WorldWideScience

Sample records for modeling land surface

  1. Hydrological land surface modelling

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois

    to imperfect model forecasts. It remains a crucial challenge to account for system uncertainty, so as to provide model outputs accompanied by a quantified confidence interval. Properly characterizing and reducing uncertainty opens-up the opportunity for risk-based decision-making and more effective emergency...... and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface...... temperature are explored in a multi-objective calibration experiment to optimize the parameters in a SVAT model in the Sahel. The two satellite derived variables were effective at constraining most land-surface and soil parameters. A data assimilation framework is developed and implemented with an integrated...

  2. Hydrological land surface modelling

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois

    Recent advances in integrated hydrological and soil-vegetation-atmosphere transfer (SVAT) modelling have led to improved water resource management practices, greater crop production, and better flood forecasting systems. However, uncertainty is inherent in all numerical models ultimately leading...... and disaster management. The objective of this study is to develop and investigate methods to reduce hydrological model uncertainty by using supplementary data sources. The data is used either for model calibration or for model updating using data assimilation. Satellite estimates of soil moisture and surface...... hydrological and tested by assimilating synthetic hydraulic head observations in a catchment in Denmark. Assimilation led to a substantial reduction of model prediction error, and better model forecasts. Also, a new assimilation scheme is developed to downscale and bias-correct coarse satellite derived soil...

  3. Modelling land surface - atmosphere interactions

    DEFF Research Database (Denmark)

    Rasmussen, Søren Højmark

    related to inaccurate land surface modelling, e.g. enhanced warm bias in warm dry summer months. Coupling the regional climate model to a hydrological model shows the potential of improving the surface flux simulations in dry periods and the 2 m air temperature in general. In the dry periods......The study is investigates modelling of land surface – atmosphere interactions in context of fully coupled climatehydrological model. With a special focus of under what condition a fully coupled model system is needed. Regional climate model inter-comparison projects as ENSEMBLES have shown bias...... representation of groundwater in the hydrological model is found to important and this imply resolving the small river valleys. Because, the important shallow groundwater is found in the river valleys. If the model does not represent the shallow groundwater then the area mean surface flux calculation...

  4. Modeling Land Surface Phenology Using Earthlight

    Science.gov (United States)

    Henebry, G. M.

    2005-12-01

    Microwave radiometers have long been used in earth observation, but the coarse spatial resolution of the data has discouraged its use in investigations of the vegetated land surface. The Advanced Microwave Scanning Radiometer (AMSR-E) on the Aqua satellite acquires multifrequency observations twice daily (1:30 and 13:30). From these brightness temperatures come two data products relevant to land surface phenology: soil moisture and vegetation water content. Although the nominal spatial resolution of these products is coarse (25 km), the fine temporal sampling allows characterization of the diel variation in surface moisture as contained in the uppermost soil layer and bound in the vegetation canopy. The ephermal dynamics of surficial soil moisture are difficult to validate due to the scale discrepancy between the 625 sq km coverage of a single pixel and the sparse network of weather stations. In contrast, canopy dynamics are more readily validated using finer spatial resolution data products and/or ecoregionalizations. For sites in the North American Great Plains and Northern Eurasia dominated by herbaceous vegetation, I will present land surface phenologies modeled using emitted earthlight and compare them with land surface phenologies modeled using reflected sunlight. I will also explore whether some key climate modes have a significant effect on the microwave-retrieved land surface phenologies.

  5. Land-surface modelling in hydrological perspective

    DEFF Research Database (Denmark)

    Overgaard, Jesper; Rosbjerg, Dan; Butts, M.B.

    2006-01-01

    The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. We choose to focus on energy-based approaches, ......, and the difficulties inherent in various evaluation procedures are presented. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed....

  6. Land-surface modelling in hydrological perspective

    DEFF Research Database (Denmark)

    Overgaard, Jesper; Rosbjerg, Dan; Butts, M.B.

    2006-01-01

    The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. We choose to focus on energy-based approaches......, and the difficulties inherent in various evaluation procedures are presented. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed....

  7. Improving land surface models with FLUXNET data

    Directory of Open Access Journals (Sweden)

    Y. -P. Wang

    2009-07-01

    Full Text Available There is a growing consensus that land surface models (LSMs that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF. MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT – we would expect similar parameter

  8. Improving land surface models with FLUXNET data

    Directory of Open Access Journals (Sweden)

    M. Williams

    2009-03-01

    Full Text Available There is a growing consensus that land surface models (LSMs that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for fusing LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF. MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs from MDF can be used to interpret model process validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT – we would expect similar parameter estimates among sites

  9. Revising Hydrology of a Land Surface Model

    Science.gov (United States)

    Le Vine, Nataliya; Butler, Adrian; McIntyre, Neil; Jackson, Christopher

    2015-04-01

    Land Surface Models (LSMs) are key elements in guiding adaptation to the changing water cycle and the starting points to develop a global hyper-resolution model of the terrestrial water, energy and biogeochemical cycles. However, before this potential is realised, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. An important limitation is the simplistic or non-existent representation of the deep subsurface in LSMs; and another is the lack of connection of LSM parameterisations to relevant hydrological information. In this context, the paper uses a case study of the JULES (Joint UK Land Environmental Simulator) LSM applied to the Kennet region in Southern England. The paper explores the assumptions behind JULES hydrology, adapts the model structure and optimises the coupling with the ZOOMQ3D regional groundwater model. The analysis illustrates how three types of information can be used to improve the model's hydrology: a) observations, b) regionalized information, and c) information from an independent physics-based model. It is found that: 1) coupling to the groundwater model allows realistic simulation of streamflows; 2) a simple dynamic lower boundary improves upon JULES' stationary unit gradient condition; 3) a 1D vertical flow in the unsaturated zone is sufficient; however there is benefit in introducing a simple dual soil moisture retention curve; 4) regionalized information can be used to describe soil spatial heterogeneity. It is concluded that relatively simple refinements to the hydrology of JULES and its parameterisation method can provide a substantial step forward in realising its potential as a high-resolution multi-purpose model.

  10. Mycorrhizal fungi and global land surface models?

    Science.gov (United States)

    Brzostek, E. R.; Fisher, J. B.; Shi, M.; Phillips, R.

    2013-12-01

    In the current generation of Land Surface Models (LSMs), the representation of coupled carbon (C) and nutrient cycles does not account for allocation of C by plants to mycorrhizal fungi in exchange for limiting nutrients. Given that the amount of C transferred to mycorrhizae can exceed 20% of net primary production (NPP), mycorrhizae can supply over half of the nitrogen (N) needed to support NPP, and that large majority of plants form associations with mycorrhizae; integrating these mechanisms into LSMs may significantly alter our understanding of the role of the terrestrial biosphere in mitigating climate change. Here, we present results from the integration of a mycorrhizal framework into a cutting-edge global plant nitrogen model -- Fixation & Uptake of Nitrogen (FUN; Fisher et al., 2010) -- that can be coupled into existing LSMs. In this mycorrhizal framework, the C cost of N acquisition varies as a function of mycorrhizal type with: (1) plants that support arbuscular mycorrhizae (AM) benefiting when N is plentiful and (2) plants that support ectomycorrhizae (ECM) benefiting when N is limiting. At the plot scale (15 x 15m), the My-FUN model improved predictions of retranslocation, N uptake, and the amount of C transferred into the soil relative to the base model across 45 plots that vary in mycorrhizal type in Indiana, USA. At the ecosystem scale, when we coupled this new framework into the Community Land Model (CLM-CN), the model estimated lower C uptake than the base model and more accurately predicted C uptake at the Morgan Monroe State Forest AmeriFlux site. These results suggest that the inclusion of a mycorrhizal framework into LSMs will enhance our ability to predict feedbacks between global change and the terrestrial biosphere.

  11. Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

    Science.gov (United States)

    Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

    2011-01-01

    Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

  12. Land surface Verification Toolkit (LVT – a generalized framework for land surface model evaluation

    Directory of Open Access Journals (Sweden)

    S. V. Kumar

    2012-02-01

    Full Text Available Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS, it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

  13. Land surface Verification Toolkit (LVT – a generalized framework for land surface model evaluation

    Directory of Open Access Journals (Sweden)

    S. V. Kumar

    2012-06-01

    Full Text Available Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS, it supports hydrological data products from non-LIS environments as well. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

  14. Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

    Science.gov (United States)

    Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Kumar, Sujay; Ringerud, Sarah

    2014-01-01

    Land surface microwave emissivity affects remote sensing of both the atmosphere and the land surface. The dynamical behavior of microwave emissivity over a very diverse sample of land surface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the land surface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate land surface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale land surface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of land surfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

  15. Spatial Aggregation of Land Surface Characteristics: Impact of resolution of remote sensing data on land surface modelling

    NARCIS (Netherlands)

    Pelgrum, H.

    2000-01-01

    Land surface models describe the exchange of heat, moisture and momentum between the land surface and the atmosphere. These models can be solved regionally using remote sensing measurements as input. Input variables which can be derived from remote sensing measurements are surface albedo, surface te

  16. Enhancing the representation of subgrid land surface characteristics in land surface models

    Directory of Open Access Journals (Sweden)

    Y. Ke

    2013-03-01

    Full Text Available Land surface heterogeneity has long been recognized as important to represent in the land surface models. In most existing land surface models, the spatial variability of surface cover is represented as subgrid composition of multiple surface cover types. In this study, we developed a new subgrid classification method (SGC that accounts for the topographic variability of the vegetation cover. Each model grid cell was represented with a number of elevation classes and each elevation class was further described by a number of vegetation types. The numbers of elevation classes and vegetation types were variable and optimized for each model grid so that the spatial variability of both elevation and vegetation can be reasonably explained given a pre-determined total number of classes. The subgrid structure of the Community Land Model (CLM was used as an example to illustrate the newly developed method in this study. With similar computational burden as the current subgrid vegetation representation in CLM, the new method is able to explain at least 80% of the total subgrid Plant Functional Types (PFTs and greatly reduced the variations of elevation within each subgrid class compared to the baseline method where a single elevation class is assigned to each subgrid PFT. The new method was also evaluated against two other subgrid methods (SGC1 and SGC2 that assigned fixed numbers of elevation and vegetation classes for each model grid with different perspectives of surface cover classification. Implemented at five model resolutions (0.1°, 0.25°, 0.5°, 1.0° and 2.0° with three maximum-allowed total number of classes Nclass of 24, 18 and 12 representing different computational burdens over the North America (NA continent, the new method showed variable performances compared to the SGC1 and SGC2 methods. However, the advantage of the SGC method over the other two methods clearly emerged at coarser model resolutions and with moderate computational

  17. Real Time Land-Surface Hydrologic Modeling Over Continental US

    Science.gov (United States)

    Houser, Paul R.

    1998-01-01

    The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP to impose ad hoc corrections to the land surface states to prevent this drift. A proposed methodology is to develop Land Data Assimilation schemes (LDAS), which are uncoupled models forced with observations, and not affected by NWP forcing biases. The proposed research is being implemented as a real time operation using an existing Surface Vegetation Atmosphere Transfer Scheme (SVATS) model at a 40 km degree resolution across the United States to evaluate these critical science questions. The model will be forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing GIS vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations.

  18. Advanced microwave forward model for the land surface data assimilation

    Science.gov (United States)

    Park, Chang-Hwan; Pause, Marion; Gayler, Sebastian; Wollschlaeger, Ute; Jackson, Thomas J.; LeDrew, Ellsworth; Behrendt, Andreas; Wulfmeyer, Volker

    2015-04-01

    From local to global scales, microwave remote-sensing techniques can provide temporally and spatially highly resolved observations of land surface properties including soil moisture and temperature as well as the state of vegetation. These variables are critical for agricultural productivity and water resource management. Furthermore, having accurate information of these variables allows us to improve the performances of numerical weather forecasts and climate prediction models. However, it is challenging to translate a measured brightness temperature into the multiple land surface properties because of the inherent inversion problem. In this study, we introduce a novel forward model for microwave remote sensing to resolve this inversion problem and to close the gap between land surface modeling and observations. It is composed of the Noah-MP land surface model as well as new models for the dielectric mixing and the radiative transfer. For developing a realistic forward operator, the land surface model must simulate soil and vegetation processes properly. The Noah-MP land surface model provides an excellent starting point because it contains already a sophisticated soil texture and land cover data set. Soil moisture transport is derived using the Richards equation in combination with a set of soil hydraulic parameters. Vegetation properties are considered using several photosynthesis models with different complexity. The energy balance is closed for the top soil and the vegetation layers. The energy flux becomes more realistic due to including not only the volumetric ratio of land surface properties but also their surface fraction as sub-grid scale information (semitile approach). Dielectric constant is the fundamental link to quantify the land surface properties. Our physical based new dielectric-mixing model is superior to previous calibration and semi-empirical approaches. Furthermore, owing to the consideration of the oversaturated surface dielectric behaviour

  19. Development of high resolution land surface parameters for the Community Land Model

    Directory of Open Access Journals (Sweden)

    Y. Ke

    2012-06-01

    Full Text Available There is a growing need for high-resolution land surface parameters as land surface models are being applied at increasingly higher spatial resolution offline as well as in regional and global models. The default land surface parameters for the most recent version of the Community Land Model (i.e. CLM 4.0 are at 0.5° or coarser resolutions, released with the model from the National Center for Atmospheric Research (NCAR. Plant Functional Types (PFTs, vegetation properties such as Leaf Area Index (LAI, Stem Area Index (SAI, and non-vegetated land covers were developed using remotely-sensed datasets retrieved in late 1990's and the beginning of this century. In this study, we developed new land surface parameters for CLM 4.0, specifically PFTs, LAI, SAI and non-vegetated land cover composition, at 0.05° resolution globally based on the most recent MODIS land cover and improved MODIS LAI products. Compared to the current CLM 4.0 parameters, the new parameters produced a decreased coverage by bare soil and trees, but an increased coverage by shrub, grass, and cropland. The new parameters result in a decrease in global seasonal LAI, with the biggest decrease in boreal forests; however, the new parameters also show a large increase in LAI in tropical forest. Differences between the new and the current parameters are mainly caused by changes in the sources of remotely sensed data and the representation of land cover in the source data. The new high-resolution land surface parameters have been used in a coupled land-atmosphere model (WRF-CLM applied to the western US to demonstrate their use in high-resolution modeling. Future work will include global offline CLMsimulations to examine the impacts of source data resolution and subsequent land parameter changes on simulated land surface processes.

  20. Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

    Science.gov (United States)

    Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija

    2012-01-01

    Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

  1. Evapotranspiration and runoff from large land areas: Land surface hydrology for atmospheric general circulation models

    Science.gov (United States)

    Famiglietti, J. S.; Wood, Eric F.

    1993-01-01

    A land surface hydrology parameterization for use in atmospheric GCM's is presented. The parameterization incorporates subgrid scale variability in topography, soils, soil moisture and precipitation. The framework of the model is the statistical distribution of a topography-soils index, which controls the local water balance fluxes, and is therefore taken to represent the large land area. Spatially variable water balance fluxes are integrated with respect to the topography-soils index to yield our large topography-soils distribution, and interval responses are weighted by the probability of occurrence of the interval. Grid square averaged land surface fluxes result. The model functions independently as a macroscale water balance model. Runoff ratio and evapotranspiration efficiency parameterizations are derived and are shown to depend on the spatial variability of the above mentioned properties and processes, as well as the dynamics of land surface-atmosphere interactions.

  2. Photosynthesis sensitivity to climate change in land surface models

    Science.gov (United States)

    Manrique-Sunen, Andrea; Black, Emily; Verhoef, Anne; Balsamo, Gianpaolo

    2016-04-01

    Accurate representation of vegetation processes within land surface models is key to reproducing surface carbon, water and energy fluxes. Photosynthesis determines the amount of CO2 fixated by plants as well as the water lost in transpiration through the stomata. Photosynthesis is calculated in land surface models using empirical equations based on plant physiological research. It is assumed that CO2 assimilation is either CO2 -limited, radiation -limited ; and in some models export-limited (the speed at which the products of photosynthesis are used by the plant) . Increased levels of atmospheric CO2 concentration tend to enhance photosynthetic activity, but the effectiveness of this fertilization effect is regulated by environmental conditions and the limiting factor in the photosynthesis reaction. The photosynthesis schemes at the 'leaf level' used by land surface models JULES and CTESSEL have been evaluated against field photosynthesis observations. Also, the response of photosynthesis to radiation, atmospheric CO2 and temperature has been analysed for each model, as this is key to understanding the vegetation response that climate models using these schemes are able to reproduce. Particular emphasis is put on the limiting factor as conditions vary. It is found that while at present day CO2 concentrations export-limitation is only relevant at low temperatures, as CO2 levels rise it becomes an increasingly important restriction on photosynthesis.

  3. Development of high resolution land surface parameters for the Community Land Model

    Directory of Open Access Journals (Sweden)

    Y. Ke

    2012-11-01

    Full Text Available There is a growing need for high-resolution land surface parameters as land surface models are being applied at increasingly higher spatial resolution offline as well as in regional and global models. The default land surface parameters for the most recent version of the Community Land Model (i.e. CLM 4.0 are at 0.5° or coarser resolutions, released with the Community Earth System Model (CESM. Plant Functional Types (PFTs, vegetation properties such as Leaf Area Index (LAI, Stem Area Index (SAI, and non-vegetated land covers were developed using remotely sensed datasets retrieved in late 1990's and the beginning of this century. In this study, we developed new land surface parameters for CLM 4.0, specifically PFTs, LAI, SAI and non-vegetated land cover composition, at 0.05° resolution globally based on the most recent MODIS land cover and improved MODIS LAI products. Compared to the current CLM 4.0 parameters, the new parameters produced a decreased coverage by bare soil and trees, but an increased coverage by shrub, grass, and cropland. The new parameters result in a decrease in global seasonal LAI, with the biggest decrease in boreal forests; however, the new parameters also show a large increase in LAI in tropical forest. Differences between the new and the current parameters are mainly caused by changes in the sources of remotely sensed data and the representation of land cover in the source data. Advantages and disadvantages of each dataset were discussed in order to provide guidance on the use of the data. The new high-resolution land surface parameters have been used in a coupled land-atmosphere model (WRF-CLM applied to the western US to demonstrate their use in high-resolution modeling. A remapping method from the latitude/longitude grid of the CLM data to the WRF grids with map projection was also demonstrated. Future work will include global offline CLM simulations to examine the impacts of source data resolution and subsequent land

  4. Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

    Directory of Open Access Journals (Sweden)

    F. Zabel

    2010-10-01

    Full Text Available Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

  5. High-resolution Continental Scale Land Surface Model incorporating Land-water Management in United States

    Science.gov (United States)

    Shin, S.; Pokhrel, Y. N.

    2016-12-01

    Land surface models have been used to assess water resources sustainability under changing Earth environment and increasing human water needs. Overwhelming observational records indicate that human activities have ubiquitous and pertinent effects on the hydrologic cycle; however, they have been crudely represented in large scale land surface models. In this study, we enhance an integrated continental-scale land hydrology model named Leaf-Hydro-Flood to better represent land-water management. The model is implemented at high resolution (5km grids) over the continental US. Surface water and groundwater are withdrawn based on actual practices. Newly added irrigation, water diversion, and dam operation schemes allow better simulations of stream flows, evapotranspiration, and infiltration. Results of various hydrologic fluxes and stores from two sets of simulation (one with and the other without human activities) are compared over a range of river basin and aquifer scales. The improved simulations of land hydrology have potential to build consistent modeling framework for human-water-climate interactions.

  6. Improving land surface emissivty parameter for land surface models using portable FTIR and remote sensing observation in Taklimakan Desert

    Science.gov (United States)

    Liu, Yongqiang; Mamtimin, Ali; He, Qing

    2014-05-01

    Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply assumption, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, 0.96 for soil and wetland in the Global and Regional Assimilation and Prediction System (GRAPES) Common Land Model (CoLM). This is the so-called emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the emissivity induces errors in modeling the surface energy budget over Taklimakan Desert where ɛ is far smaller than original value. One feasible solution to this problem is to apply the accurate broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity required by land surface models. In order to calibrate the regression equations, using a portable Fourier Transform infrared (FTIR) spectrometer instrument, crossing Taklimakan Desert along with highway from north to south, to measure the accurate broadband emissivity. The observed emissivity data show broadband ɛ around 0.89-0.92. To examine the impact of improved ɛ to radiative energy redistribution, simulation studies were conducted using offline CoLM. The results illustrate that large impacts of surface ɛ occur over desert, with changes up in surface skin temperature, as well as evident changes in sensible heat fluxes. Keywords: Taklimakan Desert, surface broadband emissivity, Fourier Transform infrared spectrometer, MODIS, CoLM

  7. A New Approach for Parameter Optimization in Land Surface Model

    Institute of Scientific and Technical Information of China (English)

    LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin

    2011-01-01

    In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.

  8. Land Surface Models Evaluation for Two Different Land-Cover Types: Cropland and Forest

    Directory of Open Access Journals (Sweden)

    Daeun Kim

    2016-02-01

    Full Text Available Land Surface Model (LSM is an important tool used to understand the complicated hydro-meteorological flux interaction systems between the land surface and atmosphere in hydrological cycles. Over the past few decades, LSMs have further developed to more accurately estimate weather and climate hydrological processes. Common Land Model (CLM and Noah Land Surface Model (Noah LSM are used in this paper to estimate the hydro-meteorological fluxes for model applicability assessment at two different flux tower sites in Korea during the summer monsoon season. The estimated fluxes such as net radiation (RN, sensible heat flux (H, latent heat flux (LE, ground heat flux (G, and soil temperature (Ts were compared with the observed data from flux towers. The simulated RN from both models corresponded well with the in situ data. The root-mean-square error (RMSE values were 39 - 44 W m-2 for the CLM and 45 - 50 W m-2 for the Noah LSM while the H and LE showed relatively larger discrepancies with each observation. The estimated Ts from the CLM corresponded comparatively well with the observed soil temperature. The CLM estimations generally showed better statistical results than those from the Noah LSM, even though the estimated hydro-meteorological fluxes from both models corresponded reasonably with the observations. A sensitivity test indicated that differences according to different locations between the estimations from models and observations were caused by field conditions including the land-cover type and soil texture. In addition the estimated RN, H, LE, and G were more sensitive than the estimated Ts in both models.

  9. Forest biomass allometry in global land surface models

    Science.gov (United States)

    Wolf, Adam; Ciais, Philippe; Bellassen, Valentin; Delbart, Nicolas; Field, Christopher B.; Berry, Joseph A.

    2011-09-01

    A number of global land surface models simulate photosynthesis, respiration, and disturbance, important flows in the carbon cycle that are widely tested against flux towers and CO2 concentration gradients. The resulting forest biomass is examined in this paper for its resemblance to realistic stands, which are characterized using allometric theory. The simulated biomass pools largely do not conform to widely observed allometry, particularly for young stands. The best performing models had an explicit treatment of stand-thinning processes, which brought the slope of the allometry of these models closer to observations. Additionally, models that had relatively shorter wood turnover times performed were generally closer to observed allometries. The discrepancy between the pool distribution between models and data suggests estimates of NEE have biases when integrated over the long term, as compared to observed biomass data, and could therefore compromise long-term predictions of land carbon sources and sinks. We think that this presents a practical obstacle for improving models by informing them better with data. The approach taken in this paper, examining biomass pools allometrically, offers a simple approach to improving the characteristic behaviors of global models with the relatively sparse data that is available globally by forest inventory.

  10. Land Surface Modeling Applications for Famine Early Warning

    Science.gov (United States)

    McNally, A.; Verdin, J. P.; Peters-Lidard, C. D.; Arsenault, K. R.; Wang, S.; Kumar, S.; Shukla, S.; Funk, C. C.; Pervez, M. S.; Fall, G. M.; Karsten, L. R.

    2015-12-01

    AGU 2015 Fall Meeting Session ID#: 7598 Remote Sensing Applications for Water Resources Management Land Surface Modeling Applications for Famine Early Warning James Verdin, USGS EROS Christa Peters-Lidard, NASA GSFC Amy McNally, NASA GSFC, UMD/ESSIC Kristi Arsenault, NASA GSFC, SAIC Shugong Wang, NASA GSFC, SAIC Sujay Kumar, NASA GSFC, SAIC Shrad Shukla, UCSB Chris Funk, USGS EROS Greg Fall, NOAA Logan Karsten, NOAA, UCAR Famine early warning has traditionally required close monitoring of agro-climatological conditions, putting them in historical context, and projecting them forward to anticipate end-of-season outcomes. In recent years, it has become necessary to factor in the effects of a changing climate as well. There has also been a growing appreciation of the linkage between food security and water availability. In 2009, Famine Early Warning Systems Network (FEWS NET) science partners began developing land surface modeling (LSM) applications to address these needs. With support from the NASA Applied Sciences Program, an instance of the Land Information System (LIS) was developed to specifically support FEWS NET. A simple crop water balance model (GeoWRSI) traditionally used by FEWS NET took its place alongside the Noah land surface model and the latest version of the Variable Infiltration Capacity (VIC) model, and LIS data readers were developed for FEWS NET precipitation forcings (NOAA's RFE and USGS/UCSB's CHIRPS). The resulting system was successfully used to monitor and project soil moisture conditions in the Horn of Africa, foretelling poor crop outcomes in the OND 2013 and MAM 2014 seasons. In parallel, NOAA created another instance of LIS to monitor snow water resources in Afghanistan, which are an early indicator of water availability for irrigation and crop production. These successes have been followed by investment in LSM implementations to track and project water availability in Sub-Saharan Africa and Yemen, work that is now underway. Adoption of

  11. Physically plausible prescription of land surface model soil moisture

    Science.gov (United States)

    Hauser, Mathias; Orth, René; Thiery, Wim; Seneviratne, Sonia

    2016-04-01

    Land surface hydrology is an important control of surface weather and climate, especially under extreme dry or wet conditions where it can amplify heat waves or floods, respectively. Prescribing soil moisture in land surface models is a valuable technique to investigate this link between hydrology and climate. It has been used for example to assess the influence of soil moisture on temperature variability, mean and extremes (Seneviratne et al. 2006, 2013, Lorenz et al., 2015). However, perturbing the soil moisture content artificially can lead to a violation of the energy and water balances. Here we present a new method for prescribing soil moisture which ensures water and energy balance closure by using only water from runoff and a reservoir term. If water is available, the method prevents soil moisture decrease below climatological values. Results from simulations with the Community Land Model (CLM) indicate that our new method allows to avoid soil moisture deficits in many regions of the world. We show the influence of the irrigation-supported soil moisture content on mean and extreme temperatures and contrast our findings with that of earlier studies. Additionally, we will assess how long into the 21st century the new method will be able to maintain present-day climatological soil moisture levels for different regions. Lorenz, R., Argüeso, D., Donat, M.G., Pitman, A.J., den Hurk, B.V., Berg, A., Lawrence, D.M., Chéruy, F., Ducharne, A., Hagemann, S. and Meier, A., 2015. Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. Journal of Geophysical Research: Atmospheres. Seneviratne, S.I., Lüthi, D., Litschi, M. and Schär, C., 2006. Land-atmosphere coupling and climate change in Europe. Nature, 443(7108), pp.205-209. Seneviratne, S.I., Wilhelm, M., Stanelle, T., Hurk, B., Hagemann, S., Berg, A., Cheruy, F., Higgins, M.E., Meier, A., Brovkin, V. and Claussen, M., 2013. Impact of soil moisture

  12. Information-Theoretic Benchmarking of Land Surface Models

    Science.gov (United States)

    Nearing, Grey; Mocko, David; Kumar, Sujay; Peters-Lidard, Christa; Xia, Youlong

    2016-04-01

    Benchmarking is a type of model evaluation that compares model performance against a baseline metric that is derived, typically, from a different existing model. Statistical benchmarking was used to qualitatively show that land surface models do not fully utilize information in boundary conditions [1] several years before Gong et al [2] discovered the particular type of benchmark that makes it possible to *quantify* the amount of information lost by an incorrect or imperfect model structure. This theoretical development laid the foundation for a formal theory of model benchmarking [3]. We here extend that theory to separate uncertainty contributions from the three major components of dynamical systems models [4]: model structures, model parameters, and boundary conditions describe time-dependent details of each prediction scenario. The key to this new development is the use of large-sample [5] data sets that span multiple soil types, climates, and biomes, which allows us to segregate uncertainty due to parameters from the two other sources. The benefit of this approach for uncertainty quantification and segregation is that it does not rely on Bayesian priors (although it is strictly coherent with Bayes' theorem and with probability theory), and therefore the partitioning of uncertainty into different components is *not* dependent on any a priori assumptions. We apply this methodology to assess the information use efficiency of the four land surface models that comprise the North American Land Data Assimilation System (Noah, Mosaic, SAC-SMA, and VIC). Specifically, we looked at the ability of these models to estimate soil moisture and latent heat fluxes. We found that in the case of soil moisture, about 25% of net information loss was from boundary conditions, around 45% was from model parameters, and 30-40% was from the model structures. In the case of latent heat flux, boundary conditions contributed about 50% of net uncertainty, and model structures contributed

  13. Evaluation of BAER surface model for aerosol optical thickness retrieval over land surface

    Directory of Open Access Journals (Sweden)

    Y. S. Chiang

    2012-04-01

    Full Text Available Estimation of surface reflectance is essential for an accurate retrieval of aerosol optical thickness (AOT by satellite remote sensing approach. Due to the variability of surface reflectance over land surfaces, a surface model is required to take into account the crucial factor controlling this variability. In the present study, we attempted to simulate surface reflectance in the short-wave channels with two methods, namely the land cover type dependent method and a two-source linear model. In the two-source linear model, we assumed that the spectral property can be described by a mixture of vegetated and non-vegetated area, and both the normalized difference vegetation index (NDVI, and the vegetation continuous field (VCF was applied to summarize this surface characteristic. By comparing our estimation with surface reflectance data derived from Moderate Resolution Imaging Spectroradiometer (MODIS, it indicated that the land cover type approach did not provide a better estimation because of inhomogeneous land cover pattern and the mixing pixel properties. For the two-source linear method, the study suggested that the use of NDVI as parameterization for vegetation fraction can reflect the spectral behavior of shortwave surface reflectance, despite of some deviation due to the averaging characteristics in our linear combination process. A channel-dependent offset and scalar factor could enhance reflectance estimation and further improve AOT retrieval by the current Bremen AErosol Retrieval (BAER approach.

  14. Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

    Science.gov (United States)

    Yang, Runhua; Cohn, Stephen E.; daSilva, Arlindo; Joiner, Joanna; Houser, Paul R.

    1997-01-01

    The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface

  15. Assimilation of neural network soil moisture in land surface models

    Science.gov (United States)

    Rodriguez-Fernandez, Nemesio; de Rosnay, Patricia; Albergel, Clement; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Richaume, Philippe; Muñoz-Sabater, Joaquin; Drusch, Matthias

    2017-04-01

    In this study a set of land surface data assimilation (DA) experiments making use of satellite derived soil moisture (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of soil moisture for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and Soil Moisture and Ocean Salinity (SMOS) data were used. The SMOS soil moisture dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a global bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and soil-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests

  16. Sensitivity of land surface modeling to parameters: An uncertainty quantification method applied to the Community Land Model

    Science.gov (United States)

    Ricciuto, D. M.; Mei, R.; Mao, J.; Hoffman, F. M.; Kumar, J.

    2015-12-01

    Uncertainties in land parameters could have important impacts on simulated water and energy fluxes and land surface states, which will consequently affect atmospheric and biogeochemical processes. Therefore, quantification of such parameter uncertainties using a land surface model is the first step towards better understanding of predictive uncertainty in Earth system models. In this study, we applied a random-sampling, high-dimensional model representation (RS-HDMR) method to analyze the sensitivity of simulated photosynthesis, surface energy fluxes and surface hydrological components to selected land parameters in version 4.5 of the Community Land Model (CLM4.5). Because of the large computational expense of conducting ensembles of global gridded model simulations, we used the results of a previous cluster analysis to select one thousand representative land grid cells for simulation. Plant functional type (PFT)-specific uniform prior ranges for land parameters were determined using expert opinion and literature survey, and samples were generated with a quasi-Monte Carlo approach-Sobol sequence. Preliminary analysis of 1024 simulations suggested that four PFT-dependent parameters (including slope of the conductance-photosynthesis relationship, specific leaf area at canopy top, leaf C:N ratio and fraction of leaf N in RuBisco) are the dominant sensitive parameters for photosynthesis, surface energy and water fluxes across most PFTs, but with varying importance rankings. On the other hand, for surface ans sub-surface runoff, PFT-independent parameters, such as the depth-dependent decay factors for runoff, play more important roles than the previous four PFT-dependent parameters. Further analysis by conditioning the results on different seasons and years are being conducted to provide guidance on how climate variability and change might affect such sensitivity. This is the first step toward coupled simulations including biogeochemical processes, atmospheric processes

  17. An Integrated Snow Radiance and Snow Physics Modeling Framework for Cold Land Surface Modeling

    Science.gov (United States)

    Kim, Edward J.; Tedesco, Marco

    2006-01-01

    Recent developments in forward radiative transfer modeling and physical land surface modeling are converging to allow the assembly of an integrated snow/cold lands modeling framework for land surface modeling and data assimilation applications. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. Together these form a flexible framework for self-consistent remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. Each element of this framework is modular so the choice of element can be tailored to match the emphasis of a particular study. For example, within our framework, four choices of a FRTM are available to simulate the brightness temperature of snow: Two models are available to model the physical evolution of the snowpack and underlying soil, and two models are available to handle the water/energy balance at the land surface. Since the framework is modular, other models-physical or statistical--can be accommodated, too. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster at the NASA Goddard Space Flight Center. The advantages of such an integrated modular framework built on the LIS will be described through examples-e.g., studies to analyze snow field experiment observations, and simulations of future satellite missions for snow and cold land processes.

  18. Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6

    Science.gov (United States)

    Benavides Pinjosovsky, Hector Simon; Thiria, Sylvie; Ottlé, Catherine; Brajard, Julien; Badran, Fouad; Maugis, Pascal

    2017-01-01

    The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by observing the land surface temperature or remote sensing data such as the brightness temperature. The paper presents the fundamental principles of the 4D-VAR assimilation, the semi-generator software YAO and a large number of experiments showing the accuracy of the adjoint code in different conditions (sites, PFTs, seasons). In addition, a distributed version is available in the case for which only the land surface temperature is observed.

  19. Land surface evapotranspiration modelling at the regional scale

    Science.gov (United States)

    Raffelli, Giulia; Ferraris, Stefano; Canone, Davide; Previati, Maurizio; Gisolo, Davide; Provenzale, Antonello

    2017-04-01

    minimal point of soil moisture that plant requires not to wilt); the field capacity (i.e. the maximum amount of water content that a soil can held); the available water content (AWC), obtained as the difference between field capacity and wilting point. Furthermore, the model considers 15 different ID of land use, with a resolution of 250 m. The model was then tested by a direct comparison with experimental data. First, the modelled water content from the surface down to 65 cm of soil depth was compared to the measured one with a Time Domain Reflectometry (TDR) in Grugliasco (TO), a non-irrigated flat permanent meadow, for years 2006-2008. Here, the soil is sandy with a slope of about 1%. Then, considering three corn farms located in the Cuneo district, the goodness of modelled irrigations was verified. The soil texture of the three farms, analysed according to the USDA criteria, is loam or silty-loam. In particular, we compared the number of irrigations done by the farmers with the ones given by the model, which irrigates as soon as the plant reaches an imposed level of water stress. We also compared the irrigation turn given by the model with the farmers' one. Then we compared the modelled water content with the one measured before and after the irrigation. We observed that the modelled irrigation occurred when the measured water content was close to the modelled wilting point. In both test cases, the model seems to reflect quite well the real behaviour of water content.

  20. Evaluation of a photosyntheses-based canopy resistance formulation in the Noah Land-surface model

    Science.gov (United States)

    Accurately representing complex land-surface processes balancing complexity and realism remains one challenge that the weather modelling community is facing nowadays. In this study, a photosynthesis-based Gas-exchange Evapotranspiration Model (GEM) is integrated into the Noah land-surface model repl...

  1. Modeling land-surface processes and land-atmosphere interactions in the community weather and regional climate WRF model (Invited)

    Science.gov (United States)

    Chen, F.; Barlage, M. J.

    2013-12-01

    The Weather Research and Forecasting (WRF) model has been widely used with high-resolution configuration in the weather and regional climate communities, and hence demands its land-surface models to treat not only fast-response processes, such as plant evapotranspiration that are important for numerical weather prediction but also slow-evolving processes such as snow hydrology and interactions between surface soil water and deep aquifer. Correctly representing urbanization, which has been traditionally ignored in coarse-resolution modeling, is critical for applying WRF to air quality and public health research. To meet these demands, numerous efforts have been undertaken to improve land-surface models (LSM) in WRF, including the recent implementation of the Noah-MP (Noah Multiple-Physics). Noah-MP uses multiple options for key sub-grid land-atmosphere interaction processes (Niu et al., 2011; Yang et al., 2011), and contains a separate vegetation canopy representing within- and under-canopy radiation and turbulent processes, a multilayer physically-based snow model, and a photosynthesis canopy resistance parameterization with a dynamic vegetation model. This paper will focus on the interactions between fast and slow land processes through: 1) a benchmarking of the Noah-MP performance, in comparison to five widely-used land-surface models, in simulating and diagnosing snow evolution for complex terrain forested regions, and 2) the effects of interactions between shallow and deep aquifers on regional weather and climate. Moreover, we will provide an overview of recent improvements of the integrated WRF-Urban modeling system, especially its hydrological enhancements that takes into account the effects of lawn irrigation, urban oasis, evaporation from pavements, anthropogenic moisture sources, and a green-roof parameterization.

  2. Modeling the impact of land surface feedbacks on post landfall tropical cyclones

    Science.gov (United States)

    Subramanian, Subashini

    The land surface is an important component of numerical models. The land surface models are modules that control energy partitioning, compute surface exchange coefficients and form the only physical boundary in a regional scale numerical model. Thus, an accurate representation of land surface is critical to compute surface fluxes, represent the boundary layer evolution and affect changes in weather systems. Land surface can affect landfalling tropical cyclones in two ways: (i) when the cyclone is offshore and land can influence cyclones by introducing dry (or moist) air that can weaken (or strengthen) the organized convective structure of cyclones, and (ii) land can affect the evolution of cyclones post landfall by modifying the surface heat fluxes and introducing additional surface drag. In this dissertation, the hypothesis that improved representation of land surface conditions will improve the prediction of landfalling tropical cyclones is tested. To that effect, a comprehensive review of land surface effects on tropical cyclones was undertaken and an idealized study was conducted to study the impact of antecedent soil temperature on the sustenance/reintensification of tropical cyclones over land. Rainfall verification for cyclone events over the Atlantic Ocean was conducted and a comparison study between land models--GFDL Slab and Noah, also considers the sensitivity of tropical cyclone models to land surface parameterizations. The recent adoption of Noah land model with hydrology products in HWRF offers a unique opportunity to couple a river routing model to HWRF to provide streamflow estimations from the HWRF model and this dissertation has outlined techniques to real time predict streamflow for United States with HWRF forcing. Results from this dissertation research indicate antecedent land surface conditions can affect tropical cyclone evolution post landfall and high soil temperature and thermally diffusive soil texture of land surface are critical factors

  3. A coupled atmosphere and multi-layer land surface model for improving heavy rainfall simulation

    Directory of Open Access Journals (Sweden)

    M. Haggag

    2008-04-01

    Full Text Available A multi-layer land surface model (SOLVEG is dynamically coupled to the non-hydrostatic atmospheric model (MM5 in order to represent better spatial variations and changes in land surface characteristics compared with the land surface parameterization schemes included in the MM5. In this coupling, calculations of the atmosphere and land surface models are carried out as independent tasks of different processors; a model coupler controls these calculations and data exchanges among models using Message Passing Interface (MPI. This coupled model is applied to the record-breaking heavy rain events occurred in Kyushu Island, the southernmost of Japan's main islands, from 20 July to 25 July in 2006. The test computations are conducted by using both the developed coupled model and the original land surface parameterization of MM5. The result of these computations shows that SOLVEG reproduce higher ground temperature than land surface parameterization schemes in the MM5. This result indicates the feedback of land surface processes between MM5 and SOLVEG plays an important role in the computation. The most pronounced difference is in the rainfall simulation that shows the importance of coupling SOLVEG and MM5. The coupled model accurately reproduces the heavy rainfall events observed in Kyushu Island compared to the original MM5 from both the spatial and temporal point of view. This paper clearly shows that realistic simulation of rainfall event strongly depends on land-surface processes interacting with cloud development that depends on surface heat and moisture fluxes, which in turn are mainly determined by land surface vegetation and soil moisture storage. Soil temperature/moisture changes significantly affect the localized precipitation and modest improvement in the land surface representation can enhance the heavy rain simulation. MM5-SOLVEG coupling shows a clear image of land surface-atmosphere interactions and the dynamic feedback on

  4. Towards a public, standardized, diagnostic benchmarking system for land surface models

    Directory of Open Access Journals (Sweden)

    G. Abramowitz

    2012-02-01

    Full Text Available We examine different conceptions of land surface model benchmarking and illustrate the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. We additionally show how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and give an example of how these expectations might be quantified. Finally, we introduce the Protocol for the Analysis of Land Surface models (PALS, a free, online land surface model benchmarking application, and show how it is structured to meet both of these goals.

  5. Towards a public, standardized, diagnostic benchmarking system for land surface models

    Directory of Open Access Journals (Sweden)

    G. Abramowitz

    2012-06-01

    Full Text Available This work examines different conceptions of land surface model benchmarking and the importance of internationally standardized evaluation experiments that specify data sets, variables, metrics and model resolutions. It additionally demonstrates how essential the definition of a priori expectations of model performance can be, based on the complexity of a model and the amount of information being provided to it, and gives an example of how these expectations might be quantified. Finally, the Protocol for the Analysis of Land Surface models (PALS is introduced – a free, online land surface model benchmarking application that is structured to meet both of these goals.

  6. Comprehensive Assessment of Land Surface, Snow, and Soil Moisture-Climate Feedbacks by Multi-model Experiments of Land Surface Models under LS3MIP

    Science.gov (United States)

    Oki, T.; Kim, H.; Hurk, B. V. D.; Krinner, G.; Derksen, C.; Seneviratne, S. I.

    2015-12-01

    The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and its predictability, including effects on the energy and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. The Land surface, snow and soil moisture model inter-comparison project (LS3MIP) experiments address together the following objectives: an evaluation of the current state of land processes including surface fluxes, snow cover and soil moisture representation in CMIP6 DECK runs (LMIP-protoDECK) a multi-model estimation of the long-term terrestrial energy/water/carbon cycles, using the surface modules of CMIP6 models under observation constrained historical (land reanalysis) and projected future (impact assessment) conditions considering land use/land cover changes. (LMIP) an assessment of the role of snow and soil moisture feedbacks in the regional response to altered climate forcings, focusing on controls of climate extremes, water availability and high-latitude climate in historical and future scenario runs (LFMIP) an assessment of the contribution of land surface processes to the current and future predictability of regional temperature/precipitation patterns. (LFMIP) These LS3MIP outcomes will contribute to the improvement of climate change projections by reducing the systematic biases from the land surface component of climate models, and a better representation of feedback mechanisms related to snow and soil moisture in climate models. Further, LS3MIP will enable the assessment of probable historical changes in energy, water, and carbon cycles over land surfaces extending more than 100 years, including spatial variability and trends in global runoff, snow cover, and soil moisture that are hard to detect purely based on observations. LS3MIP will also enable the impact assessments of climate changes on hydrological regimes and available

  7. A land surface scheme for atmospheric and hydrologic models: SEWAB (Surface Energy and Water Balance)

    Energy Technology Data Exchange (ETDEWEB)

    Mengelkamp, H.T.; Warrach, K.; Raschke, E. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Atmosphaerenphysik

    1997-12-31

    A soil-vegetation-atmosphere-transfer scheme is presented here which solves the coupled system of the Surface Energy and Water Balance (SEWAB) equations considering partly vegetated surfaces. It is based on the one-layer concept for vegetation. In the soil the diffusion equations for heat and moisture are solved on a multi-layer grid. SEWAB has been developed to serve as a land-surface scheme for atmospheric circulation models. Being forced with atmospheric data from either simulations or measurements it calculates surface and subsurface runoff that can serve as input to hydrologic models. The model has been validated with field data from the FIFE experiment and has participated in the PILPS project for intercomparison of land-surface parameterization schemes. From these experiments we feel that SEWAB reasonably well partitions the radiation and precipitation into sensible and latent heat fluxes as well as into runoff and soil moisture Storage. (orig.) [Deutsch] Ein Landoberflaechenschema wird vorgestellt, das den Transport von Waerme und Wasser zwischen dem Erdboden, der Vegetation und der Atmosphaere unter Beruecksichtigung von teilweise bewachsenem Boden beschreibt. Im Erdboden werden die Diffusionsgleichungen fuer Waerme und Feuchte auf einem Gitter mit mehreren Schichten geloest. Das Schema SEWAB (Surface Energy and Water Balance) beschreibt die Landoberflaechenprozesse in atmosphaerischen Modellen und berechnet den Oberflaechenabfluss und den Basisabfluss, die als Eingabedaten fuer hydrologische Modelle genutzt werden koennen. Das Modell wurde mit Daten des FIFE-Experiments kalibriert und hat an Vergleichsexperimenten fuer Landoberflaechen-Schemata im Rahmen des PILPS-Projektes teilgenommen. Dabei hat sich gezeigt, dass die Aufteilung der einfallenden Strahlung und des Niederschlages in den sensiblen und latenten Waermefluss und auch in Abfluss und Speicherung der Bodenfeuchte in SEWAB den beobachteten Daten recht gut entspricht. (orig.)

  8. Incorporation of water vapor transfer in the JULES land surface model: Implications for key soil variables and land surface fluxes

    Science.gov (United States)

    Garcia Gonzalez, Raquel; Verhoef, Anne; Luigi Vidale, Pier; Braud, Isabelle

    2012-05-01

    This study focuses on the mechanisms underlying water and heat transfer in upper soil layers, and their effects on soil physical prognostic variables and the individual components of the energy balance. The skill of the JULES (Joint UK Environment Simulator) land surface model (LSM) to simulate key soil variables, such as soil moisture content and surface temperature, and fluxes such as evaporation, is investigated. The Richards equation for soil water transfer, as used in most LSMs, was updated by incorporating isothermal and thermal water vapor transfer. The model was tested for three sites representative of semiarid and temperate arid climates: the Jornada site (New Mexico, USA), Griffith site (Australia), and Audubon site (Arizona, USA). Water vapor flux was found to contribute significantly to the water and heat transfer in the upper soil layers. This was mainly due to isothermal vapor diffusion; thermal vapor flux also played a role at the Jornada site just after rainfall events. Inclusion of water vapor flux had an effect on the diurnal evolution of evaporation, soil moisture content, and surface temperature. The incorporation of additional processes, such as water vapor flux among others, into LSMs may improve the coupling between the upper soil layers and the atmosphere, which in turn could increase the reliability of weather and climate predictions.

  9. Modelling the Relationship Between Land Surface Temperature and Landscape Patterns of Land Use Land Cover Classification Using Multi Linear Regression Models

    Science.gov (United States)

    Bernales, A. M.; Antolihao, J. A.; Samonte, C.; Campomanes, F.; Rojas, R. J.; dela Serna, A. M.; Silapan, J.

    2016-06-01

    The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC) and land surface temperature (LST). Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric "Effective mesh size" was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas) and looking for common predictors between LSTs of these two different farming periods.

  10. Calibration of a distributed hydrology and land surface model using energy flux measurements

    DEFF Research Database (Denmark)

    Larsen, Morten Andreas Dahl; Refsgaard, Jens Christian; Jensen, Karsten H.

    2016-01-01

    In this study we develop and test a calibration approach on a spatially distributed groundwater-surface water catchment model (MIKE SHE) coupled to a land surface model component with particular focus on the water and energy fluxes. The model is calibrated against time series of eddy flux measure...

  11. 2-way coupling the hydrological land surface model PROMET with the regional climate model MM5

    Directory of Open Access Journals (Sweden)

    F. Zabel

    2013-05-01

    Full Text Available Most land surface hydrological models (LSHMs consider land surface processes (e.g. soil–plant–atmosphere interactions, lateral water flows, snow and ice in a spatially detailed manner. The atmosphere is considered as exogenous driver, neglecting feedbacks between the land surface and the atmosphere. On the other hand, regional climate models (RCMs generally simulate land surface processes through coarse descriptions and spatial scales but include land–atmosphere interactions. What is the impact of the differently applied model physics and spatial resolution of LSHMs on the performance of RCMs? What feedback effects are induced by different land surface models? This study analyses the impact of replacing the land surface module (LSM within an RCM with a high resolution LSHM. A 2-way coupling approach was applied using the LSHM PROMET (1 × 1 km2 and the atmospheric part of the RCM MM5 (45 × 45 km2. The scaling interface SCALMET is used for down- and upscaling the linear and non-linear fluxes between the model scales. The change in the atmospheric response by MM5 using the LSHM is analysed, and its quality is compared to observations of temperature and precipitation for a 4 yr period from 1996 to 1999 for the Upper Danube catchment. By substituting the Noah-LSM with PROMET, simulated non-bias-corrected near-surface air temperature improves for annual, monthly and daily courses when compared to measurements from 277 meteorological weather stations within the Upper Danube catchment. The mean annual bias was improved from −0.85 to −0.13 K. In particular, the improved afternoon heating from May to September is caused by increased sensible heat flux and decreased latent heat flux as well as more incoming solar radiation in the fully coupled PROMET/MM5 in comparison to the NOAH/MM5 simulation. Triggered by the LSM replacement, precipitation overall is reduced; however simulated precipitation amounts are still of high uncertainty, both

  12. Spatial validation of large scale land surface models against monthly land surface temperature patterns using innovative performance metrics.

    Science.gov (United States)

    Koch, Julian; Siemann, Amanda; Stisen, Simon; Sheffield, Justin

    2016-04-01

    Land surface models (LSMs) are a key tool to enhance process understanding and to provide predictions of the terrestrial hydrosphere and its atmospheric coupling. Distributed LSMs predict hydrological states and fluxes, such as land surface temperature (LST) or actual evapotranspiration (aET), at each grid cell. LST observations are widely available through satellite remote sensing platforms that enable comprehensive spatial validations of LSMs. In spite of the availability of LST data, most validation studies rely on simple cell to cell comparisons and thus do not regard true spatial pattern information. This study features two innovative spatial performance metrics, namely EOF- and connectivity-analysis, to validate predicted LST patterns by three LSMs (Mosaic, Noah, VIC) over the contiguous USA. The LST validation dataset is derived from global High-Resolution-Infrared-Radiometric-Sounder (HIRS) retrievals for a 30 year period. The metrics are bias insensitive, which is an important feature in order to truly validate spatial patterns. The EOF analysis evaluates the spatial variability and pattern seasonality, and attests better performance to VIC in the warm months and to Mosaic and Noah in the cold months. Further, more than 75% of the LST variability can be captured by a single pattern that is strongly driven by air temperature. The connectivity analysis assesses the homogeneity and smoothness of patterns. The LSMs are most reliable at predicting cold LST patterns in the warm months and vice versa. Lastly, the coupling between aET and LST is investigated at flux tower sites and compared against LSMs to explain the identified LST shortcomings.

  13. Analysis of the Effects of Different Land Use and Land Cover Classification on Surface Meteorological Variables using WRF Model

    Science.gov (United States)

    Sati, A. P.

    2015-12-01

    The continuous population growth and the subsequent economic expansion over centuries have been the primary drivers of land use /land cover (LULC) changes resulting in the environmental changes across the globe. Most of the urban areas being developed today are on the expense of agricultural or barren lands and the changes result from various practices such as deforestation, changing agriculture practices, rapid expansion of urban centers etc.For modeling applications, classification of land use is important and periodic updates of land cover are necessary to capture change due to LULC changes.Updated land cover and land use data derived from satellites offer the possibility of consistent and regularly collected information on LULC. In this study we explore the application of Landsat based LULC classification inWeather Research and Forecasting (WRF) model in predicting the meteorology over Delhi, India. The supervised classification of Landsat 8 imagery over Delhi region is performed which update the urban extent as well as other Land use for the region. WRF model simulations are performed using LULC classification from Landsat data, United States Geological Survey (USGS) and Moderate Resolution Imaging Spectroradiometer (MODIS) for various meteorological parameters. Modifications in LULC showed a significant effect on various surface meteorological parameters such as temperature, humidity, wind circulations and other underlying surface parameters. There is a considerable improvement in the spatial distribution of the surface meteorological parameters with correction in input LULC. The study demonstrates the improved LULC classification from Landsat data than currently in vogue and their potential to improve numerical weather simulations especially for expanding urban areas.The continuous population growth and the subsequent economic expansion over centuries have been the primary drivers of land use /land cover (LULC) changes resulting in the environmental changes

  14. Results from Assimilating AMSR-E Soil Moisture Estimates into a Land Surface Model Using an Ensemble Kalman Filter in the Land Information System

    Science.gov (United States)

    Blankenship, Clay B.; Crosson, William L.; Case, Jonathan L.; Hale, Robert

    2010-01-01

    Improve simulations of soil moisture/temperature, and consequently boundary layer states and processes, by assimilating AMSR-E soil moisture estimates into a coupled land surface-mesoscale model Provide a new land surface model as an option in the Land Information System (LIS)

  15. Simulation of land surface temperatures: comparison of two climate models and satellite retrievals

    Directory of Open Access Journals (Sweden)

    J. M. Edwards

    2009-03-01

    Full Text Available Recently there has been significant progress in the retrieval of land surface temperature from satellite observations. Satellite retrievals of surface temperature offer several advantages, including broad spatial coverage, and such data are potentially of great value in assessing general circulation models of the atmosphere. Here, retrievals of the land surface temperature over the contiguous United States are compared with simulations from two climate models. The models generally simulate the diurnal range realistically, but show significant warm biases during the summer. The models' diurnal cycle of surface temperature is related to their surface flux budgets. Differences in the diurnal cycle of the surface flux budget between the models are found to be more pronounced than those in the diurnal cycle of surface temperature.

  16. Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

    Science.gov (United States)

    Baatz, Roland; Hendricks Franssen, Harrie-Jan; Han, Xujun; Hoar, Tim; Bogena, Heye; Vereecken, Harry

    2017-04-01

    Land surface models describe biogeophysical and biogeochemical processes at the land surface, and represent the lower boundary condition of atmospheric circulation models. Their key role is the quantification of mass and energy fluxes between the land surface and the atmosphere. Predictions with land surface models are affected by many unknown parameters, uncertain meteorological forcings and incomplete process understanding. Measurement data (e.g., soil moisture) can help to improve land surface model predictions. However, soil moisture products obtained from satellite remote sensing are normally available at a very coarse resolution and give information on the upper few centimetres of the soil only. Therefore, the recently developed Cosmic Ray Neutron Sensors (CRNS) are of high interest for predictions by land surface models, because they measure neutron count intensity, which is related to integral soil moisture content at the field scale (about 20 hectares). High resolution land surface models are also able to provide solutions at the same scale. In this study, we investigated whether the assimilation of soil moisture retrievals from CRNS data measured by a network of nine CRNS located in the Rur catchment in Germany (2354 km2) can improve land surface model prediction. The assimilation of soil moisture retrievals from neutron count intensity data was used to update model states and parameters of the land surface model CLM 4.5 over a two year time period, followed by a one year evaluation period without updates. This updating was done with the local ensemble transform Kalman filter. The real world experiment tested the value of CRNS using jackknifing experiments and three different initial soil maps. During the assimilation period, soil moisture predictions generally improved, for a biased soil map from an RMSE of 0.11 cm3/cm3 in the open loop run to 0.03 cm3/cm3 while during the evaluation period soil moisture predictions improved from 0.12 cm3/cm3 to 0.06 cm3

  17. Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

    OpenAIRE

    2013-01-01

    Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and t...

  18. Advancing land surface model development with satellite-based Earth observations

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Trigo, Isabel F.; Balsamo, Gianpaolo

    2017-04-01

    The land surface forms an essential part of the climate system. It interacts with the atmosphere through the exchange of water and energy and hence influences weather and climate, as well as their predictability. Correspondingly, the land surface model (LSM) is an essential part of any weather forecasting system. LSMs rely on partly poorly constrained parameters, due to sparse land surface observations. With the use of newly available land surface temperature observations, we show in this study that novel satellite-derived datasets help to improve LSM configuration, and hence can contribute to improved weather predictability. We use the Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) and validate it comprehensively against an array of Earth observation reference datasets, including the new land surface temperature product. This reveals satisfactory model performance in terms of hydrology, but poor performance in terms of land surface temperature. This is due to inconsistencies of process representations in the model as identified from an analysis of perturbed parameter simulations. We show that HTESSEL can be more robustly calibrated with multiple instead of single reference datasets as this mitigates the impact of the structural inconsistencies. Finally, performing coupled global weather forecasts we find that a more robust calibration of HTESSEL also contributes to improved weather forecast skills. In summary, new satellite-based Earth observations are shown to enhance the multi-dataset calibration of LSMs, thereby improving the representation of insufficiently captured processes, advancing weather predictability and understanding of climate system feedbacks. Orth, R., E. Dutra, I. F. Trigo, and G. Balsamo (2016): Advancing land surface model development with satellite-based Earth observations. Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-628

  19. Modeling the land surface reflectance for optical remote sensing data in rugged terrain

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively,the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model.A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper.The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.

  20. Modeling the land surface reflectance for optical remote sensing data in rugged terrain

    Institute of Scientific and Technical Information of China (English)

    WEN JianGuang; LIU QinHuo; XIAO Qing; LIU Qiang; LI XiaoWen

    2008-01-01

    A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented. Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively, the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model. A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper. The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.

  1. Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

    Directory of Open Access Journals (Sweden)

    N. Ghilain

    2012-08-01

    Full Text Available Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I, showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI and Fractional Vegetation Cover (FVC products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land

  2. Sensitivity of a general circulation model to land surface parameters in African tropical deforestation experiments

    Energy Technology Data Exchange (ETDEWEB)

    Maynard, K.; Royer, J.F. [Meteo-France CNRM, 42 Avenue G. Coriolis, 31057, Toulouse Cedex 1 (France)

    2004-06-01

    During the last two decades, several land surface schemes for use in climate, regional and/or mesoscale, hydrological and ecological models have been designed. Incorrect parametrization of land-surface processes and prescription of the surface parameters in atmospheric modeling, can result in artificial changes of the horizontal gradient of the sensible heat flux. Thus, an error in horizontal temperature gradient within the lower atmosphere may be introduced. The reliability of the model depends on the quality of boundary layer scheme implemented and its sensitivity to the bare soil and vegetation parameters. In this study, a series of sensitivity experiments has been conducted over broad time scales, using a version of the ARPEGE Climate Model coupled to the ISBA land surface scheme in order to investigate model sensitivity to separate changes in land surface parameters over Africa. Effects of perturbing vegetation cover, distribution of soil depth, albedo of vegetation, roughness length, leaf area index and minimum stomatal resistance were explored by using a simple statistical analysis. Identifying which parameters are important in controlling turbulent energy fluxes, temperature and soil moisture is dependent on which variables are used to determine sensibility, which type of vegetation and climate regime is being simulated and the magnitude and sign of the parameter change. This study does not argue that a particular parameter is important in ISBA, rather it shows that no general ranking of parameters is possible. So, it is essential to specify all land surface parameters with greater precision when attempting to determine the climate response to modification of the land surface. The implication of ISBA being sensitive to parameters that cannot be validated suggests that there will always be considerable doubt over the predictive quality of land-surface schemes. (orig.)

  3. Mapping Biophysical Parameters for Land Surface Modeling over the Continental US Using MODIS and Landsat

    Directory of Open Access Journals (Sweden)

    Lahouari Bounoua

    2015-01-01

    Full Text Available In terms of the space cities occupy, urbanization appears as a minor land transformation. However, it permanently modifies land’s ecological functions, altering its carbon, energy, and water fluxes. It is therefore necessary to develop a land cover characterization at fine spatial and temporal scales to capture urbanization’s effects on surface fluxes. We develop a series of biophysical vegetation parameters such as the fraction of photosynthetically active radiation, leaf area index, vegetation greenness fraction, and roughness length over the continental US using MODIS and Landsat products for 2001. A 13-class land cover map was developed at a climate modeling grid (CMG merging the 500 m MODIS land cover and the 30 m impervious surface area from the National Land Cover Database. The landscape subgrid heterogeneity was preserved using fractions of each class from the 500 m and 30 m into the CMG. Biophysical parameters were computed using the 8-day composite Normalized Difference Vegetation Index produced by the North American Carbon Program. In addition to urban impact assessments, this dataset is useful for the computation of surface fluxes in land, vegetation, and urban models and is expected to be widely used in different land cover and land use change applications.

  4. Assimilation of freeze-thaw observations into the NASA Catchment land surface model

    Science.gov (United States)

    Farhadi, L.; Reichle, R. H.; Delannoy, G.

    2012-12-01

    The land surface freeze-thaw (F/T) state controls hydrological and carbon cycling and thus affects water and energy exchanges at land surface. In this research an Observing System Simulation Experiment experiment is conducted using synthetically generated measurements of the F/T state for a region in North America (90-110oW longitude, 45-55oN latitude). The synthetic "truth" is generated using the NASA Catchment land surface model forced with surface meteorological fields from the Modern-Era Retrospective Reanalysis for Research and Applications (MERRA). To generate synthetic measurements, the true categorical F/T state is corrupted with a prescribed amount of F/T classification error. The assimilation experiment employs the same Catchment model except that forcing errors (relative to truth) are introduced via the application of meteorological forcing fields from the Global Land Data Assimilation System (GLDAS). A rule-based approach that incorporates model and observational errors is developed and used for assimilating the categorical F/T measurements into the land surface model (F/T analysis). The effect of the F/T analysis on land surface temperature, soil temperature and soil moisture is examined. In a real-world experiment, the synthetic F/T observations are replaced with F/T observations from the Advanced Microwave Scanning Radiometer Enhanced (AMSR-E). The ultimate goal of this project is to provide a framework for the assimilation of SMAP (Soil Moisture Active Passive) F/T observations into the NASA Catchment land surface model.

  5. MODELLING THE RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND LANDSCAPE PATTERNS OF LAND USE LAND COVER CLASSIFICATION USING MULTI LINEAR REGRESSION MODELS

    Directory of Open Access Journals (Sweden)

    A. M. Bernales

    2016-06-01

    Full Text Available The threat of the ailments related to urbanization like heat stress is very prevalent. There are a lot of things that can be done to lessen the effect of urbanization to the surface temperature of the area like using green roofs or planting trees in the area. So land use really matters in both increasing and decreasing surface temperature. It is known that there is a relationship between land use land cover (LULC and land surface temperature (LST. Quantifying this relationship in terms of a mathematical model is very important so as to provide a way to predict LST based on the LULC alone. This study aims to examine the relationship between LST and LULC as well as to create a model that can predict LST using class-level spatial metrics from LULC. LST was derived from a Landsat 8 image and LULC classification was derived from LiDAR and Orthophoto datasets. Class-level spatial metrics were created in FRAGSTATS with the LULC and LST as inputs and these metrics were analysed using a statistical framework. Multi linear regression was done to create models that would predict LST for each class and it was found that the spatial metric “Effective mesh size” was a top predictor for LST in 6 out of 7 classes. The model created can still be refined by adding a temporal aspect by analysing the LST of another farming period (for rural areas and looking for common predictors between LSTs of these two different farming periods.

  6. On the Comparison of the Global Surface Soil Moisture product and Land Surface Modeling

    Science.gov (United States)

    Delorme, B., Jr.; Ottlé, C.; Peylin, P.; Polcher, J.

    2016-12-01

    Thanks to its large spatio-temporal coverage, the new ESA CCI multi-instruments dataset offers a good opportunity to assess and improve land surface models parametrization. In this study, the ESA CCI surface soil moisture (SSM) combined product (v2.2) has been compared to the simulated top first layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to evaluate its potential of improvements with data assimilation techniques. The ambition of the work was to develop a comprehensive comparison methodology by analyzing simultaneously the temporal and spatial structures of both datasets. We analyzed the SSM synoptic, seasonal, and inter-annual variations by decomposing the signals into fast and slow components. ORCHIDEE was shown to adequately reproduce the observed SSM dynamics in terms of temporal correlation. However, these correlation scores are supposed to be strongly influenced by SSM seasonal variability and the quality of the model input forcing. Autocorrelation and spectral analyses brought out disagreements in the temporal inertia of the upper soil moisture reservoirs. By linking our results to land cover maps, we found that ORCHIDEE is more dependent on rainfall events compared to the observations in regions with sparse vegetation cover. These diflerences might be due to a wrong partition of rainfall between soil evaporation, transpiration, runofl and drainage in ORCHIDEE. To refine this analysis, a single value decomposition (SVD) of the co-variability between rainfall provided by WFDEI and soil moisture was pursued over Central Europe and South Africa. It showed that spatio-temporal co-varying patterns between ORCHIDEE and rainfall and the ESA-CCI product and rainfall are in relatively good agreement. However, the leading SVD pattern, which exhibits a strong annual cycle and explains the same portion of covariance for both datasets, explains a much larger fraction of variance for ORCHIDEE than for the ESA-CCI product

  7. Sensitivity Analysis of the Land Surface Model NOAH-MP for Different Model Fluxes

    Science.gov (United States)

    Mai, Juliane; Thober, Stephan; Samaniego, Luis; Branch, Oliver; Wulfmeyer, Volker; Clark, Martyn; Attinger, Sabine; Kumar, Rohini; Cuntz, Matthias

    2015-04-01

    Land Surface Models (LSMs) use a plenitude of process descriptions to represent the carbon, energy and water cycles. They are highly complex and computationally expensive. Practitioners, however, are often only interested in specific outputs of the model such as latent heat or surface runoff. In model applications like parameter estimation, the most important parameters are then chosen by experience or expert knowledge. Hydrologists interested in surface runoff therefore chose mostly soil parameters while biogeochemists interested in carbon fluxes focus on vegetation parameters. However, this might lead to the omission of parameters that are important, for example, through strong interactions with the parameters chosen. It also happens during model development that some process descriptions contain fixed values, which are supposedly unimportant parameters. However, these hidden parameters remain normally undetected although they might be highly relevant during model calibration. Sensitivity analyses are used to identify informative model parameters for a specific model output. Standard methods for sensitivity analysis such as Sobol indexes require large amounts of model evaluations, specifically in case of many model parameters. We hence propose to first use a recently developed inexpensive sequential screening method based on Elementary Effects that has proven to identify the relevant informative parameters. This reduces the number parameters and therefore model evaluations for subsequent analyses such as sensitivity analysis or model calibration. In this study, we quantify parametric sensitivities of the land surface model NOAH-MP that is a state-of-the-art LSM and used at regional scale as the land surface scheme of the atmospheric Weather Research and Forecasting Model (WRF). NOAH-MP contains multiple process parameterizations yielding a considerable amount of parameters (˜ 100). Sensitivities for the three model outputs (a) surface runoff, (b) soil drainage

  8. Exploring new topography-based subgrid spatial structures for improving land surface modeling

    Energy Technology Data Exchange (ETDEWEB)

    Tesfa, Teklu K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Leung, Lai-Yung Ruby [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2017-02-22

    Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation, slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Overall the local

  9. Exploring new topography-based subgrid spatial structures for improving land surface modeling

    Science.gov (United States)

    Tesfa, Teklu K.; Leung, Lai-Yung Ruby

    2017-02-01

    Topography plays an important role in land surface processes through its influence on atmospheric forcing, soil and vegetation properties, and river network topology and drainage area. Land surface models with a spatial structure that captures spatial heterogeneity, which is directly affected by topography, may improve the representation of land surface processes. Previous studies found that land surface modeling, using subbasins instead of structured grids as computational units, improves the scalability of simulated runoff and streamflow processes. In this study, new land surface spatial structures are explored by further dividing subbasins into subgrid structures based on topographic properties, including surface elevation, slope and aspect. Two methods (local and global) of watershed discretization are applied to derive two types of subgrid structures (geo-located and non-geo-located) over the topographically diverse Columbia River basin in the northwestern United States. In the global method, a fixed elevation classification scheme is used to discretize subbasins. The local method utilizes concepts of hypsometric analysis to discretize each subbasin, using different elevation ranges that also naturally account for slope variations. The relative merits of the two methods and subgrid structures are investigated for their ability to capture topographic heterogeneity and the implications of this on representations of atmospheric forcing and land cover spatial patterns. Results showed that the local method reduces the standard deviation (SD) of subgrid surface elevation in the study domain by 17 to 19 % compared to the global method, highlighting the relative advantages of the local method for capturing subgrid topographic variations. The comparison between the two types of subgrid structures showed that the non-geo-located subgrid structures are more consistent across different area threshold values than the geo-located subgrid structures. Overall the local method

  10. Observation and modeling of land surface state and convective activity over the Qinghai - Tibet Plateau

    Science.gov (United States)

    Menenti, M.; Colin, J.; Jia, L.; Ma, Y.; Foken, T.; Sobrino, J. A.; Wang, J.; Shen, X.; Ueno, K.

    2012-04-01

    The Qinghai - Tibet Plateau is characterized by a significant intra-annual variability and spatial heterogeneity of surface conditions. Snow and vegetation cover, albedo, surface temperature and wetness change very significantly during the year and from place to place. The influence of temporal changes on convective events and the onset of the monsoon has been documented by ground based measurements of land - atmosphere exchanges of heat and water. The state of the land surface over the entire Plateau can be determined by space observation of surface albedo, temperature, snow and vegetation cover and soil moisture. This provides spatial patterns in the land surface drivers of atmospheric instability: radiative forcing, land surface temperature and soil moisture contribute to trigger convective events. Heat and vapour fluxes at the land surface have been mapped at high spatial resolution and over periods of time representative of seasonal variability using MODIS and AATSR multispectral radiometric data. The response of surface temperature to vegetation phenology has been studied by using 25 years of AVHRR observations. Snow cover has been monitored by improving and re-calibrating the MODIS snow cover product. The snow water equivalent has been monitored over a period of 28 years using SMMR and SSM/I 18 and 37 GHz data and an improved algorithm. Linkages between land surface conditions, convective events and the onset of the Asian Monsoon have been investigated using two Numerical Weather Prediction Models: GRAPES in China and WRF in Japan to analyze a set of case-studies. These first experiments were aimed at evaluating the linkages of land surface conditions with intense rainfall events in the region. Using the modeling and data assimilation system GRAPES a series of experiments was performed to assess the sensitivity to different types of Land Surface Models. Combined use of medium resolution thermal infrared sensors like AATSR or MODIS with GRAPES in a Multi

  11. Microclimatic models. Estimation of components of the energy balance over land surfaces

    Energy Technology Data Exchange (ETDEWEB)

    Heikinheimo, M.; Venaelaeinen, A.; Tourula, T. [Finnish Meteorological Inst., Helsinki (Finland). Air Quality Dept.

    1996-12-31

    Climates at regional scale are strongly dependent on the interaction between atmosphere and its lower boundary, the oceans and the land surface mosaic. Land surfaces influence climate through their albedo, and the aerodynamic roughness, the processes of the biosphere and many soil hydrological properties; all these factors vary considerably geographically. Land surfaces receive a certain portion of the solar irradiance depending on the cloudiness, atmospheric transparency and surface albedo. Short-wave solar irradiance is the source of the heat energy exchange at the earth`s surface and also regulates many biological processes, e.g. photosynthesis. Methods for estimating solar irradiance, atmospheric transparency and surface albedo were reviewed during the course of this project. The solar energy at earth`s surface is consumed for heating the soil and the lower atmosphere. Where moisture is available, evaporation is one of the key components of the surface energy balance, because the conversion of liquid water into water vapour consumes heat. The evaporation process was studied by carrying out field experiments and testing parameterisation for a cultivated agricultural surface and for lakes. The micrometeorological study over lakes was carried out as part of the international `Northern Hemisphere Climatic Processes Experiment` (NOPEX/BAHC) in Sweden. These studies have been aimed at a better understanding of the energy exchange processes of the earth`s surface-atmosphere boundary for a more accurate and realistic parameterisation of the land surface in atmospheric models

  12. mRM - multiscale Routing Model for Land Surface and Hydrologic Models

    Science.gov (United States)

    Cuntz, M.; Thober, S.; Mai, J.; Samaniego, L. E.; Gochis, D. J.; Kumar, R.

    2015-12-01

    Routing streamflow through a river network is a basic step within any distributed hydrologic model. It integrates the generated runoff and allows comparison with observed discharge at the outlet of a catchment. The Muskingum routing is a textbook river routing scheme that has been implemented in Earth System Models (e.g., WRF-HYDRO), stand-alone routing schemes (e.g., RAPID), and hydrologic models (e.g., the mesoscale Hydrologic Model). Most implementations suffer from a high computational demand because the spatial routing resolution is fixed to that of the elevation model irrespective of the hydrologic modeling resolution. This is because the model parameters are scale-dependent and cannot be used at other resolutions without re-estimation. Here, we present the multiscale Routing Model (mRM) that allows for a flexible choice of the routing resolution. mRM exploits the Multiscale Parameter Regionalization (MPR) included in the open-source mesoscale Hydrologic Model (mHM, www.ufz.de/mhm) that relates model parameters to physiographic properties and allows to estimate scale-independent model parameters. mRM is currently coupled to mHM and is presented here as stand-alone Free and Open Source Software (FOSS). The mRM source code is highly modular and provides a subroutine for internal re-use in any land surface scheme. mRM is coupled in this work to the state-of-the-art land surface model Noah-MP. Simulation results using mRM are compared with those available in WRF-HYDRO for the Red River during the period 1990-2000. mRM allows to increase the routing resolution from 100m to more than 10km without deteriorating the model performance. Therefore, it speeds up model calculation by reducing the contribution of routing to total runtime from over 80% to less than 5% in the case of WRF-HYDRO. mRM thus makes discharge data available to land surface modeling with only little extra calculations.

  13. Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model

    Science.gov (United States)

    Ukkola, Anna M.; Pitman, Andy J.; Decker, Mark; De Kauwe, Martin G.; Abramowitz, Gab; Kala, Jatin; Wang, Ying-Ping

    2016-06-01

    Surface fluxes from land surface models (LSMs) have traditionally been evaluated against monthly, seasonal or annual mean states. The limited ability of LSMs to reproduce observed evaporative fluxes under water-stressed conditions has been previously noted, but very few studies have systematically evaluated these models during rainfall deficits. We evaluated latent heat fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLE) LSM across 20 flux tower sites at sub-annual to inter-annual timescales, in particular focusing on model performance during seasonal-scale rainfall deficits. The importance of key model processes in capturing the latent heat flux was explored by employing alternative representations of hydrology, leaf area index, soil properties and stomatal conductance. We found that the representation of hydrological processes was critical for capturing observed declines in latent heat during rainfall deficits. By contrast, the effects of soil properties, LAI and stomatal conductance were highly site-specific. Whilst the standard model performs reasonably well at annual scales as measured by common metrics, it grossly underestimates latent heat during rainfall deficits. A new version of CABLE, with a more physically consistent representation of hydrology, captures the variation in the latent heat flux during seasonal-scale rainfall deficits better than earlier versions, but remaining biases point to future research needs. Our results highlight the importance of evaluating LSMs under water-stressed conditions and across multiple plant functional types and climate regimes.

  14. A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

    Science.gov (United States)

    Gutmann, Ethan D.; Small, Eric E.

    2007-01-01

    Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

  15. Studying the Effect of Runoff Parameterization and Interaction between Atmosphere and Land Surface in Land Surface Schemes Used in NWP Models

    Science.gov (United States)

    Khodamorad Poor, M.; Irannejad, P.

    2009-04-01

    Land Surface Schemes that is one of the most important components in climate and numerical weather prediction models (NWP) has concentrated on surface energy and water budgets. Water budget is the hydrologic core of the land surface schemes and it is presented as the precipitation which is divided into evapotranspiration, runoff and changing in soil moisture. It is also introduced by different parameterizations among land surface schemes. Since Runoff is the major component of the water budget, unrealistic simulation of it can have some effects on the other components used in water budget and hence on the laten heat flux between atmosphere and land surface. Different representations of runoff in NWP models are relatively simple because runoff is conceptually difficult to be parameterized. Regarding that topography has a major control on the distribution of soil moisture and runoff, the main objective in this study is to find the parameterization runoff which is better to be introduced in NWP models. The algorithm used in Simple TOP Model (SIMTOP) for runoff parameterization is put in NOAH LSM utilized in Weather Research and Forecasting model (WRF). In SIMTOP, surface and subsurface runoff are considered as exponential functions of water table depth, but in NOAH LSM runoff is produced by extra maximum soil infiltration. The SIMTOP is like TOPMODEL that implemented topographic information (expressed by topographic index) and the nature of soil (indicated by reducing hydraulic conductivity with soil depth). The SIMTOP is simpler than TOPMODEL because of reducing in parameters that are needed to be calibrated. The surface runoff is the sum of two components, the first generated by infiltration excess (Horton mechanism) and the second, referring to variable contributed area, by saturation excess (Dunn mechanism). The subsurface runoff is represented by topographic control, bottom drainage and saturation excess. Although the river routing is very important for

  16. Using SMOS brightness temperature and derived surface-soil moisture to characterize surface conditions and validate land surface models.

    Science.gov (United States)

    Polcher, Jan; Barella-Ortiz, Anaïs; Piles, Maria; Gelati, Emiliano; de Rosnay, Patricia

    2017-04-01

    The SMOS satellite, operated by ESA, observes the surface in the L-band. On continental surface these observations are sensitive to moisture and in particular surface-soil moisture (SSM). In this presentation we will explore how the observations of this satellite can be exploited over the Iberian Peninsula by comparing its results with two land surface models : ORCHIDEE and HTESSEL. Measured and modelled brightness temperatures show a good agreement in their temporal evolution, but their spatial structures are not consistent. An empirical orthogonal function analysis of the brightness temperature's error identifies a dominant structure over the south-west of the Iberian Peninsula which evolves during the year and is maximum in autumn and winter. Hypotheses concerning forcing-induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for the weak spatial correlations. The analysis of spatial inconsistencies between modelled and measured TBs is important, as these can affect the estimation of geophysical variables and TB assimilation in operational models, as well as result in misleading validation studies. When comparing the surface-soil moisture of the models with the product derived operationally by ESA from SMOS observations similar results are found. The spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates is poor (ρ 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products. Other reasons have to

  17. Automation of SimSphere Land Surface Model Use as a Standalone Application and Integration With EO Data for Deriving Key Land Surface Parameters

    Science.gov (United States)

    Petropoulos, George P.; Konstas, Ioannis; Carlson, Toby N.

    2013-04-01

    Use of simulation process models has played a key role in extending our abilities to study Earth system processes and enhancing our understanding on how different components of it interplay. Use of such models combined with Earth Observation (EO) data provides a promising direction towards deriving accurately spatiotemporal estimates of key parameters characterising land surface interactions, by combining the horizontal coverage and spectral resolution of remote sensing data with the vertical coverage and fine temporal continuity of those models. SimSphere is such a software toolkit written in Java for simulating the interactions of soil, vegetation and atmosphere layers of the Earth's land surface. Its use is at present continually expanding worldwide both as an educational and as a research tool for scientific investigations. It is being used either as a stand-alone application or synergistically with EO data. Herein we present recent advancements introduced to SimSphere in different aspects of the model aiming to make its use more robust when used both as a standalone application and synergistically with EO data. We have extensively tested and updated the model code, as well as enhanced it with new functionalities. These included for example taking into account the thermal inertia variation in soil moisture, simulating additional parameters characterising land surface interactions, automating the model use when integrating it with EO data via the "triangle" method and developing batch processing operations. Use of these recently introduced to the model functionalities are illustrated herein using a variety of examples. Our work is significant to the users' community of the model and very timely, given the potential use of SimSphere in an EO-based method being under development for deriving operationally regional estimates of energy fluxes and soil moisture from EO data provided by non-commercial vendors. KEYWORDS: land surface interactions, land surface process

  18. Multi-Site Model Benchmarking: Do Land Surface Models Leak Information?

    Science.gov (United States)

    Mocko, D. M.; Nearing, G. S.; Kumar, S.

    2014-12-01

    It is widely reported that land surface models (LSMs) are unable to use all of the information available from boundary conditions [1-4]. Evidence for this is that statistical models typically out-perform physics LSMs with the same forcing data. We demonstrate that this conclusion is not necessarily correct. The statistical models don't consider parameters, and the experiments cannot distinguish between information loss and bad information (disinformation). Recent work has outlined a rigorous interpretation of model benchmarking that allows us to measure the amount of information provided by model physics and the amount of information lost due to model error [5]. Recognizing that a complete understanding of model adequacy requires treatment across multiple locations [6] allows us to expand benchmarking theory to segregate bad and missing information. The result is a benchmarking method that that can distinguish error due to parameters, forcing data, and model structure - and, unlike other approaches, does not rely on parameter estimation, which can only provide estimates of parameter uncertainty conditional on model physics. Our new benchmarking methodology was compared with the standard methodology to measure information loss in several LSMs included in the current and developmental generations of the North American Land Data Assimilation System. The classical experiments implied that each of these models lose a significant amount of information from the forcing data; however, the new methodology shows clearly that this information did not actually exist in the boundary conditions in the first place. Almost all model bias can be attributed to incorrect parameters, and that most of the LSMs actually add information (via model physics) to what is available in the boundary conditions. 1 Abramowitz, G., Geophys Res Let 32, (2005). 2 Gupta, H. V., et al., Water Resour Res 48, (2012). 3 Luo, Y. Q. et al., Biogeosciences 9, (2012). 4 Han, E., et al., J Hydromet (2014). 5

  19. An integrated modelling framework for regulated river systems in Land Surface Hydrological Models

    Science.gov (United States)

    Rehan Anis, Muhammad; razavi, Saman; Wheater, Howard

    2017-04-01

    Many of the large river systems around the world are highly regulated with numerous physical flow control and storage structures as well as a range of water abstraction rules and regulations. Most existing Land Surface Models (LSM) do not represent the modifications to the hydrological regimes introduced by water management (reservoirs, irrigation diversions, etc.). The interactions between natural hydrological processes and changes in water and energy fluxes and storage due to human interventions are important to the understanding of how these systems may respond to climate change amongst other drivers for change as well as to the assessment of their feedbacks to the climate system at regional and global scales. This study presents an integrated modelling approach to include human interventions within natural hydrological systems using a fully coupled modelling platform. The Bow River Basin in Alberta (26,200 km2), one of the most managed Canadian rivers, is used to demonstrate the approach. We have dynamically linked the MESH modelling system, which embeds the Canadian Land Surface Scheme (CLASS), with the MODSIM-DSS water management modelling tool. MESH models the natural hydrology while MODSIM optimizes the reservoir operation of 4 simulated reservoirs to satisfy demands within the study basin. MESH was calibrated for the catchments upstream the reservoirs and gave good performance (NSE = 0.81) while BIAS was only 2.3% at the catchment outlet. Without coupling with MODSIM (i.e. no regulation), simulated hydrographs at the catchment outlet were in complete disagreement with observations (NSE = 0.28). The coupled model simulated the optimization introduced by the operation of the multi-reservoir system in the Bow river basin and shows excellent agreement between observed and simulated hourly flows (NSE = 0.98). Irrigation demands are fully satisfied during summer, however, there are some shortages in winter demand from industries, which can be rectified by

  20. Impacts on the regional climate modeling and improvements of modern land surface model over the Tibet Plateau

    Science.gov (United States)

    Gao, Yanhong; Xiao, Linhong; Chen, Fei; Chen, Deliang

    2017-04-01

    Three WRF dynamical downscaling simulations were designed and conducted with two different GCM forcings and two land-surface schemes (Noah versus Noah-MP). Two of them used the same forcing (ERA) and two of them shared the same land-surface model physics (Noah-MP). Simulated surface air temperature and precipitation were assessed in terms of seasonal and annual mean Climatology, and spatial characteristics and linear trends over the TP for the period 1980-2005. Major findings are summarized in the following: 1) A common cold and wet bias exists in all three simulations regardless the type of large-scale forcing or land-surface model being used. This is especially true in the western TP during the cold season, indicating that the WRF model is still deficient in capturing cold-season processes at high elevations. However, such biases in DDM were greatly constrained compared to these in forcing GCM. The land-surface model impacts the surface air temperature and precipitation climatology and spatial distribution significantly more than the large -scale forcing. Large-scale forcing has more influence on the trends rather than on the spatial characteristics in DDM. 2) The land-surface model affects precipitation over the TP through including the surface heating differential over the TP. The heating differential caused surface heat fluxes differences resulting in a stronger or weaker upward vertical motion and divergence (convergence) at upper (low) levels. This in turn brings different moist air from the ocean.

  1. Improvements of modern land surface model and impacts on the regional climate modeling in the Tibet Plateau

    Science.gov (United States)

    Gao, Y.; Linhong, X., Sr.; Chen, F.

    2016-12-01

    Three WRF dynamical downscaling simulations were designed and conducted with two different GCM forcings and two land-surface schemes (Noah versus Noah-MP). Two of them used the same forcing (ERA) and two of them shared the same land-surface model physics (Noah-MP). Simulated surface air temperature and precipitation were assessed in terms of seasonal and annual mean climatology, and spatial characteristics and linear trends over the TP for the period 1980-2005. Major findings are summarized in the following: 1) A common cold and wet bias exists in all three simulations regardless the type of large-scale forcing or land-surface model being used. This is especially true in the western TP during the cold season, indicating that the WRF model is still deficient in capturing cold-season processes at high elevations. However, such biases in DDM were greatly constrained compared to these in forcing GCM. The land-surface model impacts the surface air temperature and precipitation climatology and spatial distribution significantly more than the large -scale forcing. Large-scale forcing has more influence on the trends rather than on the spatial characteristics in DDM. 2) The land-surface model affects precipitation over the TP through including the surface heating differential over the TP. The heating differential caused surface heat fluxes differences resulting in a stronger or weaker upward vertical motion and divergence (convergence) at upper (low) levels. This in turn brings different moist air from the ocean.

  2. Implementing surface parameter aggregation rules in the CCM3 global climate model: regional responses at the land surface

    Directory of Open Access Journals (Sweden)

    M. A. Arain

    1999-01-01

    Full Text Available The land-surface parameters required as input to a GCM grid box (typically a few degrees are often set to be those of the dominant vegetation type within the grid box. This paper discusses the use and effect of aggregation rules for specifying effective values of these land cover parameters by taking into account the relative proportion of each land-cover type within each individual grid box. Global land-cover classification data at 1 km resolution were used to define Biosphere Atmosphere Transfer Scheme (BATS specific aggregate (using aggregation rules land-cover parameters. Comparison of the values of the aggregate parameters and those defined using the single dominant vegetation type (default parameters shows significant differences in some regions, particularly in the semi-desert and in forested regions, e.g. the Sahara Desert and the tropical forest of South America. These two different sets of parameters were used as input data for two 10-year simulations of the NCAR CCM3 model coupled to the BATS land-surface scheme. Statistical analyses comparing the results of the two model runs showed that the resulting effects on the land-surface diagnostics are significant only in specific regions. For example, the sensible heat flux in the Sahara Desert calculated for the aggregate parameter run increased due to the marked increase in the minimum stomatal resistance and the decrease in fractional vegetation cover in the aggregate parameters over the default parameters. The modelled global precipitation and surface air temperature fields were compared to observations: there is a general improvement in the performance of the aggregate parameter run over the default parameter run in areas where the differences between the aggregate and default parameter run are significant. However, most of the difference between the modelled and observed fields is attributable to other model deficiencies. It can be concluded that the use of aggregation rules to derive

  3. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; Velde, van der R.; Vekerdy, Z.; Su, Z.

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado

  4. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; van der Velde, R.; Vekerdy, Z.; Su, Zhongbo

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park

  5. Assimilation of satellite observed snow albedo in a land surface model

    NARCIS (Netherlands)

    Malik, M.J.; van der Velde, R.; Vekerdy, Z.; Su, Zhongbo

    2012-01-01

    This study assesses the impact of assimilating satellite-observed snow albedo on the Noah land surface model (LSM)-simulated fluxes and snow properties. A direct insertion technique is developed to assimilate snow albedo into Noah and is applied to three intensive study areas in North Park (Colorado

  6. The Study of CoLM (Common Land Model) over Gobi Desert Surface

    Science.gov (United States)

    Wang, Chao; Xiao, Tiangui; Jia, La; Du, Jun; Wen, Xiaohang

    2017-04-01

    By using the observation data, e.g. energy fluxes, temperature, soil temperature, at Dunhuang Gobi station in northwest China during Land-Atmosphere Interaction Experiment. The simulation capability of the Common Land Model (CoLM) was evaluated. Furthermore, the new value of albedo in gobi surface at Dunhuang, and empirical relation of the surface thermal exchange coefficient calculated by observation data, were used to improve the capability of CoLM. The main conclusions are as follows: 1. Daily variation trend of the sensible heat and net radiation flux were estimated well in unmodified CoLM experiment, but the extreme value of energy flux were different from observation data, especially at midday. The model overestimates the sensible heat and underestimates the net radiation. 2. The albedo of gobi in model is 0.32, and it is higher than 0.26, which is calculated by observation data. Using the new value we conducted the simulation, and the net radiation is closer to observation, but the surface temperature and sensible heat were not meeting our expectation. 3. As the new empirical relationship of the surface thermal exchange coefficient was used to modify the thermal aerodynamic impedance, the simulated soil surface temperature was significantly closer to the observed data. Meanwhile, the simulated surface sensible heat and the net radiation fluxes were also improved. The energy flux can be simulated reasonable in the modified CoLM model over gobi land surface. Key words: land surface model, numerical simulation, parameterization scheme Acknowledgements This study was supported by National Natural Science Foundation of China Fund Project (91337215, 41575066), National Key Basic Research Program (2013CB733206), Special Fund for Meteorological Research in the Public Interest (GYHY201406015), Risk Assessment System of Significant Climate Events in Tibet (14H046), and the Scientific Research Foundation of CUIT (CRF201606).

  7. Modeling directional effects in land surface temperature derived from geostationary satellite data

    DEFF Research Database (Denmark)

    Rasmussen, Mads Olander

    This PhD-thesis investigates the directional effects in land surface temperature (LST) estimates from the SEVIRI sensor onboard the Meteosat Second Generation (MSG) satellites. The directional effects are caused by the land surface structure (i.e. tree size and shape) interacting with the changing...... sun-target-sensor geometry. The directional effects occur because the different surface components, e.g. tree canopies and bare soil surfaces, will in many cases have significantly different temperatures. Depending on the viewing angle, different fractions of each of the components will be viewed......; shaded and sunlit canopy and background, respectively. Given data on vegetation structure and density, the model estimates the fractions of the four components as well as the directional composite temperature in the view of a sensor, given the illumination and viewing geometry. The modeling results show...

  8. Improving the representation of river-groundwater interactions in land surface modeling at the regional scale: Observational evidence and parameterization applied in the Community Land Model

    KAUST Repository

    Zampieri, Matteo

    2012-02-01

    Groundwater is an important component of the hydrological cycle, included in many land surface models to provide a lower boundary condition for soil moisture, which in turn plays a key role in the land-vegetation-atmosphere interactions and the ecosystem dynamics. In regional-scale climate applications land surface models (LSMs) are commonly coupled to atmospheric models to close the surface energy, mass and carbon balance. LSMs in these applications are used to resolve the momentum, heat, water and carbon vertical fluxes, accounting for the effect of vegetation, soil type and other surface parameters, while lack of adequate resolution prevents using them to resolve horizontal sub-grid processes. Specifically, LSMs resolve the large-scale runoff production associated with infiltration excess and sub-grid groundwater convergence, but they neglect the effect from loosing streams to groundwater. Through the analysis of observed data of soil moisture obtained from the Oklahoma Mesoscale Network stations and land surface temperature derived from MODIS we provide evidence that the regional scale soil moisture and surface temperature patterns are affected by the rivers. This is demonstrated on the basis of simulations from a land surface model (i.e., Community Land Model - CLM, version 3.5). We show that the model cannot reproduce the features of the observed soil moisture and temperature spatial patterns that are related to the underlying mechanism of reinfiltration of river water to groundwater. Therefore, we implement a simple parameterization of this process in CLM showing the ability to reproduce the soil moisture and surface temperature spatial variabilities that relate to the river distribution at regional scale. The CLM with this new parameterization is used to evaluate impacts of the improved representation of river-groundwater interactions on the simulated water cycle parameters and the surface energy budget at the regional scale. © 2011 Elsevier B.V.

  9. A Modeling Study of Land Surface Process Impacts on Inland Behavior of Typhoon Rananim (2004)

    Institute of Scientific and Technical Information of China (English)

    WEI Na; LI Ying

    2013-01-01

    On 12 August 2004,Typhoon Rananim (0414) moved inland over China and stagnated over the Poyang Lake area,resulting in torrential rainfall and severe geologic hazards.The Advanced Weather Research and Forecasting (ARW-WRF) model and its different land surface models (LSMs) were employed to study the impacts of land surface process on the inland behavior of Typhoon Rananim.Results show that simulations,coupled with LSMs or not,have no significant differences in predicting typhoon track,intensity,and largescale circulation.However,the simulations of mesoscale structure,rainfall rate,and rainfall distribution of typhoon are more reasonable with LSMs than without LSMs.Although differences are slight among LSMs,NOAH is better than the others.Based on outputs using the NOAH scheme,the interaction between land surface and typhoon was explored in this study.Notably,typhoon rainfall and cloud cover can cool land surface,but rainfall expands the underlying saturated wetland area,which exacerbates the asymmetric distribution of surface heat fluxes.Accordingly,an energy frontal zone may form in the lower troposphere that enhances ascending motion and local convection,resulting in heavier rainfall.Moreover,the expanded underlying saturated wetlands provide plentiful moisture and unstable energy for the maintenance of Typhoon Rananim and increased rainfall in return.

  10. Some practical notes on the land surface modeling in the Tibetan Plateau

    Directory of Open Access Journals (Sweden)

    K. Yang

    2009-05-01

    Full Text Available The Tibetan Plateau is a key region of land-atmosphere interactions, as it provides an elevated heat source to the middle-troposphere. The Plateau surfaces are typically characterized by alpine meadows and grasslands in the central and eastern part while by alpine deserts in the western part. This study evaluates performance of three state-of-the-art land surface models (LSMs for the Plateau typical land surfaces. The LSMs of interest are SiB2 (the Simple Biosphere, CoLM (Common Land Model, and Noah. They are run at typical alpine meadow sites in the central Plateau and typical alpine desert sites in the western Plateau.

    The identified key processes and modeling issues are as follows. First, soil stratification is a typical phenomenon beneath the alpine meadows, with dense roots and soil organic matters within the topsoil, and it controls the profile of soil moisture in the central and eastern Plateau; all models, when using default parameters, significantly under-estimate the soil moisture within the topsoil. Second, a soil surface resistance controls the surface evaporation from the alpine deserts but it has not been reasonably modeled in LSMs; an advanced scheme for soil water flow is implemented in a LSM, based on which the soil resistance is determined from soil water content and meteorological conditions. Third, an excess resistance controls sensible heat fluxes from dry bare-soil or sparsely vegetated surfaces, and all LSMs significantly under-predict the ground-air temperature gradient, which would result in higher net radiation, lower soil heat fluxes and thus higher sensible heat fluxes in the models. A parameterization scheme for this resistance has been shown to be effective to remove these biases.

  11. Coupled atmospheric, land surface, and subsurface modeling: Exploring water and energy feedbacks in three-dimensions

    Science.gov (United States)

    Davison, Jason H.; Hwang, Hyoun-Tae; Sudicky, Edward A.; Lin, John C.

    2015-12-01

    Human activities amplified by climate change pose a significant threat to the sustainability of water resources. Coupled climate-hydrologic simulations commonly predict these threats by combining shallow 1-D land surface models (LSMs) with traditional 2-D and 3-D hydrology models. However, these coupled models limit the moisture and energy-feedback dynamics to the shallow near-surface. This paper presents a novel analysis by applying an integrated variably-saturated subsurface/surface hydrology and heat transport model, HydroGeoSphere (HGS), as a land surface model (LSM). Furthermore, this article demonstrates the coupling of HGS to a simple 0-D atmospheric boundary layer (ABL) model. We then applied our coupled HGS-ABL model to three separate test cases and reproduced the strong correlation between the atmospheric energy balance to the depth of the groundwater table. From our simulations, we found that conventional LSMs may overestimate surface temperatures for extended drought periods because they underestimate the heat storage in the groundwater zone. Our final test case of the atmospheric response to drought conditions illustrated that deeper roots buffered the atmosphere better than shallow roots by maintaining higher latent heat fluxes, lower sensible heat fluxes, and lower surface and atmospheric temperatures.

  12. Simulating carbon exchange using a regional atmospheric model coupled to an advanced land-surface model

    Directory of Open Access Journals (Sweden)

    H. W. Ter Maat

    2010-08-01

    Full Text Available This paper is a case study to investigate what the main controlling factors are that determine atmospheric carbon dioxide content for a region in the centre of The Netherlands. We use the Regional Atmospheric Modelling System (RAMS, coupled with a land surface scheme simulating carbon, heat and momentum fluxes (SWAPS-C, and including also submodels for urban and marine fluxes, which in principle should include the dominant mechanisms and should be able to capture the relevant dynamics of the system. To validate the model, observations are used that were taken during an intensive observational campaign in central Netherlands in summer 2002. These include flux-tower observations and aircraft observations of vertical profiles and spatial fluxes of various variables.

    The simulations performed with the coupled regional model (RAMS-SWAPS-C are in good qualitative agreement with the observations. The station validation of the model demonstrates that the incoming shortwave radiation and surface fluxes of water and CO2 are well simulated. The comparison against aircraft data shows that the regional meteorology (i.e. wind, temperature is captured well by the model. Comparing spatially explicitly simulated fluxes with aircraft observed fluxes we conclude that in general latent heat fluxes are underestimated by the model compared to the observations but that the latter exhibit large variability within all flights. Sensitivity experiments demonstrate the relevance of the urban emissions of carbon dioxide for the carbon balance in this particular region. The same tests also show the relation between uncertainties in surface fluxes and those in atmospheric concentrations.

  13. Impact of high resolution land surface initialization in Indian summer monsoon simulation using a regional climate model

    Science.gov (United States)

    Unnikrishnan, C. K.; Rajeevan, M.; Rao, S. Vijaya Bhaskara

    2016-06-01

    The direct impact of high resolution land surface initialization on the forecast bias in a regional climate model in recent years over Indian summer monsoon region is investigated. Two sets of regional climate model simulations are performed, one with a coarse resolution land surface initial conditions and second one used a high resolution land surface data for initial condition. The results show that all monsoon years respond differently to the high resolution land surface initialization. The drought monsoon year 2009 and extended break periods were more sensitive to the high resolution land surface initialization. These results suggest that the drought monsoon year predictions can be improved with high resolution land surface initialization. Result also shows that there are differences in the response to the land surface initialization within the monsoon season. Case studies of heat wave and a monsoon depression simulation show that, the model biases were also improved with high resolution land surface initialization. These results show the need for a better land surface initialization strategy in high resolution regional models for monsoon forecasting.

  14. Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model

    Directory of Open Access Journals (Sweden)

    Y. Sun

    2013-04-01

    Full Text Available This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4. Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent – as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty

  15. Transitioning Enhanced Land Surface Initialization and Model Verification Capabilities to the Kenya Meteorological Department (KMD)

    Science.gov (United States)

    Case, Jonathan L.; Mungai, John; Sakwa, Vincent; Zavodsky, Bradley T.; Srikishen, Jayanthi; Limaye, Ashutosh; Blankenship, Clay B.

    2016-01-01

    Flooding, severe weather, and drought are key forecasting challenges for the Kenya Meteorological Department (KMD), based in Nairobi, Kenya. Atmospheric processes leading to convection, excessive precipitation and/or prolonged drought can be strongly influenced by land cover, vegetation, and soil moisture content, especially during anomalous conditions and dry/wet seasonal transitions. It is thus important to represent accurately land surface state variables (green vegetation fraction, soil moisture, and soil temperature) in Numerical Weather Prediction (NWP) models. The NASA SERVIR and the Short-term Prediction Research and Transition (SPoRT) programs in Huntsville, AL have established a working partnership with KMD to enhance its regional modeling capabilities. SPoRT and SERVIR are providing experimental land surface initialization datasets and model verification capabilities for capacity building at KMD. To support its forecasting operations, KMD is running experimental configurations of the Weather Research and Forecasting (WRF; Skamarock et al. 2008) model on a 12-km/4-km nested regional domain over eastern Africa, incorporating the land surface datasets provided by NASA SPoRT and SERVIR. SPoRT, SERVIR, and KMD participated in two training sessions in March 2014 and June 2015 to foster the collaboration and use of unique land surface datasets and model verification capabilities. Enhanced regional modeling capabilities have the potential to improve guidance in support of daily operations and high-impact weather and climate outlooks over Eastern Africa. For enhanced land-surface initialization, the NASA Land Information System (LIS) is run over Eastern Africa at 3-km resolution, providing real-time land surface initialization data in place of interpolated global model soil moisture and temperature data available at coarser resolutions. Additionally, real-time green vegetation fraction (GVF) composites from the Suomi-NPP VIIRS instrument is being incorporated

  16. Advantages of analytically computing the ground heat flux in land surface models

    Science.gov (United States)

    Pauwels, Valentijn R. N.; Daly, Edoardo

    2016-11-01

    It is generally accepted that the ground heat flux accounts for a significant fraction of the surface energy balance. In land surface models, the ground heat flux is typically estimated through a numerical solution of the heat conduction equation. Recent research has shown that this approach introduces errors in the estimation of the energy balance. In this paper, we calibrate a land surface model using a numerical solution of the heat conduction equation with four different vertical spatial resolutions. It is found that the thermal conductivity is the most sensitive parameter to the spatial resolution. More importantly, the thermal conductivity values are directly related to the spatial resolution, thus rendering any physical interpretation of this value irrelevant. The numerical solution is then replaced by an analytical solution. The results of the numerical and analytical solutions are identical when fine spatial and temporal resolutions are used. However, when using resolutions that are typical of land surface models, significant differences are found. When using the analytical solution, the ground heat flux is directly calculated without calculating the soil temperature profile. The calculation of the temperature at each node in the soil profile is thus no longer required, unless the model contains parameters that depend on the soil temperature, which in this study is not the case. The calibration is repeated, and thermal conductivity values independent of the vertical spatial resolution are obtained. The main conclusion of this study is that care must be taken when interpreting land surface model results that have been obtained using numerical ground heat flux estimates. The use of exact analytical solutions, when available, is recommended.

  17. Intercomparisons of land-surface parameterizations coupled to a limited area forecast model

    Science.gov (United States)

    Timbal, B.; Henderson-Sellers, A.

    1998-12-01

    The goal of the Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) is to improve the understanding of the interactions between the atmosphere and the continental surface in climate and weather forecast models. In PILPS Phase 4(b), selected schemes are coupled to the Limited Area Prediction System (LAPS) developed by the Australian Bureau of Meteorology. To facilitate the comparison of PILPS schemes' behavior within LAPS, a single mode of coupling is selected: explicit coupling. This type of coupling is more flexible and avoids most of the problems raised when interchanging the surface schemes. Exploratory tests are conducted. Initially, experiments are run in which the land-surface schemes use the same parameters as in their original host models. Then, in other runs, the most important surface parameters are set constant in an attempt to reduce the scatter amongst the schemes' results. In order to understand the impact of initialisation of soil moisture on the schemes' results some extreme cases (wet and dry) are performed. The partitioning between surface fluxes is studied as well as the soil moisture budget. Both regional and local results are analysed. Sensitivity between LSS is found in the precipitation field with rainfall over the Australian continent altering by about 20%, but no significant change is found in the net radiation. The scatter in the surface energy fluxes amongst the schemes is large (up to 300 W m -2 locally, during the daytime peak) but is seldom affected by the choice of surface parameters. The dynamical range of flux partitioning between extremely dry and wet initialisation varies strongly amongst the schemes. Some major shortcoming with the BUCKET approach are seen in the re-evaporation of convective precipitation over dry land, in the very large evaporation from wet surfaces and the diurnal cycle of surface temperature.

  18. Spatial and temporal patterns of land surface fluxes from remotely sensed surface temperatures within an uncertainty modelling framework

    Directory of Open Access Journals (Sweden)

    M. F. McCabe

    2005-01-01

    Full Text Available Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP and combined within the Generalised Likelihood Uncertainty Estimation (GLUE methodology to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses.

  19. Cross-validation of satellite products over France through their integration into a land surface model

    Science.gov (United States)

    Calvet, Jean-Christophe; Barbu, Alina; Carrer, Dominique; Meurey, Catherine

    2014-05-01

    Long (more than 30 years) time series of satellite-derived products over land are now available. They concern Essential Climate Variables (ECV) such as LAI, FAPAR, surface albedo, and soil moisture. The direct validation of such Climate Data Records (CDR) is not easy, as in situ observations are limited in space and time. Therefore, indirect validation has a key role. It consists in comparing the products with similar preexisting products derived from satellite observations or from land surface model (LSM) simulations. The most advanced indirect validation technique consists in integrating the products into a LSM using a data assimilation scheme. The obtained reanalysis accounts for the synergies of the various upstream products and provides statistics which can be used to monitor the quality of the assimilated observations. Meteo-France develops the ISBA-A-gs generic LSM able to represent the diurnal cycle of the surface fluxes together with the seasonal, interannual and decadal variability of the vegetation biomass. The LSM is embedded in the SURFEX modeling platform together with a simplified extended Kalman filter. These tools form a Land Data Assimilation System (LDAS). The current version of the LDAS assimilates SPOT-VGT LAI and ASCAT surface soil moisture (SSM) products over France (8km x 8km), and a passive monitoring of albedo, FAPAR and Land Surface temperature (LST) is performed (i.e., the simulated values are compared with the satellite products). The LDAS-France system is used in the European Copernicus Global Land Service (http://land.copernicus.eu/global/) to monitor the quality of upstream products. The LDAS generates statistics whose trends can be analyzed in order to detect possible drifts in the quality of the products: (1) for LAI and SSM, metrics derived from the active monitoring (i.e. assimilation) such as innovations (observations vs. model forecast), residuals (observations vs. analysis), and increments (analysis vs. model forecast) ; (2

  20. Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

    Science.gov (United States)

    Farhadi, Leila; Reichle, Rolf H.; DeLannoy, Gabrielle J. M.; Kimball, John S.

    2014-01-01

    The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

  1. A test of an optimal stomatal conductance scheme within the CABLE land surface model

    Science.gov (United States)

    De Kauwe, M. G.; Kala, J.; Lin, Y.-S.; Pitman, A. J.; Medlyn, B. E.; Duursma, R. A.; Abramowitz, G.; Wang, Y.-P.; Miralles, D. G.

    2015-02-01

    Stomatal conductance (gs) affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model (LSM). In common with many LSMs, CABLE does not differentiate between gs model parameters in relation to plant functional type (PFT), but instead only in relation to photosynthetic pathway. We constrained the key model parameter "g1", which represents plant water use strategy, by PFT, based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g1 parameter can be estimated using two long-term average (1960-1990) bioclimatic variables: (i) temperature and (ii) an indirect estimate of annual plant water availability. The new stomatal model, in conjunction with PFT parameterisations, resulted in a large reduction in annual fluxes of transpiration (~ 30% compared to the standard CABLE simulations) across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance against upscaled data products was not degraded, but did not noticeably reduce existing model-data biases. We identified assumptions relating to the coupling of the vegetation to the atmosphere and the parameterisation of the minimum stomatal conductance as areas requiring further investigation in both CABLE and potentially other LSMs. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.

  2. A test of an optimal stomatal conductance scheme within the CABLE Land Surface Model

    Directory of Open Access Journals (Sweden)

    M. G. De Kauwe

    2014-10-01

    Full Text Available Stomatal conductance (gs affects the fluxes of carbon, energy and water between the vegetated land surface and the atmosphere. We test an implementation of an optimal stomatal conductance model within the Community Atmosphere Biosphere Land Exchange (CABLE land surface model (LSM. In common with many LSMs, CABLE does not differentiate between gs model parameters in relation to plant functional type (PFT, but instead only in relation to photosynthetic pathway. We therefore constrained the key model parameter "g1" which represents a plants water use strategy by PFT based on a global synthesis of stomatal behaviour. As proof of concept, we also demonstrate that the g1 parameter can be estimated using two long-term average (1960–1990 bioclimatic variables: (i temperature and (ii an indirect estimate of annual plant water availability. The new stomatal models in conjunction with PFT parameterisations resulted in a large reduction in annual fluxes of transpiration (~ 30% compared to the standard CABLE simulations across evergreen needleleaf, tundra and C4 grass regions. Differences in other regions of the globe were typically small. Model performance when compared to upscaled data products was not degraded, though the new stomatal conductance scheme did not noticeably change existing model-data biases. We conclude that optimisation theory can yield a simple and tractable approach to predicting stomatal conductance in LSMs.

  3. Scale-consistent two-way coupling of land-surface and atmospheric models

    Science.gov (United States)

    Schomburg, A.; Venema, V.; Ament, F.; Simmer, C.

    2009-04-01

    Processes at the land surface and in the atmosphere act on different spatial scales. While in the atmosphere small-scale heterogeneity is smoothed out quickly by turbulent mixing, this is not the case at the land surface where small-scale variability of orography, land cover, soil texture, soil moisture etc. varies only slowly in time. For the modelling of the fluxes between the land-surface and the atmosphere it is consequently more scale consistent to model the surface processes at a higher spatial resolution than the atmospheric processes. The mosaic approach is one way to deal with this problem. Using this technique the Soil Vegetation Atmosphere Transfer (SVAT) scheme is solved on a higher resolution than the atmosphere, which is possible since a SVAT module generally demands considerably less computation time than the atmospheric part. The upscaling of the turbulent fluxes of sensible and latent heat at the interface to the atmosphere is realized by averaging, due to the nonlinearities involved this is a more sensible approach than averaging the soil properties and computing the fluxes in a second step. The atmospheric quantities are usually assumed to be homogeneous for all soil-subpixels pertaining to one coarse atmospheric grid box. In this work, the aim is to develop a downscaling approach in which the atmospheric quantities at the lowest model layer are disaggregated before they enter the SVAT module at the higher mosaic resolution. The overall aim is a better simulation of the heat fluxes which play an important role for the energy and moisture budgets at the surface. The disaggregation rules for the atmospheric variables will depend on high-resolution surface properties and the current atmospheric conditions. To reduce biases due to nonlinearities we will add small-scale variability according to such rules as well as noise for the variability we can not explain. The model used in this work is the COSMO-model, the weather forecast model (and regional

  4. Improving the Fit of a Land-Surface Model to Data Using its Adjoint

    Science.gov (United States)

    Raoult, Nina; Jupp, Tim; Cox, Peter; Luke, Catherine

    2016-04-01

    Land-surface models (LSMs) are crucial components of the Earth System Models (ESMs) which are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. In this study, JULES is automatically differentiated using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed, to search for locally optimum parameter sets by calibrating against observations. We present an introduction to the adJULES system and demonstrate its ability to improve the model-data fit using eddy covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the 5 Plant Functional Types (PFTS) in JULES. The optimised PFT-specific parameters improve the performance of JULES over 90% of the FLUXNET sites used in the study. These reductions in error are shown and compared to reductions found due to site-specific optimisations. Finally, we show that calculation of the 2nd derivative of JULES allows us to produce posterior probability density functions of the parameters and how knowledge of parameter values is constrained by observations.

  5. Temporal Dynamics of Soil Moisture Variability at the Landscape Scale: Implications for Land Surface Models.

    Science.gov (United States)

    Montaldo, N.; Albertson, J. D.

    2001-12-01

    Meteorological and hydrological forecasting models share soil moisture as a critical boundary condition. Partitioning of received energy at the land surface depends directly on this variable, as does the partitioning of rainfall into its possible routes over and through the soil. In Land Surface Models (LSMs) the temporal dynamic of soil moisture spatial variability is a fundamental issue in large-scale flux predictions. From remote sensing observations soil moisture values are averaged in the horizontal over rather large regions (pixels). The averaging areas will be getting even larger as we move from aircraft mounted sensors to satellite mounting. These data are to be used ultimately to estimate spatial averages of other processes that depend on soil moisture, such as, runoff generation, drainage, evaporation, sensible heat fluxes, crop yield, microbial activity, etc. Consequently, the LSMs have to predict spatial averaged flux over large region from average values of the soil moisture. But soil moisture variances affect flux predictions, which depend nonlinearly on soil moisture, because many of the other processes possess distinct threshold aspects to their nonlinear dependence on soil moisture. Through application of well-developed Reynolds averaging rules from fluid mechanics to the equation of Richards and Darcy-Buckingham, we write a conservation equation for the horizontal variance of soil moisture. And, through closure arguments, we are able to describe the individual terms that produce and destroy spatial variance through time in terms of the mean soil moisture state and other observable system properties such as vegetation and soil properties variability. Finally, we calculate land surface fluxes from second order Taylor expansion, using our soil moisture variance closure model, and the other observable system properties. In this work, we demonstrate significant improvements in land surface large-scale flux predictions using the proposed soil moisture

  6. Temporal Dynamics of Soil Moisture Variability: Implications For Land Surface Models

    Science.gov (United States)

    Montaldo, N.; Albertson, J. D.

    Meteorological and hydrological forecasting models share soil moisture as a critical boundary condition. Partitioning of received energy at the land surface depends di- rectly on this variable, as does the partitioning of rainfall into its possible routes over and through the soil. In Land Surface Models (LSMs) the temporal dynamic of soil moisture spatial variability is a fundamental issue in large-scale flux predictions. From remote sensing observations soil moisture values are averaged in the horizontal over rather large regions (pixels). The averaging areas will be getting even larger as we move from aircraft mounted sensors to satellite mounting. These data are to be used ultimately to estimate spatial averages of other processes that depend on soil moisture, such as, runoff generation, drainage, evaporation, sensible heat fluxes, crop yield, mi- crobial activity, etc. Consequently, the LSMs have to predict spatial averaged flux over large region from average values of the soil moisture. But soil moisture variances af- fect flux predictions, which depend nonlinearly on soil moisture, because many of the other processes possess distinct threshold aspects to their nonlinear dependence on soil moisture. Through application of well-developed Reynolds averaging rules from fluid mechanics to the equation of Richards and Darcy-Buckingham, we write a con- servation equation for the horizontal variance of soil moisture. And, through closure arguments, we are able to describe the individual terms that produce and destroy spa- tial variance through time in terms of the mean soil moisture state and other observable system properties such as vegetation and soil properties variability. Finally, we calcu- late land surface fluxes from second order Taylor expansion, using our soil moisture variance closure model, and the other observable system properties. In this work, we demonstrate significant improvements in land surface large-scale flux predictions us- ing the proposed

  7. Impact of land-surface elevation and riparian evapotranspiration seasonality on groundwater budget in MODFLOW models

    Science.gov (United States)

    Ajami, Hoori; Meixner, Thomas; Maddock, Thomas; Hogan, James F.; Guertin, D. Phillip

    2011-09-01

    Riparian groundwater evapotranspiration (ETg) constitutes a major component of the water balance especially in many arid and semi-arid environments. Although spatial and temporal variability of riparian ETg are controlled by climate, vegetation and subsurface characteristics, depth to water table (DTWT) is often considered the major controlling factor. Relationships between ETg rates and DTWT, referred to as ETg curves, are implemented in MODFLOW ETg packages (EVT, ETS1 and RIP-ET) with different functional forms. Here, the sensitivity of the groundwater budget in MODFLOW groundwater models to ETg parameters (including ETg curves, land-surface elevation and ETg seasonality) are investigated. A MODFLOW model of the hypothetical Dry Alkaline Valley in the Southwestern USA is used to show how spatial representation of riparian vegetation and digital elevation model (DEM) processing methods impact the water budget when RIPGIS-NET (a GIS-based ETg program) is used with MODFLOW's RIP-ET package, and results are compared with the EVT and ETS1 packages. Results show considerable impact on ETg and other groundwater budget components caused by spatial representation of riparian vegetation, vegetation type, fractional coverage areas and land-surface elevation. RIPGIS-NET enhances ETg estimation in MODFLOW by incorporating vegetation and land-surface parameters, providing a tool for ecohydrology studies, riparian ecosystem management and stream restoration.

  8. Four-stream Radiative Transfer Parameterization Scheme in a Land Surface Process Model

    Institute of Scientific and Technical Information of China (English)

    ZHOU Wenyan; GUO Pinwen; LUO Yong; Kuo-Nan LIOU; Yu GU; Yongkang XUE

    2009-01-01

    Accurate estimates of albedos are required in climate modeling. Accurate and simple schemes for radiative transfer within canopy are required for these estimates, but severe limitations exist. This paper developed a four-stream solar radiative transfer model and coupled it with a land surface process model. The radiative model uses a four-stream approximation method as in the atmosphere to obtain analytic solutions of the basic equation of canopy radiative transfer. As an analytical model, the four-stream radiative transfer model can be easily applied efficiently to improve the parameterization of land surface radiation in climate models. Our four-stream solar radiative transfer model is based on a two-stream short wave radiative transfer model. It can simulate short wave solar radiative transfer within canopy according to the relevant theory in the atmosphere. Each parameter of the basic radiative transfer equation of canopy has special geometry and optical characters of leaves or canopy. The upward or downward radiative fluxes are related to the diffuse phase function, the G-function, leaf reflectivity and transmission, leaf area index, and the solar angle of the incident beam.The four-stream simulation is compared with that of the two-stream model. The four-stream model is proved successful through its consistent modeling of canopy albedo at any solar incident angle. In order to compare and find differences between the results predicted by the four-and two-stream models, a number of numerical experiments are performed through examining the effects of different leaf area indices, leaf angle distributions, optical properties of leaves, and ground surface conditions on the canopy albcdo. Parallel experiments show that the canopy albedos predicted by the two models differ significantly when the leaf angle distribution is spherical and vertical. The results also show that the difference is particularly great for different incident solar beams.One additional

  9. Assessment of Noah land surface model with various runoff parameterizations over a Tibetan river

    Science.gov (United States)

    Zheng, Donghai; Van Der Velde, Rogier; Su, Zhongbo; Wen, Jun; Wang, Xin

    2017-02-01

    Runoff parameterizations currently adopted by the (i) Noah-MP model, (ii) Community Land Model (CLM), and (iii) CLM with variable infiltration capacity hydrology (CLM-VIC) are incorporated into the structure of Noah land surface model, and the impact of these parameterizations on the runoff simulations is investigated for a Tibetan river. Four numerical experiments are conducted with the default Noah and three aforementioned runoff parameterizations. Each experiment is forced with the same set of atmospheric forcing, vegetation, and soil parameters. In addition, the Community Earth System Model database provides the maximum surface saturated area parameter for the Noah-MP and CLM parameterizations. A single-year recurrent spin-up is adopted for the initialization of each model run to achieve equilibrium states. Comparison with discharge measurements shows that each runoff parameterization produces significant differences in the separation of total runoff into surface and subsurface components and that the soil water storage-based parameterizations (Noah and CLM-VIC) outperform the groundwater table-based parameterizations (Noah-MP and CLM) for the seasonally frozen and high-altitude Tibetan river. A parameter sensitivity experiment illustrates that this underperformance of the groundwater table-based parameterizations cannot be resolved through calibration. Further analyses demonstrate that the simulations of other surface water and energy budget components are insensitive to the selected runoff parameterizations, due to the strong control of the atmosphere on simulated land surface fluxes induced by the diurnal dependence of the roughness length for heat transfer and the large water retention capacity of the highly organic top soils over the plateau.

  10. Integrated Water Flow Model (IWFM), A Tool For Numerically Simulating Linked Groundwater, Surface Water And Land-Surface Hydrologic Processes

    Science.gov (United States)

    Dogrul, E. C.; Brush, C. F.; Kadir, T. N.

    2006-12-01

    The Integrated Water Flow Model (IWFM) is a comprehensive input-driven application for simulating groundwater flow, surface water flow and land-surface hydrologic processes, and interactions between these processes, developed by the California Department of Water Resources (DWR). IWFM couples a 3-D finite element groundwater flow process and 1-D land surface, lake, stream flow and vertical unsaturated-zone flow processes which are solved simultaneously at each time step. The groundwater flow system is simulated as a multilayer aquifer system with a mixture of confined and unconfined aquifers separated by semiconfining layers. The groundwater flow process can simulate changing aquifer conditions (confined to unconfined and vice versa), subsidence, tile drains, injection wells and pumping wells. The land surface process calculates elemental water budgets for agricultural, urban, riparian and native vegetation classes. Crop water demands are dynamically calculated using distributed soil properties, land use and crop data, and precipitation and evapotranspiration rates. The crop mix can also be automatically modified as a function of pumping lift using logit functions. Surface water diversions and groundwater pumping can each be specified, or can be automatically adjusted at run time to balance water supply with water demand. The land-surface process also routes runoff to streams and deep percolation to the unsaturated zone. Surface water networks are specified as a series of stream nodes (coincident with groundwater nodes) with specified bed elevation, conductance and stage-flow relationships. Stream nodes are linked to form stream reaches. Stream inflows at the model boundary, surface water diversion locations, and one or more surface water deliveries per location are specified. IWFM routes stream flows through the network, calculating groundwater-surface water interactions, accumulating inflows from runoff, and allocating available stream flows to meet specified or

  11. Application of a land surface model for simulating river streamflow in high latitudes

    Science.gov (United States)

    Gusev, Yeugeniy; Nasonova, Olga; Dzhogan, Larissa

    2010-05-01

    Nowadays modelling runoff from the pan-Arctic river basins, which represents nearly 50% of water flow to the Arctic Ocean, is of great interest among hydrological modelling community because these regions are very sensitive to natural and anthropogenic impacts. This motivates the necessity of increase of the accuracy of hydrological estimations, runoff predictions, and water resources assessments in high latitudes. However, in these regions, observations required for model simulations (to specify model parameters and forcing inputs) are very scarce or even absent (especially this concerns land surface parameters). At the same time river discharge measurements are usually available that makes it possible to estimate model parameters by their calibration against measured discharge. Such a situation is typical of most of the northern basins of Russia. The major goal of the work is to reveal whether a physically-based land surface model (LSM) Soil Water - Atmosphere - Plants (SWAP) is able to reproduce snowmelt and rain driven daily streamflow in high latitudes (using poor input information) with the accuracy acceptable for hydrologic applications. Three river basins, located on the north of the European part of Russia, were chosen for investigation. They are the Mezen River basin (area: area: 78 000 km2), the Pechora River basin (area: 312 000 km2) and the Severnaya Dvina River basin (area: 348 000 km2). For modeling purposes the basins were presented, respectively, by 10, 57 and 62 one-degree computational grid boxes connected by river network. A priori estimation of the land surface parameters for each grid box was based on the global one-degree datasets prepared within the framework of the International Satellite Land-Surface Climatology Project Initiative II (ISLSCP) / the Second Global Soil Wetness Project (GSWP-2). Three versions of atmospheric forcing data prepared for the basins were based on: (1) NCEP/DOE reanalysis dataset; (2) NCEP/DOE reanalysis product

  12. A new method to dynamically simulate groundwater table in land surface model VIC

    Institute of Scientific and Technical Information of China (English)

    YANG Hongwei; XIE Zhenghui

    2003-01-01

    Soil moisture plays an important role in water and energy balance in land-atmospheric interaction, but is impacted directly by the groundwater table. Dynamic variation of the groundwater table can be described mathematically by a moving boundary problem. In this paper, the moving boundary problem is reduced to a fixed boundary problem through a coordinate transformation. A new model of groundwater table simulation is developed using the mass-lumped finite element method and is coupled with the land surface model of Variable Infiltration Capacity (VIC). The simulation results show that the new model not only can simulate the groundwater table dynamically, but also can evade the choice of water table depth scale in computation with a low computation cost.

  13. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

    Directory of Open Access Journals (Sweden)

    M. Marshall

    2013-03-01

    Full Text Available Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET, a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET, driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

  14. Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

    Science.gov (United States)

    Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

    2017-04-01

    Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is

  15. PALADYN, a comprehensive land surface-vegetation-carbon cycle model of intermediate complexity

    Science.gov (United States)

    Willeit, Matteo; Ganopolski, Andrey

    2016-04-01

    PALADYN is presented, a new comprehensive and computationally efficient land surface-vegetation-carbon cycle model designed to be used in Earth system models of intermediate complexity for long-term simulations and paleoclimate studies. The model treats in a consistent manner the interaction between atmosphere, terrestrial vegetation and soil through the fluxes of energy, water and carbon. Energy, water and carbon are conserved. The model explicitly treats permafrost, both in physical processes and as important carbon pool. The model distinguishes 9 surface types of which 5 are different vegetation types, bare soil, land ice, lake and ocean shelf. Including the ocean shelf allows to treat continuous changes in sea level and shelf area associated with glacial cycles. Over each surface type the model solves the surface energy balance and computes the fluxes of sensible, latent and ground heat and upward shortwave and longwave radiation. It includes a single snow layer. The soil model distinguishes between three different macro surface types which have their own soil column: vegetation and bare soil, ice sheet and ocean shelf. The soil is vertically discretized into 5 layers where prognostic equations for temperature, water and carbon are consistently solved. Phase changes of water in the soil are explicitly considered. A surface hydrology module computes precipitation interception by vegetation, surface runoff and soil infiltration. The soil water equation is based on Darcy's law. Given soil water content, the wetland fraction is computed based on a topographic index. Photosynthesis is computed using a light use efficiency model. Carbon assimilation by vegetation is coupled to the transpiration of water through stomatal conductance. The model includes a dynamic vegetation module with 5 plant functional types competing for the gridcell share with their respective net primary productivity. Each macro surface type has its own carbon pools represented by a litter, a fast

  16. Assessing the influence of groundwater and land surface scheme in the modelling of land surface-atmosphere feedbacks over the FIFE area in Kansas, USA

    DEFF Research Database (Denmark)

    Larsen, Morten Andreas Dahl; Højmark Rasmussen, Søren; Drews, Martin

    2016-01-01

    experiments include five simulations. First MIKE SHE is forced by observed climate data in two versions i) with groundwater at a fixed uniform depth, and ii) with a dynamical groundwater component simulating shallow groundwater conditions in river valleys. iii) In a third simulation MIKE SHE is forced......The land surface-atmosphere interaction is described differently in large scale surface schemes of regional climate models and small scale spatially distributed hydrological models. In particular, the hydrological models include the influence of shallow groundwater on evapotranspiration during dry...... periods where soils are depleted and groundwater is the only water supply. These mechanisms are analysed by combining a distributed hydrological model (MIKE SHE) and a regional climate model (HIRHAM) and comparing simulation results to the FIFE area observation data in Kansas, USA. The numerical...

  17. Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

    Science.gov (United States)

    Naha, Shaini; Thakur, Praveen K.; Aggarwal, S. P.

    2016-06-01

    The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity) is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH) have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006). Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I) and Ensemble Kalman Filter (EnKF) that uses observations of snow covered area (SCA) to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU), rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD) data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed

  18. Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

    Directory of Open Access Journals (Sweden)

    S. Naha

    2016-06-01

    Full Text Available The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006. Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I and Ensemble Kalman Filter (EnKF that uses observations of snow covered area (SCA to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU, rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated

  19. Towards a simple representation of chalk hydrology in land surface modelling

    Science.gov (United States)

    Rahman, Mostaquimur; Rosolem, Rafael

    2017-01-01

    Modelling and monitoring of hydrological processes in the unsaturated zone of chalk, a porous medium with fractures, is important to optimize water resource assessment and management practices in the United Kingdom (UK). However, incorporating the processes governing water movement through a chalk unsaturated zone in a numerical model is complicated mainly due to the fractured nature of chalk that creates high-velocity preferential flow paths in the subsurface. In general, flow through a chalk unsaturated zone is simulated using the dual-porosity concept, which often involves calibration of a relatively large number of model parameters, potentially undermining applications to large regions. In this study, a simplified parameterization, namely the Bulk Conductivity (BC) model, is proposed for simulating hydrology in a chalk unsaturated zone. This new parameterization introduces only two additional parameters (namely the macroporosity factor and the soil wetness threshold parameter for fracture flow activation) and uses the saturated hydraulic conductivity from the chalk matrix. The BC model is implemented in the Joint UK Land Environment Simulator (JULES) and applied to a study area encompassing the Kennet catchment in the southern UK. This parameterization is further calibrated at the point scale using soil moisture profile observations. The performance of the calibrated BC model in JULES is assessed and compared against the performance of both the default JULES parameterization and the uncalibrated version of the BC model implemented in JULES. Finally, the model performance at the catchment scale is evaluated against independent data sets (e.g. runoff and latent heat flux). The results demonstrate that the inclusion of the BC model in JULES improves simulated land surface mass and energy fluxes over the chalk-dominated Kennet catchment. Therefore, the simple approach described in this study may be used to incorporate the flow processes through a chalk unsaturated

  20. Stable water isotopes in the coupled atmosphere–land surface model ECHAM5-JSBACH

    Directory of Open Access Journals (Sweden)

    B. Haese

    2012-10-01

    Full Text Available In this study we present first results of a new model development, ECHAM5-JSBACH-wiso, where we have incorporated the stable water isotopes H218O and HDO as tracers in the hydrological cycle of the coupled atmosphere–land surface model ECHAM5-JSBACH. The ECHAM5-JSBACH-wiso model was run under present-day climate conditions at two different resolutions (T31L19, T63L31. A comparison between ECHAM5-JSBACH-wiso and ECHAM5-wiso shows that the coupling has a strong impact on the simulated temperature and soil wetness. Caused by these changes of temperature and the hydrological cycle, the δ18O in precipitation also shows variations from −4.5‰ up to 4.5‰. One of the clearest anomalies is shown over North-East Asia where, depending on an increase of temperature, the δ18O in precipitation increases as well. In order to analyze the sensitivity of the fractionation processes over land, we compare a set of simulations with various implementations of water isotope fractionation processes over the land surface. The simulations allow us to distinguish between no fractionation, fractionation included in the evaporation flux (from bare soil and also fractionation included in both evaporation and transpiration (from water transport through plants fluxes. The simulated δ18O and δD in precipitation of these setups generally fit well with the observations and the best agreement between observation and simulation is given in the case where no fractionation over land surface is assumed.

  1. On the potential application of land surface models for drought monitoring in China

    Science.gov (United States)

    Zhang, Liang; Zhang, Huqiang; Zhang, Qiang; Li, Yaohui; Zhao, Jianhua

    2017-05-01

    The potential of using land surface models (LSMs) to monitor near-real-time drought has not been fully assessed in China yet. In this study, we analyze the performance of such a system with a land surface model (LSM) named the Australian Community Atmosphere Biosphere Land Exchange model (CABLE). The meteorological forcing datasets based on reanalysis products and corrected by observational data have been extended to near-real time for semi-operational trial. CABLE-simulated soil moisture (SM) anomalies are used to characterize drought spatial and temporal evolutions. One outstanding feature in our analysis is that with the same meteorological data, we have calculated a range of drought indices including Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI). We have assessed the similarity among these indices against observed SM over a number of regions in China. While precipitation is the dominant factor in the drought development, relationships between precipitation, evaporation, and soil moisture anomalies vary significantly under different climate regimes, resulting in different characteristics of droughts in China. The LSM-based trial system is further evaluated for the 1997/1998 drought in northern China and 2009/2010 drought in southwestern China. The system can capture the severities and temporal and spatial evolutions of these drought events well. The advantage of using a LSM-based drought monitoring system is further demonstrated by its potential to monitor other consequences of drought impacts in a more physically consistent manner.

  2. On the potential application of land surface models for drought monitoring in China

    Science.gov (United States)

    Zhang, Liang; Zhang, Huqiang; Zhang, Qiang; Li, Yaohui; Zhao, Jianhua

    2016-01-01

    The potential of using land surface models (LSMs) to monitor near-real-time drought has not been fully assessed in China yet. In this study, we analyze the performance of such a system with a land surface model (LSM) named the Australian Community Atmosphere Biosphere Land Exchange model (CABLE). The meteorological forcing datasets based on reanalysis products and corrected by observational data have been extended to near-real time for semi-operational trial. CABLE-simulated soil moisture (SM) anomalies are used to characterize drought spatial and temporal evolutions. One outstanding feature in our analysis is that with the same meteorological data, we have calculated a range of drought indices including Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI). We have assessed the similarity among these indices against observed SM over a number of regions in China. While precipitation is the dominant factor in the drought development, relationships between precipitation, evaporation, and soil moisture anomalies vary significantly under different climate regimes, resulting in different characteristics of droughts in China. The LSM-based trial system is further evaluated for the 1997/1998 drought in northern China and 2009/2010 drought in southwestern China. The system can capture the severities and temporal and spatial evolutions of these drought events well. The advantage of using a LSM-based drought monitoring system is further demonstrated by its potential to monitor other consequences of drought impacts in a more physically consistent manner.

  3. Modelling land surface fluxes of CO2 in response to climate change and nitrogen deposition

    DEFF Research Database (Denmark)

    Hansen, Kristina; Ambelas Skjøth, Carsten; Geels, Camilla

    Climate change, land use variations, and impacts of atmospheric nitrogen (N) deposition represent uncertainties for the prediction of future greenhouse gas exchange between land surfaces and the atmosphere as the mechanisms describing nutritional effects are not well developed in climate...... climate feedback mechanisms of CO2 between changes in management, land use practise, and climate change....

  4. Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model

    Science.gov (United States)

    Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.

    2012-12-01

    Agro-Land Surface Models (agro-LSM) have been developed from the coupling of specific crop models and large-scale generic vegetation models. They aim at accounting for the spatial distribution and variability of energy, water and carbon fluxes within soil-vegetation-atmosphere continuum with a particular emphasis on how crop phenology and agricultural management practice influence the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty in these models is related to the many parameters included in the models' equations. In this study, we quantify the parameter-based uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS on a multi-regional approach with data from sites in Australia, La Reunion and Brazil. First, the main source of uncertainty for the output variables NPP, GPP, and sensible heat flux (SH) is determined through a screening of the main parameters of the model on a multi-site basis leading to the selection of a subset of most sensitive parameters causing most of the uncertainty. In a second step, a sensitivity analysis is carried out on the parameters selected from the screening analysis at a regional scale. For this, a Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used. First, we quantify the sensitivity of the output variables to individual input parameters on a regional scale for two regions of intensive sugar cane cultivation in Australia and Brazil. Then, we quantify the overall uncertainty in the simulation's outputs propagated from the uncertainty in the input parameters. Seven parameters are identified by the screening procedure as driving most of the uncertainty in the agro-LSM ORCHIDEE-STICS model output at all sites. These parameters control photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), root

  5. The Effect of Errors in Snow Assimilation on Land Surface Modeling

    Science.gov (United States)

    Cosgrove, Brian A.; Houser, Paul R.; Atlas, Robert (Technical Monitor)

    2001-01-01

    The accurate portrayal of the hydrological cycle is extremely important in land surface modeling. Central to this effort is the treatment of snow, as errors in the representation of this quantity can impact practically all other modeled quantities through alterations in the water and energy balances. Although land surface model (LSM) simulations can benefit from the assimilation of snow cover and snow depth observations, they can be negatively impacted if such observations contain errors or if a model bias exists in the simulation of surface or soil temperatures. Both cases may lead to excessive melting or growth of snow packs, and to large alterations in both the energy and water balances. Such problems in the snow assimilation process, made evident by the repeated melting and replenishing of snow pack over significant areas of the United States, exists in the Eta Data Assimilation System and is a product of the EDAS system's direct insertion assimilation of snow data. Occurring on a 24 hour cycle, the repeated melting infuses the soil column with a large quantity of water that upsets the hydrological cycle. In an effort to quantify the impacts of such errors in snow assimilation on water and energy budgets, a series of Mosaic LSM simulations were performed over the 12 month period covering October 1998 to October 1999.

  6. Utilization of Hydrologic Remote Sensing Data in Land Surface Modeling and Data Assimilation: Current Status and Challenges

    Science.gov (United States)

    Kumar, Sujay V.; Peters-Lidard, Christa; Reichl, Rolf; Harrison, Kenneth; Santanello, Joseph

    2010-01-01

    Recent advances in remote sensing technologies have enabled the monitoring and measurement of the Earth's land surface at an unprecedented scale and frequency. The myriad of these land surface observations must be integrated with the state-of-the-art land surface model forecasts using data assimilation to generate spatially and temporally coherent estimates of environmental conditions. These analyses are of critical importance to real-world applications such as agricultural production, water resources management and flood, drought, weather and climate prediction. This need motivated the development of NASA Land Information System (LIS), which is an expert system encapsulating a suite of modeling, computational and data assimilation tools required to address challenging hydrological problems. LIS integrates the use of several community land surface models, use of ground and satellite based observations, data assimilation and uncertainty estimation techniques and high performance computing and data management tools to enable the assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. This presentation will focus on describing the results, challenges and lessons learned from the use of remote sensing data for improving land surface modeling, within LIS. More specifically, studies related to the improved estimation of soil moisture, snow and land surface temperature conditions through data assimilation will be discussed. The presentation will also address the characterization of uncertainty in the modeling process through Bayesian remote sensing and computational methods.

  7. Impact of high resolution land surface initialization in Indian summer monsoon simulation using a regional climate model

    Indian Academy of Sciences (India)

    C K Unnikrishnan; M Rajeevan; S Vijaya Bhaskara Rao

    2016-06-01

    The direct impact of high resolution land surface initialization on the forecast bias in a regional climatemodel in recent years over Indian summer monsoon region is investigated. Two sets of regional climatemodel simulations are performed, one with a coarse resolution land surface initial conditions and secondone used a high resolution land surface data for initial condition. The results show that all monsoonyears respond differently to the high resolution land surface initialization. The drought monsoon year2009 and extended break periods were more sensitive to the high resolution land surface initialization.These results suggest that the drought monsoon year predictions can be improved with high resolutionland surface initialization. Result also shows that there are differences in the response to the land surfaceinitialization within the monsoon season. Case studies of heat wave and a monsoon depression simulationshow that, the model biases were also improved with high resolution land surface initialization. Theseresults show the need for a better land surface initialization strategy in high resolution regional modelsfor monsoon forecasting.

  8. Benchmarking sensitivity of biophysical processes to leaf area changes in land surface models

    Science.gov (United States)

    Forzieri, Giovanni; Duveiller, Gregory; Georgievski, Goran; Li, Wei; Robestson, Eddy; Kautz, Markus; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

    2017-04-01

    Land surface models (LSM) are widely applied as supporting tools for policy-relevant assessment of climate change and its impact on terrestrial ecosystems, yet knowledge of their performance skills in representing the sensitivity of biophysical processes to changes in vegetation density is still limited. This is particularly relevant in light of the substantial impacts on regional climate associated with the changes in leaf area index (LAI) following the observed global greening. Benchmarking LSMs on the sensitivity of the simulated processes to vegetation density is essential to reduce their uncertainty and improve the representation of these effects. Here we present a novel benchmark system to assess model capacity in reproducing land surface-atmosphere energy exchanges modulated by vegetation density. Through a collaborative effort of different modeling groups, a consistent set of land surface energy fluxes and LAI dynamics has been generated from multiple LSMs, including JSBACH, JULES, ORCHIDEE, CLM4.5 and LPJ-GUESS. Relationships of interannual variations of modeled surface fluxes to LAI changes have been analyzed at global scale across different climatological gradients and compared with satellite-based products. A set of scoring metrics has been used to assess the overall model performances and a detailed analysis in the climate space has been provided to diagnose possible model errors associated to background conditions. Results have enabled us to identify model-specific strengths and deficiencies. An overall best performing model does not emerge from the analyses. However, the comparison with other models that work better under certain metrics and conditions indicates that improvements are expected to be potentially achievable. A general amplification of the biophysical processes mediated by vegetation is found across the different land surface schemes. Grasslands are characterized by an underestimated year-to-year variability of LAI in cold climates

  9. Modeling of ground temperatures in South Shetlands (Antarctic Peninsula): Forcing a land surface model with the reanalysis ERA-Interim

    Science.gov (United States)

    João Rocha, Maria; Dutra, Emanuel; Vieira, Gonçalo; Miranda, Pedro; Ramos, Miguel

    2010-05-01

    This study focus on Livingston Island (South Shetlands Antarctic Peninsula), one of the Earth's regions where warming has been more significant in the last 50 years. Our work is integrated in a project focusing on studying the influence of climate change on permafrost temperatures, which includes systematic and long-term terrain monitoring and also modeling using land surface models. A contribution will be the evaluation of the possibilities for using land surface modeling approaches to areas of the Antarctic Peninsula with lack of data on observational meteorological forcing data, as well as on permafrost temperatures. The climate variability of the Antarctic Peninsula region was studied using the new reanalysis product from European Centre for Medium-Range Weather Forecasts (ECMWF) Era-Interim and observational data from boreholes run by our group. Monthly and annual cycles of near surface climate variables are compared. The modeling approach includes the HTESSEL (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land) forced with ERA-Interim for modeling ground temperatures in the study region. The simulation results of run of HTESSEL are compared against soil temperature observations. The results show a favorable match between simulated and observed soil temperatures. The use of different forcing parameters is compared and the model vs. observation results from different results is analyzed. The main variable needing further improvement in the modeling is snow cover. The developed methodology provides a good tool for the analysis of the influence of climate variability on permafrost of the Maritime Antarctic.

  10. Sensitivity of snow cover to horizontal resolution in a land surface model

    Science.gov (United States)

    Dutra, E.; Kotlarski, S.; Viterbo, P.; Balsamo, G.; Miranda, P. M. A.; Schär, C.

    2010-09-01

    Snow cover is a highly variable land surface condition that exerts a strong control on the heat and moisture budget of the overlying atmosphere. Modeling studies based on long integrations of global circulation models (GCM) are normally carried out at very low resolution (typically coarser than 100 km) due to their high computational demand. On local scales, snow cover plays an important socioeconomic role, ranging from water management applications to outdoor recreation. These latter applications vary in horizontal resolution from a few hundred meters to a few kilometers, where small scale topography, land cover and local circulation effects play a significant role. In this study our focus will be on horizontal scales ranging from typical GCM global climate modeling to high resolution global weather forecasts. In the land surface component of a GCM (land surface model - LSM), snow cover temporal and spatial variability is mainly determined by the overlying atmospheric conditions. However, once snowfall settles on the ground, the sub-grid scale variability associated with complex terrain and land cover variability (not resolved at the model resolution) is parameterized following simple physical and/or empirical relations. The present study intends to access the impact of horizontal resolution in the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model (HTESSEL). HTESSEL is forced by the ECMWF operational weather forecasts since March 2006 to December 2009 (runs in offline/stand-alone mode). The control run is carried out at the horizontal resolution of the forecasts at TL799 (gaussian reduced grid N400 -about 25 km). Two lower horizontal resolutions are then tested: TL255 (gaussian reduced grid - about 80 km, same as the ERA-Interim reanalysis), and TL95 (gaussian reduced grid N48 - about 200 km). The length of the simulations is rather small (only 46 months), however global meteorological forcing at 25 km can only be accessed through the

  11. Estimation and Modelling of Land Surface Temperature Using Landsat 7 ETM+ Images and Fuzzy System Techniques

    Science.gov (United States)

    Bisht, K.; Dodamani, S. S.

    2016-12-01

    Modelling of Land Surface Temperature is essential for short term and long term management of environmental studies and management activities of the Earth's resources. The objective of this research is to estimate and model Land Surface Temperatures (LST). For this purpose, Landsat 7 ETM+ images period from 2007 to 2012 were used for retrieving LST and processed through MATLAB software using Mamdani fuzzy inference systems (MFIS), which includes pre-monsoon and post-monsoon LST in the fuzzy model. The Mangalore City of Karnataka state, India has been taken for this research work. Fuzzy model inputs are considered as the pre-monsoon and post-monsoon retrieved temperatures and LST was chosen as output. In order to develop a fuzzy model for LST, seven fuzzy subsets, nineteen rules and one output are considered for the estimation of weekly mean air temperature. These are very low (VL), low (L), medium low (ML), medium (M), medium high (MH), high (H) and very high (VH). The TVX (Surface Temperature Vegetation Index) and the empirical method have provided estimated LST. The study showed that the Fuzzy model M4/7-19-1 (model 4, 7 fuzzy sets, 19 rules and 1 output) which developed over Mangalore City has provided more accurate outcomes than other models (M1, M2, M3, M5). The result of this research was evaluated according to statistical rules. The best correlation coefficient (R) and root mean squared error (RMSE) between estimated and measured values for pre-monsoon and post-monsoon LST found to be 0.966 - 1.607 K and 0.963- 1.623 respectively.

  12. Multi-Spectral Satellite Imagery and Land Surface Modeling Supporting Dust Detection and Forecasting

    Science.gov (United States)

    Molthan, A.; Case, J.; Zavodsky, B.; Naeger, A. R.; LaFontaine, F.; Smith, M. R.

    2014-12-01

    Current and future multi-spectral satellite sensors provide numerous means and methods for identifying hazards associated with polluting aerosols and dust. For over a decade, the NASA Short-term Prediction Research and Transition (SPoRT) Center at Marshall Space Flight Center in Huntsville has focused on developing new applications from near real-time data sources in support of the operational weather forecasting community. The SPoRT Center achieves these goals by matching appropriate analysis tools, modeling outputs, and other products to forecast challenges, along with appropriate training and end-user feedback to ensure a successful transition. As a spinoff of these capabilities, the SPoRT Center has recently focused on developing collaborations to address challenges with the public health community, specifically focused on the identification of hazards associated with dust and pollution aerosols. Using multispectral satellite data from the SEVIRI instrument on the Meteosat series, the SPoRT team has leveraged EUMETSAT techniques for identifying dust through false color (RGB) composites, which have been used by the National Hurricane Center and other meteorological centers to identify, monitor, and predict the movement of dust aloft. Similar products have also been developed from the MODIS and VIIRS instruments onboard the Terra and Aqua, and Suomi-NPP satellites, respectively, and transitioned for operational forecasting use by offices within NOAA's National Weather Service. In addition, the SPoRT Center incorporates satellite-derived vegetation information and land surface modeling to create high-resolution analyses of soil moisture and other land surface conditions relevant to the lofting of wind-blown dust and identification of other, possible public-health vectors. Examples of land surface modeling and relevant predictions are shown in the context of operational decision making by forecast centers with potential future applications to public health arenas.

  13. Forward-looking Assimilation of MODIS-derived Snow Covered Area into a Land Surface Model

    Science.gov (United States)

    Zaitchik, Benjamin F.; Rodell, Matthew

    2008-01-01

    Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation SCA indicates only the presence or absence of snow, and not snow volume and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to non-physical artifacts in the local water balance. In this paper we present a novel assimilation algorithm that introduces MODIS SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm utilizes observations from up to 72 hours ahead of the model simulation in order to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes both during the snow season and, in some regions, on into the following spring.

  14. On the development of a coupled land surface and groundwater model

    Energy Technology Data Exchange (ETDEWEB)

    Maxwell, R.M.; Miller, N.L.

    2004-05-04

    Management of surface water quality is often complicated by interactions between surface water and groundwater. Traditional Land-Surface Models (LSM) used for numerical weather prediction, climate projection, and as inputs to water management decision support systems, do not treat the LSM lower boundary in a fully process-based fashion. LSMs have evolved from a leaky bucket to more sophisticated land surface water and energy budget models that typically have a so-called basement term to depict the bottom model layer exchange with deeper aquifers. Nevertheless, the LSM lower boundary is often assumed zero flux or the soil moisture content is set to a constant value; an approach that while mass conservative, ignores processes that can alter surface fluxes, runoff, and water quantity and quality. Conversely, groundwater models (GWM) for saturated and unsaturated water flow, while addressing important features such as subsurface heterogeneity and three-dimensional flow, often have overly simplified upper boundary conditions that ignore soil heating, runoff, snow and root-zone uptake. In the present study, a state-of-the-art LSM (CLM) and a variably-saturated GWM (ParFlow) have been coupled as a single column model. A set of simulations based on synthetic data and data from the Project for Intercomparison of Landsurface Parameterization Schemes (PILPS), version 2(d), 18-year dataset from Valdai, Russia demonstrate the temporal dynamics of this coupled modeling system. Changes in soil moisture and movement of the water table are used as indicators of mass conservation between the LSM and GWM. This study demonstrates the affect of aquifer storage and a dynamic water table on predicted watershed flow. The model's ability to capture certain cold processes such as frozen soil and freeze/thaw processes are discussed. Comparisons of the uncoupled and coupled modes are presented and the differences in simulations of soil moisture and shallow and deeper ground processes are

  15. Fuel moisture content estimation: a land-surface modelling approach applied to African savannas

    Science.gov (United States)

    Ghent, D.; Spessa, A.; Kaduk, J.; Balzter, H.

    2009-04-01

    Despite the importance of fire to the global climate system, in terms of emissions from biomass burning, ecosystem structure and function, and changes to surface albedo, current land-surface models do not adequately estimate key variables affecting fire ignition and propagation. Fuel moisture content (FMC) is considered one of the most important of these variables (Chuvieco et al., 2004). Biophysical models, with appropriate plant functional type parameterisations, are the most viable option to adequately predict FMC over continental scales at high temporal resolution. However, the complexity of plant-water interactions, and the variability associated with short-term climate changes, means it is one of the most difficult fire variables to quantify and predict. Our work attempts to resolve this issue using a combination of satellite data and biophysical modelling applied to Africa. The approach we take is to represent live FMC as a surface dryness index; expressed as the ratio between the Normalised Difference Vegetation Index (NDVI) and land-surface temperature (LST). It has been argued in previous studies (Sandholt et al., 2002; Snyder et al., 2006), that this ratio displays a statistically stronger correlation to FMC than either of the variables, considered separately. In this study, simulated FMC is constrained through the assimilation of remotely sensed LST and NDVI data into the land-surface model JULES (Joint-UK Land Environment Simulator). Previous modelling studies of fire activity in Africa savannas, such as Lehsten et al. (2008), have reported significant levels of uncertainty associated with the simulations. This uncertainty is important because African savannas are among some of the most frequently burnt ecosystems and are a major source of greenhouse trace gases and aerosol emissions (Scholes et al., 1996). Furthermore, regional climate model studies indicate that many parts of the African savannas will experience drier and warmer conditions in future

  16. Stable water isotopes in the coupled atmosphere–land surface model ECHAM5-JSBACH

    Directory of Open Access Journals (Sweden)

    B. Haese

    2013-09-01

    Full Text Available In this study we present first results of a new model development, ECHAM5-JSBACH-wiso, where we have incorporated the stable water isotopes H218O and HDO as tracers in the hydrological cycle of the coupled atmosphere–land surface model ECHAM5-JSBACH. The ECHAM5-JSBACH-wiso model was run under present-day climate conditions at two different resolutions (T31L19, T63L31. A comparison between ECHAM5-JSBACH-wiso and ECHAM5-wiso shows that the coupling has a strong impact on the simulated temperature and soil wetness. Caused by these changes of temperature and the hydrological cycle, the δ18O in precipitation also shows variations from −4‰ up to 4‰. One of the strongest anomalies is shown over northeast Asia where, due to an increase of temperature, the δ18O in precipitation increases as well. In order to analyze the sensitivity of the fractionation processes over land, we compare a set of simulations with various implementations of these processes over the land surface. The simulations allow us to distinguish between no fractionation, fractionation included in the evaporation flux (from bare soil and also fractionation included in both evaporation and transpiration (from water transport through plants fluxes. While the isotopic composition of the soil water may change for δ18O by up to +8&permil:, the simulated δ18O in precipitation shows only slight differences on the order of ±1‰. The simulated isotopic composition of precipitation fits well with the available observations from the GNIP (Global Network of Isotopes in Precipitation database.

  17. Comparing potential recharge estimates from three Land Surface Models across the western US

    Science.gov (United States)

    Niraula, Rewati; Meixner, Thomas; Ajami, Hoori; Rodell, Matthew; Gochis, David; Castro, Christopher L.

    2017-02-01

    Groundwater is a major source of water in the western US. However, there are limited recharge estimates in this region due to the complexity of recharge processes and the challenge of direct observations. Land surface Models (LSMs) could be a valuable tool for estimating current recharge and projecting changes due to future climate change. In this study, simulations of three LSMs (Noah, Mosaic and VIC) obtained from the North American Land Data Assimilation System (NLDAS-2) are used to estimate potential recharge in the western US. Modeled recharge was compared with published recharge estimates for several aquifers in the region. Annual recharge to precipitation ratios across the study basins varied from 0.01% to 15% for Mosaic, 3.2% to 42% for Noah, and 6.7% to 31.8% for VIC simulations. Mosaic consistently underestimates recharge across all basins. Noah captures recharge reasonably well in wetter basins, but overestimates it in drier basins. VIC slightly overestimates recharge in drier basins and slightly underestimates it for wetter basins. While the average annual recharge values vary among the models, the models were consistent in identifying high and low recharge areas in the region. Models agree in seasonality of recharge occurring dominantly during the spring across the region. Overall, our results highlight that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating future recharge in data limited regions.

  18. Modelling Hydrological Processes in Presence of Uncertain or Unreliable Forcing Data and Land Surface Parameters

    Science.gov (United States)

    Gusev, Ye. M.; Nasonova, O. N.; Dzhogan, L. Ya.

    2009-04-01

    Construction of a model for simulating hydrological processes, to our opinion, should be based on mathematical description of the real physical heat and water exchange processes occurring in a soil - vegetation/snow cover - atmosphere system rather than on available data. This allows one to create more universal model, which can be applied at different temporal and spatial scales and under different natural conditions. More than that, such a model can be applied for poorly-gauged basins and in the presence of uncertain/unreliable forcing data and land surface parameters, provided that reliable runoff measurements are available at least for several years. The latter is necessary for model calibration to reduce the impact of uncertainties in input data on model results. The present work is intended to confirm the above statements using the land surface model SWAP (Soil Water - Atmosphere - Plants). SWAP is a physically-based model describing the processes of heat and water exchange within a soil-vegetation/snow cover-atmosphere system (SVAS). The model can be applied both for point (or grid cell) simulations of vertical fluxes and state variables of SVAS in atmospheric science applications, and for simulating streamflow on different scales — from small catchments to continental scale river basins. The results of model validations have demonstrated that SWAP is able to reproduce (without calibration) heat and water exchange processes (in particular, hydrological processes) adequately, provided that input data of high quality are available. In poorly-gauged basins, alternative sources of information should be used. Here, the global data sets on forcing data and land surface parameters were used for simulating streamflow from two pan-Arctic river basins (the Mezen and the Pechora basins with an area of 78 000 and 324 000 sq.km, respectively), located in the northeast part of the European Russia. The Mezen and the Pechora basins were represented for modeling purposes

  19. Recent Progresses in Incorporating Human Land-Water Management into Global Land Surface Models Toward Their Integration into Earth System Models

    Science.gov (United States)

    Pokhrel, Yadu N.; Hanasaki, Naota; Wada, Yoshihide; Kim, Hyungjun

    2016-01-01

    The global water cycle has been profoundly affected by human land-water management. As the changes in the water cycle on land can affect the functioning of a wide range of biophysical and biogeochemical processes of the Earth system, it is essential to represent human land-water management in Earth system models (ESMs). During the recent past, noteworthy progress has been made in large-scale modeling of human impacts on the water cycle but sufficient advancements have not yet been made in integrating the newly developed schemes into ESMs. This study reviews the progresses made in incorporating human factors in large-scale hydrological models and their integration into ESMs. The study focuses primarily on the recent advancements and existing challenges in incorporating human impacts in global land surface models (LSMs) as a way forward to the development of ESMs with humans as integral components, but a brief review of global hydrological models (GHMs) is also provided. The study begins with the general overview of human impacts on the water cycle. Then, the algorithms currently employed to represent irrigation, reservoir operation, and groundwater pumping are discussed. Next, methodological deficiencies in current modeling approaches and existing challenges are identified. Furthermore, light is shed on the sources of uncertainties associated with model parameterizations, grid resolution, and datasets used for forcing and validation. Finally, representing human land-water management in LSMs is highlighted as an important research direction toward developing integrated models using ESM frameworks for the holistic study of human-water interactions within the Earths system.

  20. Noah-MP-Crop: Introducing dynamic crop growth in the Noah-MP land surface model

    Science.gov (United States)

    Liu, Xing; Chen, Fei; Barlage, Michael; Zhou, Guangsheng; Niyogi, Dev

    2016-12-01

    Croplands are important in land-atmosphere interactions and in the modification of local and regional weather and climate; however, they are poorly represented in the current version of the coupled Weather Research and Forecasting/Noah with multiparameterization (Noah-MP) land surface modeling system. This study introduced dynamic corn (Zea mays) and soybean (Glycine max) growth simulations and field management (e.g., planting date) into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at field scales using crop biomass data sets, surface heat fluxes, and soil moisture observations. Compared to the generic dynamic vegetation and prescribed-leaf area index (LAI)-driven methods in Noah-MP, the Noah-MP-Crop showed improved performance in simulating leaf area index (LAI) and crop biomass. This model is able to capture the seasonal and annual variability of LAI and to differentiate corn and soybean in peak values of LAI as well as the length of growing seasons. Improved simulations of crop phenology in Noah-MP-Crop led to better surface heat flux simulations, especially in the early period of growing season where current Noah-MP significantly overestimated LAI. The addition of crop yields as model outputs expand the application of Noah-MP-Crop to regional agriculture studies. There are limitations in the use of current growing degree days (GDD) criteria to predict growth stages, and it is necessary to develop a new method that combines GDD with other environmental factors, to more accurately define crop growth stages. The capability introduced in Noah-MP allows further crop-related studies and development.

  1. Utilization of satellite-derived estimates of meteorological and land surface characteristics in the Land Surface Model for vast agricultural region territory

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Volkova, Elena

    2015-04-01

    The method has been elaborated to evaluate the water and heat regime characteristics of the territory on a regional scale for the vegetation season based on a physical-mathematical model of water and heat exchange between vegetation covered land surface and atmosphere (LSM, Land Surface Model) appropriate for using satellite information on land surface and meteorological conditions. The developed model is intended for calculating soil water content, evapotranspiration (evaporation from bare soil and transpiration by vegetation), vertical water and heat fluxes as well as land surface and vegetation cover temperatures and vertical distributions of temperature and moisture in the active soil layer. Parameters of the model are soil and vegetation characteristics and input variables are meteorological characteristics. Their values have been obtained from ground-based observations at agricultural meteorological stations and satellite-based measurements by scanning radiometers AVHRR/NOAA, MODIS/EOS Terra and Aqua and SEVIRI (geostationary satellites Meteosat-9, -10). The AVHRR data have been used to build the estimates of three types of land surface temperature (LST): land skin temperature Tsg, air temperature at a level of vegetation cover Ta and efficient radiation temperature Tseff, emissivity E, normalized vegetation index NDVI, vegetation cover fraction B, leaf area index LAI, and precipitation. The set of estimates derived from MODIS data has comprised values of LST Tls, E, NDVI and LAI. The SEVIRI-based retrievals have included Tls, Ta, Е at daylight and nighttime, LAI (daily) and precipitation. The case study has been carried out for agricultural Central Black Earth region of the European Russia of 227,300 sq.km containing 7 regions of the Russian Federation for years 2009-2013 vegetation seasons. Estimates of described characteristics have been built with the help of the developed original and improved pre-existing methods and technologies of thematic processing

  2. The estimation of future surface water bodies at Olkiluoto area based on statistical terrain and land uplift models

    Energy Technology Data Exchange (ETDEWEB)

    Pohjola, J.; Turunen, J.; Lipping, T. [Tampere Univ. of Technology (Finland); Ikonen, A.

    2014-03-15

    In this working report the modelling effort of future landscape development and surface water body formation at the modelling area in the vicinity of the Olkiluoto Island is presented. Estimation of the features of future surface water bodies is based on probabilistic terrain and land uplift models presented in previous working reports. The estimation is done using a GIS-based toolbox called UNTAMO. The future surface water bodies are estimated in 10 000 years' time span with 1000 years' intervals for the safety assessment of disposal of spent nuclear fuel at the Olkiluoto site. In the report a brief overview on the techniques used for probabilistic terrain modelling, land uplift modelling and hydrological modelling are presented first. The latter part of the report describes the results of the modelling effort. The main features of the future landscape - the four lakes forming in the vicinity of the Olkiluoto Island - are identified and the probabilistic model of the shoreline displacement is presented. The area and volume of the four lakes is modelled in a probabilistic manner. All the simulations have been performed for three scenarios two of which are based on 10 realizations of the probabilistic digital terrain model (DTM) and 10 realizations of the probabilistic land uplift model. These two scenarios differ from each other by the eustatic curve used in the land uplift model. The third scenario employs 50 realizations of the probabilistic DTM while a deterministic land uplift model, derived solely from the current land uplift rate, is used. The results indicate that the two scenarios based on the probabilistic land uplift model behave in a similar manner while the third model overestimates past and future land uplift rates. The main features of the landscape are nevertheless similar also for the third scenario. Prediction results for the volumes of the future lakes indicate that a couple of highly probably lake formation scenarios can be identified

  3. Variability of basin scale water resources indicators derived from global hydrological and land surface models

    Science.gov (United States)

    Werner, Micha; Blyth, Eleanor; Schellekens, Jaap

    2016-04-01

    Global hydrological and land-surface models are becoming increasingly available, and as the resolution of these improves, as well how hydrological processes are represented, so does their potential. These offer consistent datasets at the global scale, which can be used to establish water balances and derive policy relevant indicators in medium to large basins, including those that are poorly gauged. However, differences in model structure, model parameterisation, and model forcing may result in quite different indicator values being derived, depending on the model used. In this paper we explore indicators developed using four land surface models (LSM) and five global hydrological models (GHM). Results from these models have been made available through the Earth2Observe project, a recent research initiative funded by the European Union 7th Research Framework. All models have a resolution of 0.5 arc degrees, and are forced using the same WATCH-ERA-Interim (WFDEI) meteorological re-analysis data at a daily time step for the 32 year period from 1979 to 2012. We explore three water resources indicators; an aridity index, a simplified water exploitation index; and an indicator that calculates the frequency of occurrence of root zone stress. We compare indicators derived over selected areas/basins in Europe, Colombia, Southern Africa, the Indian Subcontinent and Australia/New Zealand. The hydrological fluxes calculated show quite significant differences between the nine models, despite the common forcing dataset, with these differences reflected in the indicators subsequently derived. The results show that the variability between models is related to the different climates types, with that variability quite logically depending largely on the availability of water. Patterns are also found in the type of models that dominate different parts of the distribution of the indicator values, with LSM models providing lower values, and GHM models providing higher values in some

  4. Evaluation of the JULES land surface model in simulating catchment hydrology in Southern Africa

    Directory of Open Access Journals (Sweden)

    N. C. MacKellar

    2013-08-01

    Full Text Available Land surface models (LSMs are advanced tools which can be used to estimate energy, water and biogeochemical exchanges at regional scales. The inclusion of a river flow routing module in an LSM allows for the simulation of river discharge from a catchment and offers an approach to evaluate the response of the system to variations in climate and land-use, which can provide useful information for regional water resource management. This study offers insight into some of the pragmatic considerations of applying an LSM over a regional domain in Southern Africa. The objectives are to identify key parameter sensitivities and investigate differences between two runoff production schemes in physically contrasted catchments. The Joint UK Land Environment Simulator (JULES LSM was configured for a domain covering Southern Africa at a 0.5° resolution. The model was forced with meteorological input from the WATCH Forcing Data for the period 1981–2001 and sensitivity to various model configurations and parameter settings were tested. Both the PDM and TOPMODEL sub-grid scale runoff generation schemes were tested for parameter sensitivities, with the evaluation focussing on simulated river discharge in sub-catchments of the Orange, Okavango and Zambezi rivers. It was found that three catchments respond differently to the model configurations and there is no single runoff parameterization scheme or parameter values that yield optimal results across all catchments. The PDM scheme performs well in the upper Orange catchment, but poorly in the Okavango and Zambezi, whereas TOPMODEL grossly underestimates discharge in the upper Orange and shows marked improvement over PDM for the Okavango and Zambezi. A major shortcoming of PDM is that it does not realistically represent subsurface runoff in the deep, porous soils typical of the Okavango and Zambezi headwaters. The dry-season discharge in these catchments is therefore not replicated by PDM. TOPMODEL, however

  5. Evaluation of streamflow simulation results of land surface models in GLDAS on the Tibetan plateau

    Science.gov (United States)

    Bai, Peng; Liu, Xiaomang; Yang, Tiantian; Liang, Kang; Liu, Changming

    2016-10-01

    The Global Land Data Assimilation System (GLDAS) project estimates long-term runoff based on land surface models (LSMs) and provides a potential way to solve the issue of nonexistent streamflow data in gauge-sparse regions such as the Tibetan Plateau (TP). However, the reliability of GLDAS runoff data must be validated before being practically applied. In this study, the streamflows simulated by four LSMs (CLM, Noah, VIC, and Mosaic) in GLDAS coupled with a river routing model are evaluated against observed streamflows in five river basins on the TP. The evaluation criteria include four aspects: monthly streamflow value, seasonal cycle of streamflow, annual streamflow trend, and streamflow component partitioning. The four LSMs display varying degrees of biases in monthly streamflow simulations: systematic overestimations are found in the Noah (1.74 ≤ bias ≤ 2.75) and CLM (1.22 ≤ bias ≤ 2.53) models, whereas systematic underestimations are observed in the VIC (0.36 ≤ bias ≤ 0.85) and Mosaic (0.34 ≤ bias ≤ 0.66) models. The Noah model shows the best performance in capturing the temporal variation in monthly streamflow and the seasonal cycle of streamflow, while the VIC model performs the best in terms of bias statistics. The Mosaic model provides the best performance in modeling annual runoff trends and runoff component partitioning. The possible reasons for the different performances of the LSMs are discussed in detail. In order to achieve more accurate streamflow simulations from the LSMs in GLDAS, suggestions are made to further improve the accuracy of the forcing data and parameterization schemes in all models.

  6. Utilizing CLASIC observations and multiscale models to study the impact of improved Land surface representation on modeling cloud- convection

    Energy Technology Data Exchange (ETDEWEB)

    Niyogi, Devdutta S. [Purdue

    2013-06-07

    The CLASIC experiment was conducted over the US southern great plains (SGP) in June 2007 with an objective to lead an enhanced understanding of the cumulus convection particularly as it relates to land surface conditions. This project was design to help assist with understanding the overall improvement of land atmosphere convection initiation representation of which is important for global and regional models. The study helped address one of the critical documented deficiency in the models central to the ARM objectives for cumulus convection initiation and particularly under summer time conditions. This project was guided by the scientific question building on the CLASIC theme questions: What is the effect of improved land surface representation on the ability of coupled models to simulate cumulus and convection initiation? The focus was on the US Southern Great Plains region. Since the CLASIC period was anomalously wet the strategy has been to use other periods and domains to develop the comparative assessment for the CLASIC data period, and to understand the mechanisms of the anomalous wet conditions on the tropical systems and convection over land. The data periods include the IHOP 2002 field experiment that was over roughly same domain as the CLASIC in the SGP, and some of the DOE funded Ameriflux datasets.

  7. Water balance in the Amazon basin from a land surface model ensemble

    Energy Technology Data Exchange (ETDEWEB)

    Getirana, Augusto; Dutra, Emanuel; Guimberteau, Matthieu; Kam, Jonghun; Li, Hongyi; Decharme, Bertrand; Zhang, Zhengqiu J.; Ducharne, Agnes; Boone, Aaron; Balsamo, Gianpaolo; Rodell, Matthew; Mounirou Toure, Ally; Xue, Yongkang; Peters-Lidard, Christa D.; Kumar, Sujay V.; Arsenault, Kristi Rae; Drapeau, Guillaume; Leung, Lai-Yung R.; Ronchail, Josyane; Sheffield, Justin

    2014-12-06

    Despite recent advances in modeling and remote sensing of land surfaces, estimates of the global water budget are still fairly uncertain. The objective of this study is to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables [total water storage (TWS), evapotranspiration (ET), surface runoff (R) and baseflow (B)] are evaluated at the basin scale using both remote sensing and in situ data. Fourteen LSMs were run using meteorological forcings at a 3-hourly time step and 1-degree spatial resolution. Three experiments are performed using precipitation which has been rescaled to match monthly global GPCP and GPCC datasets and the daily HYBAM dataset for the Amazon basin. R and B are used to force the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration, and GRACE TWS estimates in different catchments. At the basin scale, simulated ET ranges from 2.39mm.d-1 to 3.26mm.d-1 and a low spatial correlation between ET and P indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget variables vary significantly as a function of both the LSM and precipitation used, but simulated TWS generally agree at the basin scale. The best water budget simulations resulted from experiments using the HYBAM dataset, mostly explained by a denser rainfall gauge network the daily rescaling.

  8. How much does weather-driven vegetation dynamics matter in land surface modelling?

    Science.gov (United States)

    Ingwersen, Joachim; Streck, Thilo

    2016-04-01

    Land surface models (LSM) are an essential part of weather and climate models as they provide the lower boundary condition for the atmospheric models. In state-of-the-art LSMs the seasonal vegetation dynamics is "frozen". The seasonal variation of vegetation state variables, such as leaf area index or green vegetation fraction, are prescribed in lookup tables. Hence, a year-by-year variation in the development of vegetation due to changing weather conditions cannot be considered. For climate simulations, this is obviously a severe drawback. The objective of the present study was to quantify the potential error in the simulation of land surface exchange processes resulting from "frozen" vegetation dynamics. For this purpose we simulated energy and water fluxes from a winter wheat stand and a maize stand in Southwest Germany. In a first set of simulations, six years (2010 to 2015) were simulated considering weather-driven vegetation dynamics. For this purpose, we coupled the generic crop growth model GECROS with the NOAH-MP model (NOAHMP-GECROS). In a second set of simulations all vegetation-related state variables of the 2010 simulation were written to an external file and were used to overwrite the vegetation-related state variables of the simulations of the years 2011-2015. The difference between both sets was taken as a measure for the potential error introduced to the LSM due to the assumption of a "frozen" vegetation dynamics. We will present first results and discuss the impact of "frozen" vegetation dynamics on climate change simulations.

  9. Reinitialised versus continuous regional climate simulations using ALARO-0 coupled to the land surface model SURFEXv5

    Science.gov (United States)

    Berckmans, Julie; Giot, Olivier; De Troch, Rozemien; Hamdi, Rafiq; Ceulemans, Reinhart; Termonia, Piet

    2017-01-01

    Dynamical downscaling in a continuous approach using initial and boundary conditions from a reanalysis or a global climate model is a common method for simulating the regional climate. The simulation potential can be improved by applying an alternative approach of reinitialising the atmosphere, combined with either a daily reinitialised or a continuous land surface. We evaluated the dependence of the simulation potential on the running mode of the regional climate model ALARO coupled to the land surface model Météo-France SURFace EXternalisée (SURFEX), and driven by the ERA-Interim reanalysis. Three types of downscaling simulations were carried out for a 10-year period from 1991 to 2000, over a western European domain at 20 km horizontal resolution: (1) a continuous simulation of both the atmosphere and the land surface, (2) a simulation with daily reinitialisations for both the atmosphere and the land surface and (3) a simulation with daily reinitialisations of the atmosphere while the land surface is kept continuous. The results showed that the daily reinitialisation of the atmosphere improved the simulation of the 2 m temperature for all seasons. It revealed a neutral impact on the daily precipitation totals during winter, but the results were improved for the summer when the land surface was kept continuous. The behaviour of the three model configurations varied among different climatic regimes. Their seasonal cycle for the 2 m temperature and daily precipitation totals was very similar for a Mediterranean climate, but more variable for temperate and continental climate regimes. Commonly, the summer climate is characterised by strong interactions between the atmosphere and the land surface. The results for summer demonstrated that the use of a daily reinitialised atmosphere improved the representation of the partitioning of the surface energy fluxes. Therefore, we recommend using the alternative approach of the daily reinitialisation of the atmosphere for

  10. Enhancing Global Land Surface Hydrology Estimates from the NASA MERRA Reanalysis Using Precipitation Observations and Model Parameter Adjustments

    Science.gov (United States)

    Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally

    2011-01-01

    The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.

  11. Enhancing Global Land Surface Hydrology Estimates from the NASA MERRA Reanalysis Using Precipitation Observations and Model Parameter Adjustments

    Science.gov (United States)

    Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally

    2011-01-01

    The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.

  12. Global Assessment of Land Surface Temperature From Geostationary Satellites and Model Estimates

    Science.gov (United States)

    Reichle, Rolf H.; Liu, Q.; Minnis, P.; daSilva, A. M., Jr.; Palikonda, R.; Yost, C. R.

    2012-01-01

    Land surface (or 'skin') temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research we compare two global and independent data sets: (i) LST retrievals from five geostationary satellites generated at the NASA Langley Research Center (LaRC) and (ii) LST estimates from the quasi-operational NASA GEOS-5 global modeling and assimilation system. The objective is to thoroughly understand both data sets and their systematic differences in preparation for the assimilation of the LaRC LST retrievals into GEOS-5. As expected, mean differences (MD) and root-mean-square differences (RMSD) between modeled and retrieved LST vary tremendously by region and time of day. Typical (absolute) MD values range from 1-3 K in Northern Hemisphere mid-latitude regions to near 10 K in regions where modeled clouds are unrealistic, for example in north-eastern Argentina, Uruguay, Paraguay, and southern Brazil. Typically, model estimates of LST are higher than satellite retrievals during the night and lower during the day. RMSD values range from 1-3 K during the night to 2-5 K during the day, but are larger over the 50-120 W longitude band where the LST retrievals are derived from the FY2E platform

  13. A multi-layer land surface energy budget model for implicit coupling with global atmospheric simulations

    Directory of Open Access Journals (Sweden)

    J. Ryder

    2014-12-01

    Full Text Available In Earth system modelling, a description of the energy budget of the vegetated surface layer is fundamental as it determines the meteorological conditions in the planetary boundary layer and as such contributes to the atmospheric conditions and its circulation. The energy budget in most Earth system models has long been based on a "big-leaf approach", with averaging schemes that represent in-canopy processes. Such models have difficulties in reproducing consistently the energy balance in field observations. We here outline a newly developed numerical model for energy budget simulation, as a component of the land surface model ORCHIDEE-CAN (Organising Carbon and Hydrology In Dynamic Ecosystems – CANopy. This new model implements techniques from single-site canopy models in a practical way. It includes representation of in-canopy transport, a multilayer longwave radiation budget, height-specific calculation of aerodynamic and stomatal conductance, and interaction with the bare soil flux within the canopy space. Significantly, it avoids iterations over the height of tha canopy and so maintains implicit coupling to the atmospheric model LMDz. As a first test, the model is evaluated against data from both an intensive measurement campaign and longer term eddy covariance measurements for the intensively studied Eucalyptus stand at Tumbarumba, Australia. The model performs well in replicating both diurnal and annual cycles of fluxes, as well as the gradients of sensible heat fluxes. However, the model overestimates sensible heat flux against an underestimate of the radiation budget. Improved performance is expected through the implementation of a more detailed calculation of stand albedo and a more up-to-date stomatal conductance calculation.

  14. A novel assessment of the role of land-use and land-cover change in the global carbon cycle, using a new Dynamic Global Vegetation Model version of the CABLE land surface model

    Science.gov (United States)

    Haverd, Vanessa; Smith, Benjamin; Nieradzik, Lars; Briggs, Peter; Canadell, Josep

    2017-04-01

    In recent decades, terrestrial ecosystems have sequestered around 1.2 PgC y-1, an amount equivalent to 20% of fossil-fuel emissions. This land carbon flux is the net result of the impact of changing climate and CO2 on ecosystem productivity (CO2-climate driven land sink ) and deforestation, harvest and secondary forest regrowth (the land-use change (LUC) flux). The future trajectory of the land carbon flux is highly dependent upon the contributions of these processes to the net flux. However their contributions are highly uncertain, in part because the CO2-climate driven land sink and LUC components are often estimated independently, when in fact they are coupled. We provide a novel assessment of global land carbon fluxes (1800-2015) that integrates land-use effects with the effects of changing climate and CO2 on ecosystem productivity. For this, we use a new land-use enabled Dynamic Global Vegetation Model (DGVM) version of the CABLE land surface model, suitable for use in attributing changes in terrestrial carbon balance, and in predicting changes in vegetation cover and associated effects on land-atmosphere exchange. In this model, land-use-change is driven by prescribed gross land-use transitions and harvest areas, which are converted to changes in land-use area and transfer of carbon between pools (soil, litter, biomass, harvested wood products and cleared wood pools). A novel aspect is the treatment of secondary woody vegetation via the coupling between the land-use module and the POP (Populations Order Physiology) module for woody demography and disturbance-mediated landscape heterogeneity. Land-use transitions to and from secondary forest tiles modify the patch age distribution within secondary-vegetated tiles, in turn affecting biomass accumulation and turnover rates and hence the magnitude of the secondary forest sink. The resulting secondary forest patch age distribution also influences the magnitude of the secondary forest harvest and clearance fluxes

  15. Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water

    Science.gov (United States)

    Wood, Eric F.; Roundy, Joshua K.; Troy, Tara J.; van Beek, L. P. H.; Bierkens, Marc F. P.; 4 Blyth, Eleanor; de Roo, Ad; Doell. Petra; Ek, Mike; Famiglietti, James; Gochis, David; van de Giesen, Nick; Houser, Paul; Jaffe, Peter R.; Kollet, Stefan; Lehner, Bernhard; Lettenmaier, Dennis P.; Peters-Lidard, Christa; Sivpalan, Murugesu; Sheffield, Justin; Wade, Andrew; Whitehead, Paul

    2011-01-01

    Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (approx.10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 10(exp 9) unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a grand challenge to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

  16. How can we use MODIS land surface temperature to validate long-term urban model simulations?

    Science.gov (United States)

    Hu, Leiqiu; Brunsell, Nathaniel A.; Monaghan, Andrew J.; Barlage, Michael; Wilhelmi, Olga V.

    2014-03-01

    High spatial resolution urban climate modeling is essential for understanding urban climatology and predicting the human health impacts under climate change. Satellite thermal remote-sensing data are potential observational sources for urban climate model validation with comparable spatial scales, temporal consistency, broad coverage, and long-term archives. However, sensor view angle, cloud distribution, and cloud-contaminated pixels can confound comparisons between satellite land surface temperature (LST) and modeled surface radiometric temperature. The impacts of sensor view angles on urban LST values are investigated and addressed. Three methods to minimize the confounding factors of clouds are proposed and evaluated using 10years of Moderate Resolution Imaging Spectroradiometer (MODIS) data and simulations from the High-Resolution Land Data Assimilation System (HRLDAS) over Greater Houston, Texas, U.S. For the satellite cloud mask (SCM) method, prior to comparison, the cloud mask for each MODIS scene is applied to its concurrent HRLDAS simulation. For the max/min temperature (MMT) method, the 50 warmest days and coolest nights for each data set are selected and compared to avoid cloud impacts. For the high clear-sky fraction (HCF) method, only those MODIS scenes that have a high percentage of clear-sky pixels are compared. The SCM method is recommended for validation of long-term simulations because it provides the largest sample size as well as temporal consistency with the simulations. The MMT method is best for comparison at the extremes. And the HCF method gives the best absolute temperature comparison due to the spatial and temporal consistency between simulations and observations.

  17. Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data

    NARCIS (Netherlands)

    Sutanudjaja, E.H.; Beek, L.P.H. van; Jong, S.M. de; Geer, F.C. van; Bierkens, M.F.P.

    2014-01-01

    We explore the possibility of using remotely sensed soil moisture data and in situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically based and fully coupled groundwater-land surface model operating at a daily basis and havi

  18. Can satellite land surface temperature data be used similarly to ground discharge measurements for distributed hydrological model calibration?

    NARCIS (Netherlands)

    Corbari, C.; Mancini, M.; Li, J.; Su, Zhongbo

    2015-01-01

    This study proposes a new methodology for the calibration of distributed hydrological models at basin scale by constraining an internal model variable using satellite data of land surface temperature. The model algorithm solves the system of energy and mass balances in terms of a representative equi

  19. Landing Site Selection and Surface Traverse Planning using the Lunar Mapping & Modeling Portal

    Science.gov (United States)

    Law, E.; Chang, G.; Bui, B.; Sadaqathullah, S.; Kim, R.; Dodge, K.; Malhotra, S.

    2013-12-01

    Introduction: The Lunar Mapping and Modeling Portal (LMMP), is a web-based Portal and a suite of interactive visualization and analysis tools for users to access mapped lunar data products (including image mosaics, digital elevation models, etc.) from past and current lunar missions (e.g., Lunar Reconnaissance Orbiter, Apollo, etc.), and to perform in-depth analyses to support lunar surface mission planning and system design for future lunar exploration and science missions. It has been widely used by many scientists mission planners, as well as educators and public outreach (e.g., Google Lunar XPRICE teams, RESOLVE project, museums etc.) This year, LMMP was used by the Lunar and Planetary Institute (LPI)'s Lunar Exploration internship program to perform lighting analysis and local hazard assessments, such as, slope, surface roughness and crater/boulder distribution to research landing sites and surface pathfinding and traversal. Our talk will include an overview of LMMP, a demonstration of the tools as well as a summary of the LPI Lunar Exploration summer interns' experience in using those tools.

  20. Simulation of snow microwave radiance observations using a coupled land surface- radiative transfer models

    Science.gov (United States)

    Toure, A. M.; Rodell, M.; Hoar, T. J.; Kwon, Y.; Yang, Z.; Zhang, Y.; Beaudoing, H.

    2013-12-01

    Radiance assimilation (RA) has been used in operational numerical weather forecasting for generating realistic initial and boundary conditions for the last two decades. Previous studies have shown that the same approach can be used to characterize seasonal snow. Since the penetration depth of microwaves depends essentially on snow physical properties, studies have also shown that for RA to be successful, it is crucial that the land surface model (LSM) represents with great fidelity snow physical properties such as the effective grain size, the temperature, the stratigraphy, the densification and the melt/refreeze processes. The Community Land Model version 4 (CLM4), the land model component of the Community Earth System Model (CESM), describes the physical, chemical, biological, and hydrological processes by which terrestrial ecosystems interact with climate across a variety of spatial and temporal scales. Sub-grid heterogeneity of the CLM4 is represented by fractional coverage of glacier, lake, wetland, urban, and vegetation land cover types. The vegetation portion is further divided into mosaic of plant functional types (pfts) each with its own leaf and stem area index and canopy height. Processes such as snow accumulation, depletion, densification, metamorphism, percolation, and refreezing of water are represented by a state-of-the-art multi-layer (up to five layers) snow model. Each snow layer is characterized by its thickness, ice mass, liquid water content, temperature, and effective grain radius. The model is considered to be one of the most sophisticated snow models ever within a general circulation model. One of the main challenges in simulating the radiance observed by a radiometer on-board a satellite is the spatial heterogeneity of the land within the footprint of the radiometer. Since CLM4 has the capability to represent the sub-grid heterogeneity, it is perfect candidate for a model operator for simulating the observed brightness temperature (Tb). The

  1. Assessment of roughness length schemes implemented within the Noah land surface model for high-altitude regions

    NARCIS (Netherlands)

    Zheng, D.; Velde, van der R.; Su, Z.; Booij, M.J.; Hoekstra, A.Y.; Wen, J.

    2014-01-01

    Current land surface models still have difficulties with producing reliable surface heat fluxes and skin temperature (Tsfc) estimates for high-altitude regions, which may be addressed via adequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer. In this study, th

  2. A MODELING APPROACH FOR ESTIMATING WATERSHED IMPERVIOUS SURFACE AREA FROM NATIONAL LAND COVER DATA 92

    Science.gov (United States)

    We used National Land Cover Data 92 (NLCD92), vector impervious surface data, and raster GIS overlay methods to derive impervious surface coefficients per NLCD92 class in portions of the Nfid-Atlantic physiographic region. The methods involve a vector to raster conversion of the ...

  3. High-resolution climate and land surface interactions modeling over Belgium: current state and decennial scale projections

    Science.gov (United States)

    Jacquemin, Ingrid; Henrot, Alexandra-Jane; Beckers, Veronique; Berckmans, Julie; Debusscher, Bos; Dury, Marie; Minet, Julien; Hamdi, Rafiq; Dendoncker, Nicolas; Tychon, Bernard; Hambuckers, Alain; François, Louis

    2016-04-01

    The interactions between land surface and climate are complex. Climate changes can affect ecosystem structure and functions, by altering photosynthesis and productivity or inducing thermal and hydric stresses on plant species. These changes then impact socio-economic systems, through e.g., lower farming or forestry incomes. Ultimately, it can lead to permanent changes in land use structure, especially when associated with other non-climatic factors, such as urbanization pressure. These interactions and changes have feedbacks on the climate systems, in terms of changing: (1) surface properties (albedo, roughness, evapotranspiration, etc.) and (2) greenhouse gas emissions (mainly CO2, CH4, N2O). In the framework of the MASC project (« Modelling and Assessing Surface Change impacts on Belgian and Western European climate »), we aim at improving regional climate model projections at the decennial scale over Belgium and Western Europe by combining high-resolution models of climate, land surface dynamics and socio-economic processes. The land surface dynamics (LSD) module is composed of a dynamic vegetation model (CARAIB) calculating the productivity and growth of natural and managed vegetation, and an agent-based model (CRAFTY), determining the shifts in land use and land cover. This up-scaled LSD module is made consistent with the surface scheme of the regional climate model (RCM: ALARO) to allow simulations of the RCM with a fully dynamic land surface for the recent past and the period 2000-2030. In this contribution, we analyze the results of the first simulations performed with the CARAIB dynamic vegetation model over Belgium at a resolution of 1km. This analysis is performed at the species level, using a set of 17 species for natural vegetation (trees and grasses) and 10 crops, especially designed to represent the Belgian vegetation. The CARAIB model is forced with surface atmospheric variables derived from the monthly global CRU climatology or ALARO outputs

  4. Water Balance in the Amazon Basin from a Land Surface Model Ensemble

    Science.gov (United States)

    Getirana, Augusto C. V.; Dutra, Emanuel; Guimberteau, Matthieu; Kam, Jonghun; Li, Hong-Yi; Decharme, Bertrand; Zhang, Zhengqiu; Ducharne, Agnes; Boone, Aaron; Balsamo, Gianpaolo; Rodell, Matthew; Toure, Ally M.; Xue, Yongkang; Peters-Lidard, Christa D.; Kumar, Sujay V.; Arsenault, Kristi; Drapeau, Guillaume; Leung, L. Ruby; Ronchail, Josyane; Sheffield, Justin

    2014-01-01

    Despite recent advances in land surfacemodeling and remote sensing, estimates of the global water budget are still fairly uncertain. This study aims to evaluate the water budget of the Amazon basin based on several state-ofthe- art land surface model (LSM) outputs. Water budget variables (terrestrial water storage TWS, evapotranspiration ET, surface runoff R, and base flow B) are evaluated at the basin scale using both remote sensing and in situ data. Meteorological forcings at a 3-hourly time step and 18 spatial resolution were used to run 14 LSMs. Precipitation datasets that have been rescaled to matchmonthly Global Precipitation Climatology Project (GPCP) andGlobal Precipitation Climatology Centre (GPCC) datasets and the daily Hydrologie du Bassin de l'Amazone (HYBAM) dataset were used to perform three experiments. The Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme was forced with R and B and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration datasets andGravity Recovery and ClimateExperiment (GRACE)TWSestimates in two subcatchments of main tributaries (Madeira and Negro Rivers).At the basin scale, simulated ET ranges from 2.39 to 3.26 mm day(exp -1) and a low spatial correlation between ET and precipitation indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget components vary significantly as a function of both the LSM and precipitation dataset, but simulated TWS generally agrees with GRACE estimates at the basin scale. The best water budget simulations resulted from experiments using HYBAM, mostly explained by a denser rainfall gauge network and the rescaling at a finer temporal scale.

  5. On the utility of land surface models for agricultural drought monitoring

    Directory of Open Access Journals (Sweden)

    W. T. Crow

    2012-09-01

    Full Text Available The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

  6. Quantifying the impacts of land surface schemes and dynamic vegetation on the model dependency of projected changes in surface energy and water budgets

    Science.gov (United States)

    Yu, Miao; Wang, Guiling; Chen, Haishan

    2016-03-01

    Assessing and quantifying the uncertainties in projected future changes of energy and water budgets over land surface are important steps toward improving our confidence in climate change projections. In this study, the contribution of land surface models to the inter-GCM variation of projected future changes in land surface energy and water fluxes are assessed based on output from 19 global climate models (GCMs) and offline Community Land Model version 4 (CLM4) simulations driven by meteorological forcing from the 19 GCMs. Similar offline simulations using CLM4 with its dynamic vegetation submodel are also conducted to investigate how dynamic vegetation feedback, a process that is being added to more earth system models, may amplify or moderate the intermodel variations of projected future changes. Projected changes are quantified as the difference between the 2081-2100 period from the Representative Concentration Pathway 8.5 (RCP8.5) future experiment and the 1981-2000 period from the historical simulation. Under RCP8.5, projected changes in surface water and heat fluxes show a high degree of model dependency across the globe. Although precipitation is very likely to increase in the high latitudes of the Northern Hemisphere, a high degree of model-related uncertainty exists for evapotranspiration, soil water content, and surface runoff, suggesting discrepancy among land surface models (LSMs) in simulating the surface hydrological processes and snow-related processes. Large model-related uncertainties for the surface water budget also exist in the Tropics including southeastern South America and Central Africa. These uncertainties would be reduced in the hypothetical scenario of a single near-perfect land surface model being used across all GCMs, suggesting the potential to reduce uncertainties through the use of more consistent approaches toward land surface model development. Under such a scenario, the most significant reduction is likely to be seen in the

  7. Shallow groundwater effect on land surface temperature and surface energy balance under bare soil conditions: modeling and description

    Directory of Open Access Journals (Sweden)

    F. Alkhaier

    2012-07-01

    Full Text Available Understanding when and how groundwater affects surface temperature and energy fluxes is significant for utilizing remote sensing in groundwater studies and for integrating aquifers within land surface models. To investigate the shallow groundwater effect under bare soil conditions, we numerically exposed two soil profiles to identical metrological forcing. One of the profiles had shallow groundwater. The different responses that the two profiles manifested were inspected regarding soil moisture, temperature and energy balance at the land surface. The findings showed that the two profiles differed in three aspects: the absorbed and emitted amounts of energy, the portioning out of the available energy and the heat fluency in the soil. We concluded that due to their lower albedo, shallow groundwater areas reflect less shortwave radiation and consequently get a higher magnitude of net radiation. When potential evaporation demand is sufficiently high, a large portion of the energy received by these areas is consumed for evaporation. This increases the latent heat flux and reduces the energy that could have heated the soil. Consequently, lower magnitudes of both sensible and ground heat fluxes are caused to occur. The higher soil thermal conductivity in shallow groundwater areas facilitates heat transfer between the top soil and the subsurface, i.e. soil subsurface is more thermally connected to the atmosphere. For the reliability of remote sensors in detecting shallow groundwater effect, it was concluded that this effect can be sufficiently clear to be detected if at least one of the following conditions occurs: high potential evaporation and high contrast between day and night temperatures. Under these conditions, most day and night hours are suitable for shallow groundwater depth detection.

  8. Global water balances reconstructed by multi-model offline simulations of land surface models under GSWP3 (Invited)

    Science.gov (United States)

    Oki, T.; KIM, H.; Ferguson, C. R.; Dirmeyer, P.; Seneviratne, S. I.

    2013-12-01

    As the climate warms, the frequency and severity of flood and drought events is projected to increase. Understanding the role that the land surface will play in reinforcing or diminishing these extremes at regional scales will become critical. In fact, the current development path from atmospheric (GCM) to coupled atmosphere-ocean (AOGCM) to fully-coupled dynamic earth system models (ESMs) has brought new awareness to the climate modeling community of the abundance of uncertainty in land surface parameterizations. One way to test the representativeness of a land surface scheme is to do so in off-line (uncoupled) mode with controlled, high quality meteorological forcing. When multiple land schemes are run in-parallel (with the same forcing data), an inter-comparison of their outputs can provide the basis for model confidence estimates and future model refinements. In 2003, the Global Soil Wetness Project Phase 2 (GSWP2) provided the first global multi-model analysis of land surface state variables and fluxes. It spanned the decade of 1986-1995. While it was state-of-the art at the time, physical schemes have since been enhanced, a number of additional processes and components in the water-energy-eco-systems nexus can now be simulated, , and the availability of global, long-term observationally-based datasets that can be used for forcing and validating models has grown. Today, the data exists to support century-scale off-line experiments. The ongoing follow-on to GSWP2, named GSWP3, capitalizes on these new feasibilities and model functionalities. The project's cornerstone is its century-scale (1901-2010), 3-hourly, 0.5° meteorological forcing dataset that has been dynamically downscaled from the Twentieth Century Reanalysis and bias-corrected using monthly Climate Research Unit (CRU) temperature and Global Precipitation Climatology Centre (GPCC) precipitation data. However, GSWP3 also has an important long-term future climate component that spans the 21st century

  9. Multi-site evaluation of the JULES land surface model using global and local data

    Directory of Open Access Journals (Sweden)

    D. Slevin

    2015-02-01

    Full Text Available This study evaluates the ability of the JULES land surface model (LSM to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model parameters include site-specific (local values for each flux tower site and the default parameters used in the Hadley Centre Global Environmental Model (HadGEM climate model. Firstly, gross primary productivity (GPP estimates from driving JULES with data derived from local site measurements were compared to observations from the FLUXNET network. When using local data, the model is biased with total annual GPP underestimated by 16% across all sites compared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmospheric reanalysis (on scales of 100 km or so were compared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP underestimated by 30% across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7% reduction in total annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET. Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area index (LAI. Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small number of sites, compared to using the default phenology model.

  10. Calibration of the Distributed Hydrological Model mHM using Satellite derived Land Surface Temperature

    Science.gov (United States)

    Zink, M.; Samaniego, L. E.; Cuntz, M.

    2012-12-01

    A combined investigation of the water and energy balance in hydrologic models can lead to a more accurate estimation of hydrological fluxes and state variables, such as evapotranspiration and soil moisture. Hydrologic models are usually calibrated against discharge measurements, and thus are only trained on information of few points within a catchment. This procedure does not take into account any spatio-temporal variability of fluxes or state variables. Satellite data are a useful source of information to account for this spatial distributions. The objective of this study is to calibrate the distributed hydrological model mHM with satellite derived Land Surface Temperature (LST) fields provided by the Land Surface Analysis - Satellite Application Facility (LSA-SAF). LST is preferred to other satellite products such as soil moisture or evapotranspiration due to its higher precision. LST is obtained by solving the energy balance by assuming that the soil heat flux and the storage term are negligible on a daily time step. The evapotranspiration is determined by closing the water balance in mHM. The net radiation is calculated by using the incoming short- and longwave radiation, albedo and emissivity data provided by LSA-SAF. The Multiscale Parameter Regionalization technique (MPR, Samaniego et al. 2010) is used to determine the aerodynamic resistance among other parameters. The optimization is performed within the time period 2008-2010 using three objective functions that consider 1) only discharge, 2) only LST, and 3) a combination of both. The proposed method is applied to seven major German river basins: Danube, Ems, Main, Mulde, Neckar, Saale, and Weser. The annual coefficient of correlation between LSA-SAF incoming shortwave radiation and 28 meteorological stations operated by the German Weather Service (DWD) is 0.94 (RMSE = 29 W m-2) in 2009. LSA-SAF incoming longwave radiation could be further evaluated at two eddy covariance stations with a very similar

  11. Modifications in the land surface model ORCHIDEE and application in the Tarim basin

    Science.gov (United States)

    Zhou, Xudong; Polcher, Jan; Yang, Tao; Nguyen Quang, Trung; Hirabayashi, Yukiko

    2017-04-01

    Land surface modeling in regions mixing high mountains and arid deserts remains a great challenge due to the inadequate representations of physical processes in atmospheric forcings , runoff generation, evaporation and river routing. A few key improvements were analyzed within ORCHIDEE (Organising Carbon and Hydrology in Dynamic Ecosystems) to better understand these limitations as well as quantify their influence on the water cycle over Tarim basin (TRB). The TRB is a representative endorheic basin in center Asia, with glacier and snow melting, limited precipitation but strong evaporation, high spatial heterogeneity and intensive human interference, thus challenging any land surface model. National observations on daily precipitation from China Meteorological Administration (CMA) were used to correct precipitation inputs on the basis of WATCH forcing datasets. The independent glacier melting simulation by HYOGA2 was added to the forcing to overcome the lack of glacier module in ORCHIDEE. Improvements in the snow scheme provided more accurate simulations of the soil temperature which restrict the infiltration process when the soil is frozen. In addition, a novel routing scheme with finer spatial resolution from 50km to 1km was developed based on HydroSHED map. It improves the descriptions of catchments boundaries, the flow direction and the water residence time within sub-basins that make significant difference especially for the mountainous area and flat plains. Model results with these modifications were compared through various atmospheric and hydrological variables (i.e. evaporation, soil moisture, runoff and discharge). In conclusion, the correction by the precipitation observations and involvement of glacier melting simulations increase the water input to the basin by 37.2% and 8.4% respectively, which in turn increases evaporation, soil moisture and runoff to different extents. The new snow and soil freezing scheme advance in time the spring high-water in

  12. Simulating hydrology with an isotopic land surface model in western Siberia: what do we learn from water isotopes?

    Directory of Open Access Journals (Sweden)

    F. Guglielmo

    2015-09-01

    Full Text Available Improvements in the evaluation of land surface models would translate into more reliable predictions of future climate changes, as significant uncertainties persist in the quantification and representation of the relative contributions of soil and vegetation to the water and energy cycles. In this paper, we investigate the usefulness of water stable isotopes in land surface models studying land surface processes. To achieve this, we implemented 18O and 2H and the computation of the oxygen (δ18O and deuterium (δD stable isotope composition of soil and leaf water pools in a~recent version of the land surface model ORCHIDEE. We performed point-wise simulations with this new model and evaluated its performance on vertical profiles of soil water isotope ratios measured in summer 2012 at four experimental sites located in a boreal region of the Artic zone of western Siberia. The model performed relatively well in simulating some features of the δ18O soil profiles, but poorly reproduced the d-excess profiles, at all four stations. The response of the simulated δ18O profiles to variations in key hydrological parameters revealed the importance of the choice of a correct infiltration pathway in ORCHIDEE. Our results show also that the strength of the evaporative enrichment signal plays a role in shaping the profiles, too and, therefore, the relevance of the vegetation and bare soil characterization. We investigated furthermore to which extent we are able to determine the relative contribution of the evaporation to the evapotranspiration. This study's results confirm that the use of water stable isotopes measurements helps constrain the representation of key land surface processes in land surface models.

  13. Modelling nitrate from land-surface to wells-perforations under Mediterranean agricultural land: success, failure, and future scenarios

    Science.gov (United States)

    Levy, Yehuda; Chefetz, Benny; Shapira, Roi; Kurtzman, Daniel

    2017-04-01

    Contamination of groundwater resources by nitrate due to leaching under agricultural land is probably the most troublesome agriculture-related water contamination, worldwide. Deep soil sampling (10 m) were used for calibrating vertical flow and nitrogen-transport numerical models of the unsaturated zone, under different agricultural land uses. Vegetables fields (potato and strawberries) and deciduous (persimmon) orchards in the Sharon area overlaying the coastal aquifer of Israel, were examined. Average nitrate-nitrogen fluxes below vegetables fields were 210-290 kg ha-1 a-1 and under deciduous orchards were 110-140 kg ha-1 a-1. The output water and nitrate-nitrogen fluxes of the unsaturated zone models were used as input for a three dimensional flow and nitrate-transport model in the aquifer under an area of 13.3 square kilometers of agricultural land. The area was subdivided to 4 agricultural land-uses: vegetables, deciduous, citrus orchards and non-cultivated. Fluxes of water and nitrate-nitrogen below citrus orchards were taken from a previous study in this area (Kurtzman et al., 2013, j. Contam. Hydrol.). The groundwater flow model was calibrated to well heads only by changing the hydraulic conductivity while transient recharge fluxes were constraint to the bottom-fluxes of the unsaturated zone flow models. The nitrate-transport model in the aquifer, which was fed at the top by the nitrate fluxes of the unsaturated zone models, succeeded in reconstructing the average nitrate concentration in the wells. On the other hand, this transport model failed in calculating the high concentrations in the most contaminated wells and the large spatial variability of nitrate-concentrations in the aquifer. In order to reconstruct the spatial variability and enable predictions nitrate-fluxes from the unsaturated zone were multiplied by local multipliers. This action was rationalized by the fact that the high concentrations in some wells cannot be explained by regular

  14. Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities

    Institute of Scientific and Technical Information of China (English)

    Youlong XIA; Zong-Liang YANG; Paul L. STOFFA; Mrinal K. SEN

    2005-01-01

    Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI)to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing.The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes.Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.

  15. AWRA-G: A continental scale groundwater component linked to a land surface water balance model

    Science.gov (United States)

    Joehnk, Klaus; Crosbie, Russell; Peeters, Luk; Doble, Rebecca

    2013-04-01

    The Australian Water Resources Assessment (AWRA) system is a combination of models, data sources and analysis techniques that together will describe the water balance of Australia's landscapes, rivers and groundwater systems. It is a grid based water balance model that has lumped representation of the water balance of the soil, groundwater and surface water stores for each cell. The purpose of AWRA is to operationally provide up to date, credible, comprehensive, and accurate information about the history, present state and future trajectory of the water balance across Australia with sufficient spatial and temporal detail and enable water resources management for undertaking annual water resource assessments and national water accounts. AWRA is developed to link three major components: a landscape water balance model (AWRA-L), a river routing model (AWRA-R), and a groundwater component model (AWRA-G). These three component models combined are expected to be able to model the fluxes and stores of water throughout the landscape. The groundwater component (AWRA-G) addresses an improved representation of groundwater in the AWRA system to describe basic aquifer dynamics and groundwater-surface water processes. While most continental scale land surface models do not have the capacity to allow water to flow between cells and thus ignore this element of the water balance, AWRA-G does account for lateral flows. In general, AWRA-G provides estimates of groundwater fluxes that are not incorporated into either AWRA-L and its modifications to in-cell soil and groundwater processes, or AWRA-R. The processes integrated into AWRA-G thus are lateral groundwater flow between cells in regional and intermediate groundwater flow systems, groundwater discharge to the ocean, groundwater extraction and infiltration, river losses to groundwater, recharge from overbank flooding, and interactions between deep confined systems and surficial groundwater systems. Basis of AWRA-G is a good

  16. A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) - Part 1: Model description

    Science.gov (United States)

    Masutomi, Yuji; Ono, Keisuke; Mano, Masayoshi; Maruyama, Atsushi; Miyata, Akira

    2016-11-01

    Crop growth and agricultural management can affect climate at various spatial and temporal scales through the exchange of heat, water, and gases between land and atmosphere. Therefore, simulation of fluxes for heat, water, and gases from agricultural land is important for climate simulations. A land surface model (LSM) combined with a crop growth model (CGM), called an LSM-CGM combined model, is a useful tool for simulating these fluxes from agricultural land. Therefore, we developed a new LSM-CGM combined model for paddy rice fields, the MATCRO-Rice model. The main objective of this paper is to present the full description of MATCRO-Rice. The most important feature of MATCRO-Rice is that it can consistently simulate latent and sensible heat fluxes, net carbon uptake by crop, and crop yield by exchanging variables between the LSM and CGM. This feature enables us to apply the model to a wide range of integrated issues.

  17. Predicting Summer Dryness Under a Warmer Climate: Modeling Land Surface Processes in the Midwestern United States

    Science.gov (United States)

    Winter, J. M.; Eltahir, E. A.

    2009-12-01

    One of the most significant impacts of climate change is the potential alteration of local hydrologic cycles over agriculturally productive areas. As the world’s food supply continues to be taxed by its burgeoning population, a greater percentage of arable land will need to be utilized and land currently producing food must become more efficient. This study seeks to quantify the effects of climate change on soil moisture in the American Midwest. A series of 24-year numerical experiments were conducted to assess the ability of Regional Climate Model Version 3 coupled to Integrated Biosphere Simulator (RegCM3-IBIS) and Biosphere-Atmosphere Transfer Scheme 1e (RegCM3-BATS1e) to simulate the observed hydroclimatology of the midwestern United States. Model results were evaluated using NASA Surface Radiation Budget, NASA Earth Radiation Budget Experiment, Illinois State Water Survey, Climate Research Unit Time Series 2.1, Global Soil Moisture Data Bank, and regional-scale estimations of evapotranspiration. The response of RegCM3-IBIS and RegCM3-BATS1e to a surrogate climate change scenario, a warming of 3oC at the boundaries and doubling of CO2, was explored. Precipitation increased significantly during the spring and summer in both RegCM3-IBIS and RegCM3-BATS1e, leading to additional runoff. In contrast, enhancement of evapotranspiration and shortwave radiation were modest. Soil moisture remained relatively unchanged in RegCM3-IBIS, while RegCM3-BATS1e exhibited some fall and winter wetting.

  18. The climate responses of tropical and boreal ecosystems with an improved land surface model (JULES)

    Science.gov (United States)

    Harper, Anna; Friedlingstein, Pierre; Cox, Peter; Wiltshire, Andy; Jones, Chris

    2016-04-01

    The Joint UK Land Environment Simulator (JULES) is the land surface of the next generation UK Earth System Model (UKESM1). Recently, JULES was updated with new plant functional types and physiology based on a global plant trait database. These developments improved the simulation of terrestrial gross and net primary productivity on local and global scales, and enabled a more realistic representation of the global distribution of vegetation. In this study, we explore the present-day distribution of ecosystems and their vulnerability to climate change in JULES with these improvements, focusing on tropical and boreal ecosystems. Changes to these ecosystems will have implications for biogeophysical and biogeochemical feedbacks to climate change and need to be understood. First, we examine the simulated and observed rainforest-savannah boundary, which is strongly related to annual precipitation and the maximum climatological water deficit. Second, we assess the length of growing season and biomass stored in boreal ecosystems, where 20th century warming has likely extended the growing season. In each case, we first evaluate the ability of JULES to capture observed climate-vegetation relationships and trends. Finally, we run JULES to 2100 using climate data from 3 models and 2 RCP scenarios, and examine potential 21st century changes to these ecosystems. For example, do the tropical forests shrink in response to changes in tropical rainfall seasonality? And, how does the composition of boreal ecosystems change in response to climate warming? Given the potential for climate feedbacks and the inherent value in these ecosystems, it is essential to assess their responses to a range of climate change scenarios.

  19. Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model

    Science.gov (United States)

    Girotto, Manuela; De Lannoy, Gabriëlle J. M.; Reichle, Rolf H.; Rodell, Matthew

    2016-05-01

    Observations of terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission have a coarse resolution in time (monthly) and space (roughly 150,000 km2 at midlatitudes) and vertically integrate all water storage components over land, including soil moisture and groundwater. Data assimilation can be used to horizontally downscale and vertically partition GRACE-TWS observations. This work proposes a variant of existing ensemble-based GRACE-TWS data assimilation schemes. The new algorithm differs in how the analysis increments are computed and applied. Existing schemes correlate the uncertainty in the modeled monthly TWS estimates with errors in the soil moisture profile state variables at a single instant in the month and then apply the increment either at the end of the month or gradually throughout the month. The proposed new scheme first computes increments for each day of the month and then applies the average of those increments at the beginning of the month. The new scheme therefore better reflects submonthly variations in TWS errors. The new and existing schemes are investigated here using gridded GRACE-TWS observations. The assimilation results are validated at the monthly time scale, using in situ measurements of groundwater depth and soil moisture across the U.S. The new assimilation scheme yields improved (although not in a statistically significant sense) skill metrics for groundwater compared to the open-loop (no assimilation) simulations and compared to the existing assimilation schemes. A smaller impact is seen for surface and root-zone soil moisture, which have a shorter memory and receive smaller increments from TWS assimilation than groundwater. These results motivate future efforts to combine GRACE-TWS observations with observations that are more sensitive to surface soil moisture, such as L-band brightness temperature observations from Soil Moisture Ocean Salinity (SMOS) or Soil Moisture Active Passive

  20. Performance of the JULES land surface model for UK Biogenic VOC emissions

    Science.gov (United States)

    Hayman, Garry; Comyn-Platt, Edward; Vieno, Massimo; Langford, Ben

    2017-04-01

    Emissions of biogenic non-methane volatile organic compounds (NMVOCs) are important for air quality and tropospheric composition. Through their contribution to the production of tropospheric ozone and secondary organic aerosol (SOA), biogenic VOCs indirectly contribute to climate forcing and climate feedbacks [1]. Biogenic VOCs encompass a wide range of compounds and are produced by plants for growth, development, reproduction, defence and communication [2]. There are both biological and physico-chemical controls on emissions [3]. Only a few of the many biogenic VOCs are of wider interest and only two or three (isoprene and the monoterpenes, α- and β-pinene) are represented in chemical transport models. We use the Joint UK Land Environment Simulator (JULES), the UK community land surface model, to estimate biogenic VOC emission fluxes. JULES is a process-based model that describes the water, energy and carbon balances and includes temperature, moisture and carbon stores [4, 5]. JULES currently provides emission fluxes of the 4 largest groups of biogenic VOCs: isoprene, terpenes, methanol and acetone. The JULES isoprene scheme uses gross primary productivity (GPP), leaf internal carbon and the leaf temperature as a proxy for the electron requirement for isoprene synthesis [6]. In this study, we compare JULES biogenic VOC emission estimates of isoprene and terepenes with (a) flux measurements made at selected sites in the UK and Europe and (b) gridded estimates for the UK from the EMEP/EMEP4UK atmospheric chemical transport model [7, 8], using site-specific or EMEP4UK driving meteorological data, respectively. We compare the UK-scale emission estimates with literature estimates. We generally find good agreement in the comparisons but the estimates are sensitive to the choice of the base or reference emission potentials. References (1) Unger, 2014: Geophys. Res. Lett., 41, 8563, doi:10.1002/2014GL061616; (2) Laothawornkitkul et al., 2009: New Phytol., 183, 27, doi

  1. Comment on "Simulation of Surface Ozone Pollution in the Central Gulf Coast Region Using WRF/Chem Model: Sensitivity to PBL and Land Surface Physics"

    Science.gov (United States)

    A recently published meteorology and air quality modeling study has several serious deficiencies deserving comment. The study uses the weather research and forecasting/chemistry (WRF/Chem) model to compare and evaluate boundary layer and land surface modeling options. The most se...

  2. The effect of GCM biases on global runoff simulations of a land surface model

    Science.gov (United States)

    Papadimitriou, Lamprini V.; Koutroulis, Aristeidis G.; Grillakis, Manolis G.; Tsanis, Ioannis K.

    2017-09-01

    Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided

  3. Predicting Agricultural Drought using NOAH Land Surface Model, MODIS Evapotranspiration and GRACE Terrestrial Water Storage

    Science.gov (United States)

    wu, J.; Zhang, X.

    2013-12-01

    Drought is a major natural hazard in the world which costs 6-8 billion per year in the United States. Drought monitoring and prediction are difficult because it usually develops slowly and it is hard to be recognized until it becomes severe. The severity of agricultural drought was estimated by using Soil Moisture Deficit Index (SMDI) based on soil moisture simulated by Noah land surface model. Based on general water balance and delayed response of soil moisture to the forcing of climate variables, a Multiple Linear Regression (MLR) model for agricultural drought prediction was developed, the inputs of which included data at the previous one and two months of precipitation from Parameter-elevation Regressions on Independent Slopes Model (PRISM), evapotranspiration from MODIS MOD 16 product and terrestrial water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE). The stability of the MLR model is tested using different training datasets from 2003 to 2009 with time spans of one year to six years and the results indicated that the model is stable, with very limited changes in estimated parameters between different datasets. A sensitivity analysis shows that evapotranspiration is the most significant variable affecting soil moisture change compared to precipitation and TWS. The predicted SMDI was compared with U.S. drought monitor products to evaluate its performance for the period of 2010-2012 when a severe drought occurred in the U.S. (Fig.1). The predicted SMDI successfully forecasted the severe drought in the southern U.S. in early 2012 and its expansion in the following summer. The MLR model has a high predictive skill with short-term forecast (1-2 months), while less accuracy is observed for the long-term forecast (3-6 months) (Fig.2).

  4. Wildfire Risk Mapping over the State of Mississippi: Land Surface Modeling Approach

    Energy Technology Data Exchange (ETDEWEB)

    Cooke, William H. [Mississippi State University (MSU); Mostovoy, Georgy [Mississippi State University (MSU); Anantharaj, Valentine G [ORNL; Jolly, W. Matt [USDA Forest Service

    2012-01-01

    Three fire risk indexes based on soil moisture estimates were applied to simulate wildfire probability over the southern part of Mississippi using the logistic regression approach. The fire indexes were retrieved from: (1) accumulated difference between daily precipitation and potential evapotranspiration (P-E); (2) top 10 cm soil moisture content simulated by the Mosaic land surface model; and (3) the Keetch-Byram drought index (KBDI). The P-E, KBDI, and soil moisture based indexes were estimated from gridded atmospheric and Mosaic-simulated soil moisture data available from the North American Land Data Assimilation System (NLDAS-2). Normalized deviations of these indexes from the 31-year mean (1980-2010) were fitted into the logistic regression model describing probability of wildfires occurrence as a function of the fire index. It was assumed that such normalization provides more robust and adequate description of temporal dynamics of soil moisture anomalies than the original (not normalized) set of indexes. The logistic model parameters were evaluated for 0.25 x0.25 latitude/longitude cells and for probability representing at least one fire event occurred during 5 consecutive days. A 23-year (1986-2008) forest fires record was used. Two periods were selected and examined (January mid June and mid September December). The application of the logistic model provides an overall good agreement between empirical/observed and model-fitted fire probabilities over the study area during both seasons. The fire risk indexes based on the top 10 cm soil moisture and KBDI have the largest impact on the wildfire odds (increasing it by almost 2 times in response to each unit change of the corresponding fire risk index during January mid June period and by nearly 1.5 times during mid September-December) observed over 0.25 x0.25 cells located along the state of Mississippi Coast line. This result suggests a rather strong control of fire risk indexes on fire occurrence probability

  5. Using Historical Precipitation, Temperature, and Runoff Observations to Evaluate Evaporation Formulations in Land Surface Models

    Science.gov (United States)

    Koster, Randal D.; Mahanama, P. P.

    2012-01-01

    Key to translating soil moisture memory into subseasonal precipitation and air temperature forecast skill is a realistic treatment of evaporation in the forecast system used - in particular, a realistic treatment of how evaporation responds to variations in soil moisture. The inherent soil moisture-evaporation relationships used in today's land surface models (LSMs), however, arguably reflect little more than guesswork given the lack of evaporation and soil moisture data at the spatial scales represented by regional and global models. Here we present a new approach for evaluating this critical aspect of LSMs. Seasonally averaged precipitation is used as a proxy for seasonally-averaged soil moisture, and seasonally-averaged air temperature is used as a proxy for seasonally-averaged evaporation (e.g., more evaporative cooling leads to cooler temperatures) the relationship between historical precipitation and temperature measurements accordingly mimics in certain important ways nature's relationship between soil moisture and evaporation. Additional information on the relationship is gleaned from joint analysis of precipitation and streamflow measurements. An experimental framework that utilizes these ideas to guide the development of an improved soil moisture-evaporation relationship is described and demonstrated.

  6. Inferring past land use-induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations

    Directory of Open Access Journals (Sweden)

    J. P. Boisier

    2013-03-01

    Full Text Available Regional cooling resulting from increases in surface albedo has been identified in several studies as the main biogeophysical effect of past land use-induced land cover changes (LCC on climate. However, the amplitude of this effect remains quite uncertain due to, among other factors, (a uncertainties in the extent of historical LCC and, (b differences in the way various models simulate surface albedo and more specifically its dependency on vegetation type and snow cover. We derived monthly albedo climatologies for croplands and four other land cover types from the Moderate Resolution Imaging Spectroradiometer (MODIS satellite observations. We then reconstructed the changes in surface albedo between preindustrial times and present-day by combining these climatologies with the land cover maps of 1870 and 1992 used by seven land surface models (LSMs in the context of the LUCID ("Land Use and Climate: identification of robust Impacts" intercomparison project. These reconstructions show surface albedo increases larger than 10% (absolute in winter, and larger than 2% in summer between 1870 and 1992 over areas that experienced intense deforestation in the northern temperate regions. The historical surface albedo changes estimated with MODIS data were then compared to those simulated by the various climate models participating in LUCID. The inter-model mean albedo response to LCC shows a similar spatial and seasonal pattern to the one resulting from the MODIS-based reconstructions, that is, larger albedo increases in winter than in summer, driven by the presence of snow. However, individual models show significant differences between the simulated albedo changes and the corresponding reconstructions, despite the fact that land cover change maps are the same. Our analyses suggest that the primary reason for those discrepancies is how LSMs parameterize albedo. Another reason, of secondary importance, results from differences in their simulated snow extent

  7. A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

    Science.gov (United States)

    Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.; Jedlovec, Gary J.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and

  8. The Effect of Subsurface Parameterizations on Modeled Flows in the Catchment Land Surface Model, Fortuna 2.5

    Science.gov (United States)

    Roningen, J. M.; Eylander, J. B.

    2014-12-01

    Groundwater use and management is subject to economic, legal, technical, and informational constraints and incentives at a variety of spatial and temporal scales. Planned and de facto management practices influenced by tax structures, legal frameworks, and agricultural and trade policies that vary at the country scale may have medium- and long-term effects on the ability of a region to support current and projected agricultural and industrial development. USACE is working to explore and develop global-scale, physically-based frameworks to serve as a baseline for hydrologic policy comparisons and consequence assessment, and such frameworks must include a reasonable representation of groundwater systems. To this end, we demonstrate the effects of different subsurface parameterizations, scaling, and meteorological forcings on surface and subsurface components of the Catchment Land Surface Model Fortuna v2.5 (Koster et al. 2000). We use the Land Information System 7 (Kumar et al. 2006) to process model runs using meteorological components of the Air Force Weather Agency's AGRMET forcing data from 2006 through 2011. Seasonal patterns and trends are examined in areas of the Upper Nile basin, northern China, and the Mississippi Valley. We also discuss the relevance of the model's representation of the catchment deficit with respect to local hydrogeologic structures.

  9. An Indirect Data Assimilation Scheme for Deep Soil Temperature in the Pleim-Xiu Land Surface Model

    Science.gov (United States)

    The Pleim-Xiu land surface model (PX LSM) has been improved by the addition of a 2nd indirect data assimilation scheme. The first, which was described previously, is a technique where soil moisture in nudged according to the biases in 2-m air temperature and relative humidity be...

  10. Assessing the radiative impacts of precipitating clouds on winter surface air temperatures and land surface properties in general circulation models using observations

    Science.gov (United States)

    Li, J.-L. F.; Lee, Wei-Liang; Wang, Yi-Hui; Richardson, Mark; Yu, Jia-Yuh; Suhas, E.; Fetzer, Eric; Lo, Min-Hui; Yue, Qing

    2016-10-01

    Using CloudSat-CALIPSO ice water, cloud fraction, and radiation; Clouds and the Earth's Radiant Energy System (CERES) radiation; and long-term station-measured surface air temperature (SAT), we identified a substantial underestimation of the total ice water path, total cloud fraction, land surface radiative flux, land surface temperature (LST), and SAT during Northern Hemisphere winter in Coupled Model Intercomparison Project Phase 5 (CMIP5) models. We perform sensitivity experiments with the National Center for Atmospheric Research (NCAR) Community Earth System Model version 1 (CESM1) in fully coupled modes to identify processes driving these biases. We found that biases in land surface properties are associated with the exclusion of downwelling longwave heating from precipitating ice during Northern Hemisphere winter. The land surface temperature biases introduced by the exclusion of precipitating ice radiative effects in CESM1 and CMIP5 both spatially correlate with winter biases over Eurasia and North America. The underestimated precipitating ice radiative effect leads to colder LST, associated surface energy-budget adjustments, and cooler SAT. This bias also shifts regional soil moisture state from liquid to frozen, increases snow cover, and depresses evapotranspiration (ET) and total leaf area index in Northern Hemisphere winter. The inclusion of the precipitating ice radiative effects largely reduces the model biases of surface radiative fluxes (more than 15 W m-2), SAT (up to 2-4 K), and snow cover and ET (25-30%), compared with those without snow-radiative effects.

  11. Possible Influence of the Cultivated Land Reclamation on Surface Climate in India: A WRF Model Based Simulation

    Directory of Open Access Journals (Sweden)

    Yi Qu

    2013-01-01

    Full Text Available Land use/cover change (LUCC has become one of the most important factors for the global climate change. As one of the major types of LUCC, cultivated land reclamation also has impacts on regional climate change. Most of the previous studies focused on the correlation and simulation analysis of historical LUCC and climate change, with few explorations for the impacts of future LUCC on regional climate, especially impacts of the cultivated land reclamation. This study used the Weather Research and Forecasting (WRF model to forecast the changes of energy flux and temperature based on the future cultivated land reclamation in India and then analyzed the impacts of cultivated land reclamation on climate change. The results show that cultivated land reclamation will lead to a large amount of land conversions, which will overall result in the increase in latent heat flux of regional surface as well as the decrease in sensible heat flux and further lead to changes of regional average temperature. Furthermore, the impact on climate change is seasonally different. The cultivated land reclamation mainly leads to a temperature decrease in the summer, while it leads to a temperature increase in the winter.

  12. Development of a land surface model with coupled snow and frozen soil physics

    Science.gov (United States)

    Wang, Lei; Zhou, Jing; Qi, Jia; Sun, Litao; Yang, Kun; Tian, Lide; Lin, Yanluan; Liu, Wenbin; Shrestha, Maheswor; Xue, Yongkang; Koike, Toshio; Ma, Yaoming; Li, Xiuping; Chen, Yingying; Chen, Deliang; Piao, Shilong; Lu, Hui

    2017-06-01

    Snow and frozen soil are important factors that influence terrestrial water and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new land surface model (LSM) with coupled snow and frozen soil physics was developed based on a hydrologically improved LSM (HydroSiB2). First, an energy-balance-based three-layer snow model was incorporated into HydroSiB2 (hereafter HydroSiB2-S) to provide an improved description of the internal processes of the snow pack. Second, a universal and simplified soil model was coupled with HydroSiB2-S to depict soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy was adopted as a prognostic variable instead of snow/soil temperature in the energy balance equation of the snow/frozen soil module. The newly developed models were then carefully evaluated at two typical sites of the Tibetan Plateau (TP) (one snow covered and the other snow free, both with underlying frozen soil). At the snow-covered site in northeastern TP (DY), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes in three-layer snow parameterization. At the snow-free site in southwestern TP (Ngari), HydroSiB2-SF reasonably simulated soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil parameterization in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF proved to be capable of simulating upward moisture fluxes toward the freezing front from the underlying soil layers in winter.

  13. Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

    Directory of Open Access Journals (Sweden)

    Tao Wang

    Full Text Available Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique improve the simulation accuracy of mean seasonal (October throughout May snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the

  14. Impacts of Satellite-Based Snow Albedo Assimilation on Offline and Coupled Land Surface Model Simulations.

    Science.gov (United States)

    Wang, Tao; Peng, Shushi; Krinner, Gerhard; Ryder, James; Li, Yue; Dantec-Nédélec, Sarah; Ottlé, Catherine

    2015-01-01

    Seasonal snow cover in the Northern Hemisphere is the largest component of the terrestrial cryosphere and plays a major role in the climate system through strong positive feedbacks related to albedo. The snow-albedo feedback is invoked as an important cause for the polar amplification of ongoing and projected climate change, and its parameterization across models is an important source of uncertainty in climate simulations. Here, instead of developing a physical snow albedo scheme, we use a direct insertion approach to assimilate satellite-based surface albedo during the snow season (hereafter as snow albedo assimilation) into the land surface model ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms) and assess the influences of such assimilation on offline and coupled simulations. Our results have shown that snow albedo assimilation in both ORCHIDEE and ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) improve the simulation accuracy of mean seasonal (October throughout May) snow water equivalent over the region north of 40 degrees. The sensitivity of snow water equivalent to snow albedo assimilation is more pronounced in the coupled simulation than the offline simulation since the feedback of albedo on air temperature is allowed in ORCHIDEE-LMDZ. We have also shown that simulations of air temperature at 2 meters in ORCHIDEE-LMDZ due to snow albedo assimilation are significantly improved during the spring in particular over the eastern Siberia region. This is a result of the fact that high amounts of shortwave radiation during the spring can maximize its snow albedo feedback, which is also supported by the finding that the spatial sensitivity of temperature change to albedo change is much larger during the spring than during the autumn and winter. In addition, the radiative forcing at the top of the atmosphere induced by snow albedo assimilation during the spring is estimated to be -2.50 W m-2, the magnitude of

  15. The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling

    Directory of Open Access Journals (Sweden)

    V. Maggioni

    2012-10-01

    Full Text Available The contribution of rainfall forcing errors relative to model (structural and parameter uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM, forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty or by adding randomly generated noise (representing model structure and parameter uncertainty to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

  16. Shallow groundwater effect on land surface temperature and surface energy balance under bare soil conditions: modeling and description

    Directory of Open Access Journals (Sweden)

    F. Alkhaier

    2011-09-01

    Full Text Available Appreciating when and how groundwater affects surface temperature and energy fluxes is important for utilizing remote sensing in groundwater studies and for integrating aquifers within land surface models. To explore the shallow groundwater effect, we numerically exposed two soil profiles – one having shallow groundwater – to the same meteorological forcing, and inspected their different responses regarding surface soil moisture, temperature and energy balance. We found that the two profiles differed in the absorbed and emitted amounts of energy, in portioning out the available energy and in heat fluency within the soil. We conclude that shallow groundwater areas reflect less shortwave radiation due to their lower albedo and therefore they get higher magnitude of net radiation. When potential evaporation demand is high enough, a large portion of the energy received by these areas is spent on evaporation. This makes the latent heat flux predominant, and leaves less energy to heat the soil. Consequently, this induces lower magnitudes of both sensible and ground heat fluxes. The higher soil thermal conductivity in shallow groundwater areas facilitates heat transfer between the top soil and the subsurface, i.e. soil subsurface is more thermally connected to the atmosphere. In view of remote sensors' capability of detecting shallow groundwater effect, we conclude that this effect can be sufficiently clear to be sensed if at least one of two conditions is met: high potential evaporation and big contrast in air temperature between day and night. Under these conditions, most day and night hours are suitable for shallow groundwater depth detection.

  17. Simulations of seasonal variations of stable water isotopes in land surface process model CLM

    Institute of Scientific and Technical Information of China (English)

    ZHANG XinPing; WANG XiaoYun; YANG ZongLiang; NIU GuoYue; Xie ZiChu

    2009-01-01

    In this study, we simulated and analyzed the monthly variations of stable water isotopes in different reservoirs at Manaus, Brazil, using the Community Land Model (CLM) that incorporates stable isotopic effects as a diagnostic tool for understanding stable water isotopic processes, filling the observational data gaps and predicting hydrometeorological processes. The simulation results show that the δO values in precipitation, vapor and surface runoff have distinct seasonality with the marked negative correlations with corresponding water amount. Compared with the survey results by the International Atomic Energy Agency (IAEA) in co-operation with the World Meteorological Organization (WMO), the simulations by CLM reveal the similar temporal distributions of the δO in precipitation. Moreover, the simulated amount effect between monthly δO and monthly precipitation amount, and MWL (meteoric water line) are all close to the measured values. However, the simulated seasonal difference in the δO in precipitation is distinctly smaller than observed one, and the simulated temporal distribution of the 8180 in precipitation displays the ideal bimodal seasonality rather than the observed single one. These mismatches are possibly related to the simulation capacity and the veracity in forcing data.

  18. Towards Global Simulation of Irrigation in a Land Surface Model: Multiple Cropping and Rice Paddy in Southeast Asia

    Science.gov (United States)

    Beaudoing, Hiroko Kato; Rodell, Matthew; Ozdogan, Mutlu

    2010-01-01

    Agricultural land use significantly influences the surface water and energy balances. Effects of irrigation on land surface states and fluxes include repartitioning of latent and sensible heat fluxes, an increase in net radiation, and an increase in soil moisture and runoff. We are working on representing irrigation practices in continental- to global-scale land surface simulation in NASA's Global Land Data Assimilation System (GLDAS). Because agricultural practices across the nations are diverse, and complex, we are attempting to capture the first-order reality of the regional practices before achieving a global implementation. This study focuses on two issues in Southeast Asia: multiple cropping and rice paddy irrigation systems. We first characterize agricultural practices in the region (i.e., crop types, growing seasons, and irrigation) using the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) dataset. Rice paddy extent is identified using remote sensing products. Whether irrigated or rainfed, flooded fields need to be represented and treated explicitly. By incorporating these properties and processes into a physically based land surface model, we are able to quantify the impacts on the simulated states and fluxes.

  19. Towards Global Simulation of Irrigation in a Land Surface Model: Multiple Cropping and Rice Paddy in Southeast Asia

    Science.gov (United States)

    Beaudoing, Hiroko Kato; Rodell, Matthew; Ozdogan, Mutlu

    2010-01-01

    Agricultural land use significantly influences the surface water and energy balances. Effects of irrigation on land surface states and fluxes include repartitioning of latent and sensible heat fluxes, an increase in net radiation, and an increase in soil moisture and runoff. We are working on representing irrigation practices in continental- to global-scale land surface simulation in NASA's Global Land Data Assimilation System (GLDAS). Because agricultural practices across the nations are diverse, and complex, we are attempting to capture the first-order reality of the regional practices before achieving a global implementation. This study focuses on two issues in Southeast Asia: multiple cropping and rice paddy irrigation systems. We first characterize agricultural practices in the region (i.e., crop types, growing seasons, and irrigation) using the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) dataset. Rice paddy extent is identified using remote sensing products. Whether irrigated or rainfed, flooded fields need to be represented and treated explicitly. By incorporating these properties and processes into a physically based land surface model, we are able to quantify the impacts on the simulated states and fluxes.

  20. A method for the determination of the hydraulic properties of soil from MODIS surface temperature for use in land-surface models

    Science.gov (United States)

    Gutmann, Ethan D.; Small, Eric E.

    2010-06-01

    Soil hydraulic properties (SHPs) play an important role in land-surface models, but their spatial distribution is poorly known, and it is not feasible to make field measurements of SHPs everywhere they are needed. In addition, the scale SHPs are measured on (10 cm) is substantially smaller than the scale at which land-surface models are run (>1 km). As a result, land-surface models need landscape hydraulic properties (LHPs), not SHPs. We present a method for identifying LHPs from MODIS surface temperatures. We calibrated LHPs in the Noah land-surface model using MODIS surface temperatures in 2005 at 14 sites from the Atmospheric Radiation Measurement Program (ARM) using locally observed forcing data from 2005. We then used observed flux data during this same time period for model verification. Next, we determined LHPs from MODIS surface temperature at five sites using High Resolution Land Data Assimilation forcing data from 2002. We then used these LHPS to run Noah with 2005 ARM forcing data and compared the output to the same observed 2005 fluxes. Fitting LHPs to MODIS data decreases the error in modeled latent heat flux from 98 W/m2 to 67 W/m2. Fitting LHPs to these same latent heat flux measurements decreases the error to 50 W/m2. Therefore, two thirds of the parameter estimation improvement from calibration to in situ flux data can be achieved using remotely sensed surface temperature. These results are insensitive to errors in other parameters. For example, changing albedo by 0.1 changes the saturated conductivity (Ks) by 10% and the van Genuchten "m" parameter by 1%. However, changing minimum canopy resistance by 40 s/m produced a significant but mutually compensating change in both Ks and "m."

  1. On the (in)consistency of a multi-model ensemble of the past 30 years land surface state.

    Science.gov (United States)

    Dutra, Emanuel; Schellekens, Jaap; Beck, Hylke; Balsamo, Gianpaolo

    2016-04-01

    Global land-surface and hydrological models are a fundamental tool in understanding the land-surface state and evolution either coupled to atmospheric models for climate and weather predictions or in stand-alone mode. In this study we take a recently developed dataset consisting in stand-alone simulations by 10 global hydrological and land surface models sharing the same atmospheric forcing for the period 1979-2012 (the eart2Observe dataset). This multi-model ensemble provides the first freely available dataset with such a spatial/temporal scale that allows for a characterization of the multi-model characteristics such as inter-model consistency and error-spread relationship. We will present a metric for the ensemble consistency using the concept of potential predictability, that can be interpreted as a proxy for the multi-model agreement. Initial results point to regions of low inter-model agreement in the polar and tropical regions, the latter also present when comparing globally available precipitation datasets. In addition to this, the discharge ensemble spread around the ensemble mean was compared to the error of the ensemble mean for several large-scale and small scale basins. This showed a general under-estimation of the ensemble spread, particularly in tropical basins, suggesting that the current dataset lacks the representation of the precipitation uncertainty in the input meteorological data.

  2. Coupled atmosphere and land-surface assimilation of surface observations with a single column model and ensemble data assimilation

    Science.gov (United States)

    Rostkier-Edelstein, Dorita; Hacker, Joshua P.; Snyder, Chris

    2014-05-01

    Numerical weather prediction and data assimilation models are composed of coupled atmosphere and land-surface (LS) components. If possible, the assimilation procedure should be coupled so that observed information in one module is used to correct fields in the coupled module. There have been some attempts in this direction using optimal interpolation, nudging and 2/3DVAR data assimilation techniques. Aside from satellite remote sensed observations, reference height in-situ observations of temperature and moisture have been used in these studies. Among other problems, difficulties in coupled atmosphere and LS assimilation arise as a result of the different time scales characteristic of each component and the unsteady correlation between these components under varying flow conditions. Ensemble data-assimilation techniques rely on flow dependent observations-model covariances. Provided that correlations and covariances between land and atmosphere can be adequately simulated and sampled, ensemble data assimilation should enable appropriate assimilation of observations simultaneously into the atmospheric and LS states. Our aim is to explore assimilation of reference height in-situ temperature and moisture observations into the coupled atmosphere-LS modules(simultaneously) in NCAR's WRF-ARW model using the NCAR's DART ensemble data-assimilation system. Observing system simulation experiments (OSSEs) are performed using the single column model (SCM) version of WRF. Numerical experiments during a warm season are centered on an atmospheric and soil column in the South Great Plains. Synthetic observations are derived from "truth" WRF-SCM runs for a given date,initialized and forced using North American Regional Reanalyses (NARR). WRF-SCM atmospheric and LS ensembles are created by mixing the atmospheric and soil NARR profile centered on a given date with that from another day (randomly chosen from the same season) with weights drawn from a logit-normal distribution. Three

  3. Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

    Science.gov (United States)

    Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

    2010-01-01

    Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

  4. Assessment of irrigation physics in a land surface modeling framework using non-traditional and human-practice datasets

    Science.gov (United States)

    Lawston, Patricia M.; Santanello, Joseph A., Jr.; Franz, Trenton E.; Rodell, Matthew

    2017-06-01

    Irrigation increases soil moisture, which in turn controls water and energy fluxes from the land surface to the planetary boundary layer and determines plant stress and productivity. Therefore, developing a realistic representation of irrigation is critical to understanding land-atmosphere interactions in agricultural areas. Irrigation parameterizations are becoming more common in land surface models and are growing in sophistication, but there is difficulty in assessing the realism of these schemes, due to limited observations (e.g., soil moisture, evapotranspiration) and scant reporting of irrigation timing and quantity. This study uses the Noah land surface model run at high resolution within NASA's Land Information System to assess the physics of a sprinkler irrigation simulation scheme and model sensitivity to choice of irrigation intensity and greenness fraction datasets over a small, high-resolution domain in Nebraska. Differences between experiments are small at the interannual scale but become more apparent at seasonal and daily timescales. In addition, this study uses point and gridded soil moisture observations from fixed and roving cosmic-ray neutron probes and co-located human-practice data to evaluate the realism of irrigation amounts and soil moisture impacts simulated by the model. Results show that field-scale heterogeneity resulting from the individual actions of farmers is not captured by the model and the amount of irrigation applied by the model exceeds that applied at the two irrigated fields. However, the seasonal timing of irrigation and soil moisture contrasts between irrigated and non-irrigated areas are simulated well by the model. Overall, the results underscore the necessity of both high-quality meteorological forcing data and proper representation of irrigation for accurate simulation of water and energy states and fluxes over cropland.

  5. Assessment of model behavior and acceptable forcing data uncertainty in the context of land surface soil moisture estimation

    Science.gov (United States)

    Dumedah, Gift; Walker, Jeffrey P.

    2017-03-01

    The sources of uncertainty in land surface models are numerous and varied, from inaccuracies in forcing data to uncertainties in model structure and parameterizations. Majority of these uncertainties are strongly tied to the overall makeup of the model, but the input forcing data set is independent with its accuracy usually defined by the monitoring or the observation system. The impact of input forcing data on model estimation accuracy has been collectively acknowledged to be significant, yet its quantification and the level of uncertainty that is acceptable in the context of the land surface model to obtain a competitive estimation remain mostly unknown. A better understanding is needed about how models respond to input forcing data and what changes in these forcing variables can be accommodated without deteriorating optimal estimation of the model. As a result, this study determines the level of forcing data uncertainty that is acceptable in the Joint UK Land Environment Simulator (JULES) to competitively estimate soil moisture in the Yanco area in south eastern Australia. The study employs hydro genomic mapping to examine the temporal evolution of model decision variables from an archive of values obtained from soil moisture data assimilation. The data assimilation (DA) was undertaken using the advanced Evolutionary Data Assimilation. Our findings show that the input forcing data have significant impact on model output, 35% in root mean square error (RMSE) for 5cm depth of soil moisture and 15% in RMSE for 15cm depth of soil moisture. This specific quantification is crucial to illustrate the significance of input forcing data spread. The acceptable uncertainty determined based on dominant pathway has been validated and shown to be reliable for all forcing variables, so as to provide optimal soil moisture. These findings are crucial for DA in order to account for uncertainties that are meaningful from the model standpoint. Moreover, our results point to a proper

  6. Achieving scale-independent land-surface flux estimates - Application of the Multiscale Parameter Regionalization (MPR) to the Noah-MP land-surface model across the contiguous USA

    Science.gov (United States)

    Thober, S.; Mizukami, N.; Samaniego, L. E.; Attinger, S.; Clark, M. P.; Cuntz, M.

    2016-12-01

    Land-surface models use a variety of process representations to calculate terrestrial energy, water and biogeochemical fluxes. These process descriptions are usually derived from point measurements but are scaled to much larger resolutions in applications that range from about 1 km in catchment hydrology to 100 km in climate modelling. Both, hydrologic and climate models are nowadays run on different spatial resolutions, using the exact same land surface representations. A fundamental criterion for the physical consistency of land-surface simulations across scales is that a flux estimated over a given area is independent of the spatial model resolution (i.e., the flux-matching criterion). The Noah-MP land surface model considers only one soil and land cover type per model grid cell without any representation of subgrid variability, implying a weak flux-matching. A fractional approach simulates subgrid variability but it requires a higher computational demand than using effective parameters and it is used only for land cover in current land surface schemes. A promising approach to derive scale-independent parameters is the Multiscale Parameter Regionalization (MPR) technique, which consists of two steps: first, it applies transfer functions directly to high-resolution data (such as 100 m soil maps) to derive high-resolution model parameter fields, acknowledging the full subgrid variability. Second, it upscales these high-resolution parameter fields to the model resolution by using appropriate upscaling operators. MPR has shown to improve substantially the scalability of hydrologic models. Here, we apply the MPR technique to the Noah-MP land-surface model for a large sample of basins distributed across the contiguous USA. Specifically, we evaluate the flux-matching criterion for several hydrologic fluxes such as evapotranspiration and total runoff at scales ranging from 3 km to 48 km. We also investigate a p-norm scaling operator that goes beyond the current

  7. Modelling surface runoff and water fluxes over contrasted soils in pastoral Sahel: evaluation of the ALMIP2 land surface models over the Gourma region in Mali

    Science.gov (United States)

    Land surface processes play an important role in West African monsoon variability and land –atmosphere coupling has been shown to be particularly important in the Sahel. In addition, the evolution of hydrological systems in this region, and particularly the increase of surface water and runoff coeff...

  8. Global Flood Response Using Satellite Rainfall Information Coupled with Land Surface and Routing Models

    Science.gov (United States)

    Adler, R. F.; Wu, H.

    2016-12-01

    The Global Flood Monitoring System (GFMS) (http://flood.umd.edu) has been developed and used in recent years to provide real-time flood detection, streamflow estimates and inundation calculations for most of the globe. The GFMS is driven by satellite-based precipitation, with the accuracy of the flood estimates being primarily dependent on the accuracy of the precipitation analyses and the land surface and routing models used. The routing calculations are done at both 12 km and 1 km resolution. Users of GFMS results include international and national flood response organizations. The devastating floods in October 2015 in South Carolina are analyzed indicating that the GFMS estimated streamflow is accurate and useful indicating significant flooding in the upstream basins. Further downstream the GFMS streamflow underestimates due to the presence of dams which are not accounted for in GFMS. Other examples are given for Yemen and Somalia and for Sri Lanka and southern India. A forecast flood event associated with a typhoon hitting Taiwan is also examined. One-kilometer resolution inundation mapping from GFMS holds the promise of highly useful information for flood disaster response. The algorithm is briefly described and examples are shown for recent cases where inundation estimates available from optical and Synthetic Aperture Radar (SAR) satellite sensors are available. For a case of significant flooding in Texas in May and June along the Brazos River the GFMS calculated streamflow compares favorably with the observed. Available Landsat-based (May 28) and MODIS-based (June 2) inundation analyses from U. of Colorado shows generally good agreement with the GFMS inundation calculation in most of the area where skies were clear and the optical techniques could be applied. The GFMS provides very useful disaster response information on a timely basis. However, there is still significant room for improvement, including improved precipitation information from NASA's Global

  9. Land Surface Weather Observations

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — METAR is the international standard code format for hourly surface weather observations. The acronym roughly translates from French as Aviation Routine Weather...

  10. Estimating daily surface NO2 concentrations from satellite data - a case study over Hong Kong using land use regression models

    Science.gov (United States)

    Anand, Jasdeep S.; Monks, Paul S.

    2017-07-01

    Land use regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed-effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during the period 2005-2015. In situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A, and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in situ data shows that the mixed-effects LUR model using OMI data has a high predictive power (adj. R2 = 0. 84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0. 11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

  11. Advancements in Modelling of Land Surface Energy Fluxes with Remote Sensing at Different Spatial Scales

    DEFF Research Database (Denmark)

    Guzinski, Radoslaw

    Evaporation of water from soil and its transpiration by vegetation together form a ux between the land and the atmosphere called evapotranspiration (ET). ET is a key factor in many natural and anthropogenic processes. It forms the basis of the hydrological cycle and has a strong inuence on local....... The performance of the DTD model was improved in forested ecosystems and during senescence, by taking into account the fraction of the vegetation that is green, as well as during dry conditions and in temperate climates, by modifying certain model formulations. A disaggregation algorithm was also developed...

  12. Parametrization of the Tidal Effect for Use in the Noah Land-Surface Model: Development and Validation

    Science.gov (United States)

    Lee, Young-Hee; Ahn, Kwang-Deuk; Lee, Yong Hee

    2016-12-01

    We have developed a parametrization of tidal effects for use in the Noah land-surface model and have validated the land-surface model using observations taken over a tidal flat of the western coast of South Korea. The parametrization is based on the energy budget of a water layer with varying thickness above the soil. During flood tide, heat transfer by the moving water is considered in addition to the surface energy budget. In addition, partial penetration of solar radiation through the water layer is considered in the surface energy budget, and the water thickness varying with time is used as an additional input. Seven days with clear-sky conditions and westerly winds during the daytime are selected for validation of the model. Two simulations are performed in an offline mode: a control simulation without the tidal effect (CONTROL) and a simulation with the tidal effect (TIDE). Comparisons of results have been made with eddy-covariance measurements and soil temperature data for the tidal flats. Observations show that inundation significantly reduces both sensible and latent heat fluxes during daytime, which is well simulated in the TIDE simulation. The diurnal variation and magnitude of soil temperature are better simulated in the TIDE than in the CONTROL simulation. Some underestimation of the latent heat flux over the water surface is partly attributed to the use of one layer of water and the underestimated roughness length at this site. In addition, other model deficiencies are discussed.

  13. Parametrization of the Tidal Effect for Use in the Noah Land-Surface Model: Development and Validation

    Science.gov (United States)

    Lee, Young-Hee; Ahn, Kwang-Deuk; Lee, Yong Hee

    2016-06-01

    We have developed a parametrization of tidal effects for use in the Noah land-surface model and have validated the land-surface model using observations taken over a tidal flat of the western coast of South Korea. The parametrization is based on the energy budget of a water layer with varying thickness above the soil. During flood tide, heat transfer by the moving water is considered in addition to the surface energy budget. In addition, partial penetration of solar radiation through the water layer is considered in the surface energy budget, and the water thickness varying with time is used as an additional input. Seven days with clear-sky conditions and westerly winds during the daytime are selected for validation of the model. Two simulations are performed in an offline mode: a control simulation without the tidal effect (CONTROL) and a simulation with the tidal effect (TIDE). Comparisons of results have been made with eddy-covariance measurements and soil temperature data for the tidal flats. Observations show that inundation significantly reduces both sensible and latent heat fluxes during daytime, which is well simulated in the TIDE simulation. The diurnal variation and magnitude of soil temperature are better simulated in the TIDE than in the CONTROL simulation. Some underestimation of the latent heat flux over the water surface is partly attributed to the use of one layer of water and the underestimated roughness length at this site. In addition, other model deficiencies are discussed.

  14. How well do we characterize the biophysical effects of vegetation cover change? Benchmarking land surface models against satellite observations.

    Science.gov (United States)

    Duveiller, Gregory; Forzieri, Giovanni; Robertson, Eddy; Georgievski, Goran; Li, Wei; Lawrence, Peter; Ciais, Philippe; Pongratz, Julia; Sitch, Stephen; Wiltshire, Andy; Arneth, Almut; Cescatti, Alessandro

    2017-04-01

    Changes in vegetation cover can affect the climate by altering the carbon, water and energy cycles. The main tools to characterize such land-climate interactions for both the past and future are land surface models (LSMs) that can be embedded in larger Earth System models (ESMs). While such models have long been used to characterize the biogeochemical effects of vegetation cover change, their capacity to model biophysical effects accurately across the globe remains unclear due to the complexity of the phenomena. The result of competing biophysical processes on the surface energy balance varies spatially and seasonally, and can lead to warming or cooling depending on the specific vegetation change and on the background climate (e.g. presence of snow or soil moisture). Here we present a global scale benchmarking exercise of four of the most commonly used LSMs (JULES, ORCHIDEE, JSBACH and CLM) against a dedicated dataset of satellite observations. To facilitate the understanding of the causes that lead to discrepancies between simulated and observed data, we focus on pure transitions amongst major plant functional types (PFTs): from different tree types (evergreen broadleaf trees, deciduous broadleaf trees and needleleaf trees) to either grasslands or crops. From the modelling perspective, this entails generating a separate simulation for each PFT in which all 1° by 1° grid cells are uniformly covered with that PFT, and then analysing the differences amongst them in terms of resulting biophysical variables (e.g net radiation, latent and sensible heat). From the satellite perspective, the effect of pure transitions is obtained by unmixing the signal of different 0.05° spatial resolution MODIS products (albedo, latent heat, upwelling longwave radiation) over a local moving window using PFT maps derived from the ESA Climate Change Initiative land cover map. After aggregating to a common spatial support, the observation and model-driven datasets are confronted and

  15. Improving the Vegetation Dynamic Simulation in a Land Surface Model by Using a Statistical-dynamic Canopy Interception Scheme

    Institute of Scientific and Technical Information of China (English)

    LIANG Miaoling; XIE Zhenghui

    2008-01-01

    Canopy interception of incident precipitation, as a critical component of a forest's water budget, can affect the amount of water available to the soil, and ultimately vegetation distribution and function. In this paper, a statistical-dynamic approach based on leaf area index and statistical canopy interception is used to parameterize the canopy interception process. The statistical-dynamic canopy interception scheme is implemented into the Community Land Model with dynamic global vegetation model (CLM-DGVM) to improve its dynamic vegetation simulation. The simulation for continental China by the land surface model with the new canopy interception scheme shows that the new one reasonably represents the precipitation intercepted by the canopy. Moreover, the new scheme enhances the water availability in the root zone for vegetation growth, especially in the densely vegetated and semi-arid areas, and improves the model's performance of potential vegetation simulation.

  16. Potential solar radiation and land cover contributions to digital climate surface modeling

    Science.gov (United States)

    Puig, Pol; Batalla, Meritxell; Pesquer, Lluís; Ninyerola, Miquel

    2016-04-01

    Overview: We have designed a series of ad-hoc experiments to study the role of factors that a priori have a strong weight in developing digital models of temperature and precipitation, such as solar radiation and land cover. Empirical test beds have been designed to improve climate (mean air temperature and total precipitation) digital models using statistical general techniques (multiple regression) with residual correction (interpolated with inverse weighting distance). Aim: Understand what roles these two factors (solar radiation and land cover) play to incorporate them into the process of generating mapping of temperature and rainfall. Study area: The Iberian Peninsula and supported in this, Catalonia and the Catalan Pyrenees. Data: The dependent variables used in all experiments relate to data from meteorological stations precipitation (PL), mean temperature (MT), average temperature minimum (MN) and maximum average temperature (MX). These data were obtained monthly from the AEMET (Agencia Estatal de Meteorología). Data series of stations covers the period between 1950 to 2010. Methodology: The idea is to design ad hoc, based on a sample of more equitable space statistician, to detect the role of radiation. Based on the influence of solar radiation on the temperature of the air from a quantitative point of view, the difficulty in answering this lies in the fact that there are lots of weather stations located in areas where solar radiation is similar. This suggests that the role of the radiation variable remains "off" when, instead, we intuitively think that would strongly influence the temperature. We have developed a multiple regression analysis between these meteorological variables as the dependent ones (Temperature and rainfall), and some geographical variables: altitude (ALT), latitude (LAT), continentality (CON) and solar radiation (RAD) as the independent ones. In case of the experiment with land covers, we have used the NDVI index as a proxy of land

  17. The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

    Science.gov (United States)

    Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

    2006-01-01

    Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

  18. Impact of the hard-coded parameters on the hydrologic fluxes of the land surface model Noah-MP

    Science.gov (United States)

    Cuntz, Matthias; Mai, Juliane; Samaniego, Luis; Clark, Martyn; Wulfmeyer, Volker; Attinger, Sabine; Thober, Stephan

    2016-04-01

    Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The process descriptions contain a number of parameters that can be soil or plant type dependent and are typically read from tabulated input files. Land surface models may have, however, process descriptions that contain fixed, hard-coded numbers in the computer code, which are not identified as model parameters. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the importance of the fixed values on restricting the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options, which are mostly spatially constant values. This is in addition to the 71 standard parameters of Noah-MP, which mostly get distributed spatially by given vegetation and soil input maps. We performed a Sobol' global sensitivity analysis of Noah-MP to variations of the standard and hard-coded parameters for a specific set of process options. 42 standard parameters and 75 hard-coded parameters were active with the chosen process options. The sensitivities of the hydrologic output fluxes latent heat and total runoff as well as their component fluxes were evaluated. These sensitivities were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's hydrologic output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Surface runoff is sensitive to almost all hard-coded parameters of the snow processes and the meteorological inputs. These parameter sensitivities diminish in total runoff. Assessing these parameters in model calibration would require detailed snow observations or the

  19. Evaluating radiative transfer schemes treatment of vegetation canopy architecture in land surface models

    Science.gov (United States)

    Braghiere, Renato; Quaife, Tristan; Black, Emily

    2016-04-01

    Incoming shortwave radiation is the primary source of energy driving the majority of the Earth's climate system. The partitioning of shortwave radiation by vegetation into absorbed, reflected, and transmitted terms is important for most of biogeophysical processes, including leaf temperature changes and photosynthesis, and it is currently calculated by most of land surface schemes (LSS) of climate and/or numerical weather prediction models. The most commonly used radiative transfer scheme in LSS is the two-stream approximation, however it does not explicitly account for vegetation architectural effects on shortwave radiation partitioning. Detailed three-dimensional (3D) canopy radiative transfer schemes have been developed, but they are too computationally expensive to address large-scale related studies over long time periods. Using a straightforward one-dimensional (1D) parameterisation proposed by Pinty et al. (2006), we modified a two-stream radiative transfer scheme by including a simple function of Sun zenith angle, so-called "structure factor", which does not require an explicit description and understanding of the complex phenomena arising from the presence of vegetation heterogeneous architecture, and it guarantees accurate simulations of the radiative balance consistently with 3D representations. In order to evaluate the ability of the proposed parameterisation in accurately represent the radiative balance of more complex 3D schemes, a comparison between the modified two-stream approximation with the "structure factor" parameterisation and state-of-art 3D radiative transfer schemes was conducted, following a set of virtual scenarios described in the RAMI4PILPS experiment. These experiments have been evaluating the radiative balance of several models under perfectly controlled conditions in order to eliminate uncertainties arising from an incomplete or erroneous knowledge of the structural, spectral and illumination related canopy characteristics typical

  20. Mapping the land surface for global atmosphere-biosphere models: Toward continuous distributions of vegetation's functional properties

    Science.gov (United States)

    Defries, Ruth S.; Field, Christopher B.; Fung, Inez; Justice, Christopher O.; Los, Sietse; Matson, Pamela A.; Matthews, Elaine; Mooney, Harold A.; Potter, Christopher S.; Prentice, Katharine; Sellers, Piers J.; Townshend, John R. G.; Tucker, Compton J.; Ustin, Susan L.; Vitousek, Peter M.

    1995-10-01

    Global land surface characteristics are important boundary conditions for global models that describe exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere. Existing data sets of global land cover are based on classification schemes that characterize each grid cell as a discrete vegetation type. Consequently, parameter fields derived from these data sets are dependent on the particular scheme and the number of vegetation types it includes. The functional controls on exchanges of water, energy, and carbon dioxide between the atmosphere and biosphere are now well enough understood that it is increasingly feasible to model these exchanges using a small number of vegetation characteristics that either are related to or closely related to the functional controls. Ideally, these characteristics would be mapped as continuous distributions to capture mixtures and gradients in vegetation within the cell size of the model. While such an approach makes it more difficult to build models from detailed observations at a small number of sites, it increases the potential for capturing functionally important variation within, as well as between, vegetation types. Globally, the vegetation characteristics that appear to be most important in controlling fluxes of water, energy, and carbon dioxide include (1) growth form (tree, shrub, herb), (2) seasonality of woody vegetation (deciduous, evergreen), (3) leaf type (broadleaf, coniferous), (4) photosynthetic pathway of nonwoody vegetation (C3, C4), (5) longevity (annual, perennial), and (6) type and intensity of disturbance (e.g., cultivation, fire history). Many of these characteristics can be obtained through remote sensing, though some require ground-based information. The minimum number and the identity of the required land surface characteristics almost certainly vary with the intended objective, but the philosophy of driving models with continuous distributions of a small number of land surface

  1. Modelling Angular Dependencies in Land Surface Temperatures From the SEVIRI Instrument onboard the Geostationary Meteosat Second Generation Satellites

    DEFF Research Database (Denmark)

    Rasmussen, Mads Olander; Pinheiro, AC; Proud, Simon Richard

    2010-01-01

    Satellite-based estimates of land surface temperature (LST) are widely applied as an input to models. A model output is often very sensitive to error in the input data, and high-quality inputs are therefore essential. One of the main sources of errors in LST estimates is the dependence on vegetat......Satellite-based estimates of land surface temperature (LST) are widely applied as an input to models. A model output is often very sensitive to error in the input data, and high-quality inputs are therefore essential. One of the main sources of errors in LST estimates is the dependence...... on vegetation structure and viewing and illumination geometry. Despite this, these effects are not considered in current operational LST products from neither polar-orbiting nor geostationary satellites. In this paper, we simulate the angular dependence that can be expected when estimating LST with the viewing...... by different land covers. The results show that the sun-target-sensor geometry plays a significant role in the estimated temperature, with variations strictly due to the angular configuration of more than ±3°C in some cases. On the continental scale, the average error is small except in hot-spot conditions...

  2. Integrating satellite retrieved leaf chlorophyll into land surface models for constraining simulations of water and carbon fluxes

    KAUST Repository

    Houborg, Rasmus

    2013-07-01

    In terrestrial biosphere models, key biochemical controls on carbon uptake by vegetation canopies are typically assigned fixed literature-based values for broad categories of vegetation types although in reality significant spatial and temporal variability exists. Satellite remote sensing can support modeling efforts by offering distributed information on important land surface characteristics, which would be very difficult to obtain otherwise. This study investigates the utility of satellite based retrievals of leaf chlorophyll for estimating leaf photosynthetic capacity and for constraining model simulations of water and carbon fluxes. © 2013 IEEE.

  3. Evaluation of land surface model representation of phenology: an analysis of model runs submitted to the NACP Interim Site Synthesis

    Science.gov (United States)

    Richardson, A. D.; Nacp Interim Site Synthesis Participants

    2010-12-01

    Phenology represents a critical intersection point between organisms and their growth environment. It is for this reason that phenology is a sensitive and robust integrator of the biological impacts of year-to-year climate variability and longer-term climate change on natural systems. However, it is perhaps equally important that phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating ecosystem processes, competitive interactions, and feedbacks to the climate system. Unfortunately, the phenological sub-models implemented in most state-of-the-art ecosystem models and land surface schemes are overly simplified. We quantified model errors in the representation of the seasonal cycles of leaf area index (LAI), gross ecosystem photosynthesis (GEP), and net ecosystem exchange of CO2. Our analysis was based on site-level model runs (14 different models) submitted to the North American Carbon Program (NACP) Interim Synthesis, and long-term measurements from 10 forested (5 evergreen conifer, 5 deciduous broadleaf) sites within the AmeriFlux and Fluxnet-Canada networks. Model predictions of the seasonality of LAI and GEP were unacceptable, particularly in spring, and especially for deciduous forests. This is despite an historical emphasis on deciduous forest phenology, and the perception that controls on spring phenology are better understood than autumn phenology. Errors of up to 25 days in predicting “spring onset” transition dates were common, and errors of up to 50 days were observed. For deciduous sites, virtually every model was biased towards spring onset being too early, and autumn senescence being too late. Thus, models predicted growing seasons that were far too long for deciduous forests. For most models, errors in the seasonal representation of deciduous forest LAI were highly correlated with errors in the seasonality of both GPP and NEE, indicating the importance of getting the underlying

  4. Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

    Directory of Open Access Journals (Sweden)

    Chudong Huang

    2015-07-01

    Full Text Available The coupling relationship between urban vegetation and land surface temperature (LST has been heatedly debated in a variety of environmental studies. This paper studies the urban vegetation information and LST by utilizing a series of remote sensing imagery covering the period from 1990 to 2007. Their coupling relationship is analyzed, in order to provide the basis for ecological planning and environment protection. The results show that the normalized difference vegetation index (NDVI, urban vegetation abundance (UVA and urban forest abundance (UFA are negatively correlated with LST, which means that both urban vegetation and urban forest are capable in decreasing LST. The apparent influence of urban vegetation and urban forest on LST varies with the spatial resolution of the imagery, and peaks at the resolutions ranging from 90 m to 120 m.

  5. Coupled Soil Water and Heat Transport Near the Land Surface in Arid and Semiarid Regions - Multi-Domain Modeling

    Science.gov (United States)

    Mohanty, Binayak; Yang, Zhenlei

    2016-04-01

    Understanding and simulating coupled water and heat transfer appropriately in the shallow subsurface is of vital significance for accurate prediction of soil evaporation that would improve the coupling between land surface and atmosphere, which consequently could enhance the reliability of weather as well as climate forecast. The theory of Philip and de Vries (1957), accounting for water vapor diffusion only, was considered physically incomplete and consequently extended and improved by several researchers by explicitly taking water vapor convection, dispersion or air flow into account. It is generally believed that the soil moisture is usually low in the near surface layer under highly transient field conditions, particularly in arid and semiarid regions, and that accurate characterization of water vapor transport is critical when modeling simultaneous water and heat transport in the shallow field soils. The first objective of this study is thus mainly to test existing coupled water and heat transport theories and to develop reasonable and simplified numerical models using field experimental data collected under semi-arid and arid hydro-climatic conditions. In addition, more complex multi-domain models are developed for ubiquitous heterogeneous terrestrial surfaces such as horizontal textural contrasts or structured heterogeneity including macropores (fractures, cracks, root channels, etc.). This would make coupled water and heat transfer models applicable in such non-homogeneous soils more meaningful and enhance the skill of land-atmosphere interaction models at a larger context.

  6. Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region.

    Directory of Open Access Journals (Sweden)

    Qidong Yang

    Full Text Available Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO algorithm and the land-surface process model SHAW (Simultaneous Heat and Water, the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.

  7. Impact of model developments on present and future simulations of permafrost in a global land-surface model

    Science.gov (United States)

    Chadburn, S. E.; Burke, E. J.; Essery, R. L. H.; Boike, J.; Langer, M.; Heikenfeld, M.; Cox, P. M.; Friedlingstein, P.

    2015-08-01

    There is a large amount of organic carbon stored in permafrost in the northern high latitudes, which may become vulnerable to microbial decomposition under future climate warming. In order to estimate this potential carbon-climate feedback it is necessary to correctly simulate the physical dynamics of permafrost within global Earth system models (ESMs) and to determine the rate at which it will thaw. Additional new processes within JULES, the land-surface scheme of the UK ESM (UKESM), include a representation of organic soils, moss and bedrock and a modification to the snow scheme; the sensitivity of permafrost to these new developments is investigated in this study. The impact of a higher vertical soil resolution and deeper soil column is also considered. Evaluation against a large group of sites shows the annual cycle of soil temperatures is approximately 25 % too large in the standard JULES version, but this error is corrected by the model improvements, in particular by deeper soil, organic soils, moss and the modified snow scheme. A comparison with active layer monitoring sites shows that the active layer is on average just over 1 m too deep in the standard model version, and this bias is reduced by 70 cm in the improved version. Increasing the soil vertical resolution allows the full range of active layer depths to be simulated; by contrast, with a poorly resolved soil at least 50 % of the permafrost area has a maximum thaw depth at the centre of the bottom soil layer. Thus all the model modifications are seen to improve the permafrost simulations. Historical permafrost area corresponds fairly well to observations in all simulations, covering an area between 14 and 19 million km2. Simulations under two future climate scenarios show a reduced sensitivity of permafrost degradation to temperature, with the near-surface permafrost loss per degree of warming reduced from 1.5 million km2 °C-1 in the standard version of JULES to between 1.1 and 1.2 million km2 °C-1

  8. A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over US and Illinois with SECHIBA forced by NLDAS

    OpenAIRE

    Guimberteau, M.; Perrier, A.; K. Laval; Polcher, J.

    2012-01-01

    The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and mesoscale. The model is forced by NLDAS data set at eighth degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture...

  9. Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment

    DEFF Research Database (Denmark)

    Guzinski, R.; Nieto, H.; Stisen, S.

    2015-01-01

    Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distribu...... derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.......-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case...

  10. Improved representations of coupled soil-canopy processes in the CABLE land surface model (Subversion revision 3432)

    Science.gov (United States)

    Haverd, Vanessa; Cuntz, Matthias; Nieradzik, Lars P.; Harman, Ian N.

    2016-09-01

    CABLE is a global land surface model, which has been used extensively in offline and coupled simulations. While CABLE performs well in comparison with other land surface models, results are impacted by decoupling of transpiration and photosynthesis fluxes under drying soil conditions, often leading to implausibly high water use efficiencies. Here, we present a solution to this problem, ensuring that modelled transpiration is always consistent with modelled photosynthesis, while introducing a parsimonious single-parameter drought response function which is coupled to root water uptake. We further improve CABLE's simulation of coupled soil-canopy processes by introducing an alternative hydrology model with a physically accurate representation of coupled energy and water fluxes at the soil-air interface, including a more realistic formulation of transfer under atmospherically stable conditions within the canopy and in the presence of leaf litter. The effects of these model developments are assessed using data from 18 stations from the global eddy covariance FLUXNET database, selected to span a large climatic range. Marked improvements are demonstrated, with root mean squared errors for monthly latent heat fluxes and water use efficiencies being reduced by 40 %. Results highlight the important roles of deep soil moisture in mediating drought response and litter in dampening soil evaporation.

  11. Assessing Water Availability in the Rio Grande Basin using NOAH Land Surface Model and NLDAS2 Forcing Data

    Science.gov (United States)

    Khedun, C. P.; Giardino, J. R.; Singh, V. P.; Bolten, J. D.

    2009-12-01

    The Rio Grande/Río Bravo basin is a transboundary basin shared between several states in the United States and trends along part of the border between the United States and Mexico. The basin has a varied climatology - desert with high temperature and low water resources in the northern part and a tropical climate in the southern part. The surface water in the basin is over allocated, and some cities already rely solely on ground water to meet municipal water needs. The population and urban water demand are expected to double in the next 50 years. Climate variability and change, along with persistent long-term droughts, exacerbate the problem of meeting water demands. Uncertainty in the ways these climatic phenomena will affect the water availability in the coming decades will impact the effective long-term policies and management of water resources in the basin in the United States and Mexico, and this can lead to tensions over water allocation across the border. In order to study the impact of demographic changes and climate variability and change on future water availability, it is important to develop a model that can assess the current water availability and evaluate the effect of projected climate change, based on IPCC’s scenarios, on the hydrology of the basin. For this study we developed a model based on the community NOAH land surface model (LSM) within NASA’s GSFC Land Information System. The LSM is a 1-D column model that runs in coupled or uncoupled mode, and it simulates soil moisture, soil temperature, skin temperature, snowpack depth, snow water equivalent, canopy water content, and energy flux and water flux of the surface energy and water balance. The North American Land Data Assimilation Scheme (NLDAS2) is used to drive the model. The NLDAS2 datasets extends back to 1979, thereby allowing the model to be run retrospectively for a period of 30 years. Additional model parameters include seasonal maximum snow free albedo maps, monthly greenness

  12. Land surface model performance using cosmic-ray and point-scale soil moisture measurements for calibration

    Science.gov (United States)

    Iwema, Joost; Rosolem, Rafael; Rahman, Mostaquimur; Blyth, Eleanor; Wagener, Thorsten

    2017-06-01

    At very high resolution scale (i.e. grid cells of 1 km2), land surface model parameters can be calibrated with eddy-covariance flux data and point-scale soil moisture data. However, measurement scales of eddy-covariance and point-scale data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux and soil moisture observations by replacing point-scale soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNSs) made at larger spatial scales. Five soil and evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against point-scale and Cosmic-Ray Neutron Sensor soil moisture data separately. We calibrated the model for 12 sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in latent heat flux estimation for the two calibration solutions was assessed by comparison to eddy-covariance flux data and to JULES simulations with default parameter values. Calibrations against the two soil moisture products alone did show an advantage for the cosmic-ray technique. However, further analyses of two-objective calibrations with soil moisture and latent heat flux showed no substantial differences between both calibration strategies. This was mainly caused by the limited effect of calibrating soil parameters on soil moisture dynamics and surface energy fluxes. Other factors that played a role were limited spatial variability in surface fluxes implied by soil moisture spatio-temporal stability, and data quality issues.

  13. Inter-comparison of two land-surface models applied at different scales and their feedbacks while coupled with a regional climate model

    Directory of Open Access Journals (Sweden)

    F. Zabel

    2012-03-01

    Full Text Available Downstream models are often used in order to study regional impacts of climate and climate change on the land surface. For this purpose, they are usually driven offline (i.e., 1-way with results from regional climate models (RCMs. However, the offline approach does not allow for feedbacks between these models. Thereby, the land surface of the downstream model is usually completely different to the land surface which is used within the RCM. Thus, this study aims at investigating the inconsistencies that arise when driving a downstream model offline instead of interactively coupled with the RCM, due to different feedbacks from the use of different land surface models (LSM. Therefore, two physically based LSMs which developed from different disciplinary backgrounds are compared in our study: while the NOAH-LSM was developed for the use within RCMs, PROMET was originally developed to answer hydrological questions on the local to regional scale. Thereby, the models use different physical formulations on different spatial scales and different parameterizations of the same land surface processes that lead to inconsistencies when driving PROMET offline with RCM output. Processes that contribute to these inconsistencies are, as described in this study, net radiation due to land use related albedo and emissivity differences, the redistribution of this net radiation over sensible and latent heat, for example, due to different assumptions about land use impermeability or soil hydraulic reasons caused by different plant and soil parameterizations. As a result, simulated evapotranspiration, e.g., shows considerable differences of max. 280 mm yr−1. For a full interactive coupling (i.e., 2-way between PROMET and the atmospheric part of the RCM, PROMET returns the land surface energy fluxes to the RCM and, thus, provides the lower boundary conditions for the RCM subsequently. Accordingly, the RCM responses to the replacement of the LSM with overall

  14. Regional climate model assessment of the urban land-surface forcing over central Europe

    Directory of Open Access Journals (Sweden)

    P. Huszar

    2014-11-01

    Full Text Available For the purpose of qualifying and quantifying the climate impact of cities and urban surfaces in general on climate of central Europe, the surface parameterization in regional climate model RegCM4 has been extended with the Single-layer Urban Canopy Model (SLUCM. A set of experiments was performed over the period of 2005–2009 for central Europe, either without considering urban surfaces or with the SLUCM treatment. Results show a statistically significant impact of urbanized surfaces on temperature (up to 1.5 K increase in summer as well as on the boundary layer height (increases up to 50 m. Urbanization further influences surface wind with a winter decrease up to −0.6 m s−1, though both increases and decreases were detected in summer depending on the location relative to the cities and daytime (changes up to 0.3 m s−1. Urban surfaces significantly reduce the humidity over the surface. This impacts the simulated summer precipitation rate, showing a decrease over cities of up to −2 mm day−1. Significant temperature increases are simulated over higher altitudes as well, not only within the urban canopy layer. With the urban parameterization, the climate model better describes the diurnal temperature variation, reducing the cold afternoon and evening bias of RegCM4. Sensitivity experiments were carried out to quantify the response of the meteorological conditions to changes in the parameters specific to the urban environment, such as street width, building height, albedo of the roofs and anthropogenic heat release. The results proved to be rather robust and the choice of the key SLUCM parameters impacts them only slightly (mainly temperature, boundary layer height and wind velocity. Statistically significant impacts are modelled not only over large urbanized areas, but the influence of the cities is also evident over rural areas without major urban surfaces. It is shown that this is the result of the combined effect of the distant

  15. Regional climate model assessment of the urban land-surface forcing over central Europe

    Directory of Open Access Journals (Sweden)

    P. Huszar

    2014-07-01

    Full Text Available For the purpose of qualifying and quantifying the climate impact of cities and urban surfaces in general on climate of central Europe, the surface parameterization in regional climate model RegCM4 has been extended with the Single Layer Urban Canopy Model (SLUCM. A set of experiments was performed over the period of 2005–2009 for central Europe, either without considering urban surfaces or with the SLUCM treatment. Results show a statistically significant impact of urbanized surfaces on temperature (up to 1.5 K increase in summer as well as on the boundary layer height (increases up to 50 m. Urbanization further influences surface wind with a winter decrease up to −0.6 m s−1, though both increases and decreases were detected in summer depending on the location relative to the cities and daytime (changes up to 0.3 m s−1. Urban surfaces significantly reduce evaporation and thus the humidity over the surface. This impacts the simulated summer precipitation rate, showing decrease over cities up to −2 mm day−1. Significant temperature increases are simulated over higher elevations as well, not only within the urban canopy layer. With the urban parameterization, the climate model better describes the diurnal temperature variation, reducing the cold afternoon and evening bias of RegCM4. Sensitivity experiments were carried out to quantify the response of the meteorological conditions to changes in the parameters specific to the urban environment such as street width, building height, albedo of the roofs and anthropogenic heat release. The results proved to be rather robust and the choice of the key SLUCM parameters impacts them only slightly (mainly temperature, boundary layer height and wind velocity. Statistically significant impacts are modeled not only over large urbanized areas, but the influence of the cities is also evident over rural areas without major urban surfaces. It is shown that this is the result of the combined effect of

  16. A land surface model incorporated with soil freeze/thaw and its application in GAME/Tibet

    Institute of Scientific and Technical Information of China (English)

    HU Heping; YE Baisheng; ZHOU Yuhua; TIAN Fuqiang

    2006-01-01

    Land surface process is of great importance in global climate change,moisture and heat exchange in the interface of the earth and atmosphere,human impacts on the environment and ecosystem,etc.Soil freeze/thaw plays an important role in cold land surface processes.In this work the diurnal freeze/thaw effects on energy partition in the context of GAME/Tibet are studied.A sophisticated land surface model is developed,the particular aspect of which is its physical consideration of soil freeze/thaw and vapor flux.The simultaneous water and heat transfer soil sub-model not only reflects the water flow from unfrozen zone to frozen fringe in freezing/thawing soil,but also demonstrates the change of moisture and temperature field induced by vapor flux from high temperature zone to low temperature zone,which makes the model applicable for various circumstances.The modified Picard numerical method is employed to help with the water balance and convergence of the numerical scheme.Finally,the model is applied to analyze the diurnal energy and water cycle characteristics over the Tibetan Plateau using the Game/Tibet datasets observed in May and July of 1998.Heat and energy transfer simulation shows that: (i) There exists a negative feedback mechanism between soil freeze/thaw and soil temperature/ground heat flux; (ii) during freezing period all three heat fluxes do not vary apparently,in spite of the fact that the negative soil temperature is higher than that not considering soil freeze; (iii) during thawing period,ground heat flux increases,and sensible heat flux decreases,but latent heat flux does not change much; and (iv) during freezing period,soil temperature decreases,though ground heat flux increases.

  17. Land Surface Water Mapping Using Multi-Scale Level Sets and a Visual Saliency Model from SAR Images

    Directory of Open Access Journals (Sweden)

    Chuan Xu

    2016-05-01

    Full Text Available Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS, to overcome the lack of an operational solution for automatically, rapidly and reliably extracting water from large-area and fine spatial resolution Synthetic Aperture Radar (SAR images. This paper has two main contributions, as follows: (1 The method integrated the advantages of both level sets and the visual saliency model. First, the visual saliency map was applied to detect the suspected water regions (SWR, and then the level set method only needed to be applied to the SWR regions to accurately extract the water bodies, thereby yielding a simultaneous reduction in time cost and increase in accuracy; (2 In order to make the classical Itti model more suitable for extracting water in SAR imagery, an improved texture weighted with the Itti model (TW-Itti is employed to detect those suspected water regions, which take into account texture features generated by the Gray Level Co-occurrence Matrix (GLCM algorithm, Furthermore, a novel calculation method for center-surround differences was merged into this model. The proposed method was tested on both Radarsat-2 and TerraSAR-X images, and experiments demonstrated the effectiveness of the proposed method, the overall accuracy of water mapping is 98.48% and the Kappa coefficient is 0.856.

  18. Examining the Impact of Greenspace Patterns on Land Surface Temperature by Coupling LiDAR Data with a CFD Model

    Directory of Open Access Journals (Sweden)

    Weizhong Su

    2014-09-01

    Full Text Available Understanding the link between greenspace patterns and land surface temperature is very important for mitigating the urban heat island (UHI effect and is also useful for planners and decision-makers for providing a sustainable design for urban greenspace. Although coupling remote sensing data with a computational fluid dynamics (CFD model has widely been used to examine interactions between UHI and greenspace patterns, the paper aims to examine the impact of five theoretical models of greenspace patterns on land surface temperature based on the improvement of the accuracy of CFD modeling by the combination of LiDAR data with remote sensing images to build a 3D urban model. The simulated results demonstrated that the zonal pattern always had the obvious cooling effects when there are no large buildings or terrain obstacles. For ambient environments, the building or terrain obstacles and the type of greenspace have the hugest influence on mitigating the UHI, but the greenspace area behaves as having the least cooling effect. A dotted greenspace pattern shows the best cooling effect in the central area or residential district within a city, while a radial and a wedge pattern may result in a “cold source” for the urban thermal environment.

  19. Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region

    Science.gov (United States)

    Wang, Wenli; Rinke, Annette; Moore, John C.; Ji, Duoying; Cui, Xuefeng; Peng, Shushi; Lawrence, David M.; McGuire, A. David; Burke, Eleanor J.; Chen, Xiaodong; Delire, Christine; Koven, Charles; MacDougall, Andrew; Saito, Kazuyuki; Zhang, Wenxin; Alkama, Ramdane; Bohn, Theodore J.; Ciais, Philippe; Decharme, Bertrand; Gouttevin, Isabelle; Hajima, Tomohiro; Krinner, Gerhard; Lettenmaier, Dennis P.; Miller, Paul A.; Smith, Benjamin; Sueyoshi, Tetsuo

    2016-01-01

     A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyze simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models and compare them with observations from 268 Russian stations. There are large across-model differences as expressed by simulated differences between near-surface soil and air temperatures, (ΔT), of 3 to 14 K, in the gradients between soil and air temperatures (0.13 to 0.96°C/°C), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, and hence guide improvements to the model’s conceptual structure and process parameterizations. Models with better performance apply multi-layer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (12–16 million km2). However, there is not a simple relationship between the quality of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, likely because several other factors such as differences in the treatment of soil organic matter, soil hydrology, surface energy calculations, and vegetation also provide important controls on simulated permafrost distribution.

  20. Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

    Science.gov (United States)

    De Lannoy, Gabriëlle J. M.; Reichle, Rolf H.

    2016-12-01

    Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval

  1. Modeling the Effects of Irrigation on Land Surface Fluxes and States over the Conterminous United States: Sensitivity to Input Data and Model Parameters

    Energy Technology Data Exchange (ETDEWEB)

    Leng, Guoyong; Huang, Maoyi; Tang, Qiuhong; Sacks, William J.; Lei, Huimin; Leung, Lai-Yung R.

    2013-09-16

    Previous studies on irrigation impacts on land surface fluxes/states were mainly conducted as sensitivity experiments, with limited analysis of uncertainties from the input data and model irrigation schemes used. In this study, we calibrated and evaluated the performance of irrigation water use simulated by the Community Land Model version 4 (CLM4) against observations from agriculture census. We investigated the impacts of irrigation on land surface fluxes and states over the conterminous United States (CONUS) and explored possible directions of improvement. Specifically, we found large uncertainty in the irrigation area data from two widely used sources and CLM4 tended to produce unrealistically large temporal variations of irrigation demand for applications at the water resources region scale over CONUS. At seasonal to interannual time scales, the effects of irrigation on surface energy partitioning appeared to be large and persistent, and more pronounced in dry than wet years. Even with model calibration to yield overall good agreement with the irrigation amounts from the National Agricultural Statistics Service (NASS), differences between the two irrigation area datasets still dominate the differences in the interannual variability of land surface response to irrigation. Our results suggest that irrigation amount simulated by CLM4 can be improved by (1) calibrating model parameter values to account for regional differences in irrigation demand and (2) accurate representation of the spatial distribution and intensity of irrigated areas.

  2. Land surface parameterization and modeling over desert%沙漠陆面过程参数化与模拟

    Institute of Scientific and Technical Information of China (English)

    郑辉; 刘树华

    2013-01-01

    In desert,the climate is hot and dry,the vegetation is sparse,the land surface physical processes are significantly different from those in other regions.By using the data measured in Badanjilin desert,several key land surface parameters were revised.We established a Desert Land Surface Model (DLSM).The model was compared with Noah land surface model and observation data.In this study,the Badanjilin desert surface albedo is 0.273,the emissivity is 0.950,surface roughness is 1.55×10-3 m,the soil heat capacity is 1.08×106 J · m 3 · K-1 and diffusivity is 3.34×10 7m2 ·s.Radiation transfer,sensible heat transfer and soil heat conduction are the key physical processes affecting land surface energy balance.With adequate parameterization of these three processes,the DLSM reasonably simulates the land atmosphere interaction processes over Bandanjilin desert.The root mean square errors of modeled solar radiation flux,longwave radiation flux and sensible heat flux were 7.98,6.14,33.9 W · m-2respectively,which were comparable with the results,7.98,7.72,46.6 W · m-2,from NOAH.Surface albedo is the most important land surface parameter in desert.By increasing 5% of the albedo,the reflected solar radiation increased by 5%,and the sensible heat flux decreased by 2.83%.The results are beneficial to the study on land surface parameterization,modeling and climate simulation.%沙漠地区植被稀疏、干旱少雨,其陆面物理过程具有与全球其它地区显著不同的特点.本文利用巴丹吉林沙漠观测资料,分析和计算了地表反照率、比辐射率、粗糙度和土壤热容量、热传导系数等关键陆面过程参数,建立了适合于沙漠地区的陆面过程模式DLSM (Desert Land Surface Model),并与NOAH陆面过程模式的模拟结果和观测资料进行了比较.结果表明:巴丹吉林沙漠地表反照率为0.273,比辐射率为0.950,地表粗糙度为1.55×10-3rn,土壤热容量和热扩散系数分别为1.08×106 J·m-3·K-1

  3. Characteristics of organic soil in black spruce forests: implications for the application of land surface and ecosystem models in cold regions

    Science.gov (United States)

    Shuhua Yi; Kristen Manies; Jennifer Harden; David. McGuire

    2009-01-01

    Soil organic layers (OL) play an important role in land-atmosphere exchanges of water, energy and carbon in cold environments. The proper implementation of OL in land surface and ecosystem models is important for predicting dynamic responses to climate warming. Based on the analysis of OL samples of black spruce (Picea mariana), we recommend that...

  4. Wireless Channel Characterization: Modeling the 5 GHz Microwave Landing System Extension Band for Future Airport Surface Communications

    Science.gov (United States)

    Matolak, D. W.; Apaza, Rafael; Foore, Lawrence R.

    2006-01-01

    We describe a recently completed wideband wireless channel characterization project for the 5 GHz Microwave Landing System (MLS) extension band, for airport surface areas. This work included mobile measurements at large and small airports, and fixed point-to-point measurements. Mobile measurements were made via transmission from the air traffic control tower (ATCT), or from an airport field site (AFS), to a receiving ground vehicle on the airport surface. The point-to-point measurements were between ATCT and AFSs. Detailed statistical channel models were developed from all these measurements. Measured quantities include propagation path loss and power delay profiles, from which we obtain delay spreads, frequency domain correlation (coherence bandwidths), fading amplitude statistics, and channel parameter correlations. In this paper we review the project motivation, measurement coordination, and illustrate measurement results. Example channel modeling results for several propagation conditions are also provided, highlighting new findings.

  5. Modeling directional effects in land surface temperature derived from geostationary satellite data

    DEFF Research Database (Denmark)

    Rasmussen, Mads Olander

    varying magnitude and sign on both diurnal and seasonal scales, which will have implications if using LST products in downstream applications like hydrological or soil vegetation atmosphere transfer (SVAT) models. The directional effects will cause uncertainties in LST estimates that are different...... in terms of timing than the uncertainties in data from polar orbiting sensors, which will cause discrepancies between measurements from the two types of sensors. An assessment of the performance of current LST algorithms from MSG SEVIRI for semi-arid West Africa was carried out, using data from two field...... the illumination geometry changes both over the course of the day and with the seasons. In the present study, the directional effects are assessed at different scales using a modeling approach. The model applied, the Modified Geometry Projection (MGP) model, represents the surface as a composite of four components...

  6. Parameterizing atmosphere-land surface exchange for climate models with satellite data: A case study for the Southern Great Plains CART site

    Science.gov (United States)

    Gao, W.

    High-resolution satellite data provide detailed, quantitative descriptions of land surface characteristics over large areas so that objective scale linkage becomes feasible. With the aid of satellite data, researchers examined the linearity of processes scaled up from 30 m to 15 km. If the phenomenon is scale invariant, then the aggregated value of a function or flux is equivalent to the function computed from aggregated values of controlling variables. The linear relation may be realistic for limited land areas having no large surface contrasts to cause significant horizontal exchange. However, for areas with sharp surface contrasts, horizontal exchange and different dynamics in the atmospheric boundary may induce nonlinear interactions, such as at interfaces of land-water, forest-farm land, and irrigated crops-desert steppe. The linear approach, however, represents the simplest scenario and is useful for developing an effective scheme for incorporating subgrid land surface processes into large-scale models. Our studies focus on coupling satellite data and ground measurements with a satellite-data-driven land surface model to parameterize surface fluxes for large-scale climate models. In this case study, we used surface spectral reflectance data from satellite remote sensing to characterize spatial and temporal changes in vegetation and associated surface parameters in an area of about 350 x 400 km covering the southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site of the US Department of Energy's Atmospheric Radiation Measurement (ARM) Program.

  7. LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project - aims, setup and expected outcome

    Science.gov (United States)

    van den Hurk, Bart; Kim, Hyungjun; Krinner, Gerhard; Seneviratne, Sonia I.; Derksen, Chris; Oki, Taikan; Douville, Hervé; Colin, Jeanne; Ducharne, Agnès; Cheruy, Frederique; Viovy, Nicholas; Puma, Michael J.; Wada, Yoshihide; Li, Weiping; Jia, Binghao; Alessandri, Andrea; Lawrence, Dave M.; Weedon, Graham P.; Ellis, Richard; Hagemann, Stefan; Mao, Jiafu; Flanner, Mark G.; Zampieri, Matteo; Materia, Stefano; Law, Rachel M.; Sheffield, Justin

    2016-08-01

    The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode ("LMIP", building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework ("LFMIP", building upon the GLACE-CMIP blueprint).

  8. LS3MIP (v1.0) Contribution to CMIP6: The Land Surface, Snow and Soil Moisture Model Intercomparison Project Aims, Setup and Expected Outcome.

    Science.gov (United States)

    Van Den Hurk, Bart; Kim, Hyungjun; Krinner, Gerhard; Seneviratne, Sonia I.; Derksen, Chris; Oki, Taikan; Douville, Herve; Colin, Jeanne; Ducharne, Agnes; Cheruy, Frederique; Viovy, Nicholas; Puma, Michael J.; Wada, Yoshide; Li, Weiping; Jia, Binghao; Alessandri, Andrea; Lawrence, Dave M.; Weedon, Graham P.; Ellis, Richard; Hagemann, Stefan

    2016-01-01

    The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems).The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (LMIP, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (LFMIP, building upon the GLACE-CMIP blueprint).

  9. Detection of open water dynamics with ENVISAT ASAR in support of land surface modelling at high latitudes

    Directory of Open Access Journals (Sweden)

    A. Bartsch

    2012-02-01

    Full Text Available Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4. The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km. The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected.

    C-band data from ENVISAT ASAR (Advanced SAR operating in wide swath mode (150 m resolution were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian

  10. Coupled carbon-nitrogen land surface modelling for UK agricultural landscapes using JULES and JULES-ECOSSE-FUN (JEF)

    Science.gov (United States)

    Comyn-Platt, Edward; Clark, Douglas; Blyth, Eleanor

    2016-04-01

    The UK is required to provide accurate estimates of the UK greenhouse gas (GHG; CO2, CH4 and N2O) emissions for the UNFCCC (United Nations Framework Convention on Climate Change). Process based land surface models (LSMs), such as the Joint UK Land Environment Simulator (JULES), attempt to provide such estimates based on environmental (e.g. land use and soil type) and meteorological conditions. The standard release of JULES focusses on the water and carbon cycles, however, it has long been suggested that a coupled carbon-nitrogen scheme could enhance simulations. This is of particular importance when estimating agricultural emission inventories where the carbon cycle is effectively managed via the human application of nitrogen based fertilizers. JULES-ECOSSE-FUN (JEF) links JULES with the Estimation of Carbon in Organic Soils - Sequestration and Emission (ECOSSE) model and the Fixation and Uptake of Nitrogen (FUN) model as a means of simulating C:N coupling. This work presents simulations from the standard release of JULES and the most recent incarnation of the JEF coupled system at the point and field scale. Various configurations of JULES and JEF were calibrated and fine-tuned based on comparisons with observations from three UK field campaigns (Crichton, Harwood Forest and Brattleby) specifically chosen to represent the managed vegetation types that cover the UK. The campaigns included flux tower and chamber measurements of CO2, CH4 and N2O amongst other meteorological parameters and records of land management such as application of fertilizer and harvest date at the agricultural sites. Based on the results of these comparisons, JULES and/or JEF will be used to provide simulations on the regional and national scales in order to provide improved estimates of the total UK emission inventory.

  11. Quantifying the effect of lichen and bryophyte cover on permafrost soil within a global land surface model

    Science.gov (United States)

    Porada, Philipp; Ekici, Altug; Beer, Christian

    2016-04-01

    Vegetation near the surface, such as bryophytes and lichens, has an insulating effect on the soil at high latitudes and it can therefore protect permafrost conditions. Warming due to climate change, however, may change the average surface coverage of bryophytes and lichens. This can result in permafrost thawing associated with a release of soil carbon to the atmosphere, which may lead to a positive feedback on atmospheric CO2. Thus, it is important to predict how the bryophyte and lichen cover at high latitudes will react to environmental change. However, current global land surface models so far contain mostly empirical approaches to represent bryophytes and lichens, which makes it impractical to predict their future state and function. For this reason, we integrate a process-based model of bryophyte and lichen growth into the global land surface model JSBACH. We explicitly represent dynamic thermal properties of the bryophyte and lichen cover and their relation to climate. Subsequently, we compare simulations with and without bryophyte and lichen cover to quantify the insulating effect. We estimate an annual average cooling effect of the bryophyte and lichen cover of 2.7 K on topsoil temperature for the northern high latitudes under current climate. Locally, the cooling may reach up to 5.7 K. Moreover, we show that neglecting dynamic properties of the bryophyte and lichen cover by using a simple, empirical scheme only results in an average cooling of around 0.5 K. This suggests that bryophytes and lichens have a significant impact on soil temperature in high-latitude ecosystems and also that a process-based description of their thermal properties is necessary for a realistic representation of the cooling effect.

  12. Sensitivity of a regional climate model to land surface parameterization schemes for East Asian summer monsoon simulation

    Science.gov (United States)

    Li, Wenkai; Guo, Weidong; Xue, Yongkang; Fu, Congbin; Qiu, Bo

    2016-10-01

    Land surface processes play an important role in the East Asian Summer Monsoon (EASM) system. Parameterization schemes of land surface processes may cause uncertainties in regional climate model (RCM) studies for the EASM. In this paper, we investigate the sensitivity of a RCM to land surface parameterization (LSP) schemes for long-term simulation of the EASM. The Weather Research and Forecasting (WRF) Model coupled with four different LSP schemes (Noah-MP, CLM4, Pleim-Xiu and SSiB), hereafter referred to as Sim-Noah, Sim-CLM, Sim-PX and Sim-SSiB respectively, have been applied for 22-summer EASM simulations. The 22-summer averaged spatial distributions and strengths of downscaled large-scale circulation, 2-m temperature and precipitation are comprehensively compared with ERA-Interim reanalysis and dense station observations in China. Results show that the downscaling ability of RCM for the EASM is sensitive to LSP schemes. Furthermore, this study confirms that RCM does add more information to the EASM compared to reanalysis that imposes the lateral boundary conditions (LBC) because it provides 2-m temperature and precipitation that are with higher resolution and more realistic compared to LBC. For 2-m temperature and monsoon precipitation, Sim-PX and Sim-SSiB simulations are more consistent with observation than simulations of Sim-Noah and Sim-CLM. To further explore the physical and dynamic mechanisms behind the RCM sensitivity to LSP schemes, differences in the surface energy budget between simulations of Ens-Noah-CLM (ensemble mean averaging Sim-Noah and Sim-CLM) and Ens-PX-SSiB (ensemble mean averaging Sim-PX and Sim-SSiB) are investigated and their subsequent impacts on the atmospheric circulation are analyzed. It is found that the intensity of simulated sensible heat flux over Asian continent in Ens-Noah-CLM is stronger than that in Ens-PX-SSiB, which induces a higher tropospheric temperature in Ens-Noah-CLM than in Ens-PX-SSiB over land. The adaptive

  13. Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

    Science.gov (United States)

    Bell, Jordan R.; Case, Jonathan L.; LaFontaine, Frank J.; Kumar, Sujay V.

    2012-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and

  14. The role of land surface dynamics in glacial inception: a study with the UVic Earth System Model

    Energy Technology Data Exchange (ETDEWEB)

    Meissner, K.J.; Weaver, A.J.; Matthews, H.D. [School of Earth and Ocean Sciences, University of Victoria, Victoria (Canada); Cox, P.M. [Hadley Centre, Meteorological Office, Bracknell (United Kingdom)

    2003-12-01

    The first results of the UVic Earth System Model coupled to a land surface scheme and a dynamic global vegetation model are presented in this study. In the first part the present day climate simulation is discussed and compared to observations. We then compare a simulation of an ice age inception (forced with 116 ka BP orbital parameters and an atmospheric CO{sub 2} concentration of 240 ppm) with a preindustrial run (present day orbital parameters, atmospheric [CO{sub 2}] = 280 ppm). Emphasis is placed on the vegetation's response to the combined changes in solar radiation and atmospheric CO{sub 2} level. A southward shift of the northern treeline as well as a global decrease in vegetation carbon is observed in the ice age inception run. In tropical regions, up to 88% of broadleaf trees are replaced by shrubs and C{sub 4} grasses. These changes in vegetation cover have a remarkable effect on the global climate: land related feedbacks double the atmospheric cooling during the ice age inception as well as the reduction of the meridional overturning in the North Atlantic. The introduction of vegetation related feedbacks also increases the surface area with perennial snow significantly. (orig.)

  15. The Utility of Remotely-Sensed Land Surface Temperature from Multiple Platforms For Testing Distributed Hydrologic Models over Complex Terrain

    Science.gov (United States)

    Xiang, T.; Vivoni, E. R.; Gochis, D. J.

    2011-12-01

    Land surface temperature (LST) is a key parameter in watershed energy and water budgets that is relatively unexplored as a validation metric for distributed hydrologic models. Ground-based or remotely-sensed LST datasets can provide insights into a model's ability in reproducing water and energy fluxes across a large range of terrain, vegetation, soil and meteorological conditions. As a result, spatiotemporal LST observations can serve as a strong constraint for distributed simulations and can augment other available in-situ data. LST fields are particular useful in mountainous areas where temperature varies with terrain properties and time-variable surface conditions. In this study, we collect and process remotely-sensed fields from several satellite platforms - Landsat 5/7, MODIS and ASTER - to capture spatiotemporal LST dynamics at multiple resolutions and with frequent repeat visits. We focus our analysis of these fields over the Sierra Los Locos basin (~100 km2) in Sonora, Mexico, for a period encompassing the Soil Moisture Experiment in 2004 and the North American Monsoon Experiment (SMEX04-NAME). Satellite observations are verified using a limited set of ground data from manual sampling at 30 locations and continuous measurements at 2 sites. First, we utilize the remotely-sensed fields to understand the summer seasonal evolution of LST in the basin in response to the arrival of summer storms and the vigorous ecosystem greening organized along elevation bands. Then, we utilize the ground and remote-sensing datasets to test the distributed predictions of the TIN-based Real-time Integrated Basin Simulator (tRIBS) under conditions accounting static and dynamic vegetation patterns. Basin-averaged and distributed comparisons are carried out for two different terrain products (INEGI aerial photogrammetry and ASTER stereo processing) used to derive the distributed model domain. Results from the comparisons are discussed in light of the utility of remotely-sensed LST

  16. ORCHIDEE-CROP (v0, a new process based Agro-Land Surface Model: model description and evaluation over Europe

    Directory of Open Access Journals (Sweden)

    X. Wu

    2015-06-01

    Full Text Available The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2] could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0, which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0 model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE, latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI, aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0 reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0 across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE of 11.0–54.2 %, crop yield, as well as

  17. ORCHIDEE-CROP (v0), a new process based Agro-Land Surface Model: model description and evaluation over Europe

    Science.gov (United States)

    Wu, X.; Vuichard, N.; Ciais, P.; Viovy, N.; de Noblet-Ducoudré, N.; Wang, X.; Magliulo, V.; Wattenbach, M.; Vitale, L.; Di Tommasi, P.; Moors, E. J.; Jans, W.; Elbers, J.; Ceschia, E.; Tallec, T.; Bernhofer, C.; Grünwald, T.; Moureaux, C.; Manise, T.; Ligne, A.; Cellier, P.; Loubet, B.; Larmanou, E.; Ripoche, D.

    2015-06-01

    The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2 %), crop yield, as well as of the daily

  18. Do land surface models need to include differential plant species responses to drought? Examining model predictions across a latitudinal gradient in Europe

    OpenAIRE

    M. G. De Kauwe; S.-X. Zhou; B. E. Medlyn; A. J. Pitman; Y.-P. Wang; R. A. Duursma; I. C. Prentice

    2015-01-01

    Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models, realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable ...

  19. Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe

    OpenAIRE

    M. G. De Kauwe; Zhou, S.-X.; B. E. Medlyn; A. J. Pitman; Wang, Y.-P.; R. A. Duursma; I. C. Prentice

    2015-01-01

    Future climate change has the potential to increase drought in many regions of the globe, making it essential that land surface models (LSMs) used in coupled climate models realistically capture the drought responses of vegetation. Recent data syntheses show that drought sensitivity varies considerably among plants from different climate zones, but state-of-the-art LSMs currently assume the same drought sensitivity for all vegetation. We tested whether variable drought sensi...

  20. Sensitivity Analysis and Parameter Identifiability of the Land Surface Model JULES at the point scale in permeable catchments

    Science.gov (United States)

    Bakopoulou, C.; Bulygina, N.; Butler, A. P.; McIntyre, N. R.

    2012-04-01

    Land surface models (LSMs) are recognised as important components of Global Circulation Models (GCMs). Simulating exchanges of the moisture, carbon and energy between land surface and atmosphere in a consistent manner requires physics-based LSMs of high complexity, fine vertical resolution and a large number of parameters that need to be estimated. The "physics" that is incorporated in such models is generally based on our knowledge of point (or very small) scale hydrological processes. Therefore, while larger GCM grid-scale performance may be the ultimate goal, the ability of the model to simulate the point-scale processes is, intuitively, a pre-requisite for its reliable use at larger scales. Critical evaluation of model performance and parameter uncertainty at point scales is therefore a rational starting point for critical evaluation of LSMs; and identification of optimal parameter sets at the point scale is a significant stage of the model evaluation at larger scales. The Joint UK Land Environment Simulator (JULES) is a complex LSM, which is used to represent surface exchanges in the UK Met Office's forecast and climate change models. This complexity necessitates a large number of model parameters (in total 108) some of which are incapable of being measured directly at large (i.e. kilometer) scales. For this reason, a parameter sensitivity analysis is a vital confidence building process within the framework of every LSM, and as a part of the calibration strategy. The problem of JULES parameter estimation and uncertainty at the point scale with a view to assessing the accuracy and the uncertainty in the default parameter values is addressed. The sensitivity of the JULES output of soil moisture is examined using parameter response surface analysis. The implemented technique is based on the Regional Sensitivity Analysis method (RSA), which evaluates the model response surface over a region of parameter space using Monte Carlo sampling. The modified version of RSA

  1. The Role of Surface Energy Exchange for Simulating Wind Inflow: An Evaluation of Multiple Land Surface Models in WRF for the Southern Great Plains Site Field Campaign Report

    Energy Technology Data Exchange (ETDEWEB)

    Wharton, Sonia [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Simpson, Matthew [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Osuna, Jessica [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Newman, Jennifer [National Renewable Energy Lab. (NREL), Golden, CO (United States); Biraud, Sebastien [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2016-05-01

    The Weather Research and Forecasting (WRF) model is used to investigate choice of land surface model (LSM) on the near-surface wind profile, including heights reached by multi-megawatt wind turbines. Simulations of wind profiles and surface energy fluxes were made using five LSMs of varying degrees of sophistication in dealing with soil-plant-atmosphere feedbacks for the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains (SGP) Central Facility in Oklahoma. Surface-flux and wind-profile measurements were available for validation. The WRF model was run for three two-week periods during which varying canopy and meteorological conditions existed. The LSMs predicted a wide range of energy-flux and wind-shear magnitudes even during the cool autumn period when we expected less variability. Simulations of energy fluxes varied in accuracy by model sophistication, whereby LSMs with very simple or no soil-plant-atmosphere feedbacks were the least accurate; however, the most complex models did not consistently produce more accurate results. Errors in wind shear also were sensitive to LSM choice and were partially related to the accuracy of energy flux data. The variability of LSM performance was relatively high, suggesting that LSM representation of energy fluxes in the WRF model remains a significant source of uncertainty for simulating wind turbine inflow conditions.

  2. The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins

    KAUST Repository

    Yang, Zong-Liang

    2011-06-24

    The augmented Noah land surface model described in the first part of the two-part series was evaluated here over global river basins. Across various climate zones, global-scale tests can reveal a model\\'s weaknesses and strengths that a local-scale testing cannot. In addition, global-scale tests are more challenging than local- and catchment-scale tests. Given constant model parameters (e. g., runoff parameters) across global river basins, global-scale tests are more stringent. We assessed model performance against various satellite and ground-based observations over global river basins through six experiments that mimic a transition from the original Noah LSM to the fully augmented version. The model shows transitional improvements in modeling runoff, soil moisture, snow, and skin temperature, despite considerable increase in computational time by the fully augmented Noah-MP version compared to the original Noah LSM. The dynamic vegetation model favorably captures seasonal and spatial variability of leaf area index and green vegetation fraction. We also conducted 36 ensemble experiments with 36 combinations of optional schemes for runoff, leaf dynamics, stomatal resistance, and the β factor. Runoff schemes play a dominant and different role in controlling soil moisture and its relationship with evapotranspiration compared to ecological processes such as β the factor, vegetation dynamics, and stomatal resistance. The 36-member ensemble mean of runoff performs better than any single member over the world\\'s 50 largest river basins, suggesting a great potential of land-based ensemble simulations for climate prediction. Copyright © 2011 by the American Geophysical Union.

  3. An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model

    Science.gov (United States)

    Fang, Li; Hain, Christopher R.; Zhan, Xiwu; Anderson, Martha C.

    2016-06-01

    Significant advances have been achieved in generating soil moisture (SM) products from satellite remote sensing and/or land surface modeling with reasonably good accuracy in recent years. However, the discrepancies among the different SM data products can be considerably large, which hampers their usage in various applications. The bias of one SM product from another is well recognized in the literature. Bias estimation and spatial correction methods have been documented for assimilating satellite SM product into land surface and hydrologic models. Nevertheless, understanding the characteristics of each of these SM data products is required for many applications where the most accurate data products are desirable. This study inter-compares five SM data products from three different sources with each other, and evaluates them against in situ SM measurements over 14-year period from 2000 to 2013. Specifically, three microwave (MW) satellite based data sets provided by ESA's Climate Change Initiative (CCI) (CCI-merged, -active and -passive products), one thermal infrared (TIR) satellite based product (ALEXI), and the Noah land surface model (LSM) simulations. The in-situ SM measurements are collected from the North American Soil Moisture Database (NASMD), which involves more than 600 ground sites from a variety of networks. They are used to evaluate the accuracies of these five SM data products. In general, each of the five SM products is capable of capturing the dry/wet patterns over the study period. However, the absolute SM values among the five products vary significantly. SM simulations from Noah LSM are more stable relative to the satellite-based products. All TIR and MW satellite based products are relatively noisier than the Noah LSM simulations. Even though MW satellite based SM retrievals have been predominantly used in the past years, SM retrievals of the ALEXI model based on TIR satellite observations demonstrate skills equivalent to all the MW satellite

  4. Using NOAA-AVHRR estimates of land surface temperature for regional agrometeoro[lo]gical modelling

    NARCIS (Netherlands)

    Wit, de A.J.W.; Boogaard, H.L.; Diepen, van C.A.

    2004-01-01

    This paper presents a case study on the use of features derived from remote sensing data for mapping the highly fragmented semideciduous Atlantic forest in Brazil. Innovative aspects of this research include the evaluation of different feature sets in order to improve land cover mapping. The feature

  5. Joint State and Parameter Estimation for Two Land Surface Models Using the Ensemble Kalman Filter and Particle Filter

    Science.gov (United States)

    Zhang, Hongjuan; Hendricks-Franssen, Harrie-Jan; Han, Xujun; Vrugt, Jasper A.; Vereecken, Harry

    2016-04-01

    Land surface models (LSMs) resolve the water and energy balance with different parameters and state variables. Many of the parameters of these models cannot be measured directly in the field, and require calibration against flux and soil moisture data. Two LSMs are used in our work: Variable Infiltration Capacity Hydrologic Model (VIC) and the Community Land Model (CLM). Temporal variations in soil moisture content at 5, 20 and 50 cm depth in the Rollesbroich experimental watershed in Germany are simulated in both LSMs. Data assimilation (DA) provides a good way to jointly estimate soil moisture content and soil properties of the resolved soil domain. Four DA methods combined with the two LSMs are used in our work: the Ensemble Kalman Filter (EnKF) using state augmentation or dual estimation, the Residual Resampling Particle Filter (RRPF) and Markov chain Monte Carlo Particle Filter (MCMCPF). These four DA methods are tuned and calibrated for a five month period, and subsequently evaluated for another five month period. Performances of the two LSMs and the four DA methods are compared. Our results show that all DA methods improve the estimation of soil moisture content of the VIC and CLM models, especially if the soil hydraulic properties (VIC), the maximum baseflow velocity (VIC) and/or soil texture (CLM) are jointly estimated with soil moisture content. The augmentation and dual estimation methods performed slightly better than RRPF and MCMCPF in the evaluation period. The differences in simulated soil moisture content between CLM and VIC were larger than variations among the DA methods. The CLM performed better than the VIC model. The strong underestimation of soil moisture content in the third layer of the VIC model is likely related to an inadequate parameterization of groundwater drainage.

  6. Evaluation of root water uptake in the ISBA-A-gs land surface model using agricultural yield statistics over France

    Directory of Open Access Journals (Sweden)

    N. Canal

    2014-05-01

    Full Text Available The interannual variability of cereal grain yield and permanent grassland dry matter yield is simulated over French sites by the Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs generic Land Surface Model (LSM. The two soil profile schemes available in the model are used to simulate the above-ground biomass (Bag of cereals and grasslands: a 2-layer force-restore (FR-2L bulk reservoir model and a multi-layer diffusion (DIF model. The DIF model is implemented with or without deep soil layers below the root-zone. The evaluation of the various root water uptake models is achieved by using the French agricultural statistics of Agreste over the 1994–2010 period at 45 cropland and 48 grassland sites, for a range of rooting depths. The number of sites where the simulated annual maximum Bag presents a significant correlation with the yield observations is used as a metric to benchmark the root water uptake models. Significant correlations (p value < 0.01 are found for up to 29% of the cereal sites and 77% of the grassland sites. It is found that modelling additional subroot zone base flow soil layers does not improve (and may even degrade the representation of the interannual variability of the vegetation above-ground biomass. These results are particularly robust for grasslands as calibrated simulations are able to represent the extreme 2003 and 2007 years corresponding to unfavourable and favourable fodder production, respectively.

  7. Towards a satellite driven land surface model using SURFEX modelling platform Offline Data Assimilation: an assessment of the method over Europe and the Mediterranean basin

    Science.gov (United States)

    Albergel, Clément; Munier, Simon; Leroux, Delphine; Fairbairn, David; Dorigo, Wouter; Decharme, Bertrand; Calvet, Jean-Christophe

    2017-04-01

    Modelling platforms including Land Surface Models (LSMs), forced by gridded atmospheric variables and coupled to river routing models are necessary to increase our understanding of the terrestrial water cycle. These LSMs need to simulate biogeophysical variables like Surface and Root Zone Soil Moisture (SSM, RZSM), Leaf Area Index (LAI) in a way that is fully consistent with the representation of surface/energy fluxes and river discharge simulations. Global SSM and LAI products are now operationally available from spaceborne instruments and they can be used to constrain LSMs through Data Assimilation (DA) techniques. In this study, an offline data assimilation system implemented in Météo-France's modelling platform (SURFEX) is tested over Europe and the Mediterranean basin to increase prediction accuracy for land surface variables. The resulting Land Data Assimilation System (LDAS) makes use of a simplified Extended Kalman Filter (SEKF). It is able to ingests information from satellite derived (i) SSM from the latest version of the ESA Climate Change Initiative as well as (ii) LAI from the Copernicus GLS project to constrain the multilayer, CO2-responsive version of the Interactions Between Soil, Biosphere, and Atmosphere model (ISBA) coupled with Météo-France's version of the Total Runoff Integrating Pathways continental hydrological system (ISBA-CTRIP). ERA-Interim observations based atmospheric forcing with precipitations corrected from Global Precipitation Climatology Centre observations (GPCC) is used to force ISBA-CTRIP at a resolution of 0.5 degree over 2000-2015. The model sensitivity to the assimilated observations is presented and a set of statistical diagnostics used to evaluate the impact of assimilating SSM and LAI on different model biogeophysical variables are provided. It is demonstrated that the assimilation scheme works effectively. The SEKF is able to extract useful information from the data signal at the grid scale and distribute the RZSM

  8. DISAGGREGATION OF GOES LAND SURFACE TEMPERATURES USING SURFACE EMISSIVITY

    Science.gov (United States)

    Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfo...

  9. PALADYN v1.0, a comprehensive land surface-vegetation-carbon cycle model of intermediate complexity

    Science.gov (United States)

    Willeit, Matteo; Ganopolski, Andrey

    2016-10-01

    PALADYN is presented; it is a new comprehensive and computationally efficient land surface-vegetation-carbon cycle model designed to be used in Earth system models of intermediate complexity for long-term simulations and paleoclimate studies. The model treats in a consistent manner the interaction between atmosphere, terrestrial vegetation and soil through the fluxes of energy, water and carbon. Energy, water and carbon are conserved. PALADYN explicitly treats permafrost, both in physical processes and as an important carbon pool. It distinguishes nine surface types: five different vegetation types, bare soil, land ice, lake and ocean shelf. Including the ocean shelf allows the treatment of continuous changes in sea level and shelf area associated with glacial cycles. Over each surface type, the model solves the surface energy balance and computes the fluxes of sensible, latent and ground heat and upward shortwave and longwave radiation. The model includes a single snow layer. Vegetation and bare soil share a single soil column. The soil is vertically discretized into five layers where prognostic equations for temperature, water and carbon are consistently solved. Phase changes of water in the soil are explicitly considered. A surface hydrology module computes precipitation interception by vegetation, surface runoff and soil infiltration. The soil water equation is based on Darcy's law. Given soil water content, the wetland fraction is computed based on a topographic index. The temperature profile is also computed in the upper part of ice sheets and in the ocean shelf soil. Photosynthesis is computed using a light use efficiency model. Carbon assimilation by vegetation is coupled to the transpiration of water through stomatal conductance. PALADYN includes a dynamic vegetation module with five plant functional types competing for the grid cell share with their respective net primary productivity. PALADYN distinguishes between mineral soil carbon, peat carbon, buried

  10. Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model

    Science.gov (United States)

    Decharme, Bertrand; Brun, Eric; Boone, Aaron; Delire, Christine; Le Moigne, Patrick; Morin, Samuel

    2016-04-01

    In this study we analyzed how an improved representation of snowpack processes and soil properties in the multilayer snow and soil schemes of the Interaction Soil-Biosphere-Atmosphere (ISBA) land surface model impacts the simulation of soil temperature profiles over northern Eurasian regions. For this purpose, we refine ISBA's snow layering algorithm and propose a parameterization of snow albedo and snow compaction/densification adapted from the detailed Crocus snowpack model. We also include a dependency on soil organic carbon content for ISBA's hydraulic and thermal soil properties. First, changes in the snowpack parameterization are evaluated against snow depth, snow water equivalent, surface albedo, and soil temperature at a 10 cm depth observed at the Col de Porte field site in the French Alps. Next, the new model version including all of the changes is used over northern Eurasia to evaluate the model's ability to simulate the snow depth, the soil temperature profile, and the permafrost characteristics. The results confirm that an adequate simulation of snow layering and snow compaction/densification significantly impacts the snowpack characteristics and the soil temperature profile during winter, while the impact of the more accurate snow albedo computation is dominant during the spring. In summer, the accounting for the effect of soil organic carbon on hydraulic and thermal soil properties improves the simulation of the soil temperature profile. Finally, the results confirm that this last process strongly influences the simulation of the permafrost active layer thickness and its spatial distribution.

  11. A comprehensive assessment of land surface-atmosphere interactions in a WRF/Urban modeling system for Indianapolis, IN

    Directory of Open Access Journals (Sweden)

    Daniel P. Sarmiento

    2017-05-01

    Full Text Available As part of the Indianapolis Flux (INFLUX experiment, the accuracy and biases of simulated meteorological fields were assessed for the city of Indianapolis, IN. The INFLUX project allows for a unique opportunity to conduct an extensive observation-to-model comparison in order to assess model errors for the following meteorological variables: latent heat and sensible heat fluxes, air temperature near the surface and in the planetary boundary layer (PBL, wind speed and direction, and PBL height. In order to test the sensitivity of meteorological simulations to different model packages, a set of simulations was performed by implementing different PBL schemes, urban canopy models (UCMs, and a model subroutine that was created in order to reduce an inherent model overestimation of urban land cover. It was found that accurately representing the amount of urban cover in the simulations reduced the biases in most cases during the summertime (SUMMER simulations. The simulations that used the BEP urban canopy model and the Bougeault & Lacarrere (BouLac PBL scheme had the smallest biases in the wintertime (WINTER simulations for most meteorological variables, with the exception being wind direction. The model configuration chosen had a larger impact on model errors during the WINTER simulations, whereas the differences between most of the model configurations during the SUMMER simulations were not statistically significant. By learning the behaviors of different PBL schemes and urban canopy models, researchers can start to understand the expected biases in certain model configurations for their own simulations and have a hypothesis as to the potential errors and biases that might occur when using a multi-physics ensemble based modeling approach.

  12. Comparison of Total Water Storage Anomalies from Global Hydrologic and Land Surface Models and New GRACE Satellite Solutions

    Science.gov (United States)

    Scanlon, B. R.; Zhang, Z.; Sun, A.; Save, H.; Mueller Schmied, H.; Wada, Y.; Doll, P. M.; Eisner, S.

    2016-12-01

    There is Increasing interest in global hydrology based on modeling and remote sensing, highlighting the need to compare output from modeling and remote sensing approaches. Here we evaluate simulated terrestrial Total Water Storage anomalies (TWSA) from global hydrologic models (GHMs: WGHM and PRC-GLOBWB) and global land surface models (LSMs, such as GLDAS NOAH, MOSAIC, VIC, and CLM) using newly released GRACE mascons solutions from the Univ. of Texas Center for Space Research. The comparisons are based on monthly TWS anomalies over 13 years (April 2002 - April 2015) for 176 basins globally. Performance metrics include scatter plots of simulated and GRACE observed TWSA by basin with median slopes for different models indicating bias, correlations (shape and timing of TWS time series), and variability ratio (standard deviation of model TWSA/std. dev. GRACE observed TWSA), with optimal values of 1 indicating perfect agreement. The GRACE data were also disaggregated into long-term trends and seasonal amplitudes. Modeled TWS anomalies are biased low by 20 - 30% relative to GRACE TWSA with similar bias levels for basins in different size classes but greater bias with increasing basin aridity. Discrepancies between models and GRACE TWSA are greatest for long-term trends in TWSA with 60 - 95% underestimation of GRACE TWSA by models. There is good agreement in seasonal amplitudes from models and GRACE ( 0.9 for models with little impact of basin size or climate for most models. These comparisons highlight reliable model performance in terms of seasonal amplitudes in TWSA and underestimation of long-term trends in TWSA and in arid basins.

  13. Frost Monitoring and Forecasting Using MODIS Land Surface Temperature Data and a Numerical Weather Prediction Model Forecasts for Eastern Africa

    Science.gov (United States)

    Kabuchanga, Eric; Flores, Africa; Malaso, Susan; Mungai, John; Sakwa, Vincent; Shaka, Ayub; Limaye, Ashutosh

    2014-01-01

    Frost is a major challenge across Eastern Africa, severely impacting agricultural farms. Frost damages have wide ranging economic implications on tea and coffee farms, which represent a major economic sector. Early monitoring and forecasting will enable farmers to take preventive actions to minimize the losses. Although clearly important, timely information on when to protect crops from freezing is relatively limited. MODIS Land Surface Temperature (LST) data, derived from NASA's Terra and Aqua satellites, and 72-hr weather forecasts from the Kenya Meteorological Service's operational Weather Research Forecast model are enabling the Regional Center for Mapping of Resources for Development (RCMRD) and the Tea Research Foundation of Kenya to provide timely information to farmers in the region. This presentation will highlight an ongoing collaboration among the Kenya Meteorological Service, RCMRD, and the Tea Research Foundation of Kenya to identify frost events and provide farmers with potential frost forecasts in Eastern Africa.

  14. Evaluating the effects of urbanization and land-use planning using ground-water and surface-water models

    Science.gov (United States)

    Hunt, R.J.; Steuer, J.J.

    2001-01-01

    Why are the effects of urbanization a concern? As the city of Middleton, Wisconsin, and its surroundings continue to develop, the Pheasant Branch watershed (fig.l) is expected to undergo urbanization. For the downstream city of Middleton, urbanization in the watershed can mean increased flood peaks, water volume and pollutant loads. More subtly, it may also reduce water that sustains the ground-water system (called "recharge") and adversely affect downstream ecosystems that depend on ground water such as the Pheasant Branch Springs (hereafter referred to as the Springs). The relation of stormwater runoff and reduced ground-water recharge is complex because the surface-water system is coupled to the underlying ground-water system. In many cases there is movement of water from one system to the other that varies seasonally or daily depending on changing conditions. Therefore, it is difficult to reliably determine the effects of urbanization on stream baseflow and spring flows without rigorous investigation. Moreover, mitigating adverse effects after development has occurred can be expensive and administratively difficult. Overlying these concerns are issues such as stewardship of the resource, the rights of the public, and land owners' rights both of those developing their land and those whose land is affected by this development. With the often- contradictory goals, a scientific basis for assessing effects of urbanization and effectiveness of mitigation measures helps ensure fair and constructive decision-making. The U.S. Geological Survey, in cooperation with the City of Middleton and Wisconsin Department of Natural Resources, completed a study that helps address these issues through modeling of the hydrologic system. This Fact Sheet discusses the results of this work.

  15. A statistical adjustment approach for climate projections of snow conditions in mountain regions using energy balance land surface models

    Science.gov (United States)

    Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu

    2017-04-01

    Projections of future climate change have been increasingly called for lately, as the reality of climate change has been gradually accepted and societies and governments have started to plan upcoming mitigation and adaptation policies. In mountain regions such as the Alps or the Pyrenees, where winter tourism and hydropower production are large contributors to the regional revenue, particular attention is brought to current and future snow availability. The question of the vulnerability of mountain ecosystems as well as the occurrence of climate-related hazards such as avalanches and debris-flows is also under consideration. In order to generate projections of snow conditions, however, downscaling global climate models (GCMs) by using regional climate models (RCMs) is not sufficient to capture the fine-scale processes and thresholds at play. In particular, the altitudinal resolution matters, since the phase of precipitation is mainly controlled by the temperature which is altitude-dependent. Simulations from GCMs and RCMs moreover suffer from biases compared to local observations, due to their rather coarse spatial and altitudinal resolution, and often provide outputs at too coarse time resolution to drive impact models. RCM simulations must therefore be adjusted using empirical-statistical downscaling and error correction methods, before they can be used to drive specific models such as energy balance land surface models. In this study, time series of hourly temperature, precipitation, wind speed, humidity, and short- and longwave radiation were generated over the Pyrenees and the French Alps for the period 1950-2100, by using a new approach (named ADAMONT for ADjustment of RCM outputs to MOuNTain regions) based on quantile mapping applied to daily data, followed by time disaggregation accounting for weather patterns selection. We first introduce a thorough evaluation of the method using using model runs from the ALADIN RCM driven by a global reanalysis over the

  16. Assessing climate change impacts on runoff from karstic watersheds: NASA/GISS land-surface model improvement

    Science.gov (United States)

    Blake, Reginald Alexander

    The off-line version of the Goddard Institute for Space Studies (GISS) land-surface hydrological model over- predicted run-off from the karstic Rio Cobre watershed in Jamaica. To assess possible climate change impacts on runoff from the watershed, the model's simulation of observed runoff was improved by adding to it a karst component that has pipe flow features. The improved model was tested on two other karstic watersheds (Yangtze - China and Rio Grande - USA) and the results were encouraging. The impacts that possible climate change may have on the three karstic watersheds were then assessed. The assessment indicates that in a doubled carbon dioxide climate, the Rio Cobre and the Rio Grande may experience decreases in runoff, especially in low flow periods. The Yangtze, on the other hand, may not experience decreases in total runoff, but its peak flow which now occurs in July may be attenuated and shifted to September. The results of the study also show that climate feedbacks convolute climate change assessments and that different results can be obtained from the same climate change scenario depending on the choice of the modeling methodology-that is, on whether the models are coupled or uncoupled.

  17. Estimation of Land Surface Parameters by LDAS-UT: Model Development and Validation on Tanashi Field Experiment

    Science.gov (United States)

    Lu, H.; Koike, T.; Yang, K.; Li, X.; Graf, T.; Boussetta, S.; Tsutsui, H.; Kuria, D. N.

    2007-12-01

    The estimation of soil moisture and surface energy fluxes at various temporal and spatial scales remains to be an outstanding problem in hydrologic and meteorological researches. Remote sensed data retrieval algorithms, land surface models and data assimilation systems are highly expected to provide a solution to this problem. But the parameters required by those algorithms and systems, such as the soil texture, porosity, roughness parameters and so on, are highly variable or unavailable. In this study, a land data assimilation system (LDAS- UT) is employed to inversely estimate the optimal values of those land surface parameters with meteorological forcing data and remote sensed data. And a field experiment is designed to provide a well-controlled data set for the system validation. The Tanashi experiment has been in operation since November, 2006 in the farm of the University of Tokyo. Continuous ground measurements of meteorological variables, soil moisture and temperature profiles and vegetation status have been taken over a plot, in which winter wheat was planted. At the same time, the ground based microwave radiometers (GBMR) are employed to provide accurate field measurements of brightness temperature up-welling from the plot, at the frequencies of 6.925, 10.65, 18.7, 23.8, 36.5 and 89 GHz. The LDAS_UT is then run with using data obtained from this experiment to retrieval parameters for two periods. One is the period from December 2006 to February 2007, the germination period of winter wheat, and during which the vegetation effects are small. The second period is from April to May 2007, during which the winter wheat was developing rapidly. The optimize parameters were compared with the in situ observed ¡®real' ones. It found that, for the first period, the retrieved parameters are close to the ¡®real' values, while for the second period, the gap between the retrieved parameters and the ¡®real' values are much bigger. The difference between the

  18. Application of one-dimensional land-surface model to tropical glaciers in Bolivia (16°S)

    Science.gov (United States)

    Leonardini, Gonzalo; Yamazaki, Takeshi; Asaoka, Yoshihiro; Ramírez, Edson

    2013-04-01

    In the Bolivian Andes is distributed an important number of tropical glaciers. According to previous works, these glaciers have been characterized by an accelerated retreat and melting in the last years and its runoff has been used as water resources for many purposes for the local population. The main goal of this research is to estimate the amount of snow/ice melt in some specific glaciers and to find the main meteorological control-factors and differences using a land-surface model. A one-dimensional multi-layer model has been adapted to the study of Bolivian glaciers in tropical conditions. In the model, the snow and soil components are considered, and the information of the ice component is introduced in the initial condition; the boundary condition is provided by the energy balance at the atmosphere-glacier and at the glacier-soil interphases. The model can calculate profiles of density, temperature and liquid water content as well as snowmelt and energy exchange between the atmosphere and the snow/ice surface. It has been applied to Zongo Glacier (16°S) in the southern part of the Bolivian Andes and it has been validated through the whole hydrological year 2005-2006 using the data provided by GLACIOCLIM (les GLaCIers un Observatoire du CLIMat). The surface temperature, the net radiation and the profile of snow/ice are simulated reasonably and the diurnal and seasonal changes of latent and sensible heats agreed with the previous works on the same glacier. We planned to extend this study to Condoriri and Huayna West Glaciers located in the same area of the study, and where we started meteorological observations at the end of 2011. This study is carried out by GRANDE (Glacier Retreat impact Assessment and National policy Development) project supported by JST/JICA, SATREPS (Science and Technology Research Partnership for Sustainable Development).

  19. Land surface processes and Sahel climate

    Science.gov (United States)

    Nicholson, Sharon

    2000-02-01

    This paper examines the question of land surface-atmosphere interactions in the West African Sahel and their role in the interannual variability of rainfall. In the Sahel, mean rainfall decreased by 25-40% between 1931-1960 and 1968-1997; every year in the 1950s was wet, and nearly every year since 1970 has been anomalously dry. Thus the intensity and multiyear persistence of drought conditions are unusual and perhaps unique features of Sahel climate. This article presents arguments for the role of land surface feedback in producing these features and reviews research relevant to land surface processes in the region, such as results from the 1992 Hydrologic Atmospheric Pilot Experiment (HAPEX)-Sahel experiment and recent studies on aerosols and on the issue of desertification in the region, a factor implicated by some as a cause of the changes in rainfall. Included also is a summary of evidence of feedback on meteorological processes, presented from both model results and observations. The reviewed studies demonstrate numerous ways in which the state of the land surface can influence interactions with the atmosphere. Surface hydrology essentially acts to delay and prolong the effects of meteorological drought. Each evaporative component of the surface water balance has its own timescale, with the presence of vegetation affecting the process both by delaying and prolonging the return of soil moisture to the atmosphere but at the same time accelerating the process through the evaporation of canopy-intercepted water. Hence the vegetation structure, including rooting depth, can modulate the land-atmosphere interaction. Such processes take on particular significance in the Sahel, where there is a high degree of recycling of atmospheric moisture and where the meteorological processes from the scale of boundary layer development to mesoscale disturbance generation are strongly influenced by moisture. Simple models of these feedback processes and their various timescales

  20. Impact of Rain Snow Threshold Temperature on Snow Depth Simulation in Land Surface and Regional Atmospheric Models

    Institute of Scientific and Technical Information of China (English)

    WEN Lijuan; Nidhi NAGABHATLA; L(U) Shihua; Shih-Yu WANG

    2013-01-01

    This study investigates the impact of rain snow threshold (RST) temperatures on snow depth simulation using the Community Land Model (CLM) and the Weather Research and Forecasting model (WRF coupled with the CLM and hereafter referred to as WRF_CLM),and the difference in impacts.Simulations were performed from 17 December 1994 to 30 May 1995 in the French Alps.Results showed that both the CLM and the WRF_CLM were able to represent a fair simulation of snow depth with actual terrain height and 2.5℃ RST temperature.When six RST methods were applied to the simulation using WRF_CLM,the simulated snow depth was the closest to observations using 2.5℃ RST temperature,followed by that with Pipes',USACE,Kienzle's,Dai's,and 0℃ RST temperature methods.In the case of using CLM,simulated snow depth was the closest to the observation with Dai's method,followed by with USACE,Pipes',2.5℃ RST temperature,Kienzle's,and 0℃ RST temperature method.The snow depth simulation using the WRF_CLM was comparatively sensitive to changes in RST temperatures,because the RST temperature was not only the factor to partition snow and rainfall.In addition,the simulated snow related to RST temperature could induce a significant feedback by influencing the meteorological variables forcing the land surface model in WRF_CLM.In comparison,the above variables did not change with changes in RST in CLM.Impacts of RST temperatures on snow depth simulation could also be influenced by the patterns of temperature and precipitation,spatial resolution,and input terrain heights.

  1. The Land Surface Temperature Impact to Land Cover Types

    Science.gov (United States)

    Ibrahim, I.; Abu Samah, A.; Fauzi, R.; Noor, N. M.

    2016-06-01

    Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R2 = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

  2. Assimilating the Cosmic-Ray Soil Moisture Observing System Measurements for Land Surface Hydrologic Model Parameter Estimation Using the Ensemble Kalman Filter

    Science.gov (United States)

    Xiao, D.; Shi, Y.; Li, L.

    2015-12-01

    Parameter estimation is generally required for land surface models (LSMs) and hydrologic models to reproduce observed water and energy fluxes in different watersheds. Using soil moisture observations for parameter estimation in addition to discharge and land surface temperature observations can improve the prediction of land surface and subsurface processes. Due to their representativity, point measurements cannot capture the watershed-scale soil moisture conditions and may lead to notable bias in watershed soil moisture predictions if used for model calibration. The intermediate-scale cosmic-ray soil moisture observing system (COSMOS) provides average soil water content measurement over a footprint of 0.34 m2 and depths up to 50 cm, and may provide better calibration data for low-order watersheds. In this study, we will test using COSMOS observations for Flux-PIHM parameter and state estimation via the ensemble Kalman filter (EnKF). Flux-PIHM is a physically-based land surface hydrologic model that couples the Penn State Integrated Hydrologic Model (PIHM) with the Noah land surface model. Synthetic data experiments will be performed at the Shale Hills watershed (area: 0.08 km2, smaller than COSMOS footprint) and the Garner Run watershed (1.34 km2, larger than COSMOS footprint) in the Shale Hills Susquehanna Critical Zone Observatory in central Pennsylvania. COSMOS observations will be assimilated into Flux-PIHM using the EnKF, in addition to discharge and land surface temperature (LST) observations. The accuracy of EnKF estimated parameters and water and energy flux predictions will be evaluated. In addition, the results will be compared with assimilating point soil moisture measurement (in addition to discharge and LST), to assess the effects of using different scales of soil moisture observations for parameter estimation. The results at Shale Hills and Garner Run will be compared to test whether performance of COSMOS data assimilation is affected by the size of

  3. Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

    Science.gov (United States)

    Reager, John T.; Thomas, Alys C.; Sproles, Eric A.; Rodell, Matthew; Beaudoing, Hiroko K.; Li, Bailing; Famiglietti, James S.

    2015-01-01

    We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA's Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE's coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.

  4. Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

    Science.gov (United States)

    Reager, John T.; Thomas, Alys C.; Sproles, Eric A.; Rodell, Matthew; Beaudoing, Hiroko K.; Li, Bailing; Famiglietti, James S.

    2015-01-01

    We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA's Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE's coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.

  5. Assessment of the interannual variability of agricultural yields in France using satellite data and a generic land surface model

    Science.gov (United States)

    Canal, Nicolas; Calvet, Jean-Christophe; Szczypta, Camille

    2013-04-01

    The generic ISBA-A-gs Land Surface Model (LSM) is used to simulate the interannual variability of the maximum above-ground biomass (Bagm) of cereals and grasslands in France. Agricultural statistics are used to optimize the maximal available soil water content (MaxAWC) of the model. For a number of administrative units, significant correlations between the simulated Bagm and the agricultural yield statistics are found over the 1994-2010 period. It is shown that the interannual variability of Bagm and of the simulated soil moisture correlate at given key periods. Significant correlations are found between ten-daily averaged simulated soil moisture and the simulated (observed) Bagm (yields). The corresponding plant growth stage is determined through the Leaf Area Index (LAI). Moreover, it is shown that the interannual variability of the modelled LAI and of the new satellite-derived GEOLAND2 LAI are consistent. The predictive value of both simulated and observed LAI on the agricultural yield (10 to 40 days before harvest) is investigated. The scores are used to benchmark different configurations of the model. In particular two contrasting representations of the soil moisture profile are considered: (1) one root-zone layer, (2) several soil layers with an explicit representation of diffusion processes and an exponential root density profile, with or without a deep soil layer below the root-zone.

  6. Constraining the process-based land surface model ORCHIDEE by nutrient enrichment and forest management experiments in Sweden

    Science.gov (United States)

    Sofie Lansø, Anne; Resovsky, Alex; Guenet, Bertrand; Peylin, Philippe; Vuichard, Nicolas; Messina, Palmira; Smith, Benjamin; Ryder, James; Naudts, Kim; Chen, Yiying; Otto, Juliane; McGrath, Matthew; Valade, Aude; Luyssaert, Sebastiaan

    2017-04-01

    Understanding the coupling between carbon (C) and nitrogen (N) cycling in terrestrial ecosystems is key to predicting global change. While numerous experimental studies have demonstrated the positive response of stand-level photosynthesis and net primary production (NPP) to atmospheric CO2 enrichment, N availability has been shown to exert an important control on the timing and magnitude of such responses. Forest management is also a key driver of C storage in such ecosystems but interactions between forest management and the N cycle as a C storage driver are not well known. In this study, we use data from N-fertilization experiments at two long-term forest manipulation sites in Sweden to inform and improve the representation of C and N interaction in the ORCHIDEE land surface model. Our version of the model represents the union of two ORCHIDEE branches; 1) ORCHIDEE-CN, which resolves processes related to terrestrial C and N cycling, and 2) ORCHIDEE-CAN, which integrates a multi-layer canopy structure and includes representation of forest management practices. Using this new model branch, referred to as ORCHIDEE-CN-CAN, we simulate the growth patterns of managed forests both with and without N limitations. Combining our simulated results with measurements of various ecosystem parameters (such as soil N) will aid in ecosystem model development, reducing structural uncertainty and optimizing parameter settings in global change simulations.

  7. State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter

    Directory of Open Access Journals (Sweden)

    H. Zhang

    2017-09-01

    Full Text Available Land surface models (LSMs use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L and the Community Land Model (CLM using a 5-month calibration (assimilation period (March–July 2012 of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012. As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC or fractions of sand, clay, and organic matter of each layer (CLM are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the

  8. Simulating the carbon, water, energy budgets and greenhouse gas emissions of arctic soils with the ISBA land surface model

    Science.gov (United States)

    Morel, Xavier; Decharme, Bertrand; Delire, Christine

    2017-04-01

    Permafrost soils and boreal wetlands represent an important challenge for future climate simulations. Our aim is to be able to correctly represent the most important thermal, hydrologic and carbon cycle related processes in boreal areas with our land surface model ISBA (Masson et al, 2013). This is particularly important since ISBA is part of the CNRM-CM Climate Model (Voldoire et al, 2012), that is used for projections of future climate changes. To achieve this goal, we replaced the one layer original soil carbon module based on the CENTURY model (Parton et al, 1987) by a multi-layer soil carbon module that represents C pools and fluxes (CO2 and CH4), organic matter decomposition, gas diffusion (Khvorostyanov et al., 2008), CH4 ebullition and plant-mediated transport, and cryoturbation (Koven et al., 2009). The carbon budget of the new model is closed. The soil carbon module is tightly coupled to the ISBA energy and water budget module that solves the one-dimensional Fourier law and the mixed-form of the Richards equation explicitly to calculate the time evolution of the soil energy and water budgets (Boone et al., 2000; Decharme et al. 2011). The carbon, energy and water modules are solved using the same vertical discretization. Snowpack processes are represented by a multi-layer snow model (Decharme et al, 2016). We test this new model on a pair of monitoring sites in Greenland, one in a permafrost area (Zackenberg Ecological Research Operations, Jensen et al, 2014) and the other in a region without permafrost (Nuuk Ecological Research Operations, Jensen et al, 2013); both sites are established within the GeoBasis part of the Greenland Ecosystem Monitoring (GEM) program. The site of Chokurdakh, in a permafrost area of Siberia is is our third studied site. We test the model's ability to represent the physical variables (soil temperature and water profiles, snow height), the energy and water fluxes as well as the carbon dioxyde and methane fluxes. We also test the

  9. Advancements in Modelling of Land Surface Energy Fluxes with Remote Sensing at Different Spatial Scales

    DEFF Research Database (Denmark)

    Guzinski, Radoslaw

    uxes, such as sensible heat ux, ground heat ux and net radiation, are also necessary. While it is possible to measure those uxes with ground-based instruments at local scales, at region scales they usually need to be modelled or estimated with the help of satellite remote sensing data. Even though....... The performance of the DTD model was improved in forested ecosystems and during senescence, by taking into account the fraction of the vegetation that is green, as well as during dry conditions and in temperate climates, by modifying certain model formulations. A disaggregation algorithm was also developed...

  10. Further evaluation of wetland emission estimates from the JULES land surface model using SCIAMACHY and GOSAT atmospheric column methane measurements

    Science.gov (United States)

    Hayman, Garry; Comyn-Platt, Edward; McNorton, Joey; Chipperfield, Martyn; Gedney, Nicola

    2016-04-01

    The atmospheric concentration of methane began rising again in 2007 after a period of near-zero growth [1,2], with the largest increases observed over polar northern latitudes and the Southern Hemisphere in 2007 and in the tropics since then. The observed inter-annual variability in atmospheric methane concentrations and the associated changes in growth rates have variously been attributed to changes in different methane sources and sinks [2,3]. Wetlands are generally accepted as being the largest, but least well quantified, single natural source of CH4, with global emission estimates ranging from 142-284 Tg yr-1 [3]. The modelling of wetlands and their associated emissions of CH4 has become the subject of much current interest [4]. We have previously used the HadGEM2 chemistry-climate model to evaluate the wetland emission estimates derived using the UK community land surface model (JULES, the Joint UK Land Earth Simulator) against atmospheric observations of methane, including SCIAMACHY total methane columns [5] up to 2007. We have undertaken a series of new HadGEM2 runs using new JULES emission estimates extended in time to the end of 2012, thereby allowing comparison with both SCIAMACHY and GOSAT atmospheric column methane measurements. We will describe the results of these runs and the implications for methane wetland emissions. References [1] Rigby, M., et al.: Renewed growth of atmospheric methane. Geophys. Res. Lett., 35, L22805, 2008; [2] Nisbet, E.G., et al.: Methane on the Rise-Again, Science 343, 493, 2014; [3] Kirschke, S., et al.,: Three decades of global methane sources and sinks, Nature Geosciences, 6, 813-823, 2013; [4] Melton, J. R., et al.: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10, 753-788, 2013; [5] Hayman, G.D., et al.: Comparison of the HadGEM2 climate-chemistry model against in situ and SCIAMACHY atmospheric methane data, Atmos. Chem

  11. Impact of soil parameter and physical process on reproducibility of hydrological processes by land surface model in semiarid grassland

    Science.gov (United States)

    Miyazaki, S.; Yorozu, K.; Asanuma, J.; Kondo, M.; Saito, K.

    2014-12-01

    The land surface model (LSM) takes part in the land-atmosphere interaction on the earth system model for the climate change research. In this study, we evaluated the impact of soil parameters and physical process on reproducibility of hydrological process by LSM Minimal Advanced Treatments of Surface Interaction and RunOff (MATSIRO; Takata et al, 2003, GPC) forced by the meteorological data observed at grassland in semiarid climate in China and Mongolia. The testing of MATSIRO was carried out offline mode over the semiarid grassland sites at Tongyu (44.42 deg. N, 122.87 deg. E, altitude: 184m) in China, Kherlen Bayan Ulaan (KBU; 47.21 deg. N, 108.74 deg. E, altitude: 1235m) and Arvaikheer (46.23 N, 102.82E, altitude: 1,813m) in Mongolia. Although all sites locate semiarid grassland, the climate condition is different among sites, which the annual air temperature and precipitation are 5.7 deg. C and 388mm (Tongyu), 1.2 deg.C and 180mm (KBU), and 0.4 deg. C and 245mm(Arvaikheer). We can evaluate the effect of climate condition on the model performance. Three kinds of experiments have been carried out, which was run with the default parameters (CTL), the observed parameters (OBS) for soil physics and hydrology, and vegetation, and refined MATSIRO with the effect of ice in thermal parameters and unfrozen water below the freezing with same parameters as OBS run (OBSr). The validation data has been provided by CEOP(http://www.ceop.net/) , RAISE(http://raise.suiri.tsukuba.ac.jp/), GAME-AAN (Miyazaki et al., 2004, JGR) for Tongyu, KBU, and Arvaikheer, respectively. The reproducibility of the net radiation, the soil temperature (Ts), and latent heat flux (LE) were well reproduced by OBS and OBSr run. The change of soil physical and hydraulic parameter affected the reproducibility of soil temperature (Ts) and soil moisture (SM) as well as energy flux component especially for the sensible heat flux (H) and soil heat flux (G). The reason for the great improvement on the

  12. A modeling study on the effect of urban land surface forcing to regional meteorology and air quality over South China

    Science.gov (United States)

    Zhu, Kuanguang; Xie, Min; Wang, Tijian; Cai, Junxiong; Li, Songbing; Feng, Wen

    2017-03-01

    The change of land-use from natural to artificial surface induced by urban expansion can deeply impact the city environment. In this paper, the model WRF/Chem is applied to explore the effect of this change on regional meteorology and air quality over South China, where people have witnessed a rapid rate of urbanization. Two sets of urban maps are adopted to stand for the pre-urbanization and the present urban land-use distributions. Month-long simulations are conducted for January and July, 2014. The results show that urban expansion can obviously change the weather conditions around the big cities of South China. Especially in the Pearl River Delta region (PRD), the urban land-use change can increase the sensible heat flux by 40 W/m2 in January and 80 W/m2 in July, while decrease the latent heat flux about -50 W/m2 in January and -120 W/m2 in July. In the consequent, 2-m air temperature (T2) increases as much as 1 °C and 2 °C (respective to January and July), planetary boundary layer height (PBLH) rises up by 100-150 m and 300 m, 10-m wind speed (WS10) decreases by -1.2 m/s and -0.3 m/s, and 2-m specific humidity is reduced by -0.8 g/kg and -1.5 g/kg. Also, the precipitation in July can be increased as much as 120 mm, with more heavy rains and rainstorms. These variations of meteorological factors can significantly impact the spatial and vertical distribution of air pollutants as well. In PRD, the enhanced updraft can reduce the surface concentrations of PM10 by -40 μg/m3 (30%) in January and -80 μg/m3 (50%) in July, but produce a correlating increase in the concentrations at higher atmospheric layers. However, according to the increase in T2 and the decrease in surface NO, the surface concentrations of O3 in PRD can increase by 2-6 ppb in January and 8-12 ppb in July. Meanwhile, there is a significant increase in the O3 concentrations at upper layers above PRD, which should be attributed to the increase in air temperature and the enhanced upward transport of

  13. Real-Time Global Flood Estimation Using Satellite-Based Precipitation and a Coupled Land Surface and Routing Model

    Science.gov (United States)

    Wu, Huan; Adler, Robert F.; Tian, Yudong; Huffman, George J.; Li, Hongyi; Wang, JianJian

    2014-01-01

    A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50 deg. N - 50 deg. S at relatively high spatial (approximately 12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is approximately 0.9 and the false alarm ratio is approximately 0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30 deg. S - 30 deg. N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.

  14. ASSESSMENT OF EARLY SEASON AGRICULTURAL DROUGHT THROUGH LAND SURFACE WATER INDEX (LSWI AND SOIL WATER BALANCE MODEL

    Directory of Open Access Journals (Sweden)

    K. Chandrasekar

    2012-08-01

    Full Text Available An attempt was made to address the early season agriculture drought, by monitoring the surface soil wetness during 2010 cropping seasons in the states of Andhra Pradesh and Tamil Nadu. Short Wave Infrared (SWIR based Land Surface Water Index (LSWI and Soil Water Balance (SWB model using inputs from remote sensing and ancillary data were used to monitor early season agriculture drought. During the crop season, investigation was made on LSWI characteristics and its response to the rainfall. It was observed that the Rate of Increase (RoI of LSWI was the highest during the fortnights when the onset of monsoon occurred. The study showed that LSWI is sensitive to the onset of monsoon and initiation of cropping season. The second part of this study attempted to develop a simple book keeping – bucket type – water tight soil water balance model to derive the top 30cm profile soil moisture using climatic, soil and crop parameters as the basic inputs. Soil moisture derived from the model was used to compute the Area Conducive for Sowing (ACS during the sowing window of the cropping season. The soil moisture was validated spatially and temporally with the ground observed soil moisture values. The ACS was compared with the RoI of LSWI. The results showed that the RoI was high during the sowing window whenever the ACS was greater than 50% of the district area. The observation was consistent in all the districts of the two states. Thus the analysis revealed the potential of LSWI for early season agricultural drought management.

  15. Modeling concentration patterns of agricultural and urban micropollutants in surface waters in catchment of mixed land use

    Science.gov (United States)

    Stamm, C.; Scheidegger, R.; Bader, H. P.

    2012-04-01

    Organic micropollutants detected in surface waters can originate from agricultural and urban sources. Depending on the use of the compounds, the temporal loss patterns vary substantially. Therefore models that simulate water quality in watersheds of mixed land use have to account for all relevant sources. We present here simulation results of a transport model that describes the dynamic of several biocidal compounds as well as the behaviour of human pharmaceuticals. The model consists of the sub-model Rexpo simulating the transfer of the compounds from the point of application to the stream in semi-lumped manner. The river sub-model, which is programmed in the Aquasim software, describes the fate of the compounds in the stream. Both sub-models are process-based. The Rexpo sub-model was calibrated at the scale of a small catchment of 25 km2, which is inhabited by about 12'000 people. Based on the resulting model parameters the loss dynamics of two herbicides (atrazine, isoproturon) and a compound of mixed urban and agricultural use (diuron) were predicted for two nested catchment of 212 and 1696 km2, respectively. The model output was compared to observed time-series of concentrations and loads obtained for the entire year 2009. Additionally, the fate of two pharmaceuticals with constant input (carbamazepine, diclofenac) was simulated for improving the understanding of possible degradation processes. The simulated loads and concentrations of the biocidal compounds differed by a factor of 2 to 3 from the observations. In general, the seasonal patterns were well captured by the model. However, a detailed analysis of the seasonality revealed substantial input uncertainty for the application of the compounds. The model results also demonstrated that for the dynamics of rain-driven losses of biocidal compounds the semi-lumped approach of the Rexpo sub-model was sufficient. Only for simulating the photolytic degradation of diclofenac in the stream the detailed

  16. Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

    CSIR Research Space (South Africa)

    Marshall, M

    2013-03-01

    Full Text Available . The psychometric constant (γ : 0.066 kPa ◦C−1), slope of the saturation-to-vapor pressure curve (1), RN, and G make up the Priestley–Taylor formula- tion for PET. Equation (1) is a modified version of the orig- inal PT-JPL model, in which the soil and canopy...

  17. Improving Soil Moisture and Temperature Profile and Surface Turbulent Fluxes Estimations in Irrigated Field by Assimilating Multi-source Data into Land Surface Model

    Science.gov (United States)

    Chen, Weijing; Huang, Chunlin; Shen, Huanfeng; Wang, Weizhen

    2016-04-01

    The optimal estimation of hydrothermal conditions in irrigation field is restricted by the deficiency of accurate irrigation information (when and how much to irrigate). However, the accurate estimation of soil moisture and temperature profile and surface turbulent fluxes are crucial to agriculture and water management in irrigated field. In the framework of land surface model, soil temperature is a function of soil moisture - subsurface moisture influences the heat conductivity at the interface of layers and the heat storage in different layers. In addition, soil temperature determines the phase of soil water content with the transformation between frozen and unfrozen. Furthermore, surface temperature affects the partitioning of incoming radiant energy into ground (sensible and latent heat flux), as a consequence changes the delivery of soil moisture and temperature. Given the internal positive interaction lying in these variables, we attempt to retrieve the accurate estimation of soil moisture and temperature profile via assimilating the observations from the surface under unknown irrigation. To resolve the input uncertainty of imprecise irrigation quantity, original EnKS is implemented with inflation and localization (referred to as ESIL) aiming at solving the underestimation of the background error matrix and the extension of observation information from the top soil to the bottom. EnKS applied in this study includes the states in different time points which tightly connect with adjacent ones. However, this kind of relationship gradually vanishes along with the increase of time interval. Thus, the localization is also employed to readjust temporal scale impact between states and filter out redundant or invalid correlation. Considering the parameter uncertainty which easily causes the systematic deviation of model states, two parallel filters are designed to recursively estimate both states and parameters. The study area consists of irrigated farmland and is

  18. Integrated Landsat Image Analysis and Hydrologic Modeling to Detect Impacts of 25-Year Land-Cover Change on Surface Runoff in a Philippine Watershed

    Directory of Open Access Journals (Sweden)

    Enrico Paringit

    2011-05-01

    Full Text Available Landsat MSS and ETM+ images were analyzed to detect 25-year land-cover change (1976–2001 in the critical Taguibo Watershed in Mindanao Island, Southern Philippines. This watershed has experienced historical modifications of its land-cover due to the presence of logging industries in the 1950s, and continuous deforestation due to illegal logging and slash-and-burn agriculture in the present time. To estimate the impacts of land-cover change on watershed runoff, land-cover information derived from the Landsat images was utilized to parameterize a GIS-based hydrologic model. The model was then calibrated with field-measured discharge data and used to simulate the responses of the watershed in its year 2001 and year 1976 land-cover conditions. The availability of land-cover information on the most recent state of the watershed from the Landsat ETM+ image made it possible to locate areas for rehabilitation such as barren and logged-over areas. We then created a “rehabilitated” land-cover condition map of the watershed (re-forestation of logged-over areas and agro-forestation of barren areas and used it to parameterize the model and predict the runoff responses of the watershed. Model results showed that changes in land-cover from 1976 to 2001 were directly related to the significant increase in surface runoff. Runoff predictions showed that a full rehabilitation of the watershed, especially in barren and logged-over areas, will be likely to reduce the generation of a huge volume of runoff during rainfall events. The results of this study have demonstrated the usefulness of multi-temporal Landsat images in detecting land-cover change, in identifying areas for rehabilitation, and in evaluating rehabilitation strategies for management of tropical watersheds through its use in hydrologic modeling.

  19. Using reactive transport codes to provide mechanistic biogeochemistry representations in global land surface models: CLM-PFLOTRAN 1.0

    Directory of Open Access Journals (Sweden)

    G. Tang

    2015-12-01

    Full Text Available We explore coupling to a configurable subsurface reactive transport code as a flexible and extensible approach to biogeochemistry in land surface models; our goal is to facilitate testing of alternative models and incorporation of new understanding. A reaction network with the CLM-CN decomposition, nitrification, denitrification, and plant uptake is used as an example. We implement the reactions in the open-source PFLOTRAN code, coupled with the Community Land Model (CLM, and test at Arctic, temperate, and tropical sites. To make the reaction network designed for use in explicit time stepping in CLM compatible with the implicit time stepping used in PFLOTRAN, the Monod substrate rate-limiting function with a residual concentration is used to represent the limitation of nitrogen availability on plant uptake and immobilization. To achieve accurate, efficient, and robust numerical solutions, care needs to be taken to use scaling, clipping, or log transformation to avoid negative concentrations during the Newton iterations. With a tight relative update tolerance to avoid false convergence, an accurate solution can be achieved with about 50 % more computing time than CLM in point mode site simulations using either the scaling or clipping methods. The log transformation method takes 60–100 % more computing time than CLM. The computing time increases slightly for clipping and scaling; it increases substantially for log transformation for half saturation decrease from 10−3 to 10−9 mol m−3, which normally results in decreasing nitrogen concentrations. The frequent occurrence of very low concentrations (e.g. below nanomolar can increase the computing time for clipping or scaling by about 20 %; computing time can be doubled for log transformation. Caution needs to be taken in choosing the appropriate scaling factor because a small value caused by a negative update to a small concentration may diminish the update and result in false convergence even

  20. Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

    Science.gov (United States)

    De Lannoy, Gabrielle J.M.; Reichle, Rolf H.; Houser, Paul R.; Arsenault, Kristi R.; Verhoest, Niko E.C.; Paulwels, Valentijn R.N.

    2009-01-01

    An ensemble Kalman filter (EnKF) is used in a suite of synthetic experiments to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of satellite retrievals) into fine-scale (1 km) model simulations. Coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (re-gridding) to the fine-scale model resolution prior to data assimilation. In either case observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated fine-scale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the fine-scale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

  1. Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling

    DEFF Research Database (Denmark)

    Vlassova, Lidia; Perez-Cabello, Fernando; Nieto Solana, Hector;

    2014-01-01

    Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global ch...

  2. Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling

    DEFF Research Database (Denmark)

    Vlassova, Lidia; Perez-Cabello, Fernando; Nieto Solana, Hector;

    2014-01-01

    Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global...

  3. Modeling nitrate from land surface to wells' perforations under agricultural land: success, failure, and future scenarios in a Mediterranean case study

    Science.gov (United States)

    Levy, Yehuda; Shapira, Roi H.; Chefetz, Benny; Kurtzman, Daniel

    2017-07-01

    Contamination of groundwater resources by nitrate leaching under agricultural land is probably the most troublesome agriculture-related water contamination worldwide. Contaminated areas often show large spatial variability of nitrate concentration in wells. In this study, we tried to assess whether this spatial variability can be characterized on the basis of land use and standard agricultural practices. Deep soil sampling (10 m) was used to calibrate vertical flow and nitrogen-transport numerical models of the unsaturated zone under different agricultural land uses. Vegetable fields (potato and strawberry) and deciduous orchards (persimmon) in the Sharon area overlying the coastal aquifer of Israel were examined. Average nitrate-nitrogen fluxes below vegetable fields were 210-290 kg ha-1 yr-1 and under deciduous orchards were 110-140 kg ha-1 yr-1. The output water and nitrate-nitrogen fluxes of the unsaturated-zone models were used as input data for a three-dimensional flow and nitrate-transport model in the aquifer under an area of 13.3 km2 of agricultural land. The area was subdivided into four agricultural land uses: vegetables, deciduous orchards, citrus orchards, and non-cultivated. Fluxes of water and nitrate-nitrogen below citrus orchards were taken from a previous study in the area. The groundwater flow model was calibrated to well heads by changing the hydraulic conductivity. The nitrate-transport model, which was fed by the above-mentioned models of the unsaturated zone, succeeded in reconstructing the average nitrate concentration in the wells. However, this transport model failed in calculating the high concentrations in the most contaminated wells and the large spatial variability of nitrate concentrations in the aquifer. To reconstruct the spatial variability and enable predictions, nitrate fluxes from the unsaturated zone were multiplied by local multipliers. This action was rationalized by the fact that the high concentrations in some wells cannot

  4. Improvement of boreal vegetation modelling and climate interactions through the introduction of new bryophyte and artic-shrub plant functional types in a land surface model.

    Science.gov (United States)

    Druel, Arsène; Krinner, Gerhard; Peylin, Philippe; Ciais, Philippe; Viovy, Nicolas; Peregon, Anna

    2016-04-01

    Boreal and tundra vegetation, which represents 22% of the global land area, has had a significant impact on climate through changes of albedo, snow cover, soil thermal dynamics, etc. However, it is frequently poorly represented in earth system models used for climate predictions. We improved the description of high-latitude vegetation and its interactions with the environment in the ORCHIDEE land surface model by creating new plant functional types with specific biogeochemical and biophysical properties: boreal shrubs, bryophytes (mosses and lichens) and boreal C3 grasses. The introduction of shrub specificities allows for an intermediate stratum between trees and grasses, with a new carbon allometry within the plant, inducing new interactions between wooden species and their environment, especially the complex snow-shrubs interaction. Similarly, the introduction of non-vascular plants (i.e. bryophytes) involves numerous changes both in physical and biological processes, such as the response of photosynthesis to surface humidity, the decomposition of carbon and the soil thermal conductivity. These changes in turn lead to new processes and interactions between vegetation and moisture (soil and air), carbon cycle, energy balance, etc. For the boreal C3 grasses we did not include new processes compared to the generic C3 grass PFT, but improved the realism of the carbon and water budgets with new boreal adjusted parameters. We assess the performance of the modified ORCHIDEE land surface model and in particular its ability to represent the new plant types (their phenology etc.), and evaluate the effects of these new PFTs on the simulated energy, water and carbon balances of boreal ecosystems. The potential impact of these refinements on future climate simulations will be discussed.

  5. Intercomparison of the Performance of CLM3, NOAH, RUC, and STD Land Surface Schemes in the Weather and Research Forecasting Model

    Science.gov (United States)

    Jin, J.; Miller, N.

    2007-12-01

    The Community Land Model version 3 (CLM3) developed by the National Center for Atmospheric Research (NCAR) was coupled into the Weather Research and Forecasting (WRF) Model version 2.2. The performance of WRF-CLM3 in predicting regional climate was quantitatively compared with that of WRF coupled to the soil thermal diffusion (STD), Rapid Update Cycle, and NOAH Land Surface Schemes. These land surface schemes represent a range of complexity within land-surface schemes. CLM3 is the most sophisticated model, with detailed snow and vegetation processes. The STD scheme is oversimplified, which only calculates soil temperature and neglects vegetation and snow physics. The RUC and NOAH schemes are intermediate in the detail, and the major deference between them is that RUC has a multi-layer snow scheme, and Noah has a single snow layer lumped with the topmost soil layer. WRF was driven by the National Centers for Environmental Prediction Reanalysis data II with each of these land surface schemes for one-year simulations over the period, 1 October 1995 to 30 September 1996, resulting in four one-year simulations for intercomparison. Each simulation has 30km-10km two-way nested domains. The 30 km domain includes the western U.S. and eastern Pacific, and the inner domain includes California and parts of Nevada, Oregon, and the eastern Pacific. Our analysis shows that WRF-CLM3 outperforms WRF-RUC, WRF-NOAH, and WRF-STD in simulating temperature and snow when compared with observations. The WRF-STD scheme, which does not include snow and vegetation processes resulted in the poorest results, with a dramatic overestimation of surface air temperature. However, regardless of the land surface scheme chosen, WRF reasonably well reproduces the winter precipitation, a major water resource for California, suggesting that the linkage between land surface processes and precipitation is not explicit. In general, land surface schemes play a significant role in the simulation of regional

  6. Improved Analyses and Forecasts of Snowpack, Runoff and Drought through Remote Sensing and Land Surface Modeling in Southeastern Europe

    Science.gov (United States)

    Matthews, D.; Brilly, M.; Gregoric, G.; Polajnar, J.; Kobold, M.; Zagar, M.; Knoblauch, H.; Staudinger, M.; Mecklenburg, S.; Lehning, M.; Schweizer, J.; Balint, G.; Cacic, I.; Houser, P.; Pozzi, W.

    2008-12-01

    European hydrometeorological services and research centers are faced with increasing challenges from extremes of weather and climate that require significant investments in new technology and better utilization of existing human and natural resources to provide improved forecasts. Major advances in remote sensing, observation networks, data assimilation, numerical modeling, and communications continue to improve our ability to disseminate information to decision-makers and stake holders. This paper identifies gaps in current technologies, key research and decision-maker teams, and recommends means for moving forward through focused applied research and integration of results into decision support tools. This paper reports on the WaterNet - NASA Water Cycle Solutions Network contacts in Europe and summarizes progress in improving water cycle related decision-making using NASA research results. Products from the Hydrologic Sciences Branch, Goddard Space Flight Center, NASA, Land Information System's (LIS) Land Surface Models (LSM), the SPoRT, CREW , and European Space Agency (ESA), and Joint Research Center's (JRC) natural hazards products, and Swiss Federal Institute for Snow and Avalanche Research's (SLF), and others are discussed. They will be used in collaboration with the ESA and the European Commission to provide solutions for improved prediction of water supplies and stream flow, and droughts and floods, and snow avalanches in the major river basins serviced by EARS, ZAMG, SLF, Vituki Consult, and other European forecast centers. This region of Europe includes the Alps and Carpathian Mountains and is an area of extreme topography with abrupt 2000 m mountains adjacent to the Adriatic Sea. These extremes result in the highest precipitation ( > 5000 mm) in Europe in Montenegro and low precipitation of 300-400 mm at the mouth of the Danube during droughts. The current flood and drought forecasting systems have a spatial resolution of 9 km, which is currently being

  7. ORCHIDEE-SRC v1.0: an extension of the land surface model ORCHIDEE for simulating short rotation coppice poplar plantations

    OpenAIRE

    De Groote, T.; D. Zona; Broeckx, L. S.; Verlinden, M. S.; Luyssaert, S.; Bellassen, V.; Vuichard, N.; R. Ceulemans; Gobin, A.; Janssens, I. A.

    2015-01-01

    Modelling biomass production and the environmental impact of short rotation coppice (SRC) plantations is necessary for planning their deployment, as they are becoming increasingly important for global energy production. This paper describes the modification of the widely used land surface model ORCHIDEE for stand-scale simulations of SRC plantations. The model uses weather data, soil texture and species-specific parameters to predict the aboveground (harvestable) biomass...

  8. A dynamically-coupled groundwater, land surface and regional climate model to predict seasonal watershed flow and groundwater response, FINAL LDRD REPORT.

    Energy Technology Data Exchange (ETDEWEB)

    Maxwell, R; Kollet, S; Chow, F; Granvold, P; Duan, Q

    2007-02-23

    This final report is organized in four sections. Section 1 is the project summary (below), Section 2 is a submitted manuscript that describes the offline, or spinup simulations in detail, Section 3 is also a submitted manuscript that describes the online, or fully-coupled simulations in detail and Section 3, which is report that describes work done via a subcontract with UC Berkeley. The goal of this project was to develop and apply a coupled regional climate, land-surface, groundwater flow model as a means to further understand important mass and energy couplings between regional climate, the land surface, and groundwater. The project involved coupling three distinct submodels that are traditionally used independently with abstracted and potentially oversimplified (inter-model) boundary conditions. This coupled model lead to (1) an improved understanding of the sensitivity and importance of coupled physical processes from the subsurface to the atmosphere; (2) a new tool for predicting hydrologic conditions (rainfall, temperature, snowfall, snowmelt, runoff, infiltration and groundwater flow) at the watershed scale over a range of timeframes; (3) a simulation of hydrologic response of a characteristic watershed that will provide insight into the certainty of hydrologic forecasting, dominance and sensitivity of groundwater dynamics on land-surface fluxes; and (4) a more realistic model representation of weather predictions, precipitation and temperature, at the regional scale. Regional climate models are typically used for the simulation of weather, precipitation and temperature behavior over 10-1000 km domains for weather or climate prediction purposes, and are typically driven by boundary conditions derived from global climate models (GCMs), observations or both. The land or ocean surface typically represents a bottom boundary condition of these models, where important mass (water) and energy fluxes are approximated. The viability and influence of these

  9. A dynamically-coupled groundwater, land surface and regional climate model to predict seasonal watershed flow and groundwater response, FINAL LDRD REPORT.

    Energy Technology Data Exchange (ETDEWEB)

    Maxwell, R; Kollet, S; Chow, F; Granvold, P; Duan, Q

    2007-02-23

    This final report is organized in four sections. Section 1 is the project summary (below), Section 2 is a submitted manuscript that describes the offline, or spinup simulations in detail, Section 3 is also a submitted manuscript that describes the online, or fully-coupled simulations in detail and Section 3, which is report that describes work done via a subcontract with UC Berkeley. The goal of this project was to develop and apply a coupled regional climate, land-surface, groundwater flow model as a means to further understand important mass and energy couplings between regional climate, the land surface, and groundwater. The project involved coupling three distinct submodels that are traditionally used independently with abstracted and potentially oversimplified (inter-model) boundary conditions. This coupled model lead to (1) an improved understanding of the sensitivity and importance of coupled physical processes from the subsurface to the atmosphere; (2) a new tool for predicting hydrologic conditions (rainfall, temperature, snowfall, snowmelt, runoff, infiltration and groundwater flow) at the watershed scale over a range of timeframes; (3) a simulation of hydrologic response of a characteristic watershed that will provide insight into the certainty of hydrologic forecasting, dominance and sensitivity of groundwater dynamics on land-surface fluxes; and (4) a more realistic model representation of weather predictions, precipitation and temperature, at the regional scale. Regional climate models are typically used for the simulation of weather, precipitation and temperature behavior over 10-1000 km domains for weather or climate prediction purposes, and are typically driven by boundary conditions derived from global climate models (GCMs), observations or both. The land or ocean surface typically represents a bottom boundary condition of these models, where important mass (water) and energy fluxes are approximated. The viability and influence of these

  10. Using dry spell dynamics of land surface temperature to evaluate large-scale model representation of soil moisture control on evapotranspiration

    Science.gov (United States)

    Taylor, Christopher M.; Harris, Philip P.; Gallego-Elvira, Belen; Folwell, Sonja S.

    2017-04-01

    The soil moisture control on the partition of land surface fluxes between sensible and latent heat is a key aspect of land surface models used within numerical weather prediction and climate models. As soils dry out, evapotranspiration (ET) decreases, and the excess energy is used to warm the atmosphere. Poor simulations of this dynamic process can affect predictions of mean, and in particular, extreme air temperatures, and can introduce substantial biases into projections of climate change at regional scales. The lack of reliable observations of fluxes and root zone soil moisture at spatial scales that atmospheric models use (typically from 1 to several hundred kilometres), coupled with spatial variability in vegetation and soil properties, makes it difficult to evaluate the flux partitioning at the model grid box scale. To overcome this problem, we have developed techniques to use Land Surface Temperature (LST) to evaluate models. As soils dry out, LST rises, so it can be used under certain circumstances as a proxy for the partition between sensible and latent heat. Moreover, long time series of reliable LST observations under clear skies are available globally at resolutions of the order of 1km. Models can exhibit large biases in seasonal mean LST for various reasons, including poor description of aerodynamic coupling, uncertainties in vegetation mapping, and errors in down-welling radiation. Rather than compare long-term average LST values with models, we focus on the dynamics of LST during dry spells, when negligible rain falls, and the soil moisture store is drying out. The rate of warming of the land surface, or, more precisely, its warming rate relative to the atmosphere, emphasises the impact of changes in soil moisture control on the surface energy balance. Here we show the application of this approach to model evaluation, with examples at continental and global scales. We can compare the behaviour of both fully-coupled land-atmosphere models, and land

  11. Toward Transfer Functions for Land Surface Phenologies

    Science.gov (United States)

    Henebry, G. M.

    2010-12-01

    A key problem in projecting future landscapes is simulating the associated land surface phenologies (or LSPs). A recent study of land surface models concluded that the representations of crop phenologies among the models diverged sufficiently to impede a useful intercomparison of simulation results from their associated climate models. Grassland phenologies are far more complicated than cropland phenologies due to multiple forcing factors, photosynthetic pathways (C3 vs C4), and spatial heterogeneities in both resource availabilities and land management practices. Furthermore, many tallgrass species (such as switchgrass) are widely distributed across temperature, but not moisture, gradients, resulting in significant ecotypic variation across the species' geographic range. Thus, how feasible is "transplanting" tallgrass LSPs across isotherms—but along isohyets—to simulate a shift in cultivation from maize-soy to switchgrass? Prior work has shown a quadratic model can provide a parsimonious link between a Normalized Difference Vegetation Index (or NDVI) time series and thermal time, measured in terms of accumulated growing degree-days (or AGDD). Moreover, the thermal time to peak NDVI (or TTP) is a simple function of the parameter coefficients of fitted model. I fitted quadratic models to MODIS NDVI and weather station data at multiple sites across the Northern Great Plains over ten growing seasons, 2000-2009. There is a strong latitudinal gradient in TTP that results in part from a quasi-linear gradient in accumulated daylight hours (or ADH) between 30 and 50 degrees north. However, AGDD improves upon ADH by providing sensitivity to the variability of growing season weather. In the quadratic parameter coefficients there is a geographic pattern apparent as a function of TTP, although it is more variable at shorter TTPs. Using these patterns, an LSP transfer function was implemented along a latitudinal transect to simulate switchgrass cultivation in areas now

  12. Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

    Science.gov (United States)

    Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Vasilenko, Eugene; Volkova, Elena; Kukharsky, Alexander

    2017-04-01

    The model of water and heat exchange between vegetation covered territory and atmosphere (LSM, Land Surface Model) for vegetation season has been developed to calculate soil water content, evapotranspiration, infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat balances components as well as soil surface and vegetation cover temperatures and depth distributions of moisture and temperature. The LSM is suited for utilizing satellite-derived estimates of precipitation, land surface temperature and vegetation characteristics and soil surface humidity for each pixel. Vegetation and meteorological characteristics being the model parameters and input variables, correspondingly, have been estimated by ground observations and thematic processing measurement data of scanning radiometers AVHRR/NOAA, SEVIRI/Meteosat-9, -10 (MSG-2, -3) and MSU-MR/Meteor-M № 2. Values of soil surface humidity has been calculated from remote sensing data of scatterometers ASCAT/MetOp-A, -B. The case study has been carried out for the territory of part of the agricultural Central Black Earth Region of European Russia with area of 227300 km2 located in the forest-steppe zone for years 2012-2015 vegetation seasons. The main objectives of the study have been: - to built estimates of precipitation, land surface temperatures (LST) and vegetation characteristics from MSU-MR measurement data using the refined technologies (including algorithms and programs) of thematic processing satellite information matured on AVHRR and SEVIRI data. All technologies have been adapted to the area of interest; - to investigate the possibility of utilizing satellite-derived estimates of values above in the LSM including verification of obtained estimates and development of procedure of their inputting into the model. From the AVHRR data there have been built the estimates of precipitation, three types of LST: land skin temperature Tsg, air temperature at a level of

  13. Impact of Spin-up Forcing on Vegetation States Simulated by a Dynamic Global Vegetation Model Coupledwith a Land Surface Model

    Institute of Scientific and Technical Information of China (English)

    LI Fang; ZENG Xiaodong; SONG Xiang; TIAN Dongxiao; SHAO Pu; ZHANG Dongling

    2011-01-01

    A dynamic global vegetation model (DGVM) coupled with a land surface model (LSM) is generally initialized using a spin-up process to derive a physically-consistent initial condition. Spin-up forcing, which is the atmospheric forcing used to drive the coupled model to equilibrium solutions in the spin-up process,varies across earlier studies. In the present study, the impact of the spin-up forcing in the initialization stage on the fractional coverages (FCs) of plant functional type (PFT) in the subsequent simulation stage are assessed in seven classic climate regions by a modified Community Land Model's Dynamic Global Vegetation Model (CLM-DGVM). Results show that the impact of spin-up forcing is considerable in all regions except the tropical rainforest climate region (TR) and the wet temperate climate region (WM). In the tropical monsoon climate region (TM), the TR and TM transition region (TR-TM), the dry temperate climate region (DM), the highland climate region (H), and the boreal forest climate region (BF), where FCs are affected by climate non-negligibly, the discrepancies in initial FCs, which represent long-term cumulative response of vegetation to different climate anomalies, are large. Moreover, the large discrepancies in initial FCs usually decay slowly because there are trees or shrubs in the five regions. The intrinsic growth timescales of FCs for tree PFTs and shrub PFTs are long, and the variation of FCs of tree PFTs or shrub PFTs can affect that of grass PFTs.

  14. Lunar Landing Operational Risk Model

    Science.gov (United States)

    Mattenberger, Chris; Putney, Blake; Rust, Randy; Derkowski, Brian

    2010-01-01

    Characterizing the risk of spacecraft goes beyond simply modeling equipment reliability. Some portions of the mission require complex interactions between system elements that can lead to failure without an actual hardware fault. Landing risk is currently the least characterized aspect of the Altair lunar lander and appears to result from complex temporal interactions between pilot, sensors, surface characteristics and vehicle capabilities rather than hardware failures. The Lunar Landing Operational Risk Model (LLORM) seeks to provide rapid and flexible quantitative insight into the risks driving the landing event and to gauge sensitivities of the vehicle to changes in system configuration and mission operations. The LLORM takes a Monte Carlo based approach to estimate the operational risk of the Lunar Landing Event and calculates estimates of the risk of Loss of Mission (LOM) - Abort Required and is Successful, Loss of Crew (LOC) - Vehicle Crashes or Cannot Reach Orbit, and Success. The LLORM is meant to be used during the conceptual design phase to inform decision makers transparently of the reliability impacts of design decisions, to identify areas of the design which may require additional robustness, and to aid in the development and flow-down of requirements.

  15. An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling

    Science.gov (United States)

    Hain, Christopher R.; Crow, Wade T.; Mecikalski, John R.; Anderson, Martha C.; Holmes, Thomas

    2011-08-01

    Remotely sensed soil moisture studies have mainly focused on retrievals using active and passive microwave (MW) sensors, which provide measurements that are directly related to soil moisture (SM). MW sensors have obvious advantages such as the ability to retrieve through nonprecipitating cloud cover which provides shorter repeat cycles. However, MW sensors offer coarse spatial resolution and suffer from reduced retrieval skill over moderate to dense vegetation. A unique avenue for filling these information gaps is to exploit the retrieval of SM from thermal infrared (TIR) observations, which can provide SM information under vegetation cover and at significantly higher resolutions than MW. Previously, an intercomparison of TIR-based and MW-based SM has not been investigated in the literature. Here a series of analyses are proposed to study relationships between SM products during a multiyear period (2003-2008) from a passive MW retrieval (AMSR-E), a TIR based model (ALEXI), and a land surface model (Noah) over the continental United States. The three analyses used in this study include (1) a spatial anomaly correlation analysis, (2) a temporal correlation analysis, and (3) a triple collocation error estimation technique. In general, the intercomparison shows that the TIR and MW methods provide complementary information about the current SM state. TIR can provide SM information over moderate to dense vegetation, a large information gap in current MW methods, while serving as an additional independent source of SM information over low to moderate vegetation. The complementary nature of SM information from MW and TIR sensors implies a potential for integration within an advanced SM data assimilation system.

  16. Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

    Directory of Open Access Journals (Sweden)

    D. Slevin

    2017-07-01

    Full Text Available This study evaluates the ability of the JULES land surface model (LSM to simulate gross primary productivity (GPP on regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON, were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0. 5° × 0. 5° spatial resolution, the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE, by 25 and 8 %, respectively, and CARDAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0. 5° × 0. 5°, 1° × 1° and 2° × 2° had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year−1. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year−1. On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5° N–5° S and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30

  17. The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements

    KAUST Repository

    Niu, Guo-Yue

    2011-06-24

    This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP\\'s performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework. Copyright © 2011 by the American Geophysical Union.

  18. Mosaic versus dual source approaches for modelling the surface energy balance of a semi-arid land

    Directory of Open Access Journals (Sweden)

    G. Boulet

    1999-01-01

    Full Text Available Two-layer parameterisation of the surface energy budget proves to be realistic for sparse but homogeneously distributed vegetation. For semi-arid land surfaces however, sparse vegetation is usually interspersed by large patches of unshaded bare soil which may interact directly with the atmosphere with little interference with the vegetation. Therefore such surfaces might not be realistically represented by a two-layer parameterisation. The objective of this study is to investigate the issue of representing water and energy transfer processes in arid and semi-arid regions. Two different surface schemes, namely the classic two layer (one-compartment approach and a two adjacent compartment ('mosaic' approach are used. The performance of both schemes is documented using data sets collected over two sparsely vegetated surfaces in the San Pedro river basin: homogeneously distributed grassland and heterogeneously distributed shrubs. In the latter case the mosaic scheme seems to be more realistic given the quality of the temperature estimates. But no clear statement can be made on the efficiency of both schemes for the total fluxes. Over each site, we investigate the possibility of artificially modifying some of the surface parameters in order to get the surface fluxes simulated by the one-compartment scheme to reproduce the two-compartment ones. The 'cost' associated with this process in terms of surface temperature estimates is eventually discussed.

  19. Sensitivity of Land Surfaces Model to Dynamic Land Surface Parameters%陆面过程模型对下垫面参数动态变化的敏感性分析

    Institute of Scientific and Technical Information of China (English)

    蔡福; 周广胜; 李荣平; 明惠青

    2011-01-01

    Using continuous flux data, meteorological data and biological data in 2006 (from June 1 to August 9) from Jinzhou agricultural ecosystem research station, based on BATSIe model, the sensitivity of land surface model to dynamic assignment of roughness( Z0 ), leaf area index(LAI) and fractional vegetation coverage (FVEG)and albedo(α) were investigated. The results show that dynamic assignment of Z0 has effect on simulating surface soil temperature(SST) and sensible heat flux (SH) especially on the time when maize field surface covered changes from bare soil to vegetation. Dynamic LAIplays important role in improving the simulation of SST, net absorbed solar energy flux(Frs), SH and surface soil water content(SWC), at the same time affects simulation of latent heat flux (LE). Dynamic FVEG affects obviously simulations of all above-mentioned variables and shows greater sensitivity when they are smaller. Also, dynamical change of a can affect simulations of SST, LE and SH, especially for the latter. Furthermore, the interactions among different dynamic parameters are ignored by the model. The improvement of single land surface parameter might be helpful for simulating one or multivariate but not for all variables. In short, it is necessary to set up a parameterization scheme with the interactions of different land surface parameters.%利用2006年锦州玉米农田生态系统野外观测站动态连续的通量、气象及生物因子观测数据,分析了BATSl e陆面模型对动态的粗糙度(Z0)、叶面积指数(LAI)、植被履盖度(FVEG)及反照率(α)变化的敏感性.结果表明:Z0的动态变化对表层土壤温度(SST)和感热(SH)的模拟有一定影响,主要发生在玉米农田从裸土向有植被覆盖转变这一阶段.LAI的动态赋值可以改善SST、净入射短波辐射(frs)、SH和SWC的模拟效果,对潜热(LE)的模拟也有一定影响.动态FVEG对上述各变量的模拟影响最为明显,当FVEG较小时敏感性最大.α的动

  20. BLAZE, a novel Fire-Model for the CABLE Land-Surface Model applied to a Re-Assessment of the Australian Continental Carbon Budget

    Science.gov (United States)

    Nieradzik, L. P.; Haverd, V. E.; Briggs, P.; Meyer, C. P.; Canadell, J.

    2015-12-01

    Fires play a major role in the carbon-cycle and the development of global vegetation, especially on the continent of Australia, where vegetation is prone to frequent fire occurences and where regional composition and stand-age distribution is regulated by fire. Furthermore, the probable changes of fire behaviour under a changing climate are still poorly understood and require further investigation.In this presentation we introduce the fire-model BLAZE (BLAZe induced land-atmosphere flux Estimator), designed for a novel approach to simulate fire-frequencies, fire-intensities, fire related fluxes and the responses in vegetation. Fire frequencies are prescribed using SIMFIRE (Knorr et al., 2014) or GFED3 (e.g. Giglio et al., 2013). Fire-Line-Intensity (FLI) is computed from meteorological information and fuel loads which are state variables within the C-cycle component of CABLE (Community Atmosphere-Biosphere-Land Exchange model). This FLI is used as an input to the tree-demography model POP(Population-Order-Physiology; Haverd et al., 2014). Within POP the fire-mortality depends on FLI and tree height distribution. Intensity-dependent combustion factors (CF) are then generated for and applied to live and litter carbon pools as well as the transfers from live pools to litter caused by fire. Thus, both fire and stand characteristics are taken into account which has a legacy effect on future events. Gross C-CO2 emissions from Australian wild fires are larger than Australian territorial fossil fuel emissions. However, the net effect of fire on the Australian terrestrial carbon budget is unknown. We address this by applying the newly-developed fire module, integrated within the CABLE land surface model, and optimised for the Australian region, to a reassessment of the Australian Terrestrial Carbon Budget.

  1. Evaluation of land surface model simulations of evapotranspiration over a 12 year crop succession: impact of the soil hydraulic properties

    Directory of Open Access Journals (Sweden)

    S. Garrigues

    2014-10-01

    Full Text Available Evapotranspiration has been recognized as one of the most uncertain term in the surface water balance simulated by land surface models. In this study, the SURFEX/ISBA-A-gs simulations of evapotranspiration are assessed at local scale over a 12 year Mediterranean crop succession. The model is evaluated in its standard implementation which relies on the use of the ISBA pedotransfer estimates of the soil properties. The originality of this work consists in explicitly representing the succession of crop cycles and inter-crop bare soil periods in the simulations and assessing its impact on the dynamic of simulated and measured evapotranspiration over a long period of time. The analysis focuses on key soil parameters which drive the simulation of evapotranspiration, namely the rooting depth, the soil moisture at saturation, the soil moisture at field capacity and the soil moisture at wilting point. The simulations achieved with the standard values of these parameters are compared to those achieved with the in situ values. The portability of the ISBA pedotransfer functions is evaluated over a typical Mediterranean crop site. Various in situ estimates of the soil parameters are considered and distinct parametrization strategies are tested to represent the evapotranspiration dynamic over the crop succession. This work shows that evapotranspiration mainly results from the soil evaporation when it is continuously simulated over a Mediterranean crop succession. The evapotranspiration simulated with the standard surface and soil parameters of the model is largely underestimated. The deficit in cumulative evapotranspiration amounts to 24% over 12 years. The bias in daily daytime evapotranspiration is −0.24 mm day−1. The ISBA pedotransfer estimates of the soil moisture at saturation and at wilting point are overestimated which explains most of the evapotranspiration underestimation. The overestimation of the soil moisture at wilting point causes the

  2. Evaluation of land surface model simulations of evapotranspiration over a 12 year crop succession: impact of the soil hydraulic properties

    Science.gov (United States)

    Garrigues, S.; Olioso, A.; Calvet, J.-C.; Martin, E.; Lafont, S.; Moulin, S.; Chanzy, A.; Marloie, O.; Desfonds, V.; Bertrand, N.; Renard, D.

    2014-10-01

    Evapotranspiration has been recognized as one of the most uncertain term in the surface water balance simulated by land surface models. In this study, the SURFEX/ISBA-A-gs simulations of evapotranspiration are assessed at local scale over a 12 year Mediterranean crop succession. The model is evaluated in its standard implementation which relies on the use of the ISBA pedotransfer estimates of the soil properties. The originality of this work consists in explicitly representing the succession of crop cycles and inter-crop bare soil periods in the simulations and assessing its impact on the dynamic of simulated and measured evapotranspiration over a long period of time. The analysis focuses on key soil parameters which drive the simulation of evapotranspiration, namely the rooting depth, the soil moisture at saturation, the soil moisture at field capacity and the soil moisture at wilting point. The simulations achieved with the standard values of these parameters are compared to those achieved with the in situ values. The portability of the ISBA pedotransfer functions is evaluated over a typical Mediterranean crop site. Various in situ estimates of the soil parameters are considered and distinct parametrization strategies are tested to represent the evapotranspiration dynamic over the crop succession. This work shows that evapotranspiration mainly results from the soil evaporation when it is continuously simulated over a Mediterranean crop succession. The evapotranspiration simulated with the standard surface and soil parameters of the model is largely underestimated. The deficit in cumulative evapotranspiration amounts to 24% over 12 years. The bias in daily daytime evapotranspiration is -0.24 mm day-1. The ISBA pedotransfer estimates of the soil moisture at saturation and at wilting point are overestimated which explains most of the evapotranspiration underestimation. The overestimation of the soil moisture at wilting point causes the underestimation of

  3. Effects of Crop Growth and Development on Land Surface Fluxes

    Institute of Scientific and Technical Information of China (English)

    CHEN Feng; XIE Zhenghui

    2011-01-01

    In this study, the Crop Estimation through Resource and Environment Synthesis model (CERES3.0) was coupled into the Biosphere-Atmosphere Transfer Scheme (BATS), which is called BATS_CERES, to represent interactions between the land surface and crop growth processes. The effects of crop growth and development on land surface processes were then studied based on numerical simulations using the land surface models. Six sensitivity experiments by BATS show that the land surface fluxes underwent substantial changes when the leaf area index was changed from 0 to 6 n2 m-2. Numerical experiments for Yucheng and Taoyuan stations reveal that the coupled model could capture not only the responses of crop growth and development to environmental conditions, but also the feedbacks to land surface processes.For quantitative evaluation of the effects of crop growth and development on surface fluxes in China, two numerical experiments were conducted over continental China: one by BATS_CERES and one by the original BATS. Comparison of the two runs shows decreases of leaf area index and fractional vegetation cover when incorporating dynamic crops in land surface simulation, which lead to less canopy interception, vegetation transpiration, total evapotranspiration, top soil moisture, and more soil evaporation, surface runoff, and root zone soil moisture. These changes are accompanied by decreasing latent heat flux and increasing sensible heat flux in the cropland region. In addition, the comparison between the simulations and observations proved that incorporating the crop growth and development process into the land surface model could reduce the systematic biases of the simulated leaf area index and top soil moisture, hence improve the simulation of land surface fluxes.

  4. Study on a Dynamic Vegetation Model for Simulating Land Surface Flux Exchanges at Lien-Hua-Chih Flux Observation Site in Taiwan

    Science.gov (United States)

    Yeh, T. Y.; Li, M. H.; Chen, Y. Y.; Ryder, J.; McGrath, M.; Otto, J.; Naudts, K.; Luyssaert, S.; MacBean, N.; Bastrikov, V.

    2016-12-01

    Dynamic vegetation model ORCHIDEE (Organizing Carbon and Hydrology In Dynamic EcosystEms) is a state of art land surface component of the IPSL (Institute Pierre Simon Laplace) Earth System Model. It has been used world-wide to investigate variations of water, carbon, and energy exchanges between the land surface and the atmosphere. In this study we assessed the applicability of using ORCHIDEE-CAN, a new feature with 3-D CANopy structure (Naudts et al., 2015; Ryder et al., 2016), to simulate surface fluxes measured at tower-based eddy covariance fluxes at the Lien-Hua-Chih experimental watershed in Taiwan. The atmospheric forcing including radiation, air temperature, wind speed, and the dynamics of vertical canopy structure for driving the model were obtained from the observations site. Suitable combinations of default plant function types were examined to meet in-situ observations of soil moisture and leaf area index from 2009 to 2013. The simulated top layer soil moisture was ranging from 0.1 to 0.4 and total leaf area was ranging from 2.2 to 4.4, respectively. A sensitivity analysis was performed to investigate the sensitive of model parameters and model skills of ORCHIDEE-CAN on capturing seasonal variations of surface fluxes. The most sensitive parameters were suggested and calibrated by an automatic data assimilation tool ORCHDAS (ORCHIDEE Data Assimilation Systems; http://orchidas.lsce.ipsl.fr/). Latent heat, sensible heat, and carbon fluxes simulated by the model were compared with long-term observations at the site. ORCHIDEE-CAN by making use of calibrated surface parameters was used to study variations of land-atmosphere interactions on a variety of temporal scale in associations with changes in both land and atmospheric conditions. Ref: Naudts, K., et al.,: A vertically discretised canopy description for ORCHIDEE (SVN r2290) and the modifications to the energy, water and carbon fluxes, Geoscientific Model Development, 8, 2035-2065, doi:10.5194/gmd-8

  5. evaluation of land surface temperature parameterization ...

    African Journals Online (AJOL)

    user

    1 DEPARTMENT OF PHYSICS, ADEYEMI COLLEGE OF EDUCATION, ONDO, ... Surface temperature (Ts) is vital to the study of land-atmosphere interactions and climate variabilities. .... value = 0.167 m3m-3), and very low for dry days (mean.

  6. Updated global soil map for the Weather Research and Forecasting model and soil moisture initialization for the Noah land surface model

    Science.gov (United States)

    DY, C. Y.; Fung, J. C. H.

    2016-08-01

    A meteorological model requires accurate initial conditions and boundary conditions to obtain realistic numerical weather predictions. The land surface controls the surface heat and moisture exchanges, which can be determined by the physical properties of the soil and soil state variables, subsequently exerting an effect on the boundary layer meteorology. The initial and boundary conditions of soil moisture are currently obtained via National Centers for Environmental Prediction FNL (Final) Operational Global Analysis data, which are collected operationally in 1° by 1° resolutions every 6 h. Another input to the model is the soil map generated by the Food and Agriculture Organization of the United Nations - United Nations Educational, Scientific and Cultural Organization (FAO-UNESCO) soil database, which combines several soil surveys from around the world. Both soil moisture from the FNL analysis data and the default soil map lack accuracy and feature coarse resolutions, particularly for certain areas of China. In this study, we update the global soil map with data from Beijing Normal University in 1 km by 1 km grids and propose an alternative method of soil moisture initialization. Simulations of the Weather Research and Forecasting model show that spinning-up the soil moisture improves near-surface temperature and relative humidity prediction using different types of soil moisture initialization. Explanations of that improvement and improvement of the planetary boundary layer height in performing process analysis are provided.

  7. Examining the Impacts of High-Resolution Land Surface Initialization on Model Predictions of Convection in the Southeastern U.S.

    Science.gov (United States)

    Case, Jonathan L.; Kumar, Sujay V.; Santos, Pablo; Medlin, Jeffrey M.; Jedlovec, Gary J.

    2009-01-01

    One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within physics parameterizations, model resolution limitations, as well as uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture and temperature, ground fluxes, and vegetation are necessary to better simulate the interactions between the land surface and atmosphere, and ultimately improve predictions of local circulations and summertime pulse convection. The NASA Short-term Prediction Research and Transition (SPORT) Center has been conducting studies to examine the impacts of high-resolution land surface initialization data generated by offline simulations of the NASA Land Informatiot System (LIS) on subsequent numerical forecasts using the Weather Research and Forecasting (WRF) model (Case et al. 2008, to appear in the Journal of Hydrometeorology). Case et al. presents improvements to simulated sea breezes and surface verification statistics over Florida by initializing WRF with land surface variables from an offline LIS spin-up run, conducted on the exact WRF domain and resolution. The current project extends the previous work over Florida, focusing on selected case studies of typical pulse convection over the southeastern U.S., with an emphasis on improving local short-term WRF

  8. Simulation Experiments of Land Surface Physical Processes and Ecological Effect over Different Underlying Surface

    Institute of Scientific and Technical Information of China (English)

    2004-01-01

    Based on the existing Land Surface Physical Process Models(Deardorff, Dickinson, LIU, Noilhan, Seller, ZHAO), a Comprehensive Land Surface Physical Process Model (CLSPPM) is developed by considering the different physical processes of the earth's surface-vegetation-atmosphere system more completely. Compared with SiB and BATS, which are famous for their detailed parameterizations of physical variables, this simplified model is more convenient and saves much more computation time. Though simple, the feas...

  9. Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

    Directory of Open Access Journals (Sweden)

    A. L. Barbu

    2014-01-01

    Full Text Available The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM and leaf area index (LAI. This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs land surface model within the the externalised surface model (SURFEX modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function matching technique. A multivariate multi-scale land data assimilation system (LDAS based on the extended Kalman Filter (EKF is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011. The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil

  10. A Lagrangian stochastic model to demonstrate multi-scale interactions between convection and land surface heterogeneity in the atmospheric boundary layer

    Science.gov (United States)

    Parsakhoo, Zahra; Shao, Yaping

    2017-04-01

    Near-surface turbulent mixing has considerable effect on surface fluxes, cloud formation and convection in the atmospheric boundary layer (ABL). Its quantifications is however a modeling and computational challenge since the small eddies are not fully resolved in Eulerian models directly. We have developed a Lagrangian stochastic model to demonstrate multi-scale interactions between convection and land surface heterogeneity in the atmospheric boundary layer based on the Ito Stochastic Differential Equation (SDE) for air parcels (particles). Due to the complexity of the mixing in the ABL, we find that linear Ito SDE cannot represent convections properly. Three strategies have been tested to solve the problem: 1) to make the deterministic term in the Ito equation non-linear; 2) to change the random term in the Ito equation fractional, and 3) to modify the Ito equation by including Levy flights. We focus on the third strategy and interpret mixing as interaction between at least two stochastic processes with different Lagrangian time scales. The model is in progress to include the collisions among the particles with different characteristic and to apply the 3D model for real cases. One application of the model is emphasized: some land surface patterns are generated and then coupled with the Large Eddy Simulation (LES).

  11. A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.

  12. A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

    Institute of Scientific and Technical Information of China (English)

    TIAN XiangJun; XIE ZhengHui

    2008-01-01

    The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation.The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data assimilation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in consideration of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process,while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heterogeneity and soil water thawing and freezing. With the improvement of soil moisture simulation,the soil temperature-simulated precision can be also improved to some extent.

  13. ECOCLIMAP-II/Europe: a twofold database of ecosystems and surface parameters at 1 km resolution based on satellite information for use in land surface, meteorological and climate models

    Directory of Open Access Journals (Sweden)

    S. Faroux

    2013-04-01

    Full Text Available The overall objective of the present study is to introduce the new ECOCLIMAP-II database for Europe, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, already implemented at global scale. The ECOCLIMAP programme is a dual database at 1 km resolution that includes an ecosystem classification and a coherent set of land surface parameters that are primarily mandatory in meteorological modelling (notably leaf area index and albedo. Hence, the aim of this innovative physiography is to enhance the quality of initialisation and impose some surface attributes within the scope of weather forecasting and climate related studies. The strategy for implementing ECOCLIMAP-II is to depart from prevalent land cover products such as CLC2000 (Corine Land Cover and GLC2000 (Global Land Cover by splitting existing classes into new classes that possess a better regional character by virtue of the climatic environment (latitude, proximity to the sea, topography. The leaf area index (LAI from MODIS and normalized difference vegetation index (NDVI from SPOT/Vegetation (a global monitoring system of vegetation yield the two proxy variables that were considered here in order to perform a multi-year trimmed analysis between 1999 and 2005 using the K-means method. Further, meteorological applications require each land cover type to appear as a partition of fractions of 4 main surface types or tiles (nature, water bodies, sea, urban areas and, inside the nature tile, fractions of 12 plant functional types (PFTs representing generic vegetation types – principally broadleaf forest, needleleaf forest, C3 and C4 crops, grassland and bare land – as incorporated by the SVAT model ISBA (Interactions Surface Biosphere Atmosphere developed at Météo France. This landscape division also forms the cornerstone of a validation exercise. The new ECOCLIMAP-II can be verified with auxiliary land cover products at very fine and coarse resolutions by means of

  14. Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

    Science.gov (United States)

    Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

    2012-01-01

    The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters

  15. The COSMO-CLM 4.8 regional climate model coupled to regional ocean, land surface and global earth system models using OASIS3-MCT: description and performance

    Science.gov (United States)

    Will, Andreas; Akhtar, Naveed; Brauch, Jennifer; Breil, Marcus; Davin, Edouard; Ho-Hagemann, Ha T. M.; Maisonnave, Eric; Thürkow, Markus; Weiher, Stefan

    2017-04-01

    We developed a coupled regional climate system model based on the CCLM regional climate model. Within this model system, using OASIS3-MCT as a coupler, CCLM can be coupled to two land surface models (the Community Land Model (CLM) and VEG3D), the NEMO-MED12 regional ocean model for the Mediterranean Sea, two ocean models for the North and Baltic seas (NEMO-NORDIC and TRIMNP+CICE) and the MPI-ESM Earth system model.We first present the different model components and the unified OASIS3-MCT interface which handles all couplings in a consistent way, minimising the model source code modifications and defining the physical and numerical aspects of the couplings. We also address specific coupling issues like the handling of different domains, multiple usage of the MCT library and exchange of 3-D fields.We analyse and compare the computational performance of the different couplings based on real-case simulations over Europe. The usage of the LUCIA tool implemented in OASIS3-MCT enables the quantification of the contributions of the coupled components to the overall coupling cost. These individual contributions are (1) cost of the model(s) coupled, (2) direct cost of coupling including horizontal interpolation and communication between the components, (3) load imbalance, (4) cost of different usage of processors by CCLM in coupled and stand-alone mode and (5) residual cost including i.a. CCLM additional computations.Finally a procedure for finding an optimum processor configuration for each of the couplings was developed considering the time to solution, computing cost and parallel efficiency of the simulation. The optimum configurations are presented for sequential, concurrent and mixed (sequential+concurrent) coupling layouts. The procedure applied can be regarded as independent of the specific coupling layout and coupling details.We found that the direct cost of coupling, i.e. communications and horizontal interpolation, in OASIS3-MCT remains below 7 % of the CCLM stand

  16. Egypt satellite images for land surface characterization

    DEFF Research Database (Denmark)

    Hasager, Charlotte Bay

    Satellite images provide information on the land surface properties. From optical remote sensing images in the blue, green, red and near-infrared part of the electromagnetic spectrum it is possible to identify a large number of surface features. The report briefly describes different satellite...

  17. A subsurface runoff parameterization with water storage and recharge based on the Boussinesq-Storage Equation for a Land Surface Model

    Institute of Scientific and Technical Information of China (English)

    TIAN; Xiangjun; XIE; Zhenghui; ZHANG; Shenglei

    2006-01-01

    Subsurface runoff in a land surface model is usually parameterized as a single-valued function of total storage in a basin aquifer reservoir. This kind of parameterization is often single-valued function of storage-discharge under a steady or "quasi-steady" state, which cannot represent the influence of aquifer recharge on subsurface runoff generation. In this paper, a new subsurface runoff parameterization with water storage and recharge based on the Boussinesq-storage equation is developed. This model is validated by a subsurface flow separation algorithm for an example river basin, which shows that the new model can simulate the subsurface flow reasonably.

  18. Characterizing the relationship between land use land cover change and land surface temperature

    Science.gov (United States)

    Tran, Duy X.; Pla, Filiberto; Latorre-Carmona, Pedro; Myint, Soe W.; Caetano, Mario; Kieu, Hoan V.

    2017-02-01

    Exploring changes in land use land cover (LULC) to understand the urban heat island (UHI) effect is valuable for both communities and local governments in cities in developing countries, where urbanization and industrialization often take place rapidly but where coherent planning and control policies have not been applied. This work aims at determining and analyzing the relationship between LULC change and land surface temperature (LST) patterns in the context of urbanization. We first explore the relationship between LST and vegetation, man-made features, and cropland using normalized vegetation, and built-up indices within each LULC type. Afterwards, we assess the impacts of LULC change and urbanization in UHI using hot spot analysis (Getis-Ord Gi∗ statistics) and urban landscape analysis. Finally, we propose a model applying non-parametric regression to estimate future urban climate patterns using predicted land cover and land use change. Results from this work provide an effective methodology for UHI characterization, showing that (a) LST depends on a nonlinear way of LULC types; (b) hotspot analysis using Getis Ord Gi∗ statistics allows to analyze the LST pattern change through time; (c) UHI is influenced by both urban landscape and urban development type; (d) LST pattern forecast and UHI effect examination can be done by the proposed model using nonlinear regression and simulated LULC change scenarios. We chose an inner city area of Hanoi as a case-study, a small and flat plain area where LULC change is significant due to urbanization and industrialization. The methodology presented in this paper can be broadly applied in other cities which exhibit a similar dynamic growth. Our findings can represent an useful tool for policy makers and the community awareness by providing a scientific basis for sustainable urban planning and management.

  19. Spatial assessment of land surface temperature and land use/land cover in Langkawi Island

    Science.gov (United States)

    Abu Bakar, Suzana Binti; Pradhan, Biswajeet; Salihu Lay, Usman; Abdullahi, Saleh

    2016-06-01

    This study investigates the relationship between Land Surface Temperature and Land Use/Land Cover in Langkawi Island by using Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-Up Index (NDBI) and Modified Normalized Difference Water Index (MNDWI) qualitatively by using Landsat 7 ETM+ and Landsat 8 (OLI/TIRS) over the period 2002 and 2015. Pixel-based classifiers Maximum Likelihood (MLC) and Support Vector Machine (SVM), has been performed to prepare the Land Use/ Land Cover map (LU/LC) and the result shows that Support Vector Machine (SVM) achieved maximum accuracy with 90% and 90.46% compared to Maximum Likelihood (MLC) classifier with 86.62% and 86.98% respectively. The result revealed that as the impervious surface (built-up /roads) increases, the surface temperature of the area increased. However, land surface temperature decreased in the vegetated areas. Based from the linear regression between LST and NDVI, NDBI and MNDWI, these indices can be used as an indicator to monitor the impact of Land Use/Land Cover on Land Surface Temperature.

  20. On The Reproducibility of Seasonal Land-surface Climate

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, T J

    2004-10-22

    The sensitivity of the continental seasonal climate to initial conditions is estimated from an ensemble of decadal simulations of an atmospheric general circulation model with the same specifications of radiative forcings and monthly ocean boundary conditions, but with different initial states of atmosphere and land. As measures of the ''reproducibility'' of continental climate for different initial conditions, spatio-temporal correlations are computed across paired realizations of eleven model land-surface variables in which the seasonal cycle is either included or excluded--the former case being pertinent to climate simulation, and the latter to seasonal anomaly prediction. It is found that the land-surface variables which include the seasonal cycle are impacted only marginally by changes in initial conditions; moreover, their seasonal climatologies exhibit high spatial reproducibility. In contrast, the reproducibility of a seasonal land-surface anomaly is generally low, although it is substantially higher in the Tropics; its spatial reproducibility also markedly fluctuates in tandem with warm and cold phases of the El Nino/Southern Oscillation. However, the overall degree of reproducibility depends strongly on the particular land-surface anomaly considered. It is also shown that the predictability of a land-surface anomaly implied by its reproducibility statistics is consistent with what is inferred from more conventional predictability metrics. Implications of these results for climate model intercomparison projects and for operational forecasts of seasonal continental climate also are elaborated.

  1. An ensemble Kalman filter dual assimilation of thermal infrared and microwave satellite observations of soil moisture into the Noah land surface model

    Science.gov (United States)

    Hain, Christopher R.; Crow, Wade T.; Anderson, Martha C.; Mecikalski, John R.

    2012-11-01

    Studies that have assimilated remotely sensed soil moisture (SM) into land surface models (LSMs) have generally focused on retrievals from microwave (MW) sensors. However, retrievals from thermal infrared (TIR) sensors have also been shown to add unique information, especially where MW sensors are not able to provide accurate retrievals (due to, e.g., dense vegetation). In this study, we examine the assimilation of a TIR product based on surface evaporative flux estimates from the Atmosphere Land Exchange Inverse (ALEXI) model and the MW-based VU Amsterdam NASA surface SM product generated with the Land Parameter Retrieval Model (LPRM). A set of data assimilation experiments using an ensemble Kalman filter are performed over the contiguous United States to assess the impact of assimilating ALEXI and LPRM SM retrievals in isolation and together in a dual-assimilation case. The relative skill of each assimilation case is assessed through a data denial approach where a LSM is forced with an inferior precipitation data set. The ability of each assimilation case to correct for precipitation errors is quantified by comparing with a simulation forced with a higher-quality precipitation data set. All three assimilation cases (ALEXI, LPRM, and Dual assimilation) show relative improvements versus the open loop (i.e., reduced RMSD) for surface and root zone SM. In the surface zone, the dual assimilation case provides the largest improvements, followed by the LPRM case. However, the ALEXI case performs best in the root zone. Results from the data denial experiment are supported by comparisons between assimilation results and ground-based SM observations from the Soil Climate Analysis Network.

  2. A GRACE-Streamflow Land Surface Model Calibration Approach for Improved Baseflow and Water Table Simulations over the Highly Managed Upper-Nile Basin of East Africa

    Science.gov (United States)

    Nanteza, J.; Lo, M. H.; Wu, R. J.; Thomas, B. F.; Famiglietti, J. S.

    2015-12-01

    Land surface models (LSMs) are useful tools for understanding behaviors of land hydrologic variables at different time and spatial scales. LSM outputs, however, are marked with great uncertainties resulting from the simplified assumptions on the parameterization and processes of the land surface and a poor representation of both the natural and anthropogenic controls on the system. The Upper-Nile basin, over Uganda, Kenya and Tanzania, is one region that is characteristic of significant human controls on streamflow, including Lake Victoria releases. The river Nile flow from Lake Victoria follows apriori rating curves that are not simulated by LSMs. Apart from management practices; the huge storage volume of Lake Victoria also modifies the seasonal characteristics of the Upper-Nile discharge, creating small seasonal variations in stream flow. In this study we calibrate several critical parameters in the Community Land Model (CLM.v4) in a multiobjective framework using total water storage anomalies (∆TWS) from GRACE, observed total runoff (Q) and estimated baseflow (BF) over the Upper-Nile basin. The goal is to improve the CLM parameters so that the model simulates the agreed curve (apriori) streamflow and baseflow with a better accuracy. We demonstrate the significance of improved parametrization by comparing model results of ∆TWS, Q and BF with a combination of insitu and estimated observations. Preliminary results based on RMSE statistics show that with calibration, simulations of ∆TWS, Q and BF achieve higher performance. Further, an improvement in the model's capacity to simulate the water table depth is also evident with the calibration. Such results provide a basis for using CLM for other hydrologic experiments that could guide water resources management in this highly managed basin.

  3. Study on the cloud detection of GOCI by using the simulated surface reflectance from BRDF-model for the land application and meteorological utilization

    Science.gov (United States)

    Kim, Hye-Won; Yeom, Jong-Min; Woo, Sun-Hee; Chae, Tae-Byeong

    2016-04-01

    COMS (Communication, Ocean, and Meteorological Satellite) was launched at French Guiana Kourou space center on 27 June 2010. Geostationary Ocean Color Imager (GOCI), which is the first ocean color geostationary satellite in the world for observing the ocean phenomena, is able to obtain the scientific data per an hour from 00UTC to 07UTC. Moreover, the spectral channels of GOCI would enable not only monitoring for the ocean, but for extracting the information of the land surface over the Korean Peninsula, Japan, and Eastern China. Since it is extremely important to utilize GOCI data accurately for the land application, cloud pixels over the surface have to be removed. Unfortunately, infra-red (IR) channels that can easily detect the water vapor with the cloud top temperature, are not included in the GOCI sensor. In this paper, the advanced cloud masking algorithm will be proposed with visible and near-IR (NIR) bands that are within GOCI bands. The main obstacle of cloud masking with GOCI is how to handle the high variable surface reflectance, which is mainly depending on the solar zenith angle. In this study, we use semi-empirical BRDF model to simulate the surface reflectance by using 16 day composite cloudy free image. When estimating the simulated surface reflectance, same geometry for GOCI observation was applied. The simulated surface reflectance is used to discriminate cloud areas especially for the thin cloud and shows more reasonable result than original threshold methods.

  4. TerrSysMP-PDAF (version 1.0): a modular high-performance data assimilation framework for an integrated land surface-subsurface model

    Science.gov (United States)

    Kurtz, Wolfgang; He, Guowei; Kollet, Stefan J.; Maxwell, Reed M.; Vereecken, Harry; Hendricks Franssen, Harrie-Jan

    2016-04-01

    Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce certain parameterisation errors of single compartment models, model predictions are still prone to uncertainties regarding model input variables. The resulting uncertainties of model predictions can be effectively tackled by data assimilation techniques, which allow one to correct model predictions with observations taking into account both the model and measurement uncertainties. The steadily increasing availability of computational resources makes it now increasingly possible to perform data assimilation also for computationally highly demanding integrated terrestrial system models. However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface-subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). TerrSysMP is connected via a memory-based coupling approach with the pre-existing parallel data assimilation library PDAF (Parallel Data Assimilation Framework). This framework provides a fully parallel modular environment for performing data assimilation for the land surface and the subsurface compartment. A simple synthetic case study for a land surface-subsurface system (0.8 million unknowns) is used to demonstrate the effects of data assimilation in the integrated model TerrSysMP and to assess the scaling behaviour of the

  5. Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

    Science.gov (United States)

    Forman, Barton A.; Reichle, Rolf Helmut

    2014-01-01

    A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

  6. Integrating peatlands into the coupled Canadian Land Surface Scheme (CLASS) v3.6 and the Canadian Terrestrial Ecosystem Model (CTEM) v2.0

    Science.gov (United States)

    Wu, Yuanqiao; Verseghy, Diana L.; Melton, Joe R.

    2016-08-01

    Peatlands, which contain large carbon stocks that must be accounted for in the global carbon budget, are poorly represented in many earth system models. We integrated peatlands into the coupled Canadian Land Surface Scheme (CLASS) and the Canadian Terrestrial Ecosystem Model (CTEM), which together simulate the fluxes of water, energy, and CO2 at the land surface-atmosphere boundary in the family of Canadian Earth system models (CanESMs). New components and algorithms were added to represent the unique features of peatlands, such as their characteristic ground floor vegetation (mosses), the slow decomposition of carbon in the water-logged soils and the interaction between the water, energy, and carbon cycles. This paper presents the modifications introduced into the CLASS-CTEM modelling framework together with site-level evaluations of the model performance for simulated water, energy and carbon fluxes at eight different peatland sites. The simulated daily gross primary production (GPP) and ecosystem respiration are well correlated with observations, with values of the Pearson correlation coefficient higher than 0.8 and 0.75 respectively. The simulated mean annual net ecosystem production at the eight test sites is 87 g C m-2 yr-1, which is 22 g C m-2 yr-1 higher than the observed annual mean. The general peatland model compares well with other site-level and regional-level models for peatlands, and is able to represent bogs and fens under a range of climatic and geographical conditions.

  7. Remote sensing of land surface phenology

    Science.gov (United States)

    Meier, G.A.; Brown, J.F.

    2014-01-01

    Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

  8. The carbon cycle in a land surface model: modelling, validation and implementation at a global scale; Cycle du carbone dans un modele de surface continentale: modelisation, validation et mise en oeuvre a l'echelle globale

    Energy Technology Data Exchange (ETDEWEB)

    Gibelin, A.L

    2007-05-15

    ISBA-A-gs is an option of the CNRM land surface model ISBA which allows for the simulation of carbon exchanges between the terrestrial biosphere and the atmosphere. The model was implemented for the first time at the global scale as a stand-alone model. Several global simulations were performed to assess the sensitivity of the turbulent fluxes and Leaf Area Index to a doubling of the CO{sub 2} atmospheric concentration, and to the climate change simulated by the end of the 21. century. In addition, a new option of ISBA, referred to as ISBA-CC, was developed in order to simulate a more detailed ecosystem respiration by separating the autotrophic respiration and the heterotrophic respiration. The vegetation dynamics and the carbon fluxes were validated at a global scale using satellite datasets, and at a local scale using data from 26 sites of the FLUXNET network. All these results show that the model is sufficiently realistic to be coupled with a general circulation model, in order to account for interactions between the terrestrial biosphere, the atmosphere and the carbon cycle. (author)

  9. Influence of Dust and Black Carbon on the Snow Albedo in the NASA Goddard Earth Observing System Version 5 Land Surface Model

    Science.gov (United States)

    Yasunari, Teppei J.; Koster, Randal D.; Lau, K. M.; Aoki, Teruo; Sud, Yogesh C.; Yamazaki, Takeshi; Motoyoshi, Hiroki; Kodama, Yuji

    2011-01-01

    Present-day land surface models rarely account for the influence of both black carbon and dust in the snow on the snow albedo. Snow impurities increase the absorption of incoming shortwave radiation (particularly in the visible bands), whereby they have major consequences for the evolution of snowmelt and life cycles of snowpack. A new parameterization of these snow impurities was included in the catchment-based land surface model used in the National Aeronautics and Space Administration Goddard Earth Observing System version 5. Validation tests against in situ observed data were performed for the winter of 2003.2004 in Sapporo, Japan, for both the new snow albedo parameterization (which explicitly accounts for snow impurities) and the preexisting baseline albedo parameterization (which does not). Validation tests reveal that daily variations of snow depth and snow surface albedo are more realistically simulated with the new parameterization. Reasonable perturbations in the assigned snow impurity concentrations, as inferred from the observational data, produce significant changes in snowpack depth and radiative flux interactions. These findings illustrate the importance of parameterizing the influence of snow impurities on the snow surface albedo for proper simulation of the life cycle of snow cover.

  10. Influence of Dust and Black Carbon on the Snow Albedo in the NASA Goddard Earth Observing System Version 5 Land Surface Model

    Science.gov (United States)

    Yasunari, Teppei J.; Koster, Randal D.; Lau, K. M.; Aoki, Teruo; Sud, Yogesh C.; Yamazaki, Takeshi; Motoyoshi, Hiroki; Kodama, Yuji

    2011-01-01

    Present-day land surface models rarely account for the influence of both black carbon and dust in the snow on the snow albedo. Snow impurities increase the absorption of incoming shortwave radiation (particularly in the visible bands), whereby they have major consequences for the evolution of snowmelt and life cycles of snowpack. A new parameterization of these snow impurities was included in the catchment-based land surface model used in the National Aeronautics and Space Administration Goddard Earth Observing System version 5. Validation tests against in situ observed data were performed for the winter of 2003.2004 in Sapporo, Japan, for both the new snow albedo parameterization (which explicitly accounts for snow impurities) and the preexisting baseline albedo parameterization (which does not). Validation tests reveal that daily variations of snow depth and snow surface albedo are more realistically simulated with the new parameterization. Reasonable perturbations in the assigned snow impurity concentrations, as inferred from the observational data, produce significant changes in snowpack depth and radiative flux interactions. These findings illustrate the importance of parameterizing the influence of snow impurities on the snow surface albedo for proper simulation of the life cycle of snow cover.

  11. Impact of vegetation dynamics on hydrological processes in a semi-arid basin by using a land surface-hydrology coupled model

    Energy Technology Data Exchange (ETDEWEB)

    Jiao, Yang; Lei, Huimin; Yang, Dawen; Huang, Maoyi; Liu, Dengfeng; Yuan, Xing

    2017-06-03

    Land surface models (LSMs) are widely used to understand the interactions between hydrological processes and vegetation dynamics, which is important for the attribution and prediction of regional hydrological variations. However, most LSMs have large uncertainties in their representations of ecohydrological processes due to deficiencies in hydrological parameterizations. In this study, the Community Land Model version 4 (CLM4) LSM was modified with an advanced runoff generation and flow routing scheme, resulting in a new land surface-hydrology coupled model, CLM-GBHM. Both models were implemented in the Wudinghe River Basin (WRB), which is a semi-arid basin located in the middle reaches of the Yellow River, China. Compared with CLM, CLM-GBHM increased the Nash Sutcliffe efficiency for daily river discharge simulation (1965–1969) from 0.03 to 0.23 and reduced the relative bias in water table depth simulations (2010–2012) from 32.4% to 13.4%. The CLM-GBHM simulations with static, remotely sensed and model-predicted vegetation conditions showed that the vegetation in the WRB began to recover in the 2000s due to the Grain for Green Program but had not reached the same level of vegetation cover as regions in natural eco-hydrological equilibrium. Compared with a simulation using remotely sensed vegetation cover, the simulation with a dynamic vegetation model that considers only climate-induced change showed a 10.3% increase in evapotranspiration, a 47.8% decrease in runoff, and a 62.7% and 71.3% deceleration in changing trend of the outlet river discharge before and after the year 2000, respectively. This result suggests that both natural and anthropogenic factors should be incorporated in dynamic vegetation models to better simulate the eco-hydrological cycle.

  12. Effects of Initial Drivers and Land Use on WRF Modeling for Near-Surface Fields and Atmospheric Boundary Layer over the Northeastern Tibetan Plateau

    Directory of Open Access Journals (Sweden)

    Junhua Yang

    2016-01-01

    Full Text Available To improve the simulation performance of mesoscale models in the northeastern Tibetan Plateau, two reanalysis initial datasets (NCEP FNL and ERA-Interim and two MODIS (Moderate-Resolution Imaging Spectroradiometer land-use datasets (from 2001 and 2010 are used in WRF (Weather Research and Forecasting modeling. The model can reproduce the variations of 2 m temperature (T2 and 2 m relative humidity (RH2, but T2 is overestimated and RH2 is underestimated in the control experiment. After using the new initial drive and land use data, the simulation precision in T2 is improved by the correction of overestimated net energy flux at surface and the RH2 is improved due to the lower T2 and larger soil moisture. Due to systematic bias in WRF modeling for wind speed, we design another experiment that includes the Jimenez subgrid-scale orography scheme, which reduces the frequency of low wind speed and increases the frequency of high wind speed and that is more consistent with the observation. Meanwhile, the new drive and land-use data lead to lower boundary layer height and influence the potential temperature and wind speed in both the lower atmosphere and the upper layer, while the impact on water vapor mixing ratio is primarily concentrated in the lower atmosphere.

  13. Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

    Science.gov (United States)

    Cai, X.; Yang, Z.-L.; Fisher, J. B.; Zhang, X.; Barlage, M.; Chen, F.

    2016-01-01

    Climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading pollutant causing eutrophication in water bodies. The community Noah land surface model with multi-parameterization options (Noah-MP) is unique in that it is the next-generation land surface model for the Weather Research and Forecasting meteorological model and for the operational weather/climate models in the National Centers for Environmental Prediction. In this study, we add a capability to Noah-MP to simulate nitrogen dynamics by coupling the Fixation and Uptake of Nitrogen (FUN) plant model and the Soil and Water Assessment Tool (SWAT) soil nitrogen dynamics. This model development incorporates FUN's state-of-the-art concept of carbon cost theory and SWAT's strength in representing the impacts of agricultural management on the nitrogen cycle. Parameterizations for direct root and mycorrhizal-associated nitrogen uptake, leaf retranslocation, and symbiotic biological nitrogen fixation are employed from FUN, while parameterizations for nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching are based on SWAT. The coupled model is then evaluated at the Kellogg Biological Station - a Long Term Ecological Research site within the US Corn Belt. Results show that the model performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). Furthermore, the addition of nitrogen dynamics improves the modeling of net primary productivity and evapotranspiration. The model improvement is expected to advance the capability of Noah-MP to simultaneously predict weather and water quality in fully coupled Earth system models.

  14. Investigation of the impact of anthropogenic heat flux within an urban land surface model and PILPS-urban

    Science.gov (United States)

    Best, M. J.; Grimmond, C. S. B.

    2016-10-01

    Results from the first international urban model comparison experiment (PILPS-Urban) suggested that models which neglected the anthropogenic heat flux within the surface energy balance performed at least as well as models that include the source term, but this could not be explained. The analyses undertaken show that the results from PILPS-Urban were masked by the signal from including vegetation, which was identified in PILPS-Urban as being important. Including the anthropogenic heat flux does give improved performance, but the benefit is small for the site studied given the relatively small magnitude of this flux relative to other terms in the surface energy balance. However, there is no further benefit from including temporal variations in the flux at this site. The importance is expected to increase at sites with a larger anthropogenic heat flux and greater temporal variations.

  15. The retrieval of land surface albedo in rugged terrain

    NARCIS (Netherlands)

    Gao, B.; Jia, L.; Menenti, M.

    2012-01-01

    Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed s

  16. The retrieval of land surface albedo in rugged terrain

    NARCIS (Netherlands)

    Gao, B.; Jia, L.; Menenti, M.

    2012-01-01

    Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed s

  17. The retrieval of land surface albedo in rugged terrain

    NARCIS (Netherlands)

    Gao, B.; Jia, L.; Menenti, M.

    2012-01-01

    Land surface albedo may be derived from the satellite data through the estimation of a bidirectional reflectance distribution function (BRDF) model and angular integration. However many BRDF models do not consider explicitly the topography. In rugged terrain, the topography influences the observed

  18. An Explicit Representation of High Resolution River Networks using a Catchment-based Land Surface Model with the NHDPlus dataset for California Region

    Science.gov (United States)

    Liu, Z.; Kim, H.; Famiglietti, J. S.

    2011-12-01

    The main motivation of this study is to characterize how accurately we can estimate river discharge, river depth and inundation extent using an explicit representation of the river network with a catchment-based hydrological and routing modeling system (CHARMS) framework. Here we present a macroscale implementation of CHARMS over California. There are two main components in CHARMS: a land surface model based on National Center Atmospheric Research Community Land Model (CLM) 4.0, which is modified for implementation on a catchment template; and a river routing model that considers the water transport of each river reach. The river network is upscaled from the National Hydrography Dataset Plus (NHDPlus) to the Hydrologic Unit Code (HUC8) river basins. Both long-term monthly and daily streamflow simulation are generated and show reasonable results compared with gage observations. With river cross-section profile information derived from empirical relationships between channel dimensions and drainage area, river depth and floodplain extent associated with each river reach are also explicitly represented. Results have implications for assimilation of surface water altimetry and for implementation of the approach at the continental scale.

  19. A framework of benchmarking land models

    Science.gov (United States)

    Luo, Y. Q.; Randerson, J.; Abramowitz, G.; Bacour, C.; Blyth, E.; Carvalhais, N.; Ciais, P.; Dalmonech, D.; Fisher, J.; Fisher, R.; Friedlingstein, P.; Hibbard, K.; Hoffman, F.; Huntzinger, D.; Jones, C. D.; Koven, C.; Lawrence, D.; Li, D. J.; Mahecha, M.; Niu, S. L.; Norby, R.; Piao, S. L.; Qi, X.; Peylin, P.; Prentice, I. C.; Riley, W.; Reichstein, M.; Schwalm, C.; Wang, Y. P.; Xia, J. Y.; Zaehle, S.; Zhou, X. H.

    2012-02-01

    Land models, which have been developed by the modeling community in the past two decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure and evaluate performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land models. The framework includes (1) targeted aspects of model performance to be evaluated; (2) a set of benchmarks as defined references to test model performance; (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies; and (4) model improvement. Component 4 may or may not be involved in a benchmark analysis but is an ultimate goal of general modeling research. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and the land-surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics across timescales in response to both weather and climate change. Benchmarks that are used to evaluate models generally consist of direct observations, data-model products, and data-derived patterns and relationships. Metrics of measuring mismatches between models and benchmarks may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data-model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance for future improvement. Iterations between model evaluation and improvement via benchmarking shall demonstrate progress of land modeling and help establish confidence in land models for their predictions of future states of ecosystems and climate.

  20. A framework of benchmarking land models

    Directory of Open Access Journals (Sweden)

    Y. Q. Luo

    2012-02-01

    Full Text Available Land models, which have been developed by the modeling community in the past two decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure and evaluate performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land models. The framework includes (1 targeted aspects of model performance to be evaluated; (2 a set of benchmarks as defined references to test model performance; (3 metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies; and (4 model improvement. Component 4 may or may not be involved in a benchmark analysis but is an ultimate goal of general modeling research. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and the land-surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics across timescales in response to both weather and climate change. Benchmarks that are used to evaluate models generally consist of direct observations, data-model products, and data-derived patterns and relationships. Metrics of measuring mismatches between models and benchmarks may include (1 a priori thresholds of acceptable model performance and (2 a scoring system to combine data-model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance for future improvement. Iterations between model evaluation and improvement via benchmarking shall demonstrate progress of land modeling and help establish confidence in land models for their predictions of future states of ecosystems and climate.

  1. Numerical modeling and remote sensing of global water management systems: Applications for land surface modeling, satellite missions, and sustainable water resources

    Science.gov (United States)

    Solander, Kurt C.

    The ability to accurately quantify water storages and fluxes in water management systems through observations or models is of increasing importance due to the expected impacts from climate change and population growth worldwide. Here, I describe three innovative techniques developed to better understand this problem. First, a model was created to represent reservoir storage and outflow with the objective of integration into a Land Surface Model (LSM) to simulate the impacts of reservoir management on the climate system. Given this goal, storage capacity represented the lone model input required that is not already available to an LSM user. Model parameterization was linked to air temperature to allow future simulations to adapt to a changing climate, making it the first such model to mimic the potential response of a reservoir operator to climate change. Second, spatial and temporal error properties of future NASA Surface Water and Ocean Topography (SWOT) satellite reservoir operations were quantified. This work invoked the use of the SWOTsim instrument simulator, which was run over a number of synthetic and actual reservoirs so the resulting error properties could be extrapolated to the global scale. The results provide eventual users of SWOT data with a blueprint of expected reservoir error properties so such characteristics can be determined a priori for a reservoir given knowledge about its topology and anticipated repeat orbit pass over its location. Finally, data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission was used in conjunction with in-situ water use records to evaluate sustainable water use at the two-digit HUC basin scale over the contiguous United States. Results indicate that the least sustainable water management region is centered in the southwest, where consumptive water use exceeded water availability by over 100% on average for some of these basins. This work represents the first attempt at evaluating sustainable

  2. Simulating feedbacks in land use and land cover change models

    NARCIS (Netherlands)

    Verburg, P.H.

    2006-01-01

    In spite of the many advances in land use and land cover change modelling over the past decade many challenges remain. One of these challenges relates to the explicit treatment of feedback mechanisms in descriptive models of the land use system. This paper argues for model-based analysis to explore

  3. Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics

    Science.gov (United States)

    Baker, Ronald J.; Chepiga, Mary M.; Cauller, Stephen J.

    2015-01-01

    Nitrate-concentration data are used in conjunction with land-use and land-cover data to estimate median nitrate concentrations in groundwater underlying the New Jersey (NJ) Highlands Region. Sources of data on nitrate in 19,670 groundwater samples are from the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the NJ Private Well Testing Act (PWTA).

  4. Effective UV surface albedo of seasonally snow-covered lands

    Science.gov (United States)

    Tanskanen, A.; Manninen, T.

    2007-05-01

    At ultraviolet wavelengths the albedo of most natural surfaces is small with the striking exception of snow and ice. Therefore, snow cover is a major challenge for various applications based on radiative transfer modelling. The aim of this work was to determine the characteristic effective UV range surface albedo of various land cover types when covered by snow. First we selected 1 by 1 degree sample regions that met three criteria: the sample region contained dominantly subpixels of only one land cover type according to the 8 km global land cover classification product from the University of Maryland; the average slope of the sample region was less than 2 degrees according to the USGS's HYDRO1K slope data; the sample region had snow cover in March according to the NSIDC Northern Hemisphere weekly snow cover data. Next we generated 1 by 1 degree gridded 360 nm surface albedo data from the Nimbus-7 TOMS Lambertian equivalent reflectivity data, and used them to construct characteristic effective surface albedo distributions for each land cover type. The resulting distributions showed that each land cover type experiences a characteristic range of surface albedo values when covered by snow. The result is explained by the vegetation that extends upward beyond the snow cover and masks the bright snow covered surface.

  5. Evaluation of global continental hydrology as simulated by the Land-surface Processes and eXchanges Dynamic Global Vegetation Model

    Directory of Open Access Journals (Sweden)

    S. J. Murray

    2011-01-01

    Full Text Available Global freshwater resources are sensitive to changes in climate, land cover and population density and distribution. The Land-surface Processes and eXchanges Dynamic Global Vegetation Model is a recent development of the Lund-Potsdam-Jena model with improved representation of fire-vegetation interactions. It allows simultaneous consideration of the effects of changes in climate, CO2 concentration, natural vegetation and fire regime shifts on the continental hydrological cycle. Here the model is assessed for its ability to simulate large-scale spatial and temporal runoff patterns, in order to test its suitability for modelling future global water resources. Comparisons are made against observations of streamflow and a composite dataset of modelled and observed runoff (1986–1995 and are also evaluated against soil moisture data and the Palmer Drought Severity Index. The model captures the main features of the geographical distribution of global runoff, but tends to overestimate runoff in much of the Northern Hemisphere (where this can be somewhat accounted for by freshwater consumption and the unrealistic accumulation of the simulated winter snowpack in permafrost regions and the southern tropics. Interannual variability is represented reasonably well at the large catchment scale, as are seasonal flow timings and monthly high and low flow events. Further improvements to the simulation of intra-annual runoff might be achieved via the addition of river flow routing. Overestimates of runoff in some basins could likely be corrected by the inclusion of transmission losses and direct-channel evaporation.

  6. Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs land surface model: grassland case study

    Directory of Open Access Journals (Sweden)

    A. L. Barbu

    2011-02-01

    Full Text Available The performance of the joint assimilation in a land surface model of a Soil Wetness Index (SWI product provided by an exponential filter together with Leaf Area Index (LAI is investigated. The data assimilation is evaluated with different setups using the SURFEX modeling platform, for a period of seven years (2001–2007, at the SMOSREX grassland site in southwestern France. The results obtained with a Simplified Extended Kalman Filter demonstrate the effectiveness of a joint data assimilation scheme when both SWI and Leaf Area Index are merged into the ISBA-A-gs land surface model. The assimilation of a retrieved Soil Wetness Index product presents several challenges that are investigated in this study. A significant improvement of around 13% of the root-zone soil water content is obtained by assimilating dimensionless root-zone SWI data. For comparison, the assimilation of in situ surface soil moisture is considered as well. A lower impact on the root zone is noticed. Under specific conditions, the transfer of the information from the surface to the root zone was found not accurate. Also, our results indicate that the assimilation of in situ LAI data may correct a number of deficiencies in the model, such as low LAI values in the senescence phase by using a seasonal-dependent error definition for background and observations. In order to verify the specification of the errors for SWI and LAI products, a posteriori diagnostics are employed. This approach highlights the importance of the assimilation design on the quality of the analysis. The impact of data assimilation scheme on CO2 fluxes is also quantified by using measurements of net CO2 fluxes gathered at the SMOSREX site from 2005 to 2007. An improvement of about 5% in terms of rms error is obtained.

  7. Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: A multivariate real-data experiment

    Science.gov (United States)

    Shi, Yuning; Davis, Kenneth J.; Zhang, Fuqing; Duffy, Christopher J.; Yu, Xuan

    2015-09-01

    The capability of an ensemble Kalman filter (EnKF) to simultaneously estimate multiple parameters in a physically-based land surface hydrologic model using multivariate field observations is tested at a small watershed (0.08 km2). Multivariate, high temporal resolution, in situ measurements of discharge, water table depth, soil moisture, and sensible and latent heat fluxes encompassing five months of 2009 are assimilated. It is found that, for five out of the six parameters, the EnKF estimated parameter values from different test cases converge strongly, and the estimates after convergence are close to the manually calibrated parameter values. The EnKF estimated parameters and manually calibrated parameters yield similar model performance, but the EnKF sequential method significantly decreases the time and labor required for calibration. The results demonstrate that, given a limited number of multi-state, site-specific observations, an automated sequential calibration method (EnKF) can be used to optimize physically-based land surface hydrologic models.

  8. Assimilation of GRACE Terrestrial Water Storage into a Land Surface Model: Evaluation 1 and Potential Value for Drought Monitoring in Western and Central Europe

    Science.gov (United States)

    Li, Bailing; Rodell, Matthew; Zaitchik, Benjamin F.; Reichle, Rolf H.; Koster, Randal D.; van Dam, Tonie M.

    2012-01-01

    A land surface model s ability to simulate states (e.g., soil moisture) and fluxes (e.g., runoff) is limited by uncertainties in meteorological forcing and parameter inputs as well as inadequacies in model physics. In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother. GRACE data assimilation led to improved runoff correlations with gauge data in 17 out of 18 hydrological basins, even in basins smaller than the effective resolution of GRACE. Improvements in root zone soil moisture were less conclusive, partly due to the shortness of the in situ data record. In addition to improving temporal correlations, GRACE data assimilation also reduced increasing trends in simulated monthly TWS and runoff associated with increasing rates of precipitation. GRACE assimilated root zone soil moisture and TWS fields exhibited significant changes in their dryness rankings relative to those without data assimilation, suggesting that GRACE data assimilation could have a substantial impact on drought monitoring. Signals of drought in GRACE TWS correlated well with MODIS Normalized Difference Vegetation Index (NDVI) data in most areas. Although they detected the same droughts during warm seasons, drought signatures in GRACE derived TWS exhibited greater persistence than those in NDVI throughout all seasons, in part due to limitations associated with the seasonality of vegetation.

  9. A reduced-order modeling approach to represent subgrid-scale hydrological dynamics for land-surface simulations: application in a polygonal tundra landscape

    Science.gov (United States)

    Pau, G. S. H.; Bisht, G.; Riley, W. J.

    2014-09-01

    Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO2, CH4) exchanges with the atmosphere range from the molecular scale (pore-scale O2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" that reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface-subsurface isothermal simulations were performed for summer months (June-September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998-2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 103) with very small relative approximation error (constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.

  10. Ecosystem feedbacks to climate change in California: Development, testing, and analysis using a coupled regional atmosphere and land-surface model (WRF3-CLM3.5)

    Energy Technology Data Exchange (ETDEWEB)

    Subin, Z.M.; Riley, W.J.; Kueppers, L.M.; Jin, J.; Christianson, D.S.; Torn, M.S.

    2010-11-01

    A regional atmosphere model [Weather Research and Forecasting model version 3 (WRF3)] and a land surface model [Community Land Model, version 3.5 (CLM3.5)] were coupled to study the interactions between the atmosphere and possible future California land-cover changes. The impact was evaluated on California's climate of changes in natural vegetation under climate change and of intentional afforestation. The ability of WRF3 to simulate California's climate was assessed by comparing simulations by WRF3-CLM3.5 and WRF3-Noah to observations from 1982 to 1991. Using WRF3-CLM3.5, the authors performed six 13-yr experiments using historical and future large-scale climate boundary conditions from the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (GFDL CM2.1). The land-cover scenarios included historical and future natural vegetation from the Mapped Atmosphere-Plant-Soil System-Century 1 (MC1) dynamic vegetation model, in addition to a future 8-million-ha California afforestation scenario. Natural vegetation changes alone caused summer daily-mean 2-m air temperature changes of -0.7 to +1 C in regions without persistent snow cover, depending on the location and the type of vegetation change. Vegetation temperature changes were much larger than the 2-m air temperature changes because of the finescale spatial heterogeneity of the imposed vegetation change. Up to 30% of the magnitude of the summer daily-mean 2-m air temperature increase and 70% of the magnitude of the 1600 local time (LT) vegetation temperature increase projected under future climate change were attributable to the climate-driven shift in land cover. The authors projected that afforestation could cause local 0.2-1.2 C reductions in summer daily-mean 2-m air temperature and 2.0-3.7 C reductions in 1600 LT vegetation temperature for snow-free regions, primarily because of increased evapotranspiration. Because some of these temperature changes are of comparable magnitude to those

  11. Effects of change in growing season on water use efficiency of lowland rice estimated using a coupled land surface and crop growth model

    Science.gov (United States)

    Maruyama, A.; Kuwagata, T.

    2010-12-01

    The effect of changes in the growing season for rice, which are agronomical adaptation to climate change, on water use efficiency (WUE) was estimated using a coupled land surface and crop growth model. The crop growth model consisted of calculations of phenological development (Ps), growth of leaf area index (LAI) and canopy height (h). The land surface model consisted of calculations of energy budget on the surface, radiation transport and stomatal movements using the output of the crop growth model (Ps, LAI, h). An empirical relationship between stomatal conductance and phenological stage was used for this calculation. The relationship between leaf geometry and phenological stage was also used to express the change in radiation transport in the canopy. Variations in evapotranspiration (ET) were estimated using the coupled model for five different transplanting times (from March to July) based on climatic data for the Miyazaki Plain, Japan. The seasonal variation in ET showed a common pattern, where most of the ET just after transplanting consisted of evaporation (E), and the transpiration (T) increased with rice growth until heading. However, the timing of the increase in T varied with the growing seasons due to the difference of LAI growth rate. The ratios of total transpiration to evapotranspiration (T/ET) were 40, 48, 48, 46 and 36% for transplanting on March 1st, April 1st, May 1st, June 1st and July 1st, respectively. Assuming the amount of production by photosynthesis is proportional to transpiration; our results suggest that the WUE would be higher at mid growing season.

  12. A framework for benchmarking land models

    Science.gov (United States)

    Luo, Y. Q.; Randerson, J. T.; Abramowitz, G.; Bacour, C.; Blyth, E.; Carvalhais, N.; Ciais, P.; Dalmonech, D.; Fisher, J. B.; Fisher, R.; Friedlingstein, P.; Hibbard, K.; Hoffman, F.; Huntzinger, D.; Jones, C. D.; Koven, C.; Lawrence, D.; Li, D. J.; Mahecha, M.; Niu, S. L.; Norby, R.; Piao, S. L.; Qi, X.; Peylin, P.; Prentice, I. C.; Riley, W.; Reichstein, M.; Schwalm, C.; Wang, Y. P.; Xia, J. Y.; Zaehle, S.; Zhou, X. H.

    2012-10-01

    Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data-model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models

  13. A framework for benchmarking land models

    Directory of Open Access Journals (Sweden)

    Y. Q. Luo

    2012-10-01

    Full Text Available Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1 targeted aspects of model performance to be evaluated, (2 a set of benchmarks as defined references to test model performance, (3 metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4 model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1 a priori thresholds of acceptable model performance and (2 a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties

  14. Use of Land Surface Temperature Observations in a Two-Source Energy Balance Model Towards Improved Monitoring of Evapotranspiration and Drought

    Science.gov (United States)

    Hain, C.; Anderson, M. C.; Otkin, J.; Semmens, K. A.; Zhan, X.; Fang, L.; Li, Z.

    2014-12-01

    As the world's water resources come under increasing tension due to the dual stressors of climate change and population growth, accurate knowledge of water consumption through evapotranspiration (ET) over a range in spatial scales will be critical in developing adaptation strategies. However, direct validation of ET models is challenging due to lack of available observations that are sufficiently representative at the model grid scale (10-100 km). Prognostic land-surface models require accurate information about observed precipitation, soil moisture storage, groundwater, and artificial controls on water supply (e.g., irrigation, dams, etc.) to reliably link rainfall to evaporative fluxes. In contrast, diagnostic estimates of ET can be generated, with no prior knowledge of the surface moisture state, by energy balance models using thermal-infrared remote sensing of land-surface temperature (LST) as a boundary condition. One such method, the Atmosphere Land Exchange Inverse (ALEXI) model provides estimates of surface energy fluxes through the use of mid-morning change in LST and radiation inputs. The LST inputs carry valuable proxy information regarding soil moisture and its effect on soil evaporation and canopy transpiration. Additionally, the Evaporative Stress Index (ESI) representing anomalies in the ratio of actual-to-potential ET has shown to be a reliable indicator of drought. ESI maps over the continental US show good correspondence with standard drought metrics and with patterns of precipitation, but can be generated at significantly higher spatial resolution due to a limited reliance on ground observations. Furthermore, ESI is a measure of actual stress rather than potential for stress, and has physical relevance to projected crop development. Because precipitation is not used in construction of the ESI, it provides an independent assessment of drought conditions and has particular utility for real-time monitoring in regions with sparse rainfall data or

  15. The Impact Of Climate Change On Production Of Multiple Food Crops In The 21st Century- An Analysis Based On Two Land Surface Models

    Science.gov (United States)

    Song, Y.; Jain, A. K.; Lawrence, P.; Kheshgi, H. S.

    2015-12-01

    Climate change presents potential risks to global food supply. To date, understanding of climate change effects on crop production remains uncertain due to (1) uncertainties in projected climate change trends and their spatial and temporal variability; (2) uncertainties in the physiological, genetic and molecular basis of crop adaptation to climate change and adaptive management practices and (3) uncertainties in current land surface models to estimate crop adaptation to climate change. We apply the process-based land surface model, the Integrated Science Assessment model (ISAM), to assess the impact of climate change on the production of row crops (corn, soybean, rice, cotton, sugarcane and wheat) at global and regional scales. The results are compared to the corresponding simulations performed with the crop model in the Community Land Model (CLM4.5). Three questions are addressed: (1) what is the impact of different climate change projections on global crop production; (2) what is the effect of crop adaptation and adaptive management practices on projected crop production; and (3) how do model differences in ISAM and CLM4.5 impact projected global crop production and adaptive management practices over the 21st century. ISAM and CLM4.5 have been included in the Agricultural Model Intercomparison and Improvement Project (AgMIP). Both models consider the effects of temperature, light and soil water and nitrogen availability on crop photosynthesis and temperature control on crop phenology and carbon allocation. ISAM also considers the adaptation of crop phenology, carbon allocation and structures growth to drought, light stress and N stress. The effects of model differences on projected crop production are evaluated by performing the following experiments. Each model is driven with historical atmospheric forcing data (1901-2005) and projected atmospheric forcing data (2006-2100) under RCP 4.5 or RCP 8.5 from CESM CMIP5 simulations to estimate the effects of different

  16. A novel Fire-Model for the CABLE Land Surface Model applied to a Re-assessment of the Australian Continental Carbon Budget

    Science.gov (United States)

    Nieradzik, L. P.; Haverd, V. E.; Briggs, P.; Meyer, C. P.; Canadell, J.

    2014-12-01

    Fires play a major role in the carbon-cycle and the development of global vegetation, especially on the continent of Australia, where vegetation is prone to frequent fire occurences and where regional composition and stand-age distribution is regulated by fire. Furthermore, the probable changes of fire behaviour under a changing climate are still poorly understood and require further investigation.In this presentation we introduce a novel approach to simulate fire-frequencies, fire-intensities and the responses in vegetation. Fire frequencies are prescribed using SIMFIRE (Knorr et al., 2014) or GFED3 (e.g. Giglio et al., 2013). Fire-Line-Intensity (FLI) is computed from meteorological information and fuel loads which are state variables within the C-cycle component of CABLE. This FLI is used as an input to the tree-demography model POP (Population-Order-Physiology; Haverd et al., 2014). Within POP the fire-mortality depends on FLI and tree height distribution.Intensity-dependent combustion factors (CF) are then generated for and applied to live and litter carbon pools as well as the transfers from live pools to litter caused by fire. Thus, both fire and stand characteristics are taken into account which has a legacy effect on future events. Gross C-CO2 emissions from Australian wild fires are larger than Australian territorial fossil fuel emissions. However, the net effect of fire on the Australian terrestrial carbon budget is unknown. We address this by applying the newly-developed fire module, integrated within the CABLE land surface model, and optimised for the Australian region, to a reassessment of the Australian Terrestrial Carbon Budget.

  17. Developing multi-tracer approaches to constrain the parameterisation of leaf and soil CO2 and H2O exchange in land surface models

    Science.gov (United States)

    Ogée, Jerome; Wehr, Richard; Commane, Roisin; Launois, Thomas; Meredith, Laura; Munger, Bill; Nelson, David; Saleska, Scott; Zahniser, Mark; Wofsy, Steve; Wingate, Lisa

    2016-04-01

    The net flux of carbon dioxide between the land surface and the atmosphere is dominated by photosynthesis and soil respiration, two of the largest gross CO2 fluxes in the carbon cycle. More robust estimates of these gross fluxes could be obtained from the atmospheric budgets of other valuable tracers, such as carbonyl sulfide (COS) or the carbon and oxygen isotope compositions (δ13C and δ18O) of atmospheric CO2. Over the past decades, the global atmospheric flask network has measured the inter-annual and intra-annual variations in the concentrations of these tracers. However, knowledge gaps and a lack of high-resolution multi-tracer ecosystem-scale measurements have hindered the development of process-based models that can simulate the behaviour of each tracer in response to environmental drivers. We present novel datasets of net ecosystem COS, 13CO2 and CO18O exchange and vertical profile data collected over 3 consecutive growing seasons (2011-2013) at the Harvard forest flux site. We then used the process-based model MuSICA (multi-layer Simulator of the Interactions between vegetation Canopy and the Atmosphere) to include the transport, reaction, diffusion and production of each tracer within the forest and exchanged with the atmosphere. Model simulations over the three years captured well the impact of diurnally and seasonally varying environmental conditions on the net ecosystem exchange of each tracer. The model also captured well the dynamic vertical features of tracer behaviour within the canopy. This unique dataset and model sensitivity analysis highlights the benefit in the collection of multi-tracer high-resolution field datasets and the developement of multi-tracer land surface models to provide valuable constraints on photosynthesis and respiration across scales in the near future.

  18. Exploring the Interactions among Beetle-induced Changes in Catchment-scale Ecohydrology, Land Surface Fluxes and the Lower Atmosphere with a Coupled Hydrology-Atmospheric Model.

    Science.gov (United States)

    Forrester, M. M.; Maxwell, R. M.; Bearup, L. A.; Gochis, D.; Porter, A.

    2015-12-01

    The mountain pine beetle has dramatically altered ecohydrologic processes of lodgepole pine forests in western North America, having caused one of the largest insect-driven tree mortalities in recorded history. Documented and modeled responses to forest mortality include cessation of overstory transpiration, local increases in soil moisture, changes in snow accumulation and ablation, differences in groundwater and runoff contributions to streamflow, changes in sensible and latent heat partitioning, and higher surface temperatures and ground evaporation. However, the scale-sensitivity, spatial variability and interdependence of these responses, and the simultaneous process of forest recovery, mean that watershed response to infestation is often inconsistent and damped at large scales, making it difficult to capture individual hydrologic and energy components of disturbance. This study resolves complicated feedbacks from disturbance at the land surface to responses in the atmosphere with the use of the physically-based, integrated hydrologic model ParFlow, coupled to the Weather Research and Forecasting (WRF) atmospheric model. The model domain, constructed at 1-km resolution, encompasses a 25,200 square kilometer region over a Rocky Mountain headwaters catchment in Colorado. Land use and vegetation parameters within WRF were adjusted in a detailed ensemble approach to reflect beetle-induced reductions in stomatal conductivity and LAI. Results show spatially variable but generally increased soil moisture and water yield with infestation. Subsequent disturbance of the sensible and latent heat balance propagates into the atmosphere, influencing atmospheric moisture, stability and even precipitation. This work presents the applicability of a deterministic, integrated climate-hydrologic model to identify complicated physical interactions occurring with forest disturbance, which may not be discernable with simpler models or observations.

  19. A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over the US and Illinois with SECHIBA forced by NLDAS

    Directory of Open Access Journals (Sweden)

    M. Guimberteau

    2012-11-01

    Full Text Available The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and local. The model is forced by NLDAS data set at 1/8th degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over the US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters show that the roots extraction parameter has the largest impact on soil moisture, other parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new evapotranspiration computation including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the soil moisture simulation when this effect is taken into account. Finally, soil moisture sensitivity to precipitation variation is addressed and it is shown that soil moisture observations can be rather different, depending on the method of measuring field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations.

  20. A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over US and Illinois with SECHIBA forced by NLDAS

    Directory of Open Access Journals (Sweden)

    M. Guimberteau

    2012-04-01

    Full Text Available The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and mesoscale. The model is forced by NLDAS data set at eighth degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters are performed and show that the roots extraction parameter has the largest impact on soil moisture, others parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new computation of evapotranspiration including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the simulation of soil moisture when this effect is taken into account. Finally, uncertainties in forcing precipitation to simulate a realistic soil moisture are addressed and it is shown that soil moisture observations can be rather different depending on the method to measure field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations. Excepted for one station, the monthly mean correlation is around 0.9 between observation and simulation.

  1. Distributed land surface modeling with utilization of multi-sensor satellite data: application for the vast agricultural terrain in cold region

    Science.gov (United States)

    Muzylev, E.; Uspensky, A.; Gelfan, A.; Startseva, Z.; Volkova, E.; Kukharsky, A.; Romanov, P.; Alexandrovich, M.

    2012-04-01

    A technique for satellite-data-based modeling water and heat regimes of a large scale area has been developed and applied for the 227,300 km2 agricultural region in the European Russia. The core component of the technique is the physically based distributed Remote Sensing Based Land Surface Model (RSBLSM) intended for simulating transpiration by vegetation and evaporation from bare soil, vertical transfer of water and heat within soil and vegetation covers during a vegetation season as well as hydrothermal processes in soil and snow covers during a cold season, including snow accumulation and melt, dynamics of soil moisture and temperature during soil freezing and thawing, infiltration into frozen soil. Processes in the "atmosphere-snow-frozen soil" system are critical for cold region agriculture, as they control crop development in early spring before the vegetation season beginning. For assigning the model parameters as well as for preliminary calibrating and validating the model, available multi-year data sets of soil moisture/temperature profiles, evaporation, snow and soil freezing depth measured at the meteorological stations located within the study region have been utilized. To provide an appropriate parametrization of the model for the areas where ground-based measurements are unavailable, estimates have been utilized for vegetation, meteorological and snow characteristics derived from the multispectral measurements of AVHRR/NOAA (1999-2010), MODIS/EOS Terra & Aqua (2002-2010), AMSR-E/Aqua (2003-2004; 2008-2010), and SEVIRI/Meteosat-9 (2009-2010). The technologies of thematic processing the listed satellite data have been developed and applied to estimate the land surface and snow cover characteristics for the study area. The developed technologies of AVHRR data processing have been adapted to retrieve land