WorldWideScience

Sample records for modeling land surface

  1. Hydrological land surface modelling

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois

    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......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...... 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...

  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...

  4. Modelling land surface - atmosphere interactions

    DEFF Research Database (Denmark)

    Rasmussen, Søren Højmark

    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...

  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. AMMA Land surface Model Intercomparison Project (ALMIP)

    Science.gov (United States)

    Boone, A. A.; Derosnay, P.

    2007-05-01

    Extreme climatic variability has afflicted West Africa over the last half century, which has resulted in significant socio-economic consequences for the people of this region. There is therefore a need to improve seasonal to inter-annual prediction of the West-African monsoon (WAM), however, difficulties modeling the WAM arise from both the paucity of observations at sufficient space-time resolutions, and due to the complex interactions between the biosphere, atmosphere and hydrosphere over this region. In particular, there is evidence that the land surface influences the variability of the WAM over a wide range of spatio-temporal scales. A critical aspect of this coupling is the feedback between the regional atmospheric circulation and the strong meridional surface flux gradients of mass and energy. One of the main goals of the African Monsoon Multi-disciplinary Analysis (AMMA) Project is to obtain a better understanding of the physical processes influencing the West-African Monsoon (WAM) on daily to inter-annual timescales. An improved comprehension of the relevant land surface processes is being addressed through the construction of a multi-scale atmospheric and land surface parameter forcing database using a variety of sources; numerical weather prediction forecast data, remote sensing products and local scale observations. The goal of this database is to drive land surface, vegetation and hydrological models over a range of spatial scales (local to regional) in order to gain better insights into the attendant processes. This goal is being met under the auspices of the AMMA Land surface Model Intercomparison Project (ALMIP). In the recently completed Phase 1 of this project, an ensemble of state-of-the-art land surface schemes have been run in "off-line" mode (i.e. decoupled from an atmospheric model) at a regional scale over western Africa for four annual cycles (2002-5). In this talk, intercomparison results will be presented. In addition, results from a

  7. Advances in land modeling of KIAPS based on the Noah Land Surface Model

    Science.gov (United States)

    Koo, Myung-Seo; Baek, Sunghye; Seol, Kyung-Hee; Cho, Kyoungmi

    2017-08-01

    As of 2013, the Noah Land Surface Model (LSM) version 2.7.1 was implemented in a new global model being developed at the Korea Institute of Atmospheric Prediction Systems (KIAPS). This land surface scheme is further refined in two aspects, by adding new physical processes and by updating surface input parameters. Thus, the treatment of glacier land, sea ice, and snow cover are addressed more realistically. Inconsistencies in the amount of absorbed solar flux at ground level by the land surface and radiative processes are rectified. In addition, new parameters are available by using 1-km land cover data, which had usually not been possible at a global scale. Land surface albedo/emissivity climatology is newly created using Moderate-Resolution Imaging Spectroradiometer (MODIS) satellitebased data and adjusted parameterization. These updates have been applied to the KIAPS-developed model and generally provide a positive impact on near-surface weather forecasting.

  8. Land surface Verification Toolkit (LVT) - a generalized framework for land surface model evaluation

    Science.gov (United States)

    Kumar, S. V.; Peters-Lidard, C. D.; Santanello, J.; Harrison, K.; Liu, Y.; Shaw, M.

    2012-06-01

    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 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.

  9. 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.

  10. 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,

  11. 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.

  12. Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies

    Data.gov (United States)

    National Aeronautics and Space Administration — The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of land surfaces...

  13. Soil Structure - A Neglected Component of Land-Surface Models

    Science.gov (United States)

    Fatichi, S.; Or, D.; Walko, R. L.; Vereecken, H.; Kollet, S. J.; Young, M.; Ghezzehei, T. A.; Hengl, T.; Agam, N.; Avissar, R.

    2017-12-01

    Soil structure is largely absent in most standard sampling and measurements and in the subsequent parameterization of soil hydraulic properties deduced from soil maps and used in Earth System Models. The apparent omission propagates into the pedotransfer functions that deduce parameters of soil hydraulic properties primarily from soil textural information. Such simple parameterization is an essential ingredient in the practical application of any land surface model. Despite the critical role of soil structure (biopores formed by decaying roots, aggregates, etc.) in defining soil hydraulic functions, only a few studies have attempted to incorporate soil structure into models. They mostly looked at the effects on preferential flow and solute transport pathways at the soil profile scale; yet, the role of soil structure in mediating large-scale fluxes remains understudied. Here, we focus on rectifying this gap and demonstrating potential impacts on surface and subsurface fluxes and system wide eco-hydrologic responses. The study proposes a systematic way for correcting the soil water retention and hydraulic conductivity functions—accounting for soil-structure—with major implications for near saturated hydraulic conductivity. Modification to the basic soil hydraulic parameterization is assumed as a function of biological activity summarized by Gross Primary Production. A land-surface model with dynamic vegetation is used to carry out numerical simulations with and without the role of soil-structure for 20 locations characterized by different climates and biomes across the globe. Including soil structure affects considerably the partition between infiltration and runoff and consequently leakage at the base of the soil profile (recharge). In several locations characterized by wet climates, a few hundreds of mm per year of surface runoff become deep-recharge accounting for soil-structure. Changes in energy fluxes, total evapotranspiration and vegetation productivity

  14. Modeling land-surface/atmosphere dynamics for CHAMMP

    International Nuclear Information System (INIS)

    Gutowski, W.J. Jr.

    1993-01-01

    Project progress is described on a DOE CHAMP project to model the land-surface/atmosphere coupling in a heterogeneous environment. This work is a collaboration between scientists at Iowa State University and the University of New Hampshire. Work has proceeded in two areas: baseline model coupling and data base development for model validation. The core model elements (land model, atmosphere model) have been ported to the Principal Investigator's computing system and baseline coupling has commenced. The initial target data base is the set of observations from the FIFE field campaign, which is in the process of being acquired. For the remainder of the project period, additional data from the region surrounding the FIFE site and from other field campaigns will be acquired to determine how to best extrapolate results from the initial target region to the rest of the globe. In addition, variants of the coupled model will be used to perform experiments examining resolution requirements and coupling strategies for land-atmosphere coupling in a heterogeneous environment

  15. Do Lateral Flows Matter for the Hyperresolution Land Surface Modeling?

    Science.gov (United States)

    Ji, Peng; Yuan, Xing; Liang, Xin-Zhong

    2017-11-01

    Hyperresolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycle, but lateral surface and/or subsurface flows that are essential at fine scale are often neglected by most one-dimensional land surface models (LSMs). To analyze effects of lateral flows across scales, a Conjunctive Surface-Subsurface Process model, which considers soil moisture-surface flow interaction and quasi-three-dimensional subsurface flow, is implemented over a mountainous HyperHydro test bed in southwestern USA at different resolutions. Validation over more than 70 International Soil Moisture Network stations shows that there are significant improvements in soil moisture simulations from 30 km to 4 km as finer soil property and precipitation data are used, with correlation increased by 5%-16% and error decreased by 5%. Lateral surface flow has a significant influence on surface soil moisture and ground evaporation even at coarse resolution. Effect of lateral subsurface flow on soil moisture is nontrivial at 1 km or finer resolution especially over wet areas. At 100 m resolution, topography-induced lateral subsurface flow causes drier peaks and wetter valleys, decreases latent heat by 8% at peaks, while increases it by 12% at valleys. Furthermore, influences of lateral subsurface flow on ground evaporation and vegetation transpiration are more significant during dry season due to a stronger coupling between soil moisture and evapotranspiration. Therefore, it is worthy to incorporate lateral flow processes in hyperresolution LSMs to better represent water and energy heterogeneity even with limited hyperresolution meteorological and surface data.

  16. Land-Surface-Atmosphere Coupling in Observations and Models

    Directory of Open Access Journals (Sweden)

    Alan K Betts

    2009-07-01

    Full Text Available The diurnal cycle and the daily mean at the land-surface result from the coupling of many physical processes. The framework of this review is largely conceptual; looking for relationships and information in the coupling of processes in models and observations. Starting from the surface energy balance, the role of the surface and cloud albedos in the shortwave and longwave fluxes is discussed. A long-wave radiative scaling of the diurnal temperature range and the night-time boundary layer is summarized. Several aspects of the local surface energy partition are presented: the role of soilwater availability and clouds; vector methods for understanding mixed layer evolution, and the coupling between surface and boundary layer that determines the lifting condensation level. Moving to larger scales, evaporation-precipitation feedback in models is discussed; and the coupling of column water vapor, clouds and precipitation to vertical motion and moisture convergence over the Amazon. The final topic is a comparison of the ratio of surface shortwave cloud forcing to the diabatic precipitation forcing of the atmosphere in ERA-40 with observations.

  17. 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

  18. Land-atmosphere interactions due to anthropogenic and natural changes in the land surface: A numerical modeling study

    Science.gov (United States)

    Yang, Zhao

    Alterations to the land surface can be attributed to both human activity and natural variability. Human activities, such as urbanization and irrigation, can change the conditions of the land surface by altering albedo, soil moisture, aerodynamic roughness length, the partitioning of net radiation into sensible and latent heat, and other surface characteristics. On the other hand, natural variability, manifested through changes in atmospheric circulation, can also induce land surface changes. These regional scale land surface changes, induced either by humans or natural variability, can effectively modify atmospheric conditions through land-atmosphere interactions. However, only in recent decades have numerical models begun to include representations of the critical processes driving changes at the land surface, and their associated effects on the overlying atmosphere. In this work we explore three mechanisms by which changes to the land surface - both anthropogenic and naturally induced - impact the overlying atmosphere and affect regional hydroclimate. (Abstract shortened by ProQuest.).

  19. Reliable low precision simulations in land surface models

    Science.gov (United States)

    Dawson, Andrew; Düben, Peter D.; MacLeod, David A.; Palmer, Tim N.

    2017-12-01

    Weather and climate models must continue to increase in both resolution and complexity in order that forecasts become more accurate and reliable. Moving to lower numerical precision may be an essential tool for coping with the demand for ever increasing model complexity in addition to increasing computing resources. However, there have been some concerns in the weather and climate modelling community over the suitability of lower precision for climate models, particularly for representing processes that change very slowly over long time-scales. These processes are difficult to represent using low precision due to time increments being systematically rounded to zero. Idealised simulations are used to demonstrate that a model of deep soil heat diffusion that fails when run in single precision can be modified to work correctly using low precision, by splitting up the model into a small higher precision part and a low precision part. This strategy retains the computational benefits of reduced precision whilst preserving accuracy. This same technique is also applied to a full complexity land surface model, resulting in rounding errors that are significantly smaller than initial condition and parameter uncertainties. Although lower precision will present some problems for the weather and climate modelling community, many of the problems can likely be overcome using a straightforward and physically motivated application of reduced precision.

  20. Enhanced Modeling of Remotely Sensed Annual Land Surface Temperature Cycle

    Science.gov (United States)

    Zou, Z.; Zhan, W.; Jiang, L.

    2017-09-01

    Satellite thermal remote sensing provides access to acquire large-scale Land surface temperature (LST) data, but also generates missing and abnormal values resulting from non-clear-sky conditions. Given this limitation, Annual Temperature Cycle (ATC) model was employed to reconstruct the continuous daily LST data over a year. The original model ATCO used harmonic functions, but the dramatic changes of the real LST caused by the weather changes remained unclear due to the smooth sine curve. Using Aqua/MODIS LST products, NDVI and meteorological data, we proposed enhanced model ATCE based on ATCO to describe the fluctuation and compared their performances for the Yangtze River Delta region of China. The results demonstrated that, the overall root mean square errors (RMSEs) of the ATCE was lower than ATCO, and the improved accuracy of daytime was better than that of night, with the errors decreased by 0.64 K and 0.36 K, respectively. The improvements of accuracies varied with different land cover types: the forest, grassland and built-up areas improved larger than water. And the spatial heterogeneity was observed for performance of ATC model: the RMSEs of built-up area, forest and grassland were around 3.0 K in the daytime, while the water attained 2.27 K; at night, the accuracies of all types significantly increased to similar RMSEs level about 2 K. By comparing the differences between LSTs simulated by two models in different seasons, it was found that the differences were smaller in the spring and autumn, while larger in the summer and winter.

  1. Land Surface Modeling and Data Assimilation to Support Physical Precipitation Retrievals for GPM

    Science.gov (United States)

    Peters-Lidard, Christa D.; Tian. Yudong; Kumar, Sujay; Geiger, James; Choudhury, Bhaskar

    2010-01-01

    Objective: The objective of this proposal is to provide a routine land surface modeling and data assimilation capability for GPM in order to provide global land surface states that are necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in GPM, is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. Therefore, providing a robust capability to routinely provide these critical land states is essential to support GPM-era physical retrieval algorithms over land.

  2. Land Surface Model (LSM 1.0) for Ecological, Hydrological, Atmospheric Studies

    Data.gov (United States)

    National Aeronautics and Space Administration — ABSTRACT: The NCAR LSM 1.0 is a land surface model developed to examine biogeophysical and biogeochemical land-atmosphere interactions, especially the effects of...

  3. 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.

  4. Coupling a three-dimensional subsurface flow model with a land surface model to simulate stream-aquifer-land interactions

    Science.gov (United States)

    Huang, M.; Bisht, G.; Zhou, T.; Chen, X.; Dai, H.; Hammond, G. E.; Riley, W. J.; Downs, J.; Liu, Y.; Zachara, J. M.

    2016-12-01

    A fully coupled three-dimensional surface and subsurface land model is developed and applied to a site along the Columbia River to simulate three-way interactions among river water, groundwater, and land surface processes. The model features the coupling of the Community Land Model version 4.5 (CLM4.5) and a massively-parallel multi-physics reactive tranport model (PFLOTRAN). The coupled model (CLM-PFLOTRAN) is applied to a 400m×400m study domain instrumented with groundwater monitoring wells in the Hanford 300 Area along the Columbia River. CLM-PFLOTRAN simulations are performed at three different spatial resolutions over the period 2011-2015 to evaluate the impact of spatial resolution on simulated variables. To demonstrate the difference in model simulations with and without lateral subsurface flow, a vertical-only CLM-PFLOTRAN simulation is also conducted for comparison. Results show that the coupled model is skillful in simulating stream-aquifer interactions, and the land-surface energy partitioning can be strongly modulated by groundwater-river water interactions in high water years due to increased soil moisture availability caused by elevated groundwater table. In addition, spatial resolution does not seem to impact the land surface energy flux simulations, although it is a key factor for accurately estimating the mass exchange rates at the boundaries and associated biogeochemical reactions in the aquifer. The coupled model developed in this study establishes a solid foundation for understanding co-evolution of hydrology and biogeochemistry along the river corridors under historical and future hydro-climate changes.

  5. Constraining the JULES land-surface model for different land-use types using citizen-science generated hydrological data

    Science.gov (United States)

    Chou, H. K.; Ochoa-Tocachi, B. F.; Buytaert, W.

    2017-12-01

    Community land surface models such as JULES are increasingly used for hydrological assessment because of their state-of-the-art representation of land-surface processes. However, a major weakness of JULES and other land surface models is the limited number of land surface parameterizations that is available. Therefore, this study explores the use of data from a network of catchments under homogeneous land-use to generate parameter "libraries" to extent the land surface parameterizations of JULES. The network (called iMHEA) is part of a grassroots initiative to characterise the hydrological response of different Andean ecosystems, and collects data on streamflow, precipitation, and several weather variables at a high temporal resolution. The tropical Andes are a useful case study because of the complexity of meteorological and geographical conditions combined with extremely heterogeneous land-use that result in a wide range of hydrological responses. We then calibrated JULES for each land-use represented in the iMHEA dataset. For the individual land-use types, the results show improved simulations of streamflow when using the calibrated parameters with respect to default values. In particular, the partitioning between surface and subsurface flows can be improved. But also, on a regional scale, hydrological modelling was greatly benefitted from constraining parameters using such distributed citizen-science generated streamflow data. This study demonstrates the modelling and prediction on regional hydrology by integrating citizen science and land surface model. In the context of hydrological study, the limitation of data scarcity could be solved indeed by using this framework. Improved predictions of such impacts could be leveraged by catchment managers to guide watershed interventions, to evaluate their effectiveness, and to minimize risks.

  6. Challenges and opportunities in land surface modelling of savanna ecosystems

    Science.gov (United States)

    Whitley, Rhys; Beringer, Jason; Hutley, Lindsay B.; Abramowitz, Gabriel; De Kauwe, Martin G.; Evans, Bradley; Haverd, Vanessa; Li, Longhui; Moore, Caitlin; Ryu, Youngryel; Scheiter, Simon; Schymanski, Stanislaus J.; Smith, Benjamin; Wang, Ying-Ping; Williams, Mathew; Yu, Qiang

    2017-10-01

    The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and

  7. Challenges and opportunities in land surface modelling of savanna ecosystems

    Directory of Open Access Journals (Sweden)

    R. Whitley

    2017-10-01

    Full Text Available The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs, which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model

  8. Cloud-enabled large-scale land surface model simulations with the NASA Land Information System

    Science.gov (United States)

    Duffy, D.; Vaughan, G.; Clark, M. P.; Peters-Lidard, C. D.; Nijssen, B.; Nearing, G. S.; Rheingrover, S.; Kumar, S.; Geiger, J. V.

    2017-12-01

    Developed by the Hydrological Sciences Laboratory at NASA Goddard Space Flight Center (GSFC), the Land Information System (LIS) is a high-performance software framework for terrestrial hydrology modeling and data assimilation. LIS provides the ability to integrate satellite and ground-based observational products and advanced modeling algorithms to extract land surface states and fluxes. Through a partnership with the National Center for Atmospheric Research (NCAR) and the University of Washington, the LIS model is currently being extended to include the Structure for Unifying Multiple Modeling Alternatives (SUMMA). With the addition of SUMMA in LIS, meaningful simulations containing a large multi-model ensemble will be enabled and can provide advanced probabilistic continental-domain modeling capabilities at spatial scales relevant for water managers. The resulting LIS/SUMMA application framework is difficult for non-experts to install due to the large amount of dependencies on specific versions of operating systems, libraries, and compilers. This has created a significant barrier to entry for domain scientists that are interested in using the software on their own systems or in the cloud. In addition, the requirement to support multiple run time environments across the LIS community has created a significant burden on the NASA team. To overcome these challenges, LIS/SUMMA has been deployed using Linux containers, which allows for an entire software package along with all dependences to be installed within a working runtime environment, and Kubernetes, which orchestrates the deployment of a cluster of containers. Within a cloud environment, users can now easily create a cluster of virtual machines and run large-scale LIS/SUMMA simulations. Installations that have taken weeks and months can now be performed in minutes of time. This presentation will discuss the steps required to create a cloud-enabled large-scale simulation, present examples of its use, and

  9. NLDAS Mosaic Land Surface Model L4 Hourly 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data...

  10. NLDAS VIC Land Surface Model L4 Hourly 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation...

  11. NLDAS Noah Land Surface Model L4 Monthly 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation...

  12. NLDAS Mosaic Land Surface Model L4 Monthly 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data...

  13. NLDAS VIC Land Surface Model L4 Monthly 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation...

  14. NLDAS VIC Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — Abstract: This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data...

  15. NLDAS Noah Land Surface Model L4 Hourly 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation...

  16. Improvement of a land surface model for accurate prediction of surface energy and water balances

    International Nuclear Information System (INIS)

    Katata, Genki

    2009-02-01

    In order to predict energy and water balances between the biosphere and atmosphere accurately, sophisticated schemes to calculate evaporation and adsorption processes in the soil and cloud (fog) water deposition on vegetation were implemented in the one-dimensional atmosphere-soil-vegetation model including CO 2 exchange process (SOLVEG2). Performance tests in arid areas showed that the above schemes have a significant effect on surface energy and water balances. The framework of the above schemes incorporated in the SOLVEG2 and instruction for running the model are documented. With further modifications of the model to implement the carbon exchanges between the vegetation and soil, deposition processes of materials on the land surface, vegetation stress-growth-dynamics etc., the model is suited to evaluate an effect of environmental loads to ecosystems by atmospheric pollutants and radioactive substances under climate changes such as global warming and drought. (author)

  17. 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.)

  18. Evaluating the JULES Land Surface Model Energy Fluxes Using FLUXNET Data

    NARCIS (Netherlands)

    Blyth, E.; Gash, J.H.C.; Lloyd, A.J.; Pryor, M.; Weedon, G.P.; Shuttleworth, J.

    2010-01-01

    Surface energy flux measurements from a sample of 10 flux network (FLUXNET) sites selected to represent a range of climate conditions and biome types were used to assess the performance of the Hadley Centre land surface model (Joint U. K. Land Environment Simulator; JULES). Because FLUXNET data are

  19. Coupling a groundwater model with a land surface model to improve water and energy cycle simulation

    Directory of Open Access Journals (Sweden)

    W. Tian

    2012-12-01

    Full Text Available Water and energy cycles interact, making these two processes closely related. Land surface models (LSMs can describe the water and energy cycles on the land surface, but their description of the subsurface water processes is oversimplified, and lateral groundwater flow is ignored. Groundwater models (GWMs describe the dynamic movement of the subsurface water well, but they cannot depict the physical mechanisms of the evapotranspiration (ET process in detail. In this study, a coupled model of groundwater flow with a simple biosphere (GWSiB is developed based on the full coupling of a typical land surface model (SiB2 and a 3-D variably saturated groundwater model (AquiferFlow. In this coupled model, the infiltration, ET and energy transfer are simulated by SiB2 using the soil moisture results from the groundwater flow model. The infiltration and ET results are applied iteratively to drive the groundwater flow model. After the coupled model is built, a sensitivity test is first performed, and the effect of the groundwater depth and the hydraulic conductivity parameters on the ET are analyzed. The coupled model is then validated using measurements from two stations located in shallow and deep groundwater depth zones. Finally, the coupled model is applied to data from the middle reach of the Heihe River basin in the northwest of China to test the regional simulation capabilities of the model.

  20. GLDAS CLM Land Surface Model L4 Monthly 1.0 x 1.0 degree V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Common Land Model (CLM) V2.0 model in the Global Land Data Assimilation System (GLDAS)....

  1. GLDAS CLM Land Surface Model L4 3 Hourly 1.0 x 1.0 degree Subsetted V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Common Land Model (CLM) V2.0 model in the Global Land Data Assimilation System (GLDAS)....

  2. Shallow to Deep Convection Transition over a Heterogeneous Land Surface Using the Land Model Coupled Large-Eddy Simulation

    Science.gov (United States)

    Lee, J.; Zhang, Y.; Klein, S. A.

    2017-12-01

    The triggering of the land breeze, and hence the development of deep convection over heterogeneous land should be understood as a consequence of the complex processes involving various factors from land surface and atmosphere simultaneously. That is a sub-grid scale process that many large-scale models have difficulty incorporating it into the parameterization scheme partly due to lack of our understanding. Thus, it is imperative that we approach the problem using a high-resolution modeling framework. In this study, we use SAM-SLM (Lee and Khairoutdinov, 2015), a large-eddy simulation model coupled to a land model, to explore the cloud effect such as cold pool, the cloud shading and the soil moisture memory on the land breeze structure and the further development of cloud and precipitation over a heterogeneous land surface. The atmospheric large scale forcing and the initial sounding are taken from the new composite case study of the fair-weather, non-precipitating shallow cumuli at ARM SGP (Zhang et al., 2017). We model the land surface as a chess board pattern with alternating leaf area index (LAI). The patch contrast of the LAI is adjusted to encompass the weak to strong heterogeneity amplitude. The surface sensible- and latent heat fluxes are computed according to the given LAI representing the differential surface heating over a heterogeneous land surface. Separate from the surface forcing imposed from the originally modeled surface, the cases that transition into the moist convection can induce another layer of the surface heterogeneity from the 1) radiation shading by clouds, 2) adjusted soil moisture pattern by the rain, 3) spreading cold pool. First, we assess and quantifies the individual cloud effect on the land breeze and the moist convection under the weak wind to simplify the feedback processes. And then, the same set of experiments is repeated under sheared background wind with low level jet, a typical summer time wind pattern at ARM SGP site, to

  3. Evaluation of the WAMME model surface fluxes using results from the AMMA land-surface model intercomparison project

    Energy Technology Data Exchange (ETDEWEB)

    Boone, Aaron Anthony [GAME-CNRM, Meteo-France, Toulouse (France); Poccard-Leclercq, Isabelle [Universite de Nantes, LETG-Geolittomer, Nantes (France); Xue, Yongkang; Feng, Jinming [University of California at Los Angeles, Los Angeles, CA (United States); Rosnay, Patricia de [European Centre for Medium Range Weather Forecasting, Reading (United Kingdom)

    2010-07-15

    The West African monsoon (WAM) circulation and intensity have been shown to be influenced by the land surface in numerous numerical studies using regional scale and global scale atmospheric climate models (RCMs and GCMs, respectively) over the last several decades. The atmosphere-land surface interactions are modulated by the magnitude of the north-south gradient of the low level moist static energy, which is highly correlated with the steep latitudinal gradients of the vegetation characteristics and coverage, land use, and soil properties over this zone. The African Multidisciplinary Monsoon Analysis (AMMA) has organised comprehensive activities in data collection and modelling to further investigate the significance land-atmosphere feedbacks. Surface energy fluxes simulated by an ensemble of land surface models from AMMA Land-surface Model Intercomparison Project (ALMIP) have been used as a proxy for the best estimate of the ''real world'' values in order to evaluate GCM and RCM simulations under the auspices of the West African Monsoon Modelling Experiment (WAMME) project, since such large-scale observations do not exist. The ALMIP models have been forced in off-line mode using forcing based on a mixture of satellite, observational, and numerical weather prediction data. The ALMIP models were found to agree well over the region where land-atmosphere coupling is deemed to be most important (notably the Sahel), with a high signal to noise ratio (generally from 0.7 to 0.9) in the ensemble and a inter-model coefficient of variation between 5 and 15%. Most of the WAMME models simulated spatially averaged net radiation values over West Africa which were consistent with the ALMIP estimates, however, the partitioning of this energy between sensible and latent heat fluxes was significantly different: WAMME models tended to simulate larger (by nearly a factor of two) monthly latent heat fluxes than ALMIP. This results due to a positive precipitation

  4. 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...

  5. 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

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

    Science.gov (United States)

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

    2017-05-01

    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 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.

  7. 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.

  8. The Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology or Data Quality?

    Science.gov (United States)

    Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.; Or, Dani; Best, Martin J.; Johnson, Helen R.; Balsamo, Gianpaolo; Boone, Aaron; Cuntz, Matthais; Decharme, Bertrand; hide

    2016-01-01

    The PALS Land sUrface Model Benchmarking Evaluation pRoject (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave, surface air temperature and relative humidity. These results are explored here in greater detail and possible causes are investigated. We examine whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation and whether a lack of energy conservation in fluxtower data gives the empirical models an unfair advantage in the intercomparison. We demonstrate that energy conservation in the observational data is not responsible for these results. We also show that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, we present evidence suggesting that the nature of this partitioning problem is likely shared among all contributing LSMs. While we do not find a single candidate explanation forwhy land surface models perform poorly relative to empirical benchmarks in PLUMBER, we do exclude multiple possible explanations and provide guidance on where future research should focus.

  9. NLDAS Mosaic Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American...

  10. NLDAS Noah Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V002

    Data.gov (United States)

    National Aeronautics and Space Administration — This monthly climatology data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American...

  11. 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

  12. 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)

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

    Science.gov (United States)

    Farhadi, L.; Reichle, R. H.; De Lannoy, G. J.; Kimball, J. S.

    2013-12-01

    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 net primary productivity at the land surface. To support the level 4 soil moisture and carbon products (value-added, i.e. using a combination of remote sensing data and modeling) for the planned NASA Soil Moisture Active Passive (SMAP) mission, an F/T assimilation algorithm is developed 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. 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). An Observing System Simulation 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). The effect of the F/T analysis and classification error on land surface temperature and soil temperature predictions is examined in this research.

  14. 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

  15. 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.

  16. Improving weather predictability by including land-surface model parameter uncertainty

    Science.gov (United States)

    Orth, Rene; Dutra, Emanuel; Pappenberger, Florian

    2016-04-01

    The land surface forms an important component of Earth system models and interacts nonlinearly with other parts such as ocean and atmosphere. To capture the complex and heterogenous hydrology of the land surface, land surface models include a large number of parameters impacting the coupling to other components of the Earth system model. Focusing on ECMWF's land-surface model HTESSEL we present in this study a comprehensive parameter sensitivity evaluation using multiple observational datasets in Europe. We select 6 poorly constrained effective parameters (surface runoff effective depth, skin conductivity, minimum stomatal resistance, maximum interception, soil moisture stress function shape, total soil depth) and explore their sensitivity to model outputs such as soil moisture, evapotranspiration and runoff using uncoupled simulations and coupled seasonal forecasts. Additionally we investigate the possibility to construct ensembles from the multiple land surface parameters. In the uncoupled runs we find that minimum stomatal resistance and total soil depth have the most influence on model performance. Forecast skill scores are moreover sensitive to the same parameters as HTESSEL performance in the uncoupled analysis. We demonstrate the robustness of our findings by comparing multiple best performing parameter sets and multiple randomly chosen parameter sets. We find better temperature and precipitation forecast skill with the best-performing parameter perturbations demonstrating representativeness of model performance across uncoupled (and hence less computationally demanding) and coupled settings. Finally, we construct ensemble forecasts from ensemble members derived with different best-performing parameterizations of HTESSEL. This incorporation of parameter uncertainty in the ensemble generation yields an increase in forecast skill, even beyond the skill of the default system. Orth, R., E. Dutra, and F. Pappenberger, 2016: Improving weather predictability by

  17. The development and evaluation of new runoff parameterization representations coupled with Noah Land Surface Model

    Science.gov (United States)

    Zheng, Z.; Zhang, W.; Xu, J.

    2011-12-01

    As a key component of the global water cycle, runoff plays an important role in earth climate system by affecting the land surface water and energy balance. Realistic runoff parameterization within land surface model (LSM) is significant for accurate land surface modeling and numerical weather and climate prediction. Hence, optimization and refinement of runoff formulation in LSM can further improve model predictive capability of surface-to-atmosphere fluxes which influences the complex interactions between the land surface and atmosphere. Moreover, the performance of runoff simulation in LSM would essential to drought and flood prediction and warning. In this study, a new runoff parameterization named XXT (Xin'anjiang x TOPMODEL) was developed by introducing the water table depth into the soil moisture storage capacity distribution curve (SMSCC) from Xin'anjiang model for surface runoff calculation improvement and then integrating with a TOPMODEL-based groundwater scheme. Several studies had already found a strong correlation between the water table depth and land surface processes. In this runoff parameterization, the dynamic variation of surface and subsurface runoff calculation is connected in a systematic way through the change of water table depth. The XXT runoff parameterization was calibrated and validated with datasets both from observation and Weather Research & Forecasting model (WRF) outputs, the results with high Nash-efficiency coefficient indicated that it has reliable capability of runoff simulation in different climate regions. After model test, the XXT runoff parameterization is coupled with the unified Noah LSM 3.2 instead of simple water balance model (SWB) in order to alleviate the runoff simulating bias which may lead to poor energy partition and evaporation. The impact of XXT is investigated through application of a whole year (1998) simulation at surface flux site of Champaign, Illinois (40.01°N, 88.37°W). The results show that Noah

  18. 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

  19. 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.

  20. 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...... climate, weather and numerous biophysical processes, such as plant productivity. As energy is required for ET to occur, it also forms a link between the land-surface energy uxes and water uxes. Therefore, to be able to obtain reliable estimates of ET, reliable estimates of the other land-surface energy...... of this study was to look at, and improve, various approaches for modelling the land-surface energy uxes at different spatial scales. The work was done using physically-based Two-Source Energy Balance (TSEB) approach as well as semi-empirical \\Triangle" approach. The TSEB-based approach was the main focus...

  1. State-dependent errors in a land surface model across biomes inferred from eddy covariance observations on multiple timescales

    NARCIS (Netherlands)

    Wang, T.; Brender, P.; Ciais, P.; Piao, S.; Mahecha, M.D.; Chevallier, F.; Reichstein, M.; Ottle, C.; Maignan, F.; Arain, A.; Bohrer, G.; Cescatti, A.; Kiely, G.; Law, B.E.; Lutz, M.; Montagnani, L.; Moors, E.J.

    2012-01-01

    Characterization of state-dependent model biases in land surface models can highlight model deficiencies, and provide new insights into model development. In this study, artificial neural networks (ANNs) are used to estimate the state-dependent biases of a land surface model (ORCHIDEE: ORganising

  2. 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

  3. Towards an Improved Represenation of Reservoirs and Water Management in a Land Surface-Hydrology Model

    Science.gov (United States)

    Yassin, F.; Anis, M. R.; Razavi, S.; Wheater, H. S.

    2017-12-01

    Water management through reservoirs, diversions, and irrigation have significantly changed river flow regimes and basin-wide energy and water balance cycles. Failure to represent these effects limits the performance of land surface-hydrology models not only for streamflow prediction but also for the estimation of soil moisture, evapotranspiration, and feedbacks to the atmosphere. Despite recent research to improve the representation of water management in land surface models, there remains a need to develop improved modeling approaches that work in complex and highly regulated basins such as the 406,000 km2 Saskatchewan River Basin (SaskRB). A particular challenge for regional and global application is a lack of local information on reservoir operational management. To this end, we implemented a reservoir operation, water abstraction, and irrigation algorithm in the MESH land surface-hydrology model and tested it over the SaskRB. MESH is Environment Canada's Land Surface-hydrology modeling system that couples Canadian Land Surface Scheme (CLASS) with hydrological routing model. The implemented reservoir algorithm uses an inflow-outflow relationship that accounts for the physical characteristics of reservoirs (e.g., storage-area-elevation relationships) and includes simplified operational characteristics based on local information (e.g., monthly target volume and release under limited, normal, and flood storage zone). The irrigation algorithm uses the difference between actual and potential evapotranspiration to estimate irrigation water demand. This irrigation demand is supplied from the neighboring reservoirs/diversion in the river system. We calibrated the model enabled with the new reservoir and irrigation modules in a multi-objective optimization setting. Results showed that the reservoir and irrigation modules significantly improved the MESH model performance in generating streamflow and evapotranspiration across the SaskRB and that this our approach provides

  4. GLDAS Noah Land Surface Model L4 3 Hourly 1.0 x 1.0 degree Subsetted V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 2.7.1 model in the Global Land Data Assimilation System (GLDAS). The data are in...

  5. GLDAS VIC Land Surface Model L4 3 Hourly 1.0 x 1.0 degree V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Variable Infiltration Capacity (VIC) Model in the Global Land Data Assimilation System...

  6. GLDAS Mosaic Land Surface Model L4 3 Hourly 1.0 x 1.0 degree Subsetted V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Mosaic model in the Global Land Data Assimilation System (GLDAS). The data are in 1.0...

  7. GLDAS Noah Land Surface Model L4 Monthly 1.0 x 1.0 degree V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 2.7.1 model in the Global Land Data Assimilation System (GLDAS). The data are in...

  8. GLDAS Mosaic Land Surface Model L4 Monthly 1.0 x 1.0 degree V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Mosaic model in the Global Land Data Assimilation System (GLDAS). The data are in 1.0...

  9. GLDAS Noah Land Surface Model L4 Monthly 0.25 x 0.25 degree V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 2.7.1 model in the Global Land Data Assimilation System (GLDAS). The data are in...

  10. GLDAS Noah Land Surface Model L4 3 Hourly 0.25 x 0.25 degree Subsetted V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 2.7.1 model in the Global Land Data Assimilation System (GLDAS). The data are in...

  11. GLDAS VIC Land Surface Model L4 Monthly 1.0 x 1.0 degree V001

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Variable Infiltration Capacity (VIC) model in the Global Land Data Assimilation System...

  12. 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...... 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...... that the magnitude of the directional effects mainly depends on the tree cover, with moderate tree covers (20-40 %) causing the largest directional effects but with significant effects also at much sparser tree cover. The magnitude is also highly dependent on the temperature difference between the surface components...

  13. 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

  14. An integrated evaluation of land surface energy fluxes over China in seven reanalysis/modeling products

    Science.gov (United States)

    Li, Hongyu; Fu, Congbin; Guo, Weidong

    2017-08-01

    An integrated evaluation of monthly mean land surface energy fluxes over China in seven reanalysis and land model products during the period 1979-2015 is conducted. Observations from seven field sites are used to evaluate these flux products, including four reanalysis data sets and three produced by off-line land surface models. In general, the expected seasonal variations and spatial patterns in major climatic regimes are well reproduced by all reanalysis and modeling products. However, large differences among the four reanalysis products are found, while the three off-line land surface modeling products correlate well with each other. Looking at the Bowen ratio, it is found that the off-line land surface models convert a larger fraction of surface available energy into sensible heat flux compared to the reanalysis products in all climatic regimes. There are three centers of high interannual variability in sensible heat located in West China, Northeast China, and the eastern Inner Mongolia, respectively. In addition, the sensible heat flux agrees better with observations at grassland sites than at forest sites, while the latent heat flux and net radiation are significantly overestimated at forest sites in all the flux products. Besides, mean square errors of the fluxes are decomposed into biases, correlations, and differences in standard deviation. Finally, based on a ranking system adopted to quantitatively evaluate the performance of each data set, it is found that the surface energy fluxes in ERA-Interim and JRA-25 agree well with observations and the ensemble mean of all these products remains reasonably realistic as well.

  15. 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

    by HIRHAM simulated precipitation. The last two simulations include iv) a standard HIRHAM simulation, and v) a fully coupled HIRHAM-MIKE SHE simulation locally replacing the land surface scheme by MIKE SHE for the FIFE area, while HIRHAM in standard configuration is used for the remaining model area......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...... 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...

  16. 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

  17. An improved snow scheme for the ECMWF land surface model: Description and offline validation

    Science.gov (United States)

    Emanuel Dutra; Gianpaolo Balsamo; Pedro Viterbo; Pedro M. A. Miranda; Anton Beljaars; Christoph Schar; Kelly Elder

    2010-01-01

    A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and...

  18. Assessment of Land Surface Models in a High-Resolution Atmospheric Model during Indian Summer Monsoon

    Science.gov (United States)

    Attada, Raju; Kumar, Prashant; Dasari, Hari Prasad

    2018-04-01

    Assessment of the land surface models (LSMs) on monsoon studies over the Indian summer monsoon (ISM) region is essential. In this study, we evaluate the skill of LSMs at 10 km spatial resolution in simulating the 2010 monsoon season. The thermal diffusion scheme (TDS), rapid update cycle (RUC), and Noah and Noah with multi-parameterization (Noah-MP) LSMs are chosen based on nature of complexity, that is, from simple slab model to multi-parameterization options coupled with the Weather Research and Forecasting (WRF) model. Model results are compared with the available in situ observations and reanalysis fields. The sensitivity of monsoon elements, surface characteristics, and vertical structures to different LSMs is discussed. Our results reveal that the monsoon features are reproduced by WRF model with all LSMs, but with some regional discrepancies. The model simulations with selected LSMs are able to reproduce the broad rainfall patterns, orography-induced rainfall over the Himalayan region, and dry zone over the southern tip of India. The unrealistic precipitation pattern over the equatorial western Indian Ocean is simulated by WRF-LSM-based experiments. The spatial and temporal distributions of top 2-m soil characteristics (soil temperature and soil moisture) are well represented in RUC and Noah-MP LSM-based experiments during the ISM. Results show that the WRF simulations with RUC, Noah, and Noah-MP LSM-based experiments significantly improved the skill of 2-m temperature and moisture compared to TDS (chosen as a base) LSM-based experiments. Furthermore, the simulations with Noah, RUC, and Noah-MP LSMs exhibit minimum error in thermodynamics fields. In case of surface wind speed, TDS LSM performed better compared to other LSM experiments. A significant improvement is noticeable in simulating rainfall by WRF model with Noah, RUC, and Noah-MP LSMs over TDS LSM. Thus, this study emphasis the importance of choosing/improving LSMs for simulating the ISM phenomena in

  19. Assessment of Land Surface Models in a High-Resolution Atmospheric Model during Indian Summer Monsoon

    KAUST Repository

    Attada, Raju

    2018-04-17

    Assessment of the land surface models (LSMs) on monsoon studies over the Indian summer monsoon (ISM) region is essential. In this study, we evaluate the skill of LSMs at 10 km spatial resolution in simulating the 2010 monsoon season. The thermal diffusion scheme (TDS), rapid update cycle (RUC), and Noah and Noah with multi-parameterization (Noah-MP) LSMs are chosen based on nature of complexity, that is, from simple slab model to multi-parameterization options coupled with the Weather Research and Forecasting (WRF) model. Model results are compared with the available in situ observations and reanalysis fields. The sensitivity of monsoon elements, surface characteristics, and vertical structures to different LSMs is discussed. Our results reveal that the monsoon features are reproduced by WRF model with all LSMs, but with some regional discrepancies. The model simulations with selected LSMs are able to reproduce the broad rainfall patterns, orography-induced rainfall over the Himalayan region, and dry zone over the southern tip of India. The unrealistic precipitation pattern over the equatorial western Indian Ocean is simulated by WRF–LSM-based experiments. The spatial and temporal distributions of top 2-m soil characteristics (soil temperature and soil moisture) are well represented in RUC and Noah-MP LSM-based experiments during the ISM. Results show that the WRF simulations with RUC, Noah, and Noah-MP LSM-based experiments significantly improved the skill of 2-m temperature and moisture compared to TDS (chosen as a base) LSM-based experiments. Furthermore, the simulations with Noah, RUC, and Noah-MP LSMs exhibit minimum error in thermodynamics fields. In case of surface wind speed, TDS LSM performed better compared to other LSM experiments. A significant improvement is noticeable in simulating rainfall by WRF model with Noah, RUC, and Noah-MP LSMs over TDS LSM. Thus, this study emphasis the importance of choosing/improving LSMs for simulating the ISM phenomena

  20. Calibration of a distributed hydrological model using satellite data of land surface temperature

    Science.gov (United States)

    Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni

    2013-04-01

    Calibration and validation of distributed models at basin scale generally refer to external variables, which are integrated catchment model outputs, and usually depend on the comparison between simulated and observed discharges at the available rivers cross sections, which are usually very few. However distributed models allow an internal validation due to their intrinsic structure, so that internal processes and variables of the model can be controlled in each cell of the domain. In particular this work investigates the potentiality to control evapotranspiration and its spatial and temporal variability through the detection of land surface temperature (LST) from satellite remote sensing. This study proposes a methodology for the calibration of distributed hydrological models at basin scale using remote sensing data of land surface temperature. The distributed energy water balance model, Flash-flood Event-based Spatially-distributed rainfall-runoff Transformation - Energy Water Balance model (FEST-EWB) will be calibrated in the Upper Po river basin (Italy) closed at the river cross section of Ponte della Becca with a total catchment area of about 38000 km2. The model algorithm solves the system of energy and mass balances in term of the representative pixel equilibrium temperature (RET) that governs the fluxes of energy and mass over the basin domain. This equilibrium surface temperature, which is a critical model state variable, is comparable to the land surface temperature (LST) from satellite. So a pixel to pixel semi-automatic calibration procedure of soil and vegetation parameter is presented through the comparison between the model internal state variable RET and the remotely observed LST. A similar calibration procedure will also be applied performing the traditional calibration using only discharge measurements. 260 diurnal and nocturne LST MODIS products are compared with FEST-EWB land surface temperature over the 11 years of simulation from 2000 to 2010

  1. Updating representation of land surface-atmosphere feedbacks in airborne campaign modeling analysis

    Science.gov (United States)

    Huang, M.; Carmichael, G. R.; Crawford, J. H.; Chan, S.; Xu, X.; Fisher, J. A.

    2017-12-01

    An updated modeling system to support airborne field campaigns is being built at NASA Ames Pleiades, with focus on adjusting the representation of land surface-atmosphere feedbacks. The main updates, referring to previous experiences with ARCTAS-CARB and CalNex in the western US to study air pollution inflows, include: 1) migrating the WRF (Weather Research and Forecasting) coupled land surface model from Noah to improved/more complex models especially Noah-MP and Rapid Update Cycle; 2) enabling the WRF land initialization with suitably spun-up land model output; 3) incorporating satellite land cover, vegetation dynamics, and soil moisture data (i.e., assimilating Soil Moisture Active Passive data using the ensemble Kalman filter approach) into WRF. Examples are given of comparing the model fields with available aircraft observations during spring-summer 2016 field campaigns taken place at the eastern side of continents (KORUS-AQ in South Korea and ACT-America in the eastern US), the air pollution export regions. Under fair weather and stormy conditions, air pollution vertical distributions and column amounts, as well as the impact from land surface, are compared. These help identify challenges and opportunities for LEO/GEO satellite remote sensing and modeling of air quality in the northern hemisphere. Finally, we briefly show applications of this system on simulating Australian conditions, which would explore the needs for further development of the observing system in the southern hemisphere and inform the Clean Air and Urban Landscapes (https://www.nespurban.edu.au) modelers.

  2. A COUPLED LAND-SURFACE AND DRY DEPOSITION MODEL AND COMPARISON TO FIELD MEASUREMENTS OF SURFACE HEAT, MOISTURE, AND OZONE FLUXES

    Science.gov (United States)

    We have developed a coupled land-surface and dry deposition model for realistic treatment of surface fluxes of heat, moisture, and chemical dry deposition within a comprehensive air quality modeling system. A new land-surface model (LSM) with explicit treatment of soil moisture...

  3. A critical assessment of the JULES land surface model hydrology for humid tropical environments

    Science.gov (United States)

    Zulkafli, Z.; Buytaert, W.; Onof, C.; Lavado, W.; Guyot, J. L.

    2013-03-01

    Global land surface models (LSMs) such as the Joint UK Land Environment Simulator (JULES) are originally developed to provide surface boundary conditions for climate models. They are increasingly used for hydrological simulation, for instance to simulate the impacts of land use changes and other perturbations on the water cycle. This study investigates how well such models represent the major hydrological fluxes at the relevant spatial and temporal scales - an important question for reliable model applications in poorly understood, data-scarce environments. The JULES-LSM is implemented in a 360 000 km2 humid tropical mountain basin of the Peruvian Andes-Amazon at 12-km grid resolution, forced with daily satellite and climate reanalysis data. The simulations are evaluated using conventional discharge-based evaluation methods, and by further comparing the magnitude and internal variability of the basin surface fluxes such as evapotranspiration, throughfall, and surface and subsurface runoff of the model with those observed in similar environments elsewhere. We find reasonably positive model efficiencies and high correlations between the simulated and observed streamflows, but high root-mean-square errors affecting the performance in smaller, upper sub-basins. We attribute this to errors in the water balance and JULES-LSM's inability to model baseflow. We also found a tendency to under-represent the high evapotranspiration rates of the region. We conclude that strategies to improve the representation of tropical systems to be (1) addressing errors in the forcing and (2) incorporating local wetland and regional floodplain in the subsurface representation.

  4. 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.

  5. 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.

  6. 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.

  7. Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

    Directory of Open Access Journals (Sweden)

    G. Tang

    2012-08-01

    Full Text Available Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i modify a DGVM for simulating land surface water balances; (ii evaluate the modified model in simulating actual evapotranspiration (ET, soil moisture, and surface runoff at regional or watershed scales; and (iii gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ DGVM. To evaluate the model we ran LH using historical (1981–2006 climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981–2006 (R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52. The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15% with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt

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

    Science.gov (United States)

    Sun, Y.; Hou, Z.; Huang, M.; Tian, F.; Leung, L. Ruby

    2013-12-01

    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. Both deterministic least-square fitting and stochastic Markov-chain Monte Carlo (MCMC)-Bayesian inversion approaches are evaluated by applying them to CLM4 at selected sites with different climate and soil conditions. 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 sampling-based stochastic inversion approaches provides significant improvements in the model simulations compared to using default CLM4 parameter values, and that as more information comes in, the predictive intervals (ranges of posterior distributions) of the calibrated parameters become narrower. 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 bounds.

  9. 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.

  10. Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

    Science.gov (United States)

    Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.

    2016-01-01

    Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

  11. 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

  12. NLDAS VIC Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V002 (NLDAS_VIC0125_MC) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — Abstract: This data set contains a series of land surface parameters simulated from the VIC land-surface model (LSM) for Phase 2 of the North American Land Data...

  13. NLDAS Noah Land Surface Model L4 Hourly 0.125 x 0.125 degree V002 (NLDAS_NOAH0125_H) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation...

  14. NLDAS Mosaic Land Surface Model L4 Hourly 0.125 x 0.125 degree V002 (NLDAS_MOS0125_H) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data...

  15. Combining land surface models and remote sensing data to estimate evapotranspiration for drought monitoring in Europe

    Science.gov (United States)

    Cammalleri, C.; Sepulcre-Cantó, G.; Vogt, J.

    2014-10-01

    The main hydrologic feedback from the land-surface to the atmosphere is the evapotranspiration, ET, which embraces the response of both the soil and vegetated surface to the atmospheric forcing (e.g., precipitation and temperature), as well as influences locally atmospheric humidity, cloud formation and precipitation, the main driver for drought. Actual ET is regulated by several factors, including biological quantities (e.g., rooting depth, leaf area, fraction of absorbed photosynthetically active radiation) and soil water status. The ET temporal dynamic is strongly affected by rainfall deficits, and in turn it represents a robust proxy of the effects of water shortage on plants. These characteristics make ET a promising quantity for monitoring environmental drought, defined as a shortage of water availability that reduces the ecosystem productivity. In the last few decades, the capability to accurately model ET over large areas in a spatial-distributed fashion has increased notably. Most of the improvements in this field are related to the increasing availability of remote sensing data, and the achievements in modelling of ET-related quantities. Several land-surface models exploit the richness of newly available datasets, including the Community Land Model (CLM) and the Meteosat Second Generation (MSG) ET outputs. Here, the potentiality of ET maps obtained by combining land-surface models and remote sensing data through these two schemes is explored, with a special focus on the reliability of ET (and derived standardized variables) as drought indicator. Tests were performed over Europe at moderate spatial resolution (3-5 km), with the final goal to improve the estimation of soil water status as a contribution to the European Drought Observatory (EDO, http://edo.jrc.ec.europa.eu).

  16. High-Resolution Specification of the Land and Ocean Surface for Improving Regional Mesoscale Model Predictions

    Science.gov (United States)

    Case, Jonathan L.; Lazarus, Steven M.; Splitt, Michael E.; Crosson, William L.; Lapenta, William M.; Jedlovec, Gary J.; Peters-Lidard, Christa D.

    2008-01-01

    The exchange of energy and moisture between the Earth's surface and the atmospheric boundary layer plays a critical role in many meteorological processes. High-resolution, accurate representations of surface properties such as sea-surface temperature (SST), soil temperature and moisture content, ground fluxes, and vegetation are necessary to better understand the Earth-atmosphere interactions and improve numerical predictions of sensible weather. The NASA Short-term Prediction Research and Transition (SPoRT) Center has been conducting separate studies to examine the impacts of high-resolution land-surface initialization data from the Goddard Space Flight Center Land Information System (LIS) on subsequent WRF forecasts, as well as the influence of initializing WRF with SST composites derived from the MODIS instrument. This current project addresses the combined impacts of using high-resolution lower boundary data over both land (LIS data) and water (MODIS SSTs) on the subsequent daily WRF forecasts over Florida during May 2004. For this experiment, the WRF model is configured to run on a nested domain with 9- km and 3-kin grid spacing, centered on the Florida peninsula and adjacent coastal waters of the Gulf of Mexico and Atlantic Ocean. A control configuration of WRF is established to take all initial condition data from the NCEP Eta model. Meanwhile, two WRF experimental runs are configured to use high-resolution initialization data from (1) LIS land-surface data only, and (2) a combination of LIS data and high-resolution MODIS SST composites. The experiment involves running 24-hour simulations of the control WRF configuration, the MS-initialized WRF, and the LIS+MODIS-initialized WRF daily for the entire month of May 2004. All atmospheric data for initial and boundary conditions for the Control, LIS, and LIS+MODIS runs come from the NCEP Eta model on a 40-km grid. Verification statistics are generated at land surface observation sites and buoys, and the impacts

  17. Comparing atmosphere-land surface feedbacks from models within the tropics (CALM). Part 1: Evaluation of CMIP5 GCMs to simulate the land surface-atmosphere feedback

    Science.gov (United States)

    Williams, C.; Allan, R.; Kniveton, D.

    2012-04-01

    Man-made transformations to the environment, and in particular the land surface, are having a large impact on the distribution (in both time and space) of rainfall, upon which all life is reliant. From global changes in the composition of the atmosphere, through the emission of greenhouse gases and aerosols, to more localised land use and land cover changes due to an expanding population with an increasing ecological footprint, human activity has a considerable impact on the processes controlling rainfall. This is of particular importance for environmentally vulnerable regions such as many of those in the tropics. Here, widespread poverty, an extensive disease burden and pockets of political instability has resulted in a low resilience and limited adaptative capacity to climate related shocks and stresses. Recently, the 5th Climate Modelling Intercomparison Project (CMIP5) has run a number of state-of-the-art climate models using various present-day and future emission scenarios of greenhouse gases, and therefore provides an unprecedented amount of simulated model data. This paper presents the results of the first stage of a larger project, aiming to further our understanding of how the interactions between tropical rainfall and the land surface are represented in some of the latest climate model simulations. Focusing on precipitation, soil moisture and near-surface temperature, this paper compares the data from all of these models, as well as blended observational-satellite data, to see how the interactions between rainfall and the land surface differs (or agrees) between the models and reality. Firstly, in an analysis of the processes from the "observed" data, the results suggest a strong positive relationship between precipitation and soil moisture at both daily and seasonal timescales. There is a weaker and negative relationship between precipitation and temperature, and likewise between soil moisture and temperature. For all variables, the correlations are

  18. Hyper-Resolution Global Land Surface Model at Regional-to-Local Scales with observed Groundwater data assimilation

    OpenAIRE

    Singh, Raj Shekhar

    2014-01-01

    Modeling groundwater is challenging: it is not readily visible and is difficult to measure, with limited sets of observations available. Even though groundwater models can reproduce water table and head variations, considerable drift in modeled land surface states can nonetheless result from partially known geologic structure, errors in the input forcing fields, and imperfect Land Surface Model (LSM) parameterizations. These models frequently have biased results that are very different from o...

  19. Soil temperature response in Korea to a changing climate using a land surface model

    Science.gov (United States)

    Park, Seon Ki; O, Sungmin; Cassardo, Claudio

    2017-11-01

    The land surface processes play an important role in weather and climate systems through its regulation of radiation, heat, water and momentum fluxes. Soil temperature (ST) is one of the most important parameters in the land surface processes; however, there are few extensive measurements of ST with a long time series in the world. According to the CLImatology of Parameters at the Surface (CLIPS) methodology, the output of a trusted Soil-Vegetation- Atmosphere Transfer (SVAT) scheme can be utilized instead of observations to investigate the regional climate of interest. In this study, ST in South Korea is estimated in a view of future climate using the output from a trusted SVAT scheme — the University of TOrino model of land Process Interaction with Atmosphere (UTOPIA), which is driven by a regional climate model. Here characteristic changes in ST are analyzed under the IPCC A2 future climate for 2046-2055 and 2091-2100, and are compared with those under the reference climate for 1996-2005. The UTOPIA results were validated using the observed ST in the reference climate, and the model proved to produce reasonable ST in South Korea. The UTOPIA simulations indicate that ST increases due to environmental change, especially in air temperature (AT), in the future climate. The increment of ST is proportional to that of AT except for winter. In wintertime, the ST variations are different from region to region mainly due to variations in snow cover, which keeps ST from significant changes by the climate change.

  20. Reconciling Land-Ocean Moisture Transport Variability in Reanalyses with P-ET in Observationally-Driven Land Surface Models

    Science.gov (United States)

    Robertson, Franklin R.; Bosilovich, Michael G.; Roberts, Jason B.

    2016-01-01

    Vertically integrated atmospheric moisture transport from ocean to land [vertically integrated atmospheric moisture flux convergence (VMFC)] is a dynamic component of the global climate system but remains problematic in atmospheric reanalyses, with current estimates having significant multidecadal global trends differing even in sign. Continual evolution of the global observing system, particularly stepwise improvements in satellite observations, has introduced discrete changes in the ability of data assimilation to correct systematic model biases, manifesting as nonphysical variability. Land surface models (LSMs) forced with observed precipitation P and near-surface meteorology and radiation provide estimates of evapotranspiration (ET). Since variability of atmospheric moisture storage is small on interannual and longer time scales, VMFC equals P minus ET is a good approximation and LSMs can provide an alternative estimate. However, heterogeneous density of rain gauge coverage, especially the sparse coverage over tropical continents, remains a serious concern. Rotated principal component analysis (RPCA) with prefiltering of VMFC to isolate the artificial variability is used to investigate artifacts in five reanalysis systems. This procedure, although ad hoc, enables useful VMFC corrections over global land. The P minus ET estimates from seven different LSMs are evaluated and subsequently used to confirm the efficacy of the RPCA-based adjustments. Global VMFC trends over the period 1979-2012 ranging from 0.07 to minus 0.03 millimeters per day per decade are reduced by the adjustments to 0.016 millimeters per day per decade, much closer to the LSM P minus ET estimate (0.007 millimeters per day per decade). Neither is significant at the 90 percent level. ENSO (El Nino-Southern Oscillation)-related modulation of VMFC and P minus ET remains the largest global interannual signal, with mean LSM and adjusted reanalysis time series correlating at 0.86.

  1. Calibration of an integrated land surface process and radiobrightness (LSP/R) model during summertime

    Science.gov (United States)

    Judge, Jasmeet; England, Anthony W.; Metcalfe, John R.; McNichol, David; Goodison, Barry E.

    2008-01-01

    In this study, a soil vegetation and atmosphere transfer (SVAT) model was linked with a microwave emission model to simulate microwave signatures for different terrain during summertime, when the energy and moisture fluxes at the land surface are strong. The integrated model, land surface process/radiobrightness (LSP/R), was forced with weather and initial conditions observed during a field experiment. It simulated the fluxes and brightness temperatures for bare soil and brome grass in the Northern Great Plains. The model estimates of soil temperature and moisture profiles and terrain brightness temperatures were compared with the observed values. Overall, the LSP model provides realistic estimates of soil moisture and temperature profiles to be used with a microwave model. The maximum mean differences and standard deviations between the modeled and the observed temperatures (canopy and soil) were 2.6 K and 6.8 K, respectively; those for the volumetric soil moisture were 0.9% and 1.5%, respectively. Brightness temperatures at 19 GHz matched well with the observations for bare soil, when a rough surface model was incorporated indicating reduced dielectric sensitivity to soil moisture by surface roughness. The brightness temperatures of the brome grass matched well with the observations indicating that a simple emission model was sufficient to simulate accurate brightness temperatures for grass typical of that region and surface roughness was not a significant issue for grass-covered soil at 19 GHz. Such integrated SVAT-microwave models allow for direct assimilation of microwave observations and can also be used to understand sensitivity of microwave signatures to changes in weather forcings and soil conditions for different terrain types.

  2. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    NARCIS (Netherlands)

    Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.

    2017-01-01

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of

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

    Science.gov (United States)

    Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

    2013-03-01

    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.

  4. 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.

  5. Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data

    Science.gov (United States)

    Abdolghafoorian, A.; Farhadi, L.

    2017-12-01

    Accurate estimation of land surface heat and moisture fluxes, as well as root zone soil moisture, is crucial in various hydrological, meteorological, and agricultural applications. Field measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state observations that are widely available from remote sensing across a range of scale. In this work, we applies the variational data assimilation approach to estimate land surface fluxes and soil moisture profile from the implicit information contained Land Surface Temperature (LST) and Soil Moisture (SM) (hereafter the VDA model). The VDA model is focused on the estimation of three key parameters: 1- neutral bulk heat transfer coefficient (CHN), 2- evaporative fraction from soil and canopy (EF), and 3- saturated hydraulic conductivity (Ksat). CHN and EF regulate the partitioning of available energy between sensible and latent heat fluxes. Ksat is one of the main parameters used in determining infiltration, runoff, groundwater recharge, and in simulating hydrological processes. In this study, a system of coupled parsimonious energy and water model will constrain the estimation of three unknown parameters in the VDA model. The profile of SM (LST) at multiple depths is estimated using moisture diffusion (heat diffusion) equation. In this study, the uncertainties of retrieved unknown parameters and fluxes are estimated from the inverse of Hesian matrix of cost function which is computed using the Lagrangian methodology. Analysis of uncertainty provides valuable information about the accuracy of estimated parameters and their correlation and guide the formulation of a well-posed estimation problem. The results of proposed algorithm are validated with a series of experiments using a synthetic data set generated by the simultaneous heat and

  6. Modeling large-scale human alteration of land surface hydrology and climate

    Science.gov (United States)

    Pokhrel, Yadu N.; Felfelani, Farshid; Shin, Sanghoon; Yamada, Tomohito J.; Satoh, Yusuke

    2017-12-01

    Rapidly expanding human activities have profoundly affected various biophysical and biogeochemical processes of the Earth system over a broad range of scales, and freshwater systems are now amongst the most extensively altered ecosystems. In this study, we examine the human-induced changes in land surface water and energy balances and the associated climate impacts using a coupled hydrological-climate model framework which also simulates the impacts of human activities on the water cycle. We present three sets of analyses using the results from two model versions—one with and the other without considering human activities; both versions are run in offline and coupled mode resulting in a series of four experiments in total. First, we examine climate and human-induced changes in regional water balance focusing on the widely debated issue of the desiccation of the Aral Sea in central Asia. Then, we discuss the changes in surface temperature as a result of changes in land surface energy balance due to irrigation over global and regional scales. Finally, we examine the global and regional climate impacts of increased atmospheric water vapor content due to irrigation. Results indicate that the direct anthropogenic alteration of river flow in the Aral Sea basin resulted in the loss of 510 km3 of water during the latter half of the twentieth century which explains about half of the total loss of water from the sea. Results of irrigation-induced changes in surface energy balance suggest a significant surface cooling of up to 3.3 K over 1° grids in highly irrigated areas but a negligible change in land surface temperature when averaged over sufficiently large global regions. Results from the coupled model indicate a substantial change in 2 m air temperature and outgoing longwave radiation due to irrigation, highlighting the non-local (regional and global) implications of irrigation. These results provide important insights on the direct human alteration of land surface

  7. 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

  8. 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

  9. Modeling the land surface heat exchange process with the aid of moderate resolution imaging spectroradiomer images

    Science.gov (United States)

    Gao, Zhiqiang; Zhang, Wenjiang; Gao, Wei; Chang, Ni-Bin

    2009-12-01

    Most ecosystems and crops experience water stress in arid and semiarid areas of the Inner Mongolia grassland, Northern China. Yet the lack of long-term in situ monitoring data hinders the managerial capacity of changing water vapor environment, which is tied with sustaining the grassland in the Inner Mongolia. Environmental remote sensing monitoring and modeling may provide synergistic means of observing changes in thermodynamic balance during drought onset at the grassland surface, providing reliable projections accounting for variations and correlations of water vapor and heat fluxes. It is the aim of this paper to present a series of estimates of latent heat, sensible heat, and net radiation using an innovative first-principle, physics-based model (GEOMOD: GEO-model estimated the land surface heat with MODis data) with the aid of integrated satellite remote sensing and in situ eddy covariance data. Based on the energy balance principle and aerodynamics diffusion theory, the GEOMOD model is featured with MODIS (Moderate Resolution Imaging Spectroradiometer) data with 250 m spatial resolution to collectively reflect the spatial heterogeneity of surface properties, supplement missing data with the neighborhood values across both spatial and temporal domains, estimate the surface roughness height and zero-plane displacement with dynamic look-up table, and implement a fast iterative algorithm to calculate sensible heat. Its analytical framework is designed against overreliance on local micro-meteorological parameters. Practical implementation was assessed in the study area, the Xilin Gol River Basin, a typical grassland environment, Northern China. With 179 days of MODIS data in support of modeling, coincident ground-based observations between 2000 and 2006 were selected for model calibration. The findings indicate that GEOMOD performs reasonably well in modeling the land surface heat exchange process, as demonstrated by a case study of Inner Mongolia.

  10. 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.

  11. Impact of Land Surface and Forcing Parameters on the Spin-up Behaviour of Noah Land Surface Model over the Indian Sub-Continent

    Science.gov (United States)

    Bhattacharya, A.; Satyanarayana, A. N. V.; Mandal, M.

    2018-01-01

    In the present study, an attempt is made to understand the influence of land surface parameters (such as soil moisture conditions, soil type and vegetation type) and forcing parameters on the model spin-up behaviour of a land surface model (LSM), namely Noah LSM, over the Indian sub-continent. The work presented here primarily aims to understand the optimum initial conditions to achieve the least spin-up time over the subtropical conditions that exist over the region of interest. The study is presented in three major parts. In the first part, a multivariate statistical analysis, namely principle component analysis is employed to investigate how parameters such as precipitation, air temperature, soil moisture, radiation components as well as various parameters that characterize soil and vegetation types influence the model spin-up. The second part deals with the study of the impact of soil and vegetation parameters in different seasons on the model spin-up behaviour. Finally, the third part looks into the influence of initial soil moisture condition and precipitation forcing on the spin-up behaviour of the model in different seasons to obtain the optimum initial conditions for the minimum spin-up time of the model. From the study, it is seen that the soil and vegetation type, as well as the soil moisture content influence the model spin-up significantly. The present study reports that the experiments initialized just before a continuous rainfall event has the least spin-up unless the initial soil is saturated.

  12. 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....

  13. Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia

    Science.gov (United States)

    Dumedah, Gift; Walker, Jeffrey P.

    2014-12-01

    Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model.

  14. Monitoring arid-land groundwater abstraction through optimization of a land surface model with remote sensing-based evaporation

    KAUST Repository

    Lopez Valencia, Oliver Miguel

    2018-02-01

    The increase in irrigated agriculture in Saudi Arabia is having a large impact on its limited groundwater resources. While large-scale water storage changes can be estimated using satellite data, monitoring groundwater abstraction rates is largely non-existent at either farm or regional level, so water management decisions remain ill-informed. Although determining water use from space at high spatiotemporal resolutions remains challenging, a number of approaches have shown promise, particularly in the retrieval of crop water use via evaporation. Apart from satellite-based estimates, land surface models offer a continuous spatial-temporal evolution of full land-atmosphere water and energy exchanges. In this study, we first examine recent trends in terrestrial water storage depletion within the Arabian Peninsula and explore its relation to increased agricultural activity in the region using satellite data. Next, we evaluate a number of large-scale remote sensing-based evaporation models, giving insight into the challenges of evaporation retrieval in arid environments. Finally, we present a novel method aimed to retrieve groundwater abstraction rates used in irrigated fields by constraining a land surface model with remote sensing-based evaporation observations. The approach is used to reproduce reported irrigation rates over 41 center-pivot irrigation fields presenting a range of crop dynamics over the course of one year. The results of this application are promising, with mean absolute errors below 3 mm:day-1, bias of -1.6 mm:day-1, and a first rough estimate of total annual abstractions of 65.8 Mm3 (close to the estimated value using reported farm data, 69.42 Mm3). However, further efforts to address the overestimation of bare soil evaporation in the model are required. The uneven coverage of satellite data within the study site allowed us to evaluate its impact on the optimization, with a better match between observed and obtained irrigation rates on fields with

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

    Directory of Open Access Journals (Sweden)

    A. Roy

    2013-06-01

    Full Text Available 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 the temperature gradient under dry snow conditions, while it considers the liquid water content of the snowpack for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, arctic and subarctic with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.0 m2 kg–1, and a root mean square error (RMSE of 5.1 m2 kg–1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

  16. The Interplay Between Transpiration and Runoff Formulations in Land Surface Schemes Used with Atmospheric Models

    Science.gov (United States)

    Koster, Rindal D.; Milly, P. C. D.

    1997-01-01

    The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) has shown that different land surface models (LSMS) driven by the same meteorological forcing can produce markedly different surface energy and water budgets, even when certain critical aspects of the LSMs (vegetation cover, albedo, turbulent drag coefficient, and snow cover) are carefully controlled. To help explain these differences, the authors devised a monthly water balance model that successfully reproduces the annual and seasonal water balances of the different PILPS schemes. Analysis of this model leads to the identification of two quantities that characterize an LSM's formulation of soil water balance dynamics: (1) the efficiency of the soil's evaporation sink integrated over the active soil moisture range, and (2) the fraction of this range over which runoff is generated. Regardless of the LSM's complexity, the combination of these two derived parameters with rates of interception loss, potential evaporation, and precipitation provides a reasonable estimate for the LSM's simulated annual water balance. The two derived parameters shed light on how evaporation and runoff formulations interact in an LSM, and the analysis as a whole underscores the need for compatibility in these formulations.

  17. 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

  18. 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.

  19. Assimilation of remotely sensed chlorophyll fluorescence data into the land surface model CLM4

    Science.gov (United States)

    Wieneke, S.; Ahrends, H. E.; Rascher, U.; Schween, J.; Schickling, A.; Crewell, S.

    2013-12-01

    Photosynthesis is the most important exchange process of CO2 between the atmosphere and the land-surface. Therefore, the prediction of vegetation response to environmental conditions like increasing CO2 concentrations or plant stress is crucial for a reliable prediction of climate change. Photosynthesis is a complex physiological process that consists of numerous bio-physical sub-processes and chemical reactions. Spatial and temporal patterns of photosynthesis depend on dynamic plant-specific adaptation strategies to highly variable environmental conditions. Photosynthesis can be estimated using land-surface models, but, while state-of-the-art models often rely on Plant Functional Type (PFT) specific constants, they poorly simulate the dynamic adaptation of the physiological status of plant canopies in space and time. Remotely sensed sun-induced chlorophyll fluorescence (SICF) gives us now the possibility to estimate the diurnal dynamic vitality of the photosynthetic apparatus at both, the leaf and canopy levels. We installed within the framework of the Transregio32 project (www.tr32.de) automated hyperspectral fluorescence sensors at an agricultural site (winter wheat) in the Rur catchment area in West Germany at the end of July 2012. End of August, additional measurements of SIFC on nearby temperate grassland site (riparian meadow) and on a sugar beet field were performed. Spatial covering SICF data of the region were obtained during a measurement campaign using the newly developed air-borne hyperspectral sensor HyPlant on the 23 and 27 August 2012. SIFC data and data provided by eddy covariance measurements will be used to update certain model parameters that are normally set as constants. First model results demonstrate that the assimilation of SIFC into the Community Land Model 4 (CLM4) will result in a more realistic simulation of plant-specific adaptation strategies and therefore in a more realistic simulation of photosynthesis in space and time.

  20. Use of Geostationary Satellite Data to Force Land Surface Schemes within Atmospheric Mesoscale Models

    Science.gov (United States)

    Lapenta, William M.; Suggs, Ron; McNider, Richard T.; Jedlovec, Gary; Dembek, Scott R.; Goodman, H. Michael (Technical Monitor)

    2000-01-01

    A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The technique has been employed on a semi-operational basis at the GHCC within the PSU/NCAR MM5. Assimilation has been performed on a grid centered over the Southeastern US since November 1998. Results from the past year show that assimilation of the satellite data reduces both the bias and RMSE for simulations of surface air temperature and relative humidity. These findings are based on comparison of assimilation runs with a control using the simple 5-layer soil model available in MM5. A significant development in the past several months was the inclusion of the detailed Oregon State University land surface model (OSU/LSM) as an option within MM5. One of our working hypotheses has been that the assimilation technique, although simple, may provide better short-term forecasts than a detailed LSM that requires significant number initialized parameters. Preliminary results indicate that the assimilation out performs the OSU

  1. Identifying and Evaluating the Relationships that Control a Land Surface Model's Hydrological Behavior

    Science.gov (United States)

    Koster, Randal D.; Mahanama, Sarith P.

    2012-01-01

    The inherent soil moisture-evaporation relationships used in today 's land surface models (LSMs) arguably reflect a lot of guesswork given the lack of contemporaneous evaporation and soil moisture observations at the spatial scales represented by regional and global models. The inherent soil moisture-runoff relationships used in the LSMs are also of uncertain accuracy. Evaluating these relationships is difficult but crucial given that they have a major impact on how the land component contributes to hydrological and meteorological variability within the climate system. The relationships, it turns out, can be examined efficiently and effectively with a simple water balance model framework. The simple water balance model, driven with multi-decadal observations covering the conterminous United States, shows how different prescribed relationships lead to different manifestations of hydrological variability, some of which can be compared directly to observations. Through the testing of a wide suite of relationships, the simple model provides estimates for the underlying relationships that operate in nature and that should be operating in LSMs. We examine the relationships currently used in a number of different LSMs in the context of the simple water balance model results and make recommendations for potential first-order improvements to these LSMs.

  2. 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

  3. Comparison of land surface humidity between observations and CMIP5 models

    Science.gov (United States)

    Dunn, Robert J. H.; Willett, Kate M.; Ciavarella, Andrew; Stott, Peter A.

    2017-08-01

    We compare the latest observational land surface humidity dataset, HadISDH, with the latest generation of climate models extracted from the CMIP5 archive and the ERA-Interim reanalysis over the period 1973 to present. The globally averaged behaviour of HadISDH and ERA-Interim are very similar in both humidity measures and air temperature, on decadal and interannual timescales. The global average relative humidity shows a gradual increase from 1973 to 2000, followed by a steep decline in recent years. The observed specific humidity shows a steady increase in the global average during the early period but in the later period it remains approximately constant. None of the CMIP5 models or experiments capture the observed behaviour of the relative or specific humidity over the entire study period. When using an atmosphere-only model, driven by observed sea surface temperatures and radiative forcing changes, the behaviour of regional average temperature and specific humidity are better captured, but there is little improvement in the relative humidity. Comparing the observed climatologies with those from historical model runs shows that the models are generally cooler everywhere, are drier and less saturated in the tropics and extra-tropics, and have comparable moisture levels but are more saturated in the high latitudes. The spatial pattern of linear trends is relatively similar between the models and HadISDH for temperature and specific humidity, but there are large differences for relative humidity, with less moistening shown in the models over the tropics and very little at high latitudes. The observed drying in mid-latitudes is present at a much lower magnitude in the CMIP5 models. Relationships between temperature and humidity anomalies (T-q and T-rh) show good agreement for specific humidity between models and observations, and between the models themselves, but much poorer for relative humidity. The T-q correlation from the models is more steeply positive than

  4. 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.

  5. Consequences of introducing bryophytes and Arctic-shrubs in a land surface model with dynamical vegetation.

    Science.gov (United States)

    Druel, A.; Peylin, P.; Krinner, G.; Ciais, P.; Viovy, N.

    2016-12-01

    Recent developments of boreal vegetation in land surface models show the importance of new plant functional types for a better representation of physical and carbon cycle related processes in northern latitudes. In past climate transitions, shifts in northern vegetation played a crucial role, for example in the inception of the Last Glacial Maximum. With the current high-latitude warming, a greening of vegetation is observed, associated with increased shrub cover. It has thus become essential to include shifts in vegetation in models. In the ORCHIDEE land surface model with a dynamic vegetation, we introduced new parameterizations and processes associated to Arctic-shrubs, bryophytes (mosses and lichens) and boreal C3 grasses to simulate their effect on biomass, albedo, snow cover and soil thermal dynamic (including frozen soils). Specific competition and survival conditions are defined for these three plant functional types. Competition between herbaceous vegetation, shrubs and trees is based on available light. Survival conditions of shrubs include their protection from cold temperatures by snow, and the competition between C3 grasses and bryophytes depends especially on soil water-saturation conditions. The equilibrium fractional coverage of the three competing plant functional types is based on the net primary production. We compare the results from simulations with different configurations: 1) vegetation being either fixed prescribed from a satellite land cover map or dynamic and 2) plant functional types used being either the default settings of ORCHIDEE which include three different boreal tree types and one grassland type, or the latter plus the new boreal vegetation types. The simulations are run for the historical period and with an additional run of 100 years according to the RCP 4.5 and 8.5 climate scenarios. We evaluate the effect of new plant functional types on the vegetation distribution, and their consequences for energy, water and carbon fluxes

  6. Introduction of a simple-model-based land surface dataset for Europe

    Science.gov (United States)

    Orth, Rene; Seneviratne, Sonia I.

    2015-04-01

    Land surface hydrology can play a crucial role during extreme events such as droughts, floods and even heat waves. We introduce in this study a new hydrological dataset for Europe that consists of soil moisture, runoff and evapotranspiration (ET). It is derived with a simple water balance model (SWBM) forced with precipitation, temperature and net radiation. The SWBM dataset extends over the period 1984-2013 with a daily time step and 0.5° × 0.5° resolution. We employ a novel calibration approach, in which we consider 300 random parameter sets chosen from an observation-based range. Using several independent validation datasets representing soil moisture (or terrestrial water content), ET and streamflow, we identify the best performing parameter set and hence the new dataset. To illustrate its usefulness, the SWBM dataset is compared against several state-of-the-art datasets (ERA-Interim/Land, MERRA-Land, GLDAS-2-Noah, simulations of the Community Land Model Version 4), using all validation datasets as reference. For soil moisture dynamics it outperforms the benchmarks. Therefore the SWBM soil moisture dataset constitutes a reasonable alternative to sparse measurements, little validated model results, or proxy data such as precipitation indices. Also in terms of runoff the SWBM dataset performs well, whereas the evaluation of the SWBM ET dataset is overall satisfactory, but the dynamics are less well captured for this variable. This highlights the limitations of the dataset, as it is based on a simple model that uses uniform parameter values. Hence some processes impacting ET dynamics may not be captured, and quality issues may occur in regions with complex terrain. Even though the SWBM is well calibrated, it cannot replace more sophisticated models; but as their calibration is a complex task the present dataset may serve as a benchmark in future. In addition we investigate the sources of skill of the SWBM dataset and find that the parameter set has a similar

  7. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation

    Science.gov (United States)

    Guimberteau, Matthieu; Zhu, Dan; Maignan, Fabienne; Huang, Ye; Yue, Chao; Dantec-Nédélec, Sarah; Ottlé, Catherine; Jornet-Puig, Albert; Bastos, Ana; Laurent, Pierre; Goll, Daniel; Bowring, Simon; Chang, Jinfeng; Guenet, Bertrand; Tifafi, Marwa; Peng, Shushi; Krinner, Gerhard; Ducharne, Agnès; Wang, Fuxing; Wang, Tao; Wang, Xuhui; Wang, Yilong; Yin, Zun; Lauerwald, Ronny; Joetzjer, Emilie; Qiu, Chunjing; Kim, Hyungjun; Ciais, Philippe

    2018-01-01

    The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance - those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest - are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.

  8. Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model

    Directory of Open Access Journals (Sweden)

    Ally M. Toure

    2018-02-01

    Full Text Available The NASA Catchment land surface model (CLSM is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA. Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF observations from the Moderate Resolution Imaging Spectroradiometer (MODIS. Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE through the assimilation of MODIS SCF observations. Results show that data assimilation (DA improved SCF estimates compared to the open-loop model without assimilation (OL, especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL. A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from −0.017 m for OL to −0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD (0.095 m for OL, 0.093 m for DA. The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from −0.111 m for OL to −0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.

  9. NLDAS Mosaic Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V002 (NLDAS_MOS0125_MC) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American...

  10. Development of a Cryosphere Land Surface Model with Coupled Snow and Frozen Soil Processes

    Science.gov (United States)

    Wang, L.; Sun, L.; Yang, K.; Tian, L.

    2015-12-01

    In this study, a land surface model with coupled snow and frozen soil physics has been developed by improving the formulations of snow and frozen soil for a hydrologically-improved land surface model (HydroSiB2). First, an energy-balance based 3-layer snow model has been incorporated into the HydroSiB2 (hereafter HydroSiB2-S) for an improved description of internal processes of snow pack. Second, a universal and simplified soil model has been coupled with HydroSiB2-S to enable the calculation of soil water freezing and thawing (hereafter HydroSiB2-SF). In order to avoid the instability caused by the uncertainty in estimating water phase changes, enthalpy is 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 rigorously evaluated at two typical sites over Tibetan Plateau (one snowy and the other non-snowy, with both underlying frozen soil). At the snowy site in northeast TP (DY in the upper Hei River), HydroSiB2-SF demonstrated significant improvements over HydroSiB2-F (that is the model same as HydroSiB2-SF but using the original single-layer snow module of HydroSiB2), showing the importance of snow internal processes described by 3-layer snow parameterization. At the non-snowy site in southwest TP (Ngari, extremely dry), HydroSiB2-SF gave reasonable simulations of soil water phase changes while HydroSiB2-S did not, indicating the crucial role of frozen soil module in depicting the soil thermal and water dynamics. Finally, HydroSiB2-SF was proved capable of simulating upward moisture fluxes towards freezing front from the unfrozen soil layers below in winter.

  11. 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

  12. 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.

  13. 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.

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

    International Nuclear Information System (INIS)

    Pohjola, J.; Turunen, J.; Lipping, T.; Ikonen, A.

    2014-03-01

    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 with other

  15. Coupling a three-dimensional subsurface flow and transport model with a land surface model to simulate stream–aquifer–land interactions (CP v1.0)

    Energy Technology Data Exchange (ETDEWEB)

    Bisht, Gautam; Huang, Maoyi; Zhou, Tian; Chen, Xingyuan; Dai, Heng; Hammond, Glenn E.; Riley, William J.; Downs, Janelle L.; Liu, Ying; Zachara, John M.

    2017-01-01

    A fully coupled three-dimensional surface and subsurface land model is developed and applied to a site along the Columbia River to simulate three-way interactions among river water, groundwater, and land surface processes. The model features the coupling of the Community Land Model version 4.5 (CLM4.5) and a massively parallel multiphysics reactive transport model (PFLOTRAN). The coupled model, named CP v1.0, is applied to a 400 m × 400 m study domain instrumented with groundwater monitoring wells along the Columbia River shoreline. CP v1.0 simulations are performed at three spatial resolutions (i.e., 2, 10, and 20 m) over a 5-year period to evaluate the impact of hydroclimatic conditions and spatial resolution on simulated variables. Results show that the coupled model is capable of simulating groundwater–river-water interactions driven by river stage variability along managed river reaches, which are of global significance as a result of over 30 000 dams constructed worldwide during the past half-century. Our numerical experiments suggest that the land-surface energy partitioning is strongly modulated by groundwater–river-water interactions through expanding the periodically inundated fraction of the riparian zone, and enhancing moisture availability in the vadose zone via capillary rise in response to the river stage change. Meanwhile, CLM4.5 fails to capture the key hydrologic process (i.e., groundwater–river-water exchange) at the site, and consequently simulates drastically different water and energy budgets. Furthermore, spatial resolution is found to significantly impact the accuracy of estimated the mass exchange rates at the boundaries of the aquifer, and it becomes critical when surface and subsurface become more tightly coupled with groundwater table within 6 to 7 meters below the surface. Inclusion of lateral subsurface flow influenced both the surface energy budget and subsurface transport processes as a result of river-water intrusion

  16. Coupling a three-dimensional subsurface flow and transport model with a land surface model to simulate stream–aquifer–land interactions (CP v1.0

    Directory of Open Access Journals (Sweden)

    G. Bisht

    2017-12-01

    Full Text Available A fully coupled three-dimensional surface and subsurface land model is developed and applied to a site along the Columbia River to simulate three-way interactions among river water, groundwater, and land surface processes. The model features the coupling of the Community Land Model version 4.5 (CLM4.5 and a massively parallel multiphysics reactive transport model (PFLOTRAN. The coupled model, named CP v1.0, is applied to a 400 m × 400 m study domain instrumented with groundwater monitoring wells along the Columbia River shoreline. CP v1.0 simulations are performed at three spatial resolutions (i.e., 2, 10, and 20 m over a 5-year period to evaluate the impact of hydroclimatic conditions and spatial resolution on simulated variables. Results show that the coupled model is capable of simulating groundwater–river-water interactions driven by river stage variability along managed river reaches, which are of global significance as a result of over 30 000 dams constructed worldwide during the past half-century. Our numerical experiments suggest that the land-surface energy partitioning is strongly modulated by groundwater–river-water interactions through expanding the periodically inundated fraction of the riparian zone, and enhancing moisture availability in the vadose zone via capillary rise in response to the river stage change. Meanwhile, CLM4.5 fails to capture the key hydrologic process (i.e., groundwater–river-water exchange at the site, and consequently simulates drastically different water and energy budgets. Furthermore, spatial resolution is found to significantly impact the accuracy of estimated the mass exchange rates at the boundaries of the aquifer, and it becomes critical when surface and subsurface become more tightly coupled with groundwater table within 6 to 7 meters below the surface. Inclusion of lateral subsurface flow influenced both the surface energy budget and subsurface transport processes as a result

  17. Coupling a three-dimensional subsurface flow and transport model with a land surface model to simulate stream-aquifer-land interactions (CP v1.0)

    Science.gov (United States)

    Bisht, Gautam; Huang, Maoyi; Zhou, Tian; Chen, Xingyuan; Dai, Heng; Hammond, Glenn E.; Riley, William J.; Downs, Janelle L.; Liu, Ying; Zachara, John M.

    2017-12-01

    A fully coupled three-dimensional surface and subsurface land model is developed and applied to a site along the Columbia River to simulate three-way interactions among river water, groundwater, and land surface processes. The model features the coupling of the Community Land Model version 4.5 (CLM4.5) and a massively parallel multiphysics reactive transport model (PFLOTRAN). The coupled model, named CP v1.0, is applied to a 400 m × 400 m study domain instrumented with groundwater monitoring wells along the Columbia River shoreline. CP v1.0 simulations are performed at three spatial resolutions (i.e., 2, 10, and 20 m) over a 5-year period to evaluate the impact of hydroclimatic conditions and spatial resolution on simulated variables. Results show that the coupled model is capable of simulating groundwater-river-water interactions driven by river stage variability along managed river reaches, which are of global significance as a result of over 30 000 dams constructed worldwide during the past half-century. Our numerical experiments suggest that the land-surface energy partitioning is strongly modulated by groundwater-river-water interactions through expanding the periodically inundated fraction of the riparian zone, and enhancing moisture availability in the vadose zone via capillary rise in response to the river stage change. Meanwhile, CLM4.5 fails to capture the key hydrologic process (i.e., groundwater-river-water exchange) at the site, and consequently simulates drastically different water and energy budgets. Furthermore, spatial resolution is found to significantly impact the accuracy of estimated the mass exchange rates at the boundaries of the aquifer, and it becomes critical when surface and subsurface become more tightly coupled with groundwater table within 6 to 7 meters below the surface. Inclusion of lateral subsurface flow influenced both the surface energy budget and subsurface transport processes as a result of river-water intrusion into the

  18. 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.

  19. Land surface model evaluation using a new soil moisture dataset from Kamennaya Steppe, Russia

    Science.gov (United States)

    Atkins, T.; Robock, A.; Speranskaya, N.

    2004-12-01

    The land surface affects the atmosphere through the transfer of energy and moisture and serves as the lower boundary in numerical weather prediction and climate models. To obtain good forecasts, these models must therefore accurately portray the land surface. Actual in situ measurements are vital for testing and developing these models. It is with this in mind that we have obtained a dataset of soil moisture, soil temperature and meteorological measurements from Kamennaya Steppe, Russia. The meteorological dataset spans the time period 1965-1991, while the soil moisture dataset runs from 1956-1991. The soil moisture dataset contains gravimetric volumetric total soil moisture measurements for 10 layers taken from forest, agricultural and grassland soils. The meteorological dataset contains 3-hourly measurements of precipitation, temperature, wind speed, pressure and relative humidity. We obtained longwave and shortwave radiation data from standard formulae. The data will be made available to the public via the Rutgers University Center for Environmental Prediction Global Soil Moisture Data Bank. Soil temperature is important in determining the timing, duration and intensity of runoff and snowmelt, particularly at the beginning and end of the winter when the ground is only partially frozen. Soil temperature can in turn be affected by the vertical distribution of roots. The soil temperature data are for 1969-1991. The data are daily averaged for every 20 cm to 1.2 meters in depth. These data are used to investigate the natural sensitivity of soil temperature to vegetation type and root distribution. We also use the temperature data, as well as water balance and snowfall data to test the sensitivity of the Noah land surface model (LSM) soil temperature to vertical root distribution, and what effect that has on the hydrology of the site. In addition to soil temperature data, we also have soil moisture data for several vegetation types. We compare the soil moisture time

  20. Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

    Directory of Open Access Journals (Sweden)

    Maria Lumbierres

    2017-04-01

    Full Text Available Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources—particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP derived from MODIS, which we calibrate and validate in the Doñana National Park’s marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI correlated best with ground-truth data of biomass production at five locations from 2001–2015 used to calibrate the models (R2 = 0.65. Estimators based on a single MODIS NDVI image performed worse (R2 ≤ 0.41. The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems.

  1. Quality assurance of in-situ measurements of land surface albedo: A model-based approach

    Science.gov (United States)

    Adams, Jennifer; Gobron, Nadine; Widlowski, Jean-Luc; Mio, Corrado

    2016-04-01

    This paper presents the development of a model-based framework for assessing the quality of in-situ measurements of albedo used to validate land surface albedo products. Using a 3D Monte Carlo Ray Tracing (MCRT) radiative transfer model, a quality assurance framework is built based on simulated field measurements of albedo within complex 3D canopies and under various illumination scenarios. This method provides an unbiased approach in assessing the quality of field measurements, and is also able to trace the contributions of two main sources of uncertainty in field-measurements of albedo; those resulting from 1) the field measurement protocol, such as height or placement of field measurement within the canopy, and 2) intrinsic factors of the 3D canopy under specific illumination characteristics considered, such as the canopy structure and landscape heterogeneity, tree heights, ecosystem type and season.

  2. 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

  3. 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.

  4. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

    Science.gov (United States)

    Suzuki, Kazuyoshi; Zupanski, Milija

    2018-01-01

    In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere-land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

  5. The Importance of Representing Certain Key Vegetation Canopy Processes Explicitly in a Land Surface Model

    Science.gov (United States)

    Napoly, A.; Boone, A. A.; Martin, E.; Samuelsson, P.

    2015-12-01

    Land surface models are moving to more detailed vegetation canopy descriptions in order to better represent certain key processes, such as Carbon dynamics and snowpack evolution. Since such models are usually applied within coupled numerical weather prediction or spatially distributed hydrological models, these improvements must strike a balance between computational cost and complexity. The consequences of simplified or composite canopy approaches can be manifested in terms of increased errors with respect to soil temperatures, estimates of the diurnal cycle of the turbulent fluxes or snow canopy interception and melt. Vegetated areas and particularly forests are modeled in a quite simplified manner in the ISBA land surface model. However, continuous developments of surface processes now require a more accurate description of the canopy. A new version of the the model now includes a multi energy balance (MEB) option to explicitly represent the canopy and the forest floor. It will be shown that certain newly included processes such as the shading effect of the vegetation, the explicit heat capacity of the canopy, and the insulating effect of the forest floor turn out to be essential. A detailed study has been done for four French forested sites. It was found that the MEB option significantly improves the ground heat flux (RMSE decrease from 50W/m2 to 10W/m2 on average) and soil temperatures when compared against measurements. Also the sensible heat flux calculation was improved primarily owing to a better phasing with the solar insulation owing to a lower vegetation heat capacity. However, the total latent heat flux is less modified compared to the classical ISBA simulation since it is more related to water uptake and the formulation of the stomatal resistance (which are unchanged). Next, a benchmark over 40 Fluxnet sites (116 cumulated years) was performed and compared with results from the default composite soil-vegetation version of ISBA. The results show

  6. 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.

  7. 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; hide

    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.

  8. High Resolution Land Surface Modeling over the NEF Basin in the Chilean Patagonia

    Science.gov (United States)

    Somos-Valenzuela, M. A.

    2017-12-01

    Stakeholders and policy makers perceive that water and climate change adaptation are among the most vulnerable issues that need to be addressed. Therefore, there is a need not only from the scientific community but also from the society to use integrated methodologies that link advances in climatology with hydrology to provide data that helps us to provide adaptation strategies. The Andes and the Chilean-Argentinean Patagonia have been steadily warming up to 0.5 Celsius degrees per decades at the same time that precipitation is decreasing by 10 to 12 % per decade. In the future is expected that these trends will continue which will have impacts in the annual water budget. The Chilean Patagonia has brought a lot of attention during last decade because two hydroelectric project seek to build dams in one of the most pristine environments in the world. Also, in the Baker River basin a series of Glacier Lakes Outburst Flood have occurred which is perceived as an undeniable consequence of the effects of climate change in the glacier system. Major attention is mainly situated in the impact of climate change in glaciers contribution to sea level rise, GLOF studies given the numerous supra glacier lakes that are forming, and the study of stream flow point observation. The objectives of this research are: 1) Study the historical trends of precipitation, temperature, land cover changes and streamflow available in the Baker Basin; 2) Use a couple glacier model with a land surface model to predict the evolution of glaciers and their effects in the water availability. To address these objectives, I will analyze trends in hydro meteorology observations and correlation with trends in Land Cover Changes. Use the WRF-hydro framework to generate data in a small watershed that will allow to calibrate a high resolution hydro glaciology model to understand the partition between glaciered and non-glaciered runoff. The parameters estimated in the small domain could have the potential to

  9. Reconstruction of droughts in India using multiple land-surface models (1951-2015)

    Science.gov (United States)

    Mishra, Vimal; Shah, Reepal; Azhar, Syed; Shah, Harsh; Modi, Parth; Kumar, Rohini

    2018-04-01

    India has witnessed some of the most severe historical droughts in the current decade, and severity, frequency, and areal extent of droughts have been increasing. As a large part of the population of India is dependent on agriculture, soil moisture drought affecting agricultural activities (crop yields) has significant impacts on socio-economic conditions. Due to limited observations, soil moisture is generally simulated using land-surface hydrological models (LSMs); however, these LSM outputs have uncertainty due to many factors, including errors in forcing data and model parameterization. Here we reconstruct agricultural drought events over India during the period of 1951-2015 based on simulated soil moisture from three LSMs, the Variable Infiltration Capacity (VIC), the Noah, and the Community Land Model (CLM). Based on simulations from the three LSMs, we find that major drought events occurred in 1987, 2002, and 2015 during the monsoon season (June through September). During the Rabi season (November through February), major soil moisture droughts occurred in 1966, 1973, 2001, and 2003. Soil moisture droughts estimated from the three LSMs are comparable in terms of their spatial coverage; however, differences are found in drought severity. Moreover, we find a higher uncertainty in simulated drought characteristics over a large part of India during the major crop-growing season (Rabi season, November to February: NDJF) compared to those of the monsoon season (June to September: JJAS). Furthermore, uncertainty in drought estimates is higher for severe and localized droughts. Higher uncertainty in the soil moisture droughts is largely due to the difference in model parameterizations (especially soil depth), resulting in different persistence of soil moisture simulated by the three LSMs. Our study highlights the importance of accounting for the LSMs' uncertainty and consideration of the multi-model ensemble system for the real-time monitoring and prediction of

  10. 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; hide

    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.

  11. 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

  12. 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.

  13. 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

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

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    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.

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

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    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.

  16. 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

  17. Evaluating the Hydrologic Sensitivities of Three Land Surface Models to Bound Uncertainties in Runoff Projections

    Science.gov (United States)

    Chiao, T.; Nijssen, B.; Stickel, L.; Lettenmaier, D. P.

    2013-12-01

    Hydrologic modeling is often used to assess the potential impacts of climate change on water availability and quality. A common approach in these studies is to calibrate the selected model(s) to reproduce historic stream flows prior to the application of future climate projections. This approach relies on the implicit assumptions that the sensitivities of these models to meteorological fluctuations will remain relatively constant under climate change and that these sensitivities are similar among models if all models are calibrated to the same historic record. However, even if the models are able to capture the historic variability in hydrological variables, differences in model structure and parameter estimation contribute to the uncertainties in projected runoff, which confounds the incorporation of these results into water resource management decision-making. A better understanding of the variability in hydrologic sensitivities between different models can aid in bounding this uncertainty. In this research, we characterized the hydrologic sensitivities of three watershed-scale land surface models through a case study of the Bull Run watershed in Northern Oregon. The Distributed Hydrology Soil Vegetation Model (DHSVM), Precipitation-Runoff Modeling System (PRMS), and Variable Infiltration Capacity model (VIC) were implemented and calibrated individually to historic streamflow using a common set of long-term, gridded forcings. In addition to analyzing model performances for a historic period, we quantified the temperature sensitivity (defined as change in runoff in response to change in temperature) and precipitation elasticity (defined as change in runoff in response to change in precipitation) of these three models via perturbation of the historic climate record using synthetic experiments. By comparing how these three models respond to changes in climate forcings, this research aims to test the assumption of constant and similar hydrologic sensitivities. Our

  18. Long term, non-anthropogenic groundwater storage changes simulated by a global land surface model

    Science.gov (United States)

    Li, B.; Rodell, M.; Sheffield, J.; Wood, E. F.

    2017-12-01

    Groundwater is crucial for meeting agricultural, industrial and municipal water needs, especially in arid, semi-arid and drought impacted regions. Yet, knowledge on groundwater response to climate variability is not well understood due to lack of systematic and continuous in situ measurements. In this study, we investigate global non-anthropogenic groundwater storage variations with a land surface model driven by a 67-year (1948-204) meteorological forcing data set. Model estimates were evaluated using in situ groundwater data from the central and northeastern U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites and found to be reasonable. Empirical orthogonal function (EOF) analysis was employed to examine modes of variability of groundwater storage and their relationship with atmospheric effects such as precipitation and evapotranspiration. The result shows that the leading mode in global groundwater storage reflects the influence of the El Niño Southern Oscillation (ENSO). Consistent with the EOF analysis, global total groundwater storage reflected the low frequency variability of ENSO and decreased significantly over 1948-2014 while global ET and precipitation did not exhibit statistically significant trends. This study suggests that while precipitation and ET are the primary drivers of climate related groundwater variability, changes in other forcing fields than precipitation and temperature are also important because of their influence on ET. We discuss the need to improve model physics and to continuously validate model estimates and forcing data for future studies.

  19. Latent Heat Flux Estimate Through an Energy Water Balance Model and Land Surface Temperature from Remote Sensing

    Science.gov (United States)

    Corbari, Chiara; Sobrino, Jose A.; Mancini, Marco; Hidalgo, Victoria

    2011-01-01

    Soil moisture plays a key role in the terrestrial water cycle and is responsible for the partitioning of precipitation between runoff and infiltration. Moreover, surface soil moisture controls the redistribution of the incoming solar radiation on land surface into sensible and latent heat fluxes. Recent developments have been made to improve soil moisture dynamics predictions with hydrologic land surface models (LSMs) that compute water and energy balances between the land surface and the low atmosphere. However, most of the time soil moisture is confined to an internal numerical model variable mainly due to its intrinsic space and time variability and to the well known difficulties in assessing its value from remote sensing as from in situ measurements. In order to exploit the synergy between hydrological distributed models and thermal remote sensed data, FEST-EWB, a land surface model that solves the energy balance equation, was developed. In this hydrological model, the energy budget is solved looking for the representative thermodynamic equilibrium temperature (RET) defined as the land surface temperature that closes the energy balance equation. So using this approach, soil moisture is linked to the latent heat flux and then to LST. In this work the relationship between land surface temperature and soil moisture is analysed using LST from AHS (airborne hyperspectral scanner), with a spatial resolution of 2-4 m, LST from MODIS, with a spatial resolution of 1000 m, and thermal infrared radiometric ground measurements that are compared with the thermodynamic equilibrium temperature from the energy water balance model. Moreover soil moisture measurements were carried out during the airborne overpasses and then compared with SM from the hydrological model. An improvement of this well known inverse relationship between soil moisture and land surface temperature is obtained when the thermodynamic approach is used. The analysis of the scale effects of the different

  20. Modelling monthly runoff generation processes following land use changes: groundwater–surface runoff interactions

    Directory of Open Access Journals (Sweden)

    M. Bari

    2004-01-01

    Full Text Available A conceptual water balance model is presented to represent changes in monthly water balance following land use changes. Monthly rainfall–runoff, groundwater and soil moisture data from four experimental catchments in Western Australia have been analysed. Two of these catchments, 'Ernies' (control, fully forested and 'Lemon' (54% cleared are in a zone of mean annual rainfall of 725 mm, while 'Salmon' (control, fully forested and 'Wights' (100% cleared are in a zone with mean annual rainfall of 1125 mm. At the Salmon forested control catchment, streamflow comprises surface runoff, base flow and interflow components. In the Wights catchment, cleared of native forest for pasture development, all three components increased, groundwater levels rose significantly and stream zone saturated area increased from 1% to 15% of the catchment area. It took seven years after clearing for the rainfall–runoff generation process to stabilise in 1984. At the Ernies forested control catchment, the permanent groundwater system is 20 m below the stream bed and so does not contribute to streamflow. Following partial clearing of forest in the Lemon catchment, groundwater rose steadily and reached the stream bed by 1987. The streamflow increased in two phases: (i immediately after clearing due to reduced evapotranspiration, and (ii through an increase in the groundwater-induced stream zone saturated area after 1987. After analysing all the data available, a conceptual monthly model was created, comprising four inter-connecting stores: (i an upper zone unsaturated store, (ii a transient stream zone store, (ii a lower zone unsaturated store and (iv a saturated groundwater store. Data such as rooting depth, Leaf Area Index, soil porosity, profile thickness, depth to groundwater, stream length and surface slope were incorporated into the model as a priori defined attributes. The catchment average values for different stores were determined through matching observed and

  1. Assimilation of Gridded Terrestrial Water Storage Observations from GRACE into a Land Surface Model

    Science.gov (United States)

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

    2016-01-01

    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 km(sup 2) 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

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

    Directory of Open Access Journals (Sweden)

    Y. Masutomi

    2016-11-01

    Full Text Available 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.

  3. 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...

  4. 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

  5. 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

  6. Assimilation of Remotely Sensed Fluorescence Data into the Land-Surface Model CLM4

    Science.gov (United States)

    Wieneke, S.; Ahrends, H. E.; Rascher, U.; Schickling, A.; Schween, J.; Crewell, S.

    2012-12-01

    The most important exchange process of CO2 between the atmosphere and the land-surface is photosynthesis. Therefore, the prediction of vegetation response to increasing atmospheric CO2 concentrations is crucial for a reliable prediction of climate change. Photosynthesis is a complex physiological process that consists of numerous bio-physical sub-processes and chemical reactions. Spatial and temporal patterns of photosynthesis depend on dynamic plant-specific adaptation strategies to highly variable environmental conditions. Photosynthesis can be estimated using land-surface models, but, while state-of-the-art models often rely on plant specific constants, they poorly simulate the dynamic adaptation of the physiological status of plant canopies. Another way to estimate photosynthesis is the measurement of sun-induced chlorophyll fluorescence (SICF). Several studies over the last decade have demonstrated that SICF is a promising proxy to estimate the diurnal dynamic vitality of the photosynthetic apparatus at both the leaf and canopy levels. Recent studies have shown, that the weak SICF signal is also detectable from air- and space-borne sensors. Time series of SICF can already be derived from ground based measurements and first spatial maps from air- and space-borne sensors are available. It is expected that in the preparation of the European satellite mission FLEX further spatial and temporal data sets will become available. Although, it is not possible to derive data sets that have a diurnal and spatial resolution at the same time, spatial data sets could be used to update certain model parameters that are normally set as constants. This will result in a better simulation of plant-specific adaptation strategies. In this study we focus on the retrieval of the maximum rate of carboxylation (Vcmax), by implementing the SICF signal into the photosynthesis module of the Community Land Model 4 (CLM4; http://www.cgd.ucar.edu/tss/clm), which is based on the Farquhar

  7. 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.

  8. 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

  9. 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

  10. 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

  11. The Impact of Model and Rainfall Forcing Errors on Characterizing Soil Moisture Uncertainty in Land Surface Modeling

    Science.gov (United States)

    Maggioni, V.; Anagnostou, E. N.; Reichle, R. H.

    2013-01-01

    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.

  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. Modeling land surface hydrology sensitivity in the Colorado River Basin to historical climate variability

    Science.gov (United States)

    Whitney, K. M.; Bohn, T. J.; Vivoni, E. R.

    2017-12-01

    Over the past century, the Colorado River Basin (CRB) has experienced substantial warming and interannual climate variations, including prolonged drought periods. These patterns are projected to accelerate in the 21st century, with major consequences for water resources in the southwestern U.S. and northwestern Mexico. To evaluate future projections appropriately, however, it is important to first quantify the regional hydrologic response to historical climate variability in the CRB. In the current effort, we force the Variable Infiltration Capacity (VIC) land surface hydrology model and a river routing model with historical meteorological data to estimate water balance components and naturalized streamflow response in the CRB at 1/16o spatial resolution and at an hourly time step over the period 1950-2013. We utilize data products from satellite remote sensing to specify spatiotemporal variations in vegetation parameters and include an irrigation scheme to account for evapotranspiration from croplands in the CRB. Furthermore, we apply recent modifications in VIC to more properly account for bare soil evaporation in arid and semiarid ecosystems. Analyses of the historical model simulations are focused on quantifying the spatiotemporal variability of the soil moisture, evapotranspiration, streamflow and snowmelt response and their linkages to extreme meteorological events. Here we characterize the annual and monthly distributions, trends, and statistical extremes and central tendencies of water balance terms averaged over the CRB and its sub-basins for the entire study period 1950-2013. By building a model-based hydrologic climatology and catalog of historical extreme events for the CRB, we aim to construct a basis for future activities that analyze the impact of statistically downscaled climate change projections on the hydrology of the CRB and its urban areas.

  14. Simulating damage for wind storms in the land surface model ORCHIDEE-CAN (revision 4262)

    Science.gov (United States)

    Chen, Yi-Ying; Gardiner, Barry; Pasztor, Ferenc; Blennow, Kristina; Ryder, James; Valade, Aude; Naudts, Kim; Otto, Juliane; McGrath, Matthew J.; Planque, Carole; Luyssaert, Sebastiaan

    2018-03-01

    Earth system models (ESMs) are currently the most advanced tools with which to study the interactions among humans, ecosystem productivity, and the climate. The inclusion of storm damage in ESMs has long been hampered by their big-leaf approach, which ignores the canopy structure information that is required for process-based wind-throw modelling. Recently the big-leaf assumptions in the large-scale land surface model ORCHIDEE-CAN were replaced by a three-dimensional description of the canopy structure. This opened the way to the integration of the processes from the small-scale wind damage risk model ForestGALES into ORCHIDEE-CAN. The integration of ForestGALES into ORCHIDEE-CAN required, however, developing numerically efficient solutions to deal with (1) landscape heterogeneity, i.e. account for newly established forest edges for the parameterization of gusts; (2) downscaling spatially and temporally aggregated wind fields to obtain more realistic wind speeds that would represents gusts; and (3) downscaling storm damage within the 2500 km2 pixels of ORCHIDEE-CAN. This new version of ORCHIDEE-CAN was parameterized over Sweden. Subsequently, the performance of the model was tested against data for historical storms in southern Sweden between 1951 and 2010 and south-western France in 2009. In years without big storms, here defined as a storm damaging less than 15 × 106 m3 of wood in Sweden, the model error is 1.62 × 106 m3, which is about 100 % of the observed damage. For years with big storms, such as Gudrun in 2005, the model error increased to 5.05 × 106 m3, which is between 10 and 50 % of the observed damage. When the same model parameters were used over France, the model reproduced a decrease in leaf area index and an increase in albedo, in accordance with SPOT-VGT and MODIS records following the passing of Cyclone Klaus in 2009. The current version of ORCHIDEE-CAN (revision 4262) is therefore expected to have the capability to capture the dynamics of

  15. 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

  16. 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.

  17. Modelling monthly runoff generation processes following land use changes: groundwater-surface runoff interactions

    Science.gov (United States)

    Bari, M.; Smettem, K. R. J.

    A conceptual water balance model is presented to represent changes in monthly water balance following land use changes. Monthly rainfall-runoff, groundwater and soil moisture data from four experimental catchments in Western Australia have been analysed. Two of these catchments, "Ernies" (control, fully forested) and "Lemon" (54% cleared) are in a zone of mean annual rainfall of 725 mm, while "Salmon" (control, fully forested) and "Wights" (100% cleared) are in a zone with mean annual rainfall of 1125 mm. At the Salmon forested control catchment, streamflow comprises surface runoff, base flow and interflow components. In the Wights catchment, cleared of native forest for pasture development, all three components increased, groundwater levels rose significantly and stream zone saturated area increased from 1% to 15% of the catchment area. It took seven years after clearing for the rainfall-runoff generation process to stabilise in 1984. At the Ernies forested control catchment, the permanent groundwater system is 20 m below the stream bed and so does not contribute to streamflow. Following partial clearing of forest in the Lemon catchment, groundwater rose steadily and reached the stream bed by 1987. The streamflow increased in two phases: (i) immediately after clearing due to reduced evapotranspiration, and (ii) through an increase in the groundwater-induced stream zone saturated area after 1987. After analysing all the data available, a conceptual monthly model was created, comprising four inter-connecting stores: (i) an upper zone unsaturated store, (ii) a transient stream zone store, (ii) a lower zone unsaturated store and (iv) a saturated groundwater store. Data such as rooting depth, Leaf Area Index, soil porosity, profile thickness, depth to groundwater, stream length and surface slope were incorporated into the model as a priori defined attributes. The catchment average values for different stores were determined through matching observed and predicted

  18. 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...

  19. On the assessment of urban land-surface impacts on climate in regional climate model simulations over Central Europe

    Science.gov (United States)

    Huszar, Peter; Belda, Michal; Halenka, Tomas

    2016-04-01

    When aiming higher resolution in dynamical downscaling, which is common trend in CORDEX activities, the effects of land use and land use changes are playing increasing role. This is especially true for the urban areas, which in high resolution can occupy significant part of a single gridbox, if not being even bigger in case of big cities or megacities. Moreover, the role of cities will increase in future, as the population within the urban areas is growing faster, with the estimate for Europe of about 84% living in cities. For the purpose of qualifying and quantifying the impact of cities and in general the urban surfaces on climate, the surface parameterization in regional climate model RegCM4 has been coupled with the Single Layer Urban Canopy Model (SLUCM), which can be used both in dynamic scale within BATS scheme and in a more detailed SUBBATS scale to treat the surface on a higher resolution subgrid. A set of experiments was performed over the period of 2005-2009 over central Europe, either without considering urban surfaces and with the SLUCM treatment. Results show a statistically significant impact of urbanized surfaces on temperature (up to 1.5 K increase in summer), on the boundary layer height (ZPBL, increases up to 50 m). Additionally, the version of land-surface scheme using CLM is tested and effect of the urban environment, which is included in the CLM scheme, will be assessed. Both versions will be compared and validated using EOBS data.

  20. 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...

  1. Modelling the variation of land surface temperature as determinant of risk of heat-related health events

    Directory of Open Access Journals (Sweden)

    Goudreau Sophie

    2011-01-01

    Full Text Available Abstract Background The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological conditions based on satellite and meteorological data. Methods A general linear model was used to estimate surface temperatures using 15 LANDSAT 5 and LANDSAT 7 images for Quebec Province, Canada between 1987 and 2002 and spanning the months of June to August. The images encompassed both rural and urban landscapes and predictors included: meteorological records of temperature and wind speed, distance to major water bodies, Normalized Differential Vegetation Index (NDVI, land cover (built and bare land, water, or vegetation, latitude, longitude, and week of the year. Results The model explained 77% of the variance in surface temperature, accounting for both temporal and spatial variations. The standard error of estimates was 1.42°C. Land cover and NDVI were strong predictors of surface temperature. Conclusions This study suggests that a statistical approach to estimating surface temperature incorporating both spatially explicit satellite data and time-varying meteorological data may be relevant to assessing exposure to heat during the warm season in the Quebec. By allowing the estimation of space- and time-specific surface temperatures, this model may also be used to assess the possible impacts of land use changes under various meteorological conditions. It can be applied to assess heat exposure within a large population and at relatively fine-grained scale. It may be used to evaluate the acute health effect of heat exposure over long time frames. The method proposed here could be replicated in other areas around the globe for which satellite data and meteorological data is available.

  2. Modelling the variation of land surface temperature as determinant of risk of heat-related health events.

    Science.gov (United States)

    Kestens, Yan; Brand, Allan; Fournier, Michel; Goudreau, Sophie; Kosatsky, Tom; Maloley, Matthew; Smargiassi, Audrey

    2011-01-21

    The evaluation of exposure to ambient temperatures in epidemiological studies has generally been based on records from meteorological stations which may not adequately represent local temperature variability. Here we propose a spatially explicit model to estimate local exposure to temperatures of large populations under various meteorological conditions based on satellite and meteorological data. A general linear model was used to estimate surface temperatures using 15 LANDSAT 5 and LANDSAT 7 images for Quebec Province, Canada between 1987 and 2002 and spanning the months of June to August. The images encompassed both rural and urban landscapes and predictors included: meteorological records of temperature and wind speed, distance to major water bodies, Normalized Differential Vegetation Index (NDVI), land cover (built and bare land, water, or vegetation), latitude, longitude, and week of the year. The model explained 77% of the variance in surface temperature, accounting for both temporal and spatial variations. The standard error of estimates was 1.42°C. Land cover and NDVI were strong predictors of surface temperature. This study suggests that a statistical approach to estimating surface temperature incorporating both spatially explicit satellite data and time-varying meteorological data may be relevant to assessing exposure to heat during the warm season in the Quebec. By allowing the estimation of space- and time-specific surface temperatures, this model may also be used to assess the possible impacts of land use changes under various meteorological conditions. It can be applied to assess heat exposure within a large population and at relatively fine-grained scale. It may be used to evaluate the acute health effect of heat exposure over long time frames. The method proposed here could be replicated in other areas around the globe for which satellite data and meteorological data is available.

  3. Modeling Future Land Cover Changes and Their Effects on the Land Surface Temperatures in the Saudi Arabian Eastern Coastal City of Dammam

    Directory of Open Access Journals (Sweden)

    Muhammad Tauhidur Rahman

    2017-05-01

    Full Text Available Over the past several decades, Saudi cities have experienced rapid urban developments and land use and land cover (LULC changes. These developments will have numerous short- and long-term consequences including increasing the land surface temperature (LST of these cities. This study investigated the effects of LULC changes on the LST for the eastern coastal city of Dammam. Using Landsat imagery, the study first detected the LULC using the maximum likelihood classification method and derived the LSTs for the years 1990, 2002, and 2014. Using the classified results, it then modeled the future LULC for 2026 using the Cellular Automata Markov (CAM model. Finally, using three thematic indices and linear regression analysis, it then modeled the LST for 2026 as well. The built-up area in Dammam increased by 28.9% between 1990 and 2014. During this period, the average LSTs for the LULC classes increased as well, with bare soil and built-up area having the highest LST. By 2026, the urban area is expected to encompass 55% of the city and 98% of the land cover is envisioned to have average LSTs over 41 °C. Such high temperatures will make it difficult for the residents to live in the area.

  4. Understanding Changes in Modeled Land Surface Characteristics Prior to Lightning-Initiated Holdover Fire Breakout

    Science.gov (United States)

    Schultz, Christopher J.; Case, Jonathan L.; Hain, Christopher R.; White, Kristopher; Wachter, J. Brent; Nauslar, Nicholas; MacNamara, Brittany

    2018-01-01

    Lightning initiated wildfires are only 16% of the total number of wildfires within the United States, but account for 56% of the acreage burned. One of the challenges with lightning-initiated wildfires is their ability to "holdover" which means smolder for up to 2+ weeks before breaking out into a full fledged fire. This work helps characterize the percentage of holdover events due to lightning, and helps quantify changes in the land surface characteristics to help understand trends in soil moisture and vegetation stress that potentially contribute to the fire breaking out into a full wildfire.

  5. 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.

  6. 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...

  7. 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...... to increase the spatial resolution of the reliable DTD-modelled fluxes from 1 km to 30 m. Furthermore, synergies between remote sensing based models and distributed hydrological models were studied with the aim of improving spatial performance of the hydrological models through incorporation of remote sensing......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...

  8. 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

  9. 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

  10. Assessing the Regional/Diurnal Bias between Satellite Retrievals and GEOS-5/MERRA Model Estimates of Land Surface Temperature

    Science.gov (United States)

    Scarino, B. R.; Smith, W. L., Jr.; Minnis, P.; Bedka, K. M.

    2017-12-01

    Atmospheric models rely on high-accuracy, high-resolution initial radiometric and surface conditions for better short-term meteorological forecasts, as well as improved evaluation of global climate models. Continuous remote sensing of the Earth's energy budget, as conducted by the Clouds and Earth's Radiant Energy System (CERES) project, allows for near-realtime evaluation of cloud and surface radiation properties. It is unfortunately common for there to be bias between atmospheric/surface radiation models and Earth-observations. For example, satellite-observed surface skin temperature (Ts), an important parameter for characterizing the energy exchange at the ground/water-atmosphere interface, can be biased due to atmospheric adjustment assumptions and anisotropy effects. Similarly, models are potentially biased by errors in initial conditions and regional forcing assumptions, which can be mitigated through assimilation with true measurements. As such, when frequent, broad-coverage, and accurate retrievals of satellite Ts are available, important insights into model estimates of Ts can be gained. The Satellite ClOud and Radiation Property retrieval System (SatCORPS) employs a single-channel thermal-infrared method to produce anisotropy-corrected Ts over clear-sky land and ocean surfaces from data taken by geostationary Earth orbit (GEO) satellite imagers. Regional and diurnal changes in model land surface temperature (LST) performance can be assessed owing to the somewhat continuous measurements of the LST offered by GEO satellites - measurements which are accurate to within 0.2 K. A seasonal, hourly comparison of satellite-observed LST with the NASA Goddard Earth Observing System Version 5 (GEOS-5) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) LST estimates is conducted to reveal regional and diurnal biases. This assessment is an important first step for evaluating the effectiveness of Ts assimilation, as well for determining the

  11. 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.

  12. 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.

  13. Modeling the Space-Time Destiny of Pan-Arctic Permafrost DOC in a Global Land Surface Model: Feedback Implications

    Science.gov (United States)

    Bowring, S.; Lauerwald, R.; Guenet, B.; Zhu, D.; Ciais, P.

    2017-12-01

    Most global climate models do not represent the unique permafrost soil environment and its respective processes. This significantly contributes to uncertainty in estimating their responses, and that of the planet at large, to warming. Here, the production, transport and atmospheric release of dissolved organic carbon (DOC) from high-latitude permafrost soils into inland waters and the ocean is explicitly represented for the first time in the land surface component (ORCHIDEE-MICT) of a CMIP6 global climate model (IPSL). This work merges two models that are able to mechanistically simulate complex processes for 1) snow, ice and soil phenomena in high latitude environments, and 2) DOC production and lateral transport through soils and the river network, respectively, at 0.5° to 2° resolution. The resulting model is subjected to a wide range of input forcing data, parameter testing and contentious feedback phenomena, including microbial heat generation as the active layer deepens. We present results for the present and future Pan-Arctic and Eurasia, with a focus on the Lena and Mackenzie River basins, and show that soil DOC concentrations, their riverine transport and atmospheric evasion are reasonably well represented as compared to observed stocks, fluxes and seasonality. We show that most basins exhibit large increases in DOC transport and riverine CO2 evasion across the suite of RCP scenarios to 2100. We also show that model output is strongly influenced by choice of input forcing data. The riverine component of what is known as the `boundless carbon cycle' is little-recognized in global climate modeling. Hydrological mobilization to the river network results either in sedimentary settling or atmospheric `evasion', presently amounting to 0.5-1.8 PgC yr-1. Our work aims at filling in these knowledge gaps, and the response of these DOC-related processes to thermal forcing. Potential feedbacks owing to such a response are of particular relevance, given the magnitude

  14. 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

    , distributed hydrological model, while the energy-balance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy......-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....... The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps...

  15. Impact of reflecting land surface on radiation environment over Hornsund, Spitsbergen – a model study for cloudless skies

    Directory of Open Access Journals (Sweden)

    Rozwadowska Anna

    2017-06-01

    Full Text Available This paper addresses the influence of land topography and cover on 3D radiative effects under cloudless skies in the Hornsund area, Spitsbergen, Svalbard. The authors used Monte Carlo simulations of solar radiation transfer over a heterogeneous surface to study the impact of a non-uniform surface on: (1 the spatial distribution of irradiance transmittance at the fjord surface under cloudless skies; (2 the spectral shortwave aerosol radiative forcing at the fjord surface; (3 normalized nadir radiance at the Top Of the Atmosphere (TOA over the fjord. The modelled transmittances and radiances over the fjord are compared to the transmittances and radiances over the open ocean under the same conditions. The dependence of the 3D radiative effects on aerosol optical thickness, aerosol type, surface albedo distribution, solar azimuth and zenith angle and spectral channel is discussed. The analysis was done for channels 3 (459-479 nm and 2 (841-876 nm of the MODIS radiometer. In the simulations a flat water surface was assumed. The study shows that snow-covered land surrounding the fjord strongly modifies the radiation environment over the fjord surface. The enhancement of the mean irradiance transmittance over the fjord with respect to the open ocean is up to 0.06 for channel 3. The enhancement exceeds 0.11 in the vicinity of sunlit cliffs. The influence of the snow-covered land on the TOA radiance over the fjord in channel 3 is comparable to the impact of an increase in aerosol optical thickness of over 100%, and in lateral fjords of up to several hundred percent. The increase in TOA radiance is wavelength dependent. These effects may affect retrievals of aerosol optical thickness.

  16. Improvements to the Noah Land Surface Model in WRF-CMAQ, and its Application to Future Changes in the Chesapeake Bay Region

    Science.gov (United States)

    Regional, state, and local environmental regulatory agencies often use Eulerian meteorological and air quality models to investigate the potential impacts of climate, emissions, and land use changes on nutrient loading and air quality. The Noah land surface model in WRF could be...

  17. 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

  18. Inter-annual variabilities in biogeophysical feedback of terrestrial ecosystem to atmosphere using a land surface model

    Science.gov (United States)

    Seo, C.; Hong, S.; Jeong, H. M.; Jeon, J.

    2017-12-01

    Biogeophysical processes of terrestrial ecosystem such as water vapor and energy flux are the key features to understand ecological feedback to atmospheric processes and thus role of terrestrial ecosystem in climate system. For example, it has been recently known that the ecological feedback through water vapor and energy flux results in regulating regional weathers and climates which is one of the fundamental functions of terrestrial ecosystem. In regional scale, water vapor flux has been known to give negative feedback to atmospheric warming, while energy flux from the surface has been known to positive feedback. In this study, we explored the inter-annual variabilities in these two biogeophysical features to see how the climate regulating functions of terrestrial ecosystem have been changed with climate change. We selected a land surface model involving vegetation dynamics that is forced by atmospheric data from NASA including precipitation, temperature, wind, surface pressure, humidity, and incoming radiations. From the land surface model, we simulated 60-year water vapor and energy fluxes from 1961 to 2010, and calculates feedbacks of terrestrial ecosystem as in radiation amount into atmosphere. Then, we analyzed the inter-annual variabilities in the feedbacks. The results showed that some mid-latitude areas showing very high variabilities in precipitation showed higher positive feedback and/or lower negative feedback. These results suggest deterioration of the biogeophyisical factor of climate regulating function over those regions.

  19. Land surface albedo bias in climate models and its association with tropical rainfall

    Science.gov (United States)

    Levine, Xavier J.; Boos, William R.

    2017-06-01

    The influence of surface albedo on tropical precipitation is widely appreciated, but albedo bias over snow-free areas in climate models has been studied little. Here historical Coupled Model Intercomparison Project Phase 5 simulations are shown to exhibit large multimodel mean bias and intermodel variability in boreal summer mean surface broadband shortwave albedo. Intermodel variability in this albedo is globally coherent over vegetated regions and correlates with intermodel tropical precipitation variability. Evidence supports the hypothesis that these spatially coherent albedo variations cause precipitation variations. Specifically, spatial structures of albedo and precipitation variations are distinct, suggesting the latter do not cause the former by darkening soil. Furthermore, simulated interannual albedo variance is small compared to intermodel albedo variance, while the ratio of interannual to intermodel precipitation variance is much larger. Finally, imposing the dominant pattern of intermodel albedo variability in one climate model causes a precipitation change with structure similar to that of the intermodel variability.

  20. Integrating Nutrient Enrichment and Forest Management Experiments in Sweden to Constrain the Process-Based Land Surface Model ORCHIDEE

    Science.gov (United States)

    Resovsky, A.; Luyssaert, S.; Guenet, B.; Peylin, P.; Lansø, A. S.; Vuichard, N.; Messina, P.; Smith, B.; Ryder, J.; Naudts, K.; Chen, Y.; Otto, J.; McGrath, M.; Valade, A.

    2017-12-01

    Understanding coupling between carbon (C) and nitrogen (N) cycling in forest ecosystems is key to predicting global change. Numerous experimental studies have demonstrated the positive response of stand-level photosynthesis and net primary production (NPP) to atmospheric CO2 enrichment, while N availability has been shown to exert an important control on the timing and magnitude of such responses. However, several factors complicate efforts to precisely represent ecosystem-level C and N cycling in the current generation of land surface models (LSMs), including sparse in-situ data, uncertainty with regard to key state variables and disregard for the effects of natural and anthropogenic forest management. In this study, we incorporate empirical data from N-fertilization experiments at two long-term manipulation sites in Sweden to improve the representation of C and N interaction in the ORCHIDEE land surface model. Our version of the model represents the union of two existing 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 aim to replicate the growth patterns of managed forests both with and without N limitations. Our hope is that the results, in combination with measurements of various ecosystem parameters (such as soil N) will facilitate LSM optimization, inform future model development, and reduce structural uncertainty in global change predictions.

  1. Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

    Science.gov (United States)

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

    2016-01-01

    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 degree 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

  2. Furthering our Understanding of Land Surface Interactions using SVAT modelling: Results from SimSphere's Validation

    Science.gov (United States)

    North, Matt; Petropoulos, George; Ireland, Gareth; Rendal, Daisy; Carlson, Toby

    2015-04-01

    demonstrated greatest simulation accuracies within ecologically stable sites, where low inter-annual change in vegetation phenology was exhibited, such as open woodland savannah, shrub land and mulga woodland, whereas poorer simulation accuracies were attained in cropland and grazing pasture sites. This study results present its first comprehensive validation. It is also very timely due to the rapidly expanding global use of the model, both as a standalone tool used for research, education and training in several institutions worldwide, but also for its synergistic applications to Earth Observation data. Currently, several space agencies are evaluating the model 's use synergistically with Earth Observation data in providing spatio-temporal estimates of energy fluxes and / or soil moisture at operational level. Key Words: SimSphere, Validation, FLUXNET, SVAT, Shortwave Incoming Solar Radiation, Net Radiation, Latent Heat, Sensible Heat, Air Temperature

  3. 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.

  4. Does Land-Surface Heterogeneity Affect the Evolution of Hurricanes after their Landfall in the Eastern US - A Numerical Study with the Ocean-Land-Atmosphere Model (OLAM)

    Science.gov (United States)

    Avissar, R.; Walko, R. L.

    2006-12-01

    The Ocean-Land-Atmosphere Model (OLAM) has been developed to extend the capabilities of the Regional Atmospheric Modeling System (RAMS) to a global modeling framework. OLAM is a new model with regard to its dynamic core, grid configuration, memory structure, and numerical solution technique. Instead of the Boussinesq approximation used in RAMS, OLAM solves the full compressible Navier-Stokes equations in conservation form using finite-volume numerical operators that conserve mass, momentum, and energy to machine precision. In place of RAMS' structured multiple nested grids and hexahedral grid cells on a polar stereographic projection, OLAM uses a single unstructured grid and pentahedral (prism) grid cells (with a triangular footprint) which conform to the sphere without a coordinate transformation. OLAM's grid topology enables local mesh refinement to any degree without the need for special grid nesting algorithms; all communication between regions of different resolution is accomplished seamlessly by flux-conservative advective and diffusive transport. OLAM represents topography using a form of the volume-fraction or shaved grid cell method in which model levels are strictly horizontal, rather than terrain-following, and therefore intersect topography. Grid cell face areas, which explicitly appear in the finite volume equations and are pre- computed and stored, are reduced in proportion to any blockage by topography, thereby correctly regulating inter-cell transport and preventing advective flux normal to the ground surface. Apart from its dynamic core and grid configuration, OLAM bears a strong resemblance to RAMS. Both models share the same physical parameterizations for microphysics, land and vegetation water and energy balances, radiative transfer, and sub-grid cumulus convection. Model coding structure, I/O file formats, and methods of compiling, initializing, and executing the models are very similar or identical. OLAM is particularly well designed to

  5. Improving the performance of temperature index snowmelt model of SWAT by using MODIS land surface temperature data.

    Science.gov (United States)

    Yang, Yan; Onishi, Takeo; Hiramatsu, Ken

    2014-01-01

    Simulation results of the widely used temperature index snowmelt model are greatly influenced by input air temperature data. Spatially sparse air temperature data remain the main factor inducing uncertainties and errors in that model, which limits its applications. Thus, to solve this problem, we created new air temperature data using linear regression relationships that can be formulated based on MODIS land surface temperature data. The Soil Water Assessment Tool model, which includes an improved temperature index snowmelt module, was chosen to test the newly created data. By evaluating simulation performance for daily snowmelt in three test basins of the Amur River, performance of the newly created data was assessed. The coefficient of determination (R (2)) and Nash-Sutcliffe efficiency (NSE) were used for evaluation. The results indicate that MODIS land surface temperature data can be used as a new source for air temperature data creation. This will improve snow simulation using the temperature index model in an area with sparse air temperature observations.

  6. Analysis and modelling of surface Urban Heat Island in 20 Canadian cities under climate and land-cover change.

    Science.gov (United States)

    Gaur, Abhishek; Eichenbaum, Markus Kalev; Simonovic, Slobodan P

    2018-01-15

    Surface Urban Heat Island (SUHI) is an urban climate phenomenon that is expected to respond to future climate and land-use land-cover change. It is important to further our understanding of physical mechanisms that govern SUHI phenomenon to enhance our ability to model future SUHI characteristics under changing geophysical conditions. In this study, SUHI phenomenon is quantified and modelled at 20 cities distributed across Canada. By analyzing MODerate Resolution Imaging Spectroradiometer (MODIS) sensed surface temperature at the cities over 2002-2012, it is found that 16 out of 20 selected cities have experienced a positive SUHI phenomenon while 4 cities located in the prairies region and high elevation locations have experienced a negative SUHI phenomenon in the past. A statistically significant relationship between observed SUHI magnitude and city elevation is also recorded over the observational period. A Physical Scaling downscaling model is then validated and used to downscale future surface temperature projections from 3 GCMs and 2 extreme Representative Concentration Pathways in the urban and rural areas of the cities. Future changes in SUHI magnitudes between historical (2006-2015) and future timelines: 2030s (2026-2035), 2050s (2046-2055), and 2090s (2091-2100) are estimated. Analysis of future projected changes indicate that 15 (13) out of 20 cities can be expected to experience increases in SUHI magnitudes in future under RCP 2.6 (RCP 8.5). A statistically significant relationship between projected future SUHI change and current size of the cities is also obtained. The study highlights the role of city properties (i.e. its size, elevation, and surrounding land-cover) towards shaping their current and future SUHI characteristics. The results from this analysis will help decision-makers to manage Canadian cities more efficiently under rapidly changing geophysical and demographical conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Stepwise sensitivity analysis from qualitative to quantitative: Application to the terrestrial hydrological modeling of a Conjunctive Surface-Subsurface Process (CSSP) land surface model

    Science.gov (United States)

    Gan, Yanjun; Liang, Xin-Zhong; Duan, Qingyun; Choi, Hyun Il; Dai, Yongjiu; Wu, Huan

    2015-06-01

    An uncertainty quantification framework was employed to examine the sensitivities of 24 model parameters from a newly developed Conjunctive Surface-Subsurface Process (CSSP) land surface model (LSM). The sensitivity analysis (SA) was performed over 18 representative watersheds in the contiguous United States to examine the influence of model parameters in the simulation of terrestrial hydrological processes. Two normalized metrics, relative bias (RB) and Nash-Sutcliffe efficiency (NSE), were adopted to assess the fit between simulated and observed streamflow discharge (SD) and evapotranspiration (ET) for a 14 year period. SA was conducted using a multiobjective two-stage approach, in which the first stage was a qualitative SA using the Latin Hypercube-based One-At-a-Time (LH-OAT) screening, and the second stage was a quantitative SA using the Multivariate Adaptive Regression Splines (MARS)-based Sobol' sensitivity indices. This approach combines the merits of qualitative and quantitative global SA methods, and is effective and efficient for understanding and simplifying large, complex system models. Ten of the 24 parameters were identified as important across different watersheds. The contribution of each parameter to the total response variance was then quantified by Sobol' sensitivity indices. Generally, parameter interactions contribute the most to the response variance of the CSSP, and only 5 out of 24 parameters dominate model behavior. Four photosynthetic and respiratory parameters are shown to be influential to ET, whereas reference depth for saturated hydraulic conductivity is the most influential parameter for SD in most watersheds. Parameter sensitivity patterns mainly depend on hydroclimatic regime, as well as vegetation type and soil texture. This article was corrected on 26 JUN 2015. See the end of the full text for details.

  8. Using isotopes to improve impact and hydrological predictions of land-surface schemes in global climate models

    International Nuclear Information System (INIS)

    McGuffie, K.; Henderson-Sellers, A.

    2002-01-01

    Global climate model (GCM) predictions of the impact of large-scale land-use change date back to 1984 as do the earliest isotopic studies of large-basin hydrology. Despite this coincidence in interest and geography, with both papers focussed on the Amazon, there have been few studies that have tried to exploit isotopic information with the goal of improving climate model simulations of the land-surface. In this paper we analyze isotopic results from the IAEA global data base specifically with the goal of identifying signatures of potential value for improving global and regional climate model simulations of the land-surface. Evaluation of climate model predictions of the impacts of deforestation of the Amazon has been shown to be of significance by recent results which indicate impacts occurring distant from the Amazon i.e. tele-connections causing climate change elsewhere around the globe. It is suggested that these could be similar in magnitude and extent to the global impacts of ENSO events. Validation of GCM predictions associated with Amazonian deforestation are increasingly urgently required because of the additional effects of other aspects of climate change, particularly synergies occurring between forest removal and greenhouse gas increases, especially CO 2 . Here we examine three decades distributions of deuterium excess across the Amazon and use the results to evaluate the relative importance of the fractionating (partial evaporation) and non-fractionating (transpiration) processes. These results illuminate GCM scenarios of importance to the regional climate and hydrology: (i) the possible impact of increased stomatal resistance in the rainforest caused by higher levels of atmospheric CO2 [4]; and (ii) the consequences of the combined effects of deforestation and global warming on the regions climate and hydrology

  9. Livelihoods Poised Between Cold and Dry: Modeling Land Surface Phenologies and Phenometric Lapse Rates in Central Asian Highland Pastures

    Science.gov (United States)

    Henebry, G. M.; Tomaszewska, M. A.; Krehbiel, C. P.; Kelgenbaeva, K.

    2016-12-01

    To explore the vulnerability of high-elevation communities in the Kyrgyz Republic and in Uzbekistan to changing climatic, sociodemographic, and socioeconomic conditions, we assembled image time series to characterize the condition of pastures near villages at high elevation (>2000 masl) and in remote pastures at higher elevations. Here we describe the application of the convex quadratic (CxQ) model of land surface phenology to highland pasturelands for selected oblasts in the Kyrgyz Republic and in eastern Uzbekistan. We used 16 years (2000-2015) of Landsat normalized difference vegetation index (NDVI) data with MODIS land surface temperature data processed into accumulated growing degree-days. The peak height of the NDVI and the thermal time to peak are two key phenological metrics derived analytically from the fitted parameter coefficients of the CxQ model for each pixel time series. Both exhibited sensitivity to elevation, which we describe in terms of phenometric lapse rates (PLRs). Interannual variation in PLRs was expressed differently for the peak NDVI and the thermal time to peak. Peak NDVI increased with elevation up to a point but also exhibited more spatial variation in dry years than in wetter years. Thermal time to peak exhibited strong, highly significant negative linear relationships to elevation with steeper slopes in drier years. Both types of PLRs were modulated by aspect. These relationships and the associated CxQ models by elevation and aspect can provide expectations against which to detect changes in pasture status as a result of management or weather.

  10. 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

  11. Estimating Trends and Variation of Net Biome Productivity in India for 1980-2012 Using a Land Surface Model

    Science.gov (United States)

    Gahlot, Shilpa; Shu, Shijie; Jain, Atul K.; Baidya Roy, Somnath

    2017-11-01

    In this paper we explore the trend in net biome productivity (NBP) over India for the period 1980-2012 and quantify the impact of different environmental factors, including atmospheric CO2 concentrations ([CO2]), land use and land cover change, climate, and nitrogen deposition on carbon fluxes using a land surface model, Integrated Science Assessment Model. Results show that terrestrial ecosystems of India have been a carbon sink for this period. Driven by a strong CO2 fertilization effect, magnitude of NBP increased from 27.17 TgC/yr in the 1980s to 34.39 TgC/yr in the 1990s but decreased to 23.70 TgC/yr in the 2000s due to change in climate. Adoption of forest conservation, management, and reforestation policies in the past decade has promoted carbon sequestration in the ecosystems, but this effect has been offset by loss of carbon from ecosystems due to rising temperatures and decrease in precipitation.

  12. 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; hide

    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).

  13. Can we use remotely sensed land surface temperatures to evaluate and improve model simulations of the urban heat island?

    Science.gov (United States)

    Hu, L.; Monaghan, A. J.; Brunsell, N. A.; Barlage, M. J.; Feddema, J. J.; Wilhelmi, O.

    2013-12-01

    Extreme heat events are the leading cause of weather-related human mortality in the United States and in many countries world-wide, and the development of highly accurate urban climate models to predict heat waves and extreme heat events is critical. However, the heterogeneous urban surface with myriad energy and moisture fluxes increases model complexity and uncertainty. Remotely sensed land surface temperature (LST) offers advantages such as comparable spatial scale, global coverage, steady periodicity, and long-term observations, which can be applied to assess model simulations. This research proposes a sampling technique to select and compare MODIS LST and model-simulated radiative temperature for eight configurations of the High Resolution Land Data Assimilation System (HRLDAS) during 2003-2012 summers (JJA) for Houston, TX. The objective is to decrease comparison biases between MODIS and HRLDAS caused by clouds, view angles, and the LST retrieval algorithm, and to understand which urban surface properties are critical for accurate UHI simulations. The results show that the accurate description of urban fraction can effectively decrease more than 25% of RMSE for HRLDAS LST for both daytime and nighttime comparisons. Assuming irrigated vegetation in the urban area largely improved the RMSE by about 2K during the daytime, while there was no significant difference for the nighttime periods. In the most realistic scenario HRLDAS performed quite well at night, both temporally and spatially. HRLDAS daytime LST simulations are warmer than MODIS observations by approximately 5K but with relatively strong correlations. In summary, remotely sensed LST can be a good observational source for the assessment of UHI simulations, but requires careful pre-processing beforehand to avoid unrepresentative comparisons. The proposed sampling method is practical and effective for validation of long-term urban-scale model simulations.

  14. 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.; Molthan, Andrew L.

    2011-01-01

    The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from 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 new SPoRT-MODIS GVF dataset on land surface models 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. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true

  15. 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

  16. 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

  17. Comparing SMAP to Macro-scale and Hyper-resolution Land Surface Models over Continental U. S.

    Science.gov (United States)

    Pan, Ming; Cai, Xitian; Chaney, Nathaniel; Wood, Eric

    2016-04-01

    SMAP sensors collect moisture information in top soil at the spatial resolution of ~40 km (radiometer) and ~1 to 3 km (radar, before its failure in July 2015). Such information is extremely valuable for understanding various terrestrial hydrologic processes and their implications on human life. At the same time, soil moisture is a joint consequence of numerous physical processes (precipitation, temperature, radiation, topography, crop/vegetation dynamics, soil properties, etc.) that happen at a wide range of scales from tens of kilometers down to tens of meters. Therefore, a full and thorough analysis/exploration of SMAP data products calls for investigations at multiple spatial scales - from regional, to catchment, and to field scales. Here we first compare the SMAP retrievals to the Variable Infiltration Capacity (VIC) macro-scale land surface model simulations over the continental U. S. region at 3 km resolution. The forcing inputs to the model are merged/downscaled from a suite of best available data products including the NLDAS-2 forcing, Stage IV and Stage II precipitation, GOES Surface and Insolation Products, and fine elevation data. The near real time VIC simulation is intended to provide a source of large scale comparisons at the active sensor resolution. Beyond the VIC model scale, we perform comparisons at 30 m resolution against the recently developed HydroBloks hyper-resolution land surface model over several densely gauged USDA experimental watersheds. Comparisons are also made against in-situ point-scale observations from various SMAP Cal/Val and field campaign sites.

  18. Traits of surface water pollution under climate and land use changes: A remote sensing and hydrological modeling approach

    Science.gov (United States)

    Jordan, Yuyan C.; Ghulam, Abduwasit; Hartling, Sean

    2014-01-01

    In this paper, spatial and temporal trajectories of land cover/land use change (LCLUC) derived from Landsat data record are combined with hydrological modeling to explore the implication of vegetation dynamics on soil erosion and total suspended sediment (TSS) loading to surface rivers. The inter-annual coefficient of variation (CoV) of normalized difference vegetation index (NDVI) is used to screen the LCLUC and climate change. The Soil and Water Assessment Tool (SWAT) is employed to identify the monthly TSS for two times interval (1991 to 2001 and 2001 to 2011) at subbasin levels. SWAT model is calibrated from 1991 to 2001 and validated from 2002 to 2011 at three USGS gauging sites located in the study area. The Spearman's rank correlation of annual mean TSS is used to assess the temporal trends of TSS dynamics in the subbasins in the two study periods. The spatial correlation among NDVI, LCLUC, climate change and TSS loading rate changes is quantified by using linear regression model and negative/positive trend analysis. Our results showed that higher rainfall yields contribute to higher TSS loading into surface waters. A higher inter-annual accumulated vegetation index and lower inter-annual CoV distributed over the uplands resulted in a lower TSS loading rate, while a relatively low vegetation index with larger CoV observed over lowlands resulted in a higher TSS loading rate. The TSS loading rate at the basin outlet increased with the decrease of annual NDVI due to expanding urban areas in the watershed. The results also suggested nonlinearity between the trends of TSS loading with any of a specific land cover change because of the fact that the contribution of a factor can be influenced by the effects of other factors. However, dominant factors that shape the relationship between the trend of TSS loading and specific land cover changes were detected. The change of forest showed a negative relationship while agriculture and pasture demonstrated positive

  19. VIS and NIR land surface albedo sensitivity of the Ent Terrestrial Biosphere Model to forcing leaf area index

    Science.gov (United States)

    Montes, C.; Kiang, N. Y.; Ni-Meister, W.; Yang, W.; Schaaf, C.; Aleinov, I. D.; Jonas, J.; Zhao, F. A.; Yao, T.; Wang, Z.; Sun, Q.; Carrer, D.

    2016-12-01

    Land surface albedo is a major controlling factor in vegetation-atmosphere transfers, modifying the components of the energy budget, the ecosystem productivity and patterns of regional and global climate. General Circulation Models (GCMs) are coupled to Dynamic Global Vegetation Models (DGVMs) to solve vegetation albedo by using simple schemes prescribing albedo based on vegetation classification, and approximations of canopy radiation transport for multiple plant functional types (PFTs). In this work, we aim at evaluating the sensitivity of the NASA Ent Terrestrial Biosphere Model (TBM), a demographic DGVM coupled to the NASA Goddard Institute for Space Studies (GISS) GCM, in estimating VIS and NIR surface albedo by using variable forcing leaf area index (LAI). The Ent TBM utilizes a new Global Vegetation Structure Dataset (GVSD) to account for geographically varying vegetation tree heights and densities, as boundary conditions to the gap-probability based Analytical Clumped Two-Stream (ACTS) canopy radiative transfer scheme (Ni-Meister et al., 2010). Land surface and vegetation characteristics for the Ent GVSD are obtained from a number of earth observation platforms and algorithms, including the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover and plant functional types (PFTs) (Friedl et al., 2010), soil albedo derived from MODIS (Carrer et al., 2014), and vegetation height from the Geoscience Laser Altimeter System (GLAS) on board ICESat (Ice, Cloud, and land Elevation Satellite) (Simard et al., 2011; Tang et al., 2014). Three LAI products are used as input to ACTS/Ent TBM: MODIS MOD15A2H product (Yang et al., 2006), Beijing Normal University LAI (Yuan et al., 2011), and Global Data Sets of Vegetation (LAI3g) (Zhu et al. 2013). The sensitivity of the Ent TBM VIS and NIR albedo to the three LAI products is assessed, compared against the previous GISS GCM vegetation classification and prescribed Lambertian albedoes (Matthews, 1984), and against

  20. Quantifying Modern Recharge to the Nubian Sandstone Aquifer System: Inferences from GRACE and Land Surface Models

    Science.gov (United States)

    Mohamed, A.; Sultan, M.; Ahmed, M.; Yan, E.

    2014-12-01

    The Nubian Sandstone Aquifer System (NSAS) is shared by Egypt, Libya, Chad and Sudanand is one of the largest (area: ~ 2 × 106 km2) groundwater systems in the world. Despite its importance to the population of these countries, major hydrological parameters such as modern recharge and extraction rates remain poorly investigated given: (1) the large extent of the NSAS, (2) the absence of comprehensive monitoring networks, (3) the general inaccessibility of many of the NSAS regions, (4) difficulties in collecting background information, largely included in unpublished governmental reports, and (5) limited local funding to support the construction of monitoring networks and/or collection of field and background datasets. Data from monthly Gravity Recovery and Climate Experiment (GRACE) gravity solutions were processed (Gaussian smoothed: 100 km; rescaled) and used to quantify the modern recharge to the NSAS during the period from January 2003 to December 2012. To isolate the groundwater component in GRACE data, the soil moisture and river channel storages were removed using the outputs from the most recent Community Land Model version 4.5 (CLM4.5). GRACE-derived recharge calculations were performed over the southern NSAS outcrops (area: 835 × 103 km2) in Sudan and Chad that receive average annual precipitation of 65 km3 (77.5 mm). GRACE-derived recharge rates were estimated at 2.79 ± 0.98 km3/yr (3.34 ± 1.17 mm/yr). If we take into account the total annual extraction rates (~ 0.4 km3; CEDARE, 2002) from Chad and Sudan the average annual recharge rate for the NSAS could reach up to ~ 3.20 ± 1.18 km3/yr (3.84 ± 1.42 mm/yr). Our recharge rates estimates are similar to those calculated using (1) groundwater flow modelling in the Central Sudan Rift Basins (4-8 mm/yr; Abdalla, 2008), (2) WaterGAP global scale groundwater recharge model (plans are underway for the deployment of a GRACE follow-On and GRACE-II missions, we suggest that within the next few years, GRACE

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

    Directory of Open Access Journals (Sweden)

    J. Iwema

    2017-06-01

    Full Text Available 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.

  2. Evaluating the performance of SURFEXv5 as a new land surface scheme for the ALADINcy36 and ALARO-0 models

    Science.gov (United States)

    Hamdi, R.; Degrauwe, D.; Duerinckx, A.; Cedilnik, J.; Costa, V.; Dalkilic, T.; Essaouini, K.; Jerczynki, M.; Kocaman, F.; Kullmann, L.; Mahfouf, J.-F.; Meier, F.; Sassi, M.; Schneider, S.; Váňa, F.; Termonia, P.

    2014-01-01

    The newly developed land surface scheme SURFEX (SURFace EXternalisée) is implemented into a limited-area numerical weather prediction model running operationally in a number of countries of the ALADIN and HIRLAM consortia. The primary question addressed is the ability of SURFEX to be used as a new land surface scheme and thus assessing its potential use in an operational configuration instead of the original ISBA (Interactions between Soil, Biosphere, and Atmosphere) scheme. The results show that the introduction of SURFEX either shows improvement for or has a neutral impact on the 2 m temperature, 2 m relative humidity and 10 m wind. However, it seems that SURFEX has a tendency to produce higher maximum temperatures at high-elevation stations during winter daytime, which degrades the 2 m temperature scores. In addition, surface radiative and energy fluxes improve compared to observations from the Cabauw tower. The results also show that promising improvements with a demonstrated positive impact on the forecast performance are achieved by introducing the town energy balance (TEB) scheme. It was found that the use of SURFEX has a neutral impact on the precipitation scores. However, the implementation of TEB within SURFEX for a high-resolution run tends to cause rainfall to be locally concentrated, and the total accumulated precipitation obviously decreases during the summer. One of the novel features developed in SURFEX is the availability of a more advanced surface data assimilation using the extended Kalman filter. The results over Belgium show that the forecast scores are similar between the extended Kalman filter and the classical optimal interpolation scheme. Finally, concerning the vertical scores, the introduction of SURFEX either shows improvement for or has a neutral impact in the free atmosphere.

  3. 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.

  4. Sensitivity of WRF-Chem model to land surface schemes: Assessment in a severe dust outbreak episode in the Central Mediterranean (Apulia Region)

    Science.gov (United States)

    Rizza, Umberto; Miglietta, Mario Marcello; Mangia, Cristina; Ielpo, Pierina; Morichetti, Mauro; Iachini, Chiara; Virgili, Simone; Passerini, Giorgio

    2018-03-01

    The Weather Research and Forecasting model with online coupled chemistry (WRF-Chem) is applied to simulate a severe Saharan dust outbreak event that took place over Southern Italy in March 2016. Numerical experiments have been performed applying a physics-based dust emission model, with soil properties generated from three different Land Surface Models, namely Noah, RUC and Noah-MP. The model performance in reproducing the severe desert dust outbreak is analysed using an observational dataset of aerosol and desert dust features that includes optical properties from satellite and ground-based sun-photometers, and in-situ particulate matter mass concentration (PM) data. The results reveal that the combination of the dust emission model with the RUC Land Surface Model significantly over-predicts the emitted mineral dust; on the other side, the combination with Noah or Noah-MP Land Surface Model (LSM) gives better results, especially for the daily averaged PM10.

  5. RAMI4PILPS: An intercomparison of formulations for the partitioning of solar radiation in land surface models

    Science.gov (United States)

    Widlowski, J.-L.; Pinty, B.; Clerici, M.; Dai, Y.; de Kauwe, M.; De Ridder, K.; Kallel, A.; Kobayashi, H.; Lavergne, T.; Ni-Meister, W.; Olchev, A.; Quaife, T.; Wang, S.; Yang, W.; Yang, Y.; Yuan, H.

    2011-06-01

    Remotely sensed, multiannual data sets of shortwave radiative surface fluxes are now available for assimilation into land surface schemes (LSSs) of climate and/or numerical weather prediction models. The RAMI4PILPS suite of virtual experiments assesses the accuracy and consistency of the radiative transfer formulations that provide the magnitudes of absorbed, reflected, and transmitted shortwave radiative fluxes in LSSs. RAMI4PILPS evaluates models under perfectly controlled experimental conditions in order to eliminate uncertainties arising from an incomplete or erroneous knowledge of the structural, spectral and illumination related canopy characteristics typical for model comparison with in situ observations. More specifically, the shortwave radiation is separated into a visible and near-infrared spectral region, and the quality of the simulated radiative fluxes is evaluated by direct comparison with a 3-D Monte Carlo reference model identified during the third phase of the Radiation transfer Model Intercomparison (RAMI) exercise. The RAMI4PILPS setup thus allows to focus in particular on the numerical accuracy of shortwave radiative transfer formulations and to pinpoint to areas where future model improvements should concentrate. The impact of increasing degrees of structural and spectral subgrid variability on the simulated fluxes is documented and the relevance of any thus emerging biases with respect to gross primary production estimates and shortwave radiative forcings due to snow and fire events are investigated.

  6. A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model

    Directory of Open Access Journals (Sweden)

    F. Hossain

    2004-01-01

    Full Text Available This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM. The scheme is assessed within a Monte Carlo (MC simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linear deterministic behavior between soil moisture and land surface parameters. Uncertainty in soil moisture simulation (model output is approximated through a Hermite polynomial chaos expansion of normal random variables that represent the model's parameter (model input uncertainty. The unknown coefficients of the polynomial are calculated using limited number of model simulation runs. The calibrated polynomial is then used as a fast-running proxy to the slower-running LSM to predict the degree of representativeness of a randomly sampled model parameter set. An evaluation of the scheme's efficiency in sampling is made through comparison with the fully random MC sampling (the norm for GLUE and the nearest-neighborhood sampling technique. The scheme was able to reduce computational burden of random MC sampling for GLUE in the ranges of 10%-70%. The scheme was also found to be about 10% more efficient than the nearest-neighborhood sampling method in predicting a sampled parameter set's degree of representativeness. The GLUE based on the proposed sampling scheme did not alter the essential features of the uncertainty structure in soil moisture simulation. The scheme can potentially make GLUE uncertainty estimation for any LSM more efficient as it does not impose any additional structural or distributional assumptions.

  7. Impact of groundwater capillary rises as lower boundary conditions for soil moisture in a land surface model

    Science.gov (United States)

    Vergnes, Jean-Pierre; Decharme, Bertrand; Habets, Florence

    2014-05-01

    Groundwater is a key component of the global hydrological cycle. It sustains base flow in humid climate while it receives seepage in arid region. Moreover, groundwater influences soil moisture through water capillary rise into the soil and potentially affects the energy and water budget between the land surface and the atmosphere. Despite its importance, most global climate models do not account for groundwater and their possible interaction with both the surface hydrology and the overlying atmosphere. This study assesses the impact of capillary rise from shallow groundwater on the simulated water budget over France. The groundwater scheme implemented in the Total Runoff Integrated Pathways (TRIP) river routing model in a previous study is coupled with the Interaction between Soil Biosphere Atmosphere (ISBA) land surface model. In this coupling, the simulated water table depth acts as the lower boundary condition for the soil moisture diffusivity equation. An original parameterization accounting for the subgrid elevation inside each grid cell is proposed in order to compute this fully-coupled soil lower boundary condition. Simulations are performed at high (1/12°) and low (0.5°) resolutions and evaluated over the 1989-2009 period. Compared to a free-drain experiment, upward capillary fluxes at the bottom of soil increase the mean annual evapotranspiration simulated over the aquifer domain by 3.12 % and 1.54 % at fine and low resolutions respectively. This process logically induces a decrease of the simulated recharge from ISBA to the aquifers and contributes to enhance the soil moisture memory. The simulated water table depths are then lowered, which induces a slight decrease of the simulated mean annual river discharges. However, the fully-coupled simulations compare well with river discharge and water table depth observations which confirms the relevance of the coupling formalism.

  8. The impact of the soil surface properties in water erosion seen through LandSoil model sensitivity analysis

    Science.gov (United States)

    Ciampalini, Rossano; Follain, Stéphane; Cheviron, Bruno; Le Bissonnais, Yves; Couturier, Alain; Walter, Christian

    2014-05-01

    Quantitative models of soil redistribution at the landscape scale are the current tools for understanding space-time processes in soil and landscape evolution. But models use larger and larger numbers of variables and sometimes it becomes difficult to understand their relative importance and model behaviours in critical conditions. Sensitivity analysis (SA) is widely used to clarify models behaviours, their structure giving fundamental information to ameliorate models their selves. We tested the LandSoil model (LANDscape design for SOIL conservation under soil use and climate change) a model designed for the analysis of agricultural landscape evolution at a fine spatial resolution scale [1-10 meters] and a mid-term temporal scale [10-100 years]. LandSoil is suitable for simulations from parcel to catchment scale. It is spatially distributed, event-based, and considers water and tillage erosion processes that use a dynamic representation of the agricultural landscape through parameters such as a monthly representation of soil surface properties. Our aim was to identify most significant parameters driving the model and to highlight potential particular/singular behaviours of parameter combinations and relationships. The approach was to use local sensitivity analysis, also termed 'one-factor-at-time' (OAT) which consists of a deterministic, derivative method, inquiring the local response O to a particular input factor Pi at a specified point P0 within the full input parameter space of the model expressed as: δO/δP = (O2-O1) / (P2-P1) The local sensitivity represents the partial derivatives of O with respect to Pi at the point P0. In the SA procedure the topographical entity is represented by a virtual hillslope on which soil loss and sensitivity are calculated. Virtual hillslope is inspired from the virtual catchment framework proposed by Cheviron at al. (2011): a fixed topology consisting of a 3X3 square pixel structure having 150 m length allowing to test

  9. Near-surface land disposal

    International Nuclear Information System (INIS)

    Kittel, J.H.

    1989-01-01

    The Radioactive Waste Management Handbook provides a comprehensive, systematic treatment of nuclear waste management. Near-Surface Land Disposal, the first volume, is a primary and secondary reference for the technical community. To those unfamiliar with the field, it provides a bridge to a wealth of technical information, presenting the technology associated with the near-surface disposal of low or intermediate level wastes. Coverage ranges from incipient planning to site closure and subsequent monitoring. The book discusses the importance of a systems approach during the design of new disposal facilities so that performance objectives can be achieved; gives an overview of the radioactive wastes cosigned to near-surface disposal; addresses procedures for screening and selecting sites; and emphasizes the importance of characterizing sites and obtaining reliable geologic and hydrologic data. The planning essential to the development of particular sites (land acquisition, access, layout, surface water management, capital costs, etc.) is considered, and site operations (waste receiving, inspection, emplacement, closure, stabilization, etc.) are reviewed. In addition, the book presents concepts for improved confinement of waste, important aspects of establishing a monitoring program at the disposal facility, and corrective actions available after closure to minimize release. Two analytical techniques for evaluating alternative technologies are presented. Nontechnical issues surrounding disposal, including the difficulties of public acceptance are discussed. A glossary of technical terms is included

  10. 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.

  11. Surface rights on Aboriginal lands

    International Nuclear Information System (INIS)

    McElhanney, W.L.

    1998-01-01

    Several issues regarding access and activity by petroleum industry on Aboriginal and Metis lands are discussed. Some alternative means by which both industry and Aboriginal groups can approach the matter of surface rights are presented. A historical account of how surface rights have been interpreted in the past was given. It was emphasized that the approach to surface rights compensation and negotiation for both aboriginal and industry parties must begin with adequate consultation. Rigid adherence to the usual past practice of geologically identifying locations, surveying and requesting a lease will no longer suffice. The aboriginal community must be consulted with as much lead time as possible, even assisted financially to identify traditional use areas that require protection, or cannot be disturbed, or require particular mitigation measures. Once this has been done, the operator can proceed to outline the scope of his project, detailing the timing, location, business and employment opportunities and other economic opportunities to the community. 21 refs

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

    NARCIS (Netherlands)

    Ryder, J.; Polcher, J.; Peylin, P.; Ottlé, C.; Chen, Y; van Gorsel, E.; Haverd, V.; McGrath, M.J.; Naudts, K.; Otto, J.; Valade, A; Luyssaert, S

    2014-01-01

    See, stats, and : https : / / www . researchgate . net / publication / 276534648 A - layer for simulations Article DOI : 10 . 5194 / gmdd - 7 - 8649 - 2014 CITATIONS 9 READS 155 12 , including : Some : land Master Jan French 193 , 481 SEE Eva Australian 98 , 225 SEE Vanessa The 85 SEE Juliane

  13. A 30 meter soil properties map of the contiguous United States for use in remote sensing and land surface models

    Science.gov (United States)

    Chaney, N.; Morgan, C.; McBratney, A.; Wood, E. F.; Yimam, Y.

    2016-12-01

    Soil moisture plays a critical role in the terrestrial water, energy, and biogeochemical cycles. For this reason, numerical weather prediction, global circulation models, and hydrologic monitoring systems increasingly emphasize modeling soil moisture and assimilating soil moisture remote sensing products. In both cases, the prescribed soil hydraulic properties play a pivotal role in accurately describing the soil moisture state. However, an accurate characterization of soil hydraulic properties remains a persistent challenge—existing continental soil databases are too coarse and outdated for contemporary applications. To address this challenge, we have developed the Probabilistic Remapping of SSURGO database (POLARIS); a new soil database that covers the contiguous United States (CONUS) at a 30-meter spatial resolution. POLARIS was constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm to remap the rich yet incomplete Soil Survey Geographic (SSURGO) database to create spatially complete probabilistic soil series maps over CONUS (Chaney et al., 2016). These maps are then combined with the vertical profile information of each soil series to create the corresponding maps of soil hydraulic properties and their associated uncertainties. The mapped soil hydraulic properties include soil texture, saturated hydraulic conductivity, porosity, field capacity, and wilting point. POLARIS provides a breakthrough in soil information. To illustrate this database's potential, we will both explore the database at multiple spatial scales and discuss recent land surface modeling results that have used POLARIS to simulate soil moisture at a 30-meter spatial resolution over CONUS between 2004 and 2014. We will discuss the added benefit of using POLARIS and the opportunity it presents to improve the characterization of soil hydraulic properties in land surface models and soil moisture remote sensing. References

  14. ORCHIDEE-MICT (v8.4.1, a land surface model for the high latitudes: model description and validation

    Directory of Open Access Journals (Sweden)

    M. Guimberteau

    2018-01-01

    Full Text Available The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance – those of the permanently frozen soils (permafrost, and of the great expanse of boreal forest – are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i temperature gradients between the atmosphere and deep soils, (ii the hydrological components comprising the water balance of the largest high-latitude basins, and (iii CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.

  15. 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

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

    Science.gov (United States)

    Canal, N.; Calvet, J.-C.; Decharme, B.; Carrer, D.; Lafont, S.; Pigeon, G.

    2014-12-01

    The simulation of root water uptake in land surface models is affected by large uncertainties. The difficulty in mapping soil depth and in describing the capacity of plants to develop a rooting system is a major obstacle to the simulation of the terrestrial water cycle and to the representation of the impacts of drought. In this study, long time series of agricultural statistics are used to evaluate and constrain root water uptake models. The inter-annual variability of cereal grain yield and permanent grassland dry matter yield is simulated over France 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 two-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 départements, for a range of rooting depths. The number of départements 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 neutral impact of the most refined versions of the model is found with respect to the simplified soil hydrology scheme. This shows that efforts should be made in future studies to reduce other sources of uncertainty, e.g. by using a more detailed soil and root density profile description together with satellite vegetation products. It is found that modelling additional subroot-zone base flow soil layers does not improve (and may even degrade) the representation of the inter-annual variability of the vegetation above-ground biomass. These results are

  17. Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

    Science.gov (United States)

    Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

    2017-12-01

    Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts

  18. Impact of aerodynamic resistance formulations used in two-source modeling of energy exchange from the soil and vegetation using land surface temperature

    Science.gov (United States)

    Application of the Two-Source Energy Balance (TSEB) Model using land surface temperature (LST) requires aerodynamic resistance parameterizations for the flux exchange above the canopy layer, within the canopy air space and at the soil/substrate surface. There are a number of aerodynamic resistance f...

  19. Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model

    Science.gov (United States)

    Gelati, Emiliano; Decharme, Bertrand; Calvet, Jean-Christophe; Minvielle, Marie; Polcher, Jan; Fairbairn, David; Weedon, Graham P.

    2018-04-01

    Physically consistent descriptions of land surface hydrology are crucial for planning human activities that involve freshwater resources, especially in light of the expected climate change scenarios. We assess how atmospheric forcing data uncertainties affect land surface model (LSM) simulations by means of an extensive evaluation exercise using a number of state-of-the-art remote sensing and station-based datasets. For this purpose, we use the CO2-responsive ISBA-A-gs LSM coupled with the CNRM version of the Total Runoff Integrated Pathways (CTRIP) river routing model. We perform multi-forcing simulations over the Euro-Mediterranean area (25-75.5° N, 11.5° W-62.5° E, at 0.5° resolution) from 1979 to 2012. The model is forced using four atmospheric datasets. Three of them are based on the ERA-Interim reanalysis (ERA-I). The fourth dataset is independent from ERA-Interim: PGF, developed at Princeton University. The hydrological impacts of atmospheric forcing uncertainties are assessed by comparing simulated surface soil moisture (SSM), leaf area index (LAI) and river discharge against observation-based datasets: SSM from the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects (ESA-CCI), LAI of the Global Inventory Modeling and Mapping Studies (GIMMS), and Global Runoff Data Centre (GRDC) river discharge. The atmospheric forcing data are also compared to reference datasets. Precipitation is the most uncertain forcing variable across datasets, while the most consistent are air temperature and SW and LW radiation. At the monthly timescale, SSM and LAI simulations are relatively insensitive to forcing uncertainties. Some discrepancies with ESA-CCI appear to be forcing-independent and may be due to different assumptions underlying the LSM and the remote sensing retrieval algorithm. All simulations overestimate average summer and early-autumn LAI. Forcing uncertainty impacts on simulated river discharge are

  20. 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.

  1. Evolution of 2016 drought in the Southeastern United States from a Land surface modeling perspective

    Science.gov (United States)

    Case, Jonathan L.; Zavodsky, Bradley T.

    2018-03-01

    The Southeastern United States (SEUS) climate region experienced a marked transition from excessively wet conditions early in 2016 to an exceptional drought during the Autumn. The unusually warm and dry conditions led to numerous wildfires, including the devastating Gatlinburg, Tennessee (TN) firestorm on 28-29 November. The evolution of soil wetness anomalies are highlighted through soil moisture percentiles derived from an instance of NASA's Land Information System (LIS). A 33-year soil moisture climatology simulation combined with daily, real-time county-based distributions illustrate how soil moisture began above the 96th percentile early in 2016, and declined to below the 2nd percentile in many locales by late November.

  2. Performance evaluation of land surface models and cumulus convection schemes in the simulation of Indian summer monsoon using a regional climate model

    Science.gov (United States)

    Maity, S.; Satyanarayana, A. N. V.; Mandal, M.; Nayak, S.

    2017-11-01

    In this study, an attempt has been made to investigate the sensitivity of land surface models (LSM) and cumulus convection schemes (CCS) using a regional climate model, RegCM Version-4.1 in simulating the Indian Summer Monsoon (ISM). Numerical experiments were conducted in seasonal scale (May-September) for three consecutive years: 2007, 2008, 2009 with two LSMs (Biosphere Atmosphere Transfer Scheme (BATS), Community Land Model (CLM 3.5) and five CCSs (MIT, KUO, GRELL, GRELL over land and MIT over ocean (GL_MO), GRELL over ocean and MIT over land (GO_ML)). Important synoptic features are validated using various reanalysis datasets and satellite derived products from TRMM and CRU data. Seasonally averaged surface temperature is reasonably well simulated by the model using both the LSMs along with CCSs namely, MIT, GO_ML and GL_MO schemes. Model simulations reveal slight warm bias using these schemes whereas significant cold bias is seen with KUO and GRELL schemes during all three years. It is noticed that the simulated Somali Jet (SJ) is weak in all simulations except MIT scheme in the simulations with (both BATS and CLM) in which the strength of SJ reasonably well captured. Although the model is able to simulate the Tropical Easterly Jet (TEJ) and Sub-Tropical Westerly Jet (STWJ) with all the CCSs in terms of their location and strength, the performance of MIT scheme seems to be better than the rest of the CCSs. Seasonal rainfall is not well simulated by the model. Significant underestimation of Indian Summer Monsoon Rainfall (ISMR) is observed over Central and North West India. Spatial distribution of seasonal ISMR is comparatively better simulated by the model with MIT followed by GO_ML scheme in combination with CLM although it overestimates rainfall over heavy precipitation zones. On overall statistical analysis, it is noticed that RegCM4 shows better skill in simulating ISM with MIT scheme using CLM.

  3. 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

  4. 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.

  5. Disagreement between Hydrological and Land Surface models on the water budgets in the Arctic: why is this and which of them is right?

    Science.gov (United States)

    Blyth, E.; Martinez-de la Torre, A.; Ellis, R.; Robinson, E.

    2017-12-01

    The fresh-water budget of the Artic region has a diverse range of impacts: the ecosystems of the region, ocean circulation response to Arctic freshwater, methane emissions through changing wetland extent as well as the available fresh water for human consumption. But there are many processes that control the budget including a seasonal snow packs building and thawing, freezing soils and permafrost, extensive organic soils and large wetland systems. All these processes interact to create a complex hydrological system. In this study we examine a suite of 10 models that bring all those processes together in a 25 year reanalysis of the global water budget. We assess their performance in the Arctic region. There are two approaches to modelling fresh-water flows at large scales, referred to here as `Hydrological' and `Land Surface' models. While both approaches include a physically based model of the water stores and fluxes, the Land Surface models links the water flows to an energy-based model for processes such as snow melt and soil freezing. This study will analyse the impact of that basic difference on the regional patterns of evapotranspiration, runoff generation and terrestrial water storage. For the evapotranspiration, the Hydrological models tend to have a bigger spatial range in the model bias (difference to observations), implying greater errors compared to the Land-Surface models. For instance, some regions such as Eastern Siberia have consistently lower Evaporation in the Hydrological models than the Land Surface models. For the Runoff however, the results are the other way round with a slightly higher spatial range in bias for the Land Surface models implying greater errors than the Hydrological models. A simple analysis would suggest that Hydrological models are designed to get the runoff right, while Land Surface models designed to get the evapotranspiration right. Tracing the source of the difference suggests that the difference comes from the treatment

  6. Linking Land Surface Phenology and Vegetation-Plot Databases to Model Terrestrial Plant α-Diversity of the Okavango Basin

    Directory of Open Access Journals (Sweden)

    Rasmus Revermann

    2016-04-01

    Full Text Available In many parts of Africa, spatially-explicit information on plant α-diversity, i.e., the number of species in a given area, is missing as baseline information for spatial planning. We present an approach on how to combine vegetation-plot databases and remotely-sensed land surface phenology (LSP metrics to predict plant α-diversity on a regional scale. We gathered data on plant α-diversity, measured as species density, from 999 vegetation plots sized 20 m × 50 m covering all major vegetation units of the Okavango basin in the countries of Angola, Namibia and Botswana. As predictor variables, we used MODIS LSP metrics averaged over 12 years (250-m spatial resolution and three topographic attributes calculated from the SRTM digital elevation model. Furthermore, we tested whether additional climatic data could improve predictions. We tested three predictor subsets: (1 remote sensing variables; (2 climatic variables; and (3 all variables combined. We used two statistical modeling approaches, random forests and boosted regression trees, to predict vascular plant α-diversity. The resulting maps showed that the Miombo woodlands of the Angolan Central Plateau featured the highest diversity, and the lowest values were predicted for the thornbush savanna in the Okavango Delta area. Models built on the entire dataset exhibited the best performance followed by climate-only models and remote sensing-only models. However, models including climate data showed artifacts. In spite of lower model performance, models based only on LSP metrics produced the most realistic maps. Furthermore, they revealed local differences in plant diversity of the landscape mosaic that were blurred by homogenous belts as predicted by climate-based models. This study pinpoints the high potential of LSP metrics used in conjunction with biodiversity data derived from vegetation-plot databases to produce spatial information on a regional scale that is urgently needed for basic

  7. 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.

  8. Studying the Impacts of Environmental Factors and Agricultural Management on Methane Emissions from Rice Paddies Using a Land Surface Model

    Science.gov (United States)

    Lin, T. S.; Gahlot, S.; Shu, S.; Jain, A. K.; Kheshgi, H. S.

    2017-12-01

    Continued growth in population is projected to drive increased future demand for rice and the methane emissions associated with its production. However, observational studies of methane emissions from rice have reported seemingly conflicting results and do not all support this projection. In this study we couple an ecophysiological process-based rice paddy module and a methane emission module with a land surface model, Integrated Science Assessment Model (ISAM), to study the impacts of various environmental factors and agricultural management practices on rice production and methane emissions from rice fields. This coupled modeling framework accounts for dynamic rice growth processes with adaptation of photosynthesis, rice-specific phenology, biomass accumulation, leaf area development and structures responses to water, temperature, light and nutrient stresses. The coupled model is calibrated and validated with observations from various rice cultivation fields. We find that the differing results of observational studies can be caused by the interactions of environmental factors, including climate, atmospheric CO2 concentration, and N deposition, and agricultural management practices, such as irrigation and N fertilizer applications, with rice production at spatial and temporal scales.

  9. 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.

  10. 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

  11. 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

  12. Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes

    Science.gov (United States)

    Guerrero, José-Luis; Pernica, Patricia; Wheater, Howard; Mackay, Murray; Spence, Chris

    2017-12-01

    Lakes might be sentinels of climate change, but the uncertainty in their main feedback to the atmosphere - heat-exchange fluxes - is often not considered within climate models. Additionally, these fluxes are seldom measured, hindering critical evaluation of model output. Analysis of the Canadian Small Lake Model (CSLM), a one-dimensional integral lake model, was performed to assess its ability to reproduce diurnal and seasonal variations in heat fluxes and the sensitivity of simulated fluxes to changes in model parameters, i.e., turbulent transport parameters and the light extinction coefficient (Kd). A C++ open-source software package, Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), was used to perform sensitivity analysis (SA) and identify the parameters that dominate model behavior. The generalized likelihood uncertainty estimation (GLUE) was applied to quantify the fluxes' uncertainty, comparing daily-averaged eddy-covariance observations to the output of CSLM. Seven qualitative and two quantitative SA methods were tested, and the posterior likelihoods of the modeled parameters, obtained from the GLUE analysis, were used to determine the dominant parameters and the uncertainty in the modeled fluxes. Despite the ubiquity of the equifinality issue - different parameter-value combinations yielding equivalent results - the answer to the question was unequivocal: Kd, a measure of how much light penetrates the lake, dominates sensible and latent heat fluxes, and the uncertainty in their estimates is strongly related to the accuracy with which Kd is determined. This is important since accurate and continuous measurements of Kd could reduce modeling uncertainty.

  13. 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

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

    Science.gov (United States)

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

    2017-09-01

    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 best performance for

  15. 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 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...

  16. Coupled Land Surface-Subsurface Hydrogeophysical Inverse Modeling to Estimate Soil Organic Carbon Content in an Arctic Tundra

    Science.gov (United States)

    Tran, A. P.; Dafflon, B.; Hubbard, S.

    2017-12-01

    Soil organic carbon (SOC) is crucial for predicting carbon climate feedbacks in the vulnerable organic-rich Arctic region. However, it is challenging to achieve this property due to the general limitations of conventional core sampling and analysis methods. In this study, we develop an inversion scheme that uses single or multiple datasets, including soil liquid water content, temperature and ERT data, to estimate the vertical profile of SOC content. Our approach relies on the fact that SOC content strongly influences soil hydrological-thermal parameters, and therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. The scheme includes several advantages. First, this is the first time SOC content is estimated by using a coupled hydrogeophysical inversion. Second, by using the Community Land Model, we can account for the land surface dynamics (evapotranspiration, snow accumulation and melting) and ice/liquid phase transition. Third, we combine a deterministic and an adaptive Markov chain Monte Carlo optimization algorithm to better estimate the posterior distributions of desired model parameters. Finally, the simulated subsurface variables are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using synthetic experiments. The results show that compared to inversion of single dataset, joint inversion of these datasets significantly reduces parameter uncertainty. The joint inversion approach is able to estimate SOC content within the shallow active layer with high reliability. Next, we apply the scheme to estimate OC content along an intensive ERT transect in Barrow, Alaska using multiple datasets acquired in the 2013-2015 period. The preliminary results show a good agreement between modeled and measured soil temperature, thaw layer thickness and electrical resistivity. The accuracy of estimated SOC content

  17. Hydrogeological controls of groundwater - land surface interactions

    Science.gov (United States)

    Bresciani, Etienne; Batelaan, Okke; Goderniaux, Pascal

    2017-04-01

    Interaction of groundwater with the land surface impacts a wide range of climatic, hydrologic, ecologic and geomorphologic processes. Many site-specific studies have successfully focused on measuring and modelling groundwater-surface water interaction, but upscaling or estimation at catchment or regional scale appears to be challenging. The factors controlling the interaction at regional scale are still poorly understood. In this contribution, a new 2-D (cross-sectional) analytical groundwater flow solution is used to derive a dimensionless criterion that expresses the conditions under which the groundwater outcrops at the land surface (Bresciani et al., 2016). The criterion gives insights into the functional relationships between geology, topography, climate and the locations of groundwater discharge along river systems. This sheds light on the debate about the topographic control of groundwater flow and groundwater-surface water interaction, as effectively the topography only influences the interaction when the groundwater table reaches the land surface. The criterion provides a practical tool to predict locations of groundwater discharge if a limited number of geomorphological and hydrogeological parameters (recharge, hydraulic conductivity and depth to impervious base) are known, and conversely it can provide regional estimates of the ratio of recharge over hydraulic conductivity if locations of groundwater discharge are known. A case study with known groundwater discharge locations located in South-West Brittany, France shows the feasibility of regional estimates of the ratio of recharge over hydraulic conductivity. Bresciani, E., Goderniaux, P. and Batelaan, O., 2016, Hydrogeological controls of water table-land surface interactions. Geophysical Research Letters 43(18): 9653-9661. http://dx.doi.org/10.1002/2016GL070618

  18. Parameter sensitivity analysis of a 1-D cold region lake model for land-surface schemes

    Directory of Open Access Journals (Sweden)

    J.-L. Guerrero

    2017-12-01

    Full Text Available Lakes might be sentinels of climate change, but the uncertainty in their main feedback to the atmosphere – heat-exchange fluxes – is often not considered within climate models. Additionally, these fluxes are seldom measured, hindering critical evaluation of model output. Analysis of the Canadian Small Lake Model (CSLM, a one-dimensional integral lake model, was performed to assess its ability to reproduce diurnal and seasonal variations in heat fluxes and the sensitivity of simulated fluxes to changes in model parameters, i.e., turbulent transport parameters and the light extinction coefficient (Kd. A C++ open-source software package, Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE, was used to perform sensitivity analysis (SA and identify the parameters that dominate model behavior. The generalized likelihood uncertainty estimation (GLUE was applied to quantify the fluxes' uncertainty, comparing daily-averaged eddy-covariance observations to the output of CSLM. Seven qualitative and two quantitative SA methods were tested, and the posterior likelihoods of the modeled parameters, obtained from the GLUE analysis, were used to determine the dominant parameters and the uncertainty in the modeled fluxes. Despite the ubiquity of the equifinality issue – different parameter-value combinations yielding equivalent results – the answer to the question was unequivocal: Kd, a measure of how much light penetrates the lake, dominates sensible and latent heat fluxes, and the uncertainty in their estimates is strongly related to the accuracy with which Kd is determined. This is important since accurate and continuous measurements of Kd could reduce modeling uncertainty.

  19. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    Science.gov (United States)

    Romaguera, Mireia; Vaughan, R. Greg; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F.D.

    2018-01-01

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LSTgt. We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model.In order to extract LSTgt, two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed.The results show consistent spatial and temporal LSTgt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LSTgt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LSTgt. Results further show that areas with strong topography can result in anomalously high LSTgt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LSTgt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m2 in the geothermal area, which coincides with the LSTgt flux rates obtained with the proposed approaches.The proposed approaches are a first step in estimating LSTgt

  20. Land Surface Temperature Retrieval from MODIS Data by Integrating Regression Models and the Genetic Algorithm in an Arid Region

    Directory of Open Access Journals (Sweden)

    Ji Zhou

    2014-06-01

    Full Text Available The land surface temperature (LST is one of the most important parameters of surface-atmosphere interactions. Methods for retrieving LSTs from satellite remote sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on Earth’s surface. Many split-window (SW algorithms, which can be applied to satellite sensors with two adjacent thermal channels located in the atmospheric window between 10 μm and 12 μm, require auxiliary atmospheric parameters (e.g., water vapor content. In this research, the Heihe River basin, which is one of the most arid regions in China, is selected as the study area. The Moderate-resolution Imaging Spectroradiometer (MODIS is selected as a test case. The Global Data Assimilation System (GDAS atmospheric profiles of the study area are used to generate the training dataset through radiative transfer simulation. Significant correlations between the atmospheric upwelling radiance in MODIS channel 31 and the other three atmospheric parameters, including the transmittance in channel 31 and the transmittance and upwelling radiance in channel 32, are trained based on the simulation dataset and formulated with three regression models. Next, the genetic algorithm is used to estimate the LST. Validations of the RM-GA method are based on the simulation dataset generated from in situ measured radiosonde profiles and GDAS atmospheric profiles, the in situ measured LSTs, and a pair of daytime and nighttime MOD11A1 products in the study area. The results demonstrate that RM-GA has a good ability to estimate the LSTs directly from the MODIS data without any auxiliary atmospheric parameters. Although this research is for local application in the Heihe River basin, the findings and proposed method can easily be extended to other satellite sensors and regions with arid climates and high elevations.

  1. Validation and sensitivity tests on improved parametrizations of a land surface process model (LSPM) in the Po Valley

    Energy Technology Data Exchange (ETDEWEB)

    Cassardo, C. [Alessandria, Univ. di Turin (Italy). Dipt. di Scienze e Tecnologie Avanzate; Carena, E.; Longhetto, A. [Turin Univ. (Italy). Dipt. di Fisica Generale `Amedeo Avogadro`

    1998-03-01

    The Land Surface Process Model (LSPM) has been improved with respect to the 1. version of 1994. The modifications have involved the parametrizations of the radiation terms and of turbulent heat fluxes. A parametrization of runoff has also been developed, in order to close the hydrologic balance. This 2. version of LSPM has been validated against experimental data gathered at Mottarone (Verbania, Northern Italy) during a field experiment. The results of this validation show that this new version is able to apportionate the energy into sensible and latent heat fluxes. LSPM has also been submitted to a series of sensitivity tests in order to investigate the hydrological part of the model. The physical quantities selected in these sensitivity experiments have been the initial soil moisture content and the rainfall intensity. In each experiment, the model has been forced by using the observations carried out at the synoptic stations of San Pietro Capofiume (Po Valley, Italy). The observed characteristics of soil and vegetation (not involved in the sensitivity tests) have been used as initial and boundary conditions. The results of the simulation show that LSPM can reproduce well the energy, heat and water budgets and their behaviours with varying the selected parameters. A careful analysis of the LSPM output shows also the importance to identify the effective soil type.

  2. Towards a Representation of Flexible Canopy N Stiochiometry for Land-surface Models Based on Optimality Concepts

    Science.gov (United States)

    Zaehle, S.; Caldararu, S.

    2015-12-01

    Foliar nitrogen (N) is know to acclimate to environmental conditions. One particular pertinent response is the general decline in foliar N following exposure to elevated levels of atmospheric CO2 (eCO2). Associated with reduced foliar N is an increased plant nitrogen-use efficiency, which contributes to the plants' sustained growth response to eCO2 in the absence of any counteracting litter N feedbacks. Flexible leaf N thus has important consequences for the mid- to long-term response of terrestrial ecosystems to eCO2. The current generation of land-surface models including a prognostic N cycle generally employ heuristic, and simply mass-balancing parameterisations to estimate changes in stoichiometry given altered N and carbon (C) availability. This generation generally and substantially overestimates the decline of foliar N (and thus the increase in plant nitrogen use efficiency) observed in Free Air CO2 Enrichment Experiments (FACE; Zaehle et al. 2014). In this presentation, I develop a simple, prognostic and dynamic representation of flexible foliar N for use in land-surface models by maximising the marginal gain of net assimilation with respect to the energy investment to generate foliar area and foliar N. I elucidate the underlying assumptions required to simulate the commonly observed decline in foliar N with eCO2 under different scenarios of N availability (Feng et al. 2015). References: Zaehle, Sönke, Belinda E Medlyn, Martin G De Kauwe, Anthony P Walker, Michael C Dietze, Hickler Thomas, Yiqi Luo, et al. 2014. "Evaluation of 11 Terrestrial Carbon-Nitrogen Cycle Models Against Observations From Two Temperate Free-Air CO2 Enrichment Studies." New Phytologist 202 (3): 803-22. doi:10.1111/nph.12697. Feng, Zhaozhong, Tobias RUtting, Håkan Pleijel, GORAN WALLIN, Peter B Reich, Claudia I Kammann, Paul C D Newton, Kazuhiko Kobayashi, Yunjian Luo, and Johan Uddling. 2015. "Constraints to Nitrogen Acquisition of Terrestrial Plants Under Elevated CO 2." Global

  3. 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

  4. 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.

  5. FLDAS VIC Land Surface Model L4 monthly 0.25 x 0.25 degree for Western Africa (MERRA-2 and CHIRPS) V001 (FLDAS_VIC025_C_WA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  6. FLDAS Noah Land Surface Model L4 monthly 0.1 x 0.1 degree for Eastern Africa (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_EA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  7. FLDAS Noah Land Surface Model L4 monthly 0.1 x 0.1 degree for Southern Africa (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_SA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  8. FLDAS Noah Land Surface Model L4 monthly 0.1 x 0.1 degree for Western Africa (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_WA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  9. FLDAS VIC Land Surface Model L4 monthly 0.25 x 0.25 degree for Southern Africa (MERRA-2 and CHIRPS) V001 (FLDAS_VIC025_C_SA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  10. FLDAS Noah Land Surface Model L4 daily 0.1 x 0.1 degree for Eastern Africa (GDAS and RFE2) V001 (FLDAS_NOAH01_A_EA_D) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  11. FLDAS VIC Land Surface Model L4 monthly 0.25 x 0.25 degree for Southern Africa (GDAS and RFE2) V001 (FLDAS_VIC025_A_SA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  12. FLDAS VIC Land Surface Model L4 monthly 0.25 x 0.25 degree for Eastern Africa (MERRA-2 and CHIRPS) V001 (FLDAS_VIC025_C_EA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  13. FLDAS Noah Land Surface Model L4 daily 0.1 x 0.1 degree for Southern Africa (GDAS and RFE2) V001 (FLDAS_NOAH01_A_SA_D) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  14. FLDAS Noah Land Surface Model L4 monthly 0.1 x 0.1 degree for Western Africa (GDAS and RFE2) V001 (FLDAS_NOAH01_A_WA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  15. FLDAS VIC Land Surface Model L4 daily 0.25 x 0.25 degree for Western Africa (GDAS and RFE2) V001 (FLDAS_VIC025_A_WA_D) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  16. FLDAS VIC Land Surface Model L4 monthly 0.25 x 0.25 degree for Eastern Africa (GDAS and RFE2) V001 (FLDAS_VIC025_A_EA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  17. FLDAS VIC Land Surface Model L4 daily 0.25 x 0.25 degree for Eastern Africa (GDAS and RFE2) V001 (FLDAS_VIC025_A_EA_D) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  18. FLDAS VIC Land Surface Model L4 monthly 0.25 x 0.25 degree for Western Africa (GDAS and RFE2) V001 (FLDAS_VIC025_A_WA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  19. FLDAS VIC Land Surface Model L4 Daily 0.25 x 0.25 degree for Southern Africa (MERRA-2 and CHIRPS) V001 (FLDAS_VIC025_A_SA_D) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the VIC model in the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation...

  20. FLDAS Noah Land Surface Model L4 daily 0.1 x 0.1 degree for Western Africa (GDAS and RFE2) V001 (FLDAS_NOAH01_A_WA_D) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  1. FLDAS Noah Land Surface Model L4 monthly 0.1 x 0.1 degree for Eastern Africa (GDAS and RFE2) V001 (FLDAS_NOAH01_A_EA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  2. FLDAS Noah Land Surface Model L4 monthly 0.1 x 0.1 degree for Southern Africa (GDAS and RFE2) V001 (FLDAS_NOAH01_A_SA_M) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS NET) Land Data...

  3. Flood Simulations and Uncertainty Analysis for the Pearl River Basin Using the Coupled Land Surface and Hydrological Model System

    Directory of Open Access Journals (Sweden)

    Yongnan Zhu

    2017-06-01

    Full Text Available The performances of hydrological simulations for the Pearl River Basin in China were analysed using the Coupled Land Surface and Hydrological Model System (CLHMS. Three datasets, including East Asia (EA, high-resolution gauge satellite-merged China Merged Precipitation Analysis (CMPA-Daily, and the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE daily precipitation were used to drive the CLHMS model to simulate daily hydrological processes from 1998 to 2006. The results indicate that the precipitation data was the most important source of uncertainty in the hydrological simulation. The simulated streamflow driven by the CMPA-Daily agreed well with observations, with a Pearson correlation coefficient (PMC greater than 0.70 and an index of agreement (IOA similarity coefficient greater than 0.82 at Liuzhou, Shijiao, and Wuzhou Stations. Comparison of the Nash-Sutcliffe efficiency coefficient (NSE shows that the peak flow simulation ability of CLHMS driven with the CMPA-Daily rainfall is relatively superior to that with the EA and APHRODITE datasets. The simulation results for the high-flow periods in 1998 and 2005 indicate that the CLHMS is promising for its future application in the flood simulation and prediction.

  4. Inferring CO2 Fluxes from OCO-2 for Assimilation into Land Surface Models to Calculate Net Ecosystem Exchange

    Science.gov (United States)

    Prouty, R.; Radov, A.; Halem, M.; Nearing, G. S.

    2016-12-01

    Investigations of mid to high latitude atmospheric CO2 show a growing seasonal amplitude. Land surface models poorly predict net ecosystem exchange (NEE) and are unable to substantiate these sporadic observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. A global, seasonal investigation of CO2-flux is then necessary in order to assimilate into land surface models for improving the prediction of annual NEE. The Atmospheric Radiation Measurement program (ARM) of DOE collects CO2-flux measurements (in addition to CO2 concentration and various other meteorological quantities) at several towers located around the globe at half hour temporal frequencies. CO2-fluxes are calculated via the eddy covariance technique, which utilizes CO2-densities and wind velocities to calculate CO2-fluxes. The global coverage of CO2 concentrations as provided by the Orbiting Carbon Observatory (OCO-2) provide satellite-derived CO2 concentrations all over the globe. A framework relating the satellite-inferred CO2 concentrations collocated with the ground-based ARM as well as Ameriflux stations would enable calculations of CO2-fluxes far from the station sites around the entire globe. Regression techniques utilizing deep-learning neural networks may provide such a framework. Additionally, meteorological reanalysis allows for the replacement of the ARM multivariable meteorological variables needed to infer the CO2-fluxes. We present the results of inferring CO2-fluxes from OCO-2 CO2 concentrations for a two year period, Sept. 2014- Sept. 2016 at the ARM station located near Oklahoma City. A feed-forward neural network (FFNN) is used to infer relationships between the following data sets: F([ARM CO2-density], [ARM Meteorological Data]) = [ARM CO2-Flux] F([OCO-2 CO2-density],[ARM Meteorological Data]) = [ARM CO2-Flux] F([ARM CO2-density],[Meteorological Reanalysis]) = [ARM CO2-Flux

  5. What is the importance of climate model bias when projecting the impacts of climate change on land surface processes?

    Energy Technology Data Exchange (ETDEWEB)

    Liu, M. L.; Rajagopalan, K.; Chung, S. H.; Jiang, X.; Harrison, J. H.; Nergui, T.; Guenther, Alex B.; Miller, C.; Reyes, J.; Tague, C. L.; Choate, J. S.; Salathe, E.; Stockle, Claudio O.; Adam, J. C.

    2014-05-16

    Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables(evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These im20 pact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However

  6. Land use change affecting our (modelled) climate

    Science.gov (United States)

    Weiß, M.; van den Hurk, B.

    2012-04-01

    Up until now, climate models often have used a static representation of land cover characteristics and only recently, the impact of a changing land surface on climate and climate simulations has attracted more attention. With climate scenarios to the fifth Assessment Report currently in preparation, this time considering different representative concentration pathways and associated land cover scenarios, we are still building up process-knowledge on the strength of land-atmosphere coupling. This study contributes to our understanding of the impact of land cover changes on the atmosphere by running global simulations with the EC-Earth climate model, using different historical land use data as well as two land use scenarios to analyse the following aspects in more detail: On the one hand the impact of land cover change via modified albedo, and on the other hand its impact via modified surface resistance, rooting depth and soil-moisture capacity on available energy, evapotranspiration, wind and temperature.

  7. Evaluation of snow and frozen soil parameterization in a cryosphere land surface modeling framework in the Tibetan Plateau

    Science.gov (United States)

    Zhou, J.

    2017-12-01

    Snow and frozen soil are important components in the Tibetan Plateau, and influence the water cycle and energy balances through snowpack accumulation and melt and soil freeze-thaw. In this study, a new cryosphere land surface model (LSM) with coupled snow and frozen soil parameterization 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.

  8. A field evaluation of soil moisture modelling with the Soil, Vegetation, and Snow (SVS) land surface model using evapotranspiration observations as forcing data

    Science.gov (United States)

    Maheu, Audrey; Anctil, François; Gaborit, Étienne; Fortin, Vincent; Nadeau, Daniel F.; Therrien, René

    2018-03-01

    To address certain limitations with their current operational model, Environment and Climate Change Canada recently developed the Soil, Vegetation, and Snow (SVS) land surface model and the representation of subsurface hydrological processes was targeted as an area for improvement. The objective of this study is to evaluate the ability of HydroSVS, the component of SVS responsible for the vertical redistribution of water, to simulate soil moisture under snow-free conditions when using flux-tower observations of evapotranspiration as forcing data. We assessed (1) model fidelity by comparing soil moisture modelled with HydroSVS to point-scale measurements of volumetric soil water content and (2) model complexity by comparing the performance of HydroSVS to that of HydroGeoSphere, a state-of-the-art integrated surface and subsurface hydrologic model. To do this, we performed one-dimensional soil column simulations at four sites of the AmeriFlux network. Results indicate that under Mediterranean and temperate climates, HydroSVS satisfactorily simulated soil moisture (Nash-Sutcliffe efficiency between 0.26 and 0.70; R2 ≥ 0.80), with a performance comparable to HydroGeoSphere (Nash-Sutcliffe efficiency ≥0.60; R2 ≥ 0.80). However, HydroSVS performed weakly under a semiarid climate while HydroGeoSphere performed relatively well. By decoupling the magnitude and sourcing of evapotranspiration, this study proposes a powerful diagnostic tool to evaluate the representation of subsurface hydrological processes in land surface models. Overall, this study highlights the potential of SVS for hydrological applications.

  9. 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

  10. 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.

  11. Coupling Nitrogen Transport and Transformation Model with Land Surface Scheme SABAE-HW and its Application on the Canadian Prairies

    Science.gov (United States)

    Hejazi, A.; Woodbury, A. D.; Loukili, Y.; Akinremi, W.

    2012-12-01

    The main goal of the present research is to contribute to the understanding of nutrient transport and transformations in soil and its impact on groundwater on a large scale. This paper specifically integrates the physical, chemical and biochemical nitrogen transport processes with a spatial and temporal Land Surface Scheme (LSS). Because solute transport highly depends on soil moisture and soil temperature, a vertical soil nitrogen transport and transformations model was coupled with the SABAE-HW model. Since manure is one of the most commonly available sources of nutrients, it is assumed that the main source of organic N is from animal manure in this study. A-single-pool nitrogen transformation is designed to simulate nitrogen dynamics. Mineralization and nitrification are modeled using the first order kinetics. The performance of the integrated model (SABAE-HWS) is calibrated and verified using 3 years field data from Carberry site in central Canada, Manitoba. Two rates of hog manure (2500 and 7500 gal/acre) were investigated to study the distribution of soil ammonium and soil nitrate within the 120 cm of soil profile. The results clearly showed that there is a good agreement between observed and simulated soil ammonium and soil nitrate for the two manure application rates in the first two years of study. However, there were significant differences between observations and simulations at lower depths with 7500 gal/acre by the end of growing season of 2004. Also, a 10-year climate data was used to evaluate the effect of manure rates on nitrate leaching at the Carberry site. The results indicated that to minimize the risk of nitrate leaching, the rate of manure application, accumulated soil nitrogen from earlier applications, and the atmospheric conditions should be all taken into account at the same time. The simulations clearly showed that to have a nitrate concentration below 10 mg/kg in the leachate, the manure rate should not exceed 2500 gal/acre. The model is

  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. Evaluation of the WRF-Urban Modeling System Coupled to Noah and Noah-MP Land Surface Models Over a Semiarid Urban Environment

    Science.gov (United States)

    Salamanca, Francisco; Zhang, Yizhou; Barlage, Michael; Chen, Fei; Mahalov, Alex; Miao, Shiguang

    2018-03-01

    We have augmented the existing capabilities of the integrated Weather Research and Forecasting (WRF)-urban modeling system by coupling three urban canopy models (UCMs) available in the WRF model with the new community Noah with multiparameterization options (Noah-MP) land surface model (LSM). The WRF-urban modeling system's performance has been evaluated by conducting six numerical experiments at high spatial resolution (1 km horizontal grid spacing) during a 15 day clear-sky summertime period for a semiarid urban environment. To assess the relative importance of representing urban surfaces, three different urban parameterizations are used with the Noah and Noah-MP LSMs, respectively, over the two major cities of Arizona: Phoenix and Tucson metropolitan areas. Our results demonstrate that Noah-MP reproduces somewhat better than Noah the daily evolution of surface skin temperature and near-surface air temperature (especially nighttime temperature) and wind speed. Concerning the urban areas, bulk urban parameterization overestimates nighttime 2 m air temperature compared to the single-layer and multilayer UCMs that reproduce more accurately the daily evolution of near-surface air temperature. Regarding near-surface wind speed, only the multilayer UCM was able to reproduce realistically the daily evolution of wind speed, although maximum winds were slightly overestimated, while both the single-layer and bulk urban parameterizations overestimated wind speed considerably. Based on these results, this paper demonstrates that the new community Noah-MP LSM coupled to an UCM is a promising physics-based predictive modeling tool for urban applications.

  14. Representation of vegetation effects on the snow-covered albedo in the Noah land surface model with multiple physics options

    OpenAIRE

    S. Park; S. K. Park

    2015-01-01

    Snow albedo plays a critical role in calculating the energy budget, but parameterization of the snow surface albedo is still under great uncertainty. It varies with snow grain size, snow cover thickness, snow age, forest shading factor and other variables. Snow albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the No...

  15. Representation of dissolved organic carbon in the JULES land surface model (vn4.4_JULES-DOCM

    Directory of Open Access Journals (Sweden)

    M. Nakhavali

    2018-02-01

    Full Text Available Current global models of the carbon (C cycle consider only vertical gas exchanges between terrestrial or oceanic reservoirs and the atmosphere, thus not considering the lateral transport of carbon from the continents to the oceans. Therefore, those models implicitly consider all of the C which is not respired to the atmosphere to be stored on land and hence overestimate the land C sink capability. A model that represents the whole continuum from atmosphere to land and into the ocean would provide a better understanding of the Earth's C cycle and hence more reliable historical or future projections. A first and critical step in that direction is to include processes representing the production and export of dissolved organic carbon in soils. Here we present an original representation of dissolved organic C (DOC processes in the Joint UK Land Environment Simulator (JULES-DOCM that integrates a representation of DOC production in terrestrial ecosystems based on the incomplete decomposition of organic matter, DOC decomposition within the soil column, and DOC export to the river network via leaching. The model performance is evaluated in five specific sites for which observations of soil DOC concentration are available. Results show that the model is able to reproduce the DOC concentration and controlling processes, including leaching to the riverine system, which is fundamental for integrating terrestrial and aquatic ecosystems. Future work should include the fate of exported DOC in the river system as well as DIC and POC export from soil.

  16. Representation of dissolved organic carbon in the JULES land surface model (vn4.4_JULES-DOCM)

    Science.gov (United States)

    Nakhavali, Mahdi; Friedlingstein, Pierre; Lauerwald, Ronny; Tang, Jing; Chadburn, Sarah; Camino-Serrano, Marta; Guenet, Bertrand; Harper, Anna; Walmsley, David; Peichl, Matthias; Gielen, Bert

    2018-02-01

    Current global models of the carbon (C) cycle consider only vertical gas exchanges between terrestrial or oceanic reservoirs and the atmosphere, thus not considering the lateral transport of carbon from the continents to the oceans. Therefore, those models implicitly consider all of the C which is not respired to the atmosphere to be stored on land and hence overestimate the land C sink capability. A model that represents the whole continuum from atmosphere to land and into the ocean would provide a better understanding of the Earth's C cycle and hence more reliable historical or future projections. A first and critical step in that direction is to include processes representing the production and export of dissolved organic carbon in soils. Here we present an original representation of dissolved organic C (DOC) processes in the Joint UK Land Environment Simulator (JULES-DOCM) that integrates a representation of DOC production in terrestrial ecosystems based on the incomplete decomposition of organic matter, DOC decomposition within the soil column, and DOC export to the river network via leaching. The model performance is evaluated in five specific sites for which observations of soil DOC concentration are available. Results show that the model is able to reproduce the DOC concentration and controlling processes, including leaching to the riverine system, which is fundamental for integrating terrestrial and aquatic ecosystems. Future work should include the fate of exported DOC in the river system as well as DIC and POC export from soil.

  17. 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

  18. Satellite-based albedo, sea surface temperature and effective land roughness maps used in the HIRLAM model for weather and climate scenarios

    Science.gov (United States)

    Hasager, C. B.; Nielsen, N. W.; Christensen, J. H.; Soegaard, H.; Boegh, E.; Rasmussen, M. S.; Jensen, N. O.

    2001-12-01

    A study is conducted on the effect of introducing maps of geophysical parameters retrieved from satellite Earth Observation data into the atmospheric model HIRLAM (HIgh Resolution Limited Area Model). . The HIRLAM system was developed by the HIRLAM project group, a cooperative project of the national weather services in Denmark, Finland, Iceland, Ireland, the Netherlands, Norway and Sweden. It is currently used by weather services in several European countries. The exchanges of sensible heat, water vapour and momentum between the land- and ocean surface and the atmosphere are very important dynamical processes in this type of model. The results from the HIRLAM model when using the improved surface boundary conditions is validated from wind and temperature data at synoptic weather stations and surface flux data from land- and ocean meteorological masts in Denmark. The results from a set of scenarios covering the hurricane in Denmark in December 1999 and several springtime cases in 2000 show improved weather forecasts. The methodology on retrieving improved boundary conditions is based on satellite image data. Maps on the geophysical parameters albedo and sea surface temperature are retrieved at a 1 km spatial resolution from NOAA AVHRR. Furthermore, land cover maps based on Landsat TM satellite data are used to assess the regional roughness. The high-resolution land roughness map (Areal Systems Information in a 25 m pixel resolution) is area-averaged into effective roughness values (15 km grid) by using a non-linear aggregation technique (QJRMS 1999, vol 125, 2075-2102). The area-averaging is highly non-linear due to the turbulent physical processes involved. Thus the effective surface conditions cannot be obtained by simple averaging but only by a flow model taking horizontal advection into consideration. The effect of hedges in the landscape is included as a correction index based on a vector-based map. The land surface fluxes of heat and water vapour is also

  19. Simulating the Effects of Irrigation over the U.S. in a Land Surface Model Based on Satellite Derived Agricultural Data

    Science.gov (United States)

    Ozdogan, Mutlu; Rodell, Matthew; Beaudoing, Hiroko Kato; Toll, David L.

    2009-01-01

    A novel method is introduced for integrating satellite derived irrigation data and high-resolution crop type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land-atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here we show that application of the new irrigation scheme over the continental US significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In our experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental US during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W/m from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W/m(sup 2), 20 W/m(sup 2), 5 mm/day, 0.3 mm/day, and 100 mm, respectively. These results are highly relevant to continental- to global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. Based on the results presented here, we expect that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA's Global Forecast System (GFS).

  20. 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.

  1. 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

  2. ENVISAT Land Surface Processes. Phase 2

    Science.gov (United States)

    vandenHurk, B. J. J. M.; Su, Z.; Verhoef, W.; Menenti, M.; Li, Z.-L.; Wan, Z.; Moene, A. F.; Roerink, G.; Jia, I.

    2002-01-01

    This is a progress report of the 2nd phase of the project ENVISAT- Land Surface Processes, which has a 3-year scope. In this project, preparative research is carried out aiming at the retrieval of land surface characteristics from the ENVISAT sensors MERIS and AATSR, for assimilation into a system for Numerical Weather Prediction (NWP). Where in the 1st phase a number of first shot experiments were carried out (aiming at gaining experience with the retrievals and data assimilation procedures), the current 2nd phase has put more emphasis on the assessment and improvement of the quality of the retrieved products. The forthcoming phase will be devoted mainly to the data assimilation experiments and the assessment of the added value of the future ENVISAT products for NWP forecast skill. Referring to the retrieval of albedo, leaf area index and atmospheric corrections, preliminary radiative transfer calculations have been carried out that should enable the retrieval of these parameters once AATSR and MERIS data become available. However, much of this work is still to be carried out. An essential part of work in this area is the design and implementation of software that enables an efficient use of MODTRAN(sub 4) radiative transfer code, and during the current project phase familiarization with these new components has been achieved. Significant progress has been made with the retrieval of component temperatures from directional ATSR-images, and the calculation of surface turbulent heat fluxes from these data. The impact of vegetation cover on the retrieved component temperatures appears manageable, and preliminary comparison of foliage temperature to air temperatures were encouraging. The calculation of surface fluxes using the SEBI concept,which includes a detailed model of the surface roughness ratio, appeared to give results that were in reasonable agreement with local measurements with scintillometer devices. The specification of the atmospheric boundary conditions

  3. 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

  4. The interactions between soil-biosphere-atmosphere land surface model with a multi-energy balance (ISBA-MEB) option in SURFEXv8 - Part 1: Model description

    Science.gov (United States)

    Boone, Aaron; Samuelsson, Patrick; Gollvik, Stefan; Napoly, Adrien; Jarlan, Lionel; Brun, Eric; Decharme, Bertrand

    2017-02-01

    Land surface models (LSMs) are pushing towards improved realism owing to an increasing number of observations at the local scale, constantly improving satellite data sets and the associated methodologies to best exploit such data, improved computing resources, and in response to the user community. As a part of the trend in LSM development, there have been ongoing efforts to improve the representation of the land surface processes in the interactions between the soil-biosphere-atmosphere (ISBA) LSM within the EXternalized SURFace (SURFEX) model platform. The force-restore approach in ISBA has been replaced in recent years by multi-layer explicit physically based options for sub-surface heat transfer, soil hydrological processes, and the composite snowpack. The representation of vegetation processes in SURFEX has also become much more sophisticated in recent years, including photosynthesis and respiration and biochemical processes. It became clear that the conceptual limits of the composite soil-vegetation scheme within ISBA had been reached and there was a need to explicitly separate the canopy vegetation from the soil surface. In response to this issue, a collaboration began in 2008 between the high-resolution limited area model (HIRLAM) consortium and Météo-France with the intention to develop an explicit representation of the vegetation in ISBA under the SURFEX platform. A new parameterization has been developed called the ISBA multi-energy balance (MEB) in order to address these issues. ISBA-MEB consists in a fully implicit numerical coupling between a multi-layer physically based snowpack model, a variable-layer soil scheme, an explicit litter layer, a bulk vegetation scheme, and the atmosphere. It also includes a feature that permits a coupling transition of the snowpack from the canopy air to the free atmosphere. It shares many of the routines and physics parameterizations with the standard version of ISBA. This paper is the first of two parts; in part one

  5. Using Flux Site Observations to Calibrate Root System Architecture Stencils for Water Uptake of Plant Functional Types in Land Surface Models.

    Science.gov (United States)

    Bouda, M.

    2017-12-01

    Root system architecture (RSA) can significantly affect plant access to water, total transpiration, as well as its partitioning by soil depth, with implications for surface heat, water, and carbon budgets. Despite recent advances in land surface model (LSM) descriptions of plant hydraulics, RSA has not been included because of its three-dimensional complexity, which makes RSA modelling generally too computationally costly. This work builds upon the recently introduced "RSA stencil," a process-based 1D layered model that captures the dynamic shifts in water potential gradients of 3D RSA in response to heterogeneous soil moisture profiles. In validations using root systems calibrated to the rooting profiles of four plant functional types (PFT) of the Community Land Model, the RSA stencil predicts plant water potentials within 2% of the outputs of full 3D models, despite its trivial computational cost. In transient simulations, the RSA stencil yields improved predictions of water uptake and soil moisture profiles compared to a 1D model based on root fraction alone. Here I show how the RSA stencil can be calibrated to time-series observations of soil moisture and transpiration to yield a water uptake PFT definition for use in terrestrial models. This model-data integration exercise aims to improve LSM predictions of soil moisture dynamics and, under water-limiting conditions, surface fluxes. These improvements can be expected to significantly impact predictions of downstream variables, including surface fluxes, climate-vegetation feedbacks and soil nutrient cycling.

  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. The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

    Directory of Open Access Journals (Sweden)

    D. Verfaillie

    2017-11-01

    Full Text Available We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods, the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally

  8. The method ADAMONT v1.0 for statistical adjustment of climate projections applicable to energy balance land surface models

    Science.gov (United States)

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

    2017-11-01

    We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model (RCM) using an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method ultimately produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed for any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature and precipitation but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the intervariable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for

  9. 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

  10. Communicating why land surface heterogeneity matters

    Science.gov (United States)

    Tague, C.; Burke, W.; Bart, R. R.; Turpin, E.; Wood, T.; Gordon, D.

    2017-12-01

    As hydrologic scientists, we know that land surface heterogeneity can have nuanced and sometimes dramatic impacts on the water cycle. Land surface characteristics, including the structure and composition of vegetation and soil storage and drainage properties, alter how incoming precipitation is translated into streamflow and evapotranspiration. Land surface heterogeneity can explain why this partitioning of incoming precipitation cannot always be computed by a simple water budget calculation. We also know that land surface characteristics are dynamic - vegetation grows and changes with fire, disease and human actions and these changes will alter the partitioning of water - how much so, however depends itself on other site characteristics - soil water storage and the timing and magnitude of precipitation. This complex impact of space-time dynamics on the water cycle is something we need to effectively communicate to non-experts. For example, we may want to explain why sometimes forest management practices increase water availability but sometimes they don't - or why the impacts of urbanization or fire are location specific. If we do not communicate these dependencies we risk over-simplifying and eroding scientific credibility when observed effects don't match simple generalizations. On the other hand excessive detail can overwhelm and disengage audiences. So how do we help different communities public, private landowners, other scientists, NGOs, governments to better understand the role of space-time heterogeneity. To address this issue, we present some results from ongoing work that looks at the impact of fuel treatment of forest ecohydrology. This work stem from a collaboration between an ecohydrologic modeling team, social-scientists, a visual artist and compute graphics students. We use a coupled model, validated with field measurements, to show why spatial heterogeneity matters for understanding the impact of fuel treatments on the water cycle for the Sierra

  11. Improved characterization of root zone soil moisture in land surface models by assimilation of groundwater level data. An example with TerrSysMP.

    Science.gov (United States)

    Hendricks Franssen, Harrie-Jan; Zhang, Hongjuan; Kurtz, Wolfgang; Kollet, Stefan; Vereecken, Harry

    2017-04-01

    Land surface model predictions are affected by uncertainty with respect to parameters, atmospheric forcings and process representation. Therefore, constraining land surface model predictions by assimilation of soil moisture data is of great interest, using techniques like the Ensemble Kalman Filter. Soil moisture is a key variable in land surface models linking the water and energy cycles. However, various studies found that assimilation of remotely sensed soil moisture content improved root zone soil moisture characterization only marginally. In addition, below densely vegetated areas measured remotely sensed soil moisture content is unreliable. In this study, we explored groundwater level data as an additional information source to be used in data assimilation to constrain root zone soil moisture characterization and land surface model predictions. In order to extract as much information as possible from groundwater level data we used the model TerrSysMP, which represents groundwater better than classical land surface models considering lateral subsurface flow, and fully coupled interactions between the vadose zone and groundwater. The assimilation of groundwater level data in integrated hydrological models like TerrSysMP is challenging. If groundwater level data are assimilated in terms of pressure information and used to update pressure in the vadose zone and aquifer, unrealistic updates may be generated in the upper vadose zone during (very) dry conditions as probability density functions of pressure are highly skewed. On the other hand, if groundwater level data are assimilated in terms of soil moisture values (equal to porosity in the aquifer) and used to update soil moisture in the vadose zone, under specific conditions which we will detail in the presentation updating also does not work well. We tested different data assimilation strategies in synthetic experiments and found that assimilating groundwater level data in terms of pressure, but updating model

  12. 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.

  13. 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

  14. SMEX02 Land Surface Information: Soils Database

    Data.gov (United States)

    National Aeronautics and Space Administration — The Soil Moisture Experiment 2002 (SMEX02) took place in Ames, Iowa USA between 25 June and 12 July 2002. The NASA Land Surface Hydrology Data Archive maintains an...

  15. 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

  16. Geospatial approach for estimating land surface evapotranspiration

    Science.gov (United States)

    Singh, Ramesh K.

    Reliably and accurately quantifying evapotranspiration (ET) in a spatial and temporal domain is important in water management at the local, regional, and global scales. With advances in image processing and hardware computational ability, energy balance models which utilize remote sensing images are being increasingly utilized for quantifying ET and used as inputs in hydrologic modeling. The objectives of this research were to evaluate and improve some of the energy balance models for estimating land surface ET, and develop a framework for estimating seasonal ET from temporal satellite images. Surface Energy Balance Algorithm for Land (SEBAL) model was used to estimate energy fluxes for south-central Nebraska using Landsat images. Results were compared with Bowen Ratio Energy Balance System (BREBS) field measurements. SEBAL estimated ET images were also used for computing crop coefficients (K c) for maize, soybean, sorghum, and alfalfa under irrigated and dryland conditions. Performances of four remote sensing based models for estimating soil heat flux (G) were analyzed. A new model was developed for remotely estimating G. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) model was also used for estimating energy fluxes using Landsat images. The METRIC model was modified by incorporating the Priestley-Taylor (PT) approach. The SEBAL model estimated net radiation (Rn) with a root mean square error (RMSE) of 65 W m-2 (r2 = 0.76). Calibrating G locally reduced RMSE from 80 W m-2 to 20 W m-2. The SEBAL model yielded sensible heat flux (H) with RMSE of 108 W m -2 (r2=0.23), and ET with an RMSE of 1.04 mm day -1(r2 = 0.73). Validation of Kc regression for irrigated maize resulted in RMSE of 0.21 (r2=0.74). The METRIC model estimated Rn, G, and H with RMSE values of 45 W m -2 (r2=0.85), 19 W m-2 (r2=0.85), and 113 W m-2 (r2=0.50), respectively. The modified METRIC model reduced the RMSE of H from 113 W m-2 to 91 W m -2 and that for

  17. 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

    Science.gov (United States)

    Faroux, S.; Kaptué Tchuenté, A. T.; Roujean, J.-L.; Masson, V.; Martin, E.; Le Moigne, P.

    2013-04-01

    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 versatile land

  18. 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

  19. 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

  20. Insights in time dependent cross compartment sensitivities from ensemble simulations with the fully coupled subsurface-land surface-atmosphere model TerrSysMP

    Science.gov (United States)

    Schalge, Bernd; Rihani, Jehan; Haese, Barbara; Baroni, Gabriele; Erdal, Daniel; Haefliger, Vincent; Lange, Natascha; Neuweiler, Insa; Hendricks-Franssen, Harrie-Jan; Geppert, Gernot; Ament, Felix; Kollet, Stefan; Cirpka, Olaf; Saavedra, Pablo; Han, Xujun; Attinger, Sabine; Kunstmann, Harald; Vereecken, Harry; Simmer, Clemens

    2017-04-01

    Currently, an integrated approach to simulating the earth system is evolving where several compartment models are coupled to achieve the best possible physically consistent representation. We used the model TerrSysMP, which fully couples subsurface, land surface and atmosphere, in a synthetic study that mimicked the Neckar catchment in Southern Germany. A virtual reality run at a high resolution of 400m for the land surface and subsurface and 1.1km for the atmosphere was made. Ensemble runs at a lower resolution (800m for the land surface and subsurface) were also made. The ensemble was generated by varying soil and vegetation parameters and lateral atmospheric forcing among the different ensemble members in a systematic way. It was found that the ensemble runs deviated for some variables and some time periods largely from the virtual reality reference run (the reference run was not covered by the ensemble), which could be related to the different model resolutions. This was for example the case for river discharge in the summer. We also analyzed the spread of model states as function of time and found clear relations between the spread and the time of the year and weather conditions. For example, the ensemble spread of latent heat flux related to uncertain soil parameters was larger under dry soil conditions than under wet soil conditions. Another example is that the ensemble spread of atmospheric states was more influenced by uncertain soil and vegetation parameters under conditions of low air pressure gradients (in summer) than under conditions with larger air pressure gradients in winter. The analysis of the ensemble of fully coupled model simulations provided valuable insights in the dynamics of land-atmosphere feedbacks which we will further highlight in the presentation.

  1. Reforestation of surface mines on lands of VICC Land Company

    Science.gov (United States)

    Thomas F. Evans

    1980-01-01

    This Virginia coal company's surface mined lands show an adequate stocking of tree seedlings in terms of number per acre, but the distribution of seedlings has been affected by past reclamation practices. Natural reseeding has been an important contributor to the present seedling stock.

  2. Assimilation of the ESA CCI Soil Moisture ACTIVE and PASSIVE Product into the SURFEX Land Surface Model using the Ensemble Transform Kalman Filter

    Science.gov (United States)

    Blyverket, J.; Hamer, P.; Bertino, L.; Lahoz, W. A.

    2017-12-01

    The European Space Agency Climate Change Initiative for soil moisture (ESA CCI SM) was initiated in 2012 for a period of six years, the objective for this period was to produce the most complete and consistent global soil moisture data record based on both active and passive sensors. The ESA CCI SM products consist of three surface soil moisture datasets: The ACTIVE product and the PASSIVE product were created by fusing scatterometer and radiometer soil moisture data, respectively. The COMBINED product is a blended product based on the former two datasets. In this study we assimilate globally both the ACTIVE and PASSIVE product at a 25 km spatial resolution. The different satellite platforms have different overpass times, an observation is mapped to the hours 00.00, 06.00, 12.00 or 18.00 if it falls within a 3 hour window centred at these times. We use the SURFEX land surface model with the ISBA diffusion scheme for the soil hydrology. For the assimilation routine we apply the Ensemble Transform Kalman Filter (ETKF). The land surface model is driven by perturbed MERRA-2 atmospheric forcing data, which has a temporal resolution of one hour and is mapped to the SURFEX model grid. Bias between the land surface model and the ESA CCI product is removed by cumulative distribution function (CDF) matching. This work is a step towards creating a global root zone soil moisture product from the most comprehensive satellite surface soil moisture product available. As a first step we consider the period from 2010 - 2016. This allows for comparison against other global root zone soil moisture products (SMAP Level 4, which is independent of the ESA CCI SM product).

  3. 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

    Science.gov (United States)

    Faroux, S.; Kaptué Tchuenté, A. T.; Roujean, J.-L.; Masson, V.; Martin, E.; Le Moigne, P.

    2012-11-01

    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 NDVI from SPOT/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 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 versatile land occupation nomenclatures.

  4. 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...

  5. Inclusion of Solar Elevation Angle in Land Surface Albedo Parameterization Over Bare Soil Surface.

    Science.gov (United States)

    Zheng, Zhiyuan; Wei, Zhigang; Wen, Zhiping; Dong, Wenjie; Li, Zhenchao; Wen, Xiaohang; Zhu, Xian; Ji, Dong; Chen, Chen; Yan, Dongdong

    2017-12-01

    Land surface albedo is a significant parameter for maintaining a balance in surface energy. It is also an important parameter of bare soil surface albedo for developing land surface process models that accurately reflect diurnal variation characteristics and the mechanism behind the solar spectral radiation albedo on bare soil surfaces and for understanding the relationships between climate factors and spectral radiation albedo. Using a data set of field observations, we conducted experiments to analyze the variation characteristics of land surface solar spectral radiation and the corresponding albedo over a typical Gobi bare soil underlying surface and to investigate the relationships between the land surface solar spectral radiation albedo, solar elevation angle, and soil moisture. Based on both solar elevation angle and soil moisture measurements simultaneously, we propose a new two-factor parameterization scheme for spectral radiation albedo over bare soil underlying surfaces. The results of numerical simulation experiments show that the new parameterization scheme can more accurately depict the diurnal variation characteristics of bare soil surface albedo than the previous schemes. Solar elevation angle is one of the most important factors for parameterizing bare soil surface albedo and must be considered in the parameterization scheme, especially in arid and semiarid areas with low soil moisture content. This study reveals the characteristics and mechanism of the diurnal variation of bare soil surface solar spectral radiation albedo and is helpful in developing land surface process models, weather models, and climate models.

  6. 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)

  7. Land Surface Precipitation and Hydrology in MERRA-2

    Science.gov (United States)

    Reichle, R.; Koster, R.; Draper, C.; Liu, Q.; Girotto, M.; Mahanama, S.; De Lannoy, G.; Partyka, G.

    2017-01-01

    The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), provides global, 1-hourly estimates of land surface conditions for 1980-present at 50-km resolution. Outside of the high latitudes, MERRA-2 uses observations-based precipitation data products to correct the precipitation falling on the land surface. This paper describes the precipitation correction method and evaluates the MERRA-2 land surface precipitation and hydrology. Compared to monthly GPCPv2.2 observations, the corrected MERRA-2 precipitation (M2CORR) is better than the precipitation generated by the atmospheric models within the cyclingMERRA-2 system and the earlier MERRA reanalysis. Compared to 3-hourlyTRMM observations, the M2CORR diurnal cycle has better amplitude but less realistic phasing than MERRA-2 model-generated precipitation. Because correcting the precipitation within the coupled atmosphere-land modeling system allows the MERRA-2 near-surface air temperature and humidity to respond to the improved precipitation forcing, MERRA-2 provides more self-consistent surface meteorological data than were available from the earlier, offline MERRA-Land reanalysis. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. The MERRA-2 and MERRA-Land surface and root zone soil moisture skill vs. in situ measurements is slightly higher than that of ERA-Interim Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim Land. Seasonal anomaly R values against naturalized stream flow measurements in

  8. 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.

  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. 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

    2017-08-01

    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.

  11. Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions

    International Nuclear Information System (INIS)

    Cai, X.; Zhang, X.

    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. Here 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.

  12. Remote sensing of land surface phenology

    Science.gov (United States)

    Meier, G.A.; Brown, Jesslyn 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.

  13. 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.

  14. Turbulent flow over an interactive alternating land-water surface

    Science.gov (United States)

    Van Heerwaarden, C.; Mellado, J. P.

    2014-12-01

    The alternating land-water surface is a challenging surface to represent accurately in weather and climate models, but it is of great importance for the surface energy balance in polar regions. The complexity of this surface lies in the fact that secondary circulations, which form at the boundary of water and land, interact strongly with the surface energy balance. Due to its large heat capacity, the water temperature adapts slowly to the flow, thus the properties of the atmosphere determine the uptake of energy from the water. In order to study this complex system in a simpler way, retaining only the most essential physics, we have simplified the full surface energy balance including radiation. We have derived a boundary condition that mimics the full balance and can be formulated as a so-called Robin boundary condition: a linear combination of Dirichlet (fixed temperature) and Neumann (fixed temperature gradient) ones. By spatially varying the coefficients, we are able to express land and water using this boundary condition. We have done a series of direct numerical simulations in which we generate artificial land-water patterns from noise created from a Gaussian spectrum centered around a dominant wave number. This method creates realistic random patterns, but we are still in control of the length scales. We show that the system can manifest itself in three regimes: micro-, meso- and macro-scale. In the micro-scale, we find perfect mixing of the near-surface atmosphere that results in identical air properties over water and land. In the meso-scale, secondary circulations alter the heat exchange considerably by advecting air between land and water. In addition, they bring the surface temperature of the land closer to that of the air, thereby modulating the energy loss due to outgoing longwave radiation. In the macro-scale regime, the flow over land and water become independent of each other and only the large scale forcings determine the energy balance.

  15. FLDAS Noah Land Surface Model L4 Monthly Climatology 0.1 x 0.1 degree for Eastern Africa (MERRA-2 and CHIRPS) V001 (FLDAS_NOAH01_C_EA_MC) at GES DISC

    Data.gov (United States)

    National Aeronautics and Space Administration — The monthly climatology data set contains a series of land surface parameters simulated from the Noah 3.3 model in the Famine Early Warning Systems Network (FEWS...

  16. 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

  17. Impact of the use of a CO2 responsive land surface model in simulating the effect of climate change on the hydrology of French Mediterranean basins

    Science.gov (United States)

    Queguiner, S.; Martin, E.; Lafont, S.; Calvet, J.-C.; Faroux, S.; Quintana-Seguí, P.

    2011-10-01

    In order to evaluate the uncertainty associated with the impact model in climate change studies, a CO2 responsive version of the land surface model ISBA (ISBA-A-gs) is compared with its standard version in a climate impact assessment study. The study is performed over the French Mediterranean basin using the Safran-Isba-Modcou chain. A downscaled A2 regional climate scenario is used to force both versions of ISBA, and the results of the two land surface models are compared for the present climate and for that at the end of the century. Reasonable agreement is found between models and with discharge observations. However, ISBA-A-gs has a lower mean evapotranspiration and a higher discharge than ISBA-Standard. Results for the impact of climate change are coherent on a yearly basis for evapotranspiration, total runoff, and discharge. However, the two versions of ISBA present contrasting seasonal variations. ISBA-A-gs develops a different vegetation cycle. The growth of the vegetation begins earlier and reaches a slightly lower maximum than in the present climate. This maximum is followed by a rapid decrease in summertime. In consequence, the springtime evapotranspiration is significantly increased when compared to ISBA-Standard, while the autumn evapotranspiration is lower. On average, discharge changes are more significant at the regional scale with ISBA-A-gs.

  18. Building and calibrating a large-extent and high resolution coupled groundwater-land surface model using globally available data-sets

    Science.gov (United States)

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

    2012-12-01

    The current generation of large-scale hydrological models generally lacks a groundwater model component simulating lateral groundwater flow. Large-scale groundwater models are rare due to a lack of hydro-geological data required for their parameterization and a lack of groundwater head data required for their calibration. In this study, we propose an approach to develop a large-extent fully-coupled land surface-groundwater model by using globally available datasets and calibrate it using a combination of discharge observations and remotely-sensed soil moisture data. The underlying objective is to devise a collection of methods that enables one to build and parameterize large-scale groundwater models in data-poor regions. The model used, PCR-GLOBWB-MOD, has a spatial resolution of 1 km x 1 km and operates on a daily basis. It consists of a single-layer MODFLOW groundwater model that is dynamically coupled to the PCR-GLOBWB land surface model. This fully-coupled model accommodates two-way interactions between surface water levels and groundwater head dynamics, as well as between upper soil moisture states and groundwater levels, including a capillary rise mechanism to sustain upper soil storage and thus to fulfill high evaporation demands (during dry conditions). As a test bed, we used the Rhine-Meuse basin, where more than 4000 groundwater head time series have been collected for validation purposes. The model was parameterized using globally available data-sets on surface elevation, drainage direction, land-cover, soil and lithology. Next, the model was calibrated using a brute force approach and massive parallel computing, i.e. by running the coupled groundwater-land surface model for more than 3000 different parameter sets. Here, we varied minimal soil moisture storage and saturated conductivities of the soil layers as well as aquifer transmissivities. Using different regularization strategies and calibration criteria we compared three calibration scenarios

  19. 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

  20. Study of drought processes in Spain by means of offline Land-Surface Model simulations. Evaluation of model sensitivity to the meteorological forcing dataset.

    Science.gov (United States)

    Quintana-Seguí, Pere; Míguez-Macho, Gonzalo; Barella-Ortiz, Anaïs

    2017-04-01

    Drought affects different aspects of the continental water cycle, from precipitation (meteorological drought), to soil moisture (agricultural drought), streamflow, lake volume and piezometric levels (hydrological drought). The spatial and temporal scales of drought, together with its propagation through the system must be well understood. Drought is a hazard impacting all climates and regions of the world; but in some areas, such as Spain, its societal impacts may be especially severe, creating water resources related tensions between regions and sectors. Indices are often used to characterize different aspects of drought. Similar indices can be built for precipitation (SPI), soil moisture (SSMI), and streamflow (SSI), allowing to analyse the temporal scales of drought and its spatial patterns. Precipitation and streamflow data are abundant in Spain; however soil moisture data is scarce. Land-Surface Models (LSM) physically simulate the continental water cycle and, thus, are appropriate tools to quantify soil moisture and other relevant variables and processes. These models can be run offline, forced by a gridded dataset of meteorological variables, usually a re-analysis. The quality of the forcing dataset affects the quality of the subsequent modeling results and is, thus, crucial. The objective of this study is to investigate how sensitive LSM simulations are to the forcing dataset, with a focus on drought. A global and a local dataset are used at different resolutions. The global dataset is the eartH2Observe dataset, which is based on ERA-Interim. The local dataset is the SAFRAN meteorological analysis system. The LSMs used are SURFEX and LEAFHYDRO. Standardized indices of the relevant variables are produced for all the simulations performed. Then, we analyze how differently drought propagates through the system in the different simulations and how similar are spatial and temporal scales of drought. The results of this study will be useful to understand the

  1. Evaluation of Land Surface Models in Reproducing Satellite-Derived LAI over the High-Latitude Northern Hemisphere. Part I: Uncoupled DGVMs

    Directory of Open Access Journals (Sweden)

    Ning Zeng

    2013-10-01

    Full Text Available Leaf Area Index (LAI represents the total surface area of leaves above a unit area of ground and is a key variable in any vegetation model, as well as in climate models. New high resolution LAI satellite data is now available covering a period of several decades. This provides a unique opportunity to validate LAI estimates from multiple vegetation models. The objective of this paper is to compare new, satellite-derived LAI measurements with modeled output for the Northern Hemisphere. We compare monthly LAI output from eight land surface models from the TRENDY compendium with satellite data from an Artificial Neural Network (ANN from the latest version (third generation of GIMMS AVHRR NDVI data over the period 1986–2005. Our results show that all the models overestimate the mean LAI, particularly over the boreal forest. We also find that seven out of the eight models overestimate the length of the active vegetation-growing season, mostly due to a late dormancy as a result of a late summer phenology. Finally, we find that the models report a much larger positive trend in LAI over this period than the satellite observations suggest, which translates into a higher trend in the growing season length. These results highlight the need to incorporate a larger number of more accurate plant functional types in all models and, in particular, to improve the phenology of deciduous trees.

  2. 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.

  3. 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.

  4. 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

  5. OPAL Netlogo Land Condition Model

    Science.gov (United States)

    2014-08-15

    ER D C/ CE RL T R- 14 -1 2 Optimal Allocation of Land for Training and Non-training Uses (OPAL) OPAL Netlogo Land Condition Model...characteristics in tallgrass prairie. Journal of Vegetation Science 8:541–546. Biondini, M. E., B. D. Patton , and P. E. Nyren. 1998. Grazing intensity and

  6. 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

  7. Using Land Surface Phenology to Detect Land Use Change in the Northern Great Plains

    Science.gov (United States)

    Nguyen, L. H.; Henebry, G. M.

    2017-12-01

    The Northern Great Plains of the US have been undergoing many types of land cover / land use change over the past two decades, including expansion of irrigation, conversion of grassland to cropland, biofuels production, urbanization, and fossil fuel mining. Much of the literature on these changes has relied on post-classification change detection based on a limited number of observations per year. Here we demonstrate an approach to characterize land dynamics through land surface phenology (LSP) by synergistic use of image time series at two scales. Our study areas include regions of interest (ROIs) across the Northern Great Plains located within Landsat path overlap zones to boost the number of valid observations (free of clouds or snow) each year. We first compute accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperature (MOD11A2 and MYD11A2). Using Landsat Collection 1 surface reflectance-derived vegetation indices (NDVI, EVI), we then fit at each pixel a downward convex quadratic model linking the vegetation index to each year's progression of AGDD. This quadratic equation exhibits linearity in a mathematical sense; thus, the fitted models can be linearly mixed and unmixed using a set of LSP endmembers (defined by the fitted parameter coefficients of the quadratic model) that represent "pure" land cover types with distinct seasonal patterns found within the region, such as winter wheat, spring wheat, maize, soybean, sunflower, hay/pasture/grassland, developed/built-up, among others. Information about land cover corresponding to each endmember are provided by the NLCD (National Land Cover Dataset) and CDL (Cropland Data Layer). We use linear unmixing to estimate the likely proportion of each LSP endmember within particular areas stratified by latitude. By tracking the proportions over the 2001-2011 period, we can quantify various types of land transitions in the Northern Great Plains.

  8. 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.

  9. 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.

  10. 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.

  11. CMIP5 land surface models systematically underestimate inter-annual variability of net ecosystem exchange in semi-arid southwestern North America.

    Science.gov (United States)

    MacBean, N.; Scott, R. L.; Biederman, J. A.; Vuichard, N.; Hudson, A.; Barnes, M.; Fox, A. M.; Smith, W. K.; Peylin, P. P.; Maignan, F.; Moore, D. J.

    2017-12-01

    Recent studies based on analysis of atmospheric CO2 inversions, satellite data and terrestrial biosphere model simulations have suggested that semi-arid ecosystems play a dominant role in the interannual variability and long-term trend in the global carbon sink. These studies have largely cited the response of vegetation activity to changing moisture availability as the primary mechanism of variability. However, some land surface models (LSMs) used in these studies have performed poorly in comparison to satellite-based observations of vegetation dynamics in semi-arid regions. Further analysis is therefore needed to ensure semi-arid carbon cycle processes are well represented in global scale LSMs before we can fully establish their contribution to the global carbon cycle. In this study, we evaluated annual net ecosystem exchange (NEE) simulated by CMIP5 land surface models using observations from 20 Ameriflux sites across semi-arid southwestern North America. We found that CMIP5 models systematically underestimate the magnitude and sign of NEE inter-annual variability; therefore, the true role of semi-arid regions in the global carbon cycle may be even more important than previously thought. To diagnose the factors responsible for this bias, we used the ORCHIDEE LSM to test different climate forcing data, prescribed vegetation fractions and model structures. Climate and prescribed vegetation do contribute to uncertainty in annual NEE simulations, but the bias is primarily caused by incorrect timing and magnitude of peak gross carbon fluxes. Modifications to the hydrology scheme improved simulations of soil moisture in comparison to data. This in turn improved the seasonal cycle of carbon uptake due to a more realistic limitation on photosynthesis during water stress. However, the peak fluxes are still too low, and phenology is poorly represented for desert shrubs and grasses. We provide suggestions on model developments needed to tackle these issues in the future.

  12. Surface mining and land reclamation in Germany

    Energy Technology Data Exchange (ETDEWEB)

    Nephew, E.A.

    1972-05-01

    Mining and land restoration methods as well as planning and regulatory procedures employed in West Germany to ameliorate environmental impacts from large-scale surface mining are described. The Rhineland coalfield in North Rhine Westphalia contains some 55 billion tons of brown-coal (or lignite), making the region one of Europe's most important energy centers. The lignite is extracted from huge, open-pit mines, resulting in large areas of disturbed land. The German reclamation approach is characterized by planning and carrying out the mining process as one continuum from early planning to final restoration of land and its succeeding use. Since the coalfield is located in a populated region with settlements dating back to Roman times, whole villages lying in the path of the mining operations sometimes have to be evacuated and relocated. Even before mining begins, detailed concepts must be worked out for the new landscape which will follow: the topography, the water drainage system, lakes and forests, and the intended land-use pattern are designed and specified in advance. Early, detailed planning makes it possible to coordinate mining and concurrent land reclamation activities. The comprehensive approach permits treating the overall problem as a whole rather than dealing with its separate aspects on a piecemeal basis.

  13. 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.

    2016-03-01

    The response of crops 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 the climate. To simulate the response of temperate crops to changing climate and [CO2], which accounts for the specific phenology of crops mediated by management practice, we describe 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 is tested here using 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 seven winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (net ecosystem exchange, NEE), latent heat, and sensible heat fluxes. Additional measurements of leaf area index (LAI) and aboveground biomass and yield are used as well. Evaluation results revealed that ORCHIDEE-CROP (v0) reproduced the observed timing of crop development stages and the amplitude of the LAI changes. This is in contrast to ORCHIDEEv196 where, by default, crops have the same phenology as grass. A halving of the root mean square error for LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m-2 was obtained when ORCHIDEEv196 and ORCHIDEE-CROP (v0) were compared across the seven study sites. Improved crop phenology and carbon allocation led to a good match between modeled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2 %), crop

  14. A 60-year reconstructed high-resolution local meteorological data set in Central Sahel (1950-2009): evaluation, analysis and application to land surface modelling

    Science.gov (United States)

    Leauthaud, Crystele; Cappelaere, Bernard; Demarty, Jérôme; Guichard, Françoise; Velluet, Cécile; Kergoat, Laurent; Vischel, Théo; Grippa, Manuela; Mouhaimouni, Mohammed; Bouzou Moussa, Ibrahim; Mainassara, Ibrahim; Sultan, Benjamin

    2017-04-01

    The Sahel has experienced strong climate variability in the past decades. Understanding its implications for natural and cultivated ecosystems is pivotal in a context of high population growth and mainly agriculture-based livelihoods. However, efforts to model processes at the land-atmosphere interface are hindered, particularly when the multi-decadal timescale is targeted, as climatic data are scarce, largely incomplete and often unreliable. This study presents the generation of a long-term, high-temporal resolution, multivariate local climatic data set for Niamey, Central Sahel. The continuous series spans the period 1950-2009 at a 30-min timescale and includes ground station-based meteorological variables (precipitation, air temperature, relative and specific humidity, air pressure, wind speed, downwelling long- and short-wave radiation) as well as process-modelled surface fluxes (upwelling long- and short-wave radiation,latent, sensible and soil heat fluxes and surface temperature). A combination of complementary techniques (linear/spline regressions, a multivariate analogue method, artificial neural networks and recursive gap filling) was used to reconstruct missing meteorological data. The complete surface energy budget was then obtained for two dominant land cover types, fallow bush and millet, by applying the meteorological forcing data set to a finely field-calibrated land surface model. Uncertainty in reconstructed data was expressed by means of a stochastic ensemble of plausible historical time series. Climatological statistics were computed at sub-daily to decadal timescales and compared with local, regional and global data sets such as CRU and ERA-Interim. The reconstructed precipitation statistics, ˜1°C increase in mean annual temperature from 1950 to 2009, and mean diurnal and annual cycles for all variables were in good agreement with previous studies. The new data set, denoted NAD (Niamey Airport-derived set) and publicly available, can be used

  15. Regionalization and parameterization of hydrological processes at the land surface

    NARCIS (Netherlands)

    Dolman, A.J.; Kabat, P.; Elbers, J.A.; Bastiaanssen, W.G.M.; Ogink-Hendriks, M.J.

    1995-01-01

    Hydrological processes on the land surface play a critical role in physically based hydrological and atmospheric modelling. A series of experiments have been initiated to test and develop parametrizations of spatial heterogeneity on the full range of spatial and temporal scales considered relevant.

  16. The Impacts of Various Environments Factors and Adaptive Management Strategies on Food Crops in the 21st Century Based on a Land Surface Model

    Science.gov (United States)

    Jain, A. K.; Lin, T. S.; Lawrence, P.; Kheshgi, H. S.

    2017-12-01

    Environmental factors - characterized by increasing levels of CO2, and changes in temperature and precipitation patterns - present potential risks to global food supply. To date, understanding of environmental factors' effects on crop production remains uncertain due to (1) uncertainties in projected trends of these factors and their spatial and temporal variability; (2) uncertainties in the physiological, genetic and molecular basis of crop adaptation to adaptive management practices (e.g. change in planting time, irrigation and N fertilization etc.) and (3) uncertainties in current land surface models to estimate the response of crop production to changes in environmental factors and management strategies. In this study we apply a process-based land surface model, the Integrated Science Assessment model (ISAM), to assess the impact of various environmental factors and management strategies on the production of row crops (corn, soybean and wheat) at regional and global scales. Results are compared to corresponding simulations performed with the crop model in the Community Land Model (CLM4.5). Each model is driven with historical atmospheric forcing data (1901-2005), and projected atmospheric forcing data under RCP 4.5 or RCP 8.5 (2006-2100) from CESM CMIP5 simulations to estimate the effects of different climate change projections on potential productivity of food crops at a global scale. For each set of atmospheric forcing data, production of each crop is simulated with and without inclusion of adaptive management practices (e.g. application of irrigation, N fertilization, change in planting time and crop cultivars etc.) to assess the effect of adaptation on projected crop production over the 21st century. In detail, three questions are addressed: (1) what is the impact of different climate change projections on global crop production; (2) what is the effect of adaptive management practices on projected crop production; and (3) how do differences in model

  17. 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

  18. 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

  19. 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 (training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs 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

  20. Climate Impacts of Fire-Induced Land-Surface Changes

    Science.gov (United States)

    Liu, Y.; Hao, X.; Qu, J. J.

    2017-12-01

    One of the consequences of wildfires is the changes in land-surface properties such as removal of vegetation. This will change local and regional climate through modifying the land-air heat and water fluxes. This study investigates mechanism by developing and a parameterization of fire-induced land-surface property changes and applying it to modeling of the climate impacts of large wildfires in the United States. Satellite remote sensing was used to quantitatively evaluate the land-surface changes from large fires provided from the Monitoring Trends in Burning Severity (MTBS) dataset. It was found that the changes in land-surface properties induced by fires are very complex, depending on vegetation type and coverage, climate type, season and time after fires. The changes in LAI are remarkable only if the actual values meet a threshold. Large albedo changes occur in winter for fires in cool climate regions. The signs are opposite between the first post-fire year and the following years. Summer day-time temperature increases after fires, while nigh-time temperature changes in various patterns. The changes are larger in forested lands than shrub / grassland lands. In the parameterization scheme, the detected post-fire changes are decomposed into trends using natural exponential functions and fluctuations of periodic variations with the amplitudes also determined by natural exponential functions. The final algorithm is a combination of the trends, periods, and amplitude functions. This scheme is used with Earth system models to simulate the local and regional climate effects of wildfires.

  1. Attribution of surface temperature anomalies induced by land use and land cover changes

    Science.gov (United States)

    Rigden, Angela J.; Li, Dan

    2017-07-01

    Land use/land cover changes (LULCC) directly impact the surface temperature by modifying the radiative, physiological, and aerodynamic properties controlling the surface energy and water balances. In this study, we propose a new method to attribute changes in the surface temperature induced by LULCC to changes in radiative and turbulent heat fluxes, with the partition of turbulent fluxes controlled by aerodynamic and surface resistances. We demonstrate that previous attribution studies have overestimated the contribution of aerodynamic resistance by assuming independence between the aerodynamic resistance and the Bowen ratio. Our results further demonstrate that acceptable agreement between modeled and observed temperature anomalies does not guarantee correct attribution by the model. When performing an attribution analysis, the covariance among attributing variables needs to be taken into consideration in order to accurately interpret the results.

  2. Modeling agriculture in the Community Land Model

    Directory of Open Access Journals (Sweden)

    B. Drewniak

    2013-04-01

    Full Text Available The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above- and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types – maize, soybean, and spring wheat – into the coupled carbon–nitrogen version of the Community Land Model (CLM, to help address these questions. Here we present the new model, CLM-Crop, validated against observations from two AmeriFlux sites in the United States, planted with maize and soybean. Seasonal carbon fluxes compared well with field measurements for soybean, but not as well for maize. CLM-Crop yields were comparable with observations in countries such as the United States, Argentina, and China, although the generality of the crop model and its lack of technology and irrigation made direct comparison difficult. CLM-Crop was compared against the standard CLM3.5, which simulates crops as grass. The comparison showed improvement in gross primary productivity in regions where crops are the dominant vegetation cover. Crop yields and productivity were negatively correlated with temperature and positively correlated with precipitation, in agreement with other modeling studies. In case studies with the new crop model looking at impacts of residue management and planting date on crop yield, we found that increased residue returned to the litter pool increased crop yield, while reduced residue returns resulted in yield decreases. Using climate controls to signal planting date caused different responses in different crops. Maize and soybean had opposite reactions: when low temperature threshold resulted in early planting, maize responded with a loss of yield, but soybean yields increased. Our improvements in CLM demonstrate a new capability in the model – simulating agriculture in a realistic way, complete with

  3. Modeling agriculture in the Community Land Model

    Science.gov (United States)

    Drewniak, B.; Song, J.; Prell, J.; Kotamarthi, V. R.; Jacob, R.

    2013-04-01

    The potential impact of climate change on agriculture is uncertain. In addition, agriculture could influence above- and below-ground carbon storage. Development of models that represent agriculture is necessary to address these impacts. We have developed an approach to integrate agriculture representations for three crop types - maize, soybean, and spring wheat - into the coupled carbon-nitrogen version of the Community Land Model (CLM), to help address these questions. Here we present the new model, CLM-Crop, validated against observations from two AmeriFlux sites in the United States, planted with maize and soybean. Seasonal carbon fluxes compared well with field measurements for soybean, but not as well for maize. CLM-Crop yields were comparable with observations in countries such as the United States, Argentina, and China, although the generality of the crop model and its lack of technology and irrigation made direct comparison difficult. CLM-Crop was compared against the standard CLM3.5, which simulates crops as grass. The comparison showed improvement in gross primary productivity in regions where crops are the dominant vegetation cover. Crop yields and productivity were negatively correlated with temperature and positively correlated with precipitation, in agreement with other modeling studies. In case studies with the new crop model looking at impacts of residue management and planting date on crop yield, we found that increased residue returned to the litter pool increased crop yield, while reduced residue returns resulted in yield decreases. Using climate controls to signal planting date caused different responses in different crops. Maize and soybean had opposite reactions: when low temperature threshold resulted in early planting, maize responded with a loss of yield, but soybean yields increased. Our improvements in CLM demonstrate a new capability in the model - simulating agriculture in a realistic way, complete with fertilizer and residue management

  4. Modeled Trends in Impacts of Landing and Takeoff Aircraft Emissions on Surface Air-Quality in U.S for 2005, 2010 and 2018

    Science.gov (United States)

    Vennam, L. P.

    2014-12-01

    Understanding the present-day impacts of aircraft emissions on surface air quality is essential to plan potential mitigation policies for future growth. Stringent regulation on mobile source-related emissions in the recent past coupled with anticipated rise in the growth in aviation activity can increase the relative impacts of aviation-attributable surface air quality if adequate measures for reducing aviation emissions are not implemented. Though aircraft emissions during in-flight mode (at upper altitudes) contribute a significant (70 - 80%) proportion of the total aviation emissions, landing and takeoff (LTO) related emissions can have immediate impact on surface air quality, as most of the large airports are located in urban areas, specifically those that are designated in nonattainment for O3 and/or PM2.5. In this study, we modeled impacts of aircraft emissions during LTO cycles on surface air quality using the latest version of the CMAQ model for two contemporary years (2005, 2010) and one future year (2018). For this regional scale modeling study, we used highly resolved aircraft emissions from the FAA's Aviation Environmental Design Tool (AEDT), meteorology from NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) downscaled with the WRF model, dynamically varying chemical boundary conditions from the CAM-Chem global model (which also used the same AEDT emissions but at the global scale), and spatio-temporally resolved lightning NOx emissions estimated using National Lightning Detection Network (NLDN) flash density data. We evaluated our model results with air quality observations from surface-based networks and in-situ aircraft observation data for the contemporary years. We will present results from model evaluation using this enhanced modeling system, as well as the trajectories in aviation- related air quality (focusing on O3, NO2 and PM2.5) for the three modeling years considered in this study. These findings will help plan

  5. Structural Agricultural Land Use Modelling

    OpenAIRE

    Fezzi, Carlo; Bateman, Ian J.

    2009-01-01

    This paper develops a structural econometric model of agricultural land use and production based on the joint multi-output technology representation introduced by Chambers and Just (1989). Starting from a flexible specification of the farm profit function we derive land use allocation, input applications, crops yield and livestock number equations in a joint and theoretically consistent framework. We present an empirical application using fine-scale spatial data covering the entirety of Engla...

  6. Estimation of land surface temperature of Kaduna metropolis ...

    African Journals Online (AJOL)

    Estimation of land surface temperature of Kaduna metropolis, Nigeria using landsat images. Isa Zaharaddeen, Ibrahim I. Baba, Ayuba Zachariah. Abstract. Understanding the spatial variation of Land Surface Temperature (LST), will be helpful in urban micro climate studies. This study estimates the land surface temperature ...

  7. SMEX02 Land Surface Information: Land Use Classification

    Data.gov (United States)

    National Aeronautics and Space Administration — This data set consists of land use classification data collected for the Iowa Soil Moisture Experiment 2002 (SMEX02) study region. The land use classification image...

  8. OPAL Land Condition Model

    Science.gov (United States)

    2014-08-01

    CENTURY is a computer model of plant-soil ecosystems that simulates the dynamics of grasslands, forest , crops, and savannas with a focus on nutri...Kamnalrut, and J. L. Kinyamario. 1993. Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global

  9. On the use of the post-closure methods uncertainty band to evaluate the performance of land surface models against eddy covariance flux data

    Science.gov (United States)

    Ingwersen, Joachim; Imukova, Kristina; Högy, Petra; Streck, Thilo

    2017-04-01

    The energy balance of eddy covariance (EC) flux data is normally not closed. Therefore, at least if used for modeling, EC flux data are usually post-closed, i.e. the measured turbulent fluxes are adjusted so as to close the energy balance. At the current state of knowledge, however, it is not clear how to partition the missing energy in the right way. Eddy flux data therefore contain some uncertainty due to the unknown nature of the energy balance gap, which should be considered in model evaluation and the interpretation of simulation results. We propose to construct the post-closure method uncertainty band (PUB), which essentially designates the differences between non-adjusted flux data and flux data adjusted with the three post-closure methods (Bowen ratio, latent heat flux (LE) and sensible heat flux (H) method). To demonstrate this approach, simulations with the NOAH-MP land surface model were evaluated based on EC measurements conducted at a winter wheat stand in Southwest Germany in 2011, and the performance of the Jarvis and Ball-Berry stomatal resistance scheme was compared. The width of the PUB of the LE was up to 110 W/m2 (21% of net radiation). Our study shows that it is crucial to account for the uncertainty of EC flux data originating from lacking energy balance closure. Working with only a single post-closing method might result in severe misinterpretations in model-data comparisons.

  10. Utilization of satellite remote sensing data on land surface characteristics in water and heat balance component modeling for vegetation covered territories

    Science.gov (United States)

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

    2010-05-01

    The model of vertical water and heat transfer in the "soil-vegetation-atmosphere" system (SVAT) for vegetation covered territory has been developed, allowing assimilating satellite remote sensing data on land surface condition as well as accounting for heterogeneities of vegetation and meteorological characteristics. The model provides the calculation of water and heat balance components (such as evapotranspiration Ev, soil water content W, sensible and latent heat fluxes and others ) as well as vertical soil moisture and temperature distributions, temperatures of soil surface and foliage, land surface brightness temperature for any time interval within vegetation season. To describe the landscape diversity soil constants and leaf area index LAI, vegetation cover fraction B, and other vegetation characteristics are used. All these values are considered to be the model parameters. Territory of Kursk region with square about 15 thousands km2 situated in the Black Earth zone of Central Russia was chosen for investigation. Satellite-derived estimates of land surface characteristics have been constructed under cloud-free condition basing AVHRR/NOAA, MODIS/EOS Terra and EOS Aqua, SEVIRI/Meteosat-8, -9 data. The developed technologies of AVHRR data thematic processing have been refined providing the retrieval of surface skin brightness temperature Tsg, air foliage temperature Ta, efficient surface temperature Ts.eff and emissivity E, as well as derivation of vegetation index NDVI, B, and LAI. The linear regression estimators for Tsg, Ta and LAI have been built using representative training samples for 2003-2009 vegetation seasons. The updated software package has been applied for AVHRR data thematic processing to generate named remote sensing products for various dates of the above vegetation seasons. The error statistics of Ta, Ts.eff and Тsg derivation has been investigated for various samples using comparison with in-situ measurements that has given RMS errors in the

  11. Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

    Directory of Open Access Journals (Sweden)

    F. Schlenz

    2012-10-01

    Full Text Available Soil Moisture and Ocean Salinity (SMOS L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET and radiative transfer model (L-MEB. The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and land surface temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in southern Germany. The brightness temperature root-mean-squared errors are between 6 K and 9 K. The L-MEB parameterisation is considered appropriate under local conditions even though it might possibly be further optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (R between 0.1–0.41. SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for a considerable part of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles and the horizontal polarisation are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (R = 0.65 even though the two variables are considered independent in the

  12. Evaluation of MuSyQ land surface albedo based on LAnd surface Parameters VAlidation System (LAPVAS)

    Science.gov (United States)

    Dou, B.; Wen, J.; Xinwen, L.; Zhiming, F.; Wu, S.; Zhang, Y.

    2016-12-01

    satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAnd surface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Land surface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

  13. Analysis of relationships between land surface temperature and land use changes in the Yellow River Delta

    Science.gov (United States)

    Ning, Jicai; Gao, Zhiqiang; Meng, Ran; Xu, Fuxiang; Gao, Meng

    2017-06-01

    This study analyzed land use and land cover changes and their impact on land surface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of land surface temperature, a mono-window algorithm was applied to retrieve the regional land surface temperature. The results showed bilinear correlation between land surface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the land surface temperature and the vegetation indices are correlated negatively, and vice versa. Land surface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.

  14. A global data set of land-surface parameters

    International Nuclear Information System (INIS)

    Claussen, M.; Lohmann, U.; Roeckner, E.; Schulzweida, U.

    1994-01-01

    A global data set of land surface parameters is provided for the climate model ECHAM developed at the Max-Planck-Institut fuer Meteorologie in Hamburg. These parameters are: background (surface) albedo α, surface roughness length z 0y , leaf area index LAI, fractional vegetation cover or vegetation ratio c y , and forest ratio c F . The global set of surface parameters is constructed by allocating parameters to major exosystem complexes of Olson et al. (1983). The global distribution of ecosystem complexes is given at a resolution of 0.5 0 x 0.5 0 . The latter data are compatible with the vegetation types used in the BIOME model of Prentice et al. (1992) which is a potential candidate of an interactive submodel within a comprehensive model of the climate system. (orig.)

  15. On the predictability of land surface fluxes from meteorological variables

    Science.gov (United States)

    Haughton, Ned; Abramowitz, Gab; Pitman, Andy J.

    2018-01-01

    Previous research has shown that land surface models (LSMs) are performing poorly when compared with relatively simple empirical models over a wide range of metrics and environments. Atmospheric driving data appear to provide information about land surface fluxes that LSMs are not fully utilising. Here, we further quantify the information available in the meteorological forcing data that are used by LSMs for predicting land surface fluxes, by interrogating FLUXNET data, and extending the benchmarking methodology used in previous experiments. We show that substantial performance improvement is possible for empirical models using meteorological data alone, with no explicit vegetation or soil properties, thus setting lower bounds on a priori expectations on LSM performance. The process also identifies key meteorological variables that provide predictive power. We provide an ensemble of empirical benchmarks that are simple to reproduce and provide a range of behaviours and predictive performance, acting as a baseline benchmark set for future studies. We reanalyse previously published LSM simulations and show that there is more diversity between LSMs than previously indicated, although it remains unclear why LSMs are broadly performing so much worse than simple empirical models.

  16. Correction to "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.; Kau, K. M.; Aoki, Teruo; Sud, Yogesh C.; Yamazaki, Takeshi; Motoyoshi, Hiroki; Kokdama, Yuji

    2012-01-01

    The website information describing the forcing meteorological data used for the land surface model (LSM) simulation, which were observed at an Automated Meteorological Station CAWS) at the Sapporo District Meteorological Observatory maintained by the Japan Meteorological Agency (JMA), was missing from the text. The 1-hourly data were obtained from the website of Kisyoutoukeijouhou (Information for available JMA-observed meteorological data in the past) on the website of JMA (in Japanese) (available at: http://www.jma.go.jpijmaimenulreport.html). The measurement height information of 59.5 m for the anemometer at the Sapporo Observatory was also obtained from the website of JMA (in Japanese) (available at: http://www.jma.go.jp/jma/menu/report.html). In addition, the converted 10-m wind speed, based on the AWS/JMA data, was further converted to a 2-m wind speed prior to its use with the land model as a usual treatment of off-line Catchment simulation. Please ignore the ice absorption data on the website mentioned in paragraph [15] which was not used for our calculations (but the data on the website was mostly the same as the estimated ice absorption coefficients by the following method because they partially used the same data by Warren [1984]). We calculated the ice absorption coefficients with the method mentioned in the same paragraph, for which some of the refractive index data by Warren [1984] were used and then interpolated between wavelengths, and also mentioned in paragraph [20] for the visible (VIS) and near-infrared (NIR) ranges. The optical data we used were interpolated between wavelengths as necessary.

  17. Use of agricultural statistics to verify the interannual variability in land surface models: a case study over France with ISBA-A-gs

    Directory of Open Access Journals (Sweden)

    J.-C. Calvet

    2012-01-01

    Full Text Available In order to verify the interannual variability of the above-ground biomass of herbaceous vegetation simulated by the ISBA-A-gs land surface model, within the SURFEX modelling platform, French agricultural statistics for C3 crops and grasslands were compared with the simulations for the 1994–2008 period. While excellent correlations are obtained for grasslands, representing the interannual variability of crops is more difficult. It is shown that, the Maximum Available soil Water Capacity (MaxAWC has a large influence on the correlation between the model and the agricultural statistics. In particular, high values of MaxAWC tend to reduce the impact of the climate interannual variability on the simulated biomass. Also, high values of MaxAWC allow the simulation of a negative trend in biomass production, in relation to a marked warming trend, of about 0.12 Kyr−1 on average, affecting the daily maximum air temperature during the growing period (April–June. This trend is particularly acute in Northern France. The estimates of MaxAWC for C3 crops and grasslands, currently used in SURFEX, are about 129 mm and do not vary much. Therefore, more accurate grid-cell values of this parameter are needed.

  18. Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions.

    Science.gov (United States)

    Eeftens, Marloes; Meier, Reto; Schindler, Christian; Aguilera, Inmaculada; Phuleria, Harish; Ineichen, Alex; Davey, Mark; Ducret-Stich, Regina; Keidel, Dirk; Probst-Hensch, Nicole; Künzli, Nino; Tsai, Ming-Yi

    2016-04-18

    Land Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models. Air pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data. Model explained variance (R(2)) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2)  = 0.53) and non-alpine (R (2)  = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors. LUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study

  19. Land surface sensitivity of monsoon depressions formed over Bay of Bengal using improved high-resolution land state

    Science.gov (United States)

    Rajesh, P. V.; Pattnaik, S.; Mohanty, U. C.; Rai, D.; Baisya, H.; Pandey, P. C.

    2017-12-01

    Monsoon depressions (MDs) constitute a large fraction of the total rainfall during the Indian summer monsoon season. In this study, the impact of high-resolution land state is addressed by assessing the evolution of inland moving depressions formed over the Bay of Bengal using a mesoscale modeling system. Improved land state is generated using High Resolution Land Data Assimilation System employing Noah-MP land-surface model. Verification of soil moisture using Soil Moisture and Ocean Salinity (SMOS) and soil temperature using tower observations demonstrate promising results. Incorporating high-resolution land state yielded least root mean squared errors with higher correlation coefficient in the surface and mid tropospheric parameters. Rainfall forecasts reveal that simulations are spatially and quantitatively in accordance with observations and provide better skill scores. The improved land surface characteristics have brought about the realistic evolution of surface, mid-tropospheric parameters, vorticity and moist static energy that facilitates the accurate MDs dynamics in the model. Composite moisture budget analysis reveals that the surface evaporation is negligible compared to moisture flux convergence of water vapor, which supplies moisture into the MDs over land. The temporal relationship between rainfall and moisture convergence show high correlation, suggesting a realistic representation of land state help restructure the moisture inflow into the system through rainfall-moisture convergence feedback.

  20. Benchmarking global land surface models in CMIP5: analysis of ecosystem water use efficiency (WUE) and Budyko framework

    Science.gov (United States)

    Li, Longhui

    2015-04-01

    Twelve Earth System Models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated in terms of ecosystem water use efficiency (WUE) and Budyko framework. Simulated values of GPP and ET from ESMs were validated against with FLUXNET measurements, and the slope of linear regression between the measurement and the model ranged from 0.24 in CanESM2 to 0.8 in GISS-E2 for GPP, and from 0.51 to 0.86 for ET. The performances of 12 ESMs in simulating ET are generally better than GPP. Compared with flux-tower-based estimates by Jung et al. [Journal of Geophysical Research 116 (2011) G00J07] (JU11), all ESMs could capture the latitudinal variations of GPP and ET, but the majority of models extremely overestimated GPP and ET, particularly around the equator. The 12 ESMs showed much larger variations in latitudinal WUE. 4 of 12 ESMs predicted global annual GPP of higher than 150 Pg C year-1, and the other 8 ESMs predicted global GPP with ±15% error of the JU11 GPP. In contrast, all EMSs predicted moderate bias for global ET. The coefficient of variation (CV) of ET (0.11) is significantly less than that of GPP (0.25). More than half of 12 ESMs generally comply with the Budyko framework but some models deviated much. Spatial analysis of error in GPP and ET indicated that model results largely differ among models at different regions. This study suggested that the estimate of ET was much better than GPP. Incorporating the convergence of WUE and the Budyko framework into ESMs as constraints in the next round of CMIP scheme is expected to decrease the uncertainties of carbon and water fluxes estimates.

  1. Reconstruction of gap-free time series satellite observations of land surface temperature to model spectral soil thermal admittance

    NARCIS (Netherlands)

    Ghafarian Malamiri, H.R.

    2015-01-01

    The soil thermal properties (soil thermal conductivity, soil heat capacity and soil diffusivity) are the main parameters in the applications that need quantitative information on soil heat transfer. Conventionally, these properties are either measured in situ or estimated by semi-empirical models

  2. Surface drainage in leveled land: Implication of slope

    Directory of Open Access Journals (Sweden)

    Antoniony S. Winkler

    Full Text Available ABSTRACT In the lowlands of Rio Grande do Sul, land leveling is mostly carried out with no slope for the purpose of rice production. In this environment, soils with a low hydraulic conductivity are predominant owing to the presence of a practically impermeable B-horizon near the surface. Land leveling leads to soil accommodation resulting in the formation of depressions where water accumulates after heavy rainfalls, subsequently leading to problems with crops implanted in succession to rice, such as soybeans. The objective of this research was to quantify the areas and volumes of water accumulation in soil as a function of the slope of land leveling. Five typical leveled lowland areas were studied as a part of this research. The original areas presented slopes of 0, 0.20, 0.25, 0.28 and 0.40%, which were used to generate new digital elevation models with slopes between 0 and 0.5%. These newly generated digital models were used to map the depressions with surface water storage. In conclusion, land leveling with slopes higher than 0.1% is recommended to minimize problems with superficial water storage in rice fields.

  3. Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling

    Directory of Open Access Journals (Sweden)

    Lidia Vlassova

    2014-05-01

    Full Text Available 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 change and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean “dehesa” ecosystem projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa. Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009–2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5 with atmospheric profiles inputs (LSTref, values from Single-Channel (SC algorithm are the closest (root-mean-square deviation (RMSD = 0.50 °C; procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC shows RMSD = 0.85 °C; Mono-Window algorithm (MW presents the highest RMSD (2.34 °C with systematical LST underestimation (bias = 1.81 °C. Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 °C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components.

  4. Simulating groundwater-surface water interactions in the Canadian Prairies using a coupled land-atmosphere model (ParFlow-CLM)

    Science.gov (United States)

    Ali, M. A.; Ireson, A. M.; Keim, D.

    2015-12-01

    The Canadian prairies are cold and dry. Surface depressions are ubiquitous, and contain permanent or ephemeral ponds. The ponds are filled by snowmelt and precipitation on the ponds and lose a significant portion of their water to evaporation, but also, depending on their landscape position, may spill to other ponds or channels, recharge groundwater, or received groundwater discharge. Since precipitation and actual evaporation are closely balanced, the pond water balances are very sensitive to change in climate, and the prairies in general have been subject to damaging floods and droughts, in particular in the last decade or two. A 2.25 km2 field site at St Denis, central Saskatchewan, contains over 100 ponds, some permanent, some ephemeral, some saline, some fresh, some recharging groundwater, some receiving groundwater discharge. The site has been extensively studied for almost 50 years, with about one decade of continuous meteorological data, and three years of detailed pond level, soil moisture and temperature, and groundwater data. The objective of this study was to assess the performance of PARFLOW-CLM (a coupled land-atmosphere model) in simulating the pond-groundwater interactions at this site. Our conceptual model of the site includes soil properties that are progressively weathered with depth, and we implement this in a simplified dual permeability mathematical model of the soil hydraulic properties, whereby storage is dominated by the matrix and flow is dominated by macropores. The model performance was surprisingly good, doing quite a good job of capturing the observed groundwater and pond level dynamics. The soil freezing regime is also captured reasonably well, though the timing and pattern of the zero degree isotherm during soil thaw, which is critically important for runoff generation processes, was not captured as well. The model provides credible insights into the spatial patterns of evapotranspiration, and the seasonal dynamics of subsurface

  5. Modeling water flow, depth and inundation extent over the rivers of the Contiguous US within a Catchment-based Land Surface Modeling Framework

    Science.gov (United States)

    Liu, Z.; David, C. H.; Famiglietti, J. S.

    2013-12-01

    With population growth and increasing demand of water supply, the need for integrated continental and global scale surface water dynamics simulation systems relying on both observations and models is ever increasing. In this study we characterize how accurately we can estimate river discharge, river depth and the corresponding inundation extent over the contiguous U.S. by combining observations and models. We present a continental-scale implementation of the Catchment-based Hydrological And Routing Modeling System (CHARMS) that includes an explicit representation of the river networks from a Geographic Information System (GIS) dataset. The river networks and contributing catchment boundaries of the Contiguous U.S are upscaled from the NHDPlus dataset. The average upscaled catchment size is 2773 km2 and the unique main river channel contained in each catchment consists of several river reaches of average length 1.6 km. We derive 18 sets of empirical relationship between channel dimension (bankfull depth and bankfull width) and drainage area based on USGS gauge observations to describe river dynamics for the 18 water resource regions of the NHDPlus representation of the United States. These relationships are used to separate the main river channel and floodplain. Modeled monthly and daily streamflow show reasonable agreement with gauge observations and initial results show that basins with fewer anthropogenic modifications are more accurately simulated. Modeled monthly and daily river depth and floodplain extent associated with each river reach are also explicitly estimated over the U.S., although such simulations are more challenging to validate. Our results have implications for capturing the seasonal-to-interannual dynamics of surface water in climate models. Such a continental-scale modeling framework development would, by design, facilitate the use of existing in situ observations and be suitable for integrating the upcoming NASA Surface Water and Ocean

  6. A comparison of all-weather land surface temperature products

    Science.gov (United States)

    Martins, Joao; Trigo, Isabel F.; Ghilain, Nicolas; Goettche, Frank-M.; Ermida, Sofia; Olesen, Folke-S.; Gellens-Meulenberghs, Françoise; Arboleda, Alirio

    2017-04-01

    The Satellite Application Facility on Land Surface Analysis (LSA-SAF, http://landsaf.ipma.pt) has been providing land surface temperature (LST) estimates using SEVIRI/MSG on an operational basis since 2006. The LSA-SAF service has since been extended to provide a wide range of satellite-based quantities over land surfaces, such as emissivity, albedo, radiative fluxes, vegetation state, evapotranspiration, and fire-related variables. Being based on infra-red measurements, the SEVIRI/MSG LST product is limited to clear-sky pixels only. Several all-weather LST products have been proposed by the scientific community either based on microwave observations or using Soil-Vegetation-Atmosphere Transfer models to fill the gaps caused by clouds. The goal of this work is to provide a nearly gap-free operational all-weather LST product and compare these approaches. In order to estimate evapotranspiration and turbulent energy fluxes, the LSA-SAF solves the surface energy budget for each SEVIRI pixel, taking into account the physical and physiological processes occurring in vegetation canopies. This task is accomplished with an adapted SVAT model, which adopts some formulations and parameters of the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) model operated at the European Center for Medium-range Weather Forecasts (ECMWF), and using: 1) radiative inputs also derived by LSA-SAF, which includes surface albedo, down-welling fluxes and fire radiative power; 2) a land-surface characterization obtained by combining the ECOCLIMAP database with both LSA-SAF vegetation products and the H(ydrology)-SAF snow mask; 3) meteorological fields from ECMWF forecasts interpolated to SEVIRI pixels, and 4) soil moisture derived by the H-SAF and LST from LSA-SAF. A byproduct of the SVAT model is surface skin temperature, which is needed to close the surface energy balance. The model skin temperature corresponds to the radiative temperature of the interface between soil and atmosphere

  7. Bureau of Land Management Surface Land Ownership (2014)

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — These data were collected by the U.S. Bureau of Land Management (BLM) in New Mexico at both the New Mexico State Office and at the various field offices. This...

  8. Wintertime land surface characteristics in climatic simulations over ...

    Indian Academy of Sciences (India)

    eastward moving low-pressure synoptic weather systems, called Western Disturbances (WDs). (Pisharoty and Desai .... Land surface processes are controlled by surface roughness and albedo. Different land surfaces will have different roughness length and albedo. Table 1 illustrates vegetation types and their correspond-.

  9. A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature

    Directory of Open Access Journals (Sweden)

    Yongmin Yang

    2017-01-01

    Full Text Available The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H to analytically estimate the radiometric-convective resistance (rae via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD of H and latent heat flux (LE from OSML are 34.5 W/m2 and 46.5 W/m2 at SMACEX site and 50.1 W/m2 and 67.0 W/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System. Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation

  10. Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

    Science.gov (United States)

    Phuong Tran, Anh; Dafflon, Baptiste; Hubbard, Susan S.

    2017-09-01

    Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets - including soil liquid water content, temperature and electrical resistivity tomography (ERT) data - to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the

  11. APPLICATION OF A NEW LAND-SURFACE, DRY DEPOSITION, AND PBL MODEL IN THE MODELS-3 COMMUNITY MULTI-SCALE AIR QUALITY (CMAQ) MODEL SYSTEM

    Science.gov (United States)

    Like most air quality modeling systems, CMAQ divides the treatment of meteorological and chemical/transport processes into separate models run sequentially. A potential drawback to this approach is that it creates the illusion that these processes are minimally interdependent an...

  12. Advantages of Using Microwave Satellite Soil Moisture over Gridded Precipitation Products and Land Surface Model Output in Assessing Regional Vegetation Water Availability and Growth Dynamics for a Lateral Inflow Receiving Landscape

    NARCIS (Netherlands)

    Chen, T.; McVicar, T.R.; Wang, G.J.; Chen, X.; de Jeu, R.A.M.; Liu, Y.; Shen, H.; Zhang, F.; Dolman, A.J.

    2016-01-01

    To improve the understanding of water-vegetation relationships, direct comparative studies assessing the utility of satellite remotely sensed soil moisture, gridded precipitation products, and land surface model output are needed. A case study was investigated for a water-limited, lateral inflow

  13. THE EFFECT OF LAND USE CHANGE ON LAND SURFACE TEMPERATURE IN THE NETHERLANDS

    Directory of Open Access Journals (Sweden)

    S. Youneszadeh

    2015-12-01

    Full Text Available The Netherlands is a small country with a relatively large population which experienced a rapid rate of land use changes from 2000 to 2008 years due to the industrialization and population increase. Land use change is especially related to the urban expansion and open agriculture reduction due to the enhanced economic growth. This research reports an investigation into the application of remote sensing and geographical information system (GIS in combination with statistical methods to provide a quantitative information on the effect of land use change on the land surface temperature. In this study, remote sensing techniques were used to retrieve the land surface temperature (LST by using the MODIS Terra (MOD11A2 Satellite imagery product. As land use change alters the thermal environment, the land surface temperature (LST could be a proper change indicator to show the thermal changes in relation with land use changes. The Geographical information system was further applied to extract the mean yearly land surface temperature (LST for each land use type and each province in the 2003, 2006 and 2008 years, by using the zonal statistic techniques. The results show that, the inland water and offshore area has the highest night land surface temperature (LST. Furthermore, the Zued (South-Holland province has the highest night LST value in the 2003, 2006 and 2008 years. The result of this research will be helpful tool for urban planners and environmental scientists by providing the critical information about the land surface temperature.

  14. Research on Land Surface Thermal-Hydrologic Exchange in Southern China under Future Climate and Land Cover Scenarios

    Directory of Open Access Journals (Sweden)

    Jianwu Yan

    2013-01-01

    Full Text Available Climate change inevitably leads to changes in hydrothermal circulation. However, thermal-hydrologic exchanging caused by land cover change has also undergone ineligible changes. Therefore, studying the comprehensive effects of climate and land cover changes on land surface water and heat exchanges enables us to well understand the formation mechanism of regional climate and predict climate change with fewer uncertainties. This study investigated the land surface thermal-hydrologic exchange across southern China for the next 40 years using a land surface model (ecosystem-atmosphere simulation scheme (EASS. Our findings are summarized as follows. (i Spatiotemporal variation patterns of sensible heat flux (H and evapotranspiration (ET under the land cover scenarios (A2a or B2a and climate change scenario (A1B are unanimous. (ii Both H and ET take on a single peak pattern, and the peak occurs in June or July. (iii Based on the regional interannual variability analysis, H displays a downward trend (10% and ET presents an increasing trend (15%. (iv The annual average H and ET would, respectively, increase and decrease by about 10% when woodland converts to the cultivated land. Through this study, we recognize that land surface water and heat exchanges are affected greatly by the future climate change as well as land cover change.

  15. Effects of Seasonal Land Surface Conditions on Hydrometeorological Dynamics in South-western North America

    Science.gov (United States)

    2015-09-21

    SECURITY CLASSIFICATION OF: Arid and semiarid landscapes in regions with seasonal precipitation experience dramatic changes that alter land surface...semiarid landscapes in regions with seasonal precipitation experience dramatic changes that alter land surface conditions, including soil moisture...aerial vehicle data acquisition and high performance computing-based hydrologic modeling designed to capture, account for and predict seasonal variations

  16. Hydrologic Remote Sensing and Land Surface Data Assimilation

    Directory of Open Access Journals (Sweden)

    Hamid Moradkhani

    2008-05-01

    Full Text Available Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF and Particle filter (PF, for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law and could be a strong alternative to EnKF which is subject to some

  17. Bureau of Land Management Surface Land Ownership (2012)

    Data.gov (United States)

    Earth Data Analysis Center, University of New Mexico — These data was collected by the U.S. Bureau of Land Management (BLM) in New Mexico at both the New Mexico State Office and at the various field offices. This dataset...

  18. Pairing FLUXNET sites to validate model representations of land-use/land-cover change

    Science.gov (United States)

    Chen, Liang; Dirmeyer, Paul A.; Guo, Zhichang; Schultz, Natalie M.

    2018-01-01

    Land surface energy and water fluxes play an important role in land-atmosphere interactions, especially for the climatic feedback effects driven by land-use/land-cover change (LULCC). These have long been documented in model-based studies, but the performance of land surface models in representing LULCC-induced responses has not been investigated well. In this study, measurements from proximate paired (open versus forest) flux tower sites are used to represent observed deforestation-induced changes in surface fluxes, which are compared with simulations from the Community Land Model (CLM) and the Noah Multi-Parameterization (Noah-MP) land model. Point-scale simulations suggest the CLM can represent the observed diurnal and seasonal changes in net radiation (Rnet) and ground heat flux (G), but difficulties remain in the energy partitioning between latent (LE) and sensible (H) heat flux. The CLM does not capture the observed decreased daytime LE, and overestimates the increased H during summer. These deficiencies are mainly associated with models' greater biases over forest land-cover types and the parameterization of soil evaporation. Global gridded simulations with the CLM show uncertainties in the estimation of LE and H at the grid level for regional and global simulations. Noah-MP exhibits a similar ability to simulate the surface flux changes, but with larger biases in H, G, and Rnet change during late winter and early spring, which are related to a deficiency in estimating albedo. Differences in meteorological conditions between paired sites is not a factor in these results. Attention needs to be devoted to improving the representation of surface heat flux processes in land models to increase confidence in LULCC simulations.

  19. Merged Land and Ocean Surface Temperature, Version 3.5

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The historical Merged Land-Ocean Surface Temperature Analysis (MLOST) is derived from two independent analyses, an Extended Reconstructed Sea Surface Temperature...

  20. Land management and land-cover change have impacts of similar magnitude on surface temperature

    NARCIS (Netherlands)

    Luyssaert, S.; Jammet, M.; Stoy, P.C.; Estel, S.; Pongratz, J.; Ceschia, E.; Churkina, G.; Don, A.; Erb, K.; Ferlicoq, M.; Gielen, B.; Gruenwald, T.; Houghton, R.A.; Klumpp, K.; Knohl, A.; Kolb, T.; Kuemmerle, T.; Laurila, T.; Lohila, A.; Loustau, D.; McGrath, M.J.; Meyfroidt, P.; Moors, E.J.; Naudts, K.; Novick, K.; Otto, J.; Pilegaard, K.; Pio, C.A.; Rambal, S.; Rebmann, C.; Ryder, J.; Suyker, A.E.; Varlagin, A.; Wattenbach, M.; Dolman, A.J.

    2014-01-01

    Anthropogenic changes to land cover (LCC) remain common, but continuing land scarcity promotes the widespread intensification of land management changes (LMC) to better satisfy societal demand for food, fibre, fuel and shelter. The biophysical effects of LCC on surface climate are largely

  1. Land management and land-cover change have impacts of similar magnitude on surface temperature

    DEFF Research Database (Denmark)

    Luyssaert, Sebastiaan; Jammet, Mathilde; Stoy, Paul C.

    2014-01-01

    Anthropogenic changes to land cover (LCC) remain common, but continuing land scarcity promotes the widespread intensification of land management changes (LMC) to better satisfy societal demand for food, fibre, fuel and shelter1. The biophysical effects of LCC on surface climate are largely...

  2. Implications of the choice of land surface model schemes and reanalyzes data for initialization in model simulations with WRF to characterize atmospheric dynamics and the effect on black carbon concentrations into the Eurasian Arctic.

    Science.gov (United States)

    Cavazos Guerra, C.; Lauer, A.; Herber, A. B.; Butler, T. M.

    2016-12-01

    Realistic simulation of physical and dynamical processes happening in the Arctic surface and atmosphere, and the interacting feedbacks of these processes is still a challenge for Arctic climate modelers. This is critical when further studies involving for the example transport mechanisms and pathways of pollutants from lower latitudes into the Arctic rely on the efficiency of the model to represent atmospheric circulation, especially given the complexity of the Arctic atmosphere. In this work we evaluate model performance of the Weather Research and Forecast model (WRF) according to the choice of two land surface model schemes (Noah and NoahMP) and two reanalyzes data for initialization to create lateral boundary conditions (ERA-interim and ASR) to simulate surface and atmosphere dynamics including the location and displacement of the polar dome and other features characterizing atmospheric circulation associated to sea ice maxima/minima extent within the Eurasian Arctic conformed by the Nordic countries in Northern Europe and part of West Russia. Sensitivity analyses include simulations at 15km horizontal resolution within a period of five years from 2008 to 2012. The WRF model simulations are evaluated against surface meteorological data from automated weather stations and atmospheric profiles from radiosondes. Results show that the model is able to reproduce the main features of the atmospheric dynamics and vertical structure of the Arctic atmosphere reasonably well. The model is, however, sensitive to the choice of the reanalyses used for initialization and land surface scheme with significant biases in the simulated description of surface meteorology and winds, moisture and temperature profiles. The best choice of physical parameterization is then used in the WRF with coupled chemistry (WRF-Chem) to simulate BC concentrations in several case studies within the analyzed period in our domain and assess the role of modeled circulation in concentrations of BC

  3. Carbon Sequestration on Surface Mine Lands

    Energy Technology Data Exchange (ETDEWEB)

    Donald Graves; Christopher Barton; Richard Sweigard; Richard Warner; Carmen Agouridis

    2006-03-31

    Since the implementation of the federal Surface Mining Control and Reclamation Act of 1977 (SMCRA) in May of 1978, many opportunities have been lost for the reforestation of surface mines in the eastern United States. Research has shown that excessive compaction of spoil material in the backfilling and grading process is the biggest impediment to the establishment of productive forests as a post-mining land use (Ashby, 1998, Burger et al., 1994, Graves et al., 2000). Stability of mine sites was a prominent concern among regulators and mine operators in the years immediately following the implementation of SMCRA. These concerns resulted in the highly compacted, flatly graded, and consequently unproductive spoils of the early post-SMCRA era. However, there is nothing in the regulations that requires mine sites to be overly compacted as long as stability is achieved. It has been cultural barriers and not regulatory barriers that have contributed to the failure of reforestation efforts under the federal law over the past 27 years. Efforts to change the perception that the federal law and regulations impede effective reforestation techniques and interfere with bond release must be implemented. Demonstration of techniques that lead to the successful reforestation of surface mines is one such method that can be used to change perceptions and protect the forest ecosystems that were indigenous to these areas prior to mining. The University of Kentucky initiated a large-scale reforestation effort to address regulatory and cultural impediments to forest reclamation in 2003. During the three years of this project 383,000 trees were planted on over 556 acres in different physiographic areas of Kentucky (Table 1, Figure 1). Species used for the project were similar to those that existed on the sites before mining was initiated (Table 2). A monitoring program was undertaken to evaluate growth and survival of the planted species as a function of spoil characteristics and

  4. SAIL-Thermique: a model for land surface spectral emissivity in the thermal infrared. Evaluation and reassesment of the temperature - emissivity separation (TES) algorithm in presence of vegetation canopies.

    Science.gov (United States)

    Olioso, A.; Jacob, F.; Lesaignoux, A.

    2014-12-01

    The SAIL-Thermique model was developed to simulate thermal infrared (TIR) radiative transfers inside vegetation canopies and land surface emissivity. It is based on the SAIL model developed by Verhoef (1984) for simulating spectral reflectances in the solar domain. Due to the difficulty to measure land surface emissivity, no emissivity model was validated against ground measurements. In this study, several datasets extracted from the literature and from recent databases were used for evaluating emissivity simulations. Model simulations were performed from the knowledge of leaf area index, leaf inclination distribution, direction of viewing, and leaf and soil optical properties. As data on leaf inclination and leaf optical properties were usually not available, stochastic simulations were performed from a priori knowledges on their distribution (extracted from the literature and recent databases). Simulated 8-14 μm emissivities were favorably compared to measurements with a root mean square difference (RMSD) around 0.006 (0.004 when considering only herbaceous species). The model was then used for simulating emissivity spectra for providing information for the interpretation of TIR multispectral data from the ASTER sensor. We used the land surface emissivity simulations for re-assessing the TES algorithm used to separate emissivity and land surface temperature. We showed that the inclusion of vegetated land surfaces significantly modified the relationship between minimum emissivity and minimum maximum difference (ɛmin- MMD) which is at the heart of the TES algorithm. This relationship was originally established on the ASTER spectral library which did not include vegetated land surface (Schmugge et al. 1998). On a synthetic database, estimations of spectral emissivities and surface temperature were significantly improved when using the new ɛmin- MMD relationship in comparison to the classical one: RMSD dropped from ~0.012 to ~0.006 for spectral emissivity and from

  5. Comparative analyses of measured evapotranspiration for various land surfaces

    Science.gov (United States)

    Suat Irmak

    2016-01-01

    There is a significant lack of continuously measured ET data for multiple land surfaces in the same area to be able to make comparisons of water use rates of different agroecosystems. This research presentation will provide continuous evapotranspiration and other surface energy balance variables measured above multiple land use and management practices.

  6. Reforesting unused surface mined lands by replanting with native trees

    Science.gov (United States)

    Patrick N. Angel; James A. Burger; Carl E. Zipper; Scott Eggerud

    2012-01-01

    More than 600,000 ha (1.5 million ac) of mostly forested land in the Appalachian region were surface mined for coal under the Surface Mining Control and Reclamation Act. Today, these lands are largely unmanaged and covered with persistent herbaceous species, such as fescue (Festuca spp.) and sericea lespedeza (Lespedeza cuneata [Dum. Cours.] G. Don,) and a mix of...

  7. Multi-scale validation of a new soil freezing scheme for a land-surface model with physically-based hydrology

    Directory of Open Access Journals (Sweden)

    I. Gouttevin

    2012-04-01

    Full Text Available Soil freezing is a major feature of boreal regions with substantial impact on climate. The present paper describes the implementation of the thermal and hydrological effects of soil freezing in the land surface model ORCHIDEE, which includes a physical description of continental hydrology. The new soil freezing scheme is evaluated against analytical solutions and in-situ observations at a variety of scales in order to test its numerical robustness, explore its sensitivity to parameterization choices and confront its performance to field measurements at typical application scales.

    Our soil freezing model exhibits a low sensitivity to the vertical discretization for spatial steps in the range of a few millimetres to a few centimetres. It is however sensitive to the temperature interval around the freezing point where phase change occurs, which should be 1 °C to 2 °C wide. Furthermore, linear and thermodynamical parameterizations of the liquid water content lead to similar results in terms of water redistribution within the soil and thermal evolution under freezing. Our approach does not allow firm discrimination of the performance of one approach over the other.

    The new soil freezing scheme considerably improves the representation of runoff and river discharge in regions underlain by permafrost or subject to seasonal freezing. A thermodynamical parameterization of the liquid water content appears more appropriate for an integrated description of the hydrological processes at the scale of the vast Siberian basins. The use of a subgrid variability approach and the representation of wetlands could help capture the features of the Arctic hydrological regime with more accuracy.

    The modeling of the soil thermal regime is generally improved by the representation of soil freezing processes. In particular, the dynamics of the active layer is captured with more accuracy, which is of crucial importance in the prospect of

  8. Impacts of historic and projected land-cover, land-use, and land-management change on carbon and water fluxes: The Land Use Model Intercomparison Project (LUMIP)

    Science.gov (United States)

    Lawrence, D. M.; Lombardozzi, D. L.; Lawrence, P.; Hurtt, G. C.

    2017-12-01

    Human land-use activities have resulted in large changes to the Earth surface, with resulting implications for climate. In the future, land-use activities are likely to intensify to meet growing demands for food, fiber, and energy. The Land Use Model Intercomparison Project (LUMIP) aims to further advance understanding of the broad question of impacts of land-use and land-cover change (LULCC) as well as more detailed science questions to get at process-level attribution, uncertainty, and data requirements in more depth and sophistication than possible in a multi-model context to date. LUMIP is multi-faceted and aims to advance our understanding of land-use change from several perspectives. In particular, LUMIP includes a factorial set of land-only simulations that differ from each other with respect to the specific treatment of land use or land management (e.g., irrigation active or not, crop fertilization active or not, wood harvest on or not), or in terms of prescribed climate. This factorial series of experiments serves several purposes and is designed to provide a detailed assessment of how the specification of land-cover change and land management affects the carbon, water, and energy cycle response to land-use change. The potential analyses that are possible through this set of experiments are vast. For example, comparing a control experiment with all land management active to an experiment with no irrigation allows a multi-model assessment of whether or not the increasing use of irrigation during the 20th century is likely to have significantly altered trends of regional water and energy fluxes (and therefore climate) and/or crop yield and carbon fluxes in agricultural regions. Here, we will present preliminary results from the factorial set of experiments utilizing the Community Land Model (CLM5). The analyses presented here will help guide multi-model analyses once the full set of LUMIP simulations are available.

  9. Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0

    Directory of Open Access Journals (Sweden)

    A. Druel

    2017-12-01

    Full Text Available Simulation of vegetation–climate feedbacks in high latitudes in the ORCHIDEE land surface model was improved by the addition of three new circumpolar plant functional types (PFTs, namely non-vascular plants representing bryophytes and lichens, Arctic shrubs and Arctic C3 grasses. Non-vascular plants are assigned no stomatal conductance, very shallow roots, and can desiccate during dry episodes and become active again during wet periods, which gives them a larger phenological plasticity (i.e. adaptability and resilience to severe climatic constraints compared to grasses and shrubs. Shrubs have a specific carbon allocation scheme, and differ from trees by their larger survival rates in winter, due to protection by snow. Arctic C3 grasses have the same equations as in the original ORCHIDEE version, but different parameter values, optimised from in situ observations of biomass and net primary productivity (NPP in Siberia. In situ observations of living biomass and productivity from Siberia were used to calibrate the parameters of the new PFTs using a Bayesian optimisation procedure. With the new PFTs, we obtain a lower NPP by 31 % (from 55° N, as well as a lower roughness length (−41 %, transpiration (−33 % and a higher winter albedo (by +3.6 % due to increased snow cover. A simulation of the water balance and runoff and drainage in the high northern latitudes using the new PFTs results in an increase of fresh water discharge in the Arctic ocean by 11 % (+140 km3 yr−1, owing to less evapotranspiration. Future developments should focus on the competition between these three PFTs and boreal tree PFTs, in order to simulate their area changes in response to climate change, and the effect of carbon–nitrogen interactions.

  10. Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0)

    Science.gov (United States)

    Druel, Arsène; Peylin, Philippe; Krinner, Gerhard; Ciais, Philippe; Viovy, Nicolas; Peregon, Anna; Bastrikov, Vladislav; Kosykh, Natalya; Mironycheva-Tokareva, Nina

    2017-12-01

    Simulation of vegetation-climate feedbacks in high latitudes in the ORCHIDEE land surface model was improved by the addition of three new circumpolar plant functional types (PFTs), namely non-vascular plants representing bryophytes and lichens, Arctic shrubs and Arctic C3 grasses. Non-vascular plants are assigned no stomatal conductance, very shallow roots, and can desiccate during dry episodes and become active again during wet periods, which gives them a larger phenological plasticity (i.e. adaptability and resilience to severe climatic constraints) compared to grasses and shrubs. Shrubs have a specific carbon allocation scheme, and differ from trees by their larger survival rates in winter, due to protection by snow. Arctic C3 grasses have the same equations as in the original ORCHIDEE version, but different parameter values, optimised from in situ observations of biomass and net primary productivity (NPP) in Siberia. In situ observations of living biomass and productivity from Siberia were used to calibrate the parameters of the new PFTs using a Bayesian optimisation procedure. With the new PFTs, we obtain a lower NPP by 31 % (from 55° N), as well as a lower roughness length (-41 %), transpiration (-33 %) and a higher winter albedo (by +3.6 %) due to increased snow cover. A simulation of the water balance and runoff and drainage in the high northern latitudes using the new PFTs results in an increase of fresh water discharge in the Arctic ocean by 11 % (+140 km3 yr-1), owing to less evapotranspiration. Future developments should focus on the competition between these three PFTs and boreal tree PFTs, in order to simulate their area changes in response to climate change, and the effect of carbon-nitrogen interactions.

  11. Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5 on carbon fluxes, pools, and turnover in temperate forests

    Directory of Open Access Journals (Sweden)

    F. Montané

    2017-09-01

    Full Text Available How carbon (C is allocated to different plant tissues (leaves, stem, and roots determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI measurements to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM, the Community Land Model (CLM4.5. We ran CLM4.5 for nine temperate (including evergreen and deciduous forests in North America between 1980 and 2013 using four different C allocation schemes: i. dynamic C allocation scheme (named "D-CLM4.5" with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP; ii. an alternative dynamic C allocation scheme (named "D-Litton", where, similar to (i, C allocation is a dynamic function of annual NPP, but unlike (i includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.–iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen" and the other of observations in deciduous forests (named "F-Deciduous". D-CLM4.5 generally overestimated gross primary production (GPP and ecosystem respiration, and underestimated net ecosystem exchange (NEE. In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m−2 for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011 was highly underestimated (between 1222 and 7557 g C m−2 for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C–LAI relationship in the model did not match the

  12. Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

    Science.gov (United States)

    Montané, Francesc; Fox, Andrew M.; Arellano, Avelino F.; MacBean, Natasha; Alexander, M. Ross; Dye, Alex; Bishop, Daniel A.; Trouet, Valerie; Babst, Flurin; Hessl, Amy E.; Pederson, Neil; Blanken, Peter D.; Bohrer, Gil; Gough, Christopher M.; Litvak, Marcy E.; Novick, Kimberly A.; Phillips, Richard P.; Wood, Jeffrey D.; Moore, David J. P.

    2017-09-01

    How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i. dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.-iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m-2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g C m-2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C-LAI relationship in the model did not match the observed leaf C

  13. Evaluation of land surface model simulations of evapotranspiration over a 12-year crop succession: impact of soil hydraulic and vegetation properties

    Science.gov (United States)

    Garrigues, S.; Olioso, A.; Calvet, J. C.; Martin, E.; Lafont, S.; Moulin, S.; Chanzy, A.; Marloie, O.; Buis, S.; Desfonds, V.; Bertrand, N.; Renard, D.

    2015-07-01

    Evapotranspiration has been recognized as one of the most uncertain terms in the surface water balance simulated by land surface models. In this study, the SURFEX/ISBA-A-gs (Interaction Sol-Biosphere-Atmosphere) simulations of evapotranspiration are assessed at the field scale over a 12-year Mediterranean crop