WorldWideScience

Sample records for modelling soil moisture

  1. Modeling soil moisture memory in savanna ecosystems

    Science.gov (United States)

    Gou, S.; Miller, G. R.

    2011-12-01

    Antecedent soil conditions create an ecosystem's "memory" of past rainfall events. Such soil moisture memory effects may be observed over a range of timescales, from daily to yearly, and lead to feedbacks between hydrological and ecosystem processes. In this study, we modeled the soil moisture memory effect on savanna ecosystems in California, Arizona, and Africa, using a system dynamics model created to simulate the ecohydrological processes at the plot-scale. The model was carefully calibrated using soil moisture and evapotranspiration data collected at three study sites. The model was then used to simulate scenarios with various initial soil moisture conditions and antecedent precipitation regimes, in order to study the soil moisture memory effects on the evapotranspiration of understory and overstory species. Based on the model results, soil texture and antecedent precipitation regime impact the redistribution of water within soil layers, potentially causing deeper soil layers to influence the ecosystem for a longer time. Of all the study areas modeled, soil moisture memory of California savanna ecosystem site is replenished and dries out most rapidly. Thus soil moisture memory could not maintain the high rate evapotranspiration for more than a few days without incoming rainfall event. On the contrary, soil moisture memory of Arizona savanna ecosystem site lasts the longest time. The plants with different root depths respond to different memory effects; shallow-rooted species mainly respond to the soil moisture memory in the shallow soil. The growing season of grass is largely depended on the soil moisture memory of the top 25cm soil layer. Grass transpiration is sensitive to the antecedent precipitation events within daily to weekly timescale. Deep-rooted plants have different responses since these species can access to the deeper soil moisture memory with longer time duration Soil moisture memory does not have obvious impacts on the phenology of woody plants

  2. Misrepresentation and amendment of soil moisture in conceptual hydrological modelling

    Science.gov (United States)

    Zhuo, Lu; Han, Dawei

    2016-04-01

    Although many conceptual models are very effective in simulating river runoff, their soil moisture schemes are generally not realistic in comparison with the reality (i.e., getting the right answers for the wrong reasons). This study reveals two significant misrepresentations in those models through a case study using the Xinanjiang model which is representative of many well-known conceptual hydrological models. The first is the setting of the upper limit of its soil moisture at the field capacity, due to the 'holding excess runoff' concept (i.e., runoff begins on repletion of its storage to the field capacity). The second is neglect of capillary rise of water movement. A new scheme is therefore proposed to overcome those two issues. The amended model is as effective as its original form in flow modelling, but represents more logically realistic soil water processes. The purpose of the study is to enable the hydrological model to get the right answers for the right reasons. Therefore, the new model structure has a better capability in potentially assimilating soil moisture observations to enhance its real-time flood forecasting accuracy. The new scheme is evaluated in the Pontiac catchment of the USA through a comparison with satellite observed soil moisture. The correlation between the XAJ and the observed soil moisture is enhanced significantly from 0.64 to 0.70. In addition, a new soil moisture term called SMDS (Soil Moisture Deficit to Saturation) is proposed to complement the conventional SMD (Soil Moisture Deficit).

  3. Soil Moisture Data Assimilation in Soil Water Flow Modeling

    Science.gov (United States)

    Pachepsky, Y. A.; Guber, A.; Jacques, D.; Pan, F.; van Genuchten, M.; Cady, R. E.; Nicholson, T. J.

    2010-12-01

    Soil water flow modeling has multiple applications. This modeling is based on simplifications stemming from both conceptual uncertainty and lack of detailed knowledge about parameters. Modern soil moisture sensors can provide detailed information about changes in soil water content in time and with depth. This information can be used for data assimilation in soil water flow modeling. The ensemble Kalman filter appears to be an appropriate method for that. Earlier we demonstrated ensemble simulations of soil water flow by using sets of pedotransfer functions (empirical relationships between soil hydraulic properties and soil basic properties, such as particle size distribution, bulk density, organic carbon content, etc.). The objective of this work was to apply the data assimilation with the ensemble Kalman filter to soil water flow modeling, using soil water content monitoring with TDR probes and an ensemble of soil water flow models parameterized with different pedotransfer functions. Experiments were carried out at the Bekkevoort site, Belgium. Sixty time domain reflectometry (TDR) probes with two rods) were installed along the trench in loamy soil at 12 locations with 50-cm horizontal spacing at five depths (15, 35, 55, 75, and 95 cm). Water content and weather parameters were monitored for one year with 15 min frequency. Soil water flow was simulated using the HYDRUS6 software. Mean daily means of water contents at the observation depths were the measurements used in data assimilation. Eighteen pedotransfer functions for water retention and one for hydraulic conductivity were applied to generate ensembles to evaluate the uncertainty in simulation results, whereas the replicated measurements at each of measurement depths were used to characterize the uncertainty in data. Data assimilation appeared to be very efficient. Even assimilating measurements at a single depth provided substantial improvement in simulations at other observation depths. Results on

  4. Assimilating soil moisture into an Earth System Model

    Science.gov (United States)

    Stacke, Tobias; Hagemann, Stefan

    2017-04-01

    Several modelling studies reported potential impacts of soil moisture anomalies on regional climate. In particular for short prediction periods, perturbations of the soil moisture state may result in significant alteration of surface temperature in the following season. However, it is not clear yet whether or not soil moisture anomalies affect climate also on larger temporal and spatial scales. In an earlier study, we showed that soil moisture anomalies can persist for several seasons in the deeper soil layers of a land surface model. Additionally, those anomalies can influence root zone moisture, in particular during explicitly dry or wet periods. Thus, one prerequisite for predictability, namely the existence of long term memory, is evident for simulated soil moisture and might be exploited to improve climate predictions. The second prerequisite is the sensitivity of the climate system to soil moisture. In order to investigate this sensitivity for decadal simulations, we implemented a soil moisture assimilation scheme into the Max-Planck Institute for Meteorology's Earth System Model (MPI-ESM). The assimilation scheme is based on a simple nudging algorithm and updates the surface soil moisture state once per day. In our experiments, the MPI-ESM is used which includes model components for the interactive simulation of atmosphere, land and ocean. Artificial assimilation data is created from a control simulation to nudge the MPI-ESM towards predominantly dry and wet states. First analyses are focused on the impact of the assimilation on land surface variables and reveal distinct differences in the long-term mean values between wet and dry state simulations. Precipitation, evapotranspiration and runoff are larger in the wet state compared to the dry state, resulting in an increased moisture transport from the land to atmosphere and ocean. Consequently, surface temperatures are lower in the wet state simulations by more than one Kelvin. In terms of spatial pattern

  5. Analog modeling of transient moisture flow in unsaturated soil

    NARCIS (Netherlands)

    Wind, G.P.

    1979-01-01

    Hydraulic and electronic analog models are developed for the simulation of moisture flow and accumulation in unsaturated soil. The analog models are compared with numerical models and checked with field observations. Application of soil physical knowledge on a soil technological problem by means of

  6. 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

  7. Inverse modeling of soil characteristics from surface soil moisture observations: potential and limitations

    Directory of Open Access Journals (Sweden)

    A. Loew

    2008-01-01

    Full Text Available Land surface models (LSM are widely used as scientific and operational tools to simulate mass and energy fluxes within the soil vegetation atmosphere continuum for numerous applications in meteorology, hydrology or for geobiochemistry studies. A reliable parameterization of these models is important to improve the simulation skills. Soil moisture is a key variable, linking the water and energy fluxes at the land surface. An appropriate parameterisation of soil hydraulic properties is crucial to obtain reliable simulation of soil water content from a LSM scheme. Parameter inversion techniques have been developed for that purpose to infer model parameters from soil moisture measurements at the local scale. On the other hand, remote sensing methods provide a unique opportunity to estimate surface soil moisture content at different spatial scales and with different temporal frequencies and accuracies. The present paper investigates the potential to use surface soil moisture information to infer soil hydraulic characteristics using uncertain observations. Different approaches to retrieve soil characteristics from surface soil moisture observations is evaluated and the impact on the accuracy of the model predictions is quantified. The results indicate that there is in general potential to improve land surface model parameterisations by assimilating surface soil moisture observations. However, a high accuracy in surface soil moisture estimates is required to obtain reliable estimates of soil characteristics.

  8. Implications of complete watershed soil moisture measurements to hydrologic modeling

    Science.gov (United States)

    Engman, E. T.; Jackson, T. J.; Schmugge, T. J.

    1983-01-01

    A series of six microwave data collection flights for measuring soil moisture were made over a small 7.8 square kilometer watershed in southwestern Minnesota. These flights were made to provide 100 percent coverage of the basin at a 400 m resolution. In addition, three flight lines were flown at preselected areas to provide a sample of data at a higher resolution of 60 m. The low level flights provide considerably more information on soil moisture variability. The results are discussed in terms of reproducibility, spatial variability and temporal variability, and their implications for hydrologic modeling.

  9. Potential for Remotely Sensed Soil Moisture Data in Hydrologic Modeling

    Science.gov (United States)

    Engman, Edwin T.

    1997-01-01

    Many hydrologic processes display a unique signature that is detectable with microwave remote sensing. These signatures are in the form of the spatial and temporal distributions of surface soil moisture and portray the spatial heterogeneity of hydrologic processes and properties that one encounters in drainage basins. The hydrologic processes that may be detected include ground water recharge and discharge zones, storm runoff contributing areas, regions of potential and less than potential ET, and information about the hydrologic properties of soils and heterogeneity of hydrologic parameters. Microwave remote sensing has the potential to detect these signatures within a basin in the form of volumetric soil moisture measurements in the top few cm. These signatures should provide information on how and where to apply soil physical parameters in distributed and lumped parameter models and how to subdivide drainage basins into hydrologically similar sub-basins.

  10. Using high-resolution soil moisture modelling to assess the uncertainty of microwave remotely sensed soil moisture products at the correct spatial and temporal support

    NARCIS (Netherlands)

    Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.

    2012-01-01

    Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture con

  11. Spatial Variation of Soil Type and Soil Moisture in the Regional Atmospheric Modeling System

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, R.

    2001-06-27

    Soil characteristics (texture and moisture) are typically assumed to be initially constant when performing simulations with the Regional Atmospheric Modeling System (RAMS). Soil texture is spatially homogeneous and time-independent, while soil moisture is often spatially homogeneous initially, but time-dependent. This report discusses the conversion of a global data set of Food and Agriculture Organization (FAO) soil types to RAMS soil texture and the subsequent modifications required in RAMS to ingest this information. Spatial variations in initial soil moisture obtained from the National Center for Environmental Predictions (NCEP) large-scale models are also introduced. Comparisons involving simulations over the southeastern United States for two different time periods, one during warmer, more humid summer conditions, and one during cooler, dryer winter conditions, reveals differences in surface conditions related to increases or decreases in near-surface atmospheric moisture con tent as a result of different soil properties. Three separate simulation types were considered. The base case assumed spatially homogeneous soil texture and initial soil moisture. The second case assumed variable soil texture and constant initial soil moisture, while the third case allowed for both variable soil texture and initial soil moisture. The simulation domain was further divided into four geographically distinct regions. It is concluded there is a more dramatic impact on thermodynamic variables (surface temperature and dewpoint) than on surface winds, and a more pronounced variability in results during the summer period. While no obvious trends in surface winds or dewpoint temperature were found relative to observations covering all regions and times, improvement in surface temperatures in most regions and time periods was generally seen with the incorporation of variable soil texture and initial soil moisture.

  12. Multilayer soil model for improvement of soil moisture estimation using the small perturbation method

    Science.gov (United States)

    Song, Kaijun; Zhou, Xiaobing; Fan, Yong

    2009-12-01

    A multilayer soil model is presented for improved estimation of soil moisture content using the first-order small perturbation method (SPM) applied to measurements of radar backscattering coefficient. The total reflection coefficient of the natural bare soil including volume scattering contribution is obtained using the multilayer model. The surface reflection terms in SPM model are replaced by the total reflection coefficient from the multilayer soil surface in estimating soil moisture. The difference between the modified SPM model and the original SPM surface model is that the modified SPM model includes both the surface scattering and the volumetric scattering of the natural bare soil. Both the modified SPM model and the original SPM model are tested in soil moisture retrievals using experimental microwave backscattering coefficient data in the literature. Results show that the mean square errors between the measured data and the values estimated by the modified SPM model from all samples are 5.2%, while errors from the original SPM model are 8.4%. This indicates that the capability of estimating soil moisture by the SPM model is improved when the surface reflection terms are replaced by the total reflection coefficients of multilayer soil model over bare or very sparsely vegetation covered fields.

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

    Science.gov (United States)

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

    2017-04-01

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

  14. Observing and modeling links between soil moisture, microbes and CH4 fluxes from forest soils

    Science.gov (United States)

    Christiansen, Jesper; Levy-Booth, David; Barker, Jason; Prescott, Cindy; Grayston, Sue

    2017-04-01

    Soil moisture is a key driver of methane (CH4) fluxes in forest soils, both of the net uptake of atmospheric CH4 and emission from the soil. Climate and land use change will alter spatial patterns of soil moisture as well as temporal variability impacting the net CH4 exchange. The impact on the resultant net CH4 exchange however is linked to the underlying spatial and temporal distribution of the soil microbial communities involved in CH4 cycling as well as the response of the soil microbial community to environmental changes. Significant progress has been made to target specific CH4 consuming and producing soil organisms, which is invaluable in order to understand the microbial regulation of the CH4 cycle in forest soils. However, it is not clear as to which extent soil moisture shapes the structure, function and abundance of CH4 specific microorganisms and how this is linked to observed net CH4 exchange under contrasting soil moisture regimes. Here we report on the results from a research project aiming to understand how the CH4 net exchange is shaped by the interactive effects soil moisture and the spatial distribution CH4 consuming (methanotrophs) and producing (methanogens). We studied the growing season variations of in situ CH4 fluxes, microbial gene abundances of methanotrophs and methanogens, soil hydrology, and nutrient availability in three typical forest types across a soil moisture gradient in a temperate rainforest on the Canadian Pacific coast. Furthermore, we conducted laboratory experiments to determine whether the net CH4 exchange from hydrologically contrasting forest soils responded differently to changes in soil moisture. Lastly, we modelled the microbial mediation of net CH4 exchange along the soil moisture gradient using structural equation modeling. Our study shows that it is possible to link spatial patterns of in situ net exchange of CH4 to microbial abundance of CH4 consuming and producing organisms. We also show that the microbial

  15. Time series modeling of soil moisture dynamics on a steep mountainous hillside

    Science.gov (United States)

    Kim, Sanghyun

    2016-05-01

    The response of soil moisture to rainfall events along hillslope transects is an important hydrologic process and a critical component of interactions between soil vegetation and the atmosphere. In this context, the research described in this article addresses the spatial distribution of soil moisture as a function of topography. In order to characterize the temporal variation in soil moisture on a steep mountainous hillside, a transfer function, including a model for noise, was introduced. Soil moisture time series with similar rainfall amounts, but different wetness gradients were measured in the spring and fall. Water flux near the soil moisture sensors was modeled and mathematical expressions were developed to provide a basis for input-output modeling of rainfall and soil moisture using hydrological processes such as infiltration, exfiltration and downslope lateral flow. The characteristics of soil moisture response can be expressed in terms of model structure. A seasonal comparison of models reveals differences in soil moisture response to rainfall, possibly associated with eco-hydrological process and evapotranspiration. Modeling results along the hillslope indicate that the spatial structure of the soil moisture response patterns mainly appears in deeper layers. Similarities between topographic attributes and stochastic model structures are spatially organized. The impact of temporal and spatial discretization scales on parameter expression is addressed in the context of modeling results that link rainfall events and soil moisture.

  16. Regional soil moisture simulation for Shaanxi Province using SWAT model validation and trend analysis

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The soil moisture in Shaanxi Province,a region with complex topography,is simulated using the distributed hydrological model Soil Water Assessment Tool(SWAT).Comparison and contrast of modeled and observed soil moisture show that the SWAT model can reasonably simulate the long-term trend in soil moisture and the spatiotemporal variability of soil moisture in the region.Comparisons to NCEP/NCAR and ERA40 reanalysis of soil moisture show that the trend of variability in soil moisture simulated by SWAT is more consistent with the observed.SWAT model results suggested that high soil moisture in surface soil layers appears in the southern Shaanxi with high vegetation cover,and the Qinling mountainous region with frequent orographic precipitation.In deeper soil layers,high soil moisture appears in the river basins and plains.The regional soil moisture showed a generally decreasing trend on all soil layers from 1951 to 2004,with a stronger and significant decreasing trend in deeper soil layers,especially in the northern parts of the province.

  17. Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery

    OpenAIRE

    Xun Chai; Tingting Zhang; Yun Shao; Huaze Gong; Long Liu; Kaixin Xie

    2015-01-01

    Accurate soil moisture retrieval of a large area in high resolution is significant for plateau pasture. The object of this paper is to investigate the estimation of volumetric soil moisture in vegetated areas of plateau pasture using fully polarimetric C-band RADARSAT-2 SAR (Synthetic Aperture Radar) images. Based on the water cloud model, Chen model, and Dubois model, we proposed two developed algorithms for soil moisture retrieval and validated their performance using experimental data. We ...

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

    Science.gov (United States)

    Montaldo, N.; Albertson, J. D.

    2001-12-01

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

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

    Science.gov (United States)

    Montaldo, N.; Albertson, J. D.

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

  20. Modeling the Effects of Soil Moisture at a GPS-Interferometric Reflectometry Station

    Science.gov (United States)

    Chew, C.; Small, E. E.; Larson, K. M.; Nievinski, F. G.; Zavorotny, V.

    2011-12-01

    GPS-Interferometric Reflectometry (GPS-IR) uses ground-reflected GPS signals to estimate near-surface soil moisture. Data are recorded by high-precision, geodetic-quality GPS antennas/receivers, for example those that comprise NSF's EarthScope Plate Boundary Observatory. The ground reflections used in GPS-IR are representative of a ~1000 m2 area around an antenna. As the dielectric constant of the surface fluctuates, the phase, amplitude, and frequency of signal-to-noise ratio (SNR) data recorded by the GPS unit change. Based on field observations, it has been shown that these characteristics of the SNR data are sensitive to shallow soil moisture. A single-scattering, electrodynamic model was used to simulate SNR output over a range of soil moisture conditions. All simulations were for a 2.4 m tall antenna surrounded by a surface free of roughness or vegetation. The model was run using three different types of soil moisture profiles: constant with depth, monotonic variations with depth, and observed profiles interpolated from field data. For all profiles, amplitude, phase shift, and frequency changes were calculated from simulated SNR data. The three GPS metrics are well correlated with soil moisture content modeled at the soil surface because a majority of the incident microwave energy is reflected at the air-soil interface. When surface soil is dry relative to the underlying soil, GPS metrics are also strongly correlated with soil moisture averaged over the top 5 cm of the soil column. The relationship between GPS metrics and soil moisture averaged over 5 cm is not as strong when surface soil is relatively wet (>35% volumetric soil moisture). Interpolated profiles from field data resulted in a very strong correlation between SNR metrics and soil moisture averaged over the top 5 cm of soil, suggesting that soil moisture estimated from SNR data is useful for various hydrologic applications.

  1. [Transferability of Hyperspectral Model for Estimating Soil Organic Matter Concerned with Soil Moisture].

    Science.gov (United States)

    Chen, Yi-yun; Qi, Kun; Liu, Yao-lin; He, Jian-hua; Jiang, Qing-hu

    2015-06-01

    Hyperspectral remote sensing, known as the state-of-the-art technology in the field of remote sensing, can be used to retrieve physical and chemical properties of surface objects based on the interactions between electromagnetic waves and the objects. Soil organic matter (SOM) is one of the most important parameters used in the assessment of soil fertility. Quick estimation of SOM with hyperspectral remote sensing technique can provide essential soil data to support the development of precision agriculture. The presence of external parameters, however, may affect the modeling precision, and further handicap the transfer ability of existing model. With the aim to study the effects of soil moisture on the Vis/NIR estimation of soil organic matter, and the capacity of direct standardization(DS)algorithm in the calibration transfer, 95 soil samples collected in the Jianghan plain were rewetted and air-dried. Reflectance of these samples at 13 moisture levels was measured. Results show that the model calibrated using air-dried samples has the highest prediction accuracy. This model, however, was not suitable for SOM prediction of the rewetted samples. Prediction bias and RPD improved from -8.34-3.32 g x kg(-1) and 0.64-2.04 to 0 and 7.01, when DS algorithm was applied to the spectra of the rewetted samples. DS algorithm has been proven to be effective in removing the effects of soil moisture on the Vis/NIR estimation of SOM, ensuring a transferrable model for SOM prediction with soil samples at different moisture levels.

  2. Local and Nonlocal Impacts of Soil Moisture Initialization on AGCM Seasonal Forecasts: A Model Sensitivity Study.

    Science.gov (United States)

    Zhang, H.; Frederiksen, C. S.

    2003-07-01

    Using a version of the Australian Bureau of Meteorology Research Centre (BMRC) atmospheric general circulation model, this study investigates the model's sensitivity to different soil moisture initial conditions in its dynamically extended seasonal forecasts of June-August 1998 climate anomalies, with focus on the south and northeast China regions where severe floods occurred. The authors' primary aim is to understand the model's responses to different soil moisture initial conditions in terms of the physical and dynamical processes involved. Due to a lack of observed global soil moisture data, the efficacy of using soil moisture anomalies derived from the NCEP-NCAR reanalysis is assessed. Results show that by imposing soil moisture percentile anomalies derived from the reanalysis data into the BMRC model initial condition, the regional features of the model's simulation of seasonal precipitation and temperature anomalies are modulated. Further analyses reveal that the impacts of soil moisture conditions on the model's surface temperature forecasts are mainly from localized interactions between land surface and the overlying atmosphere. In contrast, the model's sensitivity in its forecasts of rainfall anomalies is mainly due to the nonlocal impacts of the soil moisture conditions. Over the monsoon-dominated east Asian region, the contribution from local water recycling, through surface evaporation, to the model simulation of precipitation is limited. Rather, it is the horizontal moisture transport by the regional atmospheric circulation that is the dominant factor in controlling the model rainfall. The influence of different soil moisture conditions on the model forecasts of rainfall anomalies is the result of the response of regional circulation to the anomalous soil moisture condition imposed. Results from the BMRC model sensitivity study support similar findings from other model studies that have appeared in recent years and emphasize the importance of improving

  3. Spatial variability and its scale dependency of observed and modeled soil moisture under different climate conditions

    Directory of Open Access Journals (Sweden)

    B. Li

    2012-09-01

    Full Text Available Past studies on soil moisture spatial variability have been mainly conducted in catchment scales where soil moisture is often sampled over a short time period. Because of limited climate and weather conditions, the observed soil moisture often exhibited smaller dynamic ranges which prevented the complete revelation of soil moisture spatial variability as a function of mean soil moisture. In this study, spatial statistics (mean, spatial variability and skewness of in situ soil moisture measurements (from a continuously monitored network across the US, modeled and satellite retrieved soil moisture obtained in a warm season (198 days were examined at large extent scales (>100 km over three different climate regions. The investigation on in situ measurements revealed that their spatial moments strongly depend on climates, with distinct mean, spatial variability and skewness observed in each climate zone. In addition, an upward convex shape, which was revealed in several smaller scale studies, was observed for the relationship between spatial variability of in situ soil moisture and its spatial mean across dry, intermediate, and wet climates. These climate specific features were vaguely or partially observable in modeled and satellite retrieved soil moisture estimates, which is attributed to the fact that these two data sets do not have climate specific and seasonal sensitive mean soil moisture values, in addition to lack of dynamic ranges. From the point measurements to satellite retrievals, soil moisture spatial variability decreased in each climate region. The three data sources all followed the power law in the scale dependency of spatial variability, with coarser resolution data showing stronger scale dependency than finer ones. The main findings from this study are: (1 the statistical distribution of soil moisture depends on spatial mean soil moisture values and thus need to be derived locally within any given area; (2 the boundedness of soil

  4. Multi-site assimilation of a terrestrial biosphere model (BETHY) using satellite derived soil moisture data

    Science.gov (United States)

    Wu, Mousong; Sholze, Marko

    2017-04-01

    We investigated the importance of soil moisture data on assimilation of a terrestrial biosphere model (BETHY) for a long time period from 2010 to 2015. Totally, 101 parameters related to carbon turnover, soil respiration, as well as soil texture were selected for optimization within a carbon cycle data assimilation system (CCDAS). Soil moisture data from Soil Moisture and Ocean Salinity (SMOS) product was derived for 10 sites representing different plant function types (PFTs) as well as different climate zones. Uncertainty of SMOS soil moisture data was also estimated using triple collocation analysis (TCA) method by comparing with ASCAT dataset and BETHY forward simulation results. Assimilation of soil moisture to the system improved soil moisture as well as net primary productivity(NPP) and net ecosystem productivity (NEP) when compared with soil moisture derived from in-situ measurements and fluxnet datasets. Parameter uncertainties were largely reduced relatively to prior values. Using SMOS soil moisture data for assimilation of a terrestrial biosphere model proved to be an efficient approach in reducing uncertainty in ecosystem fluxes simulation. It could be further used in regional an global assimilation work to constrain carbon dioxide concentration simulation by combining with other sources of measurements.

  5. Stochastic modelling of soil moisture dynamics in a grassland of Qilian Mountain at point scale

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Stochastic modeling of soil moisture dynamics is crucial to the quantitative understanding of plant responses to water stresses, hydrological control of nutrient cycling processes, water competition among plants, and some other ecological dynamics, and thus has become a hotspot in ecohydrology at present. In this paper, we based on the continuously monitored data of soil moisture during 2002-2005 and daily precipitation date of 1992-2006, and tried to make a probabilistic analysis of soil moisture dynamics at point scale in a grassland of Qilian Mountain by integrating the stochastic model improved by Laio and the Monte Carlo method. The results show that the inter-annual variations for the soil moisture patterns at different depths are very significant, and that the coefficient of variance (CV) of surface soil moisture (20 cm) is almost continually kept at about 0.23 whether in the rich or poor rainy years. Interestingly, it has been found that the maximal CV of soil moisture has not always appeared at the surface layer. Comparison of the analytically derived soil moisture probability density function (PDF) with the statistical distribution of the observed soil moisture data suggests that the stochastic model can reasonably describe and predict the soil moisture dynamics of the grassland in Qilian Mountain at point scale. By extracting the statistical information of the historical precipitation data in 1994-2006, and inputting them into the stochastic model, we analytically derived the long-term soil moisture PDF without considering the inter-annual climate fluctuations, and then numerically derived the one when considering the inter-annual fluctuation effects in combination with a Monte-Carlo procedure. It was found that, though the peak position of the probability density distribution significantly moved towards drought when considering the disturbance forces, and its width was narrowed, accordingly its peak value was increased, no significant bimodality was

  6. Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies

    Directory of Open Access Journals (Sweden)

    A. Loew

    2013-09-01

    Full Text Available Soil moisture is an essential climate variable (ECV of major importance for land–atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observation-based soil moisture dataset has become available that provides information on soil moisture dynamics from satellite observations (ECVSM, essential climate variable soil moisture. The present study investigates the potential and limitations of this new dataset for several applications in climate model evaluation. We compare soil moisture data from satellite observations, reanalysis and simulations from a state-of-the-art land surface model and analyze relationships between soil moisture and precipitation anomalies in the different dataset. Other potential applications like model parameter optimization or model initialization are not investigated in the present study. In a detailed regional study, we show that ECVSM is capable to capture well the interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.

  7. Monitoring soil moisture through assimilation of active microwave remote sensing observation into a hydrologic model

    Science.gov (United States)

    Liu, Qian; Zhao, Yingshi

    2015-08-01

    Soil moisture can be estimated from point measurements, hydrologic models, and remote sensing. Many researches indicated that the most promising approach for soil moisture is the integration of remote sensing surface soil moisture data and computational modeling. Although many researches were conducted using passive microwave remote sensing data in soil moisture assimilation with coarse spatial resolution, few researches were carried out using active microwave remote sensing observation. This research developed and tested an operational approach of assimilation for soil moisture prediction using active microwave remote sensing data ASAR (Advanced Synthetic Aperture Radar) in Heihe Watershed. The assimilation was based on ensemble Kalman filter (EnKF), a forward radiative transfer model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The forward radiative transfer model, as a semi-empirical backscattering model, was used to eliminate the effect of surface roughness and vegetation cover on the backscatter coefficient. The impact of topography on soil water movement and the vertical and lateral exchange of soil water were considered. We conducted experiments to assimilate active microwave remote sensing data (ASAR) observation into a hydrologic model at two field sites, which had different underlying conditions. The soil moisture ground-truth data were collected through the field Time Domain Reflectometry (TDR) tools, and were used to assess the assimilation method. The temporal evolution of soil moisture measured at point-based monitoring locations were compared with EnKF based model predictions. The results indicated that the estimate of soil moisture was improved through assimilation with ASAR observation and the soil moisture based on data assimilation can be monitored in moderate spatial resolution.

  8. Soil physical data and modeling soil moisture flow

    NARCIS (Netherlands)

    Wesseling, J.G.

    2009-01-01

    De fysische eigenschappen van grof-gestruktureerd bodemmateriaal zijn bepaald aan kunstmatig gemaakte monsters, evenals het effekt van het toevoegen van 10 vol.% organische stof. Deze eigenschappen zijn toegepast in modelberekeningen met het nieuw ontwikkelde model SoWaM. De beregeningsbehoefte van

  9. A Mulitivariate Statistical Model Describing the Compound Nature of Soil Moisture Drought

    Science.gov (United States)

    Manning, Colin; Widmann, Martin; Bevacqua, Emanuele; Maraun, Douglas; Van Loon, Anne; Vrac, Mathieu

    2017-04-01

    Soil moisture in Europe acts to partition incoming energy into sensible and latent heat fluxes, thereby exerting a large influence on temperature variability. Soil moisture is predominantly controlled by precipitation and evapotranspiration. When these meteorological variables are accumulated over different timescales, their joint multivariate distribution and dependence structure can be used to provide information of soil moisture. We therefore consider soil moisture drought as a compound event of meteorological drought (deficits of precipitation) and heat waves, or more specifically, periods of high Potential Evapotraspiration (PET). We present here a statistical model of soil moisture based on Pair Copula Constructions (PCC) that can describe the dependence amongst soil moisture and its contributing meteorological variables. The model is designed in such a way that it can account for concurrences of meteorological drought and heat waves and describe the dependence between these conditions at a local level. The model is composed of four variables; daily soil moisture (h); a short term and a long term accumulated precipitation variable (Y1 and Y_2) that account for the propagation of meteorological drought to soil moisture drought; and accumulated PET (Y_3), calculated using the Penman Monteith equation, which can represent the effect of a heat wave on soil conditions. Copula are multivariate distribution functions that allow one to model the dependence structure of given variables separately from their marginal behaviour. PCCs then allow in theory for the formulation of a multivariate distribution of any dimension where the multivariate distribution is decomposed into a product of marginal probability density functions and two-dimensional copula, of which some are conditional. We apply PCC here in such a way that allows us to provide estimates of h and their uncertainty through conditioning on the Y in the form h=h|y_1,y_2,y_3 (1) Applying the model to various

  10. Performance evaluation of WRF-Noah Land surface model estimated soil moisture for hydrological application: Synergistic evaluation using SMOS retrieved soil moisture

    Science.gov (United States)

    Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika; Dai, Qiang

    2015-10-01

    This study explores the performance of soil moisture data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis datasets using the Weather Research and Forecasting (WRF) mesoscale numerical weather model coupled with the Noah Land surface model for hydrological applications. For evaluating the performance of WRF for soil moisture estimation, three domains are taken into account. The domain with best performance is used for estimating the soil moisture deficit (SMD). Further, several approaches are presented in this study to evaluate the efficiency of WRF simulated soil moisture for SMD estimation and compared against Soil Moisture and Ocean Salinity (SMOS) downscaled and non-downscaled soil moisture. In this study, the first approach is based on the empirical relationship between WRF soil moisture and the SMD on a continuous time series basis, while the second approach is focused on the vegetation cover impact on SMD retrieval, depicted in terms of growing and non-growing seasons. The linear growing and non-growing seasonal model in combination performs well with the NSE = 0.79, RMSE = 0.011 m; Bias = 0.24 m, in comparison to linear model (NSE = 0.70, RMSE = 0.013 m; Bias = 0.01 m). The performance obtained using WRF soil moisture is comparable to SMOS level 2 product but lower than the downscaled SMOS datasets. The results indicate that methodologies could be useful for modelers working in the field of soil moisture information system and SMD estimation at a catchment scale. The study could be useful for ungauged basins that pose a challenge to hydrological modeling due to unavailability of datasets for proper model calibration and validation.

  11. Modeling Transient Root-zone Soil Moisture Dichotomies in Landscapes with Intermixed Land Covers

    Science.gov (United States)

    Patrignani, A.; Ochsner, T. E.

    2015-12-01

    Although large-scale in situ soil moisture monitoring networks are becoming increasingly valuable research tools, deficiencies of many existing networks include the small spatial support of each station, the low spatial density of stations, and the almost exclusive deployment of stations in grassland vegetation. These grassland soil moisture observations may not adequately represent the real soil moisture patterns in landscapes with intermixed land cover types. The objectives of this study were i) to compare root-zone soil moisture dynamics of two dominant vegetation types across Oklahoma, grassland (observed) and winter wheat cropland (simulated); ii) to relate the soil moisture dynamics of grassland and cropland vegetation using an artificial neural network (ANN) as a transfer function; and iii) to use the resulting ANN to estimate the soil moisture spatial patterns for a landscape of intermixed grassland and wheat cropland. Root-zone soil moisture was represented by plant available water (PAW) in the top 0.8 m of the soil profile. PAW under grassland was calculated from 18 years of soil moisture observations at 78 stations of the Oklahoma Mesonet, whereas PAW under winter wheat was simulated for the same 78 locations using a soil water balance model. Then, we trained an ANN to reproduce the simulated PAW under winter wheat using only seven inputs: day of the year, latitude and longitude, measured PAW under grassland, and percent sand, silt, and clay. The resulting ANN was used, along with grassland soil moisture observations, to estimate the detailed soil moisture pattern for a 9x9 km2 Soil Moisture Active Passive (SMAP) grid cell. The seasonal dynamics of root-zone PAW for grassland and winter wheat were strongly asynchronous, so grassland soil moisture observations rarely reflect cropland soil moisture conditions in the region. The simple ANN approach facilitated efficient and accurate prediction of the simulated PAW under winter wheat, RMSE = 24 mm, using

  12. Estimation of Soil Moisture Profile using a Simple Hydrology Model and Passive Microwave Remote Sensing

    Science.gov (United States)

    Soman, Vishwas V.; Crosson, William L.; Laymon, Charles; Tsegaye, Teferi

    1998-01-01

    Soil moisture is an important component of analysis in many Earth science disciplines. Soil moisture information can be obtained either by using microwave remote sensing or by using a hydrologic model. In this study, we combined these two approaches to increase the accuracy of profile soil moisture estimation. A hydrologic model was used to analyze the errors in the estimation of soil moisture using the data collected during Huntsville '96 microwave remote sensing experiment in Huntsville, Alabama. Root mean square errors (RMSE) in soil moisture estimation increase by 22% with increase in the model input interval from 6 hr to 12 hr for the grass-covered plot. RMSEs were reduced for given model time step by 20-50% when model soil moisture estimates were updated using remotely-sensed data. This methodology has a potential to be employed in soil moisture estimation using rainfall data collected by a space-borne sensor, such as the Tropical Rainfall Measuring Mission (TRMM) satellite, if remotely-sensed data are available to update the model estimates.

  13. Multiscale analysis of surface soil moisture dynamics in a mesoscale catchment utilizing an integrated ecohydrological model

    Science.gov (United States)

    Korres, W.; Reichenau, T. G.; Schneider, K.

    2012-12-01

    Soil moisture is one of the fundamental variables in hydrology, meteorology and agriculture, influencing the partitioning of solar energy into latent and sensible heat flux as well as the partitioning of precipitation into runoff and percolation. Numerous studies have shown that in addition to natural factors (rainfall, soil, topography etc.) agricultural management is one of the key drivers for spatio-temporal patterns of soil moisture in agricultural landscapes. Interactions between plant growth, soil hydrology and soil nitrogen transformation processes are modeled by using a dynamically coupled modeling approach. The process-based ecohydrological model components of the integrated decision support system DANUBIA are used to identify the important processes and feedbacks determining soil moisture patterns in agroecosystems. Integrative validation of plant growth and surface soil moisture dynamics serves as a basis for a spatially distributed modeling analysis of surface soil moisture patterns in the northern part of the Rur catchment (1100 sq km), Western Germany. An extensive three year dataset (2007-2009) of surface soil moisture-, plant- (LAI, organ specific biomass and N) and soil- (texture, N, C) measurements was collected. Plant measurements were carried out biweekly for winter wheat, maize, and sugar beet during the growing season. Soil moisture was measured with three FDR soil moisture stations. Meteorological data was measured with an eddy flux station. The results of the model validation showed a very good agreement between the modeled plant parameters (biomass, green LAI) and the measured parameters with values between 0.84 and 0.98 (Willmotts index of agreement). The modeled surface soil moisture (0 - 20 cm) showed also a very favorable agreement with the measurements for winter wheat and sugar beet with an RMSE between 1.68 and 3.45 Vol.-%. For maize, the RMSE was less favorable particularly in the 1.5 months prior to harvest. The modeled soil

  14. Modelling soil moisture for a grassland and a woodland site in south-east England

    Directory of Open Access Journals (Sweden)

    E. Blyth

    2002-01-01

    Full Text Available This paper describes a comparison between two soil moisture prediction models. One is MORECS (Met Office Rainfall and Evaporation Calculation Scheme, the Met Office soil moisture model that is used by agriculture, flood modellers and weather forecasters to initialise their models. The other is MOSES (Met Office Surface Exchange Scheme, modified with a runoff generation module. The models are made compatible by increasing the vegetation information available to MOSES. Both models were run with standard parameters and were driven using meteorological observations at Wallingford (1995-1997. Detailed soil moisture measurements were available at a grassland site and a woodland site in this area. The comparison between the models and the observed soil moisture indicated that, for the grassland site, MORECS dried out too quickly in the spring and, for the woodland site, was too wet. Overall, the performance of MOSES was superior. The soil moisture predicted by the new, modified MOSES will be included as a product of Nimrod - the 5 km x 5km gridded network of observed meteorological data across the UK. Keywords: Soil moisture, model, observation, field capacity

  15. The impacts of assimilating satellite soil moisture into a rainfall-runoff model in a semi-arid catchment

    Science.gov (United States)

    Soil moisture plays a key role in runoff generation processes. As a result, the assimilation of soil moisture observations into rainfall-runoff models is increasingly being investigated. Given the scarcity of ground-based in situ measurements, satellite soil moisture observations offer a valuable da...

  16. Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

    Directory of Open Access Journals (Sweden)

    S. Juglea

    2010-01-01

    Full Text Available The main goal of the SMOS (Soil Moisture and Ocean Salinity mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz radiometry. Within the context of the preparation for this mission over land, the Valencia Anchor Station experimental site, in Spain, was chosen to be one of the main test sites in Europe for the SMOS Calibration/Validation (Cal/Val activities. Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT model, SURFEX (Externalized Surface-module ISBA (Interactions between Soil-Biosphere-Atmosphere so as to simulate the surface soil moisture. The calibration as well as the validation of the ISBA model was made using in situ soil moisture measurements. It is shown that a good consistency was reached when point comparisons between simulated and in situ soil moisture measurements were made. In order to obtain an accurate soil moisture mapping over the Valencia Anchor Station (50×50 km2 area, a spatialization method has been applied. To validate the approach, a comparison with remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E and from the European Remote Sensing Satellites (ERS-Scat was performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the polarization ratio a better agreement is obtained. ERS-Scat soil moisture products were also used to be compared with the simulated spatialized soil moisture. The seasonal variations were well reproduced. However, the lack of soil moisture data over the area (45 observations for one year was a limit into completely understanding the soil moisture variability.

  17. Single Plant Root System Modeling under Soil Moisture Variation

    Science.gov (United States)

    Yabusaki, S.; Fang, Y.; Chen, X.; Scheibe, T. D.

    2016-12-01

    A prognostic Virtual Plant-Atmosphere-Soil System (vPASS) model is being developed that integrates comprehensively detailed mechanistic single plant modeling with microbial, atmospheric, and soil system processes in its immediate environment. Three broad areas of process module development are targeted: Incorporating models for root growth and function, rhizosphere interactions with bacteria and other organisms, litter decomposition and soil respiration into established porous media flow and reactive transport models Incorporating root/shoot transport, growth, photosynthesis and carbon allocation process models into an integrated plant physiology model Incorporating transpiration, Volatile Organic Compounds (VOC) emission, particulate deposition and local atmospheric processes into a coupled plant/atmosphere model. The integrated plant ecosystem simulation capability is being developed as open source process modules and associated interfaces under a modeling framework. The initial focus addresses the coupling of root growth, vascular transport system, and soil under drought scenarios. Two types of root water uptake modeling approaches are tested: continuous root distribution and constitutive root system architecture. The continuous root distribution models are based on spatially averaged root development process parameters, which are relatively straightforward to accommodate in the continuum soil flow and reactive transport module. Conversely, the constitutive root system architecture models use root growth rates, root growth direction, and root branching to evolve explicit root geometries. The branching topologies require more complex data structures and additional input parameters. Preliminary results are presented for root model development and the vascular response to temporal and spatial variations in soil conditions.

  18. Passive microwave soil moisture research

    Science.gov (United States)

    Schmugge, T.; Oneill, P. E.; Wang, J. R.

    1986-01-01

    During the four years of the AgRISTARS Program, significant progress was made in quantifying the capabilities of microwave sensors for the remote sensing of soil moisture. In this paper, a discussion is provided of the results of numerous field and aircraft experiments, analysis of spacecraft data, and modeling activities which examined the various noise factors such as roughness and vegetation that affect the interpretability of microwave emission measurements. While determining that a 21-cm wavelength radiometer was the best single sensor for soil moisture research, these studies demonstrated that a multisensor approach will provide more accurate soil moisture information for a wider range of naturally occurring conditions.

  19. Employing satellite retrieved soil moisture for parameter estimation of the hydrologic model mHM

    Science.gov (United States)

    Zink, Matthias; Mai, Juliane; Rakovec, Oldrich; Schrön, Martin; Kumar, Rohini; Schäfer, David; Samaniego, Luis

    2016-04-01

    Hydrological models are usually calibrated against observed streamflow at the catchment outlet and thus they are conditioned by an integral catchment signal. Rakovec et al. 2016 (JHM) recently demonstrated that constraining model parameters against river discharge is a necessary, but not a sufficient condition. Such a procedure ensures the fulfillment of the catchment's water balance but can lead to high predictive uncertainties of model internal states, like soil moisture, or a lack in spatial representativeness of the model. However, some hydrologic applications, as e.g. soil drought monitoring and prediction, rely on this information. Within this study we propose a framework in which the mesoscale Hydrologic Model (mHM) is calibrated with soil moisture retrievals from various sources. The aim is to condition the model on soil moisture (SM), while preserving good performance in streamflow estimation. We identify the most appropriate objective functions by conducting synthetic experiments. The best objective function is determined based on: 1) deviation between synthetic and simulated soil moisture, 2) nonparametric comparison of SM fields (e.g. copulas), and 3) by euclidian distance of model parameters, which is zero if the parameters of the synthetic data are recovered. Those objective functions performing best are used to calibrate mHM against different satellite soil moisture products, e.g. ESA-CCI, H-SAF, and in situ observations. This procedure is tested in three distinct European basins (upper Sava, Neckar, and upper Guadalquivir basin) ranging from snow domination to semi arid climatic conditions. Results obtained with the synthetic experiment indicate that objective functions focusing on the temporal dynamics of SM are preferable to objective functions aiming at spatial patterns or catchment averages. Since the deviation of soil moisture fields (1) and their copulas (2) don't lead to conclusive results, the decision of the best performing objective

  20. Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery

    Directory of Open Access Journals (Sweden)

    Xun Chai

    2015-01-01

    Full Text Available Accurate soil moisture retrieval of a large area in high resolution is significant for plateau pasture. The object of this paper is to investigate the estimation of volumetric soil moisture in vegetated areas of plateau pasture using fully polarimetric C-band RADARSAT-2 SAR (Synthetic Aperture Radar images. Based on the water cloud model, Chen model, and Dubois model, we proposed two developed algorithms for soil moisture retrieval and validated their performance using experimental data. We eliminated the effect of vegetation cover by using the water cloud model and minimized the effect of soil surface roughness by solving the Dubois equations. Two experimental campaigns were conducted in the Qinghai Lake watershed, northeastern Tibetan Plateau in September 2012 and May 2013, respectively, with simultaneous satellite overpass. Compared with the developed Chen model, the predicted soil moisture given by the developed Dubois model agreed better with field measurements in terms of accuracy and stability. The RMSE, R2, and RPD value of the developed Dubois model were (5.4, 0.8, 1.6 and (3.05, 0.78, 1.74 for the two experiments, respectively. Validation results indicated that the developed Dubois model, needing a minimum of prior information, satisfied the requirement for soil moisture inversion in the study region.

  1. CPC Soil Moisture

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The monthly data set consists of a file containing 1/2 degree monthly averaged soil moisture water height equivalents for the globe from 1948 onwards. Values are...

  2. Using high-resolution soil moisture modelling to assess the uncertainty of microwave remotely sensed soil moisture products at the correct spatial and temporal support

    Science.gov (United States)

    Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.

    2012-04-01

    Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture content throughout the river basin. Space-borne remote sensing may provide this information with a high temporal and spatial resolution and with a global coverage. Currently three microwave soil moisture products are available: AMSR-E, ASCAT and SMOS. The quality of these satellite-based products is often assessed by comparing them with in-situ observations of soil moisture. This comparison is however hampered by the difference in spatial and temporal support (i.e., resolution, scale), because the spatial resolution of microwave satellites is rather low compared to in-situ field measurements. Thus, the aim of this study is to derive a method to assess the uncertainty of microwave satellite soil moisture products at the correct spatial support. To overcome the difference in support size between in-situ soil moisture observations and remote sensed soil moisture, we used a stochastic, distributed unsaturated zone model (SWAP, van Dam (2000)) that is upscaled to the support of different satellite products. A detailed assessment of the SWAP model uncertainty is included to ensure that the uncertainty in satellite soil moisture is not overestimated due to an underestimation of the model uncertainty. We simulated unsaturated water flow up to a depth of 1.5m with a vertical resolution of 1 to 10 cm and on a horizontal grid of 1 km2 for the period Jan 2010 - Jun 2011. The SWAP model was first calibrated and validated on in-situ data of the REMEDHUS soil moisture network (Spain). Next, to evaluate the satellite products, the model was run for areas in the proximity of 79 meteorological stations in Spain, where model results were aggregated to the correct support of the satellite

  3. Modeling soil moisture processes and recharge under a melting snowpack

    Science.gov (United States)

    Flint, A.L.; Flint, L.E.; Dettinger, M.D.

    2008-01-01

    Recharge into granitic bedrock under a melting snowpack is being investigated as part of a study designed to understand hydrologic processes involving snow at Yosemite National Park in the Sierra Nevada Mountains of California. Snowpack measurements, accompanied by water content and matric potential measurements of the soil under the snowpack, allowed for estimates of infiltration into the soil during snowmelt and percolation into the bedrock. During portions of the snowmelt period, infiltration rates into the soil exceeded the permeability of the bedrock and caused ponding to be sustained at the soil-bedrock interface. During a 5-d period with little measured snowmelt, drainage of the ponded water into the underlying fractured granitic bedrock was estimated to be 1.6 cm d?1, which is used as an estimate of bedrock permeability. The numerical simulator TOUGH2 was used to reproduce the field data and evaluate the potential for vertical flow into the fractured bedrock or lateral flow at the bedrock-soil interface. During most of the snowmelt season, the snowmelt rates were near or below the bedrock permeability. The field data and model results support the notion that snowmelt on the shallow soil overlying low permeability bedrock becomes direct infiltration unless the snowmelt rate greatly exceeds the bedrock permeability. Late in the season, melt rates are double that of the bedrock permeability (although only for a few days) and may tend to move laterally at the soil-bedrock interface downgradient and contribute directly to streamflow. ?? Soil Science Society of America.

  4. Assimilation of ASCAT near-surface soil moisture into the SIM hydrological model over France

    Directory of Open Access Journals (Sweden)

    C. Draper

    2011-12-01

    Full Text Available This study examines whether the assimilation of remotely sensed near-surface soil moisture observations might benefit an operational hydrological model, specifically Météo-France's SAFRAN-ISBA-MODCOU (SIM model. Soil moisture data derived from ASCAT backscatter observations are assimilated into SIM using a Simplified Extended Kalman Filter (SEKF over 3.5 years. The benefit of the assimilation is tested by comparison to a delayed cut-off version of SIM, in which the land surface is forced with more accurate atmospheric analyses, due to the availability of additional atmospheric observations after the near-real time data cut-off. However, comparing the near-real time and delayed cut-off SIM models revealed that the main difference between them is a dry bias in the near-real time precipitation forcing, which resulted in a dry bias in the root-zone soil moisture and associated surface moisture flux forecasts. While assimilating the ASCAT data did reduce the root-zone soil moisture dry bias (by nearly 50%, this was more likely due to a bias within the SEKF, than due to the assimilation having accurately responded to the precipitation errors. Several improvements to the assimilation are identified to address this, and a bias-aware strategy is suggested for explicitly correcting the model bias. However, in this experiment the moisture added by the SEKF was quickly lost from the model surface due to the enhanced surface fluxes (particularly drainage induced by the wetter soil moisture states. Consequently, by the end of each winter, during which frozen conditions prevent the ASCAT data from being assimilated, the model land surface had returned to its original (dry-biased climate. This highlights that it would be more effective to address the precipitation bias directly, than to correct it by constraining the model soil moisture through data assimilation.

  5. A note on the Hybrid Soil Moisture Deficit Model v2.0

    Directory of Open Access Journals (Sweden)

    Schulte Rogier P.O.

    2015-12-01

    Full Text Available The Hybrid Soil Moisture Deficit (HSMD model has been used for a wide range of applications, including modelling of grassland productivity and utilisation, assessment of agricultural management opportunities such as slurry spreading, predicting nutrient emissions to the environment and risks of pathogen transfer to water. In the decade since its publication, various ad hoc modifications have been developed and the recent publication of the Irish Soil Information System has facilitated improved assessment of the spatial soil moisture dynamics. In this short note, we formally present a new version of the model (HSMD2.0, which includes two new soil drainage classes, as well as an optional module to account for the topographic wetness index at any location. In addition, we present a new Indicative Soil Drainage Map for Ireland, based on the Irish Soil Classification system, developed as part of the Irish Soil Information System.

  6. Calibration of a Hydrologic Model via Densely Distributed Soil Moisture Observations

    Science.gov (United States)

    Thorstensen, A. R.; Nguyen, P.; Hsu, K. L.; Sorooshian, S.

    2014-12-01

    The complexity of a catchment's physical heterogeneities is often addressed through calibration via observed streamflow. As hydrologic models move from lumped to distributed, and Earth observations increase in number and variety, the question is raised as to whether or not such distributed observations can be used to satisfy the possibly heterogenic calibration needs of a catchment. The goal of this study is to examine if calibration of a distributed hydrologic model using soil moisture observations can improve simulated streamflow. The NWS's Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) is used in this study. HL-RDHM uses the Sacramento Heat Transfer with enhanced Evapotranspiration for rainfall-runoff production and can convert conceptual storages to soil layers. This allows for calibration of conceptual parameters based on observed soil moisture profiles. HL-RDHM is calibrated using scalar multipliers of a-priori grids derived from soil surveys, with the premise that heterogeneity of these grids is correct. This assumption is relaxed to study the benefit of distributed calibration. Soil moisture measurements in the Turkey River Basin, which was equipped with 20 in-situ soil moisture sites for the Iowa Flood Studies campaign, were used for calibration of parameters related to soil moisture (i.e. storage and release parameters). The Shuffled Complex Evolution method was used to calibrate pixels collocated with in-situ probes based on soil moisture RMSE at point scale. Methods to allocate calibrated parameter values to remaining pixels include an averaging method, spatial interpolation, and a similarity method. Calibration was done for spring 2013, and validation for 2009 and 2011. Results show that calibration using stream gauges remains the superior method, especially for correlation. This is because calibration based on streamflow can correct peak timing by adjusting routing parameters. Such adjustments using soil moisture cannot be done

  7. Integrating coarse-scale uncertain soil moisture data into a fine-scale hydrological modelling scenario

    Directory of Open Access Journals (Sweden)

    H. Vernieuwe

    2011-06-01

    Full Text Available In a hydrological modelling scenario, often the modeller is confronted with external data, such as remotely-sensed soil moisture observations, that become available to update the model output. However, the scale triplet (spacing, extent and support of these data is often inconsistent with that of the model. Furthermore, the external data can be cursed with epistemic uncertainty. Hence, a method is needed that not only integrates the external data into the model, but that also takes into account the difference in scale and the uncertainty of the observations. In this paper, a synthetic hydrological modelling scenario is set up in which a high-resolution distributed hydrological model is run over an agricultural field. At regular time steps, coarse-scale field-averaged soil moisture data, described by means of possibility distributions (epistemic uncertainty, are retrieved by synthetic aperture radar and assimilated into the model. A method is presented that allows to integrate the coarse-scale possibility distribution of soil moisture content data with the fine-scale model-based soil moisture data. To this end, a scaling relationship between field-averaged soil moisture content data and its corresponding standard deviation is employed.

  8. SOIL moisture data intercomparison

    Science.gov (United States)

    Kerr, Yann; Rodriguez-Frenandez, Nemesio; Al-Yaari, Amen; Parens, Marie; Molero, Beatriz; Mahmoodi, Ali; Mialon, Arnaud; Richaume, Philippe; Bindlish, Rajat; Mecklenburg, Susanne; Wigneron, Jean-Pierre

    2016-04-01

    The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. Subsequently, the satellite has been in operation for over 6 years while the retrieval algorithms from Level 1 to Level 2 underwent significant evolutions as knowledge improved. Other approaches for retrieval at Level 2 over land were also investigated while Level 3 and 4 were initiated. In this présentation these improvements are assessed by inter-comparisons of the current Level 2 (V620) against the previous version (V551) and new products either using neural networks or Level 3. In addition a global evaluation of different SMOS soil moisture (SM) products is performed comparing products with those of model simulations and other satellites (AMSR E/ AMSR2 and ASCAT). Finally, all products were evaluated against in situ measurements of soil moisture (SM). The study demonstrated that the V620 shows a significant improvement (including those at level1 improving level2)) with respect to the earlier version V551. Results also show that neural network based approaches can yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved while RFI sources in several areas of the world are significantly reduced. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

  9. Modeling the Soil Moisture Parametrization in a Snow Dominated Mountainous Region

    Science.gov (United States)

    Kikine, Daniel; Sensoy, Aynur; Sorman, Arda

    2016-04-01

    The study quantifies the effects of both the soil moisture accounting and the temperature index in the event based as well as the continuous simulation of a model in a snow dominated basin. Physically based watershed model parameters are required to reproduce the historical flows and forecast the stream flows. This study demonstrates that parameterization of hydrological model is a favorable approach to perform forecasting because it employs the relationship of the calibrated model parameters and those of the watershed's physical properties. With this consideration, the temperature index (degree-day) snowmelt and the soil moisture accounting models within the Hydrologic Engineering Center's hydrologic modeling system (HEC-HMS) are applied to the Upper Euphrates watershed. The versatile 14-parameter soil moisture accounting (SMA) algorithm is utilized for a better simulation and parameterization of the watershed. The methodology was exemplified by performing various independent simulations using the meteorological data and the observed stream discharges. The soil moisture parameters were calibrated and modified according to their statistical relationships with the land use for the 2002 - 2008 period, the obtained parameter set are then validated for the 2009 - 2012 period. Model outputs are evaluated in comparison to satellite derived soil moisture and snow water equivalent data. Deterministic Numerical Weather Prediction data are used together with the conceptual model to forecast runoff for the melting period of the year 2015.

  10. Assessment of soil moisture fields from imperfect climate models with uncertain satellite observations

    Directory of Open Access Journals (Sweden)

    G. Schumann

    2009-03-01

    Full Text Available We demonstrate that global satellite products can be used to evaluate climate model soil moisture predictions but conclusions should be drawn with care. The quality of a limited area climate model (LAM was compared to a general circulation model (GCM using soil moisture data from two different Earth observing satellites within a model validation scheme that copes with the presence of uncertain data. Results showed that in the face of imperfect models and data, it is difficult to investigate the quality of current land surface schemes in simulating hydrology accurately. Nevertheless, a LAM provides, in general, a better representation of spatial patterns and dynamics of soil moisture. However, in months when data uncertainty is higher, particularly in colder months and in periods when vegetation cover and soil moisture are out of phase (e.g. August in the case of Western Europe, it is not possible to draw firm conclusions about model acceptability. Our work indicates that a higher resolution LAM has more benefits to soil moisture prediction than are due to the resolution alone and can be attributed to an overall intensification of the hydrological cycle relative to the GCM.

  11. Evaluation of random cascade hierarchical and statistical arrangement model in disaggregation of SMOS soil moisture

    Science.gov (United States)

    Hosseini, M.; Magagi, R.; Goita, K.

    2013-12-01

    Soil moisture is an important parameter in hydrology that can be derived from remote sensing. In different studies, it was shown that optical-thermal, active and passive microwave remote sensing data can be used for soil moisture estimation. However, the most promising approach to estimate soil moisture in large areas is passive microwave radiometry. Global estimation of soil moisture is now operational by using remote sensing techniques. The Advanced Microwave Scanning Radiometer-Earth Observing System Sensor (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) passive microwave radiometers that were lunched on 2002 and 2009 respectively along with the upcoming Soil Moisture Active-Passive (SMAP) satellite that was planned to be lunched in the time frame of 2014-2015 make remote sensing to be more useful in soil moisture estimation. However, the spatial resolutions of AMSR-E, SMOS and SMAP are 60 km, 40 km and 10 km respectively. These very low spatial resolutions can not show the temporal and spatial variability of soil moisture in field or small scales. So, using disaggregation methods is required to efficiently using the passive microwave derived soil moisture information in different scales. The low spatial resolutions of passive microwave satellites can be improved by using disaggregation methods. Random Cascade (RC) model (Over and Gupta, 1996) is used in this research to downscale the 40 km resolution of SMOS satellite. By using this statistical method, the SMOS soil moisture resolutions are improved to 20 km, 10 km, 5 km and 2.5 km, respectively. The data that were measured during Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) field campaign are used to do the experiments. Totally the ground data and SMOS images that were obtained during 13 different days from 7-June-2012 to 13-July-2012 are used. By comparison with ground soil moisture, it is observed that the SMOS soil moisture is underestimated for all the images and so bias amounts

  12. A non-equilibrium model for soil heating and moisture transport during extreme surface heating

    Directory of Open Access Journals (Sweden)

    W. J. Massman

    2015-03-01

    Full Text Available With increasing use of prescribed fire by land managers and increasing likelihood of wildfires due to climate change comes the need to improve modeling capability of extreme heating of soils during fires. This issue is addressed here by developing a one-dimensional non-equilibrium model of soil evaporation and transport of heat, soil moisture, and water vapor, for use with surface forcing ranging from daily solar cycles to extreme conditions encountered during fires. The model employs a linearized Crank–Nicolson scheme for the conservation equations of energy and mass and its performance is evaluated against dynamic soil temperature and moisture observations obtained during laboratory experiments on soil samples exposed to surface heat fluxes ranging between 10 000 and 50 000 W m−2. The Hertz–Knudsen equation is the basis for constructing the model's non-equilibrium evaporative source term. The model includes a dynamic residual soil moisture as a function of temperature and soil water potential, which allows the model to capture some of the dynamic aspects of the strongly bound soil moisture that seems to require temperatures well beyond 150 °C to fully evaporate. Furthermore, the model emulates the observed increase in soil moisture ahead of the drying front and the hiatus in the soil temperature rise during the strongly evaporative stage of drying. It also captures the observed rapid evaporation of soil moisture that occurs at relatively low temperatures (50–90 °C. Sensitivity analyses indicate that the model's success results primarily from the use of a temperature and moisture potential dependent condensation coefficient in the evaporative source term. The model's solution for water vapor density (and vapor pressure, which can exceed one standard atmosphere, cannot be experimentally verified, but they are supported by results from (earlier and very different models developed for somewhat different purposes and for different porous

  13. Improvement of the integration of Soil Moisture Accounting into the NRCS-CN model

    Science.gov (United States)

    Durán-Barroso, Pablo; González, Javier; Valdés, Juan B.

    2016-11-01

    Rainfall-runoff quantification is one of the most important tasks in both engineering and watershed management as it allows the identification, forecast and explanation of the watershed response. This non-linear process depends on the watershed antecedent conditions, which are commonly related to the initial soil moisture content. Although several studies have highlighted the relevance of soil moisture measures to improve flood modelling, the discussion is still open in the literature about the approach to use in lumped model. The integration of these previous conditions in the widely used rainfall-runoff models NRCS-CN (e.g. National Resources Conservation Service - Curve Number model) could be handled in two ways: using the Antecedent Precipitation Index (API) concept to modify the model parameter; or alternatively, using a Soil Moisture Accounting (SMA) procedure into the NRCS-CN, being the soil moisture a state variable. For this second option, the state variable does not have a direct physical representation. This make difficult the estimation of the initial soil moisture store level. This paper presents a new formulation that overcomes such issue, the rainfall-runoff model called RSSa. Its suitability is evaluated by comparing the RSSa model with the original NRCS-CN model and alternatives SMA procedures in 12 watersheds located in six different countries, with different climatic conditions, from Mediterranean to Semi-arid regions. The analysis shows that the new model, RSSa, performs better when compared with previously proposed CN-based models. Finally, an assessment is made of the influence of the soil moisture parameter for each watershed and the relative weight of scale effects over model parameterization.

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

    Science.gov (United States)

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

    2016-12-01

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

  15. Observations and stochastic modeling of soil moisture control on evapotranspiration in a Californian oak savanna

    Science.gov (United States)

    Chen, Xingyuan; Rubin, Yoram; Ma, Siyan; Baldocchi, Dennis

    2008-08-01

    The study of water exchange between soil, plants, and the atmosphere in response to seasonal or periodic droughts is critical to modeling the hydrologic cycle and biogeochemical processes in water-controlled ecosystems. An essential step in such studies is to characterize changes in evaporation and transpiration under water stress. The objectives of this study are to investigate how soil moisture controls the evapotranspiration in a Californian oak savanna that experiences seasonal droughts, using multiyear field observations at the daily and stand scale, and to model these controls stochastically. The influence of soil moisture on evapotranspiration at the stand scale is studied using correlations between tower-based evapotranspiration measurements and representative soil moisture obtained by aggregating point measurements. The observed pattern of this effect is found in agreement with an existing model that features a linear reduction of the evapotranspiration when soil moisture falls below a critical value. The model parameters are inferred using a Bayesian framework, and they are found to vary from year to year because of climate variability. The comparison between various aggregations of soil moisture at the stand scale from point measurements demonstrates that the spatial variability of the soil moisture and the water uptake capacity limited by the root biomass need be taken into account to produce a model that is most resistant to interannual variability. Finally, the parameterized model is used to predict the actual evapotranspiration with uncertainty estimates determined using the joint distribution of the parameters derived from the Bayesian framework. The satisfactory agreement between the predicted and measured evapotranspiration suggests that the calibrated model can be incorporated into water balance studies in the future.

  16. Modelling soil moisture at SMOS scale by use of a SVAT model over the Valencia Anchor Station

    Directory of Open Access Journals (Sweden)

    S. Juglea

    2010-05-01

    Full Text Available The main goal of the SMOS (Soil Moisture and Ocean Salinity mission is to deliver global fields of surface soil moisture and sea surface salinity using L-band (1.4 GHz radiometry. Within the context of the Science preparation for SMOS, the Valencia Anchor Station (VAS experimental site, in Spain, was chosen to be one of the main test sites in Europe for Calibration/Validation (Cal/Val activities. In this framework, the paper presents an approach consisting in accurately simulating a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over the wide VAS surface (50×50 km2. Ground and meteorological measurements over the area are used as the input of a Soil-Vegetation-Atmosphere-Transfer (SVAT model, SURFEX (Externalized Surface - module ISBA (Interactions between Soil-Biosphere-Atmosphere to simulate the spatial and temporal distribution of surface soil moisture. The calibration as well as the validation of the ISBA model are performed using in situ soil moisture measurements. It is shown that a good consistency is reached when point comparisons between simulated and in situ soil moisture measurements are made.

    Actually, an important challenge in remote sensing approaches concerns product validation. In order to obtain an representative soil moisture mapping over the Valencia Anchor Station (50×50 km2 area, a spatialization method is applied. For verification, a comparison between the simulated spatialized soil moisture and remote sensing data from the Advanced Microwave Scanning Radiometer on Earth observing System (AMSR-E and from the European Remote Sensing Satellites (ERS-SCAT is performed. Despite the fact that AMSR-E surface soil moisture product is not reproducing accurately the absolute values, it provides trustworthy information on surface soil moisture temporal variability. However, during the vegetation growing season the signal is perturbed. By using the

  17. Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study

    Science.gov (United States)

    Korres, W.; Reichenau, T. G.; Schneider, K.

    2013-08-01

    Soil moisture is a key variable in hydrology, meteorology and agriculture. Soil moisture, and surface soil moisture in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are affected by multiple natural (precipitation, soil, topography, etc.) and agro-economic (soil management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between soil moisture and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface soil moisture (top 20 cm) in an intensively used agricultural landscape (1100 km2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface soil moisture patterns in order to investigate how spatial scale affects spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface soil moisture between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m-2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled soil moisture can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface soil moisture maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface soil moisture patterns at all spatial scales depend mainly upon soil properties. Within the main growing season, larger scale

  18. Satellite observed preferential states in soil moisture

    Science.gov (United States)

    Vilasa, Luis U.; De Jeu, Richard A. M.; Dolman, Han A. J.; Wang, Guojie

    2013-04-01

    This study presents observational evidence for the existence of preferential states in soil moisture content. Recently there has been much debate about the existence, location and explanations for preferential states in soil moisture. A number of studies have provided evidence either in support or against the hypothesis of a positive feedback mechanism between soil moisture and subsequent precipitation in certain regions. Researchers who support the hypothesis that preferential states in soil moisture holds information about land atmosphere feedback base their theory on the impact of soil moisture on the evaporation process. Evaporation recycles moisture to the atmosphere and soil moisture has a direct impact on the supply part of this process but also on the partitioning of the available energy for evaporation. According to this theory, the existence of soil moisture bimodality can be used as an indication of possible land-atmosphere feedbacks, to be compared with model simulations of soil moisture feedbacks. On the other hand, other researchers argue that seasonality in the meteorological conditions in combination with the non-linearity of soil moisture response alone can induce bimodality. In this study we estimate the soil moisture bimodality at a global scale as derived from the recently available 30+ year ESA Climate Change Initative satellite soil moisture dataset. An Expectation-Maximization iterative algorithm is used to find the best Gaussian Mixture Model, pursuing the highest likelihood for soil moisture bimodality. With this approach we mapped the regions where bi-modal probability distribution of soil moisture appears for each month for the period between 1979-2010. These bimodality areas are analyzed and compared to maps of model simulations of soil moisture feedbacks. The areas where more than one preferential state exists compare surprisingly well with the map of land-atmosphere coupling strength from model simulations. This approach might

  19. Design of a global soil moisture initialization procedure for the simple biosphere model

    Science.gov (United States)

    Liston, G. E.; Sud, Y. C.; Walker, G. K.

    1993-01-01

    Global soil moisture and land-surface evapotranspiration fields are computed using an analysis scheme based on the Simple Biosphere (SiB) soil-vegetation-atmosphere interaction model. The scheme is driven with observed precipitation, and potential evapotranspiration, where the potential evapotranspiration is computed following the surface air temperature-potential evapotranspiration regression of Thomthwaite (1948). The observed surface air temperature is corrected to reflect potential (zero soil moisture stress) conditions by letting the ratio of actual transpiration to potential transpiration be a function of normalized difference vegetation index (NDVI). Soil moisture, evapotranspiration, and runoff data are generated on a daily basis for a 10-year period, January 1979 through December 1988, using observed precipitation gridded at a 4 deg by 5 deg resolution.

  20. Simultaneous assimilation of in situ soil moisture and streamflow in the SWAT model using the Extended Kalman Filter

    Science.gov (United States)

    Sun, Leqiang; Seidou, Ousmane; Nistor, Ioan; Goïta, Kalifa; Magagi, Ramata

    2016-12-01

    The Extended Kalman Filter (EKF) is used to assimilate in situ surface soil moisture and streamflow observation at the outlet of an experimental watershed outlet into a semi-distributed SWAT (Soil and Water Assessment Tool) model. Watershed scale, instead of HRU scale soil moisture was used in state vector to reduce computational burden. Numerical experiments were designed to select the best state vector which consists of streamflow and soil moisture in all vertical soil layers. Compared to open-loop model and direct-insert method, the estimate of both soil moisture and streamflow has been improved by EKF assimilation. The combined assimilation of surface soil moisture and streamflow outperforms the assimilation with only surface soil moisture or streamflow especially in the estimate of full profile soil moisture. The NSC has been improved to 0.63 from -4.45 and the RMSE has been reduced to 12.34 mm from 47.44 mm in open-loop. Such improvement is also reflected in the short term forecast of soil moisture. The improvement of streamflow prediction is relatively moderate in both simulation and forecast mode compared to quality of the soil moisture prediction. The quantification of the model error, especially the error covariance between different state variables, was found to be critical to the estimate of the state variable corresponding to the error covariance.

  1. A Polarimetric First-Order Model of Soil Moisture Effects on the DInSAR Coherence

    Directory of Open Access Journals (Sweden)

    Simon Zwieback

    2015-06-01

    Full Text Available Changes in soil moisture between two radar acquisitions can impact the observed coherence in differential interferometry: both coherence magnitude |Υ| and phase Φ are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and sign, as well as dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves–upon calibration with airborne L band data–median correlations ρ at HH polarization of 0.77 for the phase Φ, of 0.50 for |Υ|, and for the phase triplets ≡ of 0.56. The predictions are sensitive to the choice of dielectric mixing model, in particular the absorptive properties; the differences between the mixing models are found to be partially compensatable by varying the relative importance of surface and volume scattering. However, for half of the agricultural fields the Hallikainen mixing model cannot reproduce the observed sensitivities of the phase to soil moisture. In addition, the first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels HH and VV. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of soil moisture changes on the HH and VV DInSAR signals. Hence it may prove useful in separating the deformations from the moisture signals, thus yielding improved displacement estimates or new ways for inferring soil moisture.

  2. Soil Moisture Estimate Under Forest Using a Semi-Empirical Model at P-Band

    Science.gov (United States)

    Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak

    2013-01-01

    Here we present the result of a semi-empirical inversion model for soil moisture retrieval using the three backscattering coefficients: sigma(sub HH), sigma(sub VV) and sigma(sub HV). In this paper we focus on the soil moisture estimate and use the biomass as an ancillary parameter estimated automatically from the algorithm and used as a validation parameter, We will first remind the model analytical formulation. Then we will sow some results obtained with real SAR data and compare them to ground estimates.

  3. Modeling the Effect of Vegetation on Passive Microwave Remote Sensing of Soil Moisture

    Science.gov (United States)

    Liu, Y. P.; Inguva, R.; Crosson, W. L.; Coleman, T. L.; Laymon, C.; Fahsi, A.

    1998-01-01

    The effect of vegetation on passive microwave remote sensing of soil moisture is studied. The radiative transfer modeling work of Njoku and Kong is applied to a stratified medium of which the upper layer is treated as a layer of vegetation. An effective dielectric constant for this vegetation layer is computed using estimates of the dielectric constant of individual components of the vegetation layer. The horizontally-polarized brightness temperature is then computed as a function of the incidence angle. Model predictions are used to compare with the data obtained in the Huntsville '96, remote sensing of soil moisture experiment, and with predictions obtained using a correction procedure of Jackson and Schmugge.

  4. Uncertainty of Deardorff’s soil moisture model based on continuous TDR measurements for sandy loam soil

    Directory of Open Access Journals (Sweden)

    Brandyk Andrzej

    2016-03-01

    Full Text Available Knowledge on soil moisture is indispensable for a range of hydrological models, since it exerts a considerable influence on runoff conditions. Proper tools are nowadays applied in order to gain in-sight into soil moisture status, especially of uppermost soil layers, which are prone to weather changes and land use practices. In order to establish relationships between meteorological conditions and topsoil moisture, a simple model would be required, characterized by low computational effort, simple structure and low number of identified and calibrated parameters. We demonstrated, that existing model for shallow soils, considering mass exchange between two layers (the upper and the lower, as well as with the atmosphere and subsoil, worked well for sandy loam with deep ground water table in Warsaw conurbation. GLUE (Generalized Likelihood Uncertainty Estimation linked with GSA (Global Sensitivity Analysis provided for final determination of parameter values and model confidence ranges. Including the uncertainty in a model structure, caused that the median soil moisture solution of the GLUE was shifted from the one optimal in deterministic sense. From the point of view of practical model application, the main shortcoming were the underestimated water exchange rates between the lower soil layer (ranging from the depth of 0.1 to 0.2 m below ground level and subsoil. General model quality was found to be satisfactory and promising for its utilization for establishing measures to regain retention in urbanized conditions.

  5. A Soil Moisture-Heat Based Early Establishment Model of Riparian White Alder (Alnus rhombifolia)

    Science.gov (United States)

    Jablkowski, P.; Johnson, E. A.; Martin, Y. E.

    2013-12-01

    Establishment of fluvially dispersed seeds on accreted gravel-sand bars is limited by water availability in streams. Past establishment models have used the stream/water table recession rate, and maximum root growth rate to determine the elevation limit of seedling establishment. This approach neglects the role of the saturated-unsaturated vadose zone in providing water to recently germinated seedlings, the physical processes that determine the soil moisture content, and the effect moisture deficit has on seedling root growth. This study combines a soil moisture-heat budget and a seedling root growth model that responds to soil moisture availability to find the elevation limit of establishment of white alder (Alnus rhombifolia) on vertically accreted bars along the south fork Eel River in the Angelo Coast Range Reserve, California. To establish successfully, seedling roots must maintain a connection with sufficient moisture to avoid water stress. This will depend on the elevation of the bar, the stream recession rate, the root growth rate, and the diurnal cycle of soil moisture. A one-dimensional moisture-heat budget of the top 15 centimeters of sediment was validated at two locations characterized by sand and clay-gravel textures respectively, using soil moisture and temperature measurements at 5, 10 and 15 cm, net radiation, air temperature, humidity, wind velocity and precipitation measured during spring-summer stream recession. Two patterns in soil water content were apparent: an average daily moisture decrease at each depth driven by stream/water table recession, and a diurnal pattern of isothermal liquid and vapour flux increasing soil water content in the upper 15 cm between 12:00 pm and 5:00 pm PDT. To determine seedling root growth rates, white alder seedlings were grown in growth chambers under a range of reduced matric potentials using polyethylene glycol. Root length measurements were made at 4 hour intervals and a quadratic equation was fit to the root

  6. Research on the Estimation Model of Soil Moisture Content Based on the Characteristics of Thermal Infrared Data

    Institute of Scientific and Technical Information of China (English)

    Jun; XU; Jianjun; JIANG

    2013-01-01

    With the portable Fourier Transform Infrared Spectroscopy (FTIR), the reflectance spectra of soil samples with different moisture content are measured in laboratory for expounding the characteristic of radiation in the thermal infrared part of the spectrum with different soil moisture content. A model of estimating the moisture content in soil is attempted to make based on Moisture Diagnostic Index (MDI). In general,the spectral characteristic of soil emissivity in laboratory includes the following aspects.Firstly,in the region of 8.0-9.5 μm,along with the increase of soil moisture content,the emissivity of soil increases to varying degrees. The spectral curves are parallel relatively and have a tendency to become horizontal and the absorbed characteristic of reststrahlen is also weakened relatively with the increase of soil moisture in this region.Secondly,in the region of 11.0-14.0 μm,the emissivity of soil has a tendency of increasing.There is an absorption value near about 12.7 μm. As the soil moisture content increases,the depth of absorption also increases. This phenomenon may be caused by soil moisture absorption. Methods as derivative, difference and standardized ratio transformation may weaken the background noise effectively to the spectrum data. Especially using the ratio of the emissivity to the average of 8-14 μm may obviously enhance the correlation between soil moisture and soil emissivity. According to the result of correlation analysis, the 8.237 μm is regarded as the best detecting band for soil moisture content. Moreover,based on the Moisture Diagnostic Index ( MDI) in the 8.194-8.279 μm, the logarithmic model of estimating soil moisture is made.

  7. Quantification of top soil moisture patterns : Evaluation of field methods, process-based modelling, remote sensing and an integrated approach

    NARCIS (Netherlands)

    van der Kwast, J.

    2009-01-01

    There is an urgent need for operational models that can accurately predict soil moisture patterns in space and time. High spatial and temporal variability of soil moisture and its low degree of autocorrelation complicate the modelling with process-based models. The aim of this research was to evalua

  8. A model for hydraulic redistribution incorporating coupled soil-root moisture transport

    Directory of Open Access Journals (Sweden)

    G. G. Amenu

    2007-10-01

    Full Text Available One of the adaptive strategies of vegetation, particularly in water limited ecosystems, is the development of deep roots and the use of hydraulic redistribution which enables them to make optimal use of resources available throughout the soil column. Hydraulic redistribution refers to roots acting as a preferential pathway for the movement of water from wet to dry soil layers driven by the moisture gradient – be it from the shallow to deep layers or vice versa. This occurs during the nighttime while during the daytime moisture movement is driven to fulfill the transpiration demand at the canopy. In this study, we develop a model to investigate the effect of hydraulic redistribution by deep roots on the terrestrial climatology. Sierra Nevada eco-region is chosen as the study site which has wet winters and dry summers. Hydraulic redistribution enables the movement of moisture from the upper soil layers to deeper zones during the wet months and this moisture is then available to meet the transpiration demand during the late dry season. It results in significant alteration of the profiles of soil moisture and water uptake as well as increase in the canopy transpiration, carbon assimilation, and the associated water-use-efficiency during the dry summer season. This also makes the presence of roots in deeper soil layers much more important than their proportional abundance would otherwise dictate. Comparison with observations of latent heat from a flux tower demonstrates improved predictability and provides validation of the model results. Hydraulic redistribution serves as a mechanism for the interaction between the variability of deep layer soil-moisture and the land-surface climatology and could have significant implications for seasonal and sub-seasonal climate prediction.

  9. A model for hydraulic redistribution incorporating coupled soil-root moisture transport

    Directory of Open Access Journals (Sweden)

    G. G. Amenu

    2008-01-01

    Full Text Available One of the adaptive strategies of vegetation, particularly in water limited ecosystems, is the development of deep roots and the use of hydraulic redistribution which enables them to make optimal use of resources available throughout the soil column. Hydraulic redistribution refers to roots acting as a preferential pathway for the movement of water from wet to dry soil layers driven by the moisture gradient – be it from the shallow to deep layers or vice versa. This occurs during the nighttime while during the daytime moisture movement is driven to fulfill the transpiration demand at the canopy. In this study, we develop a model to investigate the effect of hydraulic redistribution by deep roots on the terrestrial climatology. Sierra Nevada eco-region is chosen as the study site which has wet winters and dry summers. Hydraulic redistribution enables the movement of moisture from the upper soil layers to deeper zones during the wet months and this moisture is then available to meet the transpiration demand during the late dry season. It results in significant alteration of the profiles of soil moisture and water uptake as well as increase in the canopy transpiration, carbon assimilation, and the associated water-use-efficiency during the dry summer season. This also makes the presence of roots in deeper soil layers much more important than their proportional abundance would otherwise dictate. Comparison with observations of latent heat from a flux tower demonstrates improved predictability and provides validation of the model results. Hydraulic redistribution serves as a mechanism for the interaction between the variability of deep layer soil-moisture and the land-surface climatology and could have significant implications for seasonal and sub-seasonal climate prediction.

  10. Soil moisture data for the validation of permafrost models using direct and indirect measurement approaches at three alpine sites

    Directory of Open Access Journals (Sweden)

    Cécile ePellet

    2016-01-01

    Full Text Available In regions affected by seasonal and permanently frozen conditions soil moisture influences the thermal regime of the ground as well as its ice content, which is one of the main factors controlling the sensitivity of mountain permafrost to climate changes. In this study, several well established soil moisture monitoring techniques were combined with data from geophysical measurements to assess the spatial distribution and temporal evolution of soil moisture at three high elevation sites with different ground properties and thermal regimes. The observed temporal evolution of measured soil moisture is characteristic for sites with seasonal freeze/thaw cycles and consistent with the respective site-specific properties, demonstrating the general applicability of continuous monitoring of soil moisture at high elevation areas. The obtained soil moisture data were then used for the calibration and validation of two different model approaches in permafrost research in order to characterize the lateral and vertical distribution of ice content in the ground. Calibration of the geophysically based four-phase model (4PM with spatially distributed soil moisture data yielded satisfactory two dimensional distributions of water-, ice- and air content. Similarly, soil moisture time series significantly improved the calibration of the one-dimensional heat and mass transfer model COUP, yielding physically consistent soil moisture and temperature data matching observations at different depths.

  11. Estimating soil hydraulic properties from soil moisture time series by inversion of a dual-permeability model

    Science.gov (United States)

    Dalla Valle, Nicolas; Wutzler, Thomas; Meyer, Stefanie; Potthast, Karin; Michalzik, Beate

    2017-04-01

    Dual-permeability type models are widely used to simulate water fluxes and solute transport in structured soils. These models contain two spatially overlapping flow domains with different parameterizations or even entirely different conceptual descriptions of flow processes. They are usually able to capture preferential flow phenomena, but a large set of parameters is needed, which are very laborious to obtain or cannot be measured at all. Therefore, model inversions are often used to derive the necessary parameters. Although these require sufficient input data themselves, they can use measurements of state variables instead, which are often easier to obtain and can be monitored by automated measurement systems. In this work we show a method to estimate soil hydraulic parameters from high frequency soil moisture time series data gathered at two different measurement depths by inversion of a simple one dimensional dual-permeability model. The model uses an advection equation based on the kinematic wave theory to describe the flow in the fracture domain and a Richards equation for the flow in the matrix domain. The soil moisture time series data were measured in mesocosms during sprinkling experiments. The inversion consists of three consecutive steps: First, the parameters of the water retention function were assessed using vertical soil moisture profiles in hydraulic equilibrium. This was done using two different exponential retention functions and the Campbell function. Second, the soil sorptivity and diffusivity functions were estimated from Boltzmann-transformed soil moisture data, which allowed the calculation of the hydraulic conductivity function. Third, the parameters governing flow in the fracture domain were determined using the whole soil moisture time series. The resulting retention functions were within the range of values predicted by pedotransfer functions apart from very dry conditions, where all retention functions predicted lower matrix potentials

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

    Science.gov (United States)

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

    2017-06-01

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

  13. The international soil moisture network: A data hosting facility for global in situ soil moisture measurements

    Science.gov (United States)

    In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land co...

  14. 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.

  15. Assimilation of ASCAT near-surface soil moisture into the French SIM hydrological model

    Directory of Open Access Journals (Sweden)

    C. Draper

    2011-06-01

    Full Text Available The impact of assimilating near-surface soil moisture into the SAFRAN-ISBA-MODCOU (SIM hydrological model over France is examined. Specifically, the root-zone soil moisture in the ISBA land surface model is constrained over three and a half years, by assimilating the ASCAT-derived surface degree of saturation product, using a Simplified Extended Kalman Filter. In this experiment ISBA is forced with the near-real time SAFRAN analysis, which analyses the variables required to force ISBA from relevant observations available before the real time data cut-off. The assimilation results are tested against ISBA forecasts generated with a higher quality delayed cut-off SAFRAN analysis. Ideally, assimilating the ASCAT data will constrain the ISBA surface state to correct for errors in the near-real time SAFRAN forcing, the most significant of which was a substantial dry bias caused by a dry precipitation bias. The assimilation successfully reduced the mean root-zone soil moisture bias, relative to the delayed cut-off forecasts, by close to 50 % of the open-loop value. The improved soil moisture in the model then led to significant improvements in the forecast hydrological cycle, reducing the drainage, runoff, and evapotranspiration biases (by 17 %, 11 %, and 70 %, respectively. When coupled to the MODCOU hydrogeological model, the ASCAT assimilation also led to improved streamflow forecasts, increasing the mean discharge ratio, relative to the delayed cut off forecasts, from 0.68 to 0.76. These results demonstrate that assimilating near-surface soil moisture observations can effectively constrain the SIM model hydrology, while also confirming the accuracy of the ASCAT surface degree of saturation product. This latter point highlights how assimilation experiments can contribute towards the difficult issue of validating remotely sensed land surface observations over large spatial scales.

  16. A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent.

  17. A land surface soil moisture data assimilation framework in consideration of the model subgrid-scale heterogeneity and soil water thawing and freezing

    Institute of Scientific and Technical Information of China (English)

    TIAN XiangJun; XIE ZhengHui

    2008-01-01

    The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation.The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data assimilation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in consideration of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process,while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heterogeneity and soil water thawing and freezing. With the improvement of soil moisture simulation,the soil temperature-simulated precision can be also improved to some extent.

  18. Assimilation of Satellite Soil Moisture observation with the Particle Filter-Markov Chain Monte Carlo and Geostatistical Modeling

    Science.gov (United States)

    Moradkhani, Hamid; Yan, Hongxiang

    2016-04-01

    Soil moisture simulation and prediction are increasingly used to characterize agricultural droughts but the process suffers from data scarcity and quality. The satellite soil moisture observations could be used to improve model predictions with data assimilation. Remote sensing products, however, are typically discontinuous in spatial-temporal coverages; while simulated soil moisture products are potentially biased due to the errors in forcing data, parameters, and deficiencies of model physics. This study attempts to provide a detailed analysis of the joint and separate assimilation of streamflow and Advanced Scatterometer (ASCAT) surface soil moisture into a fully distributed hydrologic model, with the use of recently developed particle filter-Markov chain Monte Carlo (PF-MCMC) method. A geostatistical model is introduced to overcome the satellite soil moisture discontinuity issue where satellite data does not cover the whole study region or is significantly biased, and the dominant land cover is dense vegetation. The results indicate that joint assimilation of soil moisture and streamflow has minimal effect in improving the streamflow prediction, however, the surface soil moisture field is significantly improved. The combination of DA and geostatistical approach can further improve the surface soil moisture prediction.

  19. Evaluating the Potential Use of Remotely-Sensed and Model-Simulated Soil Moisture for Agricultural Drought Risk Monitoring

    Science.gov (United States)

    Yan, Hongxiang; Moradkhani, Hamid

    2016-04-01

    Current two datasets provide spatial and temporal resolution of soil moisture at large-scale: the remotely-sensed soil moisture retrievals and the model-simulated soil moisture products. Drought monitoring using remotely-sensed soil moisture is emerging, and the soil moisture simulated using land surface models (LSMs) have been used operationally to monitor agriculture drought in United States. Although these two datasets yield important drought information, their drought monitoring skill still needs further quantification. This study provides a comprehensive assessment of the potential of remotely-sensed and model-simulated soil moisture data in monitoring agricultural drought over the Columbia River Basin (CRB), Pacific Northwest. Two satellite soil moisture datasets were evaluated, the LPRM-AMSR-E (unscaled, 2002-2011) and ESA-CCI (scaled, 1979-2013). The USGS Precipitation Runoff Modeling System (PRMS) is used to simulate the soil moisture from 1979-2011. The drought monitoring skill is quantified with two indices: drought area coverage (the ability of drought detection) and drought severity (according to USDM categories). The effects of satellite sensors (active, passive), multi-satellite combined, length of climatology, climate change effect, and statistical methods are also examined in this study.

  20. Combining modelled and remote sensing soil moisture anomalies for an operational global drought monitoring

    Science.gov (United States)

    Cammalleri, Carmelo; Vogt, Jürgen

    2017-04-01

    Soil moisture anomalies (i.e., deviations from the climatology) are often seen as a reliable tool to monitor and quantify the occurrence of drought events and their potential impacts, especially in agricultural and naturally vegetated lands. Soil moisture datasets (or their proxy) can be derived from a variety of sources, including land-surface models and thermal and microwave satellite remote sensing images. However, each data source has different advantages and drawbacks that prevent to unequivocally prefer one dataset over the others, especially in global applications that encompass a wide range of soil moisture regimes. The analysis of the spatial reliability of the different datasets at global scale is further complicated by the lack of reliable long-term soil moisture records for a ground validation over most regions. To overcome this limitation, in recent years the Triple Collocation (TC) technique has been deployed in order to quantify the likely errors associated to three mutually-independent datasets without assuming that one of them represents the "truth". In this study, three global datasets of soil moisture anomalies are investigated: the first one derived from the runs of the Lisflood hydrological model, the second one obtained from the combined active/passive microwave dataset produced in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI), and the last one derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) observations. A preliminary analysis of the three datasets aimed at detecting the areas where the TC technique can be successfully applied, hence the spatial distribution of the random error variance for each model is evaluated. This study allows providing useful advises for a robust combination of the three datasets into a single product for a more reliable global drought monitoring.

  1. Evaluation of a simple, point-scale hydrologic model in simulating soil moisture using the Delaware environmental observing system

    Science.gov (United States)

    Legates, David R.; Junghenn, Katherine T.

    2017-02-01

    Many local weather station networks that measure a number of meteorological variables (i.e., mesonetworks) have recently been established, with soil moisture occasionally being part of the suite of measured variables. These mesonetworks provide data from which detailed estimates of various hydrological parameters, such as precipitation and reference evapotranspiration, can be made which, when coupled with simple surface characteristics available from soil surveys, can be used to obtain estimates of soil moisture. The question is Can meteorological data be used with a simple hydrologic model to estimate accurately daily soil moisture at a mesonetwork site? Using a state-of-the-art mesonetwork that also includes soil moisture measurements across the US State of Delaware, the efficacy of a simple, modified Thornthwaite/Mather-based daily water balance model based on these mesonetwork observations to estimate site-specific soil moisture is determined. Results suggest that the model works reasonably well for most well-drained sites and provides good qualitative estimates of measured soil moisture, often near the accuracy of the soil moisture instrumentation. The model exhibits particular trouble in that it cannot properly simulate the slow drainage that occurs in poorly drained soils after heavy rains and interception loss, resulting from grass not being short cropped as expected also adversely affects the simulation. However, the model could be tuned to accommodate some non-standard siting characteristics.

  2. Modeling spatial and seasonal soil moisture in a semi arid hillslope: The impact of integrating soil surface seal parameters

    Science.gov (United States)

    Sela, Shai; Svoray, Tal; Assouline, Shmuel

    2010-05-01

    Modeling hillslope hydrology and the complex and coupled reaction of runoff processes to rainfall, lies in the focus of a growing number of research studies. The ability to characterize and understand the mechanisms underlying the complex hillslope soil moisture patterns, which trigger spatially variable non linear runoff initiation, still remains a current hydrological challenge especially in ungauged catchments. In humid climates, connectivity of transient moisture patches was suggested as a unifying concept for studying thresholds for subsurface flow and redistribution of soil moisture at the hillslope scale. In semiarid areas, however, transient moisture patches control also the differentiation between evaporation and surface runoff and the ability to identify a unifying concept controlling the large variability of soil moisture at the hillslope scale remains an open research gap. At the LTER Lehavim site in the center of Israel (31020' N, 34045' E) a typical hillslope (0.115 km2) was chosen offering different aspects and a classic geomorphologic banding. The annual rainfall is 290 mm, the soils are brown lithosols and arid brown loess and the dominant rock formations are Eocenean limestone and chalk with patches of calcrete. The vegetation is characterised by scattered dwarf shrubs (dominant species Sarcopoterium spinosum) and patches of herbaceous vegetation, mostly annuals, are spread between rocks and dwarf shrubs. An extensive spatial database of soil hydraulic and environmental parameters (e.g. slope, radiation, bulk density) was measured in the field and interpolated to continuous maps using geostatistical techniques and physically based modelling. To explore the effect of soil surface sealing, Mualem and Assouline (1989) equations describing the change in hydraulic parameters resulting from soil seal formation were applied. Two simple indices were developed to describe local evaporation values and contribution of water from rock outcrops to the soil

  3. Evaluating lysimeter drainage against soil deep percolation modeled with profile soil moisture, field tracer propagation, and lab measured soil hydraulic properties

    DEFF Research Database (Denmark)

    Vasquez, Vicente; Thomsen, Anton Gårde; Iversen, Bo Vangsø;

    them have been reported. To compare among methods, one year of four large-scale lysimeters drainage (D) was evaluated against modeled soil deep percolation using either profile soil moisture, bromide breakthrough curves from suction cups, or measured soil hydraulic properties in the laboratory...... model using field q, and 572 mm with the laboratory measured soil hydraulic properties. In conclusion, lysimeters presented the lowest D and can be considered as a lower bound for D; whereas either laboratory measured soil hydraulic properties or models calibrated with profile soil moisture yielded......Quantifying recharge to shallow aquifers via soil deep percolation is needed for sustainable management of water resources. This includes modeled predictions to address the effects of climate change on recharge. Different methods to estimate soil deep percolation exist but few comparisons among...

  4. Using Distributed-Hydrology-Soil-Vegetation Model to Study Road Effects on Stream flow and Soil Moisture

    Science.gov (United States)

    Cuo, L.; Giambelluca, T. W.; Ziegler, A. D.; Nullet, M. A.

    2003-12-01

    The distributed-hydrology-soil-vegetation model (DHSVM) was applied in Pang Khum Experimental Watershed (PKEW), located near 19.05\\deg N, 98.65\\deg E in the mountainous region of northern Thailand, headwaters of the Chao Phraya River system. PKEW has a highly seasonal rainfall regime, with 90% of the annual 1200-1400 mm rainfall occurring during the southwest summer monsoon. The elevation of PKEW ranges from approximately 1100 to 1500 m. Total road area including road banks is about 1.2% of the basin area. About 57% of the road area occurs on slopes steeper than 10%. All roads are unpaved. Land cover in PKEW is affected by swidden agriculture. Six land cover and nine soil classes are identified in the basin. We have been working in the area since 1997 as part of the Thailand Roads Project (TRP). Within the basin, we are monitoring microclimate at two sites, soil moisture at four sites, and rainfall at five sites. Streamflow is measured at the outlet. Based on digital elevation data, DHSVM explicitly accounts for the spatial distribution of the stream and road networks, soil depth, soil and vegetation types. The model run period, including warm up, calibration and validation, is from August 1997 to January 2001. Field measurements provide forcing data, calibration data, and guidance in parameter selection. Model calibration and validation were done by aggregating simulated hourly soil moisture and stream flow into daily values and comparing them with aggregated daily measurements. For the calibration period, RMSEs of soil moisture and streamflow were lower than the observed variability as represented by the standard deviation, median absolute deviation, and (for stream flow) interquartile range. Model performance drops in validation period, but RMSEs remain near or lower than observed variability. We ran DHSVM with and without roads to examine their effects. Significant effects of roads were found despite the very low proportion of the watershed covered by roads

  5. A large scale hydrological model combining Budyko hypothesis and stochastic soil moisture model

    Science.gov (United States)

    Cong, Z.; Zhang, X.

    2012-04-01

    Based on the Budyko hypothesis, the actual evapotranspiration, E,is controlled by the water conditions and the energy conditions, which are represented by the amount of annual precipitation, P and potential evaporation, E0, respectively. Some theoretical or empirical equations have been proposed to represent the Budyko curve. We here select Choudhury's equation to describe the Budyko curve (Mezentsev, 1954; Choudhury, 1999; Yang et al., 2008; Roderick and Farquhar, 2011). ɛ = (1+ φ -α)-1/α ,ɛ = E-,φ = E0 P P Rodriguez-Iturbe et al. (1999) proposed a stochastic soil moisture model based on a Poisson distributed rainfall assumption. Porporato et al. (2004) described the average water balance based on stochastic soil moisture model as following, γ- 1 ɛ = 1 -φ(·γ)φ--(·e-γ),γ = Zr- Γ γ- - Γ γ-,γ h φ φ where, h means the average rainfall depth, Zr means basin water storage ability. Combining these two equation, we can get the relation between α and γ. Then we develop a large scale hydrological model to estimate annual runoff from P, E0, h and Zr. ( -α)- 1/α 0.5946 Zr- R = (1- ɛ)P,ɛ = 1+ φ ,a = 0.7078γ ,γ = h This method has good performance when it is applied to estimate annual runoff in the Yellow River Basin and the Yangtze River Basin. The impacts of climate changes (P, E0 and h) and human activities (Zr) are also discussed with this method.

  6. Round Robin evaluation of soil moisture retrieval models for the MetOp-A ASCAT Instrument

    Science.gov (United States)

    Gruber, Alexander; Paloscia, Simonetta; Santi, Emanuele; Notarnicola, Claudia; Pasolli, Luca; Smolander, Tuomo; Pulliainen, Jouni; Mittelbach, Heidi; Dorigo, Wouter; Wagner, Wolfgang

    2014-05-01

    Global soil moisture observations are crucial to understand hydrologic processes, earth-atmosphere interactions and climate variability. ESA's Climate Change Initiative (CCI) project aims to create a global consistent long-term soil moisture data set based on the merging of the best available active and passive satellite-based microwave sensors and retrieval algorithms. Within the CCI, a Round Robin evaluation of existing retrieval algorithms for both active and passive instruments was carried out. In this study we present the comparison of five different retrieval algorithms covering three different modelling principles applied to active MetOp-A ASCAT L1 backscatter data. These models include statistical models (Bayesian Regression and Support Vector Regression, provided by the Institute for Applied Remote Sensing, Eurac Research Viale Druso, Italy, and an Artificial Neural Network, provided by the Institute of Applied Physics, CNR-IFAC, Italy), a semi-empirical model (provided by the Finnish Meteorological Institute), and a change detection model (provided by the Vienna University of Technology). The algorithms were applied on L1 backscatter data within the period of 2007-2011, resampled to a 12.5 km grid. The evaluation was performed over 75 globally distributed, quality controlled in situ stations drawn from the International Soil Moisture Network (ISMN) using surface soil moisture data from the Global Land Data Assimilation System (GLDAS-) Noah land surface model as second independent reference. The temporal correlation between the data sets was analyzed and random errors of the the different algorithms were estimated using the triple collocation method. Absolute soil moisture values as well as soil moisture anomalies were considered including both long-term anomalies from the mean seasonal cycle and short-term anomalies from a five weeks moving average window. Results show a very high agreement between all five algorithms for most stations. A slight

  7. DO3SE modelling of soil moisture to determine ozone flux to European forest trees

    Directory of Open Access Journals (Sweden)

    M. Schaub

    2011-12-01

    Full Text Available The DO3SE (Deposition of O3 for Stomatal Exchange model is an established tool for estimating ozone (O3 deposition, stomatal flux and impacts to a variety of vegetation types across Europe. It has been embedded within the EMEP (European Monitoring and Evaluation Programme photochemical model to provide a policy tool capable of relating the risk of vegetation damage to O3 precursor emission scenarios for use in policy formulation. A key limitation of regional flux-based risk assessments so far has been the approximation that soil water deficits are not limiting O3 flux due to the unavailability of evaluated methods for modelling soil water deficits and their influence on stomatal conductance (gsto, and ultimately O3 flux. This paper describes the development and evaluation of a method to estimate soil moisture status and its influence on gsto for a variety of forest tree species. The soil moisture module uses the Penman-Monteith energy balance method to drive water cycling through the soil-plant-atmosphere system and empirical data describing gsto relationships with pre-dawn leaf water status to estimate the biological control of transpiration. We trial four different methods to estimate this biological control of the transpiration stream, which vary from simple methods that relate soil water content or potential directly to gsto to more complex methods that incorporate hydraulic resistance and plant capacitance that control water flow through the plant system. These methods are evaluated against field data describing a variety of soil water variables, gsto and transpiration data for Norway spruce (Picea abies, Scots pine (Pinus sylvestris, birch (Betula pendula, aspen (Populus tremuloides, beech (Fagus sylvatica and holm oak (Quercus ilex collected from ten sites across Europe and North America. Modelled estimates of these variables show consistency with observed data when applying the simple empirical methods, with the timing and magnitude of

  8. On the treatment of evapotranspiration, soil moisture accounting, and aquifer recharge in monthly water balance models.

    Science.gov (United States)

    Alley, W.M.

    1984-01-01

    Several two- to six-parameter regional water balance models are examined by using 50-year records of monthly streamflow at 10 sites in New Jersey. These models include variants of the Thornthwaite-Mather model, the Palmer model, and the more recent Thomas abcd model. Prediction errors are relatively similar among the models. However, simulated values of state variables such as soil moisture storage differ substantially among the models, and fitted parameter values for different models sometimes indicated an entirely different type of basin response to precipitation.-from Author

  9. Soil Thermal and Moisture Regimes in the Canadian Regional Climate Model

    Science.gov (United States)

    Sushama, L.; Laprise, R.; Caya, D.

    2004-05-01

    Soil moisture, with its high spatial and temporal variability, is important in understanding and predicting a large number of processes including land-atmospheric interactions. In many northern-latitude regions, spring melt-water derived from the winter snow pack represents the greatest source for the yearly ground moisture budget. The ability of the Canadian Regional Climate model (CRCM4.0) with its three-layer, physically based, land-surface scheme (CLASS) to simulate the hydrological cycle, especially the soil moisture and thermal regimes, over a domain covering Eastern Canada and part of Eastern United States, is investigated. The CRCM was driven by NCEP reanalyses and was run at 45-km horizontal grid-point spacing for a five-year period from 1993-1997. The model simulates reasonably well the annual cycle of soil moisture variation. Air-soil temperature phase-space diagrams are examined for regions with (1) no snow-cover, (2) seasonal snow-cover and (3) permanent snow-cover. The annual air/soil thermal orbits help assess the nature of the heat transfer process in the subsurface qualitatively and hence in identifying areas of conductive and non-conductive regimes of the subsurface. In high-latitude cold regions with permanent snow-cover, the heat transfer is predominantly conductive, whereas in regions with seasonal snow-cover, the heat transfer is mostly non-conductive during periods of phase change. The top layers in regions of no snow-cover, in the domain considered, also exhibit seasonal nonconductive type of heat transfer. The hydrological fields such as snow-cover, precipitation and runoff are also verified against observations over two northern basins. The simulated basin average values of the various hydrological fields agree very well with observations. The closely coupled average energy partitioning and water partitioning are also simulated reasonably well in the model.

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

    Directory of Open Access Journals (Sweden)

    V. Maggioni

    2012-10-01

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

  11. On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

    Directory of Open Access Journals (Sweden)

    G. Laguardia

    2008-12-01

    Full Text Available In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000, we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999b.

    Once evaluated the effect of scale mismatch, we calculate the root mean square error and the correlation between the two soil moisture time series on a pixel basis and we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images.

    The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5 pF units. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

  12. Research on the method for retrieving soil moisture using thermal inertia model

    Institute of Scientific and Technical Information of China (English)

    LIU; Zhenhua; ZHAO; Yingshi

    2006-01-01

    In order to improve accuracy of soil moisture inversion using remote sensing, a new thermal inertia model is proposed in this paper. The improved model needs only surface maximum temperature as the temperature parameter input instead of input of the surface temperature difference, as well as the surface sensible and latent fluxes are introduced into boundary conditions of thermal conductivity equation. Furthermore, surface soil conductive heat transfer equation of two-layer model is used to solve the soil thermal inertia so that the remote sensing thermal inertia method can be applied to regions with better-covered vegetation, but usually only for the bare areas or worse vegetation covered areas. The model has been tested at several locations in the area of west Inner Mongolia. Comparing the simulation of the new model with the measurements obtained by apparent thermal inertia and by field test, the result shows that the inertia thermal model can be used to estimate soil moisture in more reasonable accuracy.

  13. From Sub-basin to Grid Scale Soil Moisture Disaggregation in SMART, A Semi-distributed Hydrologic Modeling Framework

    Science.gov (United States)

    Ajami, H.; Sharma, A.

    2016-12-01

    A computationally efficient, semi-distributed hydrologic modeling framework is developed to simulate water balance at a catchment scale. The Soil Moisture and Runoff simulation Toolkit (SMART) is based upon the delineation of contiguous and topologically connected Hydrologic Response Units (HRUs). In SMART, HRUs are delineated using thresholds obtained from topographic and geomorphic analysis of a catchment, and simulation elements are distributed cross sections or equivalent cross sections (ECS) delineated in first order sub-basins. ECSs are formulated by aggregating topographic and physiographic properties of the part or entire first order sub-basins to further reduce computational time in SMART. Previous investigations using SMART have shown that temporal dynamics of soil moisture are well captured at a HRU level using the ECS delineation approach. However, spatial variability of soil moisture within a given HRU is ignored. Here, we examined a number of disaggregation schemes for soil moisture distribution in each HRU. The disaggregation schemes are either based on topographic based indices or a covariance matrix obtained from distributed soil moisture simulations. To assess the performance of the disaggregation schemes, soil moisture simulations from an integrated land surface-groundwater model, ParFlow.CLM in Baldry sub-catchment, Australia are used. ParFlow is a variably saturated sub-surface flow model that is coupled to the Common Land Model (CLM). Our results illustrate that the statistical disaggregation scheme performs better than the methods based on topographic data in approximating soil moisture distribution at a 60m scale. Moreover, the statistical disaggregation scheme maintains temporal correlation of simulated daily soil moisture while preserves the mean sub-basin soil moisture. Future work is focused on assessing the performance of this scheme in catchments with various topographic and climate settings.

  14. A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

    Science.gov (United States)

    Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak

    2012-01-01

    A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

  15. Validating modeled soil moisture with in-situ data for agricultural drought monitoring in West Africa

    Science.gov (United States)

    McNally, A.; Yatheendradas, S.; Jayanthi, H.; Funk, C. C.; Peters-Lidard, C. D.

    2011-12-01

    The declaration of famine in Somalia on July 21, 2011 highlights the need for regional hydroclimate analysis at a scale that is relevant for agropastoral drought monitoring. A particularly critical and robust component of such a drought monitoring system is a land surface model (LSM). We are currently enhancing the Famine Early Warning Systems Network (FEWS NET) monitoring activities by configuring a custom instance of NASA's Land Information System (LIS) called the FEWS NET Land Data Assimilation System (FLDAS). Using the LIS Noah LSM, in-situ measurements, and remotely sensed data, we focus on the following question: How can Noah be best parameterized to accurately simulate hydroclimate variables associated with crop performance? Parameter value testing and validation is done by comparing modeled soil moisture against fortuitously available in-situ soil moisture observations in the West Africa. Direct testing and application of the FLDAS over African agropastoral locations is subject to some issues: [1] In many regions that are vulnerable to food insecurity ground based measurements of precipitation, evapotranspiration and soil moisture are sparse or non-existent, [2] standard landcover classes (e.g., the University of Maryland 5 km dataset), do not include representations of specific agricultural crops with relevant parameter values, and phenologies representing their growth stages from the planting date and [3] physically based land surface models and remote sensing rain data might still need to be calibrated or bias-corrected for the regions of interest. This research aims to address these issues by focusing on sites in the West African countries of Mali, Niger, and Benin where in-situ rainfall and soil moisture measurements are available from the African Monsoon Multidisciplinary Analysis (AMMA). Preliminary results from model experiments over Southern Malawi, validated with Normalized Difference Vegetation Index (NDVI) and maize yield data, show that the

  16. Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

    Directory of Open Access Journals (Sweden)

    Wooyeon Sunwoo

    2017-01-01

    Full Text Available Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall–runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC prior to a rainfall event. Soil moisture is one of the most important variables in rainfall–runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API5, ground-based soil moisture (SSMinsitu, remotely sensed surface soil moisture (SSM, and soil water index (SWI provided by the advanced scatterometer (ASCAT. To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall–runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI and ground based soil moisture data (SSMinsitu. In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with

  17. MODIS Inundation Estimate Assimilation into Soil Moisture Accounting Hydrologic Model: A Case Study in Southeast Asia

    Directory of Open Access Journals (Sweden)

    Ari Posner

    2014-11-01

    Full Text Available Flash Flood Guidance consists of indices that estimate the amount of rain of a certain duration that is needed over a given small basin in order to cause minor flooding. Backwater catchment inundation from swollen rivers or regional groundwater inputs are not significant over the spatial and temporal scales for the majority of upland flash flood prone basins, as such, these effects are not considered. However, some lowland areas and flat terrain near large rivers experience standing water long after local precipitation has ceased. NASA is producing an experimental product from the MODIS that detects standing water. These observations were assimilated into the hydrologic model in order to more accurately represent soil moisture conditions within basins, from sources of water from outside of the basin. Based on the upper soil water content, relations are used to derive an error estimate for the modeled soil saturation fraction; whereby, the soil saturation fraction model state can be updated given the availability of satellite observed inundation. Model error estimates were used in a Monte Carlo ensemble forecast of soil water and flash flood potential. Numerical experiments with six months of data (July 2011–December 2011 showed that MODIS inundation data, when assimilated to correct soil moisture estimates, increased the likelihood that bankfull flow would occur, over non-assimilated modeling, at catchment outlets for approximately 44% of basin-days during the study time period. While this is a much more realistic representation of conditions, no actual events occurred allowing for validation during the time period.

  18. Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

    Directory of Open Access Journals (Sweden)

    J. M. Schuurmans

    2010-08-01

    Full Text Available This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL, provides us the spatial distribution of daily ETact and (ii an operational physically based distributed (25 m×25 m hydrological model of a small catchment (70 km2 in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related. Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

  19. Mapping Seasonal Evapotranspiration and Root Zone Soil Moisture using a Hybrid Modeling Approach over Vineyards

    Science.gov (United States)

    Geli, H. M. E.

    2015-12-01

    Estimates of actual crop evapotranspiration (ETa) at field scale over the growing season are required for improving agricultural water management, particularly in water limited and drought prone regions. Remote sensing data from multiple platforms such as airborne and Landsat-based sensors can be used to provide these estimates. Combining these data with surface energy balance models can provide ETa estimates at sub- field scale as well as information on vegetation stress and soil moisture conditions. However, the temporal resolution of airborne and Landsat data does not allow for a continuous ETa monitoring over the course of the growing season. This study presents the application of a hybrid ETa modeling approach developed for monitoring daily ETa and root zone available water at high spatial resolutions. The hybrid ETa modeling approach couples a thermal-based energy balance model with a water balance-based scheme using data assimilation. The two source energy balance (TSEB) model is used to estimate instantaneous ETa which can be extrapolated to daily ETa using a water balance model modified to use the reflectance-based basal crop coefficient for interpolating ETa in between airborne and/or Landsat overpass dates. Moreover, since it is a water balance model, the soil moisture profile is also estimated. The hybrid ETa approach is applied over vineyard fields in central California. High resolution airborne and Landsat imagery were used to drive the hybrid model. These images were collected during periods that represented different vine phonological stages in 2013 growing season. Estimates of daily ETa and surface energy balance fluxes will be compared with ground-based eddy covariance tower measurements. Estimates of soil moisture at multiple depths will be compared with measurements.

  20. Merging Alternate Remotely-Sensed Soil Moisture Retrievals Using a Non-Static Model Combination Approach

    Directory of Open Access Journals (Sweden)

    Seokhyeon Kim

    2016-06-01

    Full Text Available Soil moisture is an important variable in the coupled hydrologic and climate system. In recent years, microwave-based soil moisture products have been shown to be a viable alternative to in situ measurements. A popular way to measure the performance of soil moisture products is to calculate the temporal correlation coefficient (R against in situ measurements or other appropriate reference datasets. In this study, an existing linear combination method improving R was modified to allow for a non-static or nonstationary model combination as the basis for improving remotely-sensed surface soil moisture. Previous research had noted that two soil moisture products retrieved using the Japan Aerospace Exploration Agency (JAXA and Land Parameter Retrieval Model (LPRM algorithms from the same Advanced Microwave Scanning Radiometer 2 (AMSR2 sensor are spatially complementary in terms of R against a suitable reference over a fixed period. Accordingly, a linear combination was proposed to maximize R using a set of spatially-varying, but temporally-fixed weights. Even though this approach showed promising results, there was room for further improvements, in particular using non-static or dynamic weights that take account of the time-varying nature of the combination algorithm being approximated. The dynamic weighting was achieved by using a moving window. A number of different window sizes was investigated. The optimal weighting factors were determined for the data lying within the moving window and then used to dynamically combine the two parent products. We show improved performance for the dynamically-combined product over the static linear combination. Generally, shorter time windows outperform the static approach, and a 60-day time window is suggested to be the optimum. Results were validated against in situ measurements collected from 124 stations over different continents. The mean R of the dynamically-combined products was found to be 0.57 and 0

  1. Soil moisture estimation with limited soil characterization for decision making

    Science.gov (United States)

    Chanzy, A.; Richard, G.; Boizard, H.; Défossez, P.

    2009-04-01

    Many decisions in agriculture are conditional to soil moisture. For instance in wet conditions, farming operations as soil tillage, organic waste spreading or harvesting may lead to degraded results and/or induce soil compaction. The development of a tool that allows the estimation of soil moisture is useful to help farmers to organize their field work in a context where farm size tends to increase as well as the need to optimize the use of expensive equipments. Soil water transfer models simulate soil moisture vertical profile evolution. These models are highly sensitive to site dependant parameters. A method to implement the mechanistic soil water and heat flow model (the TEC model) in a context of limited information (soil texture, climatic data, soil organic carbon) is proposed [Chanzy et al., 2008]. In this method the most sensitive model inputs were considered i.e. soil hydraulic properties, soil moisture profile initialization and the lower boundary conditions. The accuracy was estimated by implementing the method on several experimental cases covering a range of soils. Simulated soil moisture results were compared to soil moisture measurements. The obtained accuracy in surface soil moisture (0-30 cm) was 0.04 m3/m3. When a few soil moisture measurements are available (collected for instance by the farmer using a portable moisture sensor), significant improvement in soil moisture accuracy is obtained by assimilating the results into the model. Two assimilation strategies were compared and led to comparable results: a sequential approach, where the measurement were used to correct the simulated moisture profile when measurements are available and a variational approach which take moisture measurements to invert the TEC model and so retrieve soil hydraulic properties of the surface layer. The assimilation scheme remains however heavy in terms of computing time and so, for operational purposed fast code should be taken to simulate the soil moisture as with the

  2. The importance of spatial resolution and convective parameterisation in modelling soil moisture - precipitation feedbacks

    Science.gov (United States)

    Garcia-Carreras, Luis; Taylor, Christopher M.; Roberts, Malcolm; Marsham, John H.

    2017-04-01

    Soil moisture influences low-level temperature and humidity, which can strongly affect convective development. The location of convection in turn alters the soil moisture anomalies present on the following day, providing a feedback mechanism. Satellite observations show that in the tropics afternoon rainfall falls preferentially where the ground is drier than its surroundings. A large number of global weather and climate models, on the other hand, show a positive soil moisture - rainfall feedback, inconsistent with observations. This systematic bias will tend to exaggerate drought impacts in global atmospheric models, and points to missing fundamental processes in the models related to the coupling between the surface and convection. While the source of this error is still unclear, it has been hypothesized that the triggering of parameterisations of convection is excessively sensitive to low-level moisture, leading to convection initiating preferentially over wet soils. Here we quantify the soil-moisture - precipitation feedback sign using the same method as in Taylor et al. (Nature, 2012), which is now part of the ESMValTool model evaluation toolbox. We analyse multi-year global simulations using the Met Office Unified Model (MetUM) with different resolutions and representations of convection. Three simulations are run at 15 km grid-spacing with different representations of convection: 1. the standard operational MetUM parameterisation scheme, 2. 'convection permitting', where the parameterisations of shallow and deep convection are turned off, and 3. only the parameterisation of shallow convection is turned on. The use of the same resolution and setup, except for the representation of convection, allows us to exclude any effects from changing resolution. Additional simulations at 30, 50 and 150 km grid-spacings using the standard MetUM parameterisation of convection scheme are then used to explore the impact of resolution. All simulations show daytime

  3. Monitoring Soil Moisture Deficit Effects on Vegetation Parameters Using Radiative Transfer Models Inversion and Hyperspectral Measurements Under Controlled Conditions

    Science.gov (United States)

    Bayat, Bagher; Van der Tol, Christiaan; Verhoef, Wouter

    2016-08-01

    Plant-available soil moisture is a key element which affects plant properties in their ecosystems. This study shows Poa pratensis -a species of grass- responses to soil moisture deficit during an artificial drought episode in a greenhouse experiment. We used radiative transfer model inversion to monitor the gradual manifestation of soil moisture deficit effects on vegetation in a laboratory setting. Plots of 21 cm x 14.5 cm surface area with Poa pratensis plants that formed a closed canopy were subjected to water stress for 40 days. In a regular weekly schedule, canopy reflectance was measured. The 1-D bidirectional canopy reflectance model SAIL, coupled with the leaf optical properties model PROSPECT, was inverted using hyperspectral measurements by means of an iterative optimization method to retrieve vegetation biophysical and biochemical parameters (mainly; LAI, Cab, Cw, Cdm and Cs). The relationships between these retrieved parameters with soil moisture content were established in two separated groups; stress and non-stressed. All parameters retrieved by model inversion using canopy spectral data showed good correlation with soil moisture content in the drought episode. These parameters co- varied with soil moisture content under the stress condition (Chl: R2= 0.91, Cw: R2= 0.97, Cs: R2= 0.88 and LAI: R2=0.48) at the canopy level.

  4. Modeling the Hellenic karst catchments with the Sacramento Soil Moisture Accounting model

    Science.gov (United States)

    Katsanou, K.; Lambrakis, N.

    2017-01-01

    Karst aquifers are very complex due to the presence of dual porosity. Rain-runoff hydrological models are frequently used to characterize these aquifers and assist in their management. The calibration of such models requires knowledge of many parameters, whose quality can be directly related to the quality of the simulation results. The Sacramento Soil Moisture Accounting (SAC-SMA) model includes a number of physically based parameters that permit accurate simulations and predictions of the rain-runoff relationships. Due to common physical characteristics of mature karst structures, expressed by sharp recession limbs of the runoff hydrographs, the calibration of the model becomes relatively simple, and the values of the parameters range within narrow bands. The most sensitive parameters are those related to groundwater storage regulated by the zone of the epikarst. The SAC-SMA model was calibrated for data from the mountainous part of the Louros basin, north-western Greece, which is considered to be representative of such geological formations. Visual assessment of the hydrographs as statistical outcomes revealed that the SAC-SMA model simulated the timing and magnitude of the peak flow and the shape of recession curves well.

  5. Modeling the Hellenic karst catchments with the Sacramento Soil Moisture Accounting model

    Science.gov (United States)

    Katsanou, K.; Lambrakis, N.

    2017-05-01

    Karst aquifers are very complex due to the presence of dual porosity. Rain-runoff hydrological models are frequently used to characterize these aquifers and assist in their management. The calibration of such models requires knowledge of many parameters, whose quality can be directly related to the quality of the simulation results. The Sacramento Soil Moisture Accounting (SAC-SMA) model includes a number of physically based parameters that permit accurate simulations and predictions of the rain-runoff relationships. Due to common physical characteristics of mature karst structures, expressed by sharp recession limbs of the runoff hydrographs, the calibration of the model becomes relatively simple, and the values of the parameters range within narrow bands. The most sensitive parameters are those related to groundwater storage regulated by the zone of the epikarst. The SAC-SMA model was calibrated for data from the mountainous part of the Louros basin, north-western Greece, which is considered to be representative of such geological formations. Visual assessment of the hydrographs as statistical outcomes revealed that the SAC-SMA model simulated the timing and magnitude of the peak flow and the shape of recession curves well.

  6. Assimilation of SMOS-derived soil moisture in a fully integrated hydrological and soil-vegetation-atmosphere transfer model in Western Denmark

    DEFF Research Database (Denmark)

    Ridler, Marc-Etienne Francois; Madsen, Henrik; Stisen, Simon

    2014-01-01

    in Denmark. The objective is to determine if any additional gains can be achieved by SMOS surface soil moisture assimilation beyond the optimized model. A series of assimilation experiments were designed to (1) determine how effectively soil moisture corrections propagate downward in the soil column, (2...... cover classes. Assimilation also brought modest gains in R2 at 25 cm depth but slightly degraded the correlation at 50 cm depth. Assimilation overcorrected discharge peaks....

  7. Quantitative Analysis of Moisture Effect on Black Soil Reflectance

    Institute of Scientific and Technical Information of China (English)

    LIU Huan-Jun; ZHANG Yuan-Zhi; ZHANG Xin-Le; ZHANG Bai; SONG Kai-Shan; WANG Zong-Ming; TANG Na

    2009-01-01

    Several studies have demonstrated that soil reflectance decreases with increasing soil moisture content,or increases when the soil moisture reaches a certain content;however,there are few analyses on the quantitative relationship between soil reflectance and its moisture,especially in the case of black soils in northeast China.A new moisture adjusting method was developed to obtain soil reflectance with a smaller moisture interval to describe the quantitative relationship between soil reflectance and moisture.For the soil samples with moisture contents ranging from air-dry to saturated,the changes in soil reflectance with soil moisture can be depicted using a cubic equation.Both moisture threshold (MT) and moisture inflexion (MI) of soil reflectance can also be determined by the equation.When the moisture range was smaller than MT,soil reflectance can be simulated with a linear model.However,for samples with different soil organic matter (OM),the parameters of the linear model varied regularly with the OM content.Based on their relationship,the soil moisture can be estimated from soil reflectance in the black soil region.

  8. Configuration of the relationship of soil moistures for vertical soil profiles on a steep hillslope using a vector time series model

    Science.gov (United States)

    Kim, Sanghyun; Sun, Hanna; Jung, Sungwon

    2011-03-01

    SummaryVariation in soil moisture content throughout soil profiles during several sequential rainfalls represents the internal hydrological response on a hillslope scale. A multiplex TDR system has been operating on a mountainous hillslope to obtain the time series of soil moisture along two transects in the study area. The soil moisture modeling conducted in this study highlights our understanding of the inter-relationships between soil moistures at identical spatial locations, but at different depths. A sequential procedure was used for the time series modeling to delineate an appropriate model for application to all monitoring points. The feedback relationship of soil wetness between two different depths was expressed with the proposed vector autoregressive model. Based on the successful modeling of 31 coupled soil water histories, the vertical distributions of the stochastic model throughout the study area were obtained. The distribution of the delineated models implied a spatial distribution of the hydrological processes, such as vertical infiltration for the upper soil layers and some of the lower soil layers (38 out of 62 models), lateral redistribution and subsurface flow over bedrock mostly for the lower soil layers (24 out of 62 models) on the steep hillslope. With the use of the resultant models, applications were proposed to improve the data acquisition system, i.e. gap filling for missing data and limited prediction for an ungauged location.

  9. Linking soil moisture balance and source-responsive preferential flow models for estimating groundwater recharge

    Science.gov (United States)

    Cuthbert, M. O.; Mackay, R.; Nimmo, J. R.

    2012-04-01

    Results are presented of a detailed study into the vadose zone and shallow water table hydrodynamics of a fieldsite in Shropshire, UK. Tensiometry reveals that the loamy sand topsoil wets up via macropore flow and subsequent redistribution of moisture into the soil matrix. However, recharge does not occur until near-positive pressures are achieved at the top of the glaciofluvial outwash material that underlies the topsoil, about 1 m above the water table. Once this occurs, very rapid water table rises follow. This threshold behaviour is attributed to the vertical discontinuity in the macropore system due to seasonal ploughing of the topsoil, and a lower permeability plough/iron pan restricting matrix flow between the topsoil and the lower outwash deposits. Thus, although the wetting process in the topsoil is highly complex, a soil moisture balance model (SMBM) is shown to be skilful in predicting the initiation of preferential flow from the base of the topsoil into the lower outwash horizon. The rapidity of the response at the water table suggests that Stokes type film flow rather than Richards type capillarity dominated flow is occurring and this conjecture is tested using a range of numerical models. A variation of the source-responsive model proposed by Nimmo (2010) is shown to reproduce the observed water table dynamics well, when linked to a SMBM as the source of recharge from the topsoil. The results reveal new insights into preferential flow processes in cultivated soils. If the conceptual and numerical models can be shown to be transferable to other ploughed soils, it promises to be a very useful and practical approach to accounting for preferential flow in studies of groundwater recharge estimation. Nimmo, J. R. (2010). Theory for Source-Responsive and Free-Surface Film Modeling of Unsaturated Flow. Vadose Zone Journal, 9, 295-306.

  10. Calibration Of Hydrological Models Based On Remotely Sensed Soil Moisture And Evapotranspiration

    Science.gov (United States)

    Lopez, P.; Strohmeier, S.; Sutanudjaja, E.; Haddad, M.; Karrou, M.; Sterk, G.; Schellekens, J.; Bierkens, M. F.

    2016-12-01

    The increasing water demand over recent decades together with the climate change impacts on water resources, especially in dry areas, may lead to growing problems with water availability. Investigating and developing novel strategies to assess and manage water resources have turned into a key issue, leading to increasing efforts to enhance and improve hydrological models and datasets. Despite campaigns to increase the quality and the temporal and spatial availability of ground-based hydro-meteorological data, many river basins around the world, including the Oum Er Rbia in Morocco, still have a limited number of in-situ observations. This in turn limits the application of hydrological models. Recently developed global earth observation products may unlock a greater capability of basin scale hydrological modeling for advanced water management. This study aims to evaluate the applicability of earth observation products for hydrological model simulation in comparison with in-situ data for water resources management and water allocation of the Moroccan Oum Er Rbia river basin. Two different hydrological models (SWAT and PCR-GLOBWB) were applied to inter-compare various combinations of in-situ and global earth observation data. Global earth observation products were obtained from various sources including meteorological data from the WATCH Forcing Data methodology applied to ERA-Interim reanalysis data and the Multi-Source Weighted-Ensemble Precipitation (MSWEP); the remotely sensed ESA CCI surface soil moisture Soil Water Index combined product and the GLEAM evapotranspiration data from satellite-based observations. The daily data were provided for the time period from 1979 to 2012. Due to insufficient in-situ discharge observations available in the basin, local calibration of both hydrological models was based on global evapotranspiration and soil moisture data, covering additional aspects of the hydrological cycle to further reduce modeling uncertainty. Preliminary

  11. Snow cover and soil moisture in mountains

    Science.gov (United States)

    Wever, N.; Lehning, M.

    2012-04-01

    Soil moisture is an important parameter of the climate system. It constrains evapotranspiration of plants and it functions as a storage of water, giving it an economic value, e.g. for agriculture. Furthermore, soil moisture is an important factor for predicting flood risk. In mountainous areas with a seasonal snow cover, the spatial distribution of snow depth is strongly influencing the spatial variation of soil moisture. To assess potential flooding situations during snow melt and rain on snow events in particular but for any heavy precipitation event in the mountains, it is important to understand the influence of the snow cover on soil status with respect to liquid and solid water. Only if this is known, the reaction of the soil i.e. amount of runoff, storage or melt, on additional water input can be assessed. For an operational assessment of the soil moisture state in the Swiss Alps at 140 measurement sites for snow and avalanche forecasting (IMIS network), the SNOWPACK model has been extended with a soil module, solving the Richards equation for the matrix flow. The modelling is validated with vertical profile measurements of soil moisture at meteorological stations in an Alpine catchment near Davos, Switzerland. It was found that the combination of a physical based snowpack model with a Richards equation solver seems to provide an adequate description of soil moisture fluctuations, especially in near surface layers. Soil moisture fluctuations, both measured and modelled, are strongly reduced when a snow cover is present. The measurements also revealed a strong increase in soil moisture, accompanied by a daily cycle in soil moisture during snow melt, extending down to 120cm depth. When soil properties from literature were assumed for the soil type in the vertical profile, the daily cycle in the model during snow melt was restricted mainly to the top layers, while observations show also a reaction in deeper layers. These observations are consistent with the

  12. Calibration of a geophysically based model using soil moisture measurements in mountainous terrains

    Science.gov (United States)

    Pellet, Cécile; Hilbich, Christin; Marmy, Antoine; Hauck, Christian

    2016-04-01

    The use of geophysical methods in the field of permafrost research is well established and crucial since it is the only way to infer the composition of the subsurface material. Since geophysical measurements are indirect, ambiguities in the interpretation of the results can arise, hence the simultaneous use of several methods (e.g. electrical resistivity tomography and refraction seismics) is often necessary. The so-called four-phase model, 4PM (Hauck et al., 2011) constitutes a further step towards clarification of interpretation from geophysical measurements. It uses two well-known petrophysical relationships, namely Archie's law and an extension of Timur's time-averaged equation for seismic P-wave velocities, to quantitatively estimate the different phase contents (air, water and ice) in the ground from tomographic electric and seismic measurements. In this study, soil moisture measurements were used to calibrate the 4PM in order to assess the spatial distribution of water, ice and air content in the ground at three high elevation sites with different ground properties and thermal regimes. The datasets used here were collected as part of the SNF-project SOMOMOUNT. Within the framework of this project a network of six entirely automated soil moisture stations was installed in Switzerland along an altitudinal gradient ranging from 1'200 m. a.s.l. to 3'400 m. a.s.l. The standard instrumentation of each station comprises the installation of Frequency Domain Reflectometry (FDR) and Time Domain Reflectometry (TDR) sensors for long term monitoring coupled with repeated Electrical Resistivity Tomography (ERT) and Refraction Seismic Tomography (RST) as well as spatial FDR (S-FDR) measurements. The use of spatially distributed soil moisture data significantly improved the 4PM calibration process and a semi-automatic calibration scheme was developed. This procedure was then tested at three different locations, yielding satisfactory two dimensional distributions of water

  13. Early Soil Moisture Field Experiments

    Science.gov (United States)

    Schmugge, T.

    2008-12-01

    Before the large scale field experiments described in the call for papers, there were a number of experiments devoted to a single parameter, e.g. soil moisture. In the early 1970's, before the launch of the first microwave radiometer by NASA, there were a number of aircraft experiments to determine utility of these sensors for land observations. For soil moisture, these experiments were conducted in southwestern United States over irrigated agricultural areas which could provide a wide range of moisture conditions on a given day. The radiometers covered the wavelength range from 0.8 to 21 cm. These experiments demonstrated that it is possible to observe soil moisture variations remotely using a microwave radiometer with a sensitivity of about 3 K / unit of soil moisture. The results also showed that the longer wavelengths were better, with a radiometer at the 21 cm wavelength giving the best results. These positive results led to the development of Push Broom Microwave Radiometer (PBMR) and the Electrically Scanned Thinned Array Radiometer (ESTAR) instruments at the 21-cm wavelength. They have been used extensively in the large-scale experiments such as HAPEX-MOBILHY, FIFE, Monsoon90, SMEX, etc. The multi-beam nature of these instruments makes it possible to obtain more extensive coverage and thus to map spatial variations of surface soil moisture. Examples of the early results along with the more recent soil moisture maps will be presented.

  14. The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

    Science.gov (United States)

    Karssenberg, D.; Wanders, N.; de Roo, A.; de Jong, S.; Bierkens, M. F.

    2013-12-01

    Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system that is not directly linked to discharge, in particular the unsaturated zone, remains uncalibrated, or might be modified unrealistically. Soil moisture observations from satellites have the potential to fill this gap, as these provide the closest thing to a direct measurement of the state of the unsaturated zone, and thus are potentially useful in calibrating unsaturated zone model parameters. This is expected to result in a better identification of the complete hydrological system, potentially leading to improved forecasts of the hydrograph as well. Here we evaluate this added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: 1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? 2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to approaches that calibrate only with discharge, such that this leads to improved forecasts of soil moisture content and discharge as well? To answer these questions we use a dual state and parameter ensemble Kalman filter to calibrate the hydrological model LISFLOOD for the Upper Danube area. Calibration is done with discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS and ASCAT. Four scenarios are studied: no calibration (expert knowledge), calibration on discharge, calibration on remote sensing data (three satellites) and calibration on both discharge and remote sensing data. Using a split-sample approach, the model is calibrated for a period of 2 years and validated for the calibrated model parameters on a validation period of 10 years. Results show that calibration with discharge data improves the estimation of groundwater parameters (e.g., groundwater reservoir constant) and

  15. The influence of soil type, vegetation cover and soil moisture on spin up behaviour of a land surface model in a monsoonal region

    Science.gov (United States)

    Bhattacharya, Anwesha; Mandal, Manabottam

    2015-04-01

    Model spin-up is the process through which the model is adequately equilibrated to ensure balance between the mass fields and velocity fields. In this study, an offline one dimensional Noah land surface model is integrated recursively for three years to assess its spin-up behavior at different sites over the Indian Monsoon domain. Several numerical experiments are performed to investigate the impact of soil category, vegetation cover, initial soil moisture and subsequent dry or wet condition on model spin-up. These include simulations with the dominant soil and vegetation covers of this region, different initial soil moisture content (observed soil moisture; dry soil; moderately wet soil; saturated soil), simulations initialized at different rain conditions (no rain; infrequent rain; continuous rain) and different seasons (Winter, Spring, Summer/Pre-Monsoon, Monsoon and Autumn). It is seen that the spin-up behavior of the model depends on the soil type and vegetation cover with soil characteristics having the larger influence. Over India, the model has the longest spin-up in the case of simulations with loamy soil covered with mixed-shrub. It is noted that the model has a significantly longer spin-up when initialized with very low initial soil moisture content than with higher soil moisture content. It is also seen that in general, simulations initialized just before a continuous rainfall event have the least spin-up time. This observation is reinforced by the results from the simulations initialized in different seasons. It is seen that for monsoonal region, the model spin-up time is least for simulations initialized just before the Monsoon. Model initialized during the Monsoon rain episodes has a longer spin-up than that initialized in any other season. Furthermore, it is seen that the model has a shorter spin-up if it reaches the equilibrium state predominantly via drying process and could be as low as two months under quasi-equilibrium condition depending on

  16. Subgrid Parameterization of the Soil Moisture Storage Capacity for a Distributed Rainfall-Runoff Model

    Directory of Open Access Journals (Sweden)

    Weijian Guo

    2015-05-01

    Full Text Available Spatial variability plays an important role in nonlinear hydrologic processes. Due to the limitation of computational efficiency and data resolution, subgrid variability is usually assumed to be uniform for most grid-based rainfall-runoff models, which leads to the scale-dependence of model performances. In this paper, the scale effect on the Grid-Xinanjiang model was examined. The bias of the estimation of precipitation, runoff, evapotranspiration and soil moisture at the different grid scales, along with the scale-dependence of the effective parameters, highlights the importance of well representing the subgrid variability. This paper presents a subgrid parameterization method to incorporate the subgrid variability of the soil storage capacity, which is a key variable that controls runoff generation and partitioning in the Grid-Xinanjiang model. In light of the similar spatial pattern and physical basis, the soil storage capacity is correlated with the topographic index, whose spatial distribution can more readily be measured. A beta distribution is introduced to represent the spatial distribution of the soil storage capacity within the grid. The results derived from the Yanduhe Basin show that the proposed subgrid parameterization method can effectively correct the watershed soil storage capacity curve. Compared to the original Grid-Xinanjiang model, the model performances are quite consistent at the different grid scales when the subgrid variability is incorporated. This subgrid parameterization method reduces the recalibration necessity when the Digital Elevation Model (DEM resolution is changed. Moreover, it improves the potential for the application of the distributed model in the ungauged basin.

  17. Radar for Measuring Soil Moisture Under Vegetation

    Science.gov (United States)

    Moghaddam, Mahta; Moller, Delwyn; Rodriguez, Ernesto; Rahmat-Samii, Yahya

    2004-01-01

    A two-frequency, polarimetric, spaceborne synthetic-aperture radar (SAR) system has been proposed for measuring the moisture content of soil as a function of depth, even in the presence of overlying vegetation. These measurements are needed because data on soil moisture under vegetation canopies are not available now and are necessary for completing mathematical models of global energy and water balance with major implications for global variations in weather and climate.

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

    Science.gov (United States)

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

    2016-12-01

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

  19. Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model

    Directory of Open Access Journals (Sweden)

    Hassan A. K. M. Bhuiyan

    2017-02-01

    Full Text Available This paper presents an assessment of the applicability of using RADARSAT-2-derived soil moisture data in the Hydrologic Modelling System developed by the Hydrologic Engineering Center (HEC-HMS for flood forecasting with a case study in the Sturgeon Creek watershed in Manitoba, Canada. Spring flooding in Manitoba is generally influenced by both winter precipitation and soil moisture conditions in the fall of the previous year. As a result, the soil moisture accounting (SMA and the temperature index algorithms are employed in the simulation. Results from event and continuous simulations of HEC-HMS show that the model is suitable for flood forecasting in Manitoba. Soil moisture data from the Manitoba Agriculture field survey and RADARSAT-2 satellite were used to set the initial soil moisture for the event simulations. The results confirm the benefit of using satellite data in capturing peak flows in a snowmelt event. A sensitivity analysis of SMA parameters, such as soil storage, maximum infiltration, soil percolation, maximum canopy storage and tension storage, was performed and ranked to determine which parameters have a significant impact on the performance of the model. The results show that the soil moisture storage was the most sensitive parameter. The sensitivity analysis of initial soil moisture in a snowmelt event shows that cumulative flow and peak flow are highly influenced by the initial soil moisture setting of the model. Therefore, there is a potential to utilize RADARSAT-2-derived soil moisture for hydrological modelling in other snow-dominated Manitoba watersheds.

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

    Science.gov (United States)

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

    2010-01-01

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

  1. Correlation Between Soil Moisture and Dust Emissions: An Investigation for Global Climate Modeling

    Science.gov (United States)

    Fredrickson, Carley; Tan, Qian

    2017-01-01

    This work is using the newly available NASA SMAP soil moisture measurement data to evaluate its impact on the atmospheric dust emissions. Dust is an important component of atmospheric aerosols, which affects both climate and air quality. In this work, we focused on semi-desert regions, where dust emissions show seasonal variations due to soil moisture changes, i.e. in Sahel of Africa. We first identified three Aerosol Robotic Network (AERONET) sites in the Sahel (IER_Cinzana, Banizoumbou, and Zinder_Airport). We then utilized measurements of aerosol optical depth (AOD), fine mode fraction, size distribution, and single-scattering albedo and its wave-length dependence to select dust plumes from the available measurements We matched the latitude and longitude of the AERONET station to the corresponding SMAP data cell in the years 2015 and 2016, and calculated their correlation coefficient. Additionally, we looked at the correlation coefficient with a three-day and a five-day shift to check the impact of soil moisture on dust plumes with some time delay. Due to the arid nature of Banizoumbou and Zinder_Airport, no correlation was found to exist between local soil moisture and dust aerosol load. While IER_Cinzana had soil moisture levels above the satellite threshold of 0.02cm3/cm3, R-value approaching zero indicated no presence of a correlation. On the other hand, Ilorin demonstrated a significant negative correlation between aerosol optical depth and soil moisture. When isolating the analysis to Ilorin's dry season, a negative correlation of -0.593 was the largest dust-isolated R-value recorded, suggesting that soil moisture is driven the dust emission in this semi-desert region during transitional season.

  2. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation

    Science.gov (United States)

    Houser, Paul R.; Shuttleworth, W. James; Famiglietti, James S.; Gupta, Hoshin V.; Syed, Kamran H.; Goodrich, David C.

    1998-12-01

    The feasibility of synthesizing distributed fields of soil moisture by the novel application of four-dimensional data assimilation (4DDA) applied in a hydrological model is explored. Six 160-km2 push broom microwave radiometer (PBMR) images gathered over the Walnut Gulch experimental watershed in southeast Arizona were assimilated into the Topmodel-based Land-Atmosphere Transfer Scheme (TOPLATS) using several alternative assimilation procedures. Modification of traditional assimilation methods was required to use these high-density PBMR observations. The images were found to contain horizontal correlations that imply length scales of several tens of kilometers, thus allowing information to be advected beyond the area of the image. Information on surface soil moisture also was assimilated into the subsurface using knowledge of the surface- subsurface correlation. Newtonian nudging assimilation procedures are preferable to other techniques because they nearly preserve the observed patterns within the sampled region but also yield plausible patterns in unmeasured regions and allow information to be advected in time.

  3. Model-based surface soil moisture (SSM) retrieval algorithm using multi-temporal RISAT-1 C-band SAR data

    Science.gov (United States)

    Pandey, Dharmendra K.; Maity, Saroj; Bhattacharya, Bimal; Misra, Arundhati

    2016-05-01

    Accurate measurement of surface soil moisture of bare and vegetation covered soil over agricultural field and monitoring the changes in surface soil moisture is vital for estimation for managing and mitigating risk to agricultural crop, which requires information and knowledge to assess risk potential and implement risk reduction strategies and deliver essential responses. The empirical and semi-empirical model-based soil moisture inversion approach developed in the past are either sensor or region specific, vegetation type specific or have limited validity range, and have limited scope to explain physical scattering processes. Hence, there is need for more robust, physical polarimetric radar backscatter model-based retrieval methods, which are sensor and location independent and have wide range of validity over soil properties. In the present study, Integral Equation Model (IEM) and Vector Radiative Transfer (VRT) model were used to simulate averaged backscatter coefficients in various soil moisture (dry, moist and wet soil), soil roughness (smooth to very rough) and crop conditions (low to high vegetation water contents) over selected regions of Gujarat state of India and the results were compared with multi-temporal Radar Imaging Satellite-1 (RISAT-1) C-band Synthetic Aperture Radar (SAR) data in σ°HH and σ°HV polarizations, in sync with on field measured soil and crop conditions. High correlations were observed between RISAT-1 HH and HV with model simulated σ°HH & σ°HV based on field measured soil with the coefficient of determination R2 varying from 0.84 to 0.77 and RMSE varying from 0.94 dB to 2.1 dB for bare soil. Whereas in case of winter wheat crop, coefficient of determination R2 varying from 0.84 to 0.79 and RMSE varying from 0.87 dB to 1.34 dB, corresponding to with vegetation water content values up to 3.4 kg/m2. Artificial Neural Network (ANN) methods were adopted for model-based soil moisture inversion. The training datasets for the NNs were

  4. Modelling soil moisture under different land covers in a sub-humid environment of Western Ghats, India

    Indian Academy of Sciences (India)

    B Venkatesh; Lakshman Nandagiri; B K Purandara; V B Reddy

    2011-06-01

    The objective of this study is to apply and test a simple parametric water balance model for prediction of soil moisture regime in the presence of vegetation. The intention was to evaluate the differences in model parameterization and performance when applied to small watersheds under three different types of land covers (Acacia, degraded forest and natural forest). The watersheds selected for this purpose are located in the sub-humid climate within the Western Ghats, Karnataka, India. Model calibration and validation were performed using a dataset comprising depth-averaged soil moisture content measurements made at weekly time steps from October 2004 to December 2008. In addition to this, a sensitivity analysis was carried out with respect to the water-holding capacity of the soils with the aim of explaining the suitability and adaptation of exotic vegetation types under the prevailing climatic conditions. Results indicated reasonably good performance of the model in simulating the pattern and magnitude of weekly average soil moisture content in 150 cm deep soil layer under all three land covers. This study demonstrates that a simple, robust and parametrically parsimonious model is capable of simulating the temporal dynamics of soil moisture content under distinctly different land covers. Also, results of sensitivity analysis revealed that exotic plant species such as Acacia have adapted themselves effectively to the local climate.

  5. Spatial and temporal variability of soil electrical conductivity related to soil moisture

    OpenAIRE

    José Paulo Molin; Gustavo Di Chiacchio Faulin

    2013-01-01

    Soil electrical conductivity (ECa) is a soil quality indicator associated to attributes interesting to site-specific soil management such as soil moisture and texture. Soil ECa provides information that helps guide soil management decisions, so we performed spatial evaluation of soil moisture in two experimental fields in two consecutive years and modeled its influence on soil ECa. Soil ECa, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semiv...

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

    NARCIS (Netherlands)

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

    2014-01-01

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

  7. Soil Moisture Data Assimilation in the NASA Land Information System for Local Modeling Applications and Improved Situational Awareness

    Science.gov (United States)

    Case, Jonathan L.; Blakenship, Clay B.; Zavodsky, Bradley T.

    2014-01-01

    As part of the NASA Soil Moisture Active Passive (SMAP) Early Adopter (EA) program, the NASA Shortterm Prediction Research and Transition (SPoRT) Center has implemented a data assimilation (DA) routine into the NASA Land Information System (LIS) for soil moisture retrievals from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite. The SMAP EA program promotes application-driven research to provide a fundamental understanding of how SMAP data products will be used to improve decision-making at operational agencies. SPoRT has partnered with select NOAA/NWS Weather Forecast Offices (WFOs) that use output from a real-time regional configuration of LIS, without soil moisture DA, to initialize local numerical weather prediction (NWP) models and enhance situational awareness. Improvements to local NWP with the current LIS have been demonstrated; however, a better representation of the land surface through assimilation of SMOS (and eventually SMAP) retrievals is expected to lead to further model improvement, particularly during warm-season months. SPoRT will collaborate with select WFOs to assess the impact of soil moisture DA on operational forecast situations. Assimilation of the legacy SMOS instrument data provides an opportunity to develop expertise in preparation for using SMAP data products shortly after the scheduled launch on 5 November 2014. SMOS contains a passive L-band radiometer that is used to retrieve surface soil moisture at 35-km resolution with an accuracy of 0.04 cu cm cm (exp -3). SMAP will feature a comparable passive L-band instrument in conjunction with a 3-km resolution active radar component of slightly degraded accuracy. A combined radar-radiometer product will offer unprecedented global coverage of soil moisture at high spatial resolution (9 km) for hydrometeorological applications, balancing the resolution and accuracy of the active and passive instruments, respectively. The LIS software framework manages land surface model

  8. Assimilation of ASAR Data with a Hydrologic and Semi-empirical Backscattering Coupled Model to Estimate Soil Moisture

    Institute of Scientific and Technical Information of China (English)

    LIU Qian; WANG Mingyu; ZHAO Yingshi

    2010-01-01

    The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling.However,there is much uncertainty in the assimilation process,which affects the assimilation results.This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter(EnKF)and Genetic Algorithm(GA).A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model(DHSVM)was coupled with a semi-empirical backscattering model(Oh).The Advanced Synthetic Apertture Radar(ASAR)data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment.In order to improve the assimilation results,a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR.The EnKF and GA were used to re-initialize and re-parameterize the simulation process,respectively.The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data.The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

  9. Smart Irrigation From Soil Moisture Forecast Using Satellite And Hydro -Meteorological Modelling

    Science.gov (United States)

    Corbari, Chiara; Mancini, Marco; Ravazzani, Giovanni; Ceppi, Alessandro; Salerno, Raffaele; Sobrino, Josè

    2017-04-01

    Increased water demand and climate change impacts have recently enhanced the need to improve water resources management, even in those areas which traditionally have an abundant supply of water. The highest consumption of water is devoted to irrigation for agricultural production, and so it is in this area that efforts have to be focused to study possible interventions. The SIM project funded by EU in the framework of the WaterWorks2014 - Water Joint Programming Initiative aims at developing an operational tool for real-time forecast of crops irrigation water requirements to support parsimonious water management and to optimize irrigation scheduling providing real-time and forecasted soil moisture behavior at high spatial and temporal resolutions with forecast horizons from few up to thirty days. This study discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision irrigation use comparing different case studies in Italy, in the Netherlands, in China and Spain, characterized by different climatic conditions, water availability, crop types and irrigation techniques and water distribution rules. Herein, the applications in two operative farms in vegetables production in the South of Italy where semi-arid climatic conditions holds, two maize fields in Northern Italy in a more water reach environment with flood irrigation will be presented. This system combines state of the art mathematical models and new technologies for environmental monitoring, merging ground observed data with Earth observations. Discussion on the methodology approach is presented, comparing for a reanalysis periods the forecast system outputs with observed soil moisture and crop water needs proving the reliability of the forecasting system and its benefits. The real-time visualization of the implemented system is also presented through web-dashboards.

  10. A synergistic approach for soil moisture estimation using modified Dubois model with dual-polarized SAR and optical satellite data

    Science.gov (United States)

    Thanabalan, P.; Vidhya, R.

    2016-05-01

    This paper discusses about an estimation of soil moisture in agricultural region using SAR data with the use of HH and HV polarization. In this study the semi empirical approach derived by Dubois et al (1) was modified to work using (σdegHH) and σ°VV) so that soil moisture can be obtained for the larger area extent. The optical remote sensing is helps to monitor changes in vegetation biomass and canopy cover surface reflectance by using NDVI and LAI from which the site suitability from different land use/land cover are identified. The second use involves retrieve the backscattering coefficient valuesσ°) derived from SAR for soil moisture studies. In SAR techniques, the relative surface roughness can be directly estimate using surface roughness derivation empirical algorithms. The mid incidence angle is used to overcome the incidence angle effect and it worked successfully to this study. The modified Dubois Model (MDM) in combination with The Topp's et al (2) model is used to retrieve soil moisture. These two models have equations (HH, VV) and two independent variables i.e. root mean square height (s) and dielectric constant (epsilon). The linear regression analysis is performed and the surface roughness derived from SAR is well correlated with ground surface roughness having the value of (r2 = 0.69). By using the dielectric constant (epsilon) the modified Dubois model in combination with Topp's model are performed and the soil moisture is derived from SAR having value of (r2 = 0.60). Thus, the derived model is having good scope for soil moisture monitoring with present availability of SAR datasets.

  11. Modelling water flow and seasonal soil moisture dynamics in analluvial groundwater-fed wetland

    Directory of Open Access Journals (Sweden)

    I. Joris

    2003-01-01

    Full Text Available Complex interactions occur in riparian wetlands between groundwater, surface water and climatic conditions. Knowledge of the hydrology of these systems is necessary to understand their functioning and their value and models are a useful and probably essential tool to capture their hydrological complexity. In this study, a 2D-model describing saturated-unsaturated water flow is applied to a transect through a groundwater-fed riparian wetland located along the middle reach of the river Dijle. The transect has high levees close to the river and a depression further into the floodplain. Scaling factors are introduced to describe the variability of soil hydraulic properties along the transect. Preliminary model calculations for one year show a good agreement between model calculations and measurements and demonstrate the capability of the model to capture the internal groundwater dynamics. Seasonal variations in soil moisture are reproduced well by the model thus translating external hydrological boundary conditions to root zone conditions. The model proves to be a promising tool for assessing effects of changes in hydrological boundary conditions on vegetation type distribution and to gain more insight in the highly variable internal flow processes of riparian wetlands. Keywords: riparian wetland,eco-hydrology, upward seepage, floodplain hydrology

  12. Soil moisture modelling of a SMOS pixel: interest of using the PERSIANN database over the Valencia Anchor Station

    Directory of Open Access Journals (Sweden)

    S. Juglea

    2010-08-01

    Full Text Available In the framework of Soil Moisture and Ocean Salinity (SMOS Calibration/Validation (Cal/Val activities, this study addresses the use of the PERSIANN-CCS1database in hydrological applications to accurately simulate a whole SMOS pixel by representing the spatial and temporal heterogeneity of the soil moisture fields over a wide area (50×50 km2. The study focuses on the Valencia Anchor Station (VAS experimental site, in Spain, which is one of the main SMOS Cal/Val sites in Europe.

    A faithful representation of the soil moisture distribution at SMOS pixel scale (50×50 km2 requires an accurate estimation of the amount and temporal/spatial distribution of precipitation. To quantify the gain of using the comprehensive PERSIANN database instead of sparsely distributed rain gauge measurements, comparisons between in situ observations and satellite rainfall data are done both at point and areal scale. An overestimation of the satellite rainfall amounts is observed in most of the cases (about 66% but the precipitation occurrences are in general retrieved (about 67%.

    To simulate the high variability in space and time of surface soil moisture, a Soil Vegetation Atmosphere Transfer (SVAT model – ISBA (Interactions between Soil Biosphere Atmosphere is used. The interest of using satellite rainfall estimates as well as the influence that the precipitation events can induce on the modelling of the water content in the soil is depicted by a comparison between different soil moisture data. Point-like and spatialized simulated data using rain gauge observations or PERSIANN – CCS database as well as ground measurements are used. It is shown that a good adequacy is reached in most part of the year, the precipitation differences having less impact upon the simulated soil moisture. The behaviour of simulated surface soil moisture at SMOS scale is verified by the use of remote sensing data from the Advanced

  13. Estimating irrigated areas from satellite and model soil moisture data over the contiguous US

    Science.gov (United States)

    Zaussinger, Felix; Dorigo, Wouter; Gruber, Alexander

    2017-04-01

    Information about irrigation is crucial for a number of applications such as drought- and yield management and contributes to a better understanding of the water-cycle, land-atmosphere interactions as well as climate projections. Currently, irrigation is mainly quantified by national agricultural statistics, which do not include spatial information. The digital Global Map of Irrigated Areas (GMIA) has been the first effort to quantify irrigation at the global scale by merging these statistics with remote sensing data. Also, the MODIS-Irrigated Agriculture Dataset (MirAD-US) was created by merging annual peak MODIS-NDVI with US county level irrigation statistics. In this study we aim to map irrigated areas by confronting time series of various satellite soil moisture products with soil moisture from the ERA-Interim/Land reanalysis product. We follow the assumption that irrigation signals are not modelled in the reanalysis product, nor contributing to its forcing data, but affecting the spatially continuous remote sensing observations. Based on this assumption, spatial patterns of irrigation are derived from differences between the temporal slopes of the modelled and remotely sensed time series during the irrigation season. Results show that a combination of ASCAT and ERA-Interim/Land show spatial patterns which are in good agreement with the MIrAD-US, particularly within the Mississippi Delta, Texas and eastern Nebraska. In contrast, AMSRE shows weak agreements, plausibly due to a higher vegetation dependency of the soil moisture signal. There is no significant agreement to the MIrAD-US in California, which is possibly related to higher crop-diversity and lower field sizes. Also, a strong signal in the region of the Great Corn Belt is observed, which is generally not outlined as an irrigated area. It is not yet clear to what extent the signal obtained in the Mississippi Delta is related to re-reflection effects caused by standing water due to flood or furrow

  14. Application of HEC-HMS in a Cold Region Watershed and Use of RADARSAT-2 Soil Moisture in Initializing the Model

    OpenAIRE

    Hassan A. K. M. Bhuiyan; Heather McNairn; Jarrett Powers; Amine Merzouki

    2017-01-01

    This paper presents an assessment of the applicability of using RADARSAT-2-derived soil moisture data in the Hydrologic Modelling System developed by the Hydrologic Engineering Center (HEC-HMS) for flood forecasting with a case study in the Sturgeon Creek watershed in Manitoba, Canada. Spring flooding in Manitoba is generally influenced by both winter precipitation and soil moisture conditions in the fall of the previous year. As a result, the soil moisture accounting (SMA) and the temperatur...

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

    Science.gov (United States)

    Dumedah, Gift; Walker, Jeffrey P.

    2017-03-01

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

  16. Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

    Directory of Open Access Journals (Sweden)

    P. López López

    2017-06-01

    Full Text Available A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins; among them, the use of Earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCRaster GLOBal Water Balance (PCR-GLOBWB using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin. Daily simulations at a spatial resolution of 5  ×  5 arcmin are performed with varying parameters values for the 32-year period 1979–2010. Five different calibration scenarios are inter-compared: (i reference scenario using the hydrological model with the standard parameterization, (ii calibration using in situ-observed discharge time series, (iii calibration using the Global Land Evaporation Amsterdam Model (GLEAM actual evapotranspiration time series, (iv calibration using ESA Climate Change Initiative (CCI surface soil moisture time series and (v step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including ERA-Interim (EI, WATCH Forcing methodology applied to ERA-Interim reanalysis data (WFDEI and Multi-Source Weighted-Ensemble Precipitation data by merging gauge, satellite and reanalysis data (MSWEP. Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model

  17. Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

    Science.gov (United States)

    López López, Patricia; Sutanudjaja, Edwin H.; Schellekens, Jaap; Sterk, Geert; Bierkens, Marc F. P.

    2017-06-01

    A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins; among them, the use of Earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCRaster GLOBal Water Balance (PCR-GLOBWB) using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin. Daily simulations at a spatial resolution of 5 × 5 arcmin are performed with varying parameters values for the 32-year period 1979-2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in situ-observed discharge time series, (iii) calibration using the Global Land Evaporation Amsterdam Model (GLEAM) actual evapotranspiration time series, (iv) calibration using ESA Climate Change Initiative (CCI) surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including ERA-Interim (EI), WATCH Forcing methodology applied to ERA-Interim reanalysis data (WFDEI) and Multi-Source Weighted-Ensemble Precipitation data by merging gauge, satellite and reanalysis data (MSWEP). Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in

  18. Nematode survival in relation to soil moisture

    NARCIS (Netherlands)

    Simons, W.R.

    1973-01-01

    Established nematode populations are very persistent in the soil. It is known that they need sufficient soil moisture for movement, feeding and reproduction (fig. 5), and that there are adverse soil moisture conditions which they cannot survive. The influence of soil moisture on survival of nematode

  19. Nematode survival in relation to soil moisture

    NARCIS (Netherlands)

    Simons, W.R.

    1973-01-01

    Established nematode populations are very persistent in the soil. It is known that they need sufficient soil moisture for movement, feeding and reproduction (fig. 5), and that there are adverse soil moisture conditions which they cannot survive. The influence of soil moisture on survival

  20. 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)

  1. From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

    Directory of Open Access Journals (Sweden)

    C. Albergel

    2008-12-01

    Full Text Available A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content, the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

  2. Coupled stochastic soil moisture simulation-optimization model of deficit irrigation

    Science.gov (United States)

    Alizadeh, Hosein; Mousavi, S. Jamshid

    2013-07-01

    This study presents an explicit stochastic optimization-simulation model of short-term deficit irrigation management for large-scale irrigation districts. The model which is a nonlinear nonconvex program with an economic objective function is built on an agrohydrological simulation component. The simulation component integrates (1) an explicit stochastic model of soil moisture dynamics of the crop-root zone considering interaction of stochastic rainfall and irrigation with shallow water table effects, (2) a conceptual root zone salt balance model, and 3) the FAO crop yield model. Particle Swarm Optimization algorithm, linked to the simulation component, solves the resulting nonconvex program with a significantly better computational performance compared to a Monte Carlo-based implicit stochastic optimization model. The model has been tested first by applying it in single-crop irrigation problems through which the effects of the severity of water deficit on the objective function (net benefit), root-zone water balance, and irrigation water needs have been assessed. Then, the model has been applied in Dasht-e-Abbas and Ein-khosh Fakkeh Irrigation Districts (DAID and EFID) of the Karkheh Basin in southwest of Iran. While the maximum net benefit has been obtained for a stress-avoidance (SA) irrigation policy, the highest water profitability has been resulted when only about 60% of the water used in the SA policy is applied. The DAID with respectively 33% of total cultivated area and 37% of total applied water has produced only 14% of the total net benefit due to low-valued crops and adverse soil and shallow water table conditions.

  3. Soil moisture needs in earth sciences

    Science.gov (United States)

    Engman, Edwin T.

    1992-01-01

    The author reviews the development of passive and active microwave techniques for measuring soil moisture with respect to how the data may be used. New science programs such as the EOS, the GEWEX Continental-Scale International Project (GCIP) and STORM, a mesoscale meteorology and hydrology project, will have to account for soil moisture either as a storage in water balance computations or as a state variable in-process modeling. The author discusses future soil moisture needs such as frequency of measurement, accuracy, depth, and spatial resolution, as well as the concomitant model development that must proceed concurrently if the development in microwave technology is to have a major impact in these areas.

  4. Inference of Soil Hydrologic Parameters from Soil Moisture Monitoring Records

    Science.gov (United States)

    Chandler, D. G.; Seyfried, M. S.; McNamara, J. P.; Hwang, K.

    2015-12-01

    Soil moisture is an important control on hydrologic function, as it governs flux through the soil and responds to and determines vertical fluxes from and to the atmosphere, groundwater recharge and lateral fluxes through the soil. Most physically based hydrologic models require parameters to represent soil physical properties governing flow and retention of vadose water. The presented analysis compares four methods of objective analysis to determine field capacity, plant extraction limit (or permanent wilting point) and field saturated soil moisture content from decadal records of volumetric water content. These values are found as either data attractors or limits in the VWC records and may vary with interannual moisture availability. Results are compared to values from pedotransfer functions and discussed in terms of historic methods of measurement in soil physics.

  5. Assessment of some soil thermal conductivity models via variations in temperature and bulk density at low moisture range

    Science.gov (United States)

    Mahdavi, Seyed Mohamad; Neyshabouri, Mohammad Reza; Fujimaki, Haruyuki

    2016-08-01

    Simulation of heat transfer in soil under steady and unsteady situations requires reliable estimate of soil thermal conductivity (λ) at varying environmental conditions. In the current work several soil thermal conductivity predicting models including I) de Vries, II) Campbell, III) combined de Vries and Campbell and IV) de Vries-Nobre were evaluated for the four soils of coarse sand, sandy loam, loam and clay loam textured at varying in temperature and bulk density at low moisture range. Thermal conductivities measured by the cylindrical probe method served as the reference for models assessment. Results showed that approximately same thermal conductivities obtained by the five methods at low moisture range (θ ≤ 0.05 m3/m3). Also the de Vries and de Vries-Campbell models produced accurate than Campbell and de vries-Nobre models. The accuracy of the two models increased with soil compaction but decreased with temperature rise. Campbell model showed more reliability at higher (311.16 and 321.16 K) temperatures; but its accuracy declined with soil compaction in current work. It seems that assuming needle shape for the soil particles is far away from the reality whereas assuming spherical shapes may be more realistic and produced more satisfactory prediction of thermal conductivity. The compaction would alter particle arrangement and may increase the contact area of particles; and then make them behave more or less spherical shape.it seems thermal conductivity in solid particles increase via increasing in temperature. Since a modified mineral shape factor, g m , was developed as a combination between sphere and needle according to geometric mean particle diameter as well as bulk density and temperature as modifying factors. This factor increased the accuracy of de Vries-Nobre model up to 10.37%. Regarding nonlinear regression model, moisture content, bulk density, temperature and quartz content demonstrated significant effect on soil thermal conductivity in our

  6. Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

    Science.gov (United States)

    Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

    2011-01-01

    The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

  7. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

    Science.gov (United States)

    López López, Patricia; Wanders, Niko; Schellekens, Jaap; Renzullo, Luigi J.; Sutanudjaja, Edwin H.; Bierkens, Marc F. P.

    2016-07-01

    The coarse spatial resolution of global hydrological models (typically >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from ˜  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore

  8. Impact of rainfall spatial distribution on rainfall-runoff modelling efficiency and initial soil moisture conditions estimation

    Directory of Open Access Journals (Sweden)

    Y. Tramblay

    2011-01-01

    Full Text Available A good knowledge of rainfall is essential for hydrological operational purposes such as flood forecasting. The objective of this paper was to analyze, on a relatively large sample of flood events, how rainfall-runoff modeling using an event-based model can be sensitive to the use of spatial rainfall compared to mean areal rainfall over the watershed. This comparison was based not only on the model's efficiency in reproducing the flood events but also through the estimation of the initial conditions by the model, using different rainfall inputs. The initial conditions of soil moisture are indeed a key factor for flood modeling in the Mediterranean region. In order to provide a soil moisture index that could be related to the initial condition of the model, the soil moisture output of the Safran-Isba-Modcou (SIM model developed by Météo-France was used. This study was done in the Gardon catchment (545 km2 in South France, using uniform or spatial rainfall data derived from rain gauge and radar for 16 flood events. The event-based model considered combines the SCS runoff production model and the Lag and Route routing model. Results show that spatial rainfall increases the efficiency of the model. The advantage of using spatial rainfall is marked for some of the largest flood events. In addition, the relationship between the model's initial condition and the external predictor of soil moisture provided by the SIM model is better when using spatial rainfall, in particular when using spatial radar data with R2 values increasing from 0.61 to 0.72.

  9. Comparison Experiments of Different Model Error Schemes in Ensemble Kalman Filter Soil Moisture Assimilation

    Science.gov (United States)

    Nie, Suping; Zhu, Jiang; Luo, Yong

    2010-05-01

    The purpose of this study is to explore the performances of different model error scheme in soil moisture data assimilation. Based on the ensemble Kalman filter (EnKF) and the atmosphere-vegetation interaction model (AVIM), point-scale analysis results for three schemes, 1) covariance inflation (CI), 2) direct random disturbance (DRD), and 3) error source random disturbance (ESRD), are combined under conditions of different observational error estimations, different observation layers, and different observation intervals using a series of idealized experiments. The results shows that all these schemes obtain good assimilation results when the assumed observational error is an accurate statistical representation of the actual error used to perturb the original truth value, and the ESRD scheme has the least root mean square error (RMSE). Overestimation or underestimation of the observational errors can affect the assimilation results of CI and DRD schemes sensitively. The performances of these two schemes deteriorate obviously while the ESRD scheme keeps its capability well. When the observation layers or observation interval increase, the performances of both CI and DRD schemes decline evidently. But for the ESRD scheme, as it can assimilate multi-layer observations coordinately, the increased observations improve the assimilation results further. Moreover, as the ESRD scheme contains a certain amount of model error estimation functions in its assimilation process, it also has a good performance in assimilating sparse-time observations.

  10. A coupled force-restore model of surface temperature and soil moisture using the maximum entropy production model of heat fluxes

    Science.gov (United States)

    Huang, S.-Y.; Wang, J.

    2016-07-01

    A coupled force-restore model of surface soil temperature and moisture (FRMEP) is formulated by incorporating the maximum entropy production model of surface heat fluxes and including the gravitational drainage term. The FRMEP model driven by surface net radiation and precipitation are independent of near-surface atmospheric variables with reduced sensitivity to the uncertainties of model input and parameters compared to the classical force-restore models (FRM). The FRMEP model was evaluated using observations from two field experiments with contrasting soil moisture conditions. The modeling errors of the FRMEP predicted surface temperature and soil moisture are lower than those of the classical FRMs forced by observed or bulk formula based surface heat fluxes (bias 1 ~ 2°C versus ~4°C, 0.02 m3 m-3 versus 0.05 m3 m-3). The diurnal variations of surface temperature, soil moisture, and surface heat fluxes are well captured by the FRMEP model measured by the high correlations between the model predictions and observations (r ≥ 0.84). Our analysis suggests that the drainage term cannot be neglected under wet soil condition. A 1 year simulation indicates that the FRMEP model captures the seasonal variation of surface temperature and soil moisture with bias less than 2°C and 0.01 m3 m-3 and correlation coefficients of 0.93 and 0.9 with observations, respectively.

  11. Temperature Knowledge and Model Correlation for the Soil Moisture Active and Passive (SMAP) Reflector Mesh

    Science.gov (United States)

    Mikhaylov, Rebecca; Dawson, Douglas; Kwack, Eug

    2014-01-01

    NASA's Earth observing Soil Moisture Active & Passive (SMAP) Mission is scheduled to launch in November 2014 into a 685 km near-polar, sun synchronous orbit. SMAP will provide comprehensive global mapping measurements of soil moisture and freeze/thaw state in order to enhance understanding of the processes that link the water, energy, and carbon cycles. The primary objectives of SMAP are to improve worldwide weather and flood forecasting, enhance climate prediction, and refine drought and agriculture monitoring during its 3 year mission. The SMAP instrument architecture incorporates an L-band radar and an L-band radiometer which share a common feed horn and parabolic mesh reflector. The instrument rotates about the nadir axis at approximately 15 rpm, thereby providing a conically scanning wide swath antenna beam that is capable of achieving global coverage within 3 days. In order to make the necessary precise surface emission measurements from space, a temperature knowledge of 60 deg C for the mesh reflector is required. In order to show compliance, a thermal vacuum test was conducted using a portable solar simulator to illuminate a non flight, but flight-like test article through the quartz window of the vacuum chamber. The molybdenum wire of the antenna mesh is too fine to accommodate thermal sensors for direct temperature measurements. Instead, the mesh temperature was inferred from resistance measurements made during the test. The test article was rotated to five separate angles between 10 deg and 90 deg via chamber breaks to simulate the maximum expected on-orbit solar loading during the mission. The resistance measurements were converted to temperature via a resistance versus temperature calibration plot that was constructed from data collected in a separate calibration test. A simple thermal model of two different representations of the mesh (plate and torus) was created to correlate the mesh temperature predictions to within 60 deg C. The on-orbit mesh

  12. The perceptual trap: Experimental and modelling examples of soil moisture, hydraulic conductivity and response units in complex subsurface settings.

    Science.gov (United States)

    Jackisch, Conrad; Demand, Dominic; Allroggen, Niklas; Loritz, Ralf; Zehe, Erwin

    2017-04-01

    In order to discuss hypothesis testing in hydrology, the question of the solid foundation of such tests has to be answered. But how certain are we about our measurements of the components of the water balance and the states and dynamics of the complex systems? What implicit assumptions or bias are already embedded in our perception of the processes? How can we find light in the darkness of heterogeneity? We will contribute examples from experimental findings, modelling approaches and landscape analysis to the discussion. Example soil moisture and the soil continuum: The definition of soil moisture as fraction of water in the porous medium assumes locally well-mixed conditions. Moreover, a unique relation of soil water retention presumes instant local thermodynamic equilibrium in the pore water arrangement. We will show findings from soil moisture responses to precipitation events, from irrigation experiments, and from a model study of initial infiltration velocities. The results highlight, that the implicit assumption relating soil moisture state dynamics with actual soil water flow is biased towards the slow end of the actual velocity distribution and rather blind for preferential flow acting in a very small proportion of the pore space. Moreover, we highlight the assumption of a well-defined continuum during the extrapolation of point-scale measurements and why spatially and temporally continuous observation techniques of soil water states are essential for advancing our understanding and development of subsurface process theories. Example hydraulic conductivity: Hydraulic conductivity lies at the heart of hydrological research and modelling. Its values can range across several orders of magnitude at a single site alone. Yet, we often consider it a crisp, effective parameter. We have conducted measurements of soil hydraulic conductivity in the lab and in the field. Moreover, we assessed infiltration capacity and conducted plot-scale irrigation experiments to

  13. Measurement of soil moisture using gypsum blocks

    DEFF Research Database (Denmark)

    Friis Dela, B.

    the building. Consequently, measuring the moisture of the surrounding soil is of great importance for detecting the source of moisture in a building. Up till now, information has been needed to carry out individual calibrations for the different types of gypsum blocks available on the market and to account......For the past 50 years, gypsum blocks have been used to determine soil moisture content. This report describes a method for calibrating gypsum blocks for soil moisture measurements. Moisture conditions inside a building are strongly influenced by the moisture conditions in the soil surrounding...

  14. Measurement of soil moisture using gypsum blocks

    DEFF Research Database (Denmark)

    Friis Dela, B.

    For the past 50 years, gypsum blocks have been used to determine soil moisture content. This report describes a method for calibrating gypsum blocks for soil moisture measurements. Moisture conditions inside a building are strongly influenced by the moisture conditions in the soil surrounding...... the building. Consequently, measuring the moisture of the surrounding soil is of great importance for detecting the source of moisture in a building. Up till now, information has been needed to carry out individual calibrations for the different types of gypsum blocks available on the market and to account...

  15. Soil Moisture and Vegetation Controls on Surface Energy Balance Using the Maximum Entropy Production Model of Evapotranspiration

    Science.gov (United States)

    Wang, J.; Parolari, A.; Huang, S. Y.

    2014-12-01

    The objective of this study is to formulate and test plant water stress parameterizations for the recently proposed maximum entropy production (MEP) model of evapotranspiration (ET) over vegetated surfaces. . The MEP model of ET is a parsimonious alternative to existing land surface parameterizations of surface energy fluxes from net radiation, temperature, humidity, and a small number of parameters. The MEP model was previously tested for vegetated surfaces under well-watered and dry, dormant conditions, when the surface energy balance is relatively insensitive to plant physiological activity. Under water stressed conditions, however, the plant water stress response strongly affects the surface energy balance. This effect occurs through plant physiological adjustments that reduce ET to maintain leaf turgor pressure as soil moisture is depleted during drought. To improve MEP model of ET predictions under water stress conditions, the model was modified to incorporate this plant-mediated feedback between soil moisture and ET. We compare MEP model predictions to observations under a range of field conditions, including bare soil, grassland, and forest. The results indicate a water stress function that combines the soil water potential in the surface soil layer with the atmospheric humidity successfully reproduces observed ET decreases during drought. In addition to its utility as a modeling tool, the calibrated water stress functions also provide a means to infer ecosystem influence on the land surface state. Challenges associated with sampling model input data (i.e., net radiation, surface temperature, and surface humidity) are also discussed.

  16. Varying applicability of four different satellite-derived soil moisture products to global gridded crop model evaluation

    Science.gov (United States)

    Sakai, Toru; Iizumi, Toshichika; Okada, Masashi; Nishimori, Motoki; Grünwald, Thomas; Prueger, John; Cescatti, Alessandro; Korres, Wolfgang; Schmidt, Marius; Carrara, Arnaud; Loubet, Benjamin; Ceschia, Eric

    2016-06-01

    Satellite-derived daily surface soil moisture products have been increasingly available, but their applicability to global gridded crop model (GGCM) evaluation is unclear. This study compares four different soil moisture products with the flux tower site observation at 18 cropland sites across the world where either of maize, soybean, rice and wheat is grown. These products include the first and second versions of Climate Change Initiative Soil Moisture (CCISM-1 and CCISM-2) datasets distributed by the European Space Agency and two different AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System)-derived soil moisture datasets, separately provided by the Japan Aerospace Exploration Agency (AMSRE-J) and U.S. National Aeronautics and Space Administration (AMSRE-N). The comparison demonstrates varying reliability of these products in representing major characteristics of temporal pattern of cropland soil moisture by product and crop. Possible reasons for the varying reliability include the differences in sensors, algorithms, bands and criteria used when estimating soil moisture. Both the CCISM-1 and CCISM-2 products appear the most reliable for soybean- and wheat-growing area. However, the percentage of valid data of these products is always lower than other products due to relatively strict criteria when merging data derived from multiple sources, although the CCISM-2 product has much more data with valid retrievals than the CCISM-1 product. The reliability of the AMSRE-J product is the highest for maize- and rice-growing areas and comparable to or slightly lower than the CCISM products for soybean- and wheat-growing areas. The AMSRE-N is the least reliable in most location-crop combinations. The reliability of the products for rice-growing area is far lower than that of other upland crops likely due to the extensive use of irrigation and patch distribution of rice paddy in the area examined here. We conclude that the CCISM-1, CCISM-2 and AMSRE

  17. Estimating Subcanopy Soil Moisture with RADAR

    Science.gov (United States)

    Moghaddam, M.; Saatchi, S.; Cuenca, R. H.

    1998-01-01

    The subcanopy soil moisture of a boreal old jack pine forest is estimated using polarimetric L- and P-band AIRSAR data. Model simulations have shown that for this stand, the principal scattering mechanism responsible for radar backscatter is the double-bounce mechanism between the tree trunks and the ground.

  18. Effective roughness modelling as a tool for soil moisture retrieval from C- and L-band SAR

    Directory of Open Access Journals (Sweden)

    H. Lievens

    2011-01-01

    Full Text Available Soil moisture retrieval from Synthetic Aperture Radar (SAR using state-of-the-art back-scatter models is not fully operational at present, mainly due to difficulties involved in the parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that param-eters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C- and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.

  19. Infiltration-soil moisture redistribution under natural conditions: experimental evidence as a guideline for realizing simulation models

    Directory of Open Access Journals (Sweden)

    R. Morbidelli

    2011-06-01

    Full Text Available The evolution in time, t, of the experimental soil moisture vertical profile under natural conditions is investigated in order to address the corresponding simulation modelling. The measurements were conducted in a plot with a bare silty loam soil. The soil water content, θ, was continuously monitored at different depths, z, using a Time Domain Reflectometry (TDR system. For each profile four buriable three-rod waveguides were inserted horizontally at different depths (5, 15, 25 and 35 cm. In addition, we used sensors of air temperature and relative humidity, wind speed, solar radiation, evaporation and rain as supports for the application of selected simulation models, as well as for the detection of elements leading to their improvement. The results indicate that, under natural conditions, very different trends of the θ(z,t function can be observed in the given fine-textured soil, where the formation of a sealing layer over the parent soil requires an adjustment of the simulation modelling commonly used for hydrological applications. In particular, because of the considerable variations in the shape of the moisture content vertical profile as a function of time, a generalization of the existing models should incorporate a representation of the variability in time of the saturated hydraulic conductivity of the uppermost soil. This conclusion is supported by the fact that the observed shape of θ(z can be appropriately reproduced by adopting this approach, however the observed rainfall rate and the occurrence of freeze-thaw cycles with high soil moisture contents have to be explicitly incorporated.

  20. Infiltration-soil moisture redistribution under natural conditions: experimental evidence as a guideline for realizing simulation models

    Directory of Open Access Journals (Sweden)

    R. Morbidelli

    2011-09-01

    Full Text Available The evolution in time, t, of the experimental soil moisture vertical profile under natural conditions is investigated in order to address the corresponding simulation modelling. The measurements were conducted in a plot with a bare silty loam soil. The soil water content, θ, was continuously monitored at different depths, z, using a Time Domain Reflectometry (TDR system. Four buriable three-rod waveguides were inserted horizontally at different depths (5, 15, 25 and 35 cm. In addition, we used sensors of air temperature and relative humidity, wind speed, solar radiation, evaporation and rain as supports for the application of selected simulation models, as well as for the detection of elements leading to their improvement. The results indicate that, under natural conditions, very different trends of the θ(z, t function can be observed in the given fine-textured soil, where the formation of a sealing layer over the parent soil requires an adjustment of the simulation modelling commonly used for hydrological applications. In particular, because of the considerable variations in the shape of the moisture content vertical profile as a function of time, a generalization of the existing models should incorporate a first approximation of the variability in time of the saturated hydraulic conductivity, K1s, of the uppermost soil. This conclusion is supported by the fact that the observed shape of θ(z, t can be appropriately reproduced by adopting the proposed approach with K1s kept constant during each rainfall event but considered variable from event to event, however the observed rainfall rate and the occurrence of freeze-thaw cycles with high soil moisture contents have to be explicitly incorporated in a functional form for K1s(t.

  1. Use of the Sacramento Soil Moisture Accounting Model in Areas with Insufficient Forcing Data

    Science.gov (United States)

    Kuzmin, V.

    2009-04-01

    The Sacramento Soil Moisture Accounting model (SAC-SMA) is known as a very reliable and effective hydrological model. It is widely used by the U.S. National Weather Service (NWS) and many organizations in other countries for operational forecasting of flash floods. As a purely conceptual model, the SAC-SMA requires a periodic re-calibration. However, this procedure is not trivial in watersheds with little or no historical data, in areas with changing watershed properties, in a changing climate environment, in regions with low quality and low spatial resolution forcing data etc. In such cases, so-called physically based models with measurable parameters also may not be an alternative, because they usually require high quality forcing data and, hence, are quite expensive. Therefore, this type of models can not be implemented in countries with scarce surface observation data. To resolve this problem, we offer using a very fast and efficient automatic calibration algorithm, a Stepwise Line Search (SLS), which has been implementing in NWS since 2005, and also its modifications that were developed especially for automated operational forecasting of flash floods in regions where high resolution and high quality forcing data are not available. The SLS-family includes several simple yet efficient calibration algorithms: 1) SLS-F, which supposes simultaneous natural smoothing of the response surface by quasi-local estimation of F-indices, what allows finding the most stable and reliable parameters that can be different from "global" optima in usual sense. (Thus, this method slightly transforms the original objective function); 2) SLS-2L (Two-Loop SLS), which is suitable for basins where hydraulic properties of soil are unknown; 3) SLS-2LF, which represents a conjunction of the SLS-F and SLS-2L algorithms and allows obtaining the SAC-SMA parameters that can be transferred to ungauged catchments; 4) SLS-E, which also supposes stochastic filtering of the model input through

  2. Modelling of runoff generation and soil moisture dynamics for hillslopes and micro-catchments

    Science.gov (United States)

    Bronstert, Axel; Plate, Erich J.

    1997-11-01

    The modelling of hillslope hydrology is of great importance not only for the reason that all non-plain, i.e. hilly or mountainous, landscapes can be considered as being composed of a mosaic of hillslopes. A hillslope model may also be used for both research purposes and for application-oriented, detailed, hillslope-scale hydrological studies in conjunction with related scientific disciplines such as geotechnics, geo-chemistry and environmental technology. Despite the current limited application of multi-process and multi-dimensional hydrological models (particularly at the hillslope scale), hardly any comprehensive model has been available for operational use. In this paper we introduce a model which considers most of the relevant hillslope hydrological processes. Some recent applications are described which demonstrate its ability to narrow the stated gap in hillslope hydrological modelling. The modelling system accounts for the hydrological processes of interception, evapotranspiration, infiltration, soil-moisture movement (where the flow processes can be modelled in three dimensions), surface runoff, subsurface stormflow and streamflow discharge. The relevant process interactions are also included. Special regard has been given to consideration of state-of-the-art knowledge concerning rapid soilwater flow processes during storm conditions (e.g. macropore infiltration, lateral subsurface stormflow, return flow) and to its transfer to and inclusion within an operational modelling scheme. The model is "physically based" in the sense that its parameters have a physical meaning and can be obtained or derived from field measurements. This somewhat weaker than usual definition of a physical basis implies that some of the sub-models (still) contain empirical components, that the effects of the high spatial and temporal variability found in nature cannot always be expressed within the various physical laws, i.e. that the laws are scale dependent, and that due to

  3. Spatial and temporal variability of soil electrical conductivity related to soil moisture

    Directory of Open Access Journals (Sweden)

    José Paulo Molin

    2013-02-01

    Full Text Available Soil electrical conductivity (ECa is a soil quality indicator associated to attributes interesting to site-specific soil management such as soil moisture and texture. Soil ECa provides information that helps guide soil management decisions, so we performed spatial evaluation of soil moisture in two experimental fields in two consecutive years and modeled its influence on soil ECa. Soil ECa, moisture and clay content were evaluated by statistical, geostatistical and regression analyses. Semivariogram models, adjusted for soil moisture, had strong spatial dependence, but the relationship between soil moisture and soil ECa was obtained only in one of the experimental fields, where soil moisture and clay content range was higher. In this same field, coefficients of determinations between soil moisture and clay content were above 0.70. In the second field, the low soil moisture and clay content range explain the absence of a relationship between soil ECa and soil moisture. Data repetition over the years, suggested that ECa is a qualitative indicator in areas with high spatial variability in soil texture.

  4. Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

    Science.gov (United States)

    Ardilouze, Constantin; Batté, L.; Bunzel, F.; Decremer, D.; Déqué, M.; Doblas-Reyes, F. J.; Douville, H.; Fereday, D.; Guemas, V.; MacLachlan, C.; Müller, W.; Prodhomme, C.

    2017-02-01

    Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992-2010 period performed by five different global coupled ocean-atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land-atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.

  5. A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. Part 1; Overview

    Science.gov (United States)

    Crosson, William L.; Laymon, Charles A.; Inguva, Ramarao; Schamschula, Marius; Caulfield, John

    1998-01-01

    advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean

  6. Correcting satellite-based precipitation products from SMOS soil moisture data assimilation using two models of different complexity

    Science.gov (United States)

    Román-Cascón, Carlos; Pellarin, Thierry; Gibon, François

    2017-04-01

    Real-time precipitation information at the global scale is quite useful information for many applications. However, satellite-based precipitation products in real time are known to be biased from real values observed in situ. On the other hand, the information about precipitation contained in soil moisture data can be very useful to improve precipitation estimation, since the evolution of this variable is highly influenced by the amount of rainfall at a certain area after a rain event. In this context, the soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used to correct the precipitation provided by real-time satellite-based products such as CMORPH, TRMM-3B42RT or PERSIANN. In this work, we test an assimilation algorithm based on the data assimilation of SMOS measurements in two models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a state-of-the-art complex land-surface model (Surface Externalisée (SURFEX)). We show how the assimilation technique, based on a particle filter method, leads to the improvement of correlation and root mean squared error (RMSE) of precipitation estimates, with slightly better results for the simpler (and less expensive computationally) API model. This methodology has been evaluated for six years in ten sites around the world with different features, showing the limitations of the methodology in regions affected by mountainous terrain or by high radio-frequency interferences (RFI), which notably affect the quality of the soil moisture retrievals from brightness temperatures by SMOS. The presented results are promising for a potential near-real time application at the global scale.

  7. Divergent surface and total soil moisture projections under global warming

    Science.gov (United States)

    Berg, Alexis; Sheffield, Justin; Milly, Paul C.D.

    2017-01-01

    Land aridity has been projected to increase with global warming. Such projections are mostly based on off-line aridity and drought metrics applied to climate model outputs but also are supported by climate-model projections of decreased surface soil moisture. Here we comprehensively analyze soil moisture projections from the Coupled Model Intercomparison Project phase 5, including surface, total, and layer-by-layer soil moisture. We identify a robust vertical gradient of projected mean soil moisture changes, with more negative changes near the surface. Some regions of the northern middle to high latitudes exhibit negative annual surface changes but positive total changes. We interpret this behavior in the context of seasonal changes in the surface water budget. This vertical pattern implies that the extensive drying predicted by off-line drought metrics, while consistent with the projected decline in surface soil moisture, will tend to overestimate (negatively) changes in total soil water availability.

  8. Influence of Soil Moisture on Soil Gas Vapor Concentration for Vapor Intrusion

    OpenAIRE

    Shen, Rui; Pennell, Kelly G.; Suuberg, Eric M.

    2013-01-01

    Mathematical models have been widely used in analyzing the effects of various environmental factors in the vapor intrusion process. Soil moisture content is one of the key factors determining the subsurface vapor concentration profile. This manuscript considers the effects of soil moisture profiles on the soil gas vapor concentration away from any surface capping by buildings or pavement. The “open field” soil gas vapor concentration profile is observed to be sensitive to the soil moisture di...

  9. The impact of soil moisture on the spin up of 1-D Noah land surface model at a site in monsoonal region

    Science.gov (United States)

    Bhattacharya, A.; Mandal, M.

    2014-12-01

    Model spin-up is the process through which the model is adequately equilibrated to ensure balance between the mass fields and velocity fields. In this study, an offline 1-D Noah land surface model (LSM) has been used to investigate the impact of soil moisture on the model spin up at Kharagpur, India which is a site in monsoonal region. The model is integrated recursively for 3-years to assess its spin-up behavior. Several numerical experiments are performed to investigate the impact of initial soil moisture and subsequent dry or wet condition on model spin-up. These include simulations with different initial soil moisture content (observed soil moisture; dry soil; moderately wet soil; saturated soil), simulations initialized before different rain conditions (no rain; infrequent rain; continuous rain) and simulations initialized in different seasons (Winter, Spring, Summer/Pre-Monsoon, Monsoon and Autumn). It is noted that the model has significantly longer spin-up when initialized with very low initial soil moisture content than with higher soil moisture content. It is also seen that in general, simulations initialized just before a continuous rainfall event have the least spin-up time. In a region affected by the monsoon, such as Kharagpur, this observation is reinforced by the results from the simulations initialized in different seasons. It is seen that for monsoonal region, the model spin-up time is least for simulations initialized during Summer/Pre-monsoon. Model initialized during the Monsoon has a longer spin-up than that initialized in any other season. It appears that the model has shorter spin-up if it reaches the equilibrium state predominantly via drying process. It is also observed that the spin-up of offline 1-D Noah LSM may be as low as two months under quasi-equilibrium condition if the initial soil moisture content and time of start of simulations are chosen carefully.

  10. 'Initial' Soil Moisture Effects on the Climate in China——A Regional Climate Model Study

    Institute of Scientific and Technical Information of China (English)

    SHI Xueli

    2009-01-01

    In this study, the effects of 'initial' soil moisture (SM) in arid and semi-arid Northwestern China on subsequent climate were investigated with a regional climate model. Besides the control simulations (denoted as CTL), a series of sensitivity experi-ments were conducted, including the DRY and WET experiments, in which the simulated 'initial' SM over the region 30-50°N, 75 -105°E was only 5% and 50%, and up to 150% and 200% of the simulated value in the CTL, respectively. The results show that SM change can modify the subsequent climate in not only the SM-change region proper but also the far downstream regions in Eastern and even Northeastern China. The SM-change effects are generally more prominent in the WET than in the DRY experiments. After the SM is initially increased, the SM in the SM-change region is always higher than that in the CTL, the latent (sensible) heat flux there increases (decreases), and the surface air temperature decreases. Spatially, the most prominent changes in the WET experiments are surface air temperature decrease, geopotential height decrease and corresponding abnormal changes of cyclonic wind vectors at the mid-upper troposphere levels. Generally opposite effects exist in the DRY experiments but with much weaker intensity. In addi-tion, the differences between the results obtained from the two sets of sensitivity experiments and those of the CTL are not always consistent with the variation of the initial SM. Being different from the variation of temperature, the rainfall modifications caused by initial SM change are not so distinct and in fact they show some common features in the WET and DRY experiments. This might imply that SM is only one of the factors that impact the subsequent climate, and its effect is involved in complex processes within the atmosphere, which needs further investigation.

  11. 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

  12. Analysis and modelling of the temporal stability of throughfall and near-surface soil moisture at the plot scale in the Italian pre-Alps

    Science.gov (United States)

    Zuecco, Giulia; van Meerveld, Ilja; Penna, Daniele; Hopp, Luisa; Borga, Marco

    2016-04-01

    Understanding the spatial and temporal variation in soil moisture at different scales is crucial for hydrological sciences. In forested areas, spatial patterns in throughfall and vegetation characteristics are expected to control soil moisture variability. However, few studies have focused on the influence of throughfall spatial patterns on near-surface soil moisture. Therefore, this study aimed to: i) investigate the relation between the spatial patterns of throughfall and near-surface soil moisture at the plot scale for a forested hillslope, ii) compare the temporal stability of throughfall and soil moisture with canopy characteristics, and iii) assess the controls on the correlation between throughfall and soil moisture temporal stability by means of a dynamic soil moisture model. Throughfall and soil moisture measurements were taken in a 500 m2 experimental plot on the hillslope of a densely forested catchment (Ressi) in the Italian pre-Alps between April 2013 and March 2014. The main tree species in the plot are beech and chestnut. The median diameter at breast height of the trees in the plot is 4 cm (range 1-61 cm). Fifty buckets (collecting area: 556 cm2; capacity: 162 mm) were randomly distributed in the plot for throughfall measurements, while a bucket was also installed in a nearby open area (approximately 150 m from the experimental plot) to collect rainfall. Volumetric soil moisture content was measured at 50 points, about 30 cm upslope of each bucket, at two depths (0-7 and 0-12 cm) before and after 23 rainfall events (7.7 mm to 156 mm) using portable TDR (Time Domain Reflectivity) probes. Canopy openness and leaf area index (LAI) were determined from hemispherical pictures at each bucket. For the measured events throughfall and soil moisture spatial patterns were not significantly or only weakly correlated, likely due to the lateral and vertical redistribution of water in the soil profile during the 2-36 hour period between the end of the rainfall

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

    Science.gov (United States)

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

    2017-04-01

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

  14. A soil moisture climatology of Illinois

    Energy Technology Data Exchange (ETDEWEB)

    Hollinger, S.E.; Isard, S.A. (Illinois State Water Survey, Champaign, IL (United States) Univ. of Illinois, Urbana, IL (United States))

    1994-05-01

    Ten years of soil moisture measurements (biweekly from March through September and monthly during winter) within the top 1 m of soil at 17 grass-covered sites across Illinois are analyzed to provide a climatology of soil moisture for this important Midwest agricultural region. Soil moisture measurements were obtained with neutron probes that were calibrated for each site. Measurement errors are dependent upon the volumetric water content with errors less than 20 percent when soil moisture is above 0 percent of soil volume. Single point errors in moisture measurements from the top 1 m of soil range from 6 percent to 13 percent when volumetric soil moisture is 30 percent of soil volume. The average depletion in moisture between winter and summer over the 10-year period for the top 2 m of soil in Illinois was 72.3 mm. Three-quarters of this decrease occurred above 0.5 m and only 5 percent occurred between the 1.0-m and 2.0-m depths. The average moisture decrease between winter and summer during a wet year (1985) and a drought year (1988) in the top 2 m of soil was 64 percent and 204 percent of the average for the 10-year period, respectively. Seasonal means in soil moisture averaged for the state show the effects of different seasons and soil types on soil moisture. In the winter and spring a latitudinal gradient exists with the wetter soils in the southern part of the state. During summer and autumn there is a longitudinal gradient with the wetter soils in the eastern half of the state. The longitudinal gradient is closely associated with the depth of loess deposits.

  15. Measurements of soil respiration and simple models dependent on moisture and temperature for an Amazonian southwest tropical forest

    NARCIS (Netherlands)

    Zanchi, F.B.; Rocha, Da H.R.; Freitas, De H.C.; Kruijt, B.; Waterloo, M.J.; Manzi, A.O.

    2009-01-01

    Soil respiration plays a significant role in the carbon cycle of Amazonian tropical forests, although in situ measurements have only been poorly reported and the dependence of soil moisture and soil temperature also weakly understood. This work investigates the temporal variability of soil respirati

  16. Estimating daily root-zone soil moisture in snow-dominated regions using an empirical soil moisture diagnostic equation

    Science.gov (United States)

    Pan, Feifei; Nieswiadomy, Michael

    2016-11-01

    Soil moisture in snow-dominated regions has many important applications including evapotranspiration estimation, flood forecasting, water resource and ecosystem services management, weather prediction and climate modeling, and quantification of denudation processes. A simple and robust empirical approach to estimate root-zone soil moisture in snow-dominated regions using a soil moisture diagnostic equation that incorporates snowfall and snowmelt processes is suggested and tested. A five-water-year dataset (10/1/2010-9/30/2015) of daily precipitation, air temperature, snow water equivalent and soil moistures at three depths (i.e., 5 cm, 20 cm, and 50 cm) at each of 12 Snow Telemetry (SNOTEL) sites across Utah (37.583°N-41.883°N, 110.183°W-112.9°W), is applied to test the proposed method. The first three water years are designated as the parameter-estimation period (PEP) and the last two water years are chosen as the model-testing period (MTP). Applying the estimated soil moisture loss function parameters and other empirical parameters in the soil moisture diagnostic equation in the PEP, soil moistures in three soil columns (0-5 cm, 0-20 cm, and 0-50 cm) are estimated in the MTP. The relatively accurate soil moisture estimations compared to the observations at 12 SNOTEL sites (RMSE ⩽ 6.23 (%V/V), average RMSE = 4.28 (%V/V), correlation coefficient ⩾0.75, average correlation coefficient =0.89, the Nash-Sutcliffe efficient coefficient Ec ⩾ 0.24, average Ec = 0.72) indicate that the soil moisture diagnostic equation is capable of accurately estimating soil moisture in snow-dominated regions after the snowfall and snowmelt processes are included in the soil moisture diagnostic equation.

  17. Evaluating the influence of antecedent soil moisture on variability of the North American Monsoon precipitation in the coupled MM5/VIC modeling system

    Directory of Open Access Journals (Sweden)

    Dennis P. Lettenmaier

    2009-11-01

    Full Text Available The influence of antecedent soil moisture on North American monsoon system (NAMS precipitation variability was explored using the MM5 mesoscale model coupled with the Variable Infiltration Capacity (VIC land surface model. Sensitivity experiments were performed with extreme wet and dry initial soil moisture conditions for both the 1984 wet monsoon year and the 1989 dry year. The MM5-VIC model reproduced the key features of NAMS in 1984 and 1989 especially over northwestern Mexico. Our modeling results indicate that the land surface has memory of the initial soil wetness prescribed at the onset of the monsoon that persists over most of the region well into the monsoon season (e.g. until August. However, in contrast to the classical thermal contrast concept, where wetter soils lead to cooler surface temperatures, less land-sea thermal contrast, weaker monsoon circulations and less precipitation, the coupled model consistently demonstrated a positive soil moisture – precipitation feedback. Specifically, anomalously wet pre-monsoon soil moisture always lead to enhanced monsoon precipitation, and the reverse was also true. Both the large-scale circulation change and local land-atmospheric interactions in response to pre-monsoon soil moisture anomalies play important roles in the coupled model’s positive soil moisture – monsoon precipitation feedback. However, the former may be sensitive to the strength and location of the thermal anomalies, thus leaving open the possibility of both positive and negative soil moisture – precipitation feedbacks. Furthermore, our use of a regional model with prescribed large-scale circulation at the model boundaries leaves open the possibility that the model behavior may, to some extent, reflect its limited ability to adjust its large-scale circulation to the regional thermal changes.

  18. Estimating Soil Moisture from Satellite Microwave Observations

    Science.gov (United States)

    Owe, M.; VandeGriend, A. A.; deJeu, R.; deVries, J.; Seyhan, E.

    1998-01-01

    Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data

  19. Physically-based modeling of topographic effects on spatial evapotranspiration and soil moisture patterns in complex terrain

    Directory of Open Access Journals (Sweden)

    M. Liu

    2011-07-01

    Full Text Available Simulation with the Soil Water Atmosphere Plant (SWAP model is performed to quantify the spatial variability of evapotranspiration (ET and soil moisture content (SMC caused by topography-induced spatial wind and radiation differences. The field scale SWAP model is applied in a distributed way, i.e. for each grid, assuming linear groundwater table, identical boundary conditions and no lateral flow. Input of spatial wind and solar radiation are obtained with the adapted r.sun model and the meso-scale METRAS PC model based on physical mechanisms respectively. Both potential and actual ET, as well as the individual components of evaporation and transpiration are calculated by the model. The numerical experiments are conducted for grids at two different resolutions (100 m and 1000 m to evaluate the scale effects. At fine scale, both solar radiation and wind have a strong effect on spatial ET/SMC pattern, whereas at coarse scale, the wind effect dominates. The results show a strong spatial and temporal intra-catchment variability in daily/annual total ET and less variability in soil moisture. The spatial variability in ET is associated with a difference in total amount of runoff generated, which may lead to a significant consequence in catchment water balance, snowmelt and rainfall-runoff generation processes.

  20. Calibration of soil moisture flow simulation models aided by the active heated fiber optic distributed temperature sensing AHFO

    Science.gov (United States)

    Rodriguez-Sinobas, Leonor; Zubelzu, Sergio; Sobrino, Fernando Fernando; Sánchez, Raúl

    2017-04-01

    Most of the studies dealing with the development of water flow simulation models in soils, are calibrated using experimental data measured by soil probe sensors or tensiometers which locate at specific points in the study area. However since the beginning of the XXI century, the use of Distributed Fiber Optic Temperature Measurement for estimating temperature variation along a cable of fiber optic has been assessed in multiple environmental applications. Recently, its application combined with an active heating pulses technique (AHFO) has been reported as a sensor to estimate soil moisture. This method applies a known amount of heat to the soil and monitors the temperature evolution, which mainly depends on the soil moisture content. Thus, it allows estimations of soil water content every 12.5 cm along the fiber optic cable, as long as 1500 m , with 2 % accuracy , every second. This study presents the calibration of a soil water flow model (developed in Hydrus 2D) with the AHFO technique. The model predicts the distribution of soil water content of a green area irrigated by sprinkler irrigation. Several irrigation events have been evaluated in a green area located at the ETSI Agronómica, Agroalimentaria y Biosistemas in Madrid where an installation of 147 m of fiber optic cable at 15 cm depth is deployed. The Distribute Temperature Sensing unit was a SILIXA ULTIMA SR (Silixa Ltd, UK) and has spatial and temporal resolution of 0.29 m. Data logged in the DTS unit before, during and after the irrigation event were used to calibrate the estimations in the Hydrus 2D model during the infiltration and redistribution of soil water content within the irrigation interval. References: Karandish, F., & Šimůnek, J. (2016). A field-modeling study for assessing temporal variations of soil-water-crop interactions under water-saving irrigation strategies. Agricultural Water Management, 178, 291-303. Li, Y., Šimůnek, J., Jing, L., Zhang, Z., & Ni, L. (2014). Evaluation of

  1. Assimilation of leaf area index and surface soil moisture satellite observations into the SIM hydrological model over France

    Science.gov (United States)

    Fairbairn, David; Calvet, Jean-Christophe; Mahfouf, Jean-Francois; Barbu, Alina

    2016-04-01

    Hydrological models have a variety of uses, including flood and drought prediction and water management. The SAFRAN-ISBA-MODCOU (SIM) hydrological model consists of three stages: An atmospheric analysis (SAFRAN) over France, which forces a land surface model (ISBA-A-gs), which then provides drainage and runoff inputs to a hydrological model (MODCOU). The river discharge from MODCOU is validated using observed river discharge over France. Data assimilation (DA) combines a short model forecast from the past with observations to improve the estimate of the model state. The ISBA-A-gs representation of soil moisture and its influence by vegetation can be improved by assimilating surface soil moisture (SSM) and leaf area index (LAI) observations respectively. The Advanced Scatterometer (ASCAT) on board the MetOP satellite measures a low-frequency microwave signal, which is used to retrieve daily SSM over France. The SPOT-VGT sensor observes LAI over France at a temporal frequency of about 10 days. The Simplified Extended Kalman (SEKF) filter combines the model and observed variables by weighting them according to their respective accuracies. Although the SEKF makes incorrect linear assumptions, past experiments have shown that it improves on the model estimates of SSM and LAI. However, due to nonlinearities in the land surface model, improvements in SSM and LAI do not imply improved soil moisture fluxes (drainage, runoff and evapotranspiration). This study indirectly examines the impact of the SEKF on the soil moisture fluxes using the MODCOU hydrological model. The ISBA-A-gs model appears to underestimate the LAI for grasslands in winter and spring, which results in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess water flowing into the rivers and aquifers contributes to an overestimation of the MODCOU discharge. Assimilating LAI observations slightly increases the LAI analysis in winter and spring and therefore reduces the

  2. Physically-based modifications to the Sacramento Soil Moisture Accounting model. Part A: Modeling the effects of frozen ground on the runoff generation process

    Science.gov (United States)

    Koren, Victor; Smith, Michael; Cui, Zhengtao

    2014-11-01

    This paper presents the first of two physically-based modifications to a widely-used and well-validated hydrologic precipitation-runoff model. Here, we modify the Sacramento Soil Moisture Accounting (SAC-SMA) model to include a physically-based representation of the effects of freezing and thawing soil on the runoff generation process. This model is called the SAC-SMA Heat Transfer model (SAC-HT). The frozen ground physics are taken from the Noah land surface model which serves as the land surface component of several National Center for Environmental Prediction (NCEP) numerical weather prediction models. SAC-HT requires a boundary condition of the soil temperature at the bottom of the soil column (a climatic annual air temperature is typically used, and parameters derived from readily available soil texture data). A noteworthy feature of SAC-HT is that the frozen ground component needs no parameter calibration. SAC-HT was tested at 11 sites in the U.S. for soil temperature, one site in Russia for soil temperature and soil moisture, eight basins in the upper Midwest for the effects of frozen-ground on streamflow, and one location for frost depth. High correlation coefficients for simulated soil temperature at three depths at 11 stations were achieved. Multi-year simulations of soil moisture and soil temperature agreed very well at the Valdai, Russia test location. In eight basins affected by seasonally frozen soil in the upper Midwest, SAC-HT provided improved streamflow simulations compared to SAC-SMA when both models used a priori parameters. Further improvement was gained through calibration of the non-frozen ground a priori parameters. Frost depth computed by SAC-HT compared well with observed values in the Root River basin in Minnesota.

  3. Microwave Soil Moisture Retrieval Under Trees

    Science.gov (United States)

    O'Neill, P.; Lang, R.; Kurum, M.; Joseph, A.; Jackson, T.; Cosh, M.

    2008-01-01

    Soil moisture is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth's surface and atmosphere. Current baseline soil moisture retrieval algorithms for microwave space missions have been developed and validated only over grasslands, agricultural crops, and generally light to moderate vegetation. Tree areas have commonly been excluded from operational soil moisture retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying soil moisture. Our understanding of the microwave properties of trees of various sizes and their effect on soil moisture retrieval algorithms at L band is presently limited, although research efforts are ongoing in Europe, the United States, and elsewhere to remedy this situation. As part of this research, a coordinated sequence of field measurements involving the ComRAD (for Combined Radar/Radiometer) active/passive microwave truck instrument system has been undertaken. Jointly developed and operated by NASA Goddard Space Flight Center and George Washington University, ComRAD consists of dual-polarized 1.4 GHz total-power radiometers (LH, LV) and a quad-polarized 1.25 GHz L band radar sharing a single parabolic dish antenna with a novel broadband stacked patch dual-polarized feed, a quad-polarized 4.75 GHz C band radar, and a single channel 10 GHz XHH radar. The instruments are deployed on a mobile truck with an 19-m hydraulic boom and share common control software; real-time calibrated signals, and the capability for automated data collection for unattended operation. Most microwave soil moisture retrieval algorithms developed for use at L band frequencies are based on the tau-omega model, a simplified zero-order radiative transfer approach where scattering is largely ignored and vegetation canopies are generally treated as a bulk attenuating layer. In this approach, vegetation effects are parameterized by tau and omega, the microwave

  4. Validation of SMOS Satellite Soil Moisture Products over Tropical Region

    Science.gov (United States)

    Kanniah, Kasturi; Siang, Kang Chuen

    2016-07-01

    Calibration and validation (cal/val) activities on Soil Moisture and Ocean Salinity (SMOS) satellite derived soil moisture products has been conducted worldwide since the data has become available but not over the tropical region . This study focuses on the installation of a soil moisture data collection network over an agricultural site in a tropical region in Peninsular Malaysia, and the validation of SMOS soil moisture products. The in-situ data over one year period was analysed and validation of SMOS Soil Moisture products with these in-situ data was conducted.Bias and root mean square errors (RMSE) were computed between SMOS soil moisture products and the in-situ surface soil moisture collected at the satellite passing time (6 am and 6 pm local time). Due to the known limitations of SMOS soil moisture retrieval over vegetated areas with vegetation water content higher than 5 kgm-2, overestimation of SMOS soil moisture products to in-situ data was noticed in this study. The bias is ranging from 0.064 to 0.119 m3m-3 and the RMSE is from 0.090 to 0.158 m3m-3, when both ascending and descending data were validated. This RMSE was found to be similar to a number of studies conducted previously at different regions. However a wet bias was found during the validation, while previous validation activities at other regions showed dry biases. The result of this study is useful to support the continuous development and improvement of SMOS soil moisture retrieval model, aims to produce soil moisture products with higher accuracy, especially in the tropical region.

  5. Evaluating ESA CCI soil moisture in East Africa

    Science.gov (United States)

    McNally, Amy; Shukla, Shraddhanand; Arsenault, Kristi R.; Wang, Shugong; Peters-Lidard, Christa D.; Verdin, James P.

    2016-06-01

    To assess growing season conditions where ground based observations are limited or unavailable, food security and agricultural drought monitoring analysts rely on publicly available remotely sensed rainfall and vegetation greenness. There are also remotely sensed soil moisture observations from missions like the European Space Agency (ESA), Soil Moisture and Ocean Salinity (SMOS) and NASA's Soil Moisture Active Passive (SMAP); however, these time series are still too short to conduct studies that demonstrate the utility of these data for operational applications, or to provide historical context for extreme wet or dry events. To promote the use of remotely sensed soil moisture in agricultural drought and food security monitoring, we evaluate the quality of a 30+ year time series of merged active-passive microwave soil moisture from the ESA Climate Change Initiative (CCI-SM) over East Africa. Compared to the Normalized Difference Vegetation index (NDVI) and modeled soil moisture products, we find substantial spatial and temporal gaps in the early part of the CCI-SM record, with adequate data coverage beginning in 1992. From this point forward, growing season CCI-SM anomalies are well correlated (R > 0.5) with modeled soil moisture, and in some regions, NDVI. We use pixel-wise correlation analysis and qualitative comparisons of seasonal maps and time series to show that remotely sensed soil moisture can inform remote drought monitoring that has traditionally relied on rainfall and NDVI in moderately vegetated regions.

  6. Soil Moisture Monitorization Using GNSS Reflected Signals

    CERN Document Server

    Egido, Alejandro; Caparrini, Marco; Martin, Cristina; Farres, Esteve; Banque, Xavier

    2008-01-01

    The use of GNSS signals as a source of opportunity for remote sensing applications, GNSS-R, has been a research area of interest for more than a decade. One of the possible applications of this technique is soil moisture monitoring. The retrieval of soil moisture with GNSS-R systems is based on the variability of the ground dielectric properties associated to soil moisture. Higher concentrations of water in the soil yield a higher dielectric constant and reflectivity, which incurs in signals that reflect from the Earth surface with higher peak power. Previous investigations have demonstrated the capability of GPS bistatic scatterometers to obtain high enough signal to noise ratios in order to sense small changes in surface reflectivity. Furthermore, these systems present some advantages with respect to others currently used to retrieve soil moisture. Upcoming satellite navigation systems, such as the European Galileo, will represent an excellent source of opportunity for soil moisture remote sensing for vario...

  7. Multi-objective calibration of a hydrologic model using spatially distributed remotely sensed/in-situ soil moisture

    Science.gov (United States)

    Rajib, Mohammad Adnan; Merwade, Venkatesh; Yu, Zhiqiang

    2016-05-01

    The objective of this study is to evaluate the relative potential of spatially distributed surface and root zone soil moisture estimates in calibration of Soil and Water Assessment Tool (SWAT) toward improving its hydrologic predictability with reduced equifinality. The Upper Wabash and Cedar Creek, two agriculture-dominated watersheds in Indiana, USA are considered as test beds to implement this multi-objective SWAT calibration. The proposed calibration approach is performed using remotely sensed Advanced Microwave Scanning Radiometer-Earth Observing System surface soil moisture (∼1 cm top soil) estimates (NASA's Aqua daily level-3 gridded land surface product-version 2) in sub-basin/HRU level together with observed streamflow data at the watershed's outlet. Although application of remote sensing data in calibration improves surface soil moisture simulation, other hydrologic components such as streamflow, evapotranspiration (ET) and deeper layer moisture content in SWAT remain less affected. An extension of this approach to apply root zone soil moisture estimates from limited field sensor data showed considerable improvement in the simulation of root zone moisture content and streamflow with corresponding observed data. Difference in relative sensitivity of parameters and reduced extent of uncertainty are also evident from the proposed method, especially for parameters related to the subsurface hydrologic processes. Regardless, precise representation of vertical soil moisture stratification at different layers is difficult with current SWAT ET depletion mechanism. While the results from this study show that root zone soil moisture can play a major role in SWAT calibration, more studies including various soil moisture data products are necessary to validate the proposed approach.

  8. Process-based modeling of vegetation dynamics, snow, evapotranspiration and soil moisture patterns in an alpine catchment

    Science.gov (United States)

    Bertoldi, Giacomo; Della Chiesa, Stefano; Engel, Michael; Niedrist, Georg; Brenner, Johannes G.; Endrizzi, Stefano; Dall'Amico, Matteo; Cordano, Emanuele; Tappeiner, Ulrike; Rigon, Riccardo

    2014-05-01

    Mountain regions are particularly sensitive to climate change and at the same time they represent a key water resource not only locally but as well for lowland areas. Because of the complexity of mountain landscapes and the high climatic variability at a local scale, detailed quantification of key water budget components as snow cover, soil moisture and groundwater recharge is required. Therefore, there is a strong need to improve the capability of hydrological models to identify patterns in complex terrain (i.e. when variability of spatial characteristics counts), and to quantify changes of the water cycle components explicitly, considering interactions and feedbacks with climate and vegetation. Process-based hydrological models represent promising tools for addressing those needs. However, even if their inherent complexity sometimes limits their applicability for operational purpose, they offer great potential in terms of tools to test hypotheses, which can be verified in the field. GEOtop is a hydrological model that calculates the energy and mass exchanges between soil, vegetation, and atmosphere, accounting for land cover, water redistribution, snow processes, glacier mass budget and the effects of complex terrain and thus is one of the few models that was built with this complexity in mind. Recently, it has also been coupled with a dynamic vegetation model in order to simulate alpine grassland ecosystems. In this contribution, we want to present an application of the GEOtop model in simulating above ground biomass (Bag) production, evapotranspiration (ET), soil moisture (SM) and snow water equivalent (SWE) patterns for a catchment of about 100 km2, located in the Venosta/Vinschgau valley in the European Alps. Despite the Alps are one of the 'water towers of Europe', water scarcity issues can affect the region where the model is applied, and an intensive hydrological and ecological monitoring activity with ground observations and remote-sensing products has

  9. Relating Soil Moisture to TRMMPR Backscatter in Southern United States

    Science.gov (United States)

    Puri, S.; Stephen, H.; Ahmad, S.

    2009-12-01

    Soil Moisture is an important variable in hydrological cycle. It plays a vital role in agronomy, meteorology, and hydrology. In spite of being an important variable, soil moisture measuring stations are sparse. This is due to high cost involved in the installation of dense network of measuring stations required to map a comprehensive spatio-temporal behavior of soil moisture. Hence, there is a need to develop an alternate method to measure soil moisture. This research relates soil moisture (SM) to backscatter (σ°) obtained from Tropical Rainfall Measuring Mission Precipitation Radar (TRMMPR) and Normalized Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer. SM data is obtained from Soil Climate Analysis Network (SCAN). σ° measurements are normalized at an incidence angle of 10° at which it has the highest sensitivity to SM. An empirical model that relates SM to normalized σ° and NDVI is developed. NDVI takes into account the different vegetation densities. The relationship between model variables is approximated to be linear. The model is applied to data from 1998 to 2008 where 75% of the data is used for calibration and the remaining 25% for validation. Figure 1 shows the comparison of observed and modeled soil moisture for a site with low vegetation. Even though the model underestimates the soil moisture content, it captures the signal well and produces peaks similar to the observed soil moisture. The model performs well with a correlation of 0.71 and root mean square error of 4.0%. The accuracy of the model depends on vegetation density. Table 1 summarizes the model performance for different vegetation densities. The model performance decreases with the increase in vegetation as the leaves in the vegetation canopy attenuate the incident microwaves which reduces the penetration depth and subsequently the sensitivity to soil moisture. This research provides a new insight into the microwave remote sensing of soil

  10. NASA Soil Moisture Active Passive (SMAP) Applications

    Science.gov (United States)

    Orr, Barron; Moran, M. Susan; Escobar, Vanessa; Brown, Molly E.

    2014-05-01

    The launch of the NASA Soil Moisture Active Passive (SMAP) mission in 2014 will provide global soil moisture and freeze-thaw measurements at moderate resolution (9 km) with latency as short as 24 hours. The resolution, latency and global coverage of SMAP products will enable new applications in the fields of weather, climate, drought, flood, agricultural production, human health and national security. To prepare for launch, the SMAP mission has engaged more than 25 Early Adopters. Early Adopters are users who have a need for SMAP-like soil moisture or freeze-thaw data, and who agreed to apply their own resources to demonstrate the utility of SMAP data for their particular system or model. In turn, the SMAP mission agreed to provide Early Adopters with simulated SMAP data products and pre-launch calibration and validation data from SMAP field campaigns, modeling, and synergistic studies. The applied research underway by Early Adopters has provided fundamental knowledge of how SMAP data products can be scaled and integrated into users' policy, business and management activities to improve decision-making efforts. This presentation will cover SMAP applications including weather and climate forecasting, vehicle mobility estimation, quantification of greenhouse gas emissions, management of urban potable water supply, and prediction of crop yield. The presentation will end with a discussion of potential international applications with focus on the ESA/CEOS TIGER Initiative entitled "looking for water in Africa", the United Nations (UN) Convention to Combat Desertification (UNCCD) which carries a specific mandate focused on Africa, the UN Framework Convention on Climate Change (UNFCCC) which lists soil moisture as an Essential Climate Variable (ECV), and the UN Food and Agriculture Organization (FAO) which reported a food and nutrition crisis in the Sahel.

  11. Soil moisture and root water uptake in climate models. Research Programme Climate Changes Spatial Planning

    NARCIS (Netherlands)

    Dam, van J.C.; Metselaar, K.; Wipfler, E.L.; Feddes, R.A.; Meijgaard, van E.; Hurk, van den B.

    2011-01-01

    More accurate simulation of the energy and water balance near the Earth surface is important to improve the performance of regional climate models. We used a detailed ecohydrological model to rank the importance of vegetation and soil factors with respect to evapotranspiration modeling. The results

  12. Soil moisture and root water uptake in climate models. Research Programme Climate Changes Spatial Planning

    NARCIS (Netherlands)

    Dam, van J.C.; Metselaar, K.; Wipfler, E.L.; Feddes, R.A.; Meijgaard, van E.; Hurk, van den B.

    2011-01-01

    More accurate simulation of the energy and water balance near the Earth surface is important to improve the performance of regional climate models. We used a detailed ecohydrological model to rank the importance of vegetation and soil factors with respect to evapotranspiration modeling. The results

  13. Uneven moisture patterns in water repellent soils

    NARCIS (Netherlands)

    Dekker, L.W.; Ritsema, C.J.

    1996-01-01

    In the Netherlands, water-repellent soils are widespread and they often show irregular moisture patterns, which cause accelerated transport of water and solutes to the groundwater and surface water. Under grass cover, spatial variability in soil moisture content is high owing to fingered flow; in ar

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

    Directory of Open Access Journals (Sweden)

    Qidong Yang

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

  15. Soil moisture initialization effects in the Indian monsoon system

    OpenAIRE

    Asharaf, S.; A. Dobler; Ahrens, B.

    2011-01-01

    Towards the goal to understand the role of land-surface processes over the Indian sub-continent, a series of soil-moisture sensitivity simulations have been performed using a non-hydrostatic regional climate model COSMO-CLM. The experiments were driven by the lateral boundary conditions provided by the ERA-Interim (ECMWF) reanalysis. The simulation results show that the pre-monsoonal soil moisture has a significant influence on the monsoonal precipitation. Both, positive and negative soil-moi...

  16. Spectrum properties analysis of different soil moisture content

    Science.gov (United States)

    Fang, Shenghui; Hu, Bo; Lin, Fan

    2009-10-01

    Soil moisture content is one of the most important factors in soil business. The basic of detecting soil moisture content using remote sensing technology is to analyze the relationship between soil moisture content and emissivity. In this paper, based on the analysis of spectrum collection and processing by a portable spectrometer, a set of measure schemes were first established which can accurately measure the reflectivity and emissivity of soil spectrum with different moisture content in near-infrared and thermal infrared bands. Then we selected different bare soil areas as the areas for survey, and studied the relationship of different moisture content and the spectrum curve in the soil both of the same kind and of different kind (like the soil whose structure has been modified caused by the change of organic matter contents or soil particle size). Finally, we emphasized on the quantitative relationship between soil reflectivity & emissivity and soil moisture content using the test data, and establish a model depicting the quantitative relationship above in near-infrared and thermal infrared bands.

  17. Determining soil moisture by assimilating soil temperature measurements using the Ensemble Kalman Filter

    Science.gov (United States)

    Dong, Jianzhi; Steele-Dunne, Susan C.; Ochsner, Tyson E.; van de Giesen, Nick

    2015-12-01

    This study investigates the potential to estimate the vertical profile of soil moisture by assimilating temperature observations at a limited number of depths into a coupled heat and moisture transport model (Hydrus-1D). The method is developed with a view to assimilating temperature data from distributed temperature sensing (DTS) to estimate soil moisture at high resolution over large areas. The correlation between temperature and soil moisture in the shallow soil (top ∼ 50 cm) ensures that soil moisture can be estimated using just soil temperature observations. Synthetic tests across a range of soil textures show that with data assimilation both modeled temperature and the moisture profile are improved considerably compared to the ensemble open loop model simulations. In addition, employing data assimilation provides a means to quantitatively account for different sources of uncertainty. This is particularly relevant in the context of DTS given the influence of spatial variability in soil texture and its impact on estimation error. The data assimilation approach could also be used to determine, the number of temperature observations required and the depths at which they should be made. Results suggest that temperature observed at two depths is typically sufficient to estimate soil moisture using this approach. The root mean square error (RMSE) in soil moisture was reduced by up to 75% in the top 20 cm. Furthermore, this approach solves many of the challenges identified in the application of an inversion approach to estimate soil moisture from DTS.

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

    Science.gov (United States)

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

    2016-04-01

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

  19. GNSSProbe, penetrating GNSS signals for measuring soil moisture

    Science.gov (United States)

    Martin, Francisco; Navarro, Victor; Reppucci, Antonio; Mollfulleda, Antonio; Balzter, Heiko; Nicolas-Perea, Virginia; Kissick, Lucy

    2016-04-01

    Soil moisture content (SMC) is an essential parameter from both a scientific and economical point of view. On one hand, it is key for the understanding of hydrological. Secondly, it is a most relevant parameter for agricultural activities and water management. Wide research has been done in this field using different sensors, spanning different parts of the measured electromagnetic spectrum, leading thus several methodologies to estimate soil moisture content. However complying with requirements in terms of accuracy and spatial resolution is still a major challenge. A novel approach based on the measurement of GNSS signals penetrating a soil volume is proposed here. This model relates soil moisture content to the measured soil transmissivity, and attenuation coefficient, which are a function of the soil characteristics (i.e soil moisture content, soit type, soil temperature, etc). A preliminary experiment has been performed to demonstrate the validity of this technique, where the signal received by a GNSS-R L1/E1 RHCP antenna buried at 5, 10, and 15 cm below the surface, was compared to the one received by a GNSS-R L1/E1 RHCP antenna with clear sky visibility. Preliminary results show agreement with theoretical results based on transmissivity and with previous campaigns performed where the soil moisture were collected at two different depths (5 and 15 cm). Details related to the GNSS soil moisture modeling, instrument preparation, measurement campaign, data processing and main results will be presented at the conference.

  20. Hysteresis of soil temperature under different soil moisture and ...

    African Journals Online (AJOL)

    Jane

    2011-10-17

    Oct 17, 2011 ... temperature under three soil moisture and two fertilizer levels in solar greenhouse .... temperature is governed by the one-dimensional heat conduction equation in the soil, and the soil temperature varied sinusoidally. We.

  1. Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France

    Directory of Open Access Journals (Sweden)

    T. Paris Anguela

    2008-12-01

    Full Text Available Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport and vegetation productivity. Its characterization, offers an avenue to improve our understanding of complex land surface-atmosphere interactions. In this paper, soil moisture dynamics at soil surface (first centimeters and root-zone (up to 1.5 m depth are investigated at three spatial scales: local scale (field measurements, 8×8 km2 (hydrological model and 25×25 km2 scale (ERS scatterometer in a French watershed. This study points out the quality of surface and root-zone soil moisture data for SIM model and ERS scatterometer for a three year period. Surface soil moisture is highly variable because is more influenced by atmospheric conditions (rain, wind and solar radiation, and presents RMSE up to 0.08 m3 m−3. On the other hand, root-zone moisture presents lower variability with small RMSE (between 0.02 and 0.06 m3 m−3. These results will contribute to satellite and model verification of moisture, but also to better application of radar data for data assimilation in future.

  2. Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France

    Directory of Open Access Journals (Sweden)

    T. Paris Anguela

    2008-07-01

    Full Text Available Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport and vegetation productivity. Its characterization, offers an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, soil moisture dynamics at soil surface (first centimeters and root-zone (up to 1.5 m depth are investigated at three spatial scales: local scale (field measurements, 8×8 km2 (hydrological model and 25×25 km2 scale (ERS scatterometer in a French watershed. This study points out the quality of surface and root-zone soil moisture data for SIM model and ERS scatterometer for a three year period. Surface soil moisture is highly variable because is more influenced by atmospheric conditions (rain, wind and solar radiation, and presents RMS errors up to 0.08 m3 m−3. On the other hand, root-zone moisture presents lower variability with small RMS errors (between 0.02 and 0.06 m3 m-3. These results will contribute to satellite and model verification of moisture, but also to better application of radar data for data assimilation in future.

  3. Low soil moisture during hot periods drives apparent negative temperature sensitivity of soil respiration in a dryland ecosystem: A multi-model comparison

    Science.gov (United States)

    Tucker, Colin; Reed, Sasha C.

    2016-01-01

    Arid and semiarid ecosystems (drylands) may dominate the trajectory of biosphere-to-atmosphere carbon (C) flux over the coming century. Accordingly, understanding dryland CO2 efflux controls is important for understanding C cycling at the global-scale: key unknowns regarding how temperature and moisture interact to regulate dryland C cycling remain. Further, the patchiness of dryland vegetation can create ‘islands of fertility’, with spatially heterogeneous rates of soil respiration (Rs). At our study site in southeastern Utah, USA we added or removed litter (0 to 650% of control) in paired plots that were either associated with a shrub or with interspaces between vascular plants. We measured Rs, soil temperature, and water content (θ) on eight sampling dates between October 2013 and November 2014. Rs was highest following monsoon rains in late summer when soil temperature was ~30°C. During mid-summer, Rs was low, associated with high soil temperatures (>40°C), resulting in an apparent negative temperature sensitivity of Rs at high temperatures, and positive temperature sensitivity at low-moderate temperatures. We used Bayesian statistical methods to compare multiple competing models capturing a wide range of hypothesized relationships between temperature, moisture, and Rs. The best fit model indicates apparent negative temperature sensitivity of soil respiration at high temperatures reflects the control of soil moisture – not high temperatures – in limiting Rs. The modeled Q10 ranged from 2.7 at 5°C to 1.4 at 45°C. Litter addition had no effect on temperature sensitivity or reference respiration (Rref = Rs at 20°C and optimum moisture) beneath shrubs, and little effect on Rref in interspaces, yet Rref was 1.5 times higher beneath shrubs than in interspaces. Together, these results suggest reduced Rs often observed at high temperatures in drylands is dominated by the control of moisture, and that variable litter inputs – at least over the short

  4. Measurement and modelling of moisture-electrical resistivity relationship of fine-grained unsaturated soils and electrical anisotropy

    Science.gov (United States)

    Merritt, A. J.; Chambers, J. E.; Wilkinson, P. B.; West, L. J.; Murphy, W.; Gunn, D.; Uhlemann, S.

    2016-01-01

    A methodology for developing resistivity-moisture content relationships of materials associated with a clayey landslide is presented. Key elements of the methodology include sample selection and preparation, laboratory measurement of resistivity with changing moisture content, and the derivation of models describing the relationship between resistivity and moisture content. Laboratory resistivity measurements show that the techniques utilised (samples and square array) have considerable potential as a means of electropetrophysical calibration of engineering soils and weak rock. Experimental electrical resistivity results show a hierarchy of values dependent on sample lithology, with silty clay exhibiting the lowest resistivities, followed by siltstones and sands, which return the highest resistivities. In addition, finer grained samples show a greater degree of anisotropy between measurement orientations than coarser grained samples. However, suitability of results in light of issues such as sample cracking and electrical conduction must be identified and accounted for if the results are to be accurately up-scaled to inverted model resistivity results. The existence of directional anisotropy makes model calibration curve selection more difficult due to variability in the range of measured laboratory resistances. The use of larger measurement array size means that experimental data will be more representative of bulk lithological properties. In addition, use of electrodes with a relatively high surface area (wide diameter) help maintain low contact resistances and repeat measurement error, relative to narrow electrodes. Variation exists between the fit of experimental data and petrophysical models. Model fit is best for clay-dominated samples but fits less well for sand-dominated samples. Waxman-Smits equation is appropriately applied in this investigation as all samples have considerable clay mineral content, as is shown in non-negligible CEC results. The

  5. Modeling the effect of initial soil moisture on sorptivity and infiltration

    Science.gov (United States)

    Stewart, Ryan; Abou Najm, Majdi; Rupp, David; Selker, John

    2016-04-01

    Soil capillarity, often associated with the parameter sorptivity, is a primary control on infiltration during short-duration rainfall and irrigation events. However, most mathematical models used to quantify capillarity are only valid for dry antecedent conditions. In this study, we examine how the capillary component of sorptivity (i.e., wetting front potential) varies with initial soil water content, and use this finding to provide a simple modification to the classic Green-Ampt sorptivity model. The modified model has many practical applications, including 1) describing the relative sorptivity of a soil at various water contents; 2) quantifying saturated hydraulic conductivity from sorptivity measurements; and 3) interpreting transient time behavior of single ring infiltration (i.e., beerkan) measurements. The model is especially useful in low permeability soils, where steady-state conditions may not be attained for hours or even days, and in shrink-swell soils, where rapid infiltration measurements are often desired so as not to induce substantial material swelling.

  6. Modeling Soil Moisture in Support of the Revegetation of Military Lands in Arid Regions.

    Science.gov (United States)

    Caldwell, T. G.; McDonald, E. V.; Young, M. H.

    2003-12-01

    The National Training Center (NTC), the Army's primary mechanized maneuver training facility, covers approximately 2600 km2 within the Mojave Desert in southern California, and is the subject of ongoing studies to support the sustainability of military lands in desert environments. Revegetation of these lands by the Integrated Training Areas Management (ITAM) Program requires the identification of optimum growing conditions to reestablish desert vegetation from seed and seedling, especially with regard to the timing and abundance of plant-available water. Water content, soil water potential, and soil temperature were continuously monitored and used to calibrate the Simultaneous Heat And Water (SHAW) model at 3 re-seeded sites. Modeled irrigation scenarios were used to further evaluate the most effective volume, frequency, and timing of irrigation required to maximize revegetation success and minimize water use. Surface treatments including straw mulch, gravel mulch, soil tackifier and plastic sheet

  7. Spatio-Temporal Analysis of Model and Satellite Based Soil Moisture Estimations for Assessing Coupling Hot Spots in the Southern La Plata Basin

    Science.gov (United States)

    Bruscantini, C. A.; Karszenbaum, H.; Ruscica, R. C.; Spennemann, P.; Salvia, M.; Sorensson, A. A.; Grings, F. M.; Saulo, C.

    2015-12-01

    The southern La Plata Basin, located in southeastern South America (SESA), a region of great importance because of its hydrological characteristics, the fact that it has the largest population density and is one of the most productive regions in terms of agriculture, cattle raising and industry of the continent, has been identified as a strong hotspot between soil moisture (SM) and the atmosphere by different regional studies. Among them, Ruscica et al. (2014, Atmos. Sci. Let, Int. J. Climatol.), and Spennemann et al. (2015, Int. J. Climatol.) show, through different modeling approaches, the presence of strong soil moisture-precipitation and evapotranspiration interactions during austral summer in SESA, revealing similar hotspots. Nevertheless these studies have diverse limitations related to model assumptions and to vegetation parameterizations, as well as the lack of observational data for the evaluation of models performance (Ferguson and Wood, 2011, J. of Hydrometeorology). On the other hand, in the last decade several instruments on board satellites are providing soil moisture products globally and in a continuous way. A recent work by Grings et al. (2015, IEEE JSTARS, in press), done over the Pampas Plains in SESA showed characteristic soil moisture patterns that follow the Standardized Precipitation Index (SPI) under extreme wet and dry conditions In order to deepen and overcome some of the mentioned model limitations, this work adds satellite soil moisture and vegetation products in the spatio-temporal analysis of the regions of strong soil moisture-atmosphere interactions. The main objectives and related outcomes are: the verification of already identified regions where soil moisture anomalies may have an influence on subsequent precipitation, evapotranspiration and temperature anomalies, and the study of their seasonal characteristics and land cover influences.

  8. On-irrigator pasture soil moisture sensor

    Science.gov (United States)

    Eng-Choon Tan, Adrian; Richards, Sean; Platt, Ian; Woodhead, Ian

    2017-02-01

    In this paper, we presented the development of a proximal soil moisture sensor that measured the soil moisture content of dairy pasture directly from the boom of an irrigator. The proposed sensor was capable of soil moisture measurements at an accuracy of  ±5% volumetric moisture content, and at meter scale ground area resolutions. The sensor adopted techniques from the ultra-wideband radar to enable measurements of ground reflection at resolutions that are smaller than the antenna beamwidth of the sensor. An experimental prototype was developed for field measurements. Extensive field measurements using the developed prototype were conducted on grass pasture at different ground conditions to validate the accuracy of the sensor in performing soil moisture measurements.

  9. Exploiting optical E.O. data for soil moisture retrieval

    Science.gov (United States)

    Richter, Katja; D'Urso, Guido; Palladino, Mario; Vuolo, Francesco

    2008-10-01

    In the context of agricultural applications, the knowledge of soil moisture availability is an essential aspect for irrigation management. The microwave waveband region (SAR) has been primarily used to estimate soil moisture from Earth Observation (E.O.) data. However, the optical domain (0.4 - 2.5 μm) may as well offer the possibility to get information about soil moisture since an overall decrease of soil reflectance corresponds to increasing surface soil water content. Data from two different experiments (ESA SPARC and AgriSAR) have been exploited aiming at estimating soil moisture from optical E.O. data by using the radiative transfer model PROSAILH. A soil scale factor (α) was introduced into the model and estimated using a LUT inversion technique. Relatively high negative relationships between the α-factor and the measured soil water content (up to R2 = 0.73) could be found for several crop types with low vegetation cover. The results of this study indicate the potential to retrieve surface soil moisture information from optical E.O. data for similar soil types. The method gives the advantage of retrieving simultaneously soil and canopy characteristics from the same E.O. data sources by using a physical method of parameter estimation.

  10. Influence of Soil Moisture on Soil Gas Vapor Concentration for Vapor Intrusion.

    Science.gov (United States)

    Shen, Rui; Pennell, Kelly G; Suuberg, Eric M

    2013-10-01

    Mathematical models have been widely used in analyzing the effects of various environmental factors in the vapor intrusion process. Soil moisture content is one of the key factors determining the subsurface vapor concentration profile. This manuscript considers the effects of soil moisture profiles on the soil gas vapor concentration away from any surface capping by buildings or pavement. The "open field" soil gas vapor concentration profile is observed to be sensitive to the soil moisture distribution. The van Genuchten relations can be used for describing the soil moisture retention curve, and give results consistent with the results from a previous experimental study. Other modeling methods that account for soil moisture are evaluated. These modeling results are also compared with the measured subsurface concentration profiles in the U.S. EPA vapor intrusion database.

  11. Is Regional Root Reinforcement Controlled by Soil Moisture Variability?

    Science.gov (United States)

    Hales, T.; Ford, C. R.

    2011-12-01

    of "bound water" (water present in the cell wall), which in turn affected the strength of the cellulose fibrils that provide tensile strength. This phenomenon, which is the reason any wet wood is weaker than dry wood, results in a 50% difference in root tensile strength within the range of soil moisture measured in the field. We used a one-dimensional finite difference model to explore the effects of soil moisture on root cohesion. Our model shows that changes in the distribution of root biomass represent the primary control on root cohesion (representing up to 50% of intra-specific variability in root cohesion). Local changes in soil moisture result in ~20% change in the overall root cohesion. Our work suggest a feed-forward process in precipitation (and thus soil moisture), root strength changes, and debris flow hazard.

  12. Estimating soil moisture using the Danish polarimetric SAR

    DEFF Research Database (Denmark)

    Jiankang, Ji; Thomsen, A.; Skriver, Henning

    1995-01-01

    The results of applying data from the Danish polarimetric SAR (EMISAR) to estimate soil moisture for bare fields are presented. Fully calibrated C-band SAR images for hh, vv and cross polarizations have been used in this study. The measured surface roughness data showed that classical roughness...... autocorrelation functions (Gaussian and Exponential) were not able to fit natural surfaces well. A Gauss-Exp hybrid model which agreed better with the measured data has been proposed. Theoretical surface scattering models (POM, IEM), as well as an empirical model for retrieval of soil moisture and surface rms...... height from coand cross-polarized ratio, have been examined, but the results are less satisfactory. As soil moisture response to backscattering coefficient σo is mainly coupled to surface roughness effect for bare fields, a bilinear model coupling volumetric soil moisture mv and surface rms height σ...

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

    Science.gov (United States)

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

    2015-12-01

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

  14. Linking soil moisture balance and source-responsive models to estimate diffuse and preferential components of groundwater recharge

    Science.gov (United States)

    Cuthbert, M.O.; Mackay, R.; Nimmo, J.R.

    2012-01-01

    Results are presented of a detailed study into the vadose zone and shallow water table hydrodynamics of a field site in Shropshire, UK. A conceptual model is developed and tested using a range of numerical models, including a modified soil moisture balance model (SMBM) for estimating groundwater recharge in the presence of both diffuse and preferential flow components. Tensiometry reveals that the loamy sand topsoil wets up via macropore flow and subsequent redistribution of moisture into the soil matrix. Recharge does not occur until near-positive pressures are achieved at the top of the sandy glaciofluvial outwash material that underlies the topsoil, about 1 m above the water table. Once this occurs, very rapid water table rises follow. This threshold behaviour is attributed to the vertical discontinuity in the macropore system due to seasonal ploughing of the topsoil, and a lower permeability plough/iron pan restricting matrix flow between the topsoil and the lower outwash deposits. Although the wetting process in the topsoil is complex, a SMBM is shown to be effective in predicting the initiation of preferential flow from the base of the topsoil into the lower outwash horizon. The rapidity of the response at the water table and a water table rise during the summer period while flow gradients in the unsaturated profile were upward suggest that preferential flow is also occurring within the outwash deposits below the topsoil. A variation of the source-responsive model proposed by Nimmo (2010) is shown to reproduce the observed water table dynamics well in the lower outwash horizon when linked to a SMBM that quantifies the potential recharge from the topsoil. The results reveal new insights into preferential flow processes in cultivated soils and provide a useful and practical approach to accounting for preferential flow in studies of groundwater recharge estimation.

  15. Linking soil moisture balance and source-responsive models to estimate diffuse and preferential components of groundwater recharge

    Directory of Open Access Journals (Sweden)

    M. O. Cuthbert

    2013-03-01

    Full Text Available Results are presented of a detailed study into the vadose zone and shallow water table hydrodynamics of a field site in Shropshire, UK. A conceptual model is presented and tested using a range of numerical models, including a modified soil moisture balance model (SMBM for estimating groundwater recharge in the presence of both diffuse and preferential flow components. Tensiometry reveals that the loamy sand topsoil wets up via preferential flow and subsequent redistribution of moisture into the soil matrix. Recharge does not occur until near-positive pressures are achieved at the top of the sandy glaciofluvial outwash material that underlies the topsoil, about 1 m above the water table. Once this occurs, very rapid water table rises follow. This threshold behaviour is attributed to the vertical discontinuity in preferential flow pathways due to seasonal ploughing of the topsoil and to a lower permeability plough/iron pan restricting matrix flow between the topsoil and the lower outwash deposits. Although the wetting process in the topsoil is complex, a SMBM is shown to be effective in predicting the initiation of preferential flow from the base of the topsoil into the lower outwash horizon. The rapidity of the response at the water table and a water table rise during the summer period while flow gradients in the unsaturated profile were upward suggest that preferential flow is also occurring within the outwash deposits below the topsoil. A variation of the source-responsive model proposed by Nimmo (2010 is shown to reproduce the observed water table dynamics well in the lower outwash horizon when linked to a SMBM that quantifies the potential recharge from the topsoil. The results reveal new insights into preferential flow processes in cultivated soils and provide a useful and practical approach to accounting for preferential flow in studies of groundwater recharge estimation.

  16. 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

  17. Is soil moisture initialization important for seasonal to decadal predictions?

    Science.gov (United States)

    Stacke, Tobias; Hagemann, Stefan

    2014-05-01

    The state of soil moisture can can have a significant impact on regional climate conditions for short time scales up to several months. However, focusing on seasonal to decadal time scales, it is not clear whether the predictive skill of global a Earth System Model might be enhanced by assimilating soil moisture data or improving the initial soil moisture conditions with respect to observations. As a first attempt to provide answers to this question, we set up an experiment to investigate the life time (memory) of extreme soil moisture states in the coupled land-atmosphere model ECHAM6-JSBACH, which is part of the Max Planck Institute for Meteorology's Earth System Model (MPI-ESM). This experiment consists of an ensemble of 3 years simulations which are initialized with extreme wet and dry soil moisture states for different seasons and years. Instead of using common thresholds like wilting point or critical soil moisture, the extreme states were extracted from a reference simulation to ensure that they are within the range of simulated climate variability. As a prerequisite for this experiment, the soil hydrology in JSBACH was improved by replacing the bucket-type soil hydrology scheme with a multi-layer scheme. This new scheme is a more realistic representation of the soil, including percolation and diffusion fluxes between up to five separate layers, the limitation of bare soil evaporation to the uppermost soil layer and the addition of a long term water storage below the root zone in regions with deep soil. While the hydrological cycle is not strongly affected by this new scheme, it has some impact on the simulated soil moisture memory which is mostly strengthened due to the additional deep layer water storage. Ensemble statistics of the initialization experiment indicate perturbation lengths between just a few days up to several seasons for some regions. In general, the strongest effects are seen for wet initialization during northern winter over cold and humid

  18. Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

    Science.gov (United States)

    Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

    2017-03-01

    Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has

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

    Science.gov (United States)

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

    2016-08-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  1. The impact of different soil texture datasets on soil moisture and evapotranspiration simulated by CLM4

    Science.gov (United States)

    Yan, B.; Dickinson, R. E.

    2012-12-01

    Evapotranspiration (ET) is both a moisture flux and an energy flux. It has a substantial impact on climate. Community Land Model Version 4 (CLM4) is a widely used land surface model that simulates moisture, energy and momentum exchange between land and atmosphere. However, ET from CLM4 suffers from relatively low accuracy, especially for ground evaporation. In the parameterization of CLM4, soil texture, by determining soil hydraulic properties, affects the evolution of soil moisture and consequently ET. The three components of ET in climate models can more readily be improved after an evaluation of soil texture dataset's impact on ET simulations. Besides the IGBP-DIS (International Geosphere-Biosphere Programme Data and Information System) dataset used in CLM4, another two US multi-layer soil particle content datasets, Soil Database for the Conterminous United States (CONUS-SOIL) and Global Soil Texture and Derived Water-Holding Capacities (Webb2000), are also used. The latter two show a consistent substantial reduction of both sand and clay contents in Mississippi River Basin. CLM4 is run off line over the US with the three different soil texture datasets (Control Run, CONUS SOIL and Webb2000). Comparisons of simulated soil moisture with NCEP (National Centers for Environmental Prediction) reanalysis data show a higher agreement between CONUS SOIL and NCEP over Mississippi River Basin. Compared with Control Run, soil moisture from the other two runs increases in Western US and decreases in Eastern US, which produces a stronger west-east soil moisture gradient. The response of ET to soil moisture change differs in different climate regimes. In Mississippi River Basin, the change of ET is negligible even if soil moisture increases substantially. On the other hand, in eastern US and US Central Great Plains, ET is very sensitive to soil moisture during the warm seasons, with the change of up to 10 W/m2.

  2. Retrieving pace in vegetation growth using precipitation and soil moisture

    Science.gov (United States)

    Sohoulande Djebou, D. C.; Singh, V. P.

    2013-12-01

    The complexity of interactions between the biophysical components of the watershed increases the challenge of understanding water budget. Hence, the perspicacity of the continuum soil-vegetation-atmosphere's functionality still remains crucial for science. This study targeted the Texas Gulf watershed and evaluated the behavior of vegetation covers by coupling precipitation and soil moisture patterns. Growing season's Normalized Differential Vegetation Index NDVI for deciduous forest and grassland were used over a 23 year period as well as precipitation and soil moisture data. The role of time scales on vegetation dynamics analysis was appraised using both entropy rescaling and correlation analysis. This resulted in that soil moisture at 5 cm and 25cm are potentially more efficient to use for vegetation dynamics monitoring at finer time scale compared to precipitation. Albeit soil moisture at 5 cm and 25 cm series are highly correlated (R2>0.64), it appeared that 5 cm soil moisture series can better explain the variability of vegetation growth. A logarithmic transformation of soil moisture and precipitation data increased correlation with NDVI for the different time scales considered. Based on a monthly time scale we came out with a relationship between vegetation index and the couple soil moisture and precipitation [NDVI=a*Log(% soil moisture)+b*Log(Precipitation)+c] with R2>0.25 for each vegetation type. Further, we proposed to assess vegetation green-up using logistic regression model and transinformation entropy using the couple soil moisture and precipitation as independent variables and vegetation growth metrics (NDVI, NDVI ratio, NDVI slope) as the dependent variable. The study is still ongoing and the results will surely contribute to the knowledge in large scale vegetation monitoring. Keywords: Precipitation, soil moisture, vegetation growth, entropy Time scale, Logarithmic transformation and correlation between soil moisture and NDVI, precipitation and

  3. Determining soil moisture and soil properties in vegetated areas by assimilating soil temperatures

    Science.gov (United States)

    Dong, Jianzhi; Steele-Dunne, Susan C.; Ochsner, Tyson E.; van de Giesen, Nick

    2016-06-01

    This study addresses two critical barriers to the use of Passive Distributed Temperature Sensing (DTS) for large-scale, high-resolution monitoring of soil moisture. In recent research, a particle batch smoother (PBS) was developed to assimilate sequences of temperature data at two depths into Hydrus-1D to estimate soil moisture as well as soil thermal and hydraulic properties. However, this approach was limited to bare soil and assumed that the cable depths were perfectly known. In order for Passive DTS to be more broadly applicable as a soil hydrology research and remote sensing soil moisture product validation tool, it must be applicable in vegetated areas. To address this first limitation, the forward model (Hydrus-1D) was improved through the inclusion of a canopy energy balance scheme. Synthetic tests were used to demonstrate that without the canopy energy balance scheme, the PBS estimated soil moisture could be even worse than the open loop case (no assimilation). When the improved Hydrus-1D model was used as the forward model in the PBS, vegetation impacts on the soil heat and water transfer were well accounted for. This led to accurate and robust estimates of soil moisture and soil properties. The second limitation is that, cable depths can be highly uncertain in DTS installations. As Passive DTS uses the downward propagation of heat to extract moisture-related variations in thermal properties, accurate estimates of cable depths are essential. Here synthetic tests were used to demonstrate that observation depths can be jointly estimated with other model states and parameters. The state and parameter results were only slightly poorer than those obtained when the cable depths were perfectly known. Finally, in situ temperature data from four soil profiles with different, but known, soil textures were used to test the proposed approach. Results show good agreement between the observed and estimated soil moisture, hydraulic properties, thermal properties, and

  4. Field scale spatio-temporal soil moisture variability for trafficability and crop water availability

    Science.gov (United States)

    Carranza, Coleen; van der Ploeg, Martine; Ritsema, Coen

    2016-04-01

    Spatio-temporal patterns of soil moisture have been studied mostly for inputs in land surface models for weather and climate predictions. Remote sensing techniques for estimation of soil moisture have been explored because of the good spatial coverage at different scales. Current available satellite data provide surface soil moisture as microwave systems only measure soil moisture content up to 5cm soil depth. The OWAS1S project will focus on estimation of soil moisture from freely available Sentinel-1 datasets for operational water management in agricultural areas. As part of the project, it is essential to develop spatio-temporal methods to estimate root zone soil moisture from surface soil moisture. This will be used for crop water availability and trafficability in selected agricultural fields in the Netherlands. A network of single capacitance sensors installed per field will provide continuous measurements of soil moisture in the study area. Ground penetrating radar will be used to measure soil moisture variability within a single field for different time periods. During wetter months, optimal conditions for traffic will be assessed using simultaneous soil strength and soil moisture measurements. Towards water deficit periods, focus is on the relation (or the lack thereof) between surface soil moisture and root zone soil moisture to determine the amount of water for crops. Spatio-temporal distribution will determine important physical controls for surface and root zone soil moisture and provide insights for root-zone soil moisture. Existing models for field scale soil-water balance and data assimilation methods (e.g. Kalman filter) will be combined to estimate root zone soil moisture. Furthermore, effects of root development on soil structure and soil hydraulic properties and subsequent effects on trafficability and crop water availability will be investigated. This research project has recently started, therefore we want to present methods and framework of

  5. Sensitivity of high-temperature weather to initial soil moisture: a case study using the WRF model

    Science.gov (United States)

    Zeng, X.-M.; Wang, B.; Zhang, Y.; Song, S.; Huang, X.; Zheng, Y.; Chen, C.; Wang, G.

    2014-09-01

    Using a succession of 24 h Weather Research and Forecasting model (WRF) simulations, we investigate the sensitivity to initial soil moisture of a short-range high-temperature weather event that occurred in late July 2003 in East China. The initial soil moisture (SMOIS) in the Noah land surface scheme is adjusted (relative to the control run, CTL) for four groups of simulations: DRY25 (-25%), DRY50 (-50%), WET25 (+25%) and WET50 (+50%). Ten 24 h integrations are performed in each group. We focus on 2 m surface air temperature (SAT) greater than 35 °C (the threshold of "high-temperature" events in China) at 06:00 UTC (roughly 14:00 LT in the study domain) to analyse the occurrence of the high-temperature event. The 10-day mean results show that the 06:00 UTC SAT (SAT06) is sensitive to the SMOIS change; specifically, SAT06 exhibits an apparent increase with the SMOIS decrease (e.g. compared with CTL, DRY25 generally results in a 1 °C SAT06 increase over the land surface of East China), areas with 35 °C or higher SAT06 are the most affected, and the simulations are more sensitive to the SMOIS decrease than to the SMOIS increase, which suggests that hot weather can be amplified under low soil moisture conditions. Regarding the mechanism underlying the extremely high SAT06, sensible heat flux has been shown to directly heat the lower atmosphere, and latent heat flux has been found to be more sensitive to the SMOIS change, resulting in an overall increase in surface net radiation due to the increased greenhouse effect (e.g. with the SMOIS increase from DRY25 to CTL, the 10-day mean net radiation increases by 5 W m-2). Additionally, due to the unique and dynamic nature of the western Pacific subtropical high, negative feedback occurs between the regional atmospheric circulation and the air temperature in the lower atmosphere while positive feedback occurs in the mid-troposphere. Using a method based on an analogous temperature relationship, a detailed analysis of the

  6. Sensitivity of high-temperature weather to initial soil moisture: a case study with the WRF model

    Science.gov (United States)

    Zeng, X.-M.; Wang, B.; Zhang, Y.; Song, S.; Huang, X.; Zheng, Y.; Chen, C.; Wang, G.

    2014-05-01

    Using the Weather Research and Forecasting model (WRF), we investigate the sensitivity of simulated short-range high-temperature weather to initial soil moisture for the East China extremely hot event in late July 2003 via a succession of 24 h simulations. The initial soil moisture (SMOIS) in the Noah land surface scheme is prescribed for five groups of designed simulations, i.e., relative to the control run (CTL), SMOIS is changed by -25, -50, +25 and +50% in the DRY25, DRY50, WET25 and WET50 groups, respectively, with ten 24 h-long integrations performed in each group. We focus on above-35 °C (standard of so-called "high-temperature" event in China) 2 m surface air temperature (SAT) at 06:00 UTC (roughly 12:00 LT in the study domain) to analyze the occurrence of the high-temperature event. Ten-day mean results show that the 06:00 UTC SAT (SAT06) is sensitive to the SMOIS change, i.e., SAT06 exhibits an apparent rising with the SMOIS decrease (e.g., compared with CTL, DRY25 results in a 1 °C SAT06 rising in general over land surface of East China), areas with above-35 °C SAT06 are most affected, and the simulations are found to be more sensitive to the SMOIS decrease than to the SMOIS increase, suggesting that hot weather can be amplified under low soil moisture conditions. With regard to the mechanism of influencing the extreme high SAT06, sensible heat flux shows to directly heat the lower atmosphere, latent heat flux is found to be more sensitive to the SMOIS change and results in the overall increase of surface net radiation due to the increased greenhouse effect (e.g., with the SMOIS increase of 25% from DRY25 to CTL, the ten-day mean net radiation is increased by 5 W m-2), and a negative (positive) feedback is found between regional atmospheric circulation and air temperature in the lower atmosphere (mid-troposphere) due to the unique dynamic nature of the western Pacific subtropical high. Using a method based on an analogous temperature relationship, a

  7. Climate variability effects on spatial soil moisture dynamics

    OpenAIRE

    A. J. Teuling; Hupet, F.; R. Uijlenhoet; P. A. Troch

    2007-01-01

    We investigate the role of interannual climate variability on spatial soil moisture variability dynamics for a field site in Louvain-la-Neuve, Belgium. Observations were made during 3 years under intermediate (1999), wet (2000), and extremely dry conditions (2003). Soil moisture variability dynamics are simulated with a comprehensive model for the period 1989-2003. The results show that climate variability induces non-uniqueness and two distinct hysteresis modes in the yearly relation between...

  8. Downscaling soil moisture using multisource data in China

    Science.gov (United States)

    An, Ru; Wang, Hui-Lin; You, Jia-jun; Wang, Ying; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballardd, Jonathan Arthur; Chen, Yuehong

    2016-10-01

    Soil moisture plays an important role in the water cycle within the surface ecosystem and it is the basic condition for the growth and development of plants. Currently, the spatial resolution of most soil moisture data from remote sensing ranges from ten to several tens of kilometres whilst those observed in situ and simulated for watershed hydrology, ecology, agriculture, weather and drought research are generally less than 1 kilometre. Therefore, the existing coarse resolution remotely sensed soil moisture data needs to be down-scaled. In this paper, a universal soil moisture downscaling model through stepwise regression with moving window suitable for large areas and multi temporal has been established. Datasets comprise land surface, brightness temperature, precipitation, soil and topographic parameters from high resolution data, and active/passive microwave remotely sensed soil moisture data from Essential Climate Variables (ECV_SM) with 25 km spatial resolution were used. With this model, a total of 288 soil moisture maps of 1 km resolution from the first ten-day of January 2003 to the last tenth-day of December 2010 were derived. The in situ observations were used to validate the down-scaled ECV_SM for different land cover and land use types and seasons. In addition, various errors comparative analysis was also carried out for the down-scaled ECV_SM and original one. In general, the down-scaled soil moisture for different land cover and land use types is consistent with the in situ observations. The accuracy is relatively high in autumn and winter. The validation results show that downscaled soil moisture can be improved not only on spatial resolution, but also on estimation accuracy.

  9. Thresholds and interactive effects of soil moisture on the temperature response of soil respiration

    DEFF Research Database (Denmark)

    Lellei-Kovács, Eszter; Kovács-Láng, Edit; Botta-Dukát, Zoltán;

    2011-01-01

    Ecosystem carbon exchange is poorly understood in low-productivity, semiarid habitats. Here we studied the controls of soil temperature and moisture on soil respiration in climate change field experiment in a sandy forest-steppe. Soil CO2 efflux was measured monthly from April to November in 2003......–2008 on plots receiving either rain exclusion or nocturnal warming, or serving as ambient control. Based on this dataset, we developed and compared empirical models of temperature and moisture effects on soil respiration. Results suggest that in this semiarid ecosystem the main controlling factor for soil CO2...... efflux is soil temperature, while soil moisture has less, although significant effect on soil respiration. Clear thresholds for moisture effects on temperature sensitivity were identified at 0.6, 4.0 and 7.0vol% by almost each model, which relate well to other known limits for biological activity...

  10. Inter-comparison of hydrological model simulations with dense time series of SAR-derived soil moisture maps

    Science.gov (United States)

    Iacobellis, V.; Gioia, A.; Milella, P.; Satalino, G.; Balenzano, A.; Mattia, F.

    2012-04-01

    Over the last years, a vast number of experimental and theoretical studies has widely demonstrated the sensitivity of SAR data to soil moisture content, however, operational services integrating SAR measurements into land process models are not yet available. Important progresses in this field are expected, on the one hand, from SAR missions characterized by a short revisiting time, such as the COSMO-SkyMed or the forthcoming Sentinel-1 and ALOS-2 missions, on the other hand, from a strong effort in implementing hydrological models able to reproduce the dynamic of soil moisture content of the top layer (5 cm depth) of soil. With this latter purpose, we used the DREAM model [Manfreda et al., 2005], realized in a GIS-based approach, that explicitly takes into account the spatial heterogeneity of hydrological processes. The DREAM model carries out continuous hydrological simulations using the daily and the hourly scales. The distinctive feature of the model, which consists of evaluating the lateral flow through a water content redistribution weighted by the topographic index, was preserved. The latter provided the basis for the nested implementation of the Richard equation which has been used for evaluating vertical flows in the top soil layer (5cm).The Richard routine exploits the numerical solution proposed by Simunek et al. [2009] and runs, for each cell of the river basin, in a sub-module of 60 minutes with a vertical (i.e. depth) and temporal resolution of 1 cm and 1 s, respectively. The model was applied to the portion of the Celone at Foggia San Severo river basin downstream the San Giusto Dam, which is a tributary of the Candelaro river, in Puglia region (Southern Italy). Over this area quasi-dense time series of ALOS/PALSAR ScanSAR WB1 and COSMO-SkyMedStripMap images were acquired in 2007 and 2010, respectively. The SAR data have been used to derive time-series of soil moisture maps by means of the SMOSAR software developed for Sentinel-1 data [Balenzano et

  11. Physically-based modeling of topographic effects on spatial evapotranspiration and soil moisture patterns through radiation and wind

    Directory of Open Access Journals (Sweden)

    M. Liu

    2012-02-01

    Full Text Available In this paper, simulations with the Soil Water Atmosphere Plant (SWAP model are performed to quantify the spatial variability of both potential and actual evapotranspiration (ET, and soil moisture content (SMC caused by topography-induced spatial wind and radiation differences. To obtain the spatially distributed ET/SMC patterns, the field scale SWAP model is applied in a distributed way for both pointwise and catchment wide simulations. An adapted radiation model from r.sun and the physically-based meso-scale wind model METRAS PC are applied to obtain the spatial radiation and wind patterns respectively, which show significant spatial variation and correlation with aspect and elevation respectively. Such topographic dependences and spatial variations further propagate to ET/SMC. A strong spatial, seasonal-dependent, scale-relevant intra-catchment variability in daily/annual ET and less variability in SMC can be observed from the numerical experiments. The study concludes that topography has a significant effect on ET/SMC in the humid region where ET is a energy limited rather than water availability limited process. It affects the spatial runoff generation through spatial radiation and wind, therefore should be applied to inform hydrological model development. In addition, the methodology used in the study can serve as a general method for physically-based ET estimation for data sparse regions.

  12. Incorporating an enzymatic model of effects of temperature, moisture, and substrate supply on soil respiration into an ecosystem model for two forests of northeastern USA

    Science.gov (United States)

    Sihi, Debjani; Davidson, Eric; Chen, Min; Savage, Kathleen; Richardson, Andrew; Keenan, Trevor; Hollinger, David

    2017-04-01

    Soils represent the largest terrestrial carbon (C) pool, and microbial decomposition of soil organic matter (SOM) to carbon dioxide, also called heterotrophic respiration (Rh), is an important component of the global C cycle. Temperature sensitivity of Rh is often represented with a simple Q10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed to disentangle the confounding factors of apparent temperature sensitivity of SOM decomposition and improve performance of ecosystem models and ESMs. The objective of this work was to incorporate into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen and soluble carbon substrates to the enzymatic reaction site. However, in its current configuration, DAMM depends on assumptions or inputs from other models regarding soil C inputs. Here we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FöBAAR (Forest Biomass, Assimilation, Allocation and Respiration) by replacing FöBAAR's algorithms for Rh with those of DAMM. Classical root trenching experiments provided data to partition soil CO2 efflux into Rh (trenched plot) and root respiration (untrenched minus trenched plots). We used three years of high-frequency soil flux data from automated soil chambers (trenched and untrenched plots) and landscape-scale ecosystem fluxes from eddy covariance towers from two mid-latitude forests (Harvard Forest, MA and Howland Forest, ME) of northeastern USA to develop and validate the merged model and to quantify the

  13. SMAP Level 4 Surface and Root Zone Soil Moisture

    Science.gov (United States)

    Reichle, R.; De Lannoy, G.; Liu, Q.; Ardizzone, J.; Kimball, J.; Koster, R.

    2017-01-01

    The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone soil moisture, along with other land surface variables and their error estimates. These estimates are obtained through assimilation of SMAP brightness temperature observations into the Goddard Earth Observing System (GEOS-5) land surface model. The L4_SM product is provided at 9 km spatial and 3-hourly temporal resolution and with about 2.5 day latency. The soil moisture and temperature estimates in the L4_SM product are validated against in situ observations. The L4_SM product meets the required target uncertainty of 0.04 m(exp. 3)m(exp. -3), measured in terms of unbiased root-mean-square-error, for both surface and root zone soil moisture.

  14. Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation

    KAUST Repository

    Singh, Gurjeet

    2016-05-05

    Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA’s Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

  15. Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin

    Science.gov (United States)

    Leroux, Delphine J.; Pellarin, Thierry; Vischel, Théo; Cohard, Jean-Martial; Gascon, Tania; Gibon, François; Mialon, Arnaud; Galle, Sylvie; Peugeot, Christophe; Seguis, Luc

    2016-07-01

    Precipitation forcing is usually the main source of uncertainty in hydrology. It is of crucial importance to use accurate forcing in order to obtain a good distribution of the water throughout the basin. For real-time applications, satellite observations allow quasi-real-time precipitation monitoring like the products PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, TRMM (Tropical Rainfall Measuring Mission) or CMORPH (CPC (Climate Prediction Center) MORPHing). However, especially in West Africa, these precipitation satellite products are highly inaccurate and the water amount can vary by a factor of 2. A post-adjusted version of these products exists but is available with a 2 to 3 month delay, which is not suitable for real-time hydrologic applications. The purpose of this work is to show the possible synergy between quasi-real-time satellite precipitation and soil moisture by assimilating the latter into a hydrological model. Soil Moisture Ocean Salinity (SMOS) soil moisture is assimilated into the Distributed Hydrology Soil Vegetation Model (DHSVM) model. By adjusting the soil water content, water table depth and streamflow simulations are much improved compared to real-time precipitation without assimilation: soil moisture bias is decreased even at deeper soil layers, correlation of the water table depth is improved from 0.09-0.70 to 0.82-0.87, and the Nash coefficients of the streamflow go from negative to positive. Overall, the statistics tend to get closer to those from the reanalyzed precipitation. Soil moisture assimilation represents a fair alternative to reanalyzed rainfall products, which can take several months before being available, which could lead to a better management of available water resources and extreme events.

  16. The moisture response of soil heterotrophic respiration: interaction with soil properties

    OpenAIRE

    F. E. Moyano; N. Vasilyeva; L. Bouckaert; Cook, F; J. Craine; J. Curiel Yuste; Don, A.; Epron, D.; Formanek, P; A. Franzluebbers; Ilstedt, U; T. Kätterer; Orchard, V.; Reichstein, M.; Rey, A.

    2012-01-01

    Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the rep...

  17. The moisture response of soil heterotrophic respiration: interaction with soil properties

    OpenAIRE

    F. E. Moyano; N. Vasilyeva; L. Bouckaert; Cook, F; J. Craine; J. Curiel Yuste; Don, A.; Epron, D.; Formanek, P; A. Franzluebbers; Ilstedt, U; T. Kätterer; Orchard, V.; Reichstein, M.; Rey, A.

    2011-01-01

    Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4 % in global soil carbon stock predictions by 2100. The necessity of improving t...

  18. Can soil moisture be mapped onto the terrain?

    Directory of Open Access Journals (Sweden)

    E. M. Blyth

    2004-01-01

    Full Text Available Soil moisture heterogeneity has an effect on the rainfall–runoff characteristics of a landscape. The aggregate effect on the mean water balance over an area can be quantified successfully using models such as the PDM (Moore, 1986 and TOPMODEL (Beven and Kirkby, 1979. These rainfall–runoff models have been embedded in the large-scale land surface schemes used in meteorological models. However, there is also a requirement (e.g. model validation to identify the spatial structure of the fine-scale soil moisture heterogeneity that makes up these aggregate models. In some types of landscape, this will be dictated by topography, in others by soil characteristics, or by a combination of both. A method to distribute area-average soil moisture according to the likely effect of local topography is presented and tested. The heterogeneity of the soil moisture is described by the Xinanxiang distribution (Zhao et al., 1980, commonly used to describe the natural spatial heterogeneity of the landscape. This distribution is then mapped onto the terrain using a topographic index to locate the wettest and driest areas. Soil moisture data from the Wye catchment in Wales and from the Pang catchment in Berkshire, England, are used to test the method. It is found that soil moisture data from the Wye catchment follow the topographic index reasonably well, whereas data from the quick-draining, chalky Pang catchment do not. The conclusion that topographic index is a useful indicator only in some landscapes applies equally to using this mapping method and those models that use topographic index directly. Keywords: soil moisture, heterogeneity, topographic index, data

  19. Effect of soil moisture on chlorine deposition.

    Science.gov (United States)

    Hearn, John; Eichler, Jeffery; Hare, Christopher; Henley, Michael

    2014-02-28

    The effect of soil moisture on chlorine (Cl(2)) deposition was examined in laboratory chamber experiments at high Cl(2) exposures by measuring the concentration of chloride (Cl(-)) in soil columns. Soil mixtures with varying amounts of clay, sand, and organic matter and with moisture contents up to 20% (w/w) were exposed to ≈3×10(4)ppm Cl(2) vapor. For low water content soils, additional water increased the reaction rate as evidenced by higher Cl(-) concentration at higher soil moisture content. Results also showed that the presence of water restricted transport of Cl(2) into the soil columns and caused lower overall deposition of Cl(2) in the top 0.48-cm layer of soil when water filled ≈60% or more of the void space in the column. Numerical solutions to partial differential equations of Fick's law of diffusion and a simple rate law for Cl(2) reaction corroborated conclusions derived from the data. For the soil mixtures and conditions of these experiments, moisture content that filled 30-50% of the available void space yielded the maximum amount of Cl(2) deposition in the top 0.48cm of soil. Published by Elsevier B.V.

  20. Development of an Objective High Spatial Resolution Soil Moisture Index

    Science.gov (United States)

    Zavodsky, B.; Case, J.; White, K.; Bell, J. R.

    2015-12-01

    Drought detection, analysis, and mitigation has become a key challenge for a diverse set of decision makers, including but not limited to operational weather forecasters, climatologists, agricultural interests, and water resource management. One tool that is heavily used is the United States Drought Monitor (USDM), which is derived from a complex blend of objective data and subjective analysis on a state-by-state basis using a variety of modeled and observed precipitation, soil moisture, hydrologic, and vegetation and crop health data. The NASA Short-term Prediction Research and Transition (SPoRT) Center currently runs a real-time configuration of the Noah land surface model (LSM) within the NASA Land Information System (LIS) framework. The LIS-Noah is run at 3-km resolution for local numerical weather prediction (NWP) and situational awareness applications at select NOAA/National Weather Service (NWS) forecast offices over the Continental U.S. (CONUS). To enhance the practicality of the LIS-Noah output for drought monitoring and assessing flood potential, a 30+-year soil moisture climatology has been developed in an attempt to place near real-time soil moisture values in historical context at county- and/or watershed-scale resolutions. This LIS-Noah soil moisture climatology and accompanying anomalies is intended to complement the current suite of operational products, such as the North American Land Data Assimilation System phase 2 (NLDAS-2), which are generated on a coarser-resolution grid that may not capture localized, yet important soil moisture features. Daily soil moisture histograms are used to identify the real-time soil moisture percentiles at each grid point according to the county or watershed in which the grid point resides. Spatial plots are then produced that map the percentiles as proxies to the different USDM categories. This presentation will highlight recent developments of this gridded, objective soil moisture index, comparison to subjective

  1. An intercomparison of available soil moisture estimates from thermal infrared and passive microwave remote sensing and land surface modeling

    Science.gov (United States)

    Hain, Christopher R.; Crow, Wade T.; Mecikalski, John R.; Anderson, Martha C.; Holmes, Thomas

    2011-08-01

    Remotely sensed soil moisture studies have mainly focused on retrievals using active and passive microwave (MW) sensors, which provide measurements that are directly related to soil moisture (SM). MW sensors have obvious advantages such as the ability to retrieve through nonprecipitating cloud cover which provides shorter repeat cycles. However, MW sensors offer coarse spatial resolution and suffer from reduced retrieval skill over moderate to dense vegetation. A unique avenue for filling these information gaps is to exploit the retrieval of SM from thermal infrared (TIR) observations, which can provide SM information under vegetation cover and at significantly higher resolutions than MW. Previously, an intercomparison of TIR-based and MW-based SM has not been investigated in the literature. Here a series of analyses are proposed to study relationships between SM products during a multiyear period (2003-2008) from a passive MW retrieval (AMSR-E), a TIR based model (ALEXI), and a land surface model (Noah) over the continental United States. The three analyses used in this study include (1) a spatial anomaly correlation analysis, (2) a temporal correlation analysis, and (3) a triple collocation error estimation technique. In general, the intercomparison shows that the TIR and MW methods provide complementary information about the current SM state. TIR can provide SM information over moderate to dense vegetation, a large information gap in current MW methods, while serving as an additional independent source of SM information over low to moderate vegetation. The complementary nature of SM information from MW and TIR sensors implies a potential for integration within an advanced SM data assimilation system.

  2. Microwave Measurements of Moisture Distributions in the Upper Soil Profile

    Science.gov (United States)

    Sadeghi, A. M.; Hancock, G. D.; Waite, W. P.; Scott, H. D.; Rand, J. A.

    1984-07-01

    Laboratory and field experiments were conducted to investigate the ability of microwave remote sensing systems to detect the moisture status of a silt loam soil exhibiting abrupt changes in moisture content near the surface. Laboratory soil profiles were prepared with a discontinuous moisture boundary in the subsurface. Reflectivity measurements of these profiles were made with a bistatic reflectometer operating over the frequency ranges of 1-2 and 4-8 GHz (wavelength ranges of 30-15 and 7.5-3.75 cm, respectively). These measurements exhibited a well-developed coherent interference pattern in good agreement with a simple two-layer reflectivity model. Field measurements of bare soil surfaces were conducted for initially saturated profiles and continued for extended periods of drying. During drying, coherent interference patterns similar to those observed in the laboratory were detected. These appear to be due to steep moisture gradients occurring between drying layers near the surface. The field results were modeled by a five-segment linear moisture profile with one or two steep segments and a multilayer reflectivity program. Agreement between model and field response over the frequency range was used to estimate the depth of drying layers within the soil. These depths were monitored over the second and third drying cycles. Formation of the drying layers under field conditions appears to be influenced by drying time, tillage, and evaporative demand. In any case, it appears that the coherent effects caused by nonuniform moisture profiles may substantially affect the reflectivity of even rough soil surfaces.

  3. Inference of Soil Hydrologic Parameters from Electronic Soil Moisture Records

    Science.gov (United States)

    Chandler, David G.; Seyfried, Mark S.; McNamara, James P.; Hwang, Kyotaek

    2017-04-01

    Soil moisture is an important control on hydrologic function, as it governs vertical fluxes from and to the atmosphere, groundwater recharge and lateral fluxes through the soil. Historically, the traditional model parameters of saturation, field capacity and permanent wilting point have been determined by laboratory methods. This approach is challenged by issues of scale, boundary conditions and soil disturbance. We develop and compare four methods to determine values of field saturation, field capacity, plant extraction limit and initiation of plant water stress from long term in-situ monitoring records of TDR-measured volumetric water content (Q). The monitoring sites represent a range of soil textures, soil depths, effective precipitation and plant cover types in a semi-arid climate. The Q records exhibit attractors (high frequency values) that correspond to field capacity and the plant extraction limit at both annual and longer time scales, but the field saturation values vary by year depending on seasonal wetness in the semi-arid setting. The analysis for five sites in two watersheds is supported by comparison to values determined by a common pedotransfer function and measured soil characteristic curves. Frozen soil is identified as a complicating factor for the analysis and users are cautioned to filter data by temperature, especially for near surface soils.

  4. Data-driven modeling of hydroclimatic trends and soil moisture: Multi-scale data integration and decision support

    Science.gov (United States)

    Coopersmith, Evan Joseph

    regime curve data and facilitate the development of cluster-specific algorithms. Given the desire to enable intelligent decision-making at any location, this classification system is developed in a manner that will allow for classification anywhere in the U.S., even in an ungauged basin. Daily time series data from 428 catchments in the MOPEX database are analyzed to produce an empirical classification tree, partitioning the United States into regions of hydroclimatic similarity. In constructing a classification tree based upon 55 years of data, it is important to recognize the non-stationary nature of climate data. The shifts in climatic regimes will cause certain locations to shift their ultimate position within the classification tree, requiring decision-makers to alter land usage, farming practices, and equipment needs, and algorithms to adjust accordingly. This work adapts the classification model to address the issue of regime shifts over larger temporal scales and suggests how land-usage and farming protocol may vary from hydroclimatic shifts in decades to come. Finally, the generalizability of the hydroclimatic classification system is tested with a physically-based soil moisture model calibrated at several locations throughout the continental United States. The soil moisture model is calibrated at a given site and then applied with the same parameters at other sites within and outside the same hydroclimatic class. The model's performance deteriorates minimally if the calibration and validation location are within the same hydroclimatic class, but deteriorates significantly if the calibration and validates sites are located in different hydroclimatic classes. These soil moisture estimates at the field scale are then further refined by the introduction of LiDAR elevation data, distinguishing faster-drying peaks and ridges from slower-drying valleys. The inclusion of LiDAR enabled multiple locations within the same field to be predicted accurately despite non

  5. Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for model calibration and validation in a large ungauged catchment

    DEFF Research Database (Denmark)

    Milzow, Christian; Krogh, Pernille Engelbredt; Bauer-Gottwein, Peter

    2010-01-01

    hundred meters; and (iii) Temporal changes of the Earth’s gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are compared to simulated moisture conditions in the top soil layer. They cannot be used for model...

  6. Confidence interval estimation for an empirical model quantifying the effect of soil moisture and plant development on soybean (Glycine max (L.) Merr.) leaf conductance

    Science.gov (United States)

    In this work, we address uncertainty analysis for a model, presented in a companion paper, quantifying the effect of soil moisture and plant development on soybean (Glycine max (L.) Merr.) leaf conductance. To achieve this we present several methods for confidence interval estimation. Estimation ...

  7. Predicting root zone soil moisture using surface data

    Science.gov (United States)

    Manfreda, S.; Brocca, L.; Moramarco, T.; Melone, F.; Sheffield, J.; Fiorentino, M.

    2012-04-01

    In recent years, much effort has been given to monitoring of soil moisture from satellite remote sensing. These tools represent an extraordinary source of information for hydrological applications, but they only provide information on near-surface soil moisture. In the present work, we developed a new formulation for the estimation of the soil moisture in the root zone based on the measured value of soil moisture at the surface. The method derives from a simplified form of the soil water balance equation and for this reason all parameters adopted are physically consistent. The formulation provides a closed form of the relationship between the root zone soil moisture and the surface soil moisture with a limited number of parameters, such as: the ratio between the depth of the surface layer and the deeper layer, the water loss coefficient, and the field capacity. The method has been tested using modeled soil moisture obtained from the North American Land Data Assimilation System (NLDAS). The NLDAS is a multi-institution partnership aimed at developing a retrospective data set, using available atmospheric and land surface meteorological observations to compute the land surface hydrological budget. The NLDAS database was extremely useful for the scope of the present research since it provides simulated data over an extended area with different climatic and physical condition and moreover it provides soil moisture data averaged over different depths. In particular, we used values in the top 10 cm and 100 cm layers. One year of simulation was used to test the ability of the developed method to describe soil moisture fluctuation in the 100cm layer over the entire NLDAS domain. The method was adopted by calibrating one of its three parameters and defining the remaining two based on physical characteristics of the site (using the potential evapotranspiration and ratio between the first and the second soil layer depth). In general, the method performed better than

  8. Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

    Science.gov (United States)

    Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika

    2014-01-01

    Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

  9. Soil Moisture Remote Sensing using GPS-Interferometric Reflectometry

    Science.gov (United States)

    Chew, Clara

    Ground-reflected Global Positioning System (GPS) signals can be used opportunistically to infer changes in land-surface characteristics surrounding a GPS monument. GPS satellites transmit at L-band, and at microwave frequencies the permittivity of the ground surface changes primarily due to its moisture content. Temporal changes in ground-reflected GPS signals are thus indicative of temporal changes in the moisture content surrounding a GPS antenna. The interference pattern of the direct and reflected GPS signal for a single satellite track is recorded in signal-to-noise ratio (SNR) data. Alternating constructive and destructive interference as the satellite passes over the antenna results in a noisy oscillating wave at low satellite elevation angles, from which the phase, amplitude, and frequency (or reflector height) can be calculated. Here, an electrodynamic model that simulates SNR data is validated against field observations. The model is then used to show that temporal changes in these SNR metrics may be used to estimate changes in surface soil moisture in the top 5 cm of the soil column. Results show that changes in SNR phase are best correlated with changes in soil moisture, with an approximately linear slope. Surface roughness decreases the sensitivity of SNR phase to soil moisture, though the effect is not significant for small roughness values (moisture is to be estimated using phase data. An algorithm is presented that uses modeled relationships between canopy parameters and SNR metrics to remove seasonal vegetation effects from the phase time series, from which soil moisture time series may be estimated. Results indicate that this algorithm can successfully estimate surface soil moisture with an RMSE of 0.05 cm3 cm-3 or lower for many of the antennas that comprise the Plate Boundary Observatory (PBO) network.

  10. Groundwater-soil moisture-climate interactions: lessons from idealized model experiments with forced water table depth

    Science.gov (United States)

    Ducharne, Agnès; Lo, Min-Hui; Decharme, Bertrand; Wang, Fuxing; Cheruy, Frédérique; Ghattas, Josefine; Chien, Rong-You; lan, Chia-Wei; Colin, Jeanne; Tyteca, Sophie

    2016-04-01

    Groundwater (GW) constitutes by far the largest volume of liquid freshwater on Earth. The most active part is soil moisture (SM), recognized as a key variable of land/atmosphere interactions, especially in so-called transition zones, where/when SM varies between wet and dry values. But GW can also be stored in deeper reservoirs than soils, in particular unconfined aquifer systems, in which the saturated part is called the water table (WT). The latter is characterized by slow and mostly horizontal water flows towards the river network, with well-known buffering effects on streamflow variability. Where/when the WT is shallow enough, it can also sustain SM by means of capillary rise, thus increase evapotranspiration (ET), with potential impact on the climate system (including temperatures and precipitation). The large residence time of GW may also increase the Earth system's memory, with consequences on the persistence of extreme events, hydro-climatic predictability, and anthropogenic climate change, particularly the magnitude of regional warming. Here, our main goal is to explore the potential impacts of the water table depth (WTD) on historical climate through idealized model analyses. To this end, we force three state-of-the art land surface models (LSMs), namely CLM, ORCHIDEE, and SURFEX, with prescribed WTDs ranging from 0.5 to 10 m. The LSMs are run either off-line or coupled to their parent climate model, following LMIP/AMIP-like protocols for intercomparability. Within this framework, we want to assess the sensitivity of ET and the simulated climate to the WTD in a systematic way. In particular, we will identify and compare the patterns of the critical WTD, defined as the deepest one to achieve a significant change in ET. To this end, we estimate derivatives of ET with respect to WTD, which tell how the sensitivity of ET to a unit change in WTD evolves with WTD. In each grid-point, these derivatives can be used to define the critical WTD, given a threshold ET

  11. Modelling the effect of soil moisture and organic matter degradation on biogenic NO emissions from soils in Sahel rangeland (Mali

    Directory of Open Access Journals (Sweden)

    C. Delon

    2015-06-01

    0.96 kg(N ha−1 yr−1, and wet season average ranges from 3.36 to 5.48 ng(N m−2 s−1 (1.06 to 1.73 kg(N ha−1 yr−1. These results are of the same order as previous measurements made in several sites where the vegetation and the soil are comparable to the ones in Agoufou. This coupled vegetation–litter decomposition–emission model could be generalized at the scale of the Sahel region, and provide information where few data are available.

  12. Response of spectral vegetation indices to soil moisture in grasslands and shrublands

    Science.gov (United States)

    Zhang, L.; Ji, L.; Wylie, B.K.

    2011-01-01

    The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture. ?? 2011 Taylor & Francis.

  13. Impact of the soil hydrology scheme on simulated soil moisture memory in a GCM

    Science.gov (United States)

    Hagemann, Stefan; Stacke, Tobias

    2013-04-01

    Soil moisture-atmosphere feedback effects play an important role in several regions of the globe. For some of these regions, soil moisture memory may contribute significantly to the development of the regional climate. Identifying those regions can help to improve predictability in seasonal to decadal climate forecasts. The present study investigates how different setups of the soil hydrology scheme affect soil moisture memory simulated by the global climate model of the Max Planck Institute for Meteorology (MPI-M), ECHAM6/JSBACH. First, the standard setup applied for the CMIP5 exercise is used, in which soil water is represented by a single soil moisture reservoir. Second, a new five soil layer hydrology scheme is utilized where the previous bucket soil moisture now corresponds to the root zone soil moisture. In the standard setup, transpiration may access the whole soil moisture that is exceeding the wilting point over vegetated areas. However, in the five layer scheme, soil water below the root zone cannot be accessed by transpiration directly, but only be transported upwards into the root zone by diffusion following the Richard's equation. Thus, this below the root zone, which is not present in the standard setup, can act as buffer in the transition between wet and dry periods. A second notable difference between the two setups is the formulation of bare soil evaporation. In the standard setup, it may only occur if the whole soil moisture bucket is almost completely saturated, while in the new setup, it depends only on the saturation of the upper most soil layer. As the latter is much thinner than the root zone (bucket), bare soil evaporation can occur more frequently, especially after rainfall events. For the second setup, two further variants are considered: one where the bare soil evaporation was modified and one where a new parameter dataset of soil water holding capacities was used. Soil moisture memory of the different setups will be analysed from global

  14. Predictions of rainfall-runoff response and soil moisture dynamics in a microscale catchment using the CREW model

    Science.gov (United States)

    Lee, H.; Zehe, E.; Sivapalan, M.

    2007-02-01

    .e., water retention curve) and hydraulic conductivity-saturation relationships for the unsaturated zone. Closure relations for concentrated overland flow and saturated overland flow were derived using both theoretical arguments and simpler process models. In addition to these, to complete the specification of the REW scale balance equations, a relationship for the saturated area fraction as a function of saturated zone depth was derived for an assumed topography on the basis of TOPMODEL assumptions. These relationships were used to complete the specification of all of the REW-scale governing equations (mass and momentum balance equations, closure and geometric relations) for the Weiherbach catchment, which are then employed for constructing a numerical watershed model, named the Cooperative Community Catchment model based on the Representative Elementary Watershed approach (CREW). CREW is then used to carry out sensitivity analyses with respect to various combinations of climate, soil, vegetation and topographies, in order to test the reasonableness of the derived closure relations in the context of the complete catchment response, including interacting processes. These sensitivity analyses demonstrated that the adopted closure relations do indeed produce mostly reasonable results, and can therefore be a good basis for more careful and rigorous search for appropriate closure relations in the future. Three tests are designed to assess CREW as a large scale model for Weiherbach catchment. The first test compares CREW with distributed model CATFLOW by looking at predicted soil moisture dynamics for artificially designed initial and boundary conditions. The second test is designed to see the applicabilities of the parameter values extracted from the upscaling procedures in terms of their ability to reproduce observed hydrographs within the CREW modeling framework. The final test compares simulated soil moisture time series predicted by CREW with observed ones as a way of

  15. Predictions of rainfall-runoff response and soil moisture dynamics in a microscale catchment using the CREW model

    Directory of Open Access Journals (Sweden)

    H. Lee

    2007-01-01

    pressure-saturation (i.e., water retention curve and hydraulic conductivity-saturation relationships for the unsaturated zone. Closure relations for concentrated overland flow and saturated overland flow were derived using both theoretical arguments and simpler process models. In addition to these, to complete the specification of the REW scale balance equations, a relationship for the saturated area fraction as a function of saturated zone depth was derived for an assumed topography on the basis of TOPMODEL assumptions. These relationships were used to complete the specification of all of the REW-scale governing equations (mass and momentum balance equations, closure and geometric relations for the Weiherbach catchment, which are then employed for constructing a numerical watershed model, named the Cooperative Community Catchment model based on the Representative Elementary Watershed approach (CREW. CREW is then used to carry out sensitivity analyses with respect to various combinations of climate, soil, vegetation and topographies, in order to test the reasonableness of the derived closure relations in the context of the complete catchment response, including interacting processes. These sensitivity analyses demonstrated that the adopted closure relations do indeed produce mostly reasonable results, and can therefore be a good basis for more careful and rigorous search for appropriate closure relations in the future. Three tests are designed to assess CREW as a large scale model for Weiherbach catchment. The first test compares CREW with distributed model CATFLOW by looking at predicted soil moisture dynamics for artificially designed initial and boundary conditions. The second test is designed to see the applicabilities of the parameter values extracted from the upscaling procedures in terms of their ability to reproduce observed hydrographs within the CREW modeling framework. The final test compares simulated soil moisture time series predicted by CREW with observed

  16. Development and Assessment of the Sand Dust Prediction Model by Utilizing Microwave-Based Satellite Soil Moisture and Reanalysis Datasets in East Asian Desert Areas

    Directory of Open Access Journals (Sweden)

    Hyunglok Kim

    2017-01-01

    Full Text Available For several decades, satellite-based microwave sensors have provided valuable soil moisture monitoring in various surface conditions. We have first developed a modeled aerosol optical depth (AOD dataset by utilizing Soil Moisture and Ocean Salinity (SMOS, Advanced Microwave Scanning Radiometer 2 (AMSR2, and the Global Land Data Assimilation System (GLDAS soil moisture datasets in order to estimate dust outbreaks over desert areas of East Asia. Moderate Resolution Imaging Spectroradiometer- (MODIS- based AOD products were used as reference datasets to validate the modeled AOD (MA. The SMOS-based MA (SMOS-MA dataset showed good correspondence with observed AOD (R-value: 0.56 compared to AMSR2- and GLDAS-based MA datasets, and it overestimated AOD compared to observed AOD. The AMSR2-based MA dataset was found to underestimate AOD, and it showed a relatively low R-value (0.35 with respect to observed AOD. Furthermore, SMOS-MA products were able to simulate the short-term AOD trends, having a high R-value (0.65. The results of this study may allow us to acknowledge the utilization of microwave-based soil moisture datasets for investigation of near-real time dust outbreak predictions and short-term dust outbreak trend analysis.

  17. A Simple Runoff Model Based on Topographic Wetness Indices and Soil Moisture for Central New York Agricultural Fields

    Science.gov (United States)

    Hofmeister, K.; Georgakakos, C.; Walter, M. T.

    2014-12-01

    Nonpoint source (NPS) pollution continues to be a leading cause of surface water degradation, especially in agricultural areas. In humid regions where variable source area (VSA) hydrology dominates, such as the Northeastern US, topographic wetness indices (TWI) are good approximations of relative soil moisture patterns. Mapping areas of the landscape likely to generate saturation-excess runoff and carry NPS pollution to surface waters could allow for more efficient, targeted best management practices in agricultural fields. Given the relationship between saturation excess runoff and soil water storage, we used volumetric water content (VWC) measured in five agricultural fields in central New York over two years (2012-2014) to develop runoff probability maps based on a soil topographic index (STI). The relationship between VWC and STI was strongest during the fall season after leaf fall at all sites except one. We calculated the probability of runoff occurring based on soil moisture and precipitation distributions during the sampling period. The threshold for runoff occurs when the depth of soil water and rainfall reach saturation of the soil, and appears to be at the average porosity of the soil at all sites. Counter to our initial hypothesis of a higher probability of saturation excess runoff during the spring when conditions are wetter, some sites showed higher frequencies of runoff during the fall season.

  18. Is large-scale inverse modelling of unsaturated flow with areal average evaporation and surface soil moisture as estimated from remote sensing feasible?

    Science.gov (United States)

    Feddes, R. A.; Menenti, M.; Kabat, P.; Bastiaanssen, W. G. M.

    1993-03-01

    The potentiality of combining large-scale inverse modelling of unsaturated flow with remote sensing determination of areal evaporation and areal surface moisture is assessed. Regional latent and sensible heat fluxes are estimated indirectly using remotely sensed measurements by parameterizing the surface energy balance equation. An example of evapotranspiration mapping from northern and central Egypt is presented. The inverse problem is formulated with respect to the type of information available. Two examples of estimation of soil hydraulic properties by the dynamic one-dimensional soil-water-vegetation model SWATRE are given: one refers to a classical lysimeter scale and another one to a catchment scale. It is concluded that small-scale soil physics may describe large-scale hydrological behaviour adequately, and that the effective hydraulic parameters concerned may be derived by an inverse modelling approach. Remotely sensed data on surface reflectance, surface temperature and soil moisture content derived from multifrequency microwave techniques provide a useful data set on the mesoscale. The inverse modelling approach presented combined with a meso-scale data set on evaporation and surface soil moisture, considerable potentialities arise to determine effective meso-scale hydraulic properties.

  19. The Impact of Rainfall on Soil Moisture Dynamics in a Foggy Desert

    Science.gov (United States)

    Li, Bonan; Wang, Lixin; Kaseke, Kudzai F.; Li, Lin; Seely, Mary K.

    2016-01-01

    Soil moisture is a key variable in dryland ecosystems since it determines the occurrence and duration of vegetation water stress and affects the development of weather patterns including rainfall. However, the lack of ground observations of soil moisture and rainfall dynamics in many drylands has long been a major obstacle in understanding ecohydrological processes in these ecosystems. It is also uncertain to what extent rainfall controls soil moisture dynamics in fog dominated dryland systems. To this end, in this study, twelve to nineteen months’ continuous daily records of rainfall and soil moisture (from January 2014 to August 2015) obtained from three sites (one sand dune site and two gravel plain sites) in the Namib Desert are reported. A process-based model simulating the stochastic soil moisture dynamics in water-limited systems was used to study the relationships between soil moisture and rainfall dynamics. Model sensitivity in response to different soil and vegetation parameters under diverse soil textures was also investigated. Our field observations showed that surface soil moisture dynamics generally follow rainfall patterns at the two gravel plain sites, whereas soil moisture dynamics in the sand dune site did not show a significant relationship with rainfall pattern. The modeling results suggested that most of the soil moisture dynamics can be simulated except the daily fluctuations, which may require a modification of the model structure to include non-rainfall components. Sensitivity analyses suggested that soil hygroscopic point (sh) and field capacity (sfc) were two main parameters controlling soil moisture output, though permanent wilting point (sw) was also very sensitive under the parameter setting of sand dune (Gobabeb) and gravel plain (Kleinberg). Overall, the modeling results were not sensitive to the parameters in non-bounded group (e.g., soil hydraulic conductivity (Ks) and soil porosity (n)). Field observations, stochastic modeling

  20. Sensitivity of LISEM predicted catchment discharge to initial soil moisture content of soil profile

    NARCIS (Netherlands)

    V. Sheikh; E. van Loon; R. Hessel; V. Jetten

    2010-01-01

    This study conducts a broad sensitivity analysis, taking into account the influence of initial soil moisture content in two soil layers, layer depths, event properties, and two infiltration models. A distributed hydrology and soil erosion model (LISEM) is used. Using the terrain data from the Catsop

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

    Science.gov (United States)

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

    2016-06-01

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

  2. Soil Moisture Data Assimilation in a Hydrological Model: A Case Study in Belgium Using Large-Scale Satellite Data

    Directory of Open Access Journals (Sweden)

    Pierre Baguis

    2017-08-01

    Full Text Available In the present study, we focus on the assimilation of satellite observations for Surface Soil Moisture (SSM in a hydrological model. The satellite data are produced in the framework of the EUMETSAT project H-SAF and are based on measurements with the Advanced radar Scatterometer (ASCAT, embarked on the Meteorological Operational satellites (MetOp. The product generated with these measurements has a horizontal resolution of 25 km and represents the upper few centimeters of soil. Our approach is based on the Ensemble Kalman Filter technique (EnKF, where observation and model uncertainties are taken into account, implemented in a conceptual hydrological model. The analysis is carried out in the Demer catchment of the Scheldt River Basin in Belgium, for the period from June 2013–May 2016. In this context, two methodological advances are being proposed. First, the generation of stochastic terms, necessary for the EnKF, of bounded variables like SSM is addressed with the aid of specially-designed probability distributions, so that the bounds are never exceeded. Second, bias due to the assimilation procedure itself is removed using a post-processing technique. Subsequently, the impact of SSM assimilation on the simulated streamflow is estimated using a series of statistical measures based on the ensemble average. The differences from the control simulation are then assessed using a two-dimensional bootstrap sampling on the ensemble generated by the assimilation procedure. Our analysis shows that data assimilation combined with bias correction can improve the streamflow estimations or, at a minimum, produce results statistically indistinguishable from the control run of the hydrological model.

  3. Precipitation fields interpolated from gauge stations versus a merged radar-gauge precipitation product: influence on modelled soil moisture at local scale and at SMOS scale

    Directory of Open Access Journals (Sweden)

    J. T. dall'Amico

    2012-03-01

    Full Text Available For the validation of coarse resolution soil moisture products from missions such as the Soil Moisture and Ocean Salinity (SMOS mission, hydrological modelling of soil moisture is an important tool. The spatial distribution of precipitation is among the most crucial input data for such models. Thus, reliable time series of precipitation fields are required, but these often need to be interpolated from data delivered by scarcely distributed gauge station networks. In this study, a commercial precipitation product derived by Meteomedia AG from merging radar and gauge data is introduced as a novel means of adding the promising area-distributed information given by a radar network to the more accurate, but point-like measurements from a gauge station network. This precipitation product is first validated against an independent gauge station network. Further, the novel precipitation product is assimilated into the hydrological land surface model PROMET for the Upper Danube Catchment in southern Germany, one of the major SMOS calibration and validation sites in Europe. The modelled soil moisture fields are compared to those obtained when the operational interpolation from gauge station data is used to force the model. The results suggest that the assimilation of the novel precipitation product can lead to deviations of modelled soil moisture in the order of 0.15 m3 m−3 on small spatial (∼1 km2 and short temporal resolutions (∼1 day. As expected, after spatial aggregation to the coarser grid on which SMOS data are delivered (~195 km2, these differences are reduced to the order of 0.04 m3 m−3, which is the accuracy benchmark for SMOS. The results of both model runs are compared to brightness temperatures measured by the airborne L-band radiometer EMIRAD during the SMOS Validation Campaign 2010. Both comparisons yield equally good correlations, confirming the model's ability to

  4. A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. 2; Aggregation

    Science.gov (United States)

    Schamschula, Marius; Crosson, William L.; Inguva, Ramarao; Yates, Thomas; Laymen, Charles A.; Caulfield, John

    1998-01-01

    This is a follow up on the preceding presentation by Crosson. The grid size for remote microwave measurements is much coarser than the hydrological model computational grids. To validate the hydrological models with measurements we propose mechanisms to aggregate the hydrological model outputs for soil moisture to allow comparison with measurements. Weighted neighborhood averaging methods are proposed to facilitate the comparison. We will also discuss such complications as misalignment, rotation and other distortions introduced by a generalized sensor image.

  5. Dependence of soil respiration on soil temperature and soil moisture in successional forests in Southern China

    Science.gov (United States)

    Tang, X.-L.; Zhou, G.-Y.; Liu, S.-G.; Zhang, D.-Q.; Liu, S.-Z.; Li, J.; Zhou, C.-Y.

    2006-01-01

    The spatial and temporal variations in soil respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, soil respiration rates, soil temperature, and soil moisture were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of soil respiration and its biophysical dependence in these forests. The relationships between biophysical factors and soil respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of soil respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). Soil respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (?? SD) soil respiration rate in the DNR forests was (9.0 ?? 4.6) Mg CO2-C/hm2per year, ranging from (6.1 ?? 3.2) Mg CO2-C/hm2per year in early successional forests to (10.7 ?? 4.9) Mg CO2-C/hm2 per year in advanced successional forests. Soil respiration was correlated with both soil temperature and moisture. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of soil respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, moisture increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more soil moisture is needed to maintain their activities. ?? 2006 Institute of Botany, Chinese Academy of

  6. Dependence of Soil Respiration on Soil Temperature and Soil Moisture in Successional Forests in Southern China

    Institute of Scientific and Technical Information of China (English)

    Xu-Li Tang; Guo-Yi Zhou; Shu-Guang Liu; De-Qiang Zhang; Shi-Zhong Liu; Jiong Li; Cun-Yu Zhou

    2006-01-01

    The spatial and temporal variations in soil respiration and its relationship with biophysical factors in forests near the Tropic of Cancer remain highly uncertain. To contribute towards an improvement of actual estimates, soil respiration rates, soil temperature, and soil moisture were measured in three successional subtropical forests at the Dinghushan Nature Reserve (DNR) in southern China from March 2003 to February 2005. The overall objective of the present study was to analyze the temporal variations of soil respiration and its biophysical dependence in these forests. The relationships between biophysical factors and soil respiration rates were compared in successional forests to test the hypothesis that these forests responded similarly to biophysical factors. The seasonality of soil respiration coincided with the seasonal climate pattern, with high respiration rates in the hot humid season (April-September) and with low rates in the cool dry season (October-March). Soil respiration measured at these forests showed a clear increasing trend with the progressive succession. Annual mean (± SD) soil respiration rate in the DNR forests was (9.0±4.6) Mg CO2-C/hm2 per year, ranging from (6.1±3.2) Mg CO2-C/hm2 per year in early successional forests to (10.7±4.9) Mg CO2-C/hm2 per year in advanced successional forests. Soil respiration was correlated with both soil temperature and moisture. The T/M model, where the two biophysical variables are driving factors, accounted for 74%-82% of soil respiration variation in DNR forests. Temperature sensitivity decreased along progressive succession stages, suggesting that advanced-successional forests have a good ability to adjust to temperature. In contrast, moisture increased with progressive succession processes. This increase is caused, in part, by abundant respirators in advanced-successional forest, where more soil moisture is needed to maintain their activities.

  7. Determination of the saturated film conductivity to improve the EMFX model in describing the soil hydraulic properties over the entire moisture range

    Science.gov (United States)

    Wang, Yunquan; Ma, Jinzhu; Guan, Huade; Zhu, Gaofeng

    2017-06-01

    Difficulty in measuring hydraulic conductivity, particularly under dry conditions, calls for methods of predicting the conductivity from easily obtained soil properties. As a complement to the recently published EMFX model, a method based on two specific suction conditions is proposed to estimate saturated film conductivity from the soil water retention curve. This method reduces one fitting parameter in the previous EMFX model, making it possible to predict the hydraulic conductivity from the soil water retention curve over the complete moisture range. Model performance is evaluated with published data of soils in a broad texture range from sand to clay. The testing results indicate that 1) the modified EMFX model (namely the EMFX-K model), incorporating both capillary and adsorption forces, provides good agreement with the conductivity data over the entire moisture range; 2) a value of 0.5 for the tortuosity factor in the EMFX-K model as that in the Mualem's model gives comparable estimation of the relative conductivity associated with the capillary force; and 3) a value of -1.0 × 10-20 J for the Hamaker constant, rather than the commonly used value of -6.0 × 10-20 J, appears to be more appropriate to represent solely the effect of the van der Waals forces and to predict the film conductivity. In comparison with the commonly used van Genuchten-Mualem model, the EMFX-K model significantly improves the prediction of hydraulic conductivity under dry conditions. The sensitivity analysis result suggests that the uncertainty in the film thickness estimation is important in explaining the model underestimation of hydraulic conductivity for the soils with fine texture, in addition to the uncertainties from the measurements and the model structure. High quality data that cover the complete moisture range for a variety of soil textures are required to further test the method.

  8. The Impact of Soil Moisture Initialization On Seasonal Precipitation Forecasts

    Science.gov (United States)

    Koster, R. D.; Suarez, M. J.; Tyahla, L.; Houser, Paul (Technical Monitor)

    2002-01-01

    Some studies suggest that the proper initialization of soil moisture in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial soil moisture anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the soil moisture anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial soil moisture conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize soil moisture information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.

  9. A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

    Directory of Open Access Journals (Sweden)

    Chunggil Jung

    2017-08-01

    Full Text Available This study attempts to estimate spatial soil moisture in South Korea (99,000 km2 from January 2013 to December 2015 using a multiple linear regression (MLR model and the Terra moderate-resolution imaging spectroradiometer (MODIS land surface temperature (LST and normalized distribution vegetation index (NDVI data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs of the Korea Meteorological Administration (KMA, and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM technique and observed LST data from 71 KMA stations. The coefficient of determination (R2 of the original LST and observed LST was 0.71, and the R2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R2 values were between 0.28 and 0.67. The reason for R2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R2 and root mean square error (RMSE in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI can be used to better understand the severity of droughts with the

  10. An open-access software platform for modeling turbulent heat and moisture fluxes as well as surface soil moisture from the Synergy of VNIR/TIR EO Data and a Land Biosphere Model

    Science.gov (United States)

    Petropoulos, George; Anagnostopoulos, Vasileios

    2015-04-01

    Today, in the face of climate change, it has been recognised by the global scientific community as a topic requiring further attention and investigation. Use of simulation process models combined with Earth Observation (EO) data provides a promising direction towards deriving accurately spatiotemporal estimates of key parameters characterising land surface interactions such as latent (LE) and sensible (H) heat fluxes as well as soil surface moisture (SSM). Herein a software tool developed in Java for deriving regional estimates of LE and H fluxes (sensible and latent heat) as well as surface soil moisture from the implementation of the so-called "triangle" method is presented. The method is based on a contextual interpretation of a satellite-derived scatterplot of land surface temperature (Ts) versus a Fractional Vegetation Cover (Fr) combined with a land biosphere model. The tool offers a graphical user interface (GUI) to the user, with the aim to allow customisation of the noise removal of the dataset. Upper and lower edges of the trapezoid in the Fr versus normalized Ts diagram are automatically derived and visualised. The user can also enter various parameters to the SimSphere engine through a convenient form and visualisation of trapezoid matching for various simulation scenarios is also provided. Computationally it can handle one million scatter points with acceptable lag in the user interface. It is also multi-core friendly by using Java 8 parallel streams for conversions and prediction. The predictor training and histogram computation are the main serialisation bottlenecks. In contrast to other methods the trapezoid derivation and matching is automatic requiring little more than a customisation of noise removal and scenario definition. The tool is written in Java 8 and Java FX 8 for best performance, reduced maintenance and easy interaction. The practical usefulness of the software tool is demonstrated using a variety of examples exploiting EO data from

  11. A mathematical model of soil moisture spatial distribution on the hill slopes of the Loess Plateau

    Institute of Scientific and Technical Information of China (English)

    FU; Bojie

    2001-01-01

    in China, Ser. B, 1995, 38(2): 238-244.[14]Zhu, R. X., Zhou, L. P., Laj, C. et al., The Blake geomagnetic polarity episode recorded in Chinese Loess, Geophys. Res. Lett., 1994, 21(8): 697-700.[15]Kligfield, R., Channel, J. E. T., Widespread remagnetization of Helvetic limestones, J. Geophys. Res., 1981, 86: 1888-1900.[16]Maher, B. A., Thompson, R., Zhou, L. P., Spatial and temporal reconstruction of changes in the Asian paleomonsoon: A new mineral magnetic approach, Earth Planet Sci. Lett., 1994, 125: 461-471.[17]Liu, X. M., Rolph, T., Bloemendal, J. et al., Quantitative estimates of paleoprecipitation at Xifeng in the Loess Plateau of China, Palaeogeogr. Palaeoclim. Palaeoecol., 1995, 113: 243-248.[18]Thompson, R., Maher, B. A., Age models, sediment fluxes and paleoclimatic reconstructions for the Chinese loess and paleosol sequences, Geophys. J. Int., 1995, 123: 611-622.[19]Liu, T. S., Guo, Z. T., Liu, J. Q. et al., Variations of eastern Asian monsoon over the last 140000 years, Bull. Soc. Geol. France, 1995, 166: 221-229.[20]Guo, Z. T., Liu, T. S., Guiot, J. et al., High frequency pulses of East Asian monsoon climate in the last two glaciations: link with the North Atlatic, Climate Dynamics, 1996, 12: 701-709.[21]Han, J. M., Lü, H. Y., Wu, N. Q. et al., The magnetic susceptibility of modern soils in China and its uses for paleocli-mate reconstruction, Studia Geoph et Geod., 1996, 40: 262-275.[22]Zhu, R. X., History of anisotropy of the magnetic susceptibility and its implications: Preliminary results along an E-W transect of the Chinese Loess Plateau, Geophys. Res. Abs., 2000, 2: 226.

  12. Does Soil Moisture Influence Climate Variability and Predictability over Australia?.

    Science.gov (United States)

    Timbal, B.; Power, S.; Colman, R.; Viviand, J.; Lirola, S.

    2002-05-01

    Interannual variations of Australian climate are strongly linked to the El Niño-Southern Oscillation (ENSO) phenomenon. However, the impact of other mechanisms on prediction, such as atmosphere-land surface interactions, has been less frequently investigated. Here, the impact of soil moisture variability on interannual climate variability and predictability is examined using the Bureau of Meteorology Research Centre atmospheric general circulation model. Two sets of experiments are run, each with five different initial conditions. In the first set of experiments, soil moisture is free to vary in response to atmospheric forcing in each experiment according to a set of simple prognostic equations. A potential predictability index is computed as the ratio of the model's internal variability to its external forced variability. This estimates the level of predictability obtained assuming perfect knowledge of future ocean surface temperatures. A second set of five experiments with prescribed soil moisture is performed. A comparison between these two sets of experiments reveals that fluctuations of soil moisture increase the persistence, the variance, and the potential predictability of surface temperature and rainfall. The interrelationship between these two variables is also strongly dependent upon the soil water content. Results are particularly marked over Australia in this model. A novel feature of this study is the focus on the effectiveness of ENSO-based statistical seasonal forecasting over Australia. Forecasting skill is shown to be crucially dependent upon soil moisture variability over the continent. In fact, surface temperature forecasts in this manner are not possible without soil moisture variability. This result suggests that a better representation of land-surface interaction has the potential to increase the skill of seasonal prediction schemes.

  13. Soil moisture sensors based on metamaterials

    Directory of Open Access Journals (Sweden)

    Goran Kitić

    2012-12-01

    Full Text Available In this paper novel miniature metamaterial-based soil moisture sensors are presented. The sensors are based on resonant-type metamaterials and employ split-ring resonators (SRR, spiral resonators and fractal SRRs to achieve small dimensions, high sensitivity, and compatibility with standard planar fabrication technologies. All these features make the proposedsensors suitable for deployment in agriculture for precise mapping of soil humidity.

  14. Impact of SMOS soil moisture data assimilation on NCEP-GFS forecasts

    Science.gov (United States)

    Zhan, X.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.

    2012-04-01

    Soil moisture is one of the few critical land surface state variables that have long memory to impact the exchanges of water, energy and carbon between the land surface and atmosphere. Accurate information about soil moisture status is thus required for numerical weather, seasonal climate and hydrological forecast as well as for agricultural production forecasts, water management and many other water related economic or social activities. Since the successful launch of ESA's soil moisture ocean salinity (SMOS) mission in November 2009, about 2 years of soil moisture retrievals has been collected. SMOS is believed to be the currently best satellite sensors for soil moisture remote sensing. Therefore, it becomes interesting to examine how the collected SMOS soil moisture data are compared with other satellite-sensed soil moisture retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ soil moisture measurements, and how these data sets impact numerical weather prediction models such as the Global Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS soil moisture observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ soil moisture measurements from ground networks (such as USDA Soil Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS soil moisture simulations (analysis). The benefits and uncertainties of assimilating the satellite data products in GFS are examined by comparing the GFS forecasts of surface temperature and rainfall with and without the assimilations. From these examinations, the advantages of SMOS soil moisture data products over other satellite soil moisture data sets will be evaluated. The next step toward operationally assimilating soil moisture

  15. Investigating soil controls on soil moisture spatial variability: Numerical simulations and field observations

    Science.gov (United States)

    Wang, Tiejun; Franz, Trenton E.; Zlotnik, Vitaly A.; You, Jinsheng; Shulski, Martha D.

    2015-05-01

    Due to its complex interactions with various processes and factors, soil moisture exhibits significant spatial variability across different spatial scales. In this study, a modeling approach and field observations were used to examine the soil control on the relationship between mean (θ bar) and standard deviation (σθ) of soil moisture content. For the numerical experiments, a 1-D vadose zone model along with van Genuchten parameters generated by pedotransfer functions was used for simulating soil moisture dynamics under different climate and surface conditions. To force the model, hydrometeorological and physiological data that spanned over three years from five research sites within the continental US were used. The modeling results showed that under bare surface conditions, different forms of the θ bar -σθ relationship as observed in experimental studies were produced. For finer soils, a positive θ bar -σθ relationship gradually changed to an upward convex and a negative one from arid to humid conditions; whereas, a positive relationship existed for coarser soils, regardless of climatic conditions. The maximum σθ for finer soils was larger under semiarid conditions than under arid and humid conditions, while the maximum σθ for coarser soils increased with increasing precipitation. Moreover, vegetation tended to reduce θ bar and σθ, and thus affected the θ bar -σθ relationship. A sensitivity analysis was also conducted to examine the controls of different van Genuchten parameters on the θ bar -σθ relationship under bare surface conditions. It was found that the residual soil moisture content mainly affected σθ under dry conditions, while the saturated soil moisture content and the saturated hydraulic conductivity largely controlled σθ under wet conditions. Importantly, the upward convex θ bar -σθ relationship was mostly caused by the shape factor n that accounts for pore size distribution. Finally, measured soil moisture data from a

  16. Soil moisture and temperature algorithms and validation

    Science.gov (United States)

    Passive microwave remote sensing of soil moisture has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...

  17. SURFEX modeling of soil moisture fields over the Valencia Anchor Station and their comparison to different SMOS products and in situ measurements

    Science.gov (United States)

    Coll Pajaron, M. Amparo; Lopez-Baeza, Ernesto; Fernandez-Moran, Roberto; Samiro Khodayar-Pardo, D.

    2016-07-01

    Soil moisture is a difficult variable to obtain proper representation because of its high temporal and spatial variability. It is a significant parameter in agriculture, hydrology, meteorology and related disciplines. {it SVAT (Soil-Vegetation-Atmosphere-Transfer)} models can be used to simulate the temporal behaviour and spatial distribution of soil moisture in a given area. In this work, we use the {bf SURFEX (Surface Externalisée)} model developed at the {it Centre National de Recherches Météorologiques (CNRM)} at Météo-France (http://www.cnrm.meteo.fr/surfex/) to simulate soil moisture at the {bf Valencia Anchor Station}. SURFEX integrates the {bf ISBA (Interaction Sol-Biosphère-Atmosphère}; surfaces with vegetation) module to describe the land surfaces (http://www.cnrm.meteo.fr/isbadoc/model.html) that have been adapted to describe the land covers of our study area. The Valencia Anchor Station was chosen as a core validation site for the {it SMOS (Soil Moisture and Ocean Salinity)} mission and as one of the hydrometeorological sites for the {it HyMeX (HYdrological cycle in Mediterranean EXperiment)} programme. This site represents a reasonably homogeneous and mostly flat area of about 50x50 km2. The main cover type is vineyards (65%), followed by fruit trees, shrubs, and pine forests, and a few small scattered industrial and urban areas. Except for the vineyard growing season, the area remains mostly under bare soil conditions. In spite of its relatively flat topography, the small altitude variations of the region clearly influence climate. This oscillates between semiarid and dry sub-humid. Annual mean temperatures are between 12 ºC and 14.5 ºC, and annual precipitation is about 400-450 mm. The duration of frost free periods is from May to November, with maximum precipitation in spring and autumn. The first part of this investigation consists in simulating soil moisture fields over the Valencia Anchor Station to be compared with SMOS level-2

  18. Soil and Moisture Plan : 1971 - 1980 : Agassiz National Wildlife Refuge

    Data.gov (United States)

    US Fish and Wildlife Service, Department of the Interior — This Soil and Moisture Plan for Agassiz NWR provides an overview of the Refuge, a description of soil and moisture problems, and proposed solutions to these...

  19. An adaptive ensemble Kalman filter for soil moisture data assimilation

    Science.gov (United States)

    In a 19-year twin experiment for the Red-Arkansas river basin we assimilate synthetic surface soil moisture retrievals into the NASA Catchment land surface model. We demonstrate how poorly specified model and observation error parameters affect the quality of the assimilation products. In particul...

  20. Soil moisture from ground-based networks and the North American Land Data Assimilation System Phase 2 Model: Are the right values somewhere in between?

    Science.gov (United States)

    Caldwell, T. G.; Scanlon, B. R.; Long, D.; Young, M.

    2013-12-01

    Soil moisture is the most enigmatic component of the water balance; nonetheless, it is inherently tied to every component of the hydrologic cycle, affecting the partitioning of both water and energy at the land surface. However, our ability to assess soil water storage capacity and status through measurement or modeling is challenged by error and scale. Soil moisture is as difficult to measure as it is to model, yet land surface models and remote sensing products require some means of validation. Here we compare the three major soil moisture monitoring networks across the US, including the USDA Soil Climate Assessment Network (SCAN), NOAA Climate Reference Network (USCRN), and Cosmic Ray Soil Moisture Observing System (COSMOS) to the soil moisture simulated using the North American Land Data Assimilation System (NLDAS) Phase 2. NLDAS runs in near real-time on a 0.125° (12 km) grid over the US, producing ensemble model outputs of surface fluxes and storage. We focus primarily on soil water storage (SWS) in the upper 0-0.1 m zone from the Noah Land Surface Model and secondarily on the effects of error propagation from atmospheric forcing and soil parameterization. No scaling of the observational data was attempted. We simply compared the extracted time series at the nearest grid center from NLDAS and assessed the results by standard model statistics including root mean square error (RMSE) and mean bias estimate (MBE) of the collocated ground station. Observed and modeled data were compared at both hourly and daily mean coordinated universal time steps. In all, ~300 stations were used for 2012. SCAN sites were found to be particularly troublesome at 5- and 10-cm depths. SWS at 163 SCAN sites departed significantly from Noah with a mean R2 of 0.38 × 0.0.23, a mean RMSE of 14.9 mm with a MBE of -13.5 mm. SWS at 111 USCRN sites has a mean R2 of 0.53 × 0.20, a mean RMSE of 8.2 mm with a MBE of -3.7 mm relative to Noah. Finally, 62 COSMOS sites, the instrument with the

  1. Soil moisture mapping for aquarius

    Science.gov (United States)

    Aquarius is the first satellite to provide both passive and active L-band observations of the Earth. In addition, the instruments on Satelite de Aplicaciones Cientificas-D (SAC-D) provide complementary information for analysis and retrieval algorithms. Our research focuses on the retrieval of soil m...

  2. Modelling the effect of soil moisture and organic matter degradation on biogenic NO emissions from soils in Sahel rangeland (Mali)

    National Research Council Canada - National Science Library

    C. Delon; E. Mougin; D. Serça; M. Grippa; P. Hiernaux; M. Diawara; C. Galy-Lacaux; L. Kergoat

    2014-01-01

    .... The link between NO production in the soil and NO release to the atmosphere is investigated in this study, by taking into account vegetation litter production and degradation, microbial processes...

  3. Modelling the effect of soil moisture and organic matter degradation on biogenic NO emissions from soils in Sahel rangeland (Mali)

    National Research Council Canada - National Science Library

    C. Delon; E. Mougin; D. Serça; M. Grippa; P. Hiernaux; M. Diawara; C. Galy-Lacaux; L. Kergoat

    2015-01-01

    .... The link between NO production in the soil and NO release to the atmosphere is investigated in this study, by taking into account vegetation litter production and degradation, microbial processes...

  4. Improving Estimates of Root-zone Soil Water Content Using Soil Hydrologic Properties and Remotely Sensed Soil Moisture

    Science.gov (United States)

    Baldwin, D. C.; Miller, D. A.; Singha, K.; Davis, K. J.; Smithwick, E. A.

    2013-12-01

    Newly defined relationships between remotely sensed soil moisture and soil hydraulic parameters were used to develop fine-scale (100 m) maps of root-zone soil moisture (RZSM) content at the regional scale on a daily time-step. There are several key outcomes from our research: (1) the first multi-layer regional dataset of soil hydraulic parameters developed from gSSURGO data for hydrologic modeling efforts in the Chequemegon Ecosystem Atmospheric Study (ChEAS) region, (2) the operation and calibration of a new model for estimating soil moisture flow through the root-zone at eddy covariance towers across the U.S. using remotely sensed active and passive soil moisture products, and (3) region-wide maps of estimated root-zone soil moisture content. The project links soil geophysical analytical approaches (pedotransfer functions) to new applications in remote sensing of soil moisture that detect surface moisture (~5 cm depth). We answer two key questions in soil moisture observation and prediction: (1) How do soil hydrologic properties of U.S. soil types quantitatively relate to surface-to-subsurface water loss? And (2) Does incorporation of fine-scale soil hydrologic parameters with remotely sensed soil moisture data provide improved hindcasts of in situ RZSM content? The project meets several critical research needs in estimation of soil moisture from remote sensing. First, soil moisture is known to vary spatially with soil texture and soil hydraulic properties that do not align well with the spatial resolution of current remote sensing products of soil moisture (~ 50 km2). To address this, we leveraged new advances in gridded soil parameter information (gSSURGO) together with existing remotely sensed estimates of surface soil moisture into a newly emerging semi-empirical modeling approach called SMAR (Soil Moisture Analytical Relationship). The SMAR model was calibrated and cross-validated using existing soil moisture data from a portion of AMERIFLUX tower sites and

  5. Field-Scale Soil Moisture Sensing Using GPS Reflections: Description of the PBO H2O Soil Moisture Product

    Science.gov (United States)

    Chew, C. C.; Small, E. E.; Larson, K. M.

    2014-12-01

    Data from NSF's EarthScope Plate Boundary Observatory (PBO), and similar GPS networks worldwide, can be used to monitor the terrestrial water cycle. GPS satellites transmit L-band microwave signals, which are affected by water at Earth's surface. GPS signals take two paths: (1) the "direct" signal travels from the satellite to the antenna; (2) the "reflected" signal interacts with the Earth's surface before travelling to the antenna. The direct signal is used by geophysicists to measure position of the antenna, while the effects of reflected signals are generally ignored. Recently, our group has developed a technique to retrieve terrestrial water cycle variables from GPS reflections. The sensing footprint is intermediate in scale between in situ and remote sensing observations. Soil moisture, snow depth, and an index of vegetation water content are estimated from data collected at over 400 PBO sites. The products are updated daily and are available online. This presentation provides a description of the soil moisture product. Near-surface soil moisture is estimated at more than 100 sites in the PBO H2O network. At each site, a geodetic-quality GPS antenna records the interference pattern between the direct and ground-reflected GPS signals in signal-to-noise ratio (SNR) interferograms. The ground-reflected GPS signal is altered by changes in the permittivity of the ground surface, which is primarily a function of its water content. Temporal changes in the SNR interferogram, primarily its phase, are indicative of changes in soil moisture. SNR phase data are converted to soil moisture using relationships determined using an electrodynamic model. Soil moisture is not retrieved when there is snow or significant vegetation (> ~1 kg m-2 of vegetation water), as both affect SNR phase. When there is moderate vegetation, a correction is applied to the phase data before conversion to soil moisture. The effect of vegetation on SNR phase and the exact relationship between SNR

  6. Small-scale soil moisture determination with GPR

    Science.gov (United States)

    Igel, Jan; Preetz, Holger

    2010-05-01

    The knowledge of topsoil moisture distribution is an important input for modelling water flow and evapotranspiration which are essential processes in hydrology, meteorology, and agriculture. All these processes involve non-linear effects and thus the small-scale variability of input parameters play an important role. Using smoothed interpolations instead can cause significant biases. Lateral soil moisture distribution can be sensed by different techniques at various scales whereby geophysical methods provide spatial information which closes the gap between point measurements by classical soil scientific methods and measurements on the field or regional scale by remote sensing. Ground-penetrating radar (GPR) can be used to explore soil moisture on the field scale as propagation of electromagnetic waves is correlated to soil water content. By determining the velocity of the ground wave, which is a guided wave travelling along the soil surface, we can sense soil water content. This method has been applied to determine topsoil moisture for several years. We present a new groundwave technique which determines the velocity in between two receiving antennas which enables a higher lateral resolution (approx. 10 cm) compared to classical groundwave technique (half meter and more). We present synthetic data from finite-differences (FD) calculations as well as data from a sandbox experiment carried out under controlled conditions to demonstrate the performance of this method. Further, we carried out field measurements on two sites on a sandy soil which is used as grassland. The measurements were carried out in late summer at dry soil conditions. Soil moisture on the first site shows an isotropic pattern with correlation lengths of approx. 35 cm. We think this natural pattern is governed by rout distribution within the soil and the water uptake of vegetation. On the second site, soil moisture distribution shows a regular stripe pattern. As the land has been used as

  7. First soil moisture values from SMOS over a Sahelian region.

    Science.gov (United States)

    Gruhier, Claire; Kerr, Yann; de Rosnay, Patricia; Pellarin, Thierry; Grippa, Manuela

    2010-05-01

    Soil moisture is a crucial variable which influences the land surface processes. Numerous studies shown microwaves at low frequency are particularly performed to access to soil moisture values. SMOS (Soil Moisture and Ocean Salinity), launched the November 2th 2009, is the first space mission dedicated to soil moisture observations. Before SMOS, several soil moisture products were provided, based on active or passive microwaves measurements. Gruhier et al. (2010) analyse five of them over a Sahelian area. The results show that the range of volumetric soil moisture values obtained over Sahel is drastically different depending on the remote sensing approach used to produce soil moisture estimates. Although microwave bands currently available are not optimal, some products are in very good agreement with ground data. The main goal of this study is to introduce the first soil moisture maps from SMOS over West Africa. A first analyse of values over a Sahelian region is investigated. The study area is located in Gourma region in Mali. This site has been instrumented in the context of the AMMA project (African Monsoon Multidisciplinary Analysis) and was specifically designed to address the validation of remotely sensed soil moisture. SMOS soil moisture values was analysed with ground knowledge and placed in the context of previous soil moisture products. The high sensitivity of the L-band used by SMOS should provide very accurate soil moisture values.

  8. Soil Surface Sealing Effect on Soil Moisture at a Semiarid Hillslope: Implications for Remote Sensing Estimation

    Directory of Open Access Journals (Sweden)

    Shai Sela

    2014-08-01

    Full Text Available Robust estimation of soil moisture using microwave remote sensing depends on extensive ground sampling for calibration and validation of the data. Soil surface sealing is a frequent phenomenon in dry environments. It modulates soil moisture close to the soil surface and, thus, has the potential to affect the retrieval of soil moisture from microwave remote sensing and the validation of these data based on ground observations. We addressed this issue using a physically-based modeling approach that accounts explicitly for surface sealing at the hillslope scale. Simulated mean soil moisture at the respective layers corresponding to both the ground validation probe and the radar beam’s typical effective penetration depth were considered. A cyclic pattern was found in which, as compared to an unsealed profile, the seal layer intensifies the bias in validation during rainfall events and substantially reduces it during subsequent drying periods. The analysis of this cyclic pattern showed that, accounting for soil moisture dynamics at the soil surface, the optimal time for soil sampling following a rainfall event is a few hours in the case of an unsealed system and a few days in the case of a sealed one. Surface sealing was found to increase the temporal stability of soil moisture. In both sealed and unsealed systems, the greatest temporal stability was observed at positions with moderate slope inclination. Soil porosity was the best predictor of soil moisture temporal stability, indicating that prior knowledge regarding the soil texture distribution is crucial for the application of remote sensing validation schemes.

  9. Soil Albedo in Relation to Soil Color, Moisture and Roughness

    Science.gov (United States)

    Fontes, Adan Fimbres

    Land surface albedo is the ratio of reflected to incident solar radiation. It is a function of several surface parameters including soil color, moisture, roughness and vegetation cover. A better understanding of albedo and how it changes in relation to variations in these parameters is important in order to help improve our ability to model the effects of land surface modifications on climate. The objectives of this study were (1) To determine empirical relationships between smooth bare soil albedo and soil color, (2) To develop statistical relationships between albedo and ground-based thematic mapper (TM) measurements of spectral reflectances, (3) To determine how increased surface roughness caused by tillage reduces bare soil albedo and (4) To empirically relate albedo with TM data and other physical characteristics of mixed grass/shrubland sites at Walnut Gulch Watershed. Albedos, colors and spectral reflectances were measured by Eppley pyranometer, Chroma Meter CR-200 and a Spectron SE-590, respectively. Measurements were made on two field soils (Gila and Pima) at the Campus Agricultural Center (CAC), Tucson, AZ. Soil surface roughness was measured by a profile meter developed by the USDA/ARS. Additional measurements were made at the Maricopa Agricultural Center (MAC) for statistical model testing. Albedos of the 15 smooth, bare soils (plus silica sand) were determined by linear regression to be highly correlated (r^2 = 0.93, p > 0.01) with color values for both wet and dry soil conditions. Albedos of the same smooth bare soils were also highly correlated (r^2>=q 0.86, p > 0.01) with spectral reflectances. Testing of the linear regression equations relating albedo to soil color and spectral reflectances using the data from MAC showed a high correlation. A general nonlinear relationship given by y = 8.366ln(x) + 37.802 r^2 = 0.71 was determined between percent reduction in albedo (y) and surface roughness index (x) for wet and dry Pima and Gila field soils

  10. Towards an integrated soil moisture drought monitor for East Africa

    Directory of Open Access Journals (Sweden)

    W. B. Anderson

    2012-04-01

    Full Text Available Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI surface energy balance algorithm, and physically-based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the

  11. Towards an integrated soil moisture drought monitor for East Africa

    Directory of Open Access Journals (Sweden)

    W. B. Anderson

    2012-08-01

    Full Text Available Drought in East Africa is a recurring phenomenon with significant humanitarian impacts. Given the steep climatic gradients, topographic contrasts, general data scarcity, and, in places, political instability that characterize the region, there is a need for spatially distributed, remotely derived monitoring systems to inform national and international drought response. At the same time, the very diversity and data scarcity that necessitate remote monitoring also make it difficult to evaluate the reliability of these systems. Here we apply a suite of remote monitoring techniques to characterize the temporal and spatial evolution of the 2010–2011 Horn of Africa drought. Diverse satellite observations allow for evaluation of meteorological, agricultural, and hydrological aspects of drought, each of which is of interest to different stakeholders. Focusing on soil moisture, we apply triple collocation analysis (TCA to three independent methods for estimating soil moisture anomalies to characterize relative error between products and to provide a basis for objective data merging. The three soil moisture methods evaluated include microwave remote sensing using the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E sensor, thermal remote sensing using the Atmosphere-Land Exchange Inverse (ALEXI surface energy balance algorithm, and physically based land surface modeling using the Noah land surface model. It was found that the three soil moisture monitoring methods yield similar drought anomaly estimates in areas characterized by extremely low or by moderate vegetation cover, particularly during the below-average 2011 long rainy season. Systematic discrepancies were found, however, in regions of moderately low vegetation cover and high vegetation cover, especially during the failed 2010 short rains. The merged, TCA-weighted soil moisture composite product takes advantage of the relative strengths of each method, as judged by the

  12. Crop yield monitoring in the Sahel using root zone soil moisture anomalies derived from SMOS soil moisture data assimilation

    Science.gov (United States)

    Gibon, François; Pellarin, Thierry; Alhassane, Agali; Traoré, Seydou; Baron, Christian

    2017-04-01

    West Africa is greatly vulnerable, especially in terms of food sustainability. Mainly based on rainfed agriculture, the high variability of the rainy season strongly impacts the crop production driven by the soil water availability in the soil. To monitor this water availability, classical methods are based on daily precipitation measurements. However, the raingauge network suffers from the poor network density in Africa (1/10000km2). Alternatively, real-time satellite-derived precipitations can be used, but they are known to suffer from large uncertainties which produce significant error on crop yield estimations. The present study proposes to use root soil moisture rather than precipitation to evaluate crop yield variations. First, a local analysis of the spatiotemporal impact of water deficit on millet crop production in Niger was done, from in-situ soil moisture measurements (AMMA-CATCH/OZCAR (French Critical Zone exploration network)) and in-situ millet yield survey. Crop yield measurements were obtained for 10 villages located in the Niamey region from 2005 to 2012. The mean production (over 8 years) is 690 kg/ha, and ranges from 381 to 872 kg/ha during this period. Various statistical relationships based on soil moisture estimates were tested, and the most promising one (R>0.9) linked the 30-cm soil moisture anomalies from mid-August to mid-September (grain filling period) to the crop yield anomalies. Based on this local study, it was proposed to derive regional statistical relationships using 30-cm soil moisture maps over West Africa. The selected approach was to use a simple hydrological model, the Antecedent Precipitation Index (API), forced by real-time satellite-based precipitation (CMORPH, PERSIANN, TRMM3B42). To reduce uncertainties related to the quality of real-time rainfall satellite products, SMOS soil moisture measurements were assimilated into the API model through a Particular Filter algorithm. Then, obtained soil moisture anomalies were

  13. Soil moisture sensor calibration for organic soil surface layers

    Directory of Open Access Journals (Sweden)

    S. Bircher

    2015-12-01

    Full Text Available This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology HOBE. For the Decagon 5TE sensor such a function is currently not reported in literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified: for the Decagon 5TE apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger non-linearity in the sensor response and signal saturation in the high level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankyl

  14. Soil moisture sensor calibration for organic soil surface layers

    Science.gov (United States)

    Bircher, Simone; Andreasen, Mie; Vuollet, Johanna; Vehviläinen, Juho; Rautiainen, Kimmo; Jonard, François; Weihermüller, Lutz; Zakharova, Elena; Wigneron, Jean-Pierre; Kerr, Yann H.

    2016-04-01

    This paper's objective is to present generic calibration functions for organic surface layers derived for the soil moisture sensors Decagon ECH2O 5TE and Delta-T ThetaProbe ML2x, using material from northern regions, mainly from the Finnish Meteorological Institute's Arctic Research Center in Sodankylä and the study area of the Danish Center for Hydrology (HOBE). For the Decagon 5TE sensor such a function is currently not reported in the literature. Data were compared with measurements from underlying mineral soils including laboratory and field measurements. Shrinkage and charring during drying were considered. For both sensors all field and lab data showed consistent trends. For mineral layers with low soil organic matter (SOM) content the validity of the manufacturer's calibrations was demonstrated. Deviating sensor outputs in organic and mineral horizons were identified. For the Decagon 5TE, apparent relative permittivities at a given moisture content decreased for increased SOM content, which was attributed to an increase of bound water in organic materials with large specific surface areas compared to the studied mineral soils. ThetaProbe measurements from organic horizons showed stronger nonlinearity in the sensor response and signal saturation in the high-level data. The derived calibration fit functions between sensor response and volumetric water content hold for samples spanning a wide range of humus types with differing SOM characteristics. This strengthens confidence in their validity under various conditions, rendering them highly suitable for large-scale applications in remote sensing and land surface modeling studies. Agreement between independent Decagon 5TE and ThetaProbe time series from an organic surface layer at the Sodankylä site was significantly improved when the here-proposed fit functions were used. Decagon 5TE data also well-reflected precipitation events. Thus, Decagon 5TE network data from organic surface layers at the Sodankylä and

  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. 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

  17. Soil Temperature and Moisture Profile (STAMP) System Handbook

    Energy Technology Data Exchange (ETDEWEB)

    Cook, David R [Argonne National Lab. (ANL), Argonne, IL (United States)

    2016-11-01

    The soil temperature and moisture profile system (STAMP) provides vertical profiles of soil temperature, soil water content (soil-type specific and loam type), plant water availability, soil conductivity, and real dielectric permittivity as a function of depth below the ground surface at half-hourly intervals, and precipitation at one-minute intervals. The profiles are measured directly by in situ probes at all extended facilities of the SGP climate research site. The profiles are derived from measurements of soil energy conductivity. Atmospheric scientists use the data in climate models to determine boundary conditions and to estimate the surface energy flux. The data are also useful to hydrologists, soil scientists, and agricultural scientists for determining the state of the soil. The STAMP system replaced the SWATS system in early 2016.

  18. Impacts of soil moisture content on visual soil evaluation

    Science.gov (United States)

    Emmet-Booth, Jeremy; Forristal, Dermot; Fenton, Owen; Bondi, Giulia; Creamer, Rachel; Holden, Nick

    2017-04-01

    Visual Soil Examination and Evaluation (VSE) techniques offer tools for soil quality assessment. They involve the visual and tactile assessment of soil properties such as aggregate size and shape, porosity, redox morphology, soil colour and smell. An increasing body of research has demonstrated the reliability and utility of VSE techniques. However a number of limitations have been identified, including the potential impact of soil moisture variation during sampling. As part of a national survey of grassland soil quality in Ireland, an evaluation of the impact of soil moisture on two widely used VSE techniques was conducted. The techniques were Visual Evaluation of Soil Structure (VESS) (Guimarães et al., 2011) and Visual Soil Assessment (VSA) (Shepherd, 2009). Both generate summarising numeric scores that indicate soil structural quality, though employ different scoring mechanisms. The former requires the assessment of properties concurrently and the latter separately. Both methods were deployed on 20 sites across Ireland representing a range of soils. Additional samples were taken for soil volumetric water (θ) determination at 5-10 and 10-20 cm depth. No significant correlation was observed between θ 5-10 cm and either VSE technique. However, VESS scores were significantly related to θ 10-20 cm (rs = 0.40, sig = 0.02) while VSA scores were not (rs = -0.33, sig = 0.06). VESS and VSA scores can be grouped into quality classifications (good, moderate and poor). No significant mean difference was observed between θ 5-10 cm or θ 10-20 cm according to quality classification by either method. It was concluded that VESS scores may be affected by soil moisture variation while VSA appear unaffected. The different scoring mechanisms, where the separate assessment and scoring of individual properties employed by VSA, may limit soil moisture effects. However, moisture content appears not to affect overall structural quality classification by either method. References

  19. NIR-red spectral space based new method for soil moisture monitoring

    Institute of Scientific and Technical Information of China (English)

    ZHAN ZhiMing; QIN QiMing; GHULAN Abduwasit; WANG DongDong

    2007-01-01

    Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is developed using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coefficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.

  20. NIR-red spectral space based new method for soil moisture monitoring

    Institute of Scientific and Technical Information of China (English)

    GHULAN; Abduwasit

    2007-01-01

    Drought is a complex natural disaster that occurs frequently. Soil moisture has been the main issue in remote monitoring of drought events as the most direct and important variable describing the drought. Spatio-temporal distribution and variation of soil moisture evidently affect surface evapotranspiration, agricultural water demand, etc. In this paper, a new simple method for soil moisture monitoring is de- veloped using near-infrared versus red (NIR-red) spectral reflectance space. First, NIR-red spectral reflectance space is established using atmospheric and geometric corrected ETM+ data, which is manifested by a triangle shape, in which different surface covers have similar spatial distribution rules. Next, the model of soil moisture monitoring by remote sensing (SMMRS) is developed on the basis of the distribution characteristics of soil moisture in the NIR-red spectral reflectance space. Then, the SMMRS model is validated by comparison with field measured soil moisture data at different depths. The results showed that satellite estimated soil moisture by SMMRS is highly accordant with field measured data at 5 cm soil depth and average soil moisture at 0―20 cm soil depths, correlation coef- ficients are 0.80 and 0.87, respectively. This paper concludes that, being simple and effective, the SMMRS model has great potential to estimate surface moisture conditions.

  1. Predictions of rainfall-runoff response and soil moisture dynamics in a microscale catchment using the CREW model

    Directory of Open Access Journals (Sweden)

    H. Lee

    2006-07-01

    scale pressure-saturation (i.e., water retention curve and hydraulic conductivity-saturation relationships for the unsaturated zone. Closure relations for concentrated overland flow and saturated overland flow were derived using both theoretical arguments and simpler process models. In addition to these, to complete the specification of the REW scale balance equations, a relationship for the saturated area fraction as a function of saturated zone depth was derived for an assumed topography on the basis of TOPMODEL assumptions. These relationships were used to complete the specification of all of the REW-scale governing equations (mass and momentum balance equations, closure and geometric relations for the Weiherbach catchment, which are then employed for constructing a numerical watershed model, named the Cooperative Community Catchment model based on the Representative Elementary Watershed approach (CREW. CREW is then used to carry out sensitivity analyses with respect to various combinations of climate, soil, vegetation and topographies, in order to test the reasonableness of the derived closure relations in the context of the complete catchment response, including interacting processes. These sensitivity analyses demonstrated that the adopted closure relations do indeed produce mostly reasonable results, and can therefore be a good basis for more careful and rigorous search for appropriate closure relations in the future. Three tests are designed to assess CREW as a large scale model for Weiherbach catchment. The first test compares CREW with distributed model CATFLOW by looking at predicted soil moisture dynamics for artificially designed initial and boundary conditions. The second test is designed to see the applicabilities of the parameter values extracted from the upscaling procedures in terms of their ability to reproduce observed hydrographs within the CREW modeling framework. The final test compares simulated soil

  2. Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

    Science.gov (United States)

    Orth, René; Seneviratne, Sonia I.

    2014-12-01

    Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We

  3. The moisture response of soil heterotrophic respiration: interaction with soil properties

    DEFF Research Database (Denmark)

    Moyano, F E; Vasilyeva, N; Bouckaert, L

    2012-01-01

    Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model the heter...... predictions of the response of soil carbon to future climate scenarios will require the integration of soil-dependent moisture-respiration functions coupled with realistic representations of soil water dynamics.......Soil moisture is of primary importance for predicting the evolution of soil carbon stocks and fluxes, both because it strongly controls organic matter decomposition and because it is predicted to change at global scales in the following decades. However, the soil functions used to model...... the heterotrophic respiration response to moisture have limited empirical support and introduce an uncertainty of at least 4% in global soil carbon stock predictions by 2100. The necessity of improving the representation of this relationship in models has been highlighted in recent studies. Here we present a data...

  4. Numerical Simulation of Moisture Movement in Unsaturated Expansive Soil Slope Suffering Permeation

    Institute of Scientific and Technical Information of China (English)

    Chen Shanxiong; Yu Song; Liu Zhiguo; Xu Haibin

    2005-01-01

    This study develops a way of analyzing moisture movement in unsaturated expansive soil slope. The basic equations and the integrated finite difference method for moisture movement in unsaturated soils are briefly described, and the calculation code MFUS2 has been developed. The moisture movements in unsaturated expansive soil slopes suffering precipitation were simulated numerically. The simulation results show that expansion or contraction must be taken into account in an analysis model. A simplified equivalent model for calculating rainwater infiltration into expansive soil slopes has been developed. The simplified equivalent model divides the soil slope into two layers according to the extent of weathering of the soil mass at depth. Layer Ⅰ is intensively weathered and moisture can be fully evaporated or rapidly absorbed. The moisture movement parameters take into account the greater soil permeability caused by fissures. Layer Ⅱ is unweathered and the soil is basically undisturbed. The moisture movement parameters of homogeneous soils are applicable. The moisture movements in unsaturated expansive soil slopes suffering precipitation were simulated numerically using the simplified equivalent model. The simulation results show that the moisture movement in the expansive soil slope under rainfall permeation mainly takes place in the extensively weathered layer Ⅰ, which closely simulates the real situation.

  5. Trends and Scales of Observed Soil Moisture Variations in China

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981 1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air (TDSA) are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth.Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers,cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation,whereas in the latter, the spatial scale is controlled by topography.

  6. Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

    Directory of Open Access Journals (Sweden)

    Bert Veenendaal

    2009-12-01

    Full Text Available Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method. A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in

  7. [Effects of nitrogen fertilization, soil moisture and soil temperature on soil respiration during summer fallow season].

    Science.gov (United States)

    Zhang, Fang; Guo, Sheng-Li; Zou, Jun-Liang; Li, Ze; Zhang, Yan-Jun

    2011-11-01

    On the loess plateau, summer fallow season is a hot rainy time with intensive soil microbe activities. To evaluate the response of soil respiration to soil moisture, temperature, and N fertilization during this period is helpful for a deep understanding about the temporal and spatial variability of soil respiration and its impact factors, then a field experiment was conducted in the Changwu State Key Agro-Ecological Experimental Station, Shaanxi, China. The experiment included five N application rates: unfertilized 0 (N0), 45 (N45), 90 (N90), 135(N135), and 180 (N180) kg x hm(-2). The results showed that at the fallow stage, soil respiration rate significantly enhanced from 1.24 to 1.91 micromol x (m2 x s)(-1) and the average of soil respiration during this period [6.20 g x (m2 x d)(-1)] was close to the growing season [6.95 g x (m2 x d)(-1)]. The bivariate model of soil respiration with soil water and soil temperature was better than the single-variable model, but not so well as the three-factor model when explaining the actual changes of soil respiration. Nitrogen fertilization alone accounted for 8% of the variation soil respiration. Unlike the single-variable model, the results could provide crucial information for further research of multiple factors on soil respiration and its simulation.

  8. Soil Moisture Drought Monitoring and Forecasting Using Satellite and Climate Model Data over Southwestern China

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Xuejun; Tang, Qiuhong; Liu, Xingcai; Leng, Guoyong; Li, Zhe

    2017-01-01

    Real-time monitoring and predicting drought development with several months in advance is of critical importance for drought risk adaptation and mitigation. In this paper, we present a drought monitoring and seasonal forecasting framework based on the Variable Infiltration Capacity (VIC) hydrologic model over Southwest China (SW). The satellite precipitation data are used to force VIC model for near real-time estimate of land surface hydrologic conditions. As initialized with satellite-aided monitoring, the climate model-based forecast (CFSv2_VIC) and ensemble streamflow prediction (ESP)-based forecast (ESP_VIC) are both performed and evaluated through their ability in reproducing the evolution of the 2009/2010 severe drought over SW. The results show that the satellite-aided monitoring is able to provide reasonable estimate of forecast initial conditions (ICs) in a real-time manner. Both of CFSv2_VIC and ESP_VIC exhibit comparable performance against the observation-based estimates for the first month, whereas the predictive skill largely drops beyond 1-month. Compared to ESP_VIC, CFSv2_VIC shows better performance as indicated by the smaller ensemble range. This study highlights the value of this operational framework in generating near real-time ICs and giving a reliable prediction with 1-month ahead, which has great implications for drought risk assessment, preparation and relief.

  9. Soil Moisture Remote Sensing with GNSS-R at the Valencia Anchor Station. The SOMOSTA (Soil Moisture Station) Experiment

    Science.gov (United States)

    Lopez-Baeza, Ernesto

    2016-07-01

    In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring byGlobal Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced. L-band microwaves have very good advantages in soil moisture remote sensing, for being unaffected by clouds and the atmosphere, and for the ability to penetrate vegetation. During this experimental campaign, the ESA GNSS-R Oceanpal antenna was installed on the same tower as the ESA ELBARA-II passive microwave radiometer, both measuring instruments having similar field of view. This experiment is fruitfully framed within the ESA - China Programme of Collaboration on GNSS-R. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and two down-looking antennas for receiving LHCP (left-hand circular polarisation) and RHCP (right-hand circular polarisation) reflected signals from the soil surface. We could collect data from the three different antennas through the two channels of Oceanpal and, in addition, calibration could be performed to reduce the impact from the differing channels. Reflectivity was thus measured and soil moisture could be retrieved by the L- MEB (L-band Microwave Emission of the Biosphere) model considering the effect of vegetation optical thickness and soil roughness. By contrasting GNSS-R and ELBARA-II radiometer data, a negative correlation existed between reflectivity measured by GNSS-R and brightness temperature measured by the radiometer. The two parameters represent reflection and absorption of the soil. Soil moisture retrieved by both L-band remote sensing methods shows good agreement. In addition, correspondence with in-situ measurements and rainfall is also good.

  10. De-noising of microwave satellite soil moisture time series

    Science.gov (United States)

    Su, Chun-Hsu; Ryu, Dongryeol; Western, Andrew; Wagner, Wolfgang

    2013-04-01

    The use of satellite soil moisture data for scientific and operational hydrologic, meteorological and climatological applications is advancing rapidly due to increasing capability and temporal coverage of current and future missions. However evaluation studies of various existing remotely-sensed soil moisture products from these space-borne microwave sensors, which include AMSR-E (Advanced Microwave Scanning Radiometer) on Aqua satellite, SMOS (Soil Moisture and Ocean Salinity) mission and ASCAT (Advanced Scatterometer) on MetOp-A satellite, found them to be significantly different from in-situ observations, showing large biases and different dynamic ranges and temporal patterns (e.g., Albergel et al., 2012; Su et al., 2012). Moreover they can have different error profiles in terms of bias, variance and correlations and their performance varies with land surface characteristics (Su et al., 2012). These severely impede the effort to use soil moisture retrievals from multiple sensors concurrently in land surface modelling, cross-validation and multi-satellite blending. The issue of systematic errors present in data sets should be addressed prior to renormalisation of the data for blending and data assimilation. Triple collocation estimation technique has successfully yielded realistic error estimates (Scipal et al., 2008), but this method relies on availability of large number of coincident data from multiple independent satellite data sets. In this work, we propose, i) a conceptual framework for distinguishing systematic periodic errors in the form of false spectral resonances from non-systematic errors (stochastic noise) in remotely-sensed soil moisture data in the frequency domain; and ii) the use of digital filters to reduce the variance- and correlation-related errors in satellite data. In this work, we focus on the VUA-NASA (Vrije Universiteit Amsterdam with NASA) AMSR-E, CATDS (Centre National d'Etudes Spatiales, CNES) SMOS and TUWIEN (Vienna University of

  11. Identifying groundwater recharge connections in the Moscow (USA) sub-basin using isotopic tracers and a soil moisture routing model

    Science.gov (United States)

    Candel, Jasper; Brooks, Erin; Sánchez-Murillo, Ricardo; Grader, George; Dijksma, Roel

    2016-06-01

    Globally, aquifers are suffering from large abstractions resulting in groundwater level declines. These declines can be caused by excessive abstraction for drinking water, irrigation purposes or industrial use. Basaltic aquifers also face these conflicts. A large flood basalt area (1.1 × 105 km2) can be found in the Northwest of the USA. This Columbia River Basalt Group (CRBG) consists of a thick series of basalt flows of Miocene age. The two major hydrogeological units (Wanapum and Grand Ronde formations) are widely used for water abstraction. The mean decline over recent decades has been 0.6 m year-1. At present day, abstraction wells are drying up, and base flow of rivers is reduced. At the eastern part of CRBG, the Moscow sub-basin on the Idaho/Washington State border can be found. Although a thick poorly permeable clay layer exists on top of the basalt aquifer, groundwater level dynamics suggest that groundwater recharge occurs at certain locations. A set of wells and springs has been monitored bi-weekly for 9 months for δ18O and δ2H. Large isotopic fluctuations and d-excess values close to the meteoric water line in some wells are indicating that recharge occurs at the granite/basalt interface through lateral flow paths in and below the clay. A soil moisture routing (SMR) model showed that most recharge occurs on the granitic mountains. The basaltic aquifer receives recharge from these sedimentary zones around the granite/basalt interface. The identification of these types of areas is of major importance for future managed-aquifer recharge solutions to solve problems of groundwater depletion.

  12. Identifying groundwater recharge connections in the Moscow (USA) sub-basin using isotopic tracers and a soil moisture routing model

    Science.gov (United States)

    Candel, Jasper; Brooks, Erin; Sánchez-Murillo, Ricardo; Grader, George; Dijksma, Roel

    2016-11-01

    Globally, aquifers are suffering from large abstractions resulting in groundwater level declines. These declines can be caused by excessive abstraction for drinking water, irrigation purposes or industrial use. Basaltic aquifers also face these conflicts. A large flood basalt area (1.1 × 105 km2) can be found in the Northwest of the USA. This Columbia River Basalt Group (CRBG) consists of a thick series of basalt flows of Miocene age. The two major hydrogeological units (Wanapum and Grand Ronde formations) are widely used for water abstraction. The mean decline over recent decades has been 0.6 m year-1. At present day, abstraction wells are drying up, and base flow of rivers is reduced. At the eastern part of CRBG, the Moscow sub-basin on the Idaho/Washington State border can be found. Although a thick poorly permeable clay layer exists on top of the basalt aquifer, groundwater level dynamics suggest that groundwater recharge occurs at certain locations. A set of wells and springs has been monitored bi-weekly for 9 months for δ18O and δ2H. Large isotopic fluctuations and d-excess values close to the meteoric water line in some wells are indicating that recharge occurs at the granite/basalt interface through lateral flow paths in and below the clay. A soil moisture routing (SMR) model showed that most recharge occurs on the granitic mountains. The basaltic aquifer receives recharge from these sedimentary zones around the granite/basalt interface. The identification of these types of areas is of major importance for future managed-aquifer recharge solutions to solve problems of groundwater depletion.

  13. NASA's Soil Moisture Active Passive (SMAP) observatory

    Science.gov (United States)

    Kellogg, K.; Thurman, S.; Edelstein, W.; Spencer, M.; Chen, Gun-Shing; Underwood, M.; Njoku, E.; Goodman, S.; Jai, Benhan

    The Soil Moisture Active Passive (SMAP) mission, one of the first-tier missions recommended by the 2007 U.S. National Research Council Committee on Earth Science and Applications from Space, was confirmed in May 2012 by NASA to proceed into Implementation Phase (Phase C) with a planned launch in October 2014. SMAP will produce high-resolution and accurate global maps of soil moisture and its freeze/thaw state using data from a non-imaging synthetic aperture radar and a radiometer, both operating at L-band. Major challenges addressed by the observatory design include: (1) achieving global coverage every 2-3 days with a single observatory; (2) producing both high resolution and high accuracy soil moisture data, including through moderate vegetation; (3) using a mesh reflector antenna for L-band radiometry; (4) minimizing science data loss from terrestrial L-band radio frequency interference; (5) designing fault protection that also minimizes science data loss; (6) adapting planetary heritage avionics to meet SMAP's unique application and data volume needs; (7) ensuring observatory electromagnetic compatibility to avoid degrading science; (8) controlling a large spinning instrument with a small spacecraft; and (9) accommodating launch vehicle selection late in the observatory's development lifecycle.

  14. Soil moisture gradients and controls on a southern Appalachian hillslope from drought through recharge

    Directory of Open Access Journals (Sweden)

    J. A. Yeakley

    1998-01-01

    Full Text Available Soil moisture gradients along hillslopes in humid watersheds, although indicated by vegetation gradients and by studies using models, have been difficult to confirm empirically. While soil properties and topographic features are the two general physio-graphic factors controlling soil moisture on hillslopes, studies have shown conflicting results regarding which factor is more important. The relative importance of topographic and soil property controls was examined in an upland forested watershed at the Coweeta Hydrologic Laboratory in the southern Appalachian mountains. Soil moisture was measured along a hillslope transect with a mesic-to-xeric forest vegetation gradient over a period spanning precipitation extremes. The hillslope was transect instrumented with a time domain reflectometry (TDR network at two depths. Soil moisture was measured during a severe autumn drought and subsequent winter precipitation recharge. In the upper soil depth (0-30 cm, moisture gradients persisted throughout the measurement period, and topography exerted dominant control. For the entire root zone (0-90 cm, soil moisture gradients were found only during drought. Control on soil moisture was due to both topography and storage before drought. During and after recharge, variations in soil texture and horizon distribution exerted dominant control on soil moisture content in the root zone (0-90 cm. These results indicate that topographic factors assert more control over hillslope soil moisture during drier periods as drainage progresses, while variations in soil water storage properties are more important during wetter periods. Hillslope soil moisture gradients in southern Appalachian watersheds appear to be restricted to upper soil layers, with deeper hillslope soil moisture gradients occurring only with sufficient drought.

  15. Traditional and microirrigation with stochastic soil moisture

    Science.gov (United States)

    Vico, Giulia; Porporato, Amilcare

    2010-03-01

    Achieving a sustainable use of water resources, in view of the increased food and biofuel demand and possible climate change, will require optimizing irrigation, a highly nontrivial task given the unpredictability of rainfall and the numerous soil-plant-atmosphere interactions. Here we theoretically analyze two different irrigation schemes, a traditional scheme, consisting of the application of fixed water volumes that bring soil moisture to field capacity, and a microirrigation scheme supplying water continuously in order to avoid plant water stress. These two idealized irrigation schemes are optimal in the sense that they avoid crop water stress while minimizing water losses by percolation and runoff. Furthermore, they cover the two extremes cases of continuous and fully concentrated irrigation. For both irrigation schemes, we obtain exact solutions of the steady state soil moisture probability density function with random timing and amounts of rainfall. We also give analytical expressions for irrigation frequency and volumes under different rainfall regimes, evaporative demands, and soil types. We quantify the excess volumes required by traditional irrigation, mostly lost in runoff and deep infiltration, as a function of climate, soil, and vegetation parameters.

  16. Impact of Direct Soil Moisture and Revised Soil Moisture Index Methods on Hydrologic Predictions in an Arid Climate

    OpenAIRE

    Milad Jajarmizadeh; Sobri bin Harun; Shamsuddin Shahid; Shatirah Akib; Mohsen Salarpour

    2014-01-01

    The soil and water assessment tool (SWAT) is a physically based model that is used extensively to simulate hydrologic processes in a wide range of climates around the world. SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. The objective of the present study was to compare the performance of two approaches used for the calculation of curve numbers in SWAT, that is, the Revised Soil Moisture Index (SMI), which is based on previou...

  17. The NAFE'05/CoSMOS Data Set: Toward SMOS Soil Moisture Retrieval, Downscaling, and Assimilation

    DEFF Research Database (Denmark)

    Panciera, Rocco; Walker, Jeffrey P.; Kalma, Jetse D.

    2008-01-01

    -resolution data from SMOS; and 3) testing its assimilation into land surface models for root zone soil moisture retrieval. This paper describes the NAFE'05 and COSMOS airborne data sets together with the ground data collected in support of both aircraft campaigns. The airborne L-band acquisitions included 40 km x...... was to provide simulated Soil Moisture and Ocean Salinity (SMOS) observations using airborne L-band radiometers supported by soil moisture and other relevant ground data for the following: 1) the development of SMOS soil moisture retrieval algorithms; 2) developing approaches for downscaling the low....... The L-band data were accompanied by airborne thermal infrared and optical measurements. The ground data consisted of continuous soil moisture profile measurements at 18 monitoring sites throughout the 40 km x 40 km study area and extensive spatial near-surface soil moisture measurements concurrent...

  18. Optimizing Soil Moisture Sampling Locations for Validation Networks for SMAP

    Science.gov (United States)

    Roshani, E.; Berg, A. A.; Lindsay, J.

    2013-12-01

    Soil Moisture Active Passive satellite (SMAP) is scheduled for launch on Oct 2014. Global efforts are underway for establishment of soil moisture monitoring networks for both the pre- and post-launch validation and calibration of the SMAP products. In 2012 the SMAP Validation Experiment, SMAPVEX12, took place near Carman Manitoba, Canada where nearly 60 fields were sampled continuously over a 6 week period for soil moisture and several other parameters simultaneous to remotely sensed images of the sampling region. The locations of these sampling sites were mainly selected on the basis of accessibility, soil texture, and vegetation cover. Although these criteria are necessary to consider during sampling site selection, they do not guarantee optimal site placement to provide the most efficient representation of the studied area. In this analysis a method for optimization of sampling locations is presented which combines the state-of-art multi-objective optimization engine (non-dominated sorting genetic algorithm, NSGA-II), with the kriging interpolation technique to minimize the number of sampling sites while simultaneously minimizing the differences between the soil moisture map resulted from the kriging interpolation and soil moisture map from radar imaging. The algorithm is implemented in Whitebox Geospatial Analysis Tools, which is a multi-platform open-source GIS. The optimization framework is subject to the following three constraints:. A) sampling sites should be accessible to the crew on the ground, B) the number of sites located in a specific soil texture should be greater than or equal to a minimum value, and finally C) the number of sampling sites with a specific vegetation cover should be greater than or equal to a minimum constraint. The first constraint is implemented into the proposed model to keep the practicality of the approach. The second and third constraints are considered to guarantee that the collected samples from each soil texture categories

  19. Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

    Science.gov (United States)

    Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

    2011-01-01

    Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

  20. An inversion method for retrieving soil moisture information from satellite altimetry observations

    Science.gov (United States)

    Uebbing, Bernd; Forootan, Ehsan; Kusche, Jürgen; Braakmann-Folgmann, Anne

    2016-04-01

    ) deriving time-invariant spatial patterns (base-functions) by applying principal component analysis (PCA) to simulated soil moisture from a large-scale land surface model. (ii) Estimating time-variable soil moisture evolution by fitting these base functions of (i) to the along-track retracked backscatter coefficients in a least squares sense. (iii) Combining the estimated time-variable amplitudes and the pre-computed base-functions, which results in reconstructed (spatio-temporal) soil moisture information. We will show preliminary results that are compared to available high-resolution soil moisture model data over the region (the Australian Water Resource Assessment, AWRA model). We discuss the possibility of using altimetry-derived soil moisture estimations to improve the simulation skill of soil moisture in the Global Land Data Assimilation System (GLDAS) over Australia.

  1. Large Scale Evaluation of AMSR-E Soil Moisture Products Based on Ground Soil Moisture Network Measurements

    Science.gov (United States)

    Gruhier, C.; de Rosnay, P.; Richaume, P.; Kerr, Y.; Rudiger, C.; Boulet, G.; Walker, J. P.; Mougin, E.; Ceschia, E.; Calvet, J.

    2007-05-01

    This paper presents an evaluation of AMSR-E (Advanced Microwave Scanning Radiometer for EOS) soil moisture products, based on a comparison with three ground soil moisture networks. The selected ground sites are representative of various climatic, hydrologic and environmental conditions in temperate and semi-arid areas. They are located in the south-west of France, south-east of Australia and the Gourma region of the Sahel. These sites were respectively implemented in the framework of the projects SMOSREX (Surface Monitoring Of Soil Reservoir Experiment), SASMAS/GoREx (Scaling and Assimilation of Soil Moisture and Streamflow in the Goulburn River Experimental catchment) and AMMA (African Monsoon Multidisciplinary Analysis). In all cases, the arrangement of the soil moisture measuring sites was specifically designed to address the validation of remotely sensed soil moisture in the context of the preparation of the SMOS (Soil Moisture and Ocean Salinity) project. For the purpose of this study, 25km AMSR-E products were used, including brightness temperatures at 6.9 and 10.7 GHz, and derived soil moisture. The study is focused on the year 2005. It is based on ground soil moisture network measurements from 4 stations for SMOSREX extended to the SUDOUEST project of CESBIO, 12 stations for GoRex, and 4 stations for AMMA. Temporal and spatial features of soil moisture variability and stability is a critical issue to be addressed for remotely sensed soil moisture validation. While ground measurements provide information on soil moisture dynamics at local scale and high temporal resolution (hourly), satellite measurements are sparser in time (up to several days), but cover a larger region (25km x 25km for AMSR-E). First, a statistical analysis, including mean relative difference and Spearman rank, is conducted for the three soil moisture networks. This method is mainly based on the approach proposed by Cosh et al. (2004) for the purpose of the use of ground networks for

  2. Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

    Science.gov (United States)

    Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy

    2015-01-01

    Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

  3. Non-Parametric Evolutionary Algorithm for Estimating Root Zone Soil Moisture

    Science.gov (United States)

    Mohanty, B.; Shin, Y.; Ines, A. M.

    2013-12-01

    Prediction of root zone soil moisture is critical for water resources management. In this study, we explored a non-parametric evolutionary algorithm for estimating root zone soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term root zone soil moisture dynamics at different soil depths. Also, we analyzed rainfall occurrence probabilities and dry/wet spell lengths reproduced by this approach. The HMMGA was used to estimate the optimal state sequences (weather states) based on the precipitation history. Historical root zone soil moisture statistics were then determined based on the weather state conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km x 130 km) and Illinois (300 km x 500 km), during 1995 to 2009 and 1994 to 2010, respectively. We found that the newly developed framework performed well in predicting root zone soil moisture dynamics at both the spatial scales. Also, the reproduced rainfall occurrence probabilities and dry/wet spell lengths matched well with the observations at the spatio-temporal scales. Since the proposed algorithm requires only precipitation and historical soil moisture data from existing, established weather stations, it can serve an attractive alternative for predicting root zone soil moisture in the future using climate change scenarios and root zone soil moisture history.

  4. A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals

    Directory of Open Access Journals (Sweden)

    W. T. Crow

    2009-01-01

    Full Text Available A number of recent studies have focused on enhancing runoff prediction via the assimilation of remotely-sensed surface soil moisture retrievals into a hydrologic model. The majority of these approaches have viewed the problem from purely a state or parameter estimation perspective in which remotely-sensed soil moisture estimates are assimilated to improve the characterization of pre-storm soil moisture conditions in a hydrologic model, and consequently, its simulation of runoff response to subsequent rainfall. However, recent work has demonstrated that soil moisture retrievals can also be used to filter errors present in satellite-based rainfall accumulation products. This result implies that soil moisture retrievals have potential benefit for characterizing both antecedent moisture conditions (required to estimate sub-surface flow intensities and subsequent surface runoff efficiencies and storm-scale rainfall totals (required to estimate the total surface runoff volume. In response, this work presents a new sequential data assimilation system that exploits remotely-sensed surface soil moisture retrievals to simultaneously improve estimates of both pre-storm soil moisture conditions and storm-scale rainfall accumulations. Preliminary testing of the system, via a synthetic twin data assimilation experiment based on the Sacramento hydrologic model and data collected from the Model Parameterization Experiment, suggests that the new approach is more efficient at improving stream flow predictions than data assimilation techniques focusing solely on the constraint of antecedent soil moisture conditions.

  5. Developing a dual assimilation approach for thermal infrared and passive microwave soil moisture retrievals

    Science.gov (United States)

    Hain, Christopher Ryan

    Soil moisture plays a vital role in the partitioning of sensible and latent heat fluxes in the surface energy budget and the lack of a dense spatial and temporal network of ground-based observations provides a challenge to the initialization of the true soil moisture state in numerical weather prediction simulations. The retrieval of soil moisture using observations from both satellite-based thermal-infrared (TIR) and passive microwave (PM) sensors has been developed (Anderson et al., 2007; Hain et al., 2009; Jackson, 1993; Njoku et al., 2003). The ability of the TIR and microwave observations to diagnose soil moisture conditions within different layers of the soil profile provides an opportunity to use each in a synergistic data assimilation approach towards the goal of diagnosing the true soil moisture state from surface to root-zone. TIR and PM retrievals of soil moisture are compared to soil moisture estimates provided by a retrospective Land Information System (LIS) simulation using the NOAH LSM during the time period of 2003--2008. The TIR-based soil moisture product is provided by a retrieval of soil moisture associated with surface flux estimates from the Atmosphere-Land-Exchange-Inversion (ALEXI) model (Anderson et al., 1997; Mecikalski et al., 1999; Hain et al., 2009). The PM soil moisture retrieval is provided by the Vrijie Universiteit Amsterdam (VUA)-NASA surface soil moisture product. The VUA retrieval is based on the findings of Owe et al. (2001; 2008) using the Land Surface Parameter model (LPRM), which uses one dual polarized channel (6.925 or 10.65 GHz) for a dual-retrieval of surface soil moisture and vegetation water content. In addition, retrievals of ALEXI (TIR) and AMSR-E (PM) soil moisture are assimilated within the Land Information System using the NOAH LSM. A series of data assimilation experiments is completed with the following configuration: (a) no assimilation, (b) only ALEXI soil moisture, (c) only AMSR-E soil moisture, and (d) ALEXI

  6. Upscaling of soil moisture measurements in NW Italy

    Science.gov (United States)

    Ferraris, Stefano; Canone, Davide; Previati, Maurizio; Brunod, Christian; Ratto, Sara; Cauduro, Marco

    2015-04-01

    There is large mismatch in spatial scale between the climate and meteorological model grid, and the scale of soil and vegetation measurements. Remote sensing data can help to fit the model scale, but they cannot provide rootzone data. In this work some soil moisture datasets are analysed for the sake of providing larger scale estimation of soil moisture and water and energy fluxes. The first dataset refers to a plain site near Torino, where measurements are taken since 1997 (Baudena et al., 2012), and a mountain site close to the town. The second one is a dataset in the mountains of Valle d'Aosta (Brocca et al., 2013), where 4 years of data are available. The use of digital elevation models and vegetation maps is shown in this work. Some soil processes (e.g. Whalley et al., 2012) are usually disregarded, but in this work their possible impact is considered. References L. Brocca, A. Tarpanelli, T. Moramarco, F. Melone, S.M. Ratto, M. Cauduro, S. Ferraris, N. Berni, F. Ponziani, W. Wagner, T. Melzer (2013). Soil Moisture Estimation in Alpine Catchments through Modeling and Satellite Observations VADOSE ZONE JOURNAL, vol. 8-2, p. 1-10, doi:10.2136/vzj2012.0102 M. Baudena, I. Bevilacqua, D. Canone, S. Ferraris, M. Previati, A. Provenzale (2012). Soil water dynamics at a midlatitude test site: Field measurements and box modeling approaches. JOURNAL OF HYDROLOGY, vol. 414-415, p. 329-340, ISSN: 0022-1694, doi: 10.1016/j.jhydrol.2011.11.009 W.R. Whalley, G.P. Matthews, S. Ferraris (2012). The effect of compaction and shear deformation of saturated soil on hydraulic conductivity. SOIL & TILLAGE RESEARCH, vol. 125, p. 23-29, ISSN: 0167-1987

  7. Capacitive Soil Moisture Sensor for Plant Watering

    Science.gov (United States)

    Maier, Thomas; Kamm, Lukas

    2016-04-01

    How can you realize a water saving and demand-driven plant watering device? To achieve this you need a sensor, which precisely detects the soil moisture. Designing such a sensor is the topic of this poster. We approached this subject with comparing several physical properties of water, e.g. the conductivity, permittivity, heat capacity and the soil water potential, which are suitable to detect the soil moisture via an electronic device. For our project we have developed a sensor device, which measures the soil moisture and provides the measured values for a plant watering system via a wireless bluetooth 4.0 network. Different sensor setups have been analyzed and the final sensor is the result of many iterative steps of improvement. In the end we tested the precision of our sensor and compared the results with theoretical values. The sensor is currently being used in the Botanical Garden of the Friedrich-Alexander-University in a long-term test. This will show how good the usability in the real field is. On the basis of these findings a marketable sensor will soon be available. Furthermore a more specific type of this sensor has been designed for the EU:CROPIS Space Project, where tomato plants will grow at different gravitational forces. Due to a very small (15mm x 85mm x 1.5mm) and light (5 gramm) realisation, our sensor has been selected for the space program. Now the scientists can monitor the water content of the substrate of the tomato plants in outer space and water the plants on demand.

  8. Soil Moisture Content Monitoring Based on ERS Wind Scatterometer Data

    Institute of Scientific and Technical Information of China (English)

    WANG Jian-ming; SHI Jian-cheng; SHAO Yun; LIU Wei

    2005-01-01

    The ERS-1/2 wind scatterometer (WSC) has a low resolution cell of about 50 km but provides a high repetition rate (<4 d) and can make measurements at multiple incidence angles. In order to estimate effective surface reflectivity (related to soil moisture content) over bare soil using WSC data, an original methodology based on the advance integral equation model (AIEM) is presented, which takes advantage of its multiple view angular characteristics. This method includes two steps. First, a simplified two-parameter surface scattering model is calibrated by AIEM simulated-database over a wide parameter space. Second, regression analyses are carried out using the simulated database to build the relation between those parameters of our model at different incident angles from two observations of Mid and Fore beams. From the model simulated database, our technique works quite well in estimating Γ0. The possibility of applying the model to retrieve soil moisture is investigated using a set of data collected from the Intensive Observation Period field campaign in 1998 of the Asian Monsoon Experiment Tibet (GAME-Tibet). The retrieved values obtained for the bare land surface are consistent with ground measurements collected in these areas and the correlation coefficient between retrieved soil moisture and the measured one reaches 0.65.

  9. Radon diffusion coefficients in soils of varying moisture content

    Science.gov (United States)

    Papachristodoulou, C.; Ioannides, K.; Pavlides, S.

    2009-04-01

    Radon is a naturally occurring radioactive gas that is generated in the Earth's crust and is free to migrate through soil and be released to the atmosphere. Due to its unique properties, soil gas radon has been established as a powerful tracer used for a variety of purposes, such as exploring uranium ores, locating geothermal resources and hydrocarbon deposits, mapping geological faults, predicting seismic activity or volcanic eruptions and testing atmospheric transport models. Much attention has also been given to the radiological health hazard posed by increased radon concentrations in the living and working environment. In order to exploit radon profiles for geophysical purposes and also to predict its entry indoors, it is necessary to study its transport through soils. Among other factors, the importance of soil moisture in such studies has been largely highlighted and it is widely accepted that any measurement of radon transport parameters should be accompanied by a measurement of the soil moisture content. In principle, validation of transport models in the field is encountered by a large number of uncontrollable and varying parameters; laboratory methods are therefore preferred, allowing for experiments to be conducted under well-specified and uniform conditions. In this work, a laboratory technique has been applied for studying the effect of soil moisture content on radon diffusion. A vertical diffusion chamber was employed, in which radon was produced from a 226Ra source, was allowed to diffuse through a soil column and was finally monitored using a silicon surface barrier detector. By solving the steady-state radon diffusion equation, diffusion coefficients (D) were determined for soil samples of varying moisture content (m), from null (m=0) to saturation (m=1). For dry soil, a D value of 4.1×10-7 m2s-1 was determined, which increased moderately by a factor of ~3 for soil with low moisture content, i.e. up to m ~0.2. At higher water fractions, a decrease

  10. Comparison of temporal trends from multiple soil moisture data sets and precipitation: The implication of irrigation on regional soil moisture trend

    Science.gov (United States)

    Qiu, Jianxiu; Gao, Quanzhou; Wang, Sheng; Su, Zhenrong

    2016-06-01

    In this study, soil moisture trend during 1996-2010 in China was analyzed based on three soil moisture data sets, namely microwave-based multi-satellite surface soil moisture product released from European Space Agency's Climate Change Initiative (ESA CCI), ERA-Interim/Land reanalysis, and in-situ measurements collected from the nationwide agro-meteorological network. Taking the in-situ soil moisture as reference, it is found that ESA CCI generally captured soil moisture trend more accurately than ERA-Interim/Land did. From the spatial distribution of trend analysis results, it is seen that significant decreasing trend for summer soil moisture in northwestern China and northern Inner Mongolia, as well as the significant increasing trend for autumn soil moisture in northern China were identified by both ESA CCI and ERA-Interim/Land. This is in alignment with results from gauge-based precipitation provided by Institute of Geographic Sciences and Natural Resources Research (IGSNRR) and satellite-based precipitation from Tropical Rainfall Measuring Mission (TRMM). However, disagreements in derived trends between ESA CCI, ERA-Interim/Land and IGSNRR were observed in the southwest and north of China, especially in major irrigation regions, such as the oases in northern Xinjiang and large areas in Sichuan province. Prominent difference between soil moisture and precipitation exhibited in the extensively irrigated Huang-Huai-Hai Plain. The spatial coincidence between significantly wetting areas (identified by ESA CCI) and heavily irrigated areas, as well as the grid-based Student's t-test sampling from various irrigation levels revealed that the observed discrepancy was caused by massive anthropogenic interference in this region. Results indicate that, for regions with great magnitude of human interference, modules considering actual irrigation practice are crucial for successful modeling of soil moisture and capturing the long-term trend. Furthermore, results could

  11. Capability of meteorological drought indices for detecting soil moisture droughts

    Directory of Open Access Journals (Sweden)

    Devanmini Halwatura

    2017-08-01

    New hydrological insights for the region: For three typical soil types and climate zones in Eastern Australia, and for two soil profiles, we have found a significant correlation between the indices and soil moisture droughts detected by Hydrus-1D. The failure rates and false alarm rates for detecting the simulated soil moisture droughts were generally below 50% for both indices and both soil profiles (the Reconnaissance Drought Index at Melbourne was the only exception. However, the complexity of Hydrus-1D and the uncertainty associated with the available, regionalised soil water retention curves encourage using the indices over Hydrus-1D in absence of appropriate soil moisture monitoring data.

  12. A Novel Bias Correction Method for Soil Moisture and Ocean Salinity (SMOS Soil Moisture: Retrieval Ensembles

    Directory of Open Access Journals (Sweden)

    Ju Hyoung Lee

    2015-12-01

    Full Text Available Bias correction is a very important pre-processing step in satellite data assimilation analysis, as data assimilation itself cannot circumvent satellite biases. We introduce a retrieval algorithm-specific and spatially heterogeneous Instantaneous Field of View (IFOV bias correction method for Soil Moisture and Ocean Salinity (SMOS soil moisture. To the best of our knowledge, this is the first paper to present the probabilistic presentation of SMOS soil moisture using retrieval ensembles. We illustrate that retrieval ensembles effectively mitigated the overestimation problem of SMOS soil moisture arising from brightness temperature errors over West Africa in a computationally efficient way (ensemble size: 12, no time-integration. In contrast, the existing method of Cumulative Distribution Function (CDF matching considerably increased the SMOS biases, due to the limitations of relying on the imperfect reference data. From the validation at two semi-arid sites, Benin (moderately wet and vegetated area and Niger (dry and sandy bare soils, it was shown that the SMOS errors arising from rain and vegetation attenuation were appropriately corrected by ensemble approaches. In Benin, the Root Mean Square Errors (RMSEs decreased from 0.1248 m3/m3 for CDF matching to 0.0678 m3/m3 for the proposed ensemble approach. In Niger, the RMSEs decreased from 0.14 m3/m3 for CDF matching to 0.045 m3/m3 for the ensemble approach.

  13. A case study of spatial heterogeneity of soil moisture in the Loess Plateau,western China:A geostatistical approach

    Institute of Scientific and Technical Information of China (English)

    BI Huaxing; LI Xiaoyin; LIU Xin; GUO Mengxia; LI Jun

    2009-01-01

    Soil moisture distribution shows highly variation both spatially and temporally.This study assesses the spatial heterogeneity of soil moisture on a hill-slope scale in the Loess Plateau in West China by using a geostatistical approach.Soil moisture was measured by time-domain reflectometry (TDR) in 313 samples.Two kinds of sampling scales were used (2 × 2 m and 20 × 20m) at two soil layers (0-30 cm and 30-450 cm).The general characteristics of soil moisture were analyzed by a classical statistics method,and the spatial heterogeneity of soil moisture was analyzed using a geostatistical approach.The results showed that the spherical model is the best-fit model to simulate soil moisture on the experimental hill-slope.The parameters of this model indicated that the spatial dependence of soil moisture in the selected hill-slope was moderate.Even the 2 × 2 m sampling scale was too coarse to show the detailed spatial variances of soil moisture in this area.The dependent distance increased from 27.4 m to 494.16 m as the sampling scale became coarse (from 2 ×2 m to 20 × 20 m).A map of soil moisture was generated by using original soil moisture data and interpolated values determined by the Kriging method.The average soil moisture (area weighted) in the different layers of soil was calculated on the basis of this map (10.94% for the 0-30 cm soil layer,11.88% for the 30-60 em soil layer).This average soil moisture is lower than the corresponding average effective soil moisture,which suggests that the soil moisture is not sufficient to support vegetation in this area.

  14. Multivariate hydrological data assimilation of soil moisture and groundwater head

    Science.gov (United States)

    Zhang, Donghua; Madsen, Henrik; Ridler, Marc E.; Kidmose, Jacob; Jensen, Karsten H.; Refsgaard, Jens C.

    2016-10-01

    Observed groundwater head and soil moisture profiles are assimilated into an integrated hydrological model. The study uses the ensemble transform Kalman filter (ETKF) data assimilation method with the MIKE SHE hydrological model code. The method was firstly tested on synthetic data in a catchment of less complexity (the Karup catchment in Denmark), and later implemented using data from real observations in a larger and more complex catchment (the Ahlergaarde catchment in Denmark). In the Karup model, several experiments were designed with respect to different observation types, ensemble sizes and localization schemes, to investigate the assimilation performance. The results showed the necessity of using localization, especially when assimilating both groundwater head and soil moisture. The proposed scheme with both distance localization and variable localization was shown to be more robust and provide better results. Using the same assimilation scheme in the Ahlergaarde model, groundwater head and soil moisture were successfully assimilated into the model. The hydrological model with assimilation showed an overall improved performance compared to the model without assimilation.

  15. Soil Moisture Spatial Patterns in a Uniform Paulownia Tree Stand

    Science.gov (United States)

    Soil moisture spatial patterns have been studied at length in agricultural fields and pasture/rangelands as part of the USDA soil moisture satellite validation program, but recent research has begun to address the distribution of soil beneath a forest canopy. Forests cover a significant portion of ...

  16. Remote sensing of vegetation and soil moisture

    Science.gov (United States)

    Kong, J. A.; Shin, R. T. (Principal Investigator)

    1983-01-01

    Progress in the investigation of problems related to the remote sensing of vegetation and soil moisture is reported. Specific topics addressed include: (1) microwave scattering from periodic surfaces using a rigorous modal technique; (2) combined random rough surface and volume scattering effects; (3) the anisotropic effects of vegetation structures; (4) the application of the strong fluctuation theory to the the study of electromagnetic wave scattering from a layer of random discrete scatterers; and (5) the investigation of the scattering of a plane wave obliquely incident on a half space of densely distributed spherical dielectric scatterers using a quantum mechanical potential approach.

  17. Relative skills of soil moisture and vegetation optical depth retrievals for agricultural drought monitoring

    Science.gov (United States)

    Soil moisture condition is an important indicator for agricultural drought monitoring. Through the Land Parameter Retrieval Model (LPRM), vegetation optical depth (VOD) as well as surface soil moisture (SM) can be retrieved simultaneously from brightness temperature observations from the Advanced Mi...

  18. The impact of assumed error variances on surface soil moisture and snow depth hydrologic data assimilation

    Science.gov (United States)

    Accurate knowledge of antecedent soil moisture and snow depth conditions is often important for obtaining reliable hydrological simulations of stream flow. Data assimilation (DA) methods can be used to integrate remotely-sensed (RS) soil moisture and snow depth retrievals into a hydrology model and...

  19. Error characterization of microwave satellite soil moisture data sets using fourier analysis

    Science.gov (United States)

    Abstract: Soil moisture is a key geophysical variable in hydrological and meteorological processes. Accurate and current observations of soil moisture over mesoscale to global scales as inputs to hydrological, weather and climate modelling will benefit the predictability and understanding of these p...

  20. Evaluation of gravimetric ground truth soil moisture data collected for the agricultural soil moisture experiment, 1978 Colby, Kansas, aircraft mission

    Science.gov (United States)

    Arya, L. M.; Phinney, D. E. (Principal Investigator)

    1980-01-01

    Soil moisture data acquired to support the development of algorithms for estimating surface soil moisture from remotely sensed backscattering of microwaves from ground surfaces are presented. Aspects of field uniformity and variability of gravimetric soil moisture measurements are discussed. Moisture distribution patterns are illustrated by frequency distributions and contour plots. Standard deviations and coefficients of variation relative to degree of wetness and agronomic features of the fields are examined. Influence of sampling depth on observed moisture content an variability are indicated. For the various sets of measurements, soil moisture values that appear as outliers are flagged. The distribution and legal descriptions of the test fields are included along with examinations of soil types, agronomic features, and sampling plan. Bulk density data for experimental fields are appended, should analyses involving volumetric moisture content be of interest to the users of data in this report.

  1. High resolution modelling of soil moisture patterns with TerrSysMP: A comparison with sensor network data

    Science.gov (United States)

    Gebler, S.; Hendricks Franssen, H.-J.; Kollet, S. J.; Qu, W.; Vereecken, H.

    2017-04-01

    The prediction of the spatial and temporal variability of land surface states and fluxes with land surface models at high spatial resolution is still a challenge. This study compares simulation results using TerrSysMP including a 3D variably saturated groundwater flow model (ParFlow) coupled to the Community Land Model (CLM) of a 38 ha managed grassland head-water catchment in the Eifel (Germany), with soil water content (SWC) measurements from a wireless sensor network, actual evapotranspiration recorded by lysimeters and eddy covariance stations and discharge observations. TerrSysMP was discretized with a 10 × 10 m lateral resolution, variable vertical resolution (0.025-0.575 m), and the following parameterization strategies of the subsurface soil hydraulic parameters: (i) completely homogeneous, (ii) homogeneous parameters for different soil horizons, (iii) different parameters for each soil unit and soil horizon and (iv) heterogeneous stochastic realizations. Hydraulic conductivity and Mualem-Van Genuchten parameters in these simulations were sampled from probability density functions, constructed from either (i) soil texture measurements and Rosetta pedotransfer functions (ROS), or (ii) estimated soil hydraulic parameters by 1D inverse modelling using shuffle complex evolution (SCE). The results indicate that the spatial variability of SWC at the scale of a small headwater catchment is dominated by topography and spatially heterogeneous soil hydraulic parameters. The spatial variability of the soil water content thereby increases as a function of heterogeneity of soil hydraulic parameters. For lower levels of complexity, spatial variability of the SWC was underrepresented in particular for the ROS-simulations. Whereas all model simulations were able to reproduce the seasonal evapotranspiration variability, the poor discharge simulations with high model bias are likely related to short-term ET dynamics and the lack of information about bedrock characteristics

  2. Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture

    Science.gov (United States)

    Martens, B.; Miralles, D.; Lievens, H.; Fernández-Prieto, D.; Verhoest, N. E. C.

    2016-06-01

    Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite soil moisture observations from the Soil Moisture and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite soil moisture that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010-December 2013. SMOS soil moisture observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface soil moisture and evaporation are validated against soil moisture probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite soil moisture products. Results indicate that the modelled soil moisture from GLEAM can be improved through the assimilation of SMOS soil moisture: the average correlation coefficient between in situ measurements and the modelled soil moisture over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of soil moisture data

  3. [Simulation of cropland soil moisture based on an ensemble Kalman filter].

    Science.gov (United States)

    Liu, Zhao; Zhou, Yan-Lian; Ju, Wei-Min; Gao, Ping

    2011-11-01

    By using an ensemble Kalman filter (EnKF) to assimilate the observed soil moisture data, the modified boreal ecosystem productivity simulator (BEPS) model was adopted to simulate the dynamics of soil moisture in winter wheat root zones at Xuzhou Agro-meteorological Station, Jiangsu Province of China during the growth seasons in 2000-2004. After the assimilation of observed data, the determination coefficient, root mean square error, and average absolute error of simulated soil moisture were in the ranges of 0.626-0.943, 0.018-0.042, and 0.021-0.041, respectively, with the simulation precision improved significantly, as compared with that before assimilation, indicating the applicability of data assimilation in improving the simulation of soil moisture. The experimental results at single point showed that the errors in the forcing data and observations and the frequency and soil depth of the assimilation of observed data all had obvious effects on the simulated soil moisture.

  4. Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation

    Directory of Open Access Journals (Sweden)

    M. Zribi

    2011-01-01

    Full Text Available The present paper proposes a method for the evaluation of soil evaporation, using soil moisture estimations based on radar satellite measurements. We present firstly an approach for the estimation and monitoring of soil moisture in a semi-arid region in North Africa, using ENVISAT ASAR images, over two types of vegetation covers. The first mapping process is dedicated solely to the monitoring of moisture variability related to rainfall events, over areas in the "non-irrigated olive tree" class of land use. The developed approach is based on a simple linear relationship between soil moisture and the backscattered radar signal normalised at a reference incidence angle. The second process is proposed over wheat fields, using an analysis of moisture variability due to both rainfall and irrigation. A semi-empirical model, based on the water-cloud model for vegetation correction, is used to retrieve soil moisture from the radar signal. Moisture mapping is carried out over wheat fields, showing high variability between irrigated and non-irrigated wheat covers. This analysis is based on a large database, including both ENVISAT ASAR and simultaneously acquired ground-truth measurements (moisture, vegetation, roughness, during the 2008–2009 vegetation cycle. Finally, a semi-empirical approach is proposed in order to relate surface moisture to the difference between soil evaporation and the climate demand, as defined by the potential evaporation. Mapping of the soil evaporation is proposed.

  5. Drought monitoring with soil moisture active passive (SMAP) measurements

    Science.gov (United States)

    Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara

    2017-09-01

    Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an

  6. Calibration of Soil Moisture Measurement Using Pr2 Moisture Meter and Gravimetric-Based Approaches

    Directory of Open Access Journals (Sweden)

    Olotu Yahaya

    2016-10-01

    Full Text Available The research study strongly focused on creating strong mechanism for measuring and evaluating soil moisture content comparing PR2 capacitance moisture meter and gravimetric approach. PR2 moisture meter shows a better performance accuracy of ± 6%; 0.06 m 3 /m 3 and intercept a0 =1.8; indicating the field is heavy clay. It measures to 1000 mm depth with high precision; while realistic result could not be obtained from gravimetric method at this measuring depth. Therefore, effective soil moisture measuring, monitoring and evaluation can be achieved with PR2 moisture meter.

  7. A coupling ocean-atmosphere climatic modelling study for rainfall and soil moisture simulations on the São Francisco River basin

    Directory of Open Access Journals (Sweden)

    Regla Duthit Somoza

    2011-12-01

    Full Text Available The aim of this study was to obtain a better understanding of the Ocean-Atmosphere Global Circulation Coupling Model (CGCM performance for forecasting the interannual rainfall variability on the São Francisco River Basin, during austral summer (DJF 1997-2007. In addition, the rainfall predictions and calculated potential vapor transpiration were the input variables for the Hydrological Balance Model (HBM experiments to obtain soil moisture estimations. Simulations using CGCM were compared with forecastings based on the Atmosphere Global Circulation Model (AGCM, which has been used previously for this purpose. Even though there were systematic errors of rainfall over estimations for the Basin, the CGCM had better performance than the AGCM at the spatial representation and showed positive correlation coefficients with observation values. These facts corroborate that ocean-atmosphere coupling is an important mechanism to be taken into account for rainfall forecasting at the Brazilian southeast zone. On the other hand, the HBM-AGCM and the HBM-CGCM were quite similar in terms of correlation coefficients (0,6 for soil moisture estimation. This suggests that the corrected estimated precipitation and potential evapotranspiration (ETP resulting from climate modeling and dynamics of the AGCM and CGCM, as input data for water balance models in the seasonal scale, can be used to provide support to the best practices for the management of surface water in the basin of the São Francisco River.

  8. Use of distributed water level and soil moisture data in the evaluation of the PUMMA periurban distributed hydrological model: application to the Mercier catchment, France

    Science.gov (United States)

    Braud, Isabelle; Fuamba, Musandji; Branger, Flora; Batchabani, Essoyéké; Sanzana, Pedro; Sarrazin, Benoit; Jankowfsky, Sonja

    2016-04-01

    Distributed hydrological models are used at best when their outputs are compared not only to the outlet discharge, but also to internal observed variables, so that they can be used as powerful hypothesis-testing tools. In this paper, the interest of distributed networks of sensors for evaluating a distributed model and the underlying functioning hypotheses is explored. Two types of data are used: surface soil moisture and water level in streams. The model used in the study is the periurban PUMMA (Peri-Urban Model for landscape Management, Jankowfsky et al., 2014), that is applied to the Mercier catchment (6.7 km2) a semi-rural catchment with 14% imperviousness, located close to Lyon, France where distributed water level (13 locations) and surface soil moisture data (9 locations) are available. Model parameters are specified using in situ information or the results of previous studies, without any calibration and the model is run for four years from January 1st 2007 to December 31st 2010 with a variable time step for rainfall and an hourly time step for reference evapotranspiration. The model evaluation protocol was guided by the available data and how they can be interpreted in terms of hydrological processes and constraints for the model components and parameters. We followed a stepwise approach. The first step was a simple model water balance assessment, without comparison to observed data. It can be interpreted as a basic quality check for the model, ensuring that it conserves mass, makes the difference between dry and wet years, and reacts to rainfall events. The second step was an evaluation against observed discharge data at the outlet, using classical performance criteria. It gives a general picture of the model performance and allows to comparing it to other studies found in the literature. In the next steps (steps 3 to 6), focus was made on more specific hydrological processes. In step 3, distributed surface soil moisture data was used to assess the

  9. On the identification of representative in situ soil moisture monitoring stations for the validation of SMAP soil moisture products in Australia

    Science.gov (United States)

    Yee, Mei Sun; Walker, Jeffrey P.; Monerris, Alessandra; Rüdiger, Christoph; Jackson, Thomas J.

    2016-06-01

    The high spatio-temporal variability of soil moisture complicates the validation of remotely sensed soil moisture products using in situ monitoring stations. Therefore, a standard methodology for selecting the most representative stations for the purpose of validating satellites and land surface models is essential. Based on temporal stability and geostatistical studies using long-term soil moisture records, intensive ground measurements and airborne soil moisture products, this study investigates the representativeness of soil moisture monitoring stations within the Yanco study area for the validation of NASA's Soil Moisture Active Passive (SMAP) products at 3 km for radar, 9 km for radar-radiometer and 36 km for radiometer pixels. This resulted in the identification of a number of representative stations according to the different scales. Although the temporal stability method was found to be suitable for identifying representative stations, stations based on the mean relative difference (MRD) were not necessarily the most representative of the areal average. Moreover, those identified from standard deviation of the relative difference (SDRD) may be dry-biased. It was also found that in the presence of heterogeneous land use, stations should be weighted based on proportions of agricultural land. Airborne soil moisture products were also shown to provide useful a priori information for identifying representative locations. Finally, recommendations are made regarding the design of future networks for satellite validation, and specifically the most representative stations for the Yanco area.

  10. Modelling the Passive Microwave Signature from Land Surfaces: A Review of Recent Results and Application to the L-Band SMOS SMAP Soil Moisture Retrieval Algorithms

    Science.gov (United States)

    Wigneron, J.-P.; Jackson, T. J.; O'Neill, P.; De Lannoy, G.; De Rosnay, P.; Walker, J. P.; Ferrazzoli, P.; Mironov, V.; Bircher, S.; Grant, J. P.; hide

    2017-01-01

    Two passive microwave missions are currently operating at L-band to monitor surface soil moisture (SM) over continental surfaces. The SMOS sensor, based on an innovative interferometric technology enabling multi-angular signatures of surfaces to be measured, was launched in November 2009. The SMAP sensor, based on a large mesh reflector 6 m in diameter providing a conically scanning antenna beam with a surface incidence angle of 40deg, was launched in January of 2015. Over the last decade, an intense scientific activity has focused on the development of the SM retrieval algorithms for the two missions. This activity has relied on many field (mainly tower-based) and airborne experimental campaigns, and since 2010-2011, on the SMOS and Aquarius space-borne L-band observations. It has relied too on the use of numerical, physical and semi-empirical models to simulate the microwave brightness temperature of natural scenes for a variety of scenarios in terms of system configurations (polarization, incidence angle) and soil, vegetation and climate conditions. Key components of the inversion models have been evaluated and new parameterizations of the effects of the surface temperature, soil roughness, soil permittivity, and vegetation extinction and scattering have been developed. Among others, global maps of select radiative transfer parameters have been estimated very recently. Based on this intense activity, improvements of the SMOS and SMAP SM inversion algorithms have been proposed. Some of them have already been implemented, whereas others are currently being investigated. In this paper, we present a review of the significant progress which has been made over the last decade in this field of research with a focus on L-band, and a discussion on possible applications to the SMOS and SMAP soil moisture retrieval approaches.

  11. A spatial scaling relationship for soil moisture in a semiarid landscape, using spatial scaling relationships for pedology

    Science.gov (United States)

    Willgoose, G. R.; Chen, M.; Cohen, S.; Saco, P. M.; Hancock, G. R.

    2013-12-01

    In humid areas it is generally considered that soil moisture scales spatially according to the wetness index of the landscape. This scaling arises from lateral flow downslope of ground water within the soil zone. However, in semi-arid and drier regions, this lateral flow is small and fluxes are dominated by vertical flows driven by infiltration and evapotranspiration. Thus, in the absence of runon processes, soil moisture at a location is more driven by local factors such as soil and vegetation properties at that location rather than upstream processes draining to that point. The 'apparent' spatial randomness of soil and vegetation properties generally suggests that soil moisture for semi-arid regions is spatially random. In this presentation a new analysis of neutron probe data during summer from the Tarrawarra site near Melbourne, Australia shows persistent spatial organisation of soil moisture over several years. This suggests a link between permanent features of the catchment (e.g. soil properties) and soil moisture distribution, even though the spatial pattern of soil moisture during the 4 summers monitored appears spatially random. This and other data establishes a prima facie case that soil variations drive spatial variation in soil moisture. Accordingly, we used a previously published spatial scaling relationship for soil properties derived using the mARM pedogenesis model to simulate the spatial variation of soil grading. This soil grading distribution was used in the Rosetta pedotransfer model to derive a spatial distribution of soil functional properties (e.g. saturated hydraulic conductivity, porosity). These functional properties were then input into the HYDRUS-1D soil moisture model and soil moisture simulated for 3 years at daily resolution. The HYDRUS model used had previously been calibrated to field observed soil moisture data at our SASMAS field site. The scaling behaviour of soil moisture derived from this modelling will be discussed and

  12. Integration of Process Models and Remote Sensing for Estimating Productivity, Soil Moisture, and Energy Fluxes in a Tallgrass Prairie Ecosystem

    Science.gov (United States)

    We describe a research program aimed at integrating remotely sensed data with an ecosystem model (VELMA) and a soil-vegetation-atmosphere transfer (SVAT) model (SEBS) for generating spatially explicit, regional scale estimates of productivity (biomass) and energy\\mass exchanges i...

  13. Integration of Process Models and Remote Sensing for Estimating Productivity, Soil Moisture, and Energy Fluxes in a Tallgrass Prairie Ecosystem

    Science.gov (United States)

    We describe a research program aimed at integrating remotely sensed data with an ecosystem model (VELMA) and a soil-vegetation-atmosphere transfer (SVAT) model (SEBS) for generating spatially explicit, regional scale estimates of productivity (biomass) and energy\\mass exchanges i...

  14. A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

    Science.gov (United States)

    Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

    2011-01-01

    Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

  15. Using similarity of soil texture and hydroclimate to enhance soil moisture estimation

    Science.gov (United States)

    Coopersmith, E. J.; Minsker, B. S.; Sivapalan, M.

    2014-08-01

    Estimating soil moisture typically involves calibrating models to sparse networks of in situ sensors, which introduces considerable error in locations where sensors are not available. We address this issue by calibrating parameters of a parsimonious soil moisture model, which requires only antecedent precipitation information, at gauged locations and then extrapolating these values to ungauged locations via a hydroclimatic classification system. Fifteen sites within the Soil Climate Analysis Network (SCAN) containing multiyear time series data for precipitation and soil moisture are used to calibrate the model. By calibrating at 1 of these 15 sites and validating at another, we observe that the best results are obtained where calibration and validation occur within the same hydroclimatic class. Additionally, soil texture data are tested for their importance in improving predictions between calibration and validation sites. Results have the largest errors when calibration-validation pairs differ hydroclimatically and edaphically, improve when one of these two characteristics are aligned, and are strongest when the calibration and validation sites are hydroclimatically and edaphically similar. These findings indicate considerable promise for improving soil moisture estimation in ungauged locations by considering these similarities.

  16. The AirMOSS Level 4 Root-Zone Soil Moisture Product

    Science.gov (United States)

    Crow, W. T.; Milak, S.; Moghaddam, M.

    2015-12-01

    A critical aspect of the AirMOSS mission is the temporal interpolation of (temporally-discrete) AirMOSS Level 2/3 root-zone soil moisture retrievals into a continuous, hourly root-zone soil moisture product. This is achieved via the assimilation of AirMOSS Level 2/3 root-zone soil moisture retrievals into continuous three-dimensional hydrologic modeling of AirMOSS study sites using the Penn State Integrated Hydrologic (PIHM) model. In this presentation, we will describe the results of a comparison analysis between: 1) hourly PIHM profile soil moisture predictions, 2) AirMOSS Level 2/3 root-zone soil moisture retrievals, and 3) and profile soil moisture observations obtained via ground-based instrumentation at multiple AirMOSS study sites. Since any reasonably-sophisticated integration of remotely-sensed and modeled root-zone soil moisture estimates requires information regarding the objective accuracy of each, the results of this analysis will be used to parameterize a data assimilation approach for integrating discrete AirMOSS Level 2/3 products into a continuous integration of the PIHM model. Based on this integration approach, preliminary AirMOSS Level 4 root-zone soil moisture products will be presented and evaluated. Results will highlight the relative limitations of both the AirMOSS Level 2/3 retrievals and PIHM-based estimates and therefore justify the integrated use of both soil moisture products to create an optimized Level 4 root-zone soil moisture analysis.

  17. Soil moisture patterns in a northern coniferous forest

    Science.gov (United States)

    Thomas F. McLintock

    1959-01-01

    The trend of soil moisture during the growing season, the alternate wetting from rainfall and drying during clear weather, determines the amount of moisture available for tree growth and also fixes, in part, the environment for root growth. In much of the northern coniferous region both moisture content and root environment are in turn affected by the hummock-and-...

  18. Comparison of NOAA-CREST Soil Moisture Measurements with SMOS Products

    Science.gov (United States)

    Norouzi, H.; Forbes, A.

    2014-12-01

    In October 2014, the Soil Moisture Active and Passive mission (SMAP) will launch into a near-polar and sun- synchronous orbit. SMAP includes the first 3 KM resolution product, by both radar and radiometer sensors which will transmit useful information concentrating on the global measurements of soil moisture and freeze/thaw cycles. NOAA- CREST (National Oceanic and Atmospheric Administration- Cooperative Remote Sensing Science and Technology) deploys a series of in-situ devices into the soil, and an L-BAND Radiometer close to the site ground at the Cary Institute in Millbrook, NY. The site is important for future validation of SMAP mission. Comparing mathematical and ground based remote sensing of soil moisture is beneficial to ensure the accuracy of the measurements. The focus of this research is to analyze and compare soil moisture from ESA- SMOS (Europe Space Agency- Soil Moisture Ocean Salinity) mission and the Cary Institute's soil moisture measurements within the same time period, and location. In the interest of establishing superb authentication; comparing SMOS and ground measurements will justify the accuracy of the newly launch satellite. Discrepancies can be found between field point measurement and relatively large footprint of SMOS, which affects comparison and validation. Several techniques and statistical methods will provide a more meaningful comparison to analyze soil moisture data. The results of this project will help to provide a useful method to compare the NOAA-CREST soil moisture measurements and SMAP measurements. In conclusion, the SMAP advance technology will provide more accurate feedback for modeling numerical weather and climate models. Keywords: Soil Moisture, Precipitation, CREST-SMART, Cary Institute, In-situ, Remote Sensors Accurate Soil Moisture Data, Millbrook, N.Y., CATDS, Hydrology is the branch of science concerning properties of earth's water especially its movement in relation to land. SMOS MIRAS, SMAP, Sensors (Underground)

  19. effect of soil moisture on trace elements concentrations using ...

    African Journals Online (AJOL)

    H. Sahraoui and M. Hachicha

    2017-01-01

    Jan 1, 2017 ... produced by the water influence moisture content and corrected ... Previous studies indicated that PXRF analysis was capable of detecting soil trace elements ..... determination of some heavy metals in soil using an x-ray ...

  20. An integrated GIS application system for soil moisture data assimilation

    Science.gov (United States)

    Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

    2014-11-01

    The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

  1. Retrieval of Surface and Subsurface Moisture of Bare Soil Using Simulated Annealing

    Science.gov (United States)

    Tabatabaeenejad, A.; Moghaddam, M.

    2009-12-01

    Soil moisture is of fundamental importance to many hydrological and biological processes. Soil moisture information is vital to understanding the cycling of water, energy, and carbon in the Earth system. Knowledge of soil moisture is critical to agencies concerned with weather and climate, runoff potential and flood control, soil erosion, reservoir management, water quality, agricultural productivity, drought monitoring, and human health. The need to monitor the soil moisture on a global scale has motivated missions such as Soil Moisture Active and Passive (SMAP) [1]. Rough surface scattering models and remote sensing retrieval algorithms are essential in study of the soil moisture, because soil can be represented as a rough surface structure. Effects of soil moisture on the backscattered field have been studied since the 1960s, but soil moisture estimation remains a challenging problem and there is still a need for more accurate and more efficient inversion algorithms. It has been shown that the simulated annealing method is a powerful tool for inversion of the model parameters of rough surface structures [2]. The sensitivity of this method to measurement noise has also been investigated assuming a two-layer structure characterized by the layers dielectric constants, layer thickness, and statistical properties of the rough interfaces [2]. However, since the moisture profile varies with depth, it is sometimes necessary to model the rough surface as a layered structure with a rough interface on top and a stratified structure below where each layer is assumed to have a constant volumetric moisture content. In this work, we discretize the soil structure into several layers of constant moisture content to examine the effect of subsurface profile on the backscattering coefficient. We will show that while the moisture profile could vary in deeper layers, these layers do not affect the scattered electromagnetic field significantly. Therefore, we can use just a few layers

  2. Interactive Vegetation Phenology, Soil Moisture, and Monthly Temperature Forecasts

    Science.gov (United States)

    Koster, R. D.; Walker, G. K.

    2015-01-01

    The time scales that characterize the variations of vegetation phenology are generally much longer than those that characterize atmospheric processes. The explicit modeling of phenological processes in an atmospheric forecast system thus has the potential to provide skill to subseasonal or seasonal forecasts. We examine this possibility here using a forecast system fitted with a dynamic vegetation phenology model. We perform three experiments, each consisting of 128 independent warm-season monthly forecasts: 1) an experiment in which both soil moisture states and carbon states (e.g., those determining leaf area index) are initialized realistically, 2) an experiment in which the carbon states are prescribed to climatology throughout the forecasts, and 3) an experiment in which both the carbon and soil moisture states are prescribed to climatology throughout the forecasts. Evaluating the monthly forecasts of air temperature in each ensemble against observations, as well as quantifying the inherent predictability of temperature within each ensemble, shows that dynamic phenology can indeed contribute positively to subseasonal forecasts, though only to a small extent, with an impact dwarfed by that of soil moisture.

  3. Bayesian framework to identify the main effects of long term climatic constraints on soil moisture decline in conterminous United States

    Science.gov (United States)

    Guevara, M.; Vargas, R.

    2016-12-01

    We used a Bayesian regression framework based on Hamiltonian Monte Carlo simulations to identify the main effects of mean annual soil moisture, temperature, evapotranspiration, and precipitation, on long-term soil moisture decline across conterminous United States based on 36 years of remotely sensed available data. We found that mean soil moisture was a positive control of soil moisture decline in areas with long-term high precipitation but low evapotranspiration. Furthermore, mean soil moisture is a negative control on soil moisture decline in areas with long-term low precipitation and low evapotranspiration. In contrast, mean soil moisture had no effect on soil moisture decline in areas with long-term low precipitation and high evapotranspiration. These results highlight the importance of having accurate spatial soil moisture information to better inform earth system models to predict regional to global water balance and climate trends. These results support the current understanding of the basic physical mechanisms governing the coupling of soil moisture with temperature, precipitation, and evapotranspiration, but bring attention to high spatial heterogeneity in the constraints of soil moisture at the continental scale. The response of soil moisture to climate variability is considered to be one of the largest uncertainties for global land surface models, and resolving high spatial resolution of soil moisture is an ongoing challenge. Independent estimates of high spatial resolution of soil moisture could improve parameterizations of land surface models and cross-validate the current functions that mainly relay on precipitation, aerodynamic representation of the latent and sensible heat fluxes, and land surface cover type.

  4. Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR).

    Science.gov (United States)

    Wagner, Wolfgang; Pathe, Carsten; Doubkova, Marcela; Sabel, Daniel; Bartsch, Annett; Hasenauer, Stefan; Blöschl, Günter; Scipal, Klaus; Martínez-Fernández, José; Löw, Alexander

    2008-02-21

    The high spatio-temporal variability of soil moisture is the result of atmosphericforcing and redistribution processes related to terrain, soil, and vegetation characteristics.Despite this high variability, many field studies have shown that in the temporal domainsoil moisture measured at specific locations is correlated to the mean soil moisture contentover an area. Since the measurements taken by Synthetic Aperture Radar (SAR)instruments are very sensitive to soil moisture it is hypothesized that the temporally stablesoil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT AdvancedSynthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located inthe Duero basin, Spain. It is found that a time-invariant linear relationship is well suited forrelating local scale (pixel) and regional scale (50 km) backscatter. The observed linearmodel coefficients can be estimated by considering the scattering properties of the terrainand vegetation and the soil moisture scaling properties. For both linear model coefficients,the relative error between observed and modelled values is less than 5 % and thecoefficient of determination (R²) is 86 %. The results are of relevance for interpreting anddownscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT)and passive (SMOS, AMSR-E) instruments.

  5. The global distribution and dynamics of surface soil moisture

    Science.gov (United States)

    McColl, Kaighin A.; Alemohammad, Seyed Hamed; Akbar, Ruzbeh; Konings, Alexandra G.; Yueh, Simon; Entekhabi, Dara

    2017-01-01

    Surface soil moisture has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it difficult to quantify the global distribution and dynamics of surface soil moisture. Here we introduce a metric of soil moisture memory and use a full year of global observations from NASA's Soil Moisture Active Passive mission to show that surface soil moisture--a storage believed to make up less than 0.001% of the global freshwater budget by volume, and equivalent to an, on average, 8-mm thin layer of water covering all land surfaces--plays a significant role in the water cycle. Specifically, we find that surface soil moisture retains a median 14% of precipitation falling on land after three days. Furthermore, the retained fraction of the surface soil moisture storage after three days is highest over arid regions, and in regions where drainage to groundwater storage is lowest. We conclude that lower groundwater storage in these regions is due not only to lower precipitation, but also to the complex partitioning of the water cycle by the surface soil moisture storage layer at the land surface.

  6. Enhancing agricultural forecasting using SMOS surface soil moisture retrievals

    Science.gov (United States)

    With the onset of data availability from the ESA Soil Moisture and Ocean Salinity (SMOS) mission (Kerr and Levine, 2008) and the expected 2015 launch of the NASA Soil Moisture Active and Passive (SMAP) mission (Entekhabi et al., 2010), the next five years should see a significant expansion in our ab...

  7. On bimodality in warm season soil moisture observations

    NARCIS (Netherlands)

    Teuling, A.J.; Uijlenhoet, R.; Troch, P.A.A.

    2005-01-01

    It has recently been suggested that the bimodality in warm season soil moisture observations in Illinois is evidence of a soil moisture-precipitation feedback. Other studies however provide little evidence for a strong feedback in this region. Here we show that seasonality in the meteorological cond

  8. Climate variability effects on spatial soil moisture dynamics

    NARCIS (Netherlands)

    Teuling, A.J.; Hupet, F.; Uijlenhoet, R.; Troch, P.A.

    2007-01-01

    We investigate the role of interannual climate variability on spatial soil moisture variability dynamics for a field site in Louvain-la-Neuve, Belgium. Observations were made during 3 years under intermediate (1999), wet (2000), and extremely dry conditions (2003). Soil moisture variability dynamics

  9. Impact of Direct Soil Moisture and Revised Soil Moisture Index Methods on Hydrologic Predictions in an Arid Climate

    Directory of Open Access Journals (Sweden)

    Milad Jajarmizadeh

    2014-01-01

    Full Text Available The soil and water assessment tool (SWAT is a physically based model that is used extensively to simulate hydrologic processes in a wide range of climates around the world. SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. The objective of the present study was to compare the performance of two approaches used for the calculation of curve numbers in SWAT, that is, the Revised Soil Moisture Index (SMI, which is based on previous meteorological conditions, and the Soil Moisture Condition II (SMCII, which is based on soil features for the prediction of flow. The results showed that the sensitive parameters for the SMI method are land-use and land-cover features. However, for the SMCII method, the soil and the channel are the sensitive parameters. The performances of the SMI and SMCII methods were analyzed using various indices. We concluded that the fair performance of the SMI method in an arid region may be due to the inherent characteristics of the method since it relies mostly on previous meteorological conditions and does not account for the soil features of the catchment.

  10. Stratified drought analysis using a stochastic ensemble of simulated and in-situ soil moisture observations

    Science.gov (United States)

    Sehgal, Vinit; Sridhar, Venkataramana; Tyagi, Aditya

    2017-02-01

    This study proposes a multi-wavelet Bayesian ensemble of two Land Surface Models (LSMs) using in-situ observations for accurate estimation of soil moisture for Contiguous United States (CONUS). In the absence of a continuous, accurate in-situ soil moisture dataset at high spatial resolution, an ensemble of Noah and Mosaic LSMs is derived by performing a Bayesian Model Averaging (BMA) of several wavelet-based multi-resolution regression models (WR) of the simulated soil moisture from the LSMs and in-situ volumetric soil moisture dataset obtained from the U.S. Climate Reference Network (USCRN) field stations. This provides a proxy to the in-situ soil moisture dataset at 1/8th degree spatial resolution called Hybrid Soil Moisture (HSM) for three soil layers (1-10 cm, 10-40 cm and 40-100 cm) for the CONUS. The derived HSM is used further to study the layer-wise response of soil moisture to drought, highlighting the necessity of the ensemble approach and soil profile perspective for drought analysis. A correlation analysis between HSM, the long-term (PDSI, PHDI, SPI-9, SPI-12 and SPI-24) and the short-term (Palmer Z index, SPI-1 and SPI-6) drought indices is carried out for the nine climate regions of the U.S. indicating a higher sensitivity of soil moisture to drought conditions for the Southern U.S. Furthermore, a layer-wise soil moisture percentile approach is proposed and applied for drought reconstruction in CONUS with a focus on the Southern U.S. for the year 2011.

  11. The Effect of Row Structure on Soil Moisture Retrieval Accuracy from Passive Microwave Data

    Directory of Open Access Journals (Sweden)

    Zheng Xingming

    2014-01-01

    Full Text Available Row structure causes the anisotropy of microwave brightness temperature (TB of soil surface, and it also can affect soil moisture retrieval accuracy when its influence is ignored in the inversion model. To study the effect of typical row structure on the retrieved soil moisture and evaluate if there is a need to introduce this effect into the inversion model, two ground-based experiments were carried out in 2011. Based on the observed C-band TB, field soil and vegetation parameters, row structure rough surface assumption (Qp model and discrete model, including the effect of row structure, and flat rough surface assumption (Qp model, ignoring the effect of row structure, are used to model microwave TB of soil surface. Then, soil moisture can be retrieved, respectively, by minimizing the difference of the measured and modeled TB. The results show that soil moisture retrieval accuracy based on the row structure rough surface assumption is approximately 0.02 cm3/cm3 better than the flat rough surface assumption for vegetated soil, as well as 0.015 cm3/cm3 better for bare and wet soil. This result indicates that the effect of row structure cannot be ignored for accurately retrieving soil moisture of farmland surface when C-band is used.

  12. The effect of row structure on soil moisture retrieval accuracy from passive microwave data.

    Science.gov (United States)

    Xingming, Zheng; Kai, Zhao; Yangyang, Li; Jianhua, Ren; Yanling, Ding

    2014-01-01

    Row structure causes the anisotropy of microwave brightness temperature (TB) of soil surface, and it also can affect soil moisture retrieval accuracy when its influence is ignored in the inversion model. To study the effect of typical row structure on the retrieved soil moisture and evaluate if there is a need to introduce this effect into the inversion model, two ground-based experiments were carried out in 2011. Based on the observed C-band TB, field soil and vegetation parameters, row structure rough surface assumption (Q p model and discrete model), including the effect of row structure, and flat rough surface assumption (Q p model), ignoring the effect of row structure, are used to model microwave TB of soil surface. Then, soil moisture can be retrieved, respectively, by minimizing the difference of the measured and modeled TB. The results show that soil moisture retrieval accuracy based on the row structure rough surface assumption is approximately 0.02 cm(3)/cm(3) better than the flat rough surface assumption for vegetated soil, as well as 0.015 cm(3)/cm(3) better for bare and wet soil. This result indicates that the effect of row structure cannot be ignored for accurately retrieving soil moisture of farmland surface when C-band is used.

  13. Soil moisture responses to vapour pressure deficit in polytunnel-grown tomato under soil moisture triggered irrigation control

    Science.gov (United States)

    Goodchild, Martin; Kühn, Karl; Jenkins, Dick

    2014-05-01

    The aim of this work has been to investigate soil-to-atmosphere water transport in potted tomato plants by measuring and processing high-resolution soil moisture data against the environmental driver of vapour pressure deficit (VPD). Whilst many researchers have successfully employed sap flow sensors to determine water uptake by roots and transport through the canopy, the installation of sap flow sensors is non-trivial. This work presents an alternative method that can be integrated with irrigation controllers and data loggers that employ soil moisture feedback which can allow water uptake to be evaluated against environmental drivers such as VPD between irrigation events. In order to investigate water uptake against VPD, soil moisture measurements were taken with a resolution of 2 decimal places - and soil moisture, air temperature and relative humidity measurements were logged every 2 minutes. Data processing of the soil moisture was performed in an Excel spread sheet where changes in water transport were derived from the rate of change of soil moisture using the Slope function over 5 soil moisture readings. Results are presented from a small scale experiment using a GP2-based irrigation controller and data logger. Soil moisture feedback is provided from a single SM300 soil moisture sensor in order to regulate the soil moisture level and to assess the water flow from potted tomato plants between irrigation events. Soil moisture levels were set to avoid drainage water losses. By determining the rate of change in soil moisture between irrigation events, over a 16 day period whilst the tomato plant was in flower, it has been possible to observe very good correlation between soil water uptake and VPD - illustrating the link between plant physiology and environmental conditions. Further data is presented for a second potted tomato plant where the soil moisture level is switched between the level that avoids drainage losses and a significantly lower level. This data

  14. Modelling soil temperature and moisture and corresponding seasonality of photosynthesis and transpiration in a boreal spruce ecosystem

    Directory of Open Access Journals (Sweden)

    S. H. Wu

    2013-02-01

    Full Text Available Recovery of photosynthesis and transpiration is strongly restricted by low temperatures in air and/or soil during the transition period from winter to spring in boreal zones. The extent to which air temperature (Ta and soil temperature (Ts influence the seasonality of photosynthesis and transpiration of a boreal spruce ecosystem was investigated using a process-based ecosystem model (CoupModel together with eddy covariance (EC data from one eddy flux tower and nearby soil measurements at Knottåsen, Sweden. A Monte Carlo-based uncertainty method (GLUE provided prior and posterior distributions of simulations representing a wide range of soil conditions and performance indicators. The simulated results showed sufficient flexibility to predict the measured cold and warm Ts in the moist and dry plots around the eddy flux tower. Moreover, the model presented a general ability to describe both biotic and abiotic processes for the Norway spruce stand. The dynamics of sensible heat fluxes were well described by the corresponding latent heat fluxes and net ecosystem exchange of CO2. The parameter ranges obtained are probably valid to represent regional characteristics of boreal conifer forests, but were not easy to constrain to a smaller range than that produced by the assumed prior distributions. Finally, neglecting the soil temperature response function resulted in fewer behavioural models and probably more compensatory errors in other response functions for regulating the seasonality of ecosystem fluxes.

  15. Modelling soil temperature and moisture and corresponding seasonality of photosynthesis and transpiration in a boreal spruce ecosystem

    Directory of Open Access Journals (Sweden)

    S. H. Wu

    2012-05-01

    Full Text Available Recovery of photosynthesis and transpiration is strongly restricted by low temperatures in air and/or soil during the transition period from winter to spring in boreal zones. The extent to which air temperature (Ta and soil temperature (Ts influence the seasonality of photosynthesis and transpiration of a boreal spruce ecosystem was investigated using a process-based ecosystem model (CoupModel together with eddy covariance (EC data from one eddy flux tower and nearby soil measurements at Knottåsen, Sweden. A Monte Carlo based uncertainty method (GLUE provided prior and posterior distributions of simulations representing a wide range of soil conditions and performance indicators. The simulated results showed sufficient flexibility to predict the measured cold and warm Ts in the moist and dry plots around the eddy flux tower. Moreover, the model presented a general ability to describe both biotic and abiotic processes for the Norway spruce stand. The dynamics of sensible heat fluxes were well described the corresponding latent heat fluxes and net ecosystem exchange of CO2. The parameter ranges obtained are probably valid to represent regional characteristics of boreal conifer forests, but were not easy to constrain to a smaller range than that produced by the assumed prior distributions. Finally, neglecting the soil temperature response function resulted in fewer behavioural models and probably more compensatory errors in other response functions for regulating the seasonality of ecosystem fluxes.

  16. Estimating soil moisture distribution using polarimetric airborne SAR

    Science.gov (United States)

    Tadono, Takeo; Qong, Muhtar; Wakabayashi, Hiroyuki; Shimada, Masanobu; Shi, Jiancheng

    2000-12-01

    The goal of this study is to develop an algorithm for estimating the surface soil moisture and surface roughness using polarimetric Synthetic Aperture Radar (SAR) data. In this study, an algorithm was applied to polarimetric airborne SAR data to estimate distributions of surface soil moisture and roughness. To validate the estimated soil moisture, we simultaneously conducted an experiment in October 1999 in Tsukuba Science City, Ibaragi Prefecture of Japan. Surface soil moisture was obtained by the Time- Domain Reflectometry (TDR) method, and the horizontal profiles of the land surface height were measured by a comb- style instrument for calculating the surface roughness parameters in test sites. Because the problem is site- specific and depends upon the measurement accuracy of both the ground truth data, the SAR system including speckle noise, and the effects of vegetation and artificial constructions, such as buildings, houses, roads, and roadside trees, the comparison results did not agree well with measured and inferred soil moisture.

  17. Effects of soil moisture content and tractor wheeling intensity on traffic-induced soil compaction

    Directory of Open Access Journals (Sweden)

    Iman AHMADI

    2015-12-01

    Full Text Available Soil compaction causes deleterious effects on physical and mechanical proprieties of agricultural soils. In order to investigate the effect of soil moisture content and tractor wheeling intensity on traffic-induced soil compaction, this study was carried out on a field with clay loam soil. Soil dry bulk density and hydraulic conductivity as well as emergence percentage of corn seedlings and dry mass of the sampled mature plants were considered the dependent variables of the experiment. Independent variables consisted of soil moisture content with five levels (12, 15, 17, 19, and 21%, traffic intensity with three levels (four, two, and zero passes of tractor wheel (tractor model: John Deere 3350 from the entire area of the plot, and soil sampling depth with three levels (0-10, 10-20, and 20-30 cm. According to the results of this study, gradual increase in soil water content generally resulted in an increase in soil bulk density; moreover, increasing the tractor wheeling intensity from 0 to 4 passes increased bulk density by 13%. Furthermore, the driest soil water content had the highest and the wettest soil water content had the lowest emergence percentage of corn seedlings among the treatments; moreover, traffic intensity treatment inversely affected the emergence percentage of corn seedlings and the dry mass of mature plants. To sum up, these results indicate that, for improving water permeability and reducing dry bulk density of the examined clay loam soil, as well as better emergence of corn seedlings and ultimately increasing crop yield, it is recommended to avoid wheeling when soil moisture content is high, reduce the number of machinery wheel passes from the farm as low as possible, and restrict the wheel passes to fixed strips along the field, whenever possible.

  18. COSMOS: The COsmic-ray Soil Moisture Observing System

    Directory of Open Access Journals (Sweden)

    M. Zreda

    2012-04-01

    Full Text Available Area-average soil moisture at the sub-kilometer scale is needed but until the advent of the cosmic-ray method (Zreda et al., 2008, it was difficult to measure. This new method is now being implemented routinely in the COsmic-ray Soil Moisture Observing System (or COSMOS. The stationary cosmic-ray soil moisture probe (sometimes called "neutronavka" measures the neutrons that are generated by cosmic rays within air and soil, moderated by mainly hydrogen atoms located primarily in soil water, and emitted to the atmosphere where they mix instantaneously at a scale of hundreds of meters and whose density is inversely correlated with soil moisture. COSMOS has already deployed 53 of the eventual 500 neutronavkas distributed mainly in the USA, each generating a time series of average soil moisture over its hectometer horizontal footprint, with similar networks coming into existence around the world. This paper is written to serve a community need to better understand this novel method and the COSMOS project. We describe the cosmic-ray soil moisture measurement method, the instrument and its calibration, the design, data processing and dissemination used in COSMOS, and give example time series of soil moisture obtained from COSMOS probes.

  19. COSMOS: the COsmic-ray Soil Moisture Observing System

    Directory of Open Access Journals (Sweden)

    M. Zreda

    2012-11-01

    Full Text Available The newly-developed cosmic-ray method for measuring area-average soil moisture at the hectometer horizontal scale is being implemented in the COsmic-ray Soil Moisture Observing System (or the COSMOS. The stationary cosmic-ray soil moisture probe measures the neutrons that are generated by cosmic rays within air and soil and other materials, moderated by mainly hydrogen atoms located primarily in soil water, and emitted to the atmosphere where they mix instantaneously at a scale of hundreds of meters and whose density is inversely correlated with soil moisture. The COSMOS has already deployed more than 50 of the eventual 500 cosmic-ray probes, distributed mainly in the USA, each generating a time series of average soil moisture over its horizontal footprint, with similar networks coming into existence around the world. This paper is written to serve a community need to better understand this novel method and the COSMOS project. We describe the cosmic-ray soil moisture measurement method, the instrument and its calibration, the design, data processing and dissemination used in the COSMOS project, and give example time series of soil moisture obtained from COSMOS probes.

  20. Temporal changes of spatial soil moisture patterns: controlling factors explained with a multidisciplinary approach

    Science.gov (United States)

    Martini, Edoardo; Wollschläger, Ute; Kögler, Simon; Behrens, Thorsten; Dietrich, Peter; Reinstorf, Frido; Schmidt, Karsten; Weiler, Markus; Werban, Ulrike; Zacharias, Steffen

    2016-04-01

    Characterizing the spatial patterns of soil moisture is critical for hydrological and meteorological models, as soil moisture is a key variable that controls matter and energy fluxes and soil-vegetation-atmosphere exchange processes. Deriving detailed process understanding at the hillslope scale is not trivial, because of the temporal variability of local soil moisture dynamics. Nevertheless, it remains a challenge to provide adequate information on the temporal variability of soil moisture and its controlling factors. Recent advances in wireless sensor technology allow monitoring of soil moisture dynamics with high temporal resolution at varying scales. In addition, mobile geophysical methods such as electromagnetic induction (EMI) have been widely used for mapping soil water content at the field scale with high spatial resolution, as being related to soil apparent electrical conductivity (ECa). The objective of this study was to characterize the spatial and temporal pattern of soil moisture at the hillslope scale and to infer the controlling hydrological processes, integrating well established and innovative sensing techniques, as well as new statistical methods. We combined soil hydrological and pedological expertise with geophysical measurements and methods from digital soil mapping for designing a wireless soil moisture monitoring network. For a hillslope site within the Schäfertal catchment (Central Germany), soil water dynamics were observed during 14 months, and soil ECa was mapped on seven occasions whithin this period of time using an EM38-DD device. Using the Spearman rank correlation coefficient, we described the temporal persistence of a dry and a wet characteristic state of soil moisture as well as the switching mechanisms, inferring the local properties that control the observed spatial patterns and the hydrological processes driving the transitions. Based on this, we evaluated the use of EMI for mapping the spatial pattern of soil moisture under

  1. Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

    Science.gov (United States)

    Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt

    2010-01-01

    Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

  2. Microwave remote sensing of soil moisture with vegetation effect

    Science.gov (United States)

    Tsegaye, Teferi D.; Inguva, Ramarao; Lang, Roger H.; O'Neill, Peggy E.; Fahsi, Ahmed; Coleman, Tommy L.; Tadesse, Wubishet; Rajbhandari, Narayan B.; Aburemie, Sunnie A.; de Matthaeis, Paolo

    1999-12-01

    The objectives of this study were: to examine the sensitivity of radar backscatter, to estimate soil moisture under a corn plot and to evaluate the effectiveness and sensitivity of a Radiative Transfer Model (RTM), adapted from the earlier work of Njoku and Kong, (1977) in predicting brightness temperature from a grass plot. Microwave radar measurements were collected from plots of different vegetation cover types, vegetation density, and moisture conditions during the Huntsville 1998 field experiment. A large amount of ground data on brightness temperatures, soil moisture, and vegetation characteristics (e.g., biomass, and water content) were collected. The experiments were conducted at Alabama A&M University's, Winfred Thomas Agricultural Research Station, located near Hazel Green, Alabama. Six plots, one 50 X 60 m smooth bare plot, one 50 X 60 m grass plot, and four 30 X 50 m corn plots at full, 2/3, 1/2, and 1/3 densities were used. Radar backscatter data were obtained from a ground based truck mounted radar operating at L, C, and X bands (1.6, 4.75, and 10 GHz) with four linear polarization HH, HV, VV, and VH and two incidence angles (15 and 45 degrees). Soil moisture values were determined using Water Content Reflectometry (WCR). Three types of soil temperature sensors (Infrared Thermometer, Thermistor, and a 4-sensor averaging thermocouple probes) were used. A discrete backscatter approach model and RTM were evaluated. Comparisons between model prediction and experimental observation for HH polarization indicated good agreement for a corn full plot. The direct-reflected scattering coefficient is found to be the most dominant term for both polarization and the backscatter is also highly sensitive to soil moisture. The trends in time variation of brightness temperature are in agreement with the experimental results and the numerical results are within a few percent of the experimental results. The vegetation corrections as estimated by the Jackson and Schmugge

  3. Estimation of soil moisture in paddy field using Artificial Neural Networks

    CERN Document Server

    Arif, Chusnul; Setiawan, Budi Indra; Doi, Ryoichi

    2013-01-01

    In paddy field, monitoring soil moisture is required for irrigation scheduling and water resource allocation, management and planning. The current study proposes an Artificial Neural Networks (ANN) model to estimate soil moisture in paddy field with limited meteorological data. Dynamic of ANN model was adopted to estimate soil mois