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

2010 ◽  
Vol 7 (4) ◽  
pp. 6179-6205
Author(s):  
J. M. Schuurmans ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Latent Heat ◽  
Satellite Data ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Heat Fluxes ◽  
Model Errors ◽  
Small Catchment

Abstract. 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.

scholarly journals Remotely sensed latent heat fluxes for model error diagnosis: a case study

2011 ◽  
Vol 15 (3) ◽  
pp. 759-769 ◽  
Author(s):  
J. M. Schuurmans ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Hydrological Modelling ◽  
Heat Fluxes ◽  
Zone Model ◽  
Distributed Hydrological Model ◽  
Model Errors ◽  
Small Catchment

Abstract. This study shows that remotely sensed ETact is useful in hydrological modelling for the procedure of model calibration and shows it potential to update soil moisture predictions. 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. Assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area where the model suffers from conceptual errors (forest with deep groundwater levels). By using operational (i.e. available for community in practice) data and models we aim to show the potential and limitations of using remotely sensed ETact in the practice of hydrological modelling. We use 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. The model, used by the local water board, is a physically based distributed hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Model outcomes of ETact show values that are at least 20% lower than those estimated by SEBAL, which is due to the fact that different evapotranspiration methods are used. The spatial pattern of ETact from the hydrological model resembles the soil map, whereas the ETact from SEBAL resembles the land use map. As both ASTER and MODIS images were available for the same days, this study provides an opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2014 ◽  
Vol 18 (6) ◽  
pp. 2343-2357 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Lead Times ◽  
Validation Data ◽  
Data Set ◽  
Discharge Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

scholarly journals Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97

2007 ◽  
Vol 8 (1) ◽  
pp. 68-87 ◽  
Author(s):  
Margaret A. LeMone ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Mukul Tewari ◽  
Bart Geerts ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Latent Heat ◽  
Land Surface ◽  
Field Experiments ◽  
Time Of Day ◽  
Horizontal Distribution ◽  
Heat Fluxes ◽  
Surface Model ◽  
Green Vegetation ◽  
The Impact

Abstract Analyses of daytime fair-weather aircraft and surface-flux tower data from the May–June 2002 International H2O Project (IHOP_2002) and the April–May 1997 Cooperative Atmosphere Surface Exchange Study (CASES-97) are used to document the role of vegetation, soil moisture, and terrain in determining the horizontal variability of latent heat LE and sensible heat H along a 46-km flight track in southeast Kansas. Combining the two field experiments clearly reveals the strong influence of vegetation cover, with H maxima over sparse/dormant vegetation, and H minima over green vegetation; and, to a lesser extent, LE maxima over green vegetation, and LE minima over sparse/dormant vegetation. If the small number of cases is producing the correct trend, other effects of vegetation and the impact of soil moisture emerge through examining the slope ΔxyLE/ΔxyH for the best-fit straight line for plots of time-averaged LE as a function of time-averaged H over the area. Based on the surface energy balance, H + LE = Rnet − Gsfc, where Rnet is the net radiation and Gsfc is the flux into the soil; Rnet − Gsfc ∼ constant over the area implies an approximately −1 slope. Right after rainfall, H and LE vary too little horizontally to define a slope. After sufficient drying to produce enough horizontal variation to define a slope, a steep (∼−2) slope emerges. The slope becomes shallower and better defined with time as H and LE horizontal variability increases. Similarly, the slope becomes more negative with moister soils. In addition, the slope can change with time of day due to phase differences in H and LE. These trends are based on land surface model (LSM) runs and observations collected under nearly clear skies; the vegetation is unstressed for the days examined. LSM runs suggest terrain may also play a role, but observational support is weak.

scholarly journals High resolution land surface fluxes from satellite data (HOLAPS v1.0): evaluation and uncertainty assessment

2015 ◽  
Vol 8 (12) ◽  
pp. 10783-10841
Author(s):  
A. Loew ◽  
J. Peng ◽  
M. Borsche
Keyword(s):  
Surface Energy ◽  
Latent Heat ◽  
Satellite Data ◽  
Land Surface ◽  
Global Solar Radiation ◽  
Surface Fluxes ◽  
Heat Fluxes ◽  
Surface Radiation ◽  
Water Fluxes ◽  
Essential Components

Abstract. Surface water and energy fluxes are essential components of the Earth system. Surface latent heat fluxes provide major energy input to the atmosphere. Despite the importance of these fluxes, state-of-the-art datasets of surface energy and water fluxes largely differ. The present paper introduces a new framework for the estimation of surface energy and water fluxes at the land surface, which allows for temporally and spatially high resolved flux estimates at the global scale (HOLAPS). The framework maximizes the usage of existing long-term satellite data records and ensures internally consistent estimates of the surface radiation and water fluxes. The manuscript introduces the technical details of the developed framework and provides results of a comprehensive sensitivity and evaluation study. Overall the results indicate very good agreement with in situ observations when compared against 49 FLUXNET stations worldwide. Largest uncertainties of latent heat flux and net radiation were found to result from uncertainties in the global solar radiation flux obtained from satellite data products.

scholarly journals Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

2008 ◽  
Vol 12 (3) ◽  
pp. 751-767 ◽  
Author(s):  
T. Vischel ◽  
G. G. S. Pegram ◽  
S. Sinclair ◽  
W. Wagner ◽  
A. Bartsch
Keyword(s):  
South Africa ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Hydrological Models ◽  
Good Correspondence ◽  
Physically Based

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

Multi-scale assimilation of SMAP data: comparison between land surface and land surface-subsurface model

2020 ◽  
Author(s):  
Haojin Zhao ◽  
Roland Baatz ◽  
Carsten Montzka ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Simulation Model ◽  
Land Surface ◽  
Cosmic Ray ◽  
Remotely Sensed ◽  
Surface Model ◽  
Systematic Bias

<p>Soil moisture plays an important role in the coupled water and energy cycles of the terrestrial system. However, the characterization of soil moisture at the large spatial scale is far from trivial. To cope with this challenge, the combination of data from different sources (in situ measurements by cosmic ray neutron sensors, remotely sensed soil moisture and simulated soil moisture by models) is pursued. This is done by multiscale data assimilation, to take the different resolutions of the data into account. A large number of studies on the assimilation of remotely sensed soil moisture in land surface models has been published, which show in general only a limited improvement in the characterization of root zone soil moisture, and no improvement in the characterization of evapotranspiration. In this study it was investigated whether an improved modelling of soil moisture content, using a simulation model where the interactions between the land surface, surface water and groundwater are better represented, can contribute to extracting more information from SMAP data. In this study over North-Rhine-Westphalia, the assimilation of remotely sensed soil moisture from SMAP in the coupled land surface-subsurface model TSMP was tested. Results were compared with the assimilation in the stand-alone land surface model CLM. It was also tested whether soil hydraulic parameter estimation in combination with state updating could give additional skill compared to assimilation in CLM stand-alone and without parameter updating. Results showed that modelled soil moisture by TSMP did not show a systematic bias compared to SMAP, whereas CLM was systematically wetter than TSMP. Therefore, no prior bias correction was needed in the data assimilation. The results illustrate how the difference in simulation model and parameter estimation result in significantly different estimated soil moisture contents and evapotranspiration.  </p>

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