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

Abstract. The impact of the assimilation of surface soil moisture on the simulations of the physically based hydrological model DHSVM (Distributed Hydrology Soil Vegetation Model) is investigated in this paper for a 12 000 km catchment located in Benin, West Africa. Thanks to a large number of rain gauges spread all over the entire basin, reference simulations are performed from one year of calibration (in 2010) and two years of evaluation (2011 and 2012) based on in situ measurements of streamflow at the outlet and local observations of soil moisture at different soil depths and evapotranspiration. In a second step, several satellite products (PERSIANN, TRMM-3B42RT, and CMORPH) are used instead of in situ precipitation measurements. These products bring too much water (especially PERSIANN and CMORPH), sometimes not at the correct time of the year, which has a large impact on various hydrological variables. In order to correct for the wrong amount of input water brought by the satellite precipitation products, the SMOS satellite soil moisture observations are assimilated in the hydrological model. An optimal interpolation is implemented here using an influence radius in order to replicate the field of view of the SMOS instrument. The assimilation of SMOS data shows a positive impact on the soil moisture at different depths (5, 40, and 80 cm defined in the model), with a decrease of the bias compared to the in situ measurements. Streamflow is also positively impacted with a large improvement of the Nash efficiency coefficient after assimilation (from negative to positive for PERSIANN and CMORPH). Finally, the temporal evolution of the water table depth is also greatly improved (from 0.1–0.3 to 0.8–0.9 for PERSIANN and CMORPH). This work shows that the use of satellite precipitation products into a hydrological model can lead to large errors that can be reduced by assimilating satellite soil moisture, which has a positive impact on the estimation of hydrological variables at deeper layers and at other stages of the water cycle.

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SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

10.20944/preprints201904.0216.v1 ◽

2019 ◽

Author(s):

Nemesio Rodriguez-Fernandez ◽

Patricia de Rosnay ◽

Clement Albergel ◽

Philippe Richaume ◽

Filipe Aires ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Positive Impact ◽

Land Surface Model ◽

Surface Model ◽

Northern Asia ◽

The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

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Climate-dependent propagation of precipitation uncertainty into the water cycle

10.5194/hess-2019-660 ◽

2020 ◽

Author(s):

Ali Fallah ◽

Sungmin O ◽

Rene Orth

Keyword(s):

Hydrological Model ◽

Water Cycle ◽

State Of The Art ◽

Rain Gauge ◽

Multiple Sources ◽

Current State ◽

Wide Range ◽

The Impact ◽

Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

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Comparing Assimilation of Soil Moisture and C-band Backscatter in High Resolution Land Surface Model

10.5194/egusphere-egu2020-10277 ◽

2020 ◽

Author(s):

Leqiang Sun ◽

Stéphane Belair ◽

Marco Carrera ◽

Bernard Bilodeau

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Open Loop ◽

In Situ Measurements ◽

Lookup Table ◽

Surface Model ◽

Loop Model ◽

The Impact

<p>Canadian Space Agency (CSA) has recently started receiving and processing the images from the recently launched C-band RADARSAT Constellation Mission (RCM). The backscatter and soil moisture retrievals products from the previously launched RADARSAT-2 agree well with both in-situ measurements and surface soil moisture modeled with land surface model Soil, Vegetation, and Snow (SVS). RCM will provide those products at an even better spatial coverage and temporal resolution. In preparation of the potential operational application of RCM products in Canadian Meteorological Center (CMC), this paper presents the scenarios of assimilating either soil moisture retrieval or outright backscatter signal in a 100-meter resolution version of the Canadian Land Data Assimilation System (CaLDAS) on field scale with time interval of three hours. The soil moisture retrieval map was synthesized by extrapolating the regression relationship between in-situ measurements and open loop model output based on soil texture lookup table. Based on this, the backscatter map was then generated with the surface roughness retrieved from RADARSAT-2 images using a modified Integral Equation Model (IEM) model. Bias correction was applied to the Ensemble Kalman filter (EnKF) to mitigate the impact of nonlinear errors introduced by multi-sourced perturbations. Initial results show that the assimilation of backscatter is as effective as assimilating soil moisture retrievals. Compared to open loop, both can improve the analysis of surface moisture, particularly in terms of reducing bias. &#160;</p>

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Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station

Sensors ◽

10.3390/s19081900 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1900

Author(s):

Cong Yin ◽

Ernesto Lopez-Baeza ◽

Manuel Martin-Neira ◽

Roberto Fernandez-Moran ◽

Lei Yang ◽

...

Keyword(s):

Soil Moisture ◽

Circular Polarization ◽

European Space Agency ◽

In Situ Measurements ◽

Anchor Station ◽

L Band ◽

The Impact ◽

Band Radiometer ◽

Soil Moisture Monitoring

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.

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Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation

Water ◽

10.3390/w11040666 ◽

2019 ◽

Vol 11 (4) ◽

pp. 666 ◽

Cited By ~ 4

Author(s):

Lihua Xiong ◽

Ling Zeng

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Objective Function ◽

Hydrological Model ◽

Distributed Hydrological Model ◽

Soil Moisture Data ◽

Streamflow Data ◽

The Impact ◽

Hydrological Variables ◽

Streamflow Simulation

With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration.

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Validation of ECMWF Multi-Layer Reanalysis Soil Moisture Based on the OzNet Hydrology Network

Water ◽

10.3390/w10091123 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1123 ◽

Cited By ~ 4

Author(s):

Wenlong Jing ◽

Jia Song ◽

Xiaodan Zhao

Keyword(s):

Soil Moisture ◽

Temporal Dynamics ◽

Root Zone ◽

Water Cycle ◽

In Situ Measurements ◽

Model Errors ◽

Time Variations ◽

Mean Square Difference ◽

Terrain Features

Soil moisture reanalysis products can provide soil water information for the surface and root zone soil layers, which are significant for understanding the water cycle and climate change. However, the accuracy of multi-layer soil moisture datasets obtained from reanalysis products remains unclear in some areas. In this study, we evaluated the root zone soil moisture of the ERA-Interim soil moisture product, as well as the surface soil moisture based on in situ measurements from the OzNet hydrological measurement network over southeast Australia. In general, the ERA-Interim soil moisture product presents good agreement with in situ soil moisture values and can nicely reflect time variations, with correlation coefficient (R) values in the range of 0.73 to 0.84 and unbiased root mean square difference (ubRMSD) values from 0.035 m3·m−3 to 0.060 m3·m−3. Although the ERA-Interim soil moisture also can reflect temporal dynamics of soil moisture at root zone layer at depths of 28–100 cm, low correlations were found in winter. In addition, the ERA-Interim soil moisture product overestimates in situ measurements at depths of 0–7 cm and 7–28 cm, whereas the product shows underestimated values compared with in situ soil moisture at the root zone of 28–100 cm. Consequently, the ERA-Interim soil moisture product has both high absolute and temporal accuracy at depths of 7–28 cm, and the ERA-Interim soil moisture product can nicely capture temporal dynamics at all the evaluated soil level depths, except for the depth of 28–100 cm during the winter months. The contributions of terrain, vegetation cover, and soil texture to the model error were addressed by feature importance estimations using the random forest (RF) algorithm. Results indicate that terrain features may have an impact on the model errors. It is clear that the accuracy of the ERA-Interim soil moisture can be improved by adjusting the assimilation scheme, and the results of this study are expected to provide a comprehensive understanding of the model errors and references for optimizing the model.

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Climate-dependent propagation of precipitation uncertainty into the water cycle

10.5194/egusphere-egu2020-416 ◽

2020 ◽

Author(s):

Ali Fallah Maraghi ◽

Sungmin Oh ◽

Rene Orth

Keyword(s):

Hydrological Modeling ◽

Hydrological Model ◽

Water Cycle ◽

State Of The Art ◽

Rain Gauge ◽

Multiple Sources ◽

Current State ◽

Wide Range ◽

The Impact

<p>Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in >200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR) and (3) combination of multiple sources (MSWEP V2). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984-2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.</p>

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SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

Remote Sensing ◽

10.3390/rs11111334 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1334 ◽

Cited By ~ 7

Author(s):

Nemesio Rodríguez-Fernández ◽

Patricia de Rosnay ◽

Clement Albergel ◽

Philippe Richaume ◽

Filipe Aires ◽

...

Keyword(s):

Neural Network ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Positive Impact ◽

Land Surface Model ◽

Surface Model ◽

Northern Asia ◽

The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April–September, while NNSM alone has a significant positive effect in July–September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.

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Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

Remote Sensing ◽

10.3390/rs13020228 ◽

2021 ◽

Vol 13 (2) ◽

pp. 228

Author(s):

Jian Kang ◽

Rui Jin ◽

Xin Li ◽

Yang Zhang

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Spatial Scales ◽

Microwave Remote Sensing ◽

In Situ Measurements ◽

Spatial Density ◽

Linear Regression Method ◽

Spatiotemporal Resolution ◽

Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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Assessing an Atmospheric Correction Algorithm for Time Series of Satellite-Based Water-Leaving Reflectance Using Match-Up Sites in Australian Coastal Waters

Remote Sensing ◽

10.3390/rs13101927 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1927

Author(s):

Fuqin Li ◽

David Jupp ◽

Thomas Schroeder ◽

Stephen Sagar ◽

Joshua Sixsmith ◽

...

Keyword(s):

Coastal Waters ◽

Atmospheric Correction ◽

Correction Method ◽

In Situ Measurements ◽

Landsat 8 ◽

Correction Algorithm ◽

Simple Method ◽

North East ◽

The Impact

An atmospheric correction algorithm for medium-resolution satellite data over general water surfaces (open/coastal, estuarine and inland waters) has been assessed in Australian coastal waters. In situ measurements at four match-up sites were used with 21 Landsat 8 images acquired between 2014 and 2017. Three aerosol sources (AERONET, MODIS ocean aerosol and climatology) were used to test the impact of the selection of aerosol optical depth (AOD) and Ångström coefficient on the retrieved accuracy. The initial results showed that the satellite-derived water-leaving reflectance can have good agreement with the in situ measurements, provided that the sun glint is handled effectively. Although the AERONET aerosol data performed best, the contemporary satellite-derived aerosol information from MODIS or an aerosol climatology could also be as effective, and should be assessed with further in situ measurements. Two sun glint correction strategies were assessed for their ability to remove the glint bias. The most successful one used the average of two shortwave infrared (SWIR) bands to represent sun glint and subtracted it from each band. Using this sun glint correction method, the mean all-band error of the retrieved water-leaving reflectance at the Lucinda Jetty Coastal Observatory (LJCO) in north east Australia was close to 4% and unbiased over 14 acquisitions. A persistent bias in the other strategy was likely due to the sky radiance being non-uniform for the selected images. In regard to future options for an operational sun glint correction, the simple method may be sufficient for clear skies until a physically based method has been established.

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