Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy

Hydrological models play an essential role in data assimilation (DA) systems. However, it is a challenging task to acquire the distributed hydrological model parameters that affect the accuracy of the simulations at a grid scale. Remote sensing data provide an ideal observation for DA to estimate parameters and state variables. In this study, a special assimilation scheme was proposed to jointly estimate parameters and soil moisture (SM) by assimilating brightness temperature (TB) from the Soil Moisture and Ocean Salinity (SMOS) mission. Variable infiltration capacity (VIC) hydrological model and L-band microwave emission of the biosphere model (L-MEB) are coupled as model and observation operators, respectively. The scheme combines two stages of estimators, one for the static model parameters and the other for the dynamic state variables. The estimators approximate the posterior probability distribution of an unknown target through sequential Monte Carlo (SMC) sampling. Markov chain Monte Carlo (MCMC) and immune evolution strategy are embedded in both stages to solve particle impoverishment problems. To evaluate the effectiveness of the scheme, the estimated SM sets are compared with in-situ observations and SMOS products in Maqu on the Tibetan Plateau. Specifically, the root mean square error decreased from 0.126 to 0.087 m3m−3 for surface SM, with a slight impact on the root zone. The temporal correlation between DA results and in-situ measurements increased to 0.808 and 0.755 for surface SM (+0.057) and root zone SM (+0.040), respectively. The results demonstrate that assimilating TB has tremendous potential as an approach to improve the estimation of distributed model parameters and SMs of surface and root zone at a grid scale, and the immune evolution strategy is effective for increasing the accuracy of approximation in sampling.

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SMOS brightness temperature assimilation into the Community Land Model

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5929-2017 ◽

2017 ◽

Vol 21 (11) ◽

pp. 5929-5951 ◽

Cited By ~ 8

Author(s):

Dominik Rains ◽

Xujun Han ◽

Hans Lievens ◽

Carsten Montzka ◽

Niko E. C. Verhoest

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Extreme Event ◽

Root Zone ◽

Incident Angle ◽

Grid Cell ◽

Microwave Emission ◽

Emission Model ◽

Soil Layers ◽

Community Land Model

Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010–2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.

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A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3607-2012 ◽

2012 ◽

Vol 16 (10) ◽

pp. 3607-3620 ◽

Cited By ~ 30

Author(s):

C. Albergel ◽

G. Balsamo ◽

P. de Rosnay ◽

J. Muñoz-Sabater ◽

S. Boussetta

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

The United States ◽

Microwave Emission ◽

Bare Ground ◽

New Formulation ◽

The Impact ◽

Surface Fields

Abstract. In situ soil moisture data from 122 stations across the United States are used to evaluate the impact of a new bare ground evaporation formulation at ECMWF. In November 2010, the bare ground evaporation used in ECMWF's operational Integrated Forecasting System (IFS) was enhanced by adopting a lower stress threshold than for the vegetation, allowing a higher evaporation. It results in more realistic soil moisture values when compared to in situ data, particularly over dry areas. Use was made of the operational IFS and offline experiments for the evaluation. The latter are based on a fixed version of the IFS and make it possible to assess the impact of a single modification, while the operational analysis is based on a continuous effort to improve the analysis and modelling systems, resulting in frequent updates (a few times a year). Considering the field sites with a fraction of bare ground greater than 0.2, the root mean square difference (RMSD) of soil moisture is shown to decrease from 0.118 m3 m−3 to 0.087 m3 m−3 when using the new formulation in offline experiments, and from 0.110 m3 m−3 to 0.088 m3 m−3 in operations. It also improves correlations. Additionally, the impact of the new formulation on the terrestrial microwave emission at a global scale is investigated. Realistic and dynamically consistent fields of brightness temperature as a function of the land surface conditions are required for the assimilation of the SMOS data. Brightness temperature simulated from surface fields from two offline experiments with the Community Microwave Emission Modelling (CMEM) platform present monthly mean differences up to 7 K. Offline experiments with the new formulation present drier soil moisture, hence simulated brightness temperature with its surface fields are larger. They are also closer to SMOS remotely sensed brightness temperature.

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The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0217.1 ◽

2020 ◽

Author(s):

Rolf H. Reichle ◽

Qing Liu ◽

Joseph V. Ardizzone ◽

Wade T. Crow ◽

Gabrielle J. M. De Lannoy ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Root Zone ◽

Land Surface Model ◽

Surface Model ◽

Meteorological Forcing ◽

Earth Observing System ◽

Error Surface

AbstractSoil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 Soil Moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with ¼-degree, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, ½-degree, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the Instrumental Variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10-0.11 compared to an increase of 0.02-0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous U.S. reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.

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Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case

Hydrology and Earth System Sciences ◽

10.5194/hess-24-4793-2020 ◽

2020 ◽

Vol 24 (10) ◽

pp. 4793-4812

Author(s):

Renaud Hostache ◽

Dominik Rains ◽

Kaniska Mallick ◽

Marco Chini ◽

Ramona Pelich ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Large Scale ◽

Root Zone ◽

Meteorological Model ◽

Murray Darling Basin ◽

Ocean Salinity ◽

Physically Based ◽

Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help to reduce errors and uncertainties in soil moisture and evapotranspiration simulations with a large-scale conceptual hydro-meteorological model. In addition, this study aims to investigate whether such a conceptual modelling framework, relying on parameter calibration, can reach the performance level of more complex physically based models for soil moisture simulations at a large scale. We use the ERA-Interim publicly available forcing data set and couple the Community Microwave Emission Modelling (CMEM) platform radiative transfer model with a hydro-meteorological model to enable, therefore, soil moisture, evapotranspiration and brightness temperature simulations over the Murray–Darling basin in Australia. The hydro-meteorological model is configured using recent developments in the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application and to data availability and computational requirements. The hydrological model is first calibrated using only a sample of the Soil Moisture and Ocean Salinity (SMOS) brightness temperature observations (2010–2011). Next, SMOS brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX–CMEM model (2010–2015). For this experiment, a local ensemble transform Kalman filter is used. Our empirical results show that the SUPERFLEX–CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set-up using the Community Land Model (CLM) . This shows that a simple model, when calibrated using globally and freely available Earth observation data, can yield performance levels similar to those of a physically based (uncalibrated) model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72 for the surface and root zone soil moisture. The assimilation of SMOS brightness temperature observations into the SUPERFLEX–CMEM modelling chain improves the correlation between predicted and in situ observed surface and root zone soil moisture by 0.03 on average, showing improvements similar to those obtained using the CLM land surface model. Moreover, at the same time the assimilation improves the correlation between predicted and in situ observed monthly evapotranspiration by 0.02 on average.

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Assimilation of SMOS brightness temperature into a large-scale distributed conceptual hydrological model

10.5194/hess-2019-414 ◽

2019 ◽

Author(s):

Renaud Hostache ◽

Dominik Rains ◽

Kaniska Mallick ◽

Marco Chini ◽

Ramona Pelich ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Large Scale ◽

Hydrological Model ◽

Land Surface Model ◽

Surface Model ◽

Meteorological Model ◽

Temperature Observations

Abstract. The main objective of this study is to investigate how brightness temperature observations from satellite microwave sensors may help in reducing errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. In particular, we use as forcings the ERA-Interim public dataset and we couple the CMEM radiative transfer model with a hydro-meteorological model enabling therefore soil moisture and SMOS-like brightness temperature simulations. The hydro-meteorological model is configured using recent developments of the SUPERFLEX framework, which enables tailoring the model structure to the specific needs of the application as well as to data availability and computational requirements. In this case, the model spatial resolution is adapted to the spatial grid of the satellite data, and the soil stratification is tailored to the satellite datasets to be assimilated and the forcing data. The hydrological model is first calibrated using a sample of SMOS brightness temperature observations (period 2010–2011). Next, SMOS-derived brightness temperature observations are sequentially assimilated into the coupled SUPERFLEX-CMEM model (period 2010–2015). For this experiment, a Local Ensemble Transform Kalman Filter is used and the meteorological forcings (ERA interim-based rainfall, air and soil temperature) are perturbed to generate a background ensemble. Each time a SMOS observation is available, the SUPERFLEX state variables related to the water content in the various soil layers are updated and the model simulations are resumed until the next SMOS observation becomes available. Our empirical results show that the SUPERFLEX-CMEM modelling chain is capable of predicting soil moisture at a performance level similar to that obtained for the same study area and with a quasi-identical experimental set up using the CLM land surface model. This shows that a simple model, when carefully calibrated, can yield performance level similar to that of a much more complex model. The correlation between simulated and in situ observed soil moisture ranges from 0.62 to 0.72. The assimilation of SMOS brightness temperature observation into the SUPERFLEX-CMEM modelling chain improves the correlation between predicted and in situ observed soil moisture by 0.03 on average showing improvements similar to those obtained using the CLM land surface model.

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Soil Moisture Initialization Input Scale Effect on Parameter Value Identification of a Physically Based Distributed Hydrologic Modelling

10.5194/egusphere-egu2020-22194 ◽

2020 ◽

Author(s):

Nawa Raj Pradhan ◽

Steven Brown ◽

Ian Floyd

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Hydrological Model ◽

Root Zone ◽

Soil Column ◽

Estimation Method ◽

Model Parameters ◽

Analysis Model ◽

Soil Moisture Data ◽

Moisture Estimation

<p>Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture, and parameter value identification are critical for a physics based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide relatively accurate estimates of soil moisture, but such techniques are limited due to the need for a variety of measurement accessories, which are difficult to obtain to cover a large area sufficiently. Available satellite-based digital soil moisture data is at 9 kilometers to 50 kilometers in resolution which completely filters the soil moisture details at the hill slope scale. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column that informs nothing about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed root zone soil moisture at 30 meter resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 meter resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 meter resolution SERVES soil moisture data was resampled to 4500 meter and 9000 meter resolutions and was separately employed in the calibrated hydrological model to determine the effect soil moisture resolution &#160;has on the simulated outputs and the model parameters. It was found that the simulated discharge significantly decreased as the initial soil moisture resolution was coarsened. To compensate for this underestimated simulated discharge, the soil hydraulic conductivity value decreased logarithmically with respect to the decreased resolutions. This study will reduce parameter value identification uncertainty especially in flood and soil erosion modelling at multi scale watershed in a changing climate.</p>

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The benefit of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1569-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1569-1586

Author(s):

Jianxiu Qiu ◽

Jianzhi Dong ◽

Wade T. Crow ◽

Xiaohu Zhang ◽

Rolf H. Reichle ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Coupling Strength ◽

Root Zone ◽

In Situ Measurements ◽

In Situ Measurement ◽

Surface Model ◽

Control Factors ◽

Skill Improvement

Abstract. The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.

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Incorporating Advanced Scatterometer Surface and Root Zone Soil Moisture Products into the Calibration of a Conceptual Semi-Distributed Hydrological Model

Water ◽

10.3390/w13233366 ◽

2021 ◽

Vol 13 (23) ◽

pp. 3366

Author(s):

Martin Kubáň ◽

Juraj Parajka ◽

Rui Tong ◽

Isabella Pfeil ◽

Mariette Vreugdenhil ◽

...

Keyword(s):

Soil Moisture ◽

Forest Cover ◽

Hydrological Model ◽

Root Zone ◽

Retrieval Algorithm ◽

Multiple Objective ◽

Model Parameters ◽

Distributed Hydrological Model ◽

Runoff Generation ◽

Alpine Catchments

The role of soil moisture is widely accepted as a significant factor in the mass and energy balance of catchments as a controller in surface and subsurface runoff generation. The paper examines the potential of a new dataset based on advanced scatterometer satellite remote sensing of soil moisture (ASCAT) for multiple objective calibrations of a dual-layer, conceptual, semi-distributed hydrological model. The surface and root zone soil moisture indexes based on ASCAT data were implemented into calibration of the hydrological model. Improvements not only in the instrument specifications, i.e., better temporal and spatial sampling, but also in the higher radiometric accuracy and retrieval algorithm, were applied. The analysis was performed in 209 catchments situated in different physiographic and climate zones of Austria for the period 2007–2018. We validated the model for two validation periods. The results show that multiple objective calibrations have a substantial positive effect on constraining the model parameters. The combined use of soil moisture and discharges in the calibration improved the soil moisture simulation in more than 73% of the catchments, except for the catchments with higher forest cover percentages. Improvements also occurred in the runoff model efficiency, in more than 27% of the catchments, mostly in the watersheds with a lower mean elevation and a higher proportion of farming land use, as well as in the Alpine catchments where the runoff is not significantly influenced by snowmelt and glacier runoff.

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Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0063.1 ◽

2017 ◽

Vol 18 (10) ◽

pp. 2621-2645 ◽

Cited By ~ 92

Author(s):

Rolf H. Reichle ◽

Gabrielle J. M. De Lannoy ◽

Qing Liu ◽

Joseph V. Ardizzone ◽

Andreas Colliander ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Land Surface ◽

Root Zone ◽

Land Surface Model ◽

In Situ Measurements ◽

Surface Model ◽

Accuracy Requirement ◽

Temperature Observations

Abstract The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

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The added value of brightness temperature assimilation for the SMAP Level-4 surface and root-zone soil moisture analysis over mainland China

10.5194/hess-2020-407 ◽

2020 ◽

Author(s):

Jianxiu Qiu ◽

Jianzhi Dong ◽

Wade T. Crow ◽

Xiaohu Zhang ◽

Rolf H. Reichle ◽

...

Keyword(s):

Soil Moisture ◽

Brightness Temperature ◽

Coupling Strength ◽

Land Surface ◽

Surface Soil ◽

Root Zone ◽

Mainland China ◽

Surface Soil Moisture ◽

Control Factors

Abstract. The Soil Moisture Active Passive (SMAP) Level-4 Surface Soil Moisture and Root-Zone Soil Moisture (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the Catchment Land Surface Model (CLSM). Here, using in-situ measurements from 2474 sites in mainland China, we evaluate the performance of soil moisture estimates from L4 and from a baseline open-loop (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 data assimilation (DA) system (i.e., the performance improvement in L4 relative to OL) is attributed to 8 control factors related to the land surface modelling (LSM) and radiative transfer modeling (RTM) components of the L4 system. Results show that 77 % of the 2287 9-km EASE grid cells in mainland China that contain at least one ground station exhibit an increase in the Spearman rank correlation skill (R) with in-situ measurements for L4 SSM compared to that of OL, with an average R increase of approximately 14 % (ΔR = 0.056). RZSM skill is improved for about the same percentage of 9-km EASE grid cells, but the average R increase for RZSM is only 7 % (ΔR = 0.034). Results further show that the SSM DA efficiency is most strongly related to the error in Tb observation space, followed by the error in precipitation forcing and microwave soil roughness. For RZSM DA efficiency, the three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb error. For the skill of the L4 and OL estimates themselves, the top control factors are the precipitation error and the SSM-RZSM coupling strength error (in descending order of factor importance for ROL), both of which are related to the LSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM-RZSM coupling strength.

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