Assimilation of Remotely Sensed Soil Moisture and Snow Depth Retrievals for Drought Estimation

Abstract The accurate knowledge of soil moisture and snow conditions is important for the skillful characterization of agricultural and hydrologic droughts, which are defined as deficits of soil moisture and streamflow, respectively. This article examines the influence of remotely sensed soil moisture and snow depth retrievals toward improving estimates of drought through data assimilation. Soil moisture and snow depth retrievals from a variety of sensors (primarily passive microwave based) are assimilated separately into the Noah land surface model for the period of 1979–2011 over the continental United States, in the North American Land Data Assimilation System (NLDAS) configuration. Overall, the assimilation of soil moisture and snow datasets was found to provide marginal improvements over the open-loop configuration. Though the improvements in soil moisture fields through soil moisture data assimilation were barely at the statistically significant levels, these small improvements were found to translate into subsequent small improvements in simulated streamflow. The assimilation of snow depth datasets were found to generally improve the snow fields, but these improvements did not always translate to corresponding improvements in streamflow, including some notable degradations observed in the western United States. A quantitative examination of the percentage drought area from root-zone soil moisture and streamflow percentiles was conducted against the U.S. Drought Monitor data. The results suggest that soil moisture assimilation provides improvements at short time scales, both in the magnitude and representation of the spatial patterns of drought estimates, whereas the impact of snow data assimilation was marginal and often disadvantageous.

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Streamflow data assimilation for soil moisture analysis

Geoscientific Model Development ◽

10.5194/gmd-3-1-2010 ◽

2010 ◽

Vol 3 (1) ◽

pp. 1-12 ◽

Cited By ~ 7

Author(s):

K. Warrach-Sagi ◽

V. Wulfmeyer

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Climate Models ◽

Root Zone ◽

Weather Forecast ◽

Surface Model ◽

River Catchment ◽

Streamflow Data ◽

The Impact

Abstract. Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and, hence, the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow time series into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

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Streamflow data assimilation for soil moisture analysis

Geoscientific Model Development Discussions ◽

10.5194/gmdd-2-551-2009 ◽

2009 ◽

Vol 2 (1) ◽

pp. 551-579 ◽

Cited By ~ 1

Author(s):

K. Warrach-Sagi ◽

V. Wulfmeyer

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Climate Models ◽

Root Zone ◽

Weather Forecast ◽

Surface Model ◽

River Catchment ◽

Streamflow Data ◽

The Impact

Abstract. Streamflow depends on the soil moisture of a river catchment and can be measured with relatively high accuracy. The soil moisture in the root zone influences the latent heat flux and hence the quantity and spatial distribution of atmospheric water vapour and precipitation. As numerical weather forecast and climate models require a proper soil moisture initialization for their land surface models, we enhanced an Ensemble Kalman Filter to assimilate streamflow timeseries into the multi-layer land surface model TERRA-ML of the regional weather forecast model COSMO. The impact of streamflow assimilation was studied by an observing system simulation experiment in the Enz River catchment (located at the downwind side of the northern Black Forest in Germany). The results demonstrate a clear improvement of the soil moisture field in the catchment. We illustrate the potential of streamflow data assimilation for weather forecasting and discuss its spatial and temporal requirements for a corresponding, automated river gauging network.

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From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States

Remote Sensing ◽

10.3390/rs12122020 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2020 ◽

Cited By ~ 1

Author(s):

Anthony Mucia ◽

Bertrand Bonan ◽

Yongjun Zheng ◽

Clément Albergel ◽

Jean-Christophe Calvet

Keyword(s):

United States ◽

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Surface Model ◽

Land Data Assimilation ◽

Land Data Assimilation System ◽

The Impact ◽

Data Assimilation System ◽

Assimilation System

LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.

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Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0057.1 ◽

2020 ◽

Vol 21 (10) ◽

pp. 2293-2308 ◽

Cited By ~ 1

Author(s):

Keyhan Gavahi ◽

Peyman Abbaszadeh ◽

Hamid Moradkhani ◽

Xiwu Zhan ◽

Christopher Hain

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Root Zone ◽

Remotely Sensed ◽

Open Loop ◽

Divide And Conquer ◽

Drought Monitoring ◽

Filter Method ◽

Drought Severity ◽

Surface Model

AbstractSoil moisture (SM) and evapotranspiration (ET) are key variables of the terrestrial water cycle with a strong relationship. This study examines remotely sensed soil moisture and evapotranspiration data assimilation (DA) with the aim of improving drought monitoring. Although numerous efforts have gone into assimilating satellite soil moisture observations into land surface models to improve their predictive skills, little attention has been given to the combined use of soil moisture and evapotranspiration to better characterize hydrologic fluxes. In this study, we assimilate two remotely sensed datasets, namely, Soil Moisture Operational Product System (SMOPS) and MODIS evapotranspiration (MODIS16 ET), at 1-km spatial resolution, into the VIC land surface model by means of an evolutionary particle filter method. To achieve this, a fully parallelized framework based on model and domain decomposition using a parallel divide-and-conquer algorithm was implemented. The findings show improvement in soil moisture predictions by multivariate assimilation of both ET and SM as compared to univariate scenarios. In addition, monthly and weekly drought maps are produced using the updated root-zone soil moisture percentiles over the Apalachicola–Chattahoochee–Flint basin in the southeastern United States. The model-based estimates are then compared against the corresponding U.S. Drought Monitor (USDM) archive maps. The results are consistent with the USDM maps during the winter and spring season considering the drought extents; however, the drought severity was found to be slightly higher according to DA method. Comparing different assimilation scenarios showed that ET assimilation results in wetter conditions comparing to open-loop and univariate SM DA. The multivariate DA then combines the effects of the two variables and provides an in-between condition.

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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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Can assimilating remotely-sensed surface soil moisture data improve root-zone soil moisture predictions in the CABLE land surface model?

Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation. ◽

10.36334/modsim.2011.e4.pipunic ◽

2011 ◽

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Surface Soil ◽

Root Zone ◽

Land Surface Model ◽

Remotely Sensed ◽

Surface Model ◽

Surface Soil Moisture ◽

Soil Moisture Data

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The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

Hydrology and Earth System Sciences ◽

10.5194/hess-19-4831-2015 ◽

2015 ◽

Vol 19 (12) ◽

pp. 4831-4844 ◽

Cited By ~ 19

Author(s):

C. Draper ◽

R. Reichle

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Surface Soil ◽

Root Zone ◽

Surface Model ◽

Surface Soil Moisture ◽

Near Surface ◽

In Situ Observations ◽

The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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Cosmic Ray Neutron Sensing: Integration with land surface modelling using data assimilation for improved field-scale soil moisture estimates

10.5194/egusphere-egu2020-10419 ◽

2020 ◽

Author(s):

Amol Patil ◽

Benjamin Fersch ◽

Harrie-Jan Hendricks-Franssen ◽

Harald Kunstmann

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Spatial Resolution ◽

Land Surface ◽

Root Zone ◽

Cosmic Ray ◽

Soil Parameters ◽

Surface Model ◽

Field Scale ◽

Spatio Temporal

Soil moisture is a key variable in atmospheric modelling to resolve the partitioning of net radiation into sensible and latent heat fluxes. Therefore, high resolution spatio-temporal soil moisture estimation is getting growing attention in this decade. The recent developments to observe soil moisture at field scale (170 to 250 m spatial resolution) using Cosmic Ray Neutron Sensing (CRNS) technique has created new opportunities to better resolve land surface atmospheric interactions; however, many challenges remain such as spatial resolution mismatch and estimation uncertainties. Our study couples the Noah-MP land surface model to the Data Assimilation Research Testbed (DART) for assimilating CRN intensities to update model soil moisture. For evaluation, the spatially distributed Noah-MP was set up to simulate the land surface variables at 1 km horizontal resolution for the Rott and Ammer catchments in southern Germany. The study site comprises the TERENO-preAlpine observatory with five CRNS stations and additional CRNS measurements for summer 2019 operated by our Cosmic Sense research group. We adjusted the soil parametrization in Noah-MP to allow the usage of EU soil data along with Mualem-van Genuchten soil hydraulic parameters. We use independent observations from extensive soil moisture sensor network (SoilNet) within the vicinity of CRNS sensors for validation. Our detailed synthetic and real data experiments are evaluated for the analysis of the spatio-temporal changes in updated root zone soil moisture and for implications on the energy balance component of Noah-MP. Furthermore, we present possibilities to estimate root zone soil parameters within the data assimilation framework to enhance standalone model performance.

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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

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;

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

10.5194/egusphere-egu2020-465 ◽

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

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. &#160;

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