Assimilation of Sentinel 1 and SMAP – based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins

Abstract. The assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to monitor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM–DA in recent years (e.g. the Advanced SCATterometer – ASCAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportunity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydrological model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was carried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1-based SM–DA for improving discharge predictions, especially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spatial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time period, and thus should be supported by further research activities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-20-3059-2016 ◽

2016 ◽

Vol 20 (7) ◽

pp. 3059-3076 ◽

Cited By ~ 31

Author(s):

Patricia López López ◽

Niko Wanders ◽

Jaap Schellekens ◽

Luigi J. Renzullo ◽

Edwin H. Sutanudjaja ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Data Assimilation ◽

Spatial Resolution ◽

Hydrological Model ◽

Meteorological Data ◽

Observation System ◽

Data Set ◽

Coarse Resolution ◽

The Impact

Abstract. 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, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research.

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-10559-2015 ◽

2015 ◽

Vol 12 (10) ◽

pp. 10559-10601 ◽

Cited By ~ 5

Author(s):

P. Lopez Lopez ◽

N. Wanders ◽

J. Schellekens ◽

L. J. Renzullo ◽

E. H. Sutanudjaja ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Data Assimilation ◽

Spatial Resolution ◽

Large Scale ◽

Hydrological Model ◽

Meteorological Data ◽

Added Value ◽

Coarse Resolution ◽

The Impact

Abstract. 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 (from 0.5° downscaled 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 dataset (0.05°). Downscaled satellite derived soil moisture (from approx. 0.5° downscaled to 0.08° resolution) from 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, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can help to advance this research.

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Defining a Trade-off Between Spatial and Temporal Resolution of a Geosynchronous SAR Mission for Soil Moisture Monitoring

Remote Sensing ◽

10.3390/rs10121950 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1950 ◽

Cited By ~ 3

Author(s):

Luca Cenci ◽

Luca Pulvirenti ◽

Giorgio Boni ◽

Nazzareno Pierdicca

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Temporal Resolution ◽

Added Value ◽

Sar Images ◽

Radar Images ◽

Spatio Temporal ◽

Set Up ◽

The Impact ◽

Hydrological Applications

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial (≤1 km) and temporal (≤12 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture–data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps.

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Gap-Free Global Annual Soil Moisture: 15 km Grids for 1991–2018

10.5194/essd-2020-264 ◽

2020 ◽

Author(s):

Mario Guevara ◽

Michela Taufer ◽

Rodrigo Vargas

Keyword(s):

Pattern Recognition ◽

Soil Moisture ◽

Spatial Resolution ◽

Soil Type ◽

European Space Agency ◽

Terrain Parameters ◽

Precipitation Records ◽

The Impact ◽

Type Information

Abstract. Soil moisture is key for quantifying soil-atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency-Climate Change Initiative v4.5) soil moisture dataset, which contains more than 40 years of satellite soil moisture global grids with a spatial resolution of ~27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict the finer-grained satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating the pattern recognition of soil moisture with and without the support of bioclimatic and soil type information. The outcome is a new dataset of gap-free global mean annual soil moisture and uncertainty for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, n = 13376) and in situ precipitation records (n = 4909) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r = 0.69 to r = 0.87 with root mean squared errors (RMSE, m3/m3) around 0.03 and 0.04. Our soil moisture predictions improve: (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r = 0.30 (RMSE = 0.09, ubRMSE = 0.37) to r = 0.66 (RMSE = 0.05, ubRMSE = 0.18); and (b) the correlation with local precipitation records across boreal (from r =

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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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Gap-free global annual soil moisture: 15 km grids for 1991–2018

Earth System Science Data ◽

10.5194/essd-13-1711-2021 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1711-1735

Author(s):

Mario Guevara ◽

Michela Taufer ◽

Rodrigo Vargas

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Soil Type ◽

Temporal Trends ◽

European Space Agency ◽

Terrain Parameters ◽

Precipitation Records ◽

The Impact ◽

Type Information

Abstract. Soil moisture is key for understanding soil–plant–atmosphere interactions. We provide a soil moisture pattern recognition framework to increase the spatial resolution and fill gaps of the ESA-CCI (European Space Agency Climate Change Initiative v4.5) soil moisture dataset, which contains > 40 years of satellite soil moisture global grids with a spatial resolution of ∼ 27 km. We use terrain parameters coupled with bioclimatic and soil type information to predict finer-grained (i.e., downscaled) satellite soil moisture. We assess the impact of terrain parameters on the prediction accuracy by cross-validating downscaled soil moisture with and without the support of bioclimatic and soil type information. The outcome is a dataset of gap-free global mean annual soil moisture predictions and associated prediction variances for 28 years (1991–2018) across 15 km grids. We use independent in situ records from the International Soil Moisture Network (ISMN, 987 stations) and in situ precipitation records (171 additional stations) only for evaluating the new dataset. Cross-validated correlation between observed and predicted soil moisture values varies from r= 0.69 to r= 0.87 with root mean squared errors (RMSEs, m3 m−3) around 0.03 and 0.04. Our soil moisture predictions improve (a) the correlation with the ISMN (when compared with the original ESA-CCI dataset) from r= 0.30 (RMSE = 0.09, unbiased RMSE (ubRMSE) = 0.37) to r= 0.66 (RMSE = 0.05, ubRMSE = 0.18) and (b) the correlation with local precipitation records across boreal (from r= < 0.3 up to r= 0.49) or tropical areas (from r= < 0.3 to r= 0.46) which are currently poorly represented in the ISMN. Temporal trends show a decline of global annual soil moisture using (a) data from the ISMN (-1.5[-1.8,-1.24] %), (b) associated locations from the original ESA-CCI dataset (-0.87[-1.54,-0.17] %), (c) associated locations from predictions based on terrain parameters (-0.85[-1.01,-0.49] %), and (d) associated locations from predictions including bioclimatic and soil type information (-0.68[-0.91,-0.45] %). We provide a new soil moisture dataset that has no gaps and higher granularity together with validation methods and a modeling approach that can be applied worldwide (Guevara et al., 2020, https://doi.org/10.4211/hs.9f981ae4e68b4f529cdd7a5c9013e27e).

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Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Hydrology and Earth System Sciences ◽

10.5194/hess-24-4291-2020 ◽

2020 ◽

Vol 24 (9) ◽

pp. 4291-4316 ◽

Cited By ~ 1

Author(s):

Clément Albergel ◽

Yongjun Zheng ◽

Bertrand Bonan ◽

Emanuel Dutra ◽

Nemesio Rodríguez-Fernández ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Spatial Resolution ◽

Land Surface ◽

Initial Conditions ◽

Surface States ◽

Extreme Weather ◽

Open Loop ◽

The Impact ◽

Assimilation System

Abstract. LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states. Firstly, LDAS-Monde is run globally at 0.25∘ spatial resolution over 2010–2018. It is forced by the state-of-the-art ERA5 reanalysis (LDAS_ERA5) from the European Centre for Medium Range Weather Forecasts (ECMWF). The behaviour of the assimilation system is evaluated by comparing the analysis with the assimilated observations. Then the land surface variables (LSVs) are validated with independent satellite datasets of evapotranspiration, gross primary production, sun-induced fluorescence and snow cover. Furthermore, in situ measurements of SSM, evapotranspiration and river discharge are employed for the validation. Secondly, the global analysis is used to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. This is performed by computing anomalies of the land surface states. They display strong negative values for LAI and SSM in 2018 for two regions: north-western Europe and the Murray–Darling basin in south-eastern Australia. For those regions, LDAS-Monde is forced with the ECMWF Integrated Forecasting System (IFS) high-resolution operational analysis (LDAS_HRES, 0.10∘ spatial resolution) over 2017–2018. Monitoring capacities are studied by comparing open-loop and analysis experiments, again against the assimilated observations. Forecasting abilities are assessed by initializing 4 and 8 d LDAS_HRES forecasts of the LSVs with the LDAS_HRES assimilation run compared to the open-loop experiment. The positive impact of initialization from an analysis in forecast mode is particularly visible for LAI that evolves at a slower pace than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8 d lead time. This highlights the impact of initial conditions on LSV forecasts and the value of jointly analysing soil moisture and vegetation states.

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Influence of land cover on lightning and convective precipitation over the European continent

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133318825285 ◽

2019 ◽

Vol 43 (3) ◽

pp. 352-364 ◽

Cited By ~ 1

Author(s):

L Rivas Soriano ◽

JM Sánchez Llorente ◽

A González Zamora ◽

F de Pablo Dávila

Keyword(s):

Soil Moisture ◽

Land Cover ◽

Spatial Resolution ◽

Bare Soil ◽

Convective Precipitation ◽

General Effect ◽

Convective Activity ◽

European Continent ◽

High Soil Moisture ◽

The Impact

The impact of land cover on lightning and convective precipitation in the summertime over Europe was analysed using five-year data. Lightning data were obtained with the Optical Transient Detector (OTD) at a 0.5° × 0.5° spatial resolution and the convective precipitation data were calculated by the NCEP/DOE AMIP-II Reanalysis at a ∼1.9° × 1.9° spatial resolution. Data concerning land cover were obtained from the Global Land Cover Facility, although the original 14 categories were grouped into seven categories (water, forest, shrubland, grassland, cropland, bare ground and urban). For all latitude ranges, forested areas tend to increase convective activity during the warm period of the year, and the general effect of shrubland areas is to suppress convective activity. The behaviour of convection in relation to grasslands and croplands depends on latitude. At low latitudes both vegetation types tend to increase convection during the summer. At high latitudes, grassland and cropland areas appear to be associated with the opposite effect in relation to convection: grass suppresses and crops enhance it. Finally, bare soil tends to decrease convective activity. These results seem to be related to the impact of vegetation on soil moisture and roughness. In general, vegetation areas associated with high soil moisture contents and high values in roughness length tend to enhance convective activity.

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A Numerical Sensitivity Analysis of Soil Moisture Feedback on Convective Precipitation

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0134.1 ◽

2019 ◽

Vol 20 (1) ◽

pp. 23-44 ◽

Cited By ~ 2

Author(s):

Marika Koukoula ◽

Efthymios I. Nikolopoulos ◽

Jonilda Kushta ◽

Nikolaos S. Bartsotas ◽

George Kallos ◽

...

Keyword(s):

Soil Moisture ◽

Water Content ◽

Spatial Resolution ◽

Land Surface ◽

Atmospheric Model ◽

Atmospheric Modeling ◽

Heat Fluxes ◽

Convective Precipitation ◽

Limited Area ◽

The Impact

Abstract Of the boundary conditions that affect the simulation of convective precipitation, soil moisture is one of the most important. In this study, we explore the impact of the soil moisture on convective precipitation, and factors affecting it, through an extensive numerical experiment based on four convective precipitation events that caused moderate to severe flooding in the Gard region of southern France. High-spatial-resolution (1 km) weather simulations were performed using the integrated atmospheric model Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System (RAMS/ICLAMS). The experimental framework included comparative analysis of five simulation scenarios for each event, in which we varied the magnitude and spatial distribution of the initial volumetric water content using realistic soil moisture fields with different spatial resolution. We used precipitation and surface soil moisture from radar and satellite sensors as references for the comparison of the sensitivity tests. Our results elucidate the complexity of the relationship between soil moisture and convective precipitation, showing that the control of soil water content on partitioning land surface heat fluxes has significant impacts on convective precipitation. Additionally, it is shown how different soil moisture conditions affect the modeled microphysical structure of the clouds, which translates into further changes in the magnitude and distribution of precipitation.

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