Literature DB >> 33154604

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.

Shima Azimi1,2, Alireza B Dariane1, Sara Modanesi2, Bernhard Bauer-Marschallinger3, Rajat Bindlish4, Wolfgang Wagner3, Christian Massari2.   

Abstract

In runoff generation process, soil moisture plays an important role as it controls the magnitude of the flood events in response to the rainfall inputs. In this study, we investigated the ability of a new era of satellite soil moisture retrievals to improve the Soil & Water Assessment Tool (SWAT) daily discharge simulations via soil moisture data assimilation for two small (< 500 km2) and hydrologically different catchments located in Central Italy. We ingested 1) the Soil Moisture Active and Passive (SMAP) Enhanced L3 Radiometer Global Daily 9 km EASE-Grid soil moisture, 2) the Advanced SCATterometer (ASCAT) H113 soil moisture product released within the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) which has a nearly daily temporal resolution and sampling of 12.5 km, and 3) a fused ASCAT/Sentinel-1 (S1) satellite soil moisture product named SCATSAR-SWI with temporal and spatial sampling of 1 day and 1 km, respectively into SWAT hydrological model via the Ensemble Kalman Filter (EnKF). Different configurations were tested with the aim of exploring the effect of the hydrological regime, the land use conditions, the spatial sampling and the revisit time of the products (which controls the amount of available data to be potentially ingested). Results show a general improvement of SWAT discharge simulations for all products in terms of error and Nash Sutcliffe efficiency index. In particular, we found a relatively good behavior of both the active and the passive products in terms of low flows improvement especially for the catchment characterized by a higher baseflow component. The benefit of the higher spatial resolution of SCATSAR-SWI obtained via S1 over ASCAT was small, likely due to very challenging areas for the S1 retrieval. Eventually, better performances were obtained for the passive product in the more forested catchment. With the aim of exploring the benefit of having more frequent satellite soil moisture observations to be ingested, we tested the performance of the ASCAT product with a reduced temporal sampling obtained by temporally matching ASCAT observations to that of SMAP. The results show a significant reduction of the performance of ASCAT, suggesting that the correction frequency (due to the higher number of observations available) for small catchments is an important aspect for improving flood forecasting as it helps to adjust more frequently the pre-storm soil moisture conditions.

Entities:  

Keywords:  Data assimilation; EnKF, small basins; SWAT; Satellite soil moisture

Year:  2019        PMID: 33154604      PMCID: PMC7608049          DOI: 10.1016/j.jhydrol.2019.124367

Source DB:  PubMed          Journal:  J Hydrol (Amst)        ISSN: 0022-1694            Impact factor:   5.722


  4 in total

1.  Global-scale Evaluation of SMAP, SMOS and ASCAT Soil Moisture Products using Triple Collocation.

Authors:  Fan Chen; Wade T Crow; Rajat Bindlish; Andreas Colliander; Mariko S Burgin; Jun Asanuma; Kentaro Aida
Journal:  Remote Sens Environ       Date:  2018-05-26       Impact factor: 10.164

2.  Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product.

Authors:  Steven K Chan; Rajat Bindlish; Peggy O'Neill; Thomas Jackson; Eni Njoku; Scott Dunbar; Julian Chaubell; Jeffrey Piepmeier; Simon Yueh; Dara Entekhabi; Andreas Colliander; Fan Chen; Michael H Cosh; Todd Caldwel; Jeffrey Walker; Aaron Berg; Heather McNairn; Marc Thibeault; José Martínez-Fernández; Frederik Uldall; Mark Seyfried; David Bosch; Patrick Starks; Chandra Holifield Collins; John Prueger; Rogier van der Velde; Jun Asanuma; Michael Palecki; Eric E Small; Marek Zreda; Jean-Christophe Calvet; Wade T Crow; Yann Kerr
Journal:  Remote Sens Environ       Date:  2017-10-13       Impact factor: 10.164

3.  L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

Authors:  W T Crow; F Chen; R H Reichle; Q Liu
Journal:  Geophys Res Lett       Date:  2017-05-10       Impact factor: 4.720

4.  The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework.

Authors:  Peyman Abbaszadeh; Hamid Moradkhani; Dacian N Daescu
Journal:  Water Resour Res       Date:  2019-03-25       Impact factor: 5.240

  4 in total

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