DOI: 10.5194/hess-20-2827-2016
Scopus记录号: 2-s2.0-84979084509
论文题名: Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin
作者: Leroux D ; J ; , Pellarin T ; , Vischel T ; , Cohard J ; -M ; , Gascon T ; , Gibon F ; , Mialon A ; , Galle S ; , Peugeot C ; , Seguis L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 7 起始页码: 2827
结束页码: 2840
语种: 英语
Scopus关键词: Catchments
; Climate models
; Groundwater
; Hydrology
; Moisture
; Neural networks
; Precipitation (meteorology)
; Rain
; Rain gages
; Satellites
; Soils
; Stream flow
; Water content
; Water resources
; Climate prediction centers
; Distributed hydrological model
; Distributed hydrology soil vegetation models
; Hydrologic applications
; Precipitation estimation from remotely sensed information
; Satellite precipitation
; Soil moisture ocean salinities
; Tropical rainfall measuring missions
; Soil moisture
; artificial neural network
; hydrological cycle
; hydrological modeling
; precipitation assessment
; remote sensing
; satellite data
; SMOS
; soil moisture
; streamflow
; synergism
; TRMM
; water table
英文摘要: Precipitation forcing is usually the main source of uncertainty in hydrology. It is of crucial importance to use accurate forcing in order to obtain a good distribution of the water throughout the basin. For real-time applications, satellite observations allow quasi-real-time precipitation monitoring like the products PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, TRMM (Tropical Rainfall Measuring Mission) or CMORPH (CPC (Climate Prediction Center) MORPHing). However, especially in West Africa, these precipitation satellite products are highly inaccurate and the water amount can vary by a factor of 2. A post-adjusted version of these products exists but is available with a 2 to 3 month delay, which is not suitable for real-time hydrologic applications. The purpose of this work is to show the possible synergy between quasi-real-time satellite precipitation and soil moisture by assimilating the latter into a hydrological model. Soil Moisture Ocean Salinity (SMOS) soil moisture is assimilated into the Distributed Hydrology Soil Vegetation Model (DHSVM) model. By adjusting the soil water content, water table depth and streamflow simulations are much improved compared to real-time precipitation without assimilation: soil moisture bias is decreased even at deeper soil layers, correlation of the water table depth is improved from 0.09-0.70 to 0.82-0.87, and the Nash coefficients of the streamflow go from negative to positive. Overall, the statistics tend to get closer to those from the reanalyzed precipitation. Soil moisture assimilation represents a fair alternative to reanalyzed rainfall products, which can take several months before being available, which could lead to a better management of available water resources and extreme events. © Author(s) 2016. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78790
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: CNES, LTHE, Laboratoire d'Étude des Transferts en Hydrologie et Environnement, Grenoble, France; CNRS, CESBIO, Centre d'Etudes Spatiales de la Biosphre, Toulouse, France; University Grenoble Alpes, LTHE, Grenoble, France; CNRS, LTHE, Grenoble, France; IRD, LTHE, Grenoble, France; IRD, HydroSciences, Montpellier, France
Recommended Citation:
Leroux D,J,, Pellarin T,et al. Assimilation of SMOS soil moisture into a distributed hydrological model and impacts on the water cycle variables over the Ouémé catchment in Benin[J]. Hydrology and Earth System Sciences,2016-01-01,20(7)