Satellite-Based Soil Moisture And Precipitation For Drought Monitoring And Prediction In Xiang River Basin,China

Drought is a natural disaster that occurs frequently and has a long-term impact on agricultural production,ecological environment and economy.Therefore,accurate monitoring and prediction of drought is very important.Soil moisture and precipitation are the key variables for drought monitoring and prediction.However,the scarcity or uneven distribution of ground stations limits the acess of high-precision soil moisture and precipitation data.Remote sensing enriches the surface soil moisture and precipitation observations.Remote sensing products can provide different spatial and temporal resoluations of soil moisture and precipitation data for drought monitoring and prediction.The purpose of this study is to evaluate the performance of remote sensing soil moisture and remote sensing precipitation in drought monitoring and prediction in Xiang River Basin.The Soil Moisture Active Passive(SMAP)dataset was evaluated with the soil moisture dataset obtained from the China Land Soil Moisture Data Assimilation System(CLSMDAS).The SMAP-derived SWDI(SMAP?SWDI)was compared with the atmospheric water deficit(AWD)calculated with precipitation and evapotranspiration from meteorological stations.Besides,the climate prediction center morphing(CMORPH)technique(CMORPH-CRT),the tropical rainfall measuring mission(TRMM)multi-satellite precipitation analysis(TRMM3B42V7),and theintegrated multi-satellite retrievals for global precipitation measurement(IMERG V05)were evaluated and compared with in-situ observations.A widely-used drought index,the standardized precipitation index(SPI),was chosen to evaluate the drought monitoring utility of these selected precipitation products.The AWD was used for comparison with the drought estimation with SPI.For drought prediction,this study applies support vector machine(SVM)by using a new set of inputs to investigate the performance of in-situ and remote sensing products(CMORPH-CRT,IMERG V05 and TRMM 3B42V7)for soil moisture and SWDI forecast over the Xiang River Basin.This study also assesses whether the addition of remote sensing soil moisture as input can improve the performance of SWDI prediction.In addition,this study used the conceptual model of drought risk assessment to calculate the drought risk index of Xiang River Basin.the data used in this study included the drought index SPI and the data of Gross Domestic Product(GDP),Farmland Potential Productivity(FPP)and Atmospheric Water-holding Capacity(AWC).The main conclusions of this study are as follows:(1)The SMAP soil moisture showed acceptable accuracy and relatively good performance in most of Xiang River Basin.In terms of R-values between the soil moisture datasets obtained from CLSMDAS and SMAP,around 70% performed well and only 10%performs poorly at the grid sale.At the regional scale,the SMAP soil moisture captured the features of CLSMDAS soil moisture with the R-value is 0.62.The SMAP showed relatively good performance in drought monitoring in the Xiang River Basin with a high Pearson correlation coefficient(mean value equals to 0.6)and high drought weeks probability of detection(vary from 0.7 to 0.9)between SWDI and AWD.(2)IMERG V05 precipitation product shows highest accuracy in the Xiang River Basin.CMORPH-CRT performs better than TRMM 3B42V7 precipitation product at both catchment and grid scales.Based on evaluation results of SPI-1,IMERG V05 shows the best performance in SPI-1 estimations at both station and grid scales.The CMORPH-CRT have better performance than TRMM 3B42V7 on drought monitoring at station scale but shows the worst performance at grid scale.(3)The new set of input variables in SVM method,including P,PET,T,Rh,Rn and Ws,is suitable for drought prediction over the Xiang River Basin and the appropriate lead time for drought prediction in SVM is around two weeks.As for soil moisture prediction,in-situ P used as input in SVM method shows the best performance,followed by IMERG V05.As for SWDI prediction,the IMERG V05 precipitation product can serve as an alternative precipitation dataset for indirect SWDI prediction,while CMORPH-CRT and TRMM3B42V7 are more suitable for direct SWDI prediction.The addition of remote sensing soil moisture can improve the performance of SWDI prediction when the input precipitation sources are in-situ precipitation and CMORPH-CRT.However,the addition of remote sensing soil moisture with TRMM 3B42V7 and IMERG V05 have no improvement for SWDI prediction.(4)The drought risk index shows that the regions with higher risks are mainly concentrated in the west,southwest,east and northeast of the Xiang River Basin,and the central and northern parts of the region present less drought risk.