Evaluation of GLDAS soil moisture product over Kermanshah province, Iran

Abstract

Land surface modelling and data assimilation are advanced techniques for generating optimal fields of land surface states and fluxes. In this study, the Global Land Data Assimilation System (GLDAS) data were utilized to investigate the soil moisture variations and droughts in Kermanshah province, northwest Iran. The GLDAS soil moisture data were employed in various depths and compared with observed monthly soil moisture. The monthly and annual moisture data were processed in the Geographic Information System (GIS) environment. To compute the Standardized Precipitation Index, SPI, precipitation data from 2000 to 2014 were used, and the relationship between drought and soil moisture variation was studied. The moisture data from GLDAS had a significant correlation with the most severe wet and dry seasons. The minimum and maximum values of the SPI were determined as −2.077 and 0.931 in 2004 and 2009, respectively, which corresponded to the highest and lowest normalized soil moisture of −1.93 and 1.41. The results showed that GLDAS data can be used to reconstruct spatial and temporal moisture data series. HIGHLIGHTS Aimed to explore GLDAS data's potential for soil moisture determination.; Recorded data were compared with GLDAS estimated soil moisture to verify accuracy.; Examined soil moisture and its link with drought.; GLDAS data's potential for estimating soil moisture was explored, with implications for drought monitoring and mitigation.; Focused on drought-prone Kermanshah, needing efficient soil moisture monitoring and management.;

Keywords

• drought
• gldas
• precipitation
• soil moisture
• spi