Evaluating different drought products for assessing drought and implications on agriculture in Nepal

• (GLDAS-2.1) is identified as the most reliable soil moisture product for evaluating agricultural drought in Nepal. • The Karnali river basin is the most severely affected by agricultural drought. • Linear correlation between drought indices is weak for one to represent another. • Wheat crops in the Karnali river basin are significantly impacted by agricultural drought. • The sowing and growing phases of wheat are particularly vulnerable to drought conditions. Drought impacts adversely the agriculture sector by reducing soil moisture (SM) levels, leading to decreased crop yields and increased water scarcity, thereby threatening food security and the livelihoods of a population predominantly reliant on farming. Unlike previous studies on this region, this study uses multiple SM products to evaluate agricultural drought with the aim of identifying the area that is more vulnerable to drought. Using satellite-based and model-based SM observations Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Global Land Data Assimilation System (GLDAS-2.1), Famine Early Warning Systems Network Land Data Assimilation System (FLDAS)) along with climatology and discharge measurements, we examine the spatial and temporal drought patterns. By utilizing Soil Water Deficit Index (SWDI) alongside Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Flow Index (SFI), this research highlights the complex progression from meteorological to agricultural and hydrological droughts. The findings reveal a strong relationship between SWDI-GLDAS (SWDIgl) and the Atmospheric Water Deficit (AWD), confirming SWDIgl's reliability for identifying agricultural drought. Notably, SPEI's impact in SWDI is more evident with the three-month lag, and SPEI correlates moderately with SFI at a 12-month lag. Significant correlations were found for winter wheat during their sowing and growing period. These findings suggest that further monitoring and forecasting of agricultural drought should incorporate SWDIgl due to its strong correlation with AWD. These insights can inform targeted interventions for winter wheat during their critical growth periods to mitigate drought effects in agriculture.