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The exacerbation of floods and the extension of droughts, attributed to climate change and other human-induced factors, are posing a substantial risk to communities by causing water scarcity and insecurity. The significance of safeguarding water resources and managing them is increasingly gaining prominence. Snow is an efficient source of water for recharging groundwater compared to rainfall. This is attributed to its gradual melting process and capacity to infiltrate the soil, thereby providing sustenance to the groundwater. Thus, snow drought can be considered a major contributing factor to the issue of water scarcity. The objective of this study was to investigate the evolution of snow drought over the period spanning from 1980 to 2022, as well as its impact on agricultural drought across the Upper Mississippi River Basin (UMRB). This research employed the AgERA5 reanalysis gridded data at surface level with a spatial resolution of 0.1°, obtained from the European Center for Medium-Range Weather Forecasts (ECMWF), to assess the snow drought. An analysis is conducted for comparison between the spatial estimations of snow drought in the UMBR and two other drought indicators, namely the evaporative demand drought index (EDDI) and water deficit amounts. The effects of the El Niño and La Niña phenomena on the UMRB as well as the results of the summer drought conditions were reviewed. The results point to two important findings. The former is that the snow-drought-affected zones show an increasing trend from the past to the present in the UMRB. The latter is that severe snow droughts in the winter of a water year trigger severe agricultural droughts in the summer months of the same water year. It is seen that monitoring snow droughts is as essential as following rainfall regimes in the planning of water resources, agricultural production, and irrigation methods.

Due to the shifting climate, extreme events are being observed more frequently globally. Drought is one of the most common natural hazards that severely impacts communities in terms of economic losses and agricultural production disruption. Considering global trade, drought in an agricultural region affects the food security in other regions because of disrupted supply. Decision-makers often consult susceptibility maps when preparing mitigation plans so that the adverse impacts of a drought event can be reduced. Creating drought susceptibility maps can be demanding, requiring a lot of data (i.e., hydrological and land use), expertise, and thorough assessment to accurately picture a vulnerable region’s condition. The process also relies on complex hydrological and hydrometeorological models. The objective of this investigation is to examine the vulnerability and impact of drought and formulate maps of drought susceptibility, exposure, and risk by considering a multitude of atmospheric, physical and social indicators. Subsequent to this notion, a fuzzy logic algorithm has been devised by assigning a comprehensive array of weights to each parameter derived from an exhaustive literature review and used for a preliminary investigation for the state of Iowa. This state is located in the Corn Belt region, and its primary economic activity is agriculture. Drought susceptibility maps for the state of Iowa have been generated for the period spanning from 2015 to 2021 and validated using the Kappa coefficient. The produced drought susceptibility maps can support drought mitigation plans and decisions for communities in Iowa.