Reply on RC2

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> The typical causes of droughts are lower precipitation and/or higher than normal evaporation in a region. The region&rsquo;s characteristics and anthropogenic interventions may enhance or alleviate these events. Evaluating the multiple factors that influence droughts is complex and requires innovative approaches. To address this complexity, this study employs a combination of modelling and machine learning tools to assess the relationship between hydroclimatic characteristics and the severity of agricultural and hydrological droughts. The Soil Water Assessment Tool is used for hydrological modelling. Model outputs, soil moisture and streamflow are used to calculate the drought indicators for the subsequent drought analysis. Other simulated hydroclimatic parameters are treated as hydroclimatic drivers of droughts. A machine learning technique, the multivariate regression tree approach, is then applied to identify the hydroclimatic characteristics that govern agricultural and hydrological drought severity. The case study is the Cesar River basin (Colombia). Our research indicates that multiple parameters influence the Cesar River basin&rsquo;s exposure to agricultural and hydrological droughts. Accordingly, the basin can be divided into three distinct areas. First is the upper part of the river valley. Due to precipitation shortfalls and high potential evapotranspiration, this region is very susceptible to agricultural and hydrological droughts. The second area is the middle part of the river valley. This area is likewise very susceptible to agricultural and hydrological droughts; however, severe drought conditions are brought on by inadequate rainfall partitioning and an unbalanced water cycle that favours water loss through percolation and evapotranspiration. Third, the Zapatosa marsh and the Serrania del Perij&aacute; foothills present moderate exposure to agricultural and hydrological droughts. Mild drought conditions appear to be related to the capacity of the subbasins to retain water, which lowers evapotranspiration losses and promotes percolation. Our results show that the presented methodology, in combining hydrologic modelling and machine learning techniques, provides valuable information about an interplay between the hydroclimatic factors that influence drought severity in the Cesar River basin.