Development of a hydrometeorological drought severity composite index based on the integration of multisource characteristics and an explainable artificial intelligence model

International audience The Oum Er Rbia watershed, Morocco, is a region facing severe water stress conditions associated with the simultaneous occurrence of meteorological and hydrological droughts. This study proposes a new hydrometeorological drought composite index (HDCI) by adapting an explainable artificial intelligence (XAI) approach for synergistic integration of multisource drought-related indicators. The streamflow anomalies, hydroclimatic coefficients and water variations in dams were comparatively explored as response variables for the selection and weighting of the HDCI components using Shapley additive explanations theory (SHAP).The severity of hydrometeorological drought in Oum Er Rbia watershed is regulated by the interaction of several factors, among which the contribution of terrestrial water storage to hydrometeorological drought related to streamflow anomalies tends to become more pronounced as the number of influencing factors decreases. The new composite index is highly correlated with the reference hydrometric variables. However, heteroscedasticity between hydrometric stations influences the performance of the HDCI. Therefore, the integration of factors based on spatial dependencies represents a potential avenue for reducing the influence of spatial heterogeneities. Overall, by integrating exclusively geospatial and reanalysis data, HDCI has advantages for the assessment of hydrometeorological drought conditions at the pixel scale compared with conventional methods, which use direct measurements of hydrometric variables but are often discontinuous and unavailable in real time.