Impact-based drought detection via Interpretable Machine Learning and Causal Discovery

Drought is a slowly developing natural hazard that can affect all climatic zones and is commonly defined as a temporary but significant decrease in water availability. In Europe alone, drought impacts over the last decades have generated very large economic losses, and recent summer events have been exceptional in a long-term historical perspective. Despite extensive research on drought monitoring and management, accurately characterizing how drought drivers evolve into impacts is still a key unresolved challenge, especially when impacts result from the cumulative and interacting effects of multiple hydroclimatic anomalies rather than a single precursor.

In this work, we introduce a machine learning procedure named DRIER (Drought Detection via Regression-based Interpretable Extraction and Causal Relationships) to develop interpretable, impact-based drought indices. Unlike traditional indices that primarily look at meteorological anomalies (e.g., precipitation deficits), DRIER is designed to capture the compound nature of drought impacts, such as prolonged dry periods occurring alongside high temperatures and reduced snowpack. DRIER is a fully data-driven and automated framework that integrates: (i) non-linear feature aggregation for dimensionality reduction to preserve an interpretable representation of candidate hydro-meteorological predictors, while reducing their dimension; (ii) conditional mutual information-based feature selection to identify the most informative drought drivers; (iii) multi-task linear regression to upscale learning across multiple sub-regions, leveraging shared drought processes while preserving local heterogeneity; (iv) causal validation using the Transfer Entropy Feature Selection algorithm to confirm that the relationships identified between hydroclimatic variables and drought impacts are not merely correlative but grounded in robust causal mechanisms.

We demonstrate DRIER in the Po River Basin (Italy) by considering 10 sub-basins and using vegetation stress quantified through the Vegetation Health Index (VHI) as an impact proxy. The application shows that DRIER can capture spatially heterogeneous drought–impact relationships across sub-regions while benefiting from multi-task learning to share information where responses are correlated. Importantly, because the framework is interpretable end-to-end, the resulting impact-based index is not a black-box score: each step produces transparent, auditable outputs that identify the key hydroclimatic drivers, how they are aggregated into the index, and how they contribute (in sign and magnitude) to vegetation stress. The integrated causal discovery component further strengthens confidence in real-world use by privileging predictors consistent with robust physical mechanisms, reducing the influence of spurious correlations and supporting transferability across space and time.