Groundwater drought attribution in Norway and Sweden using interpretable LSTM models of the Standardised Groundwater Index

Detecting and attributing groundwater droughts requires models that both capture complex temporal memory and provide interpretable representations of drivers. We present a framework that models weekly Standardised Groundwater Index (SGI) at multiple wells in Norway and Sweden using a Long Short-Term Memory (LSTM) network in combination with SHapley Additive exPlanations (SHAP) to attribute drought drivers. The LSTM ingests weekly climate and meteorological predictors (e.g., precipitation, temperature) together with large-scale teleconnection indices (e.g. NAO) to learn nonlinear, lagged responses that govern groundwater anomalies. We configure relatively deep LSTM network to represent long-range dependencies controlling weekly to seasonal anomalies and apply light regularisation to preserve natural SGI variability and avoid suppression of seasonal peaks. SHAP is used post-hoc to quantify feature importance and the timing and sign of impacts on predicted SGI at both aggregated and event specific scales. This allows identifying which predictors and lag times drive rapid groundwater decline or recovery, how teleconnection phases modulate drought risk, and the spatial heterogeneity of dominant drivers across wells. The primary objective of the LSTM–SHAP framework is to deliver local, well-specific attribution across the study region, complemented by spatial maps that identify the dominant controlling features (e.g., summer precipitation or winter snow). The results demonstrate that the integrated LSTM–SHAP approach produces accurate weekly SGI estimates for monitoring purposes while providing attribution of drought drivers. This capability supports early warning, and enhances understanding of hydroclimatic influences on groundwater droughts.