Four decades of full-depth profiles reveal layer-resolved drivers of reservoir thermal regimes and episodic hypolimnetic warming

Thermal structure shapes ecological dynamics in lakes and reservoirs. Yet full-profile temperature records over multi-decades remain scarce, constraining mechanistic understanding of depth-resolved thermal changes and subseasonal extremes (e.g., surface heat waves and late-season hypolimnetic warming). In this study, we focused on Rappbode Reservoir, Germany's largest drinking-water reservoir, and compiled four decades of high-resolution, full-depth temperature profiles with concurrent hydro-meteorological records that are rarely available for stratified systems. Building on these data, we developed a novel two-step analytical framework that integrates long-term monitoring and process-based modelling to yield a high-resolution, internally consistent dataset of spatiotemporal temperature dynamics. We then applied interpretable machine learning to quantify depth-specific statistical attributions of hydro-meteorological and operational drivers, using process-based interpretation to assess mechanisms associated with late-stratification hypolimnetic warming. Our results suggested that driver contributions varied markedly with depth and stratification phase: stratification-strength metrics tied to atmospheric heat exchange (i.e., surface temperature, vertical temperature difference, Schmidt stability) showed the largest SHAP contributions from the 30 d antecedent moving average of shortwave radiation and air temperature. For hypolimnetic temperature, outflow discharge accounted for the largest contribution during late stratification. Additional process-based analyses supported a link between episodic hypolimnetic warming by up to 10 °C in four specific years and intensified deep withdrawals, which weakened the density gradient and promoted downward transport of warm upper-layer water into the hypolimnion. The dual-perspective framework developed here, which integrates process-based and machine-learning approaches, is broadly transferable for analyzing ecological processes and supporting evidence-based management in stratified waters.