Bridging Global Teleconnections and Local Data for Subseasonal-to-Seasonal Forecasting of Lake Como Inflows

Drought is a prolonged dry period characterized by a lack of precipitation leading to water shortages. Due to climate change driven by human activities, Europe is experiencing a dramatic rise in temperatures at a rate unparalleled elsewhere in the world. Consequently, precipitation patterns are shifting, and regions like Northern Italy are increasingly experiencing episodes of drought, including extreme ones. Prolonged drought periods have devastating effects on the economy of sectors heavily reliant on water availability, such as agriculture, industry, energy production, and inland waterway transport, while also jeopardizing water resources for civilian use and the health of ecosystems. These sectors could benefit from the availability of subseasonal-to-seasonal (S2S) drought forecasts to trigger anticipatory actions. However, the accuracy of existing dynamical forecast systems often falls short of the standards needed for effective integration into basin management.

To address this limitation, we propose a framework that leverages information from teleconnections, global climate variables, and meteorological data. This approach is applied to predict inflows for Lake Como (Italy) with lead times of 1 to 6 months, which are crucial for long-term reservoir management and strategic water allocation. Our framework comprises three modules. The first module investigates major climatic oscillations to determine patterns in climate variables influencing lake inflows. Mutual information masking is then applied to identify the most significant variables. The global climate variable, after being filtered using mutual information, is aggregated through Principal Component Analysis (PCA), which reduces the dimensionality of the data and captures essential spatial features, thus enhancing the model’s ability to focus on the most relevant global patterns. The second module applies a feature selection algorithm based on mutual information to construct input datasets composed of the principal components of global variables and local meteorological variables. The third and final module performs regression to predict cumulated inflows based on the selected input variables using Random Forest models.

Results highlight the promising performances achieved by the framework, demonstrating its ability to generate accurate forecasts and outperform the subseasonal and seasonal large-scale ensemble forecasts produced by the European Flood Awareness System (EFAS). The model achieves a Mean Absolute Percentage Error (MAPE) of 6.73% and a skill score of 0.96 for 1-month-ahead forecasts, 6.17% MAPE and 0.98 skill score for 3-month-ahead forecasts, and 6.00% MAPE with a skill score of 0.85 for 6-month-ahead predictions, showcasing its reliability across varying lead times.

This framework advances automated data-driven modeling for robust hydrological forecasting by employing a novel combination of filter- and wrapper-based feature selection techniques. The optimal input dataset is autonomously selected based on the predictive performance of the Random Forest model.