Extracting the most valuable information from multi-timescale hydrological forecasts for informing the operation of multipurpose water systems

Given the ever-growing accuracy of forecast products over different lead times, it’s increasingly important to understand how to efficiently select and use the most valuable information to support adaptive and robust management of water resources under changing hydroclimatic conditions. In this study, we investigate how the most valuable information from multi-scale forecasts can be selected and used to inform the optimal operation of multipurpose water reservoirs. Our framework combines Input Variable Selection algorithms supporting the selection of the most informative policy inputs, including different forecast variables over diverse lead times, with the Evolutionary Multi-Objective Direct Policy Search method for designing Pareto optimal control policies conditioned on forecast information.

We test this approach on the Lake Como system, a regulated lake in Northern Italy which is operated for preventing floods along the lake shores, providing irrigation supply to downstream users and avoiding low lake levels. Our approach allows the identification of the best subset or combination of variables and metrics extracted from a suite of forecast products. In particular, the performance of the system is evaluated using short-term local deterministic forecasts as well as sub-seasonal and seasonal large-scale ensemble forecasts provided by the European Flood Awareness System (EFAS), part of the Copernicus Emergency Management Service. The candidate variables proposed as inputs for the IVS include different statistics extracted from these forecasts, including the accumulated inflow up to different lead times, the maximum daily flow over different temporal scales and spatial domains, the ensemble forecast variance and some skill scores. The performance of the designed forecast-informed operating policies is contrasted against various benchmarks, including perfect forecasts and the climatology. Beside improving the operating policy performance, results are expected to provide insights about the intrinsic bias of forecast products and to highlight the role of forecast uncertainty in policy design.