Regional Calibration of a Lumped Hourly Hydrological Model Using a Decision-Tree Approach

Hydrological simulation in ungauged basins is essential for analysing extreme events and reconstructing historical data. A major challenge is deriving consistent model parameters that reflect basin characteristics. Regionalization methods address this by transferring information from gauged to ungauged basins, linking catchment attributes to model parameters.

An innovative approach - PArameter Set Shuffling (PASS) - uses a machine learning decision tree algorithm to establish relationships between locally calibrated parameters and basin descriptors, enabling spatially distributed and lumped parameter predictions. PASS has yielded valid results with semi-distributed hydrological models in flat terrains such as Germany and in more complex regions like the Alpine areas, but its application to lumped models remains largely unexplored.

This study investigates the performance of PASS for regionalizing an hourly lumped rainfall-runoff model, GR5H, in the eastern mountainous region of Emilia-Romagna, Italy. Specifically, the method was applied to a pool of 23 medium-small mountainous basins, using hourly discharge data covering up to 20 years for many of the catchments considered. The selection of the study region is motivated by the devastating 2023-2024 floods, causing casualties, significant losses and widespread displacement. Extensive levee breaches and damaged river gauges hindered accurate flood flow measurements.

KGE and NSE were adopted as efficiency measures in the calibration process and two independent analyses were conducted, providing additional insight into the potential, strengths, and weaknesses of these two metrics. The results demonstrate that the PASS procedure enables the attainment of good regional model efficiencies without significant loss of performance when transitioning from calibration to leave-one-out cross-validation, confirming the robustness of the methodology in handling complex terrains and diverse hydrological conditions with a simpler hydrological model. These findings highlight the potential of PASS to streamline parameter estimation for ungauged basins and provide a reliable tool for hydrological modelling with reduced computational complexity.