| 12 Feb 2026
Hydrological Auditing of LISFLOOD v4.1.1: Impacts of Model Setup on Water Balance Components in the Po River Basin
Abstract. In recent years, large-scale hydrological models have been increasingly used at regional and global scales to support decision making. Their realism in simulating water balance components is crucial for building trust across different use cases. Hydrological models may reproduce streamflow well but misrepresent other fluxes, due to internal fluxes compensations and equifinality. Therefore, alternative setups can benefit specific applications by improving the representation of relevant water balance components. "Hydrological auditing" of models, i.e. a thorough critical review of their realism beyond the calibration targets (usually streamflow), provides useful insights for both practical applications and process understanding. We present one such exercise in a representative European case study using a physically-based hydrological model (LISFLOOD), widely used for flood forecasting and water resources management. We evaluate LISFLOOD v4.1.1's performance in simulating streamflow, evapotranspiration, and overall water balance in the Po River Basin, a complex and highly managed basin in Northern Italy. Six alternative model setups are tested, including different soil layers depths and preferential flow representations. Results show that the model setup currently used in the European Flood Awareness System (EFAS) v.5 performs best in terms of streamflow simulation, particularly at the daily time step, but tends to underestimate evapotranspiration. In turn, this may lead to an overestimation of groundwater recharge and a poor water balance representation. The use of the Budyko framework as a diagnostic tool reveals that model setups without preferential flow better match the expected long-term water balance, but reduce daily streamflow performance. The study highlights the importance of evaluating model performance and auditing alternative parametrizations to ensure accurate simulations of water balance components, crucial for water resources management. We propose criteria to improve the calibration of the LISFLOOD model in a flexible and target-driven way, to better support water resources management in complex river basins.
Summary:
This study assesses a ubiquitous but often overlooked problem: hydrological models are built for a specific purpose (e.g., flood forecasting), and at some point “misused” for other tasks (e.g., drought prediction, water resources management), often without specific re-training and re-evaluation. Since any model is just a coarse abstraction of reality and suffers from model structural errors, compensation for model error happens within the allowed parameter ranges, and unphysical behavior can emerge across compartments, processes and variables. So if trained for streamflow only, a hydrological model might perform poorly on other components of the water balance, and this is the target of the presented analysis in this manuscript. The authors investigate different model setups of a specific distributed model, LISFLOOD, on the Po River Basin, with respect to streamflow prediction performance, but also through diagnostic evaluation of other fluxes.
Overall evaluation:
The authors reveal interesting contradictions between performance, parameter estimation and water balance closure when training different versions of LISFLOOD. The manuscript is very well structured and a pleasure to read. While the conclusions of the study are supported by the findings, unfortunately, the manuscript left me somewhat “uninspired” – I had hoped for more insights. Yet, the findings are worthwhile reporting and the analysis itself is nicely done, so my recommendation is to still consider this manuscript for publication, albeit not the most forward-directed paper.
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References:
Guthke, A. (2017). Defensible Model Complexity: A Call for Data-Based and Goal-Oriented Model Choice. Ground Water, 55(5).