Within STARS4Water we are focusing on multi-scale model integration through the benchmarking of land surface and hydrological models. The primary objective of this activity is to evaluate the ability of different hydrological models to simulate key components of the water cycle, including river discharge, soil moisture, evapotranspiration, snow water equivalent (SWE), and water table depth (WTD). Through systematic model intercomparison, STARS4Water has demonstrated the strengths and limitations of different modelling approaches, reinforcing the importance of benchmarking for enhancing predictive accuracy in hydrological simulations. The benchmarking process has been driven by the need to support decision-making in water management by providing robust model assessments aligned with stakeholder priorities across the seven River Basin Hubs (RBHs). The developments have been made through a collaborative approach between stakeholders and the scientific community, ensuring that the methodologies are both scientifically rigorous and practically applicable to broader water resource challenges beyond the scope of the project.
The benchmarking has assessed the performance of seven models—LISFLOOD (Europe and Drammen), CLM, ParFlow-CLM, TSMP, and wflow_sbm (Europe and Rhine)—applied at different spatial and temporal scales. These models employ varying approaches, from conceptual parameterized structures to fully physically based simulations, and are evaluated against in-situ and remotely sensed hydrological observations. The results indicate that no single model excels across all hydrological variables and regions, highlighting the importance of model intercomparison. The benchmarking also reveals significant spatial variability in model accuracy, with better performance in regions with high-quality observational datasets and lower skill in areas with complex hydrological conditions, such as snow-dominated basins or regions with heterogeneous groundwater systems.
A key outcome of this study is the recognition of the critical role that robust simulated and observed datasets play in hydrological modelling. Observed data are essential for validating models and ensuring their reliability, while high-quality simulations offer valuable insights into hydrological processes, especially in regions where in-situ measurements are limited. The combination of these datasets enhances the ability to evaluate water cycle components more comprehensively, supporting the development of more accurate forecasting systems and sustainable water management strategies.
Additionally, we explored how simulated datasets can be leveraged to construct machine learning models for hydrological applications. We demonstrated how data-driven techniques such as Long Short-Term Memory (LSTM) networks and Random Forest (RF) can exploit outputs from physically based models to refine estimations of water table depth anomalies, SWE, and groundwater storage changes, in the Seine, Drammen and Duero River Basin, respectively.
Overall, the results highlight the value of benchmarking in improving hydrological models and the necessity of integrating multiple datasets to enhance water resource assessments. Future work will focus on refining model calibration and further validating simulated outputs against observational records.
More information can be found in the following STARS4Water project Deliverable:
Aguilera, H., Gomez, V., Martinez, P., Preiml, M., Glas, M., Weerts, A., Ramos, M-H., de Lavene, A., Engeland, K., Gelati, E., Hisdal, H., Hegdahl, T., Kollet, S., Avila, L., Keller, V., Rickards, N., Kossida, M., Salamon, P., Grimaldi, S., Bisselink, B. (2025): Improving existing models based on multi-scale model integration. Horizon Europe project STARS4Water. Deliverable D3.3.
