Implementation and Evaluation of the WRF-Hydro Model for Hydrometeorological Forecasting in the Piura River Basin, Peru

Extreme hydro-meteorological events associated with the Coastal El Niño phenomenon represent a critical threat to the socioeconomic stability of the northern coast of Peru. In particular, the Piura River basin, characterized by its complex topography and short concentration times, requires precise monitoring and modeling to address these episodes. Currently, the National Meteorology and Hydrology Service of Peru (SENAMHI) employs a semi-distributed system (ARNO/VIC coupled with RAPID) for the operational assessment of flood risk. However, the increasing intensity and frequency of recent events highlights the need for tools that explicitly represent physical processes at higher resolution. This research proposes the implementation of the fully distributed WRF-Hydro model, focusing the methodology on the reconstruction and analysis of the main extreme flood events within the period covered by the PISCOp_h product, a gridded hourly precipitation observational dataset developed by SENAMHI for 2015–2020. The methodological strategy is based on generating a hybrid meteorological forcing to feed the hydrological model. For this purpose, an atmospheric simulation is carried out with WRF, forced by initial and boundary conditions from the GFS, obtaining high-resolution distributed atmospheric fields. Given the uncertainty of the modeled precipitation, the rainfall field generated by WRF is replaced by the hourly gridded observations from PISCOp_h, ensuring controlled and realistic forcing. With this configuration, model calibration and validation are performed. Calibration prioritizes the highest-magnitude events, highlighting the 2017 Coastal El Niño episode for the adjustment of physical parameters, while validation considers a set of floods recorded between 2015 and 2020, evaluating the robustness of the system. It is expected to demonstrate that this combination of atmospheric dynamics and observational accuracy constitutes a physically consistent and operationally viable tool for predicting intense floods, strengthening flood risk management in Peru.