On the selection of precipitation products for the regionalisation of hydrological model parameters

Daily streamflow data are crucial for various scientific and operational water resources applications, such as climate change impact assessment, flood forecasting, and catchment classification, among others. Streamflow is typically estimated through the implementation of hydrological models, which rely on parameters to represent hypotheses about the dominant processes in a catchment. In most cases, these parameters cannot be measured at the scales relevant for model applications and are therefore estimated through model calibration. Because most streams worldwide remain ungauged, novel parameter regionalisation techniques have been developed to predict daily streamflow over ungauged catchments. These regionalisation techniques transfer calibrated model parameters from gauged to ungauged catchments. To this end, an accurate spatio-temporal representation of crucial meteorological variables such as precipitation is essential, and therefore, most regionalisation studies have been conducted over regions with a dense network of meteorological stations. However, the characterisation of precipitation over data-scarce areas is challenging and might be subject to large uncertainties when only ground-based measurements are used. Despite that few daily regionalisation studies have used gridded precipitation products, there is no precise evaluation on how the selection of a particular precipitation product can affect the performance of the existing regionalisation techniques. Therefore, this work aims to analyse how the choice of gridded daily precipitation products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. For this purpose, we calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four precipitation products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each precipitation product. Despite differences in the spatio-temporal distribution of precipitation, all products provided good performance during calibration (median KGE's > 0.77), two independent verification periods (median KGE's > 0.70 and 0.61, for near normal and dry conditions, respectively), and regionalisation (median KGE's for the best method ranging from 0.56 to 0.63). We show how model calibration can compensate, to some extent, differences between precipitation forcings by adjusting model parameters and thus the water balance components. Feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that: i) merging precipitation products and ground-based measurements does not necessarily translate into an improved hydrological model performance; ii) a precipitation product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; iii) the spatial resolution of the precipitation products does not substantially affect the regionalisation performance; and iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.