Speaker
Description
Semi-distributed flood modelling requires an accurate representation of the spatial and temporal variability of rainfall. However, official rain-gauge networks often have limited spatial density and may fail to capture intense and localized rainfall events. Opportunistic rainfall sensors, such as personal weather stations (PWS), offer the potential to complement and enhance the spatial coverage of conventional rainfall observations. Nevertheless, heterogeneous data quality and continuously evolving network configurations pose significant challenges for hydrological modelling, given the high sensitivity of models to rainfall inputs. Therefore, the applicability of PWS rainfall data for semi-distributed modelling must be thoroughly evaluated.
In this study, twelve flood events in the Lambro basin (northern Italy) were simulated. Rainfall data from the Meteonetwork PWS platform were analysed alongside reference rain-gauge (RG) data provided by the Regional Environmental Protection Agency (ARPA) of the Lombardy Region. To evaluate the impact of PWS selection, several precipitation datasets were generated. In addition to the reference RG dataset, two groups of rainfall inputs were constructed:
(i) standalone PWS datasets, comprising: a) all available PWS observations (PWSall), b) quality-controlled PWS data (PWSqc), and c) quality-controlled PWS data from stations that were continuously active during all analysed storm events (PWSqc_c); and
(ii) combined datasets merging PWS and RG observations, denoted as e) RG+PWSall, f) RG+PWSqc, and g) RG+PWSqc_c, respectively.
Alternative rainfall inputs were generated using the inverse distance weighting (IDW) interpolation method. Rainfall hyetographs were compared at both the point scale, using RG observations as reference data (“ground truth”), and at the sub-basin scale. Their impact on hydrological modelling performance was evaluated through semi-distributed flood simulations conducted with a hydrological model previously developed by the authors of this contribution.
The results indicate that standalone PWS datasets, particularly those incorporating all available PWS observations without prior quality control, introduce substantial uncertainty in both rainfall representation and simulated flood hydrographs. The application of quality control procedures as well as the selection of continuously operating stations improve both rainfall hyetographs and model performance. The most accurate reproduction of flood hydrographs is achieved when quality-controlled PWS datasets (PWSqc and PWSqc_c) are combined with RG observations. These findings demonstrate that PWS can provide valuable complementary information to conventional monitoring networks. However, under current conditions, they cannot fully replace reference gauge networks under current conditions.
Acknowledgments
The authors would like to thank the COST Action “OpenSense” (CA20136) for supporting collaboration opportunities among the co-authors through the STSM program.