Speaker
Description
Accurate spatio-temporal representation of rainfall is essential for hydrological (i.e., rainfall-runoff) modelling, and for the later applications of hydrological models. Rainfall data are usually obtained from official raingauge networks; however, these networks are often sparce and/or even with declining number of stations. To improve spatio-temporal representation of rainfall, various opportunistic sensors have been considered, including commercial microwave links (CML). CML-based rainfall data have been used for hydrological modelling in small urban catchments for years, but their applications in large catchments is lagging behind. Development of hydrological models in large catchments requires long rainfall series, which is not the case when it comes to CML datasets. Thus, a thorough evaluation of hydrological model transferability across different rainfall inputs is essential for wider application of the CML data. In this study, a semi-distributed hydrological model developed for the peri-urban Lambro catchment in Northen Italy (Cazzaniga et al., 2022; doi: https://doi.org/10.5194/hess-26-2093-2022) is evaluated from the standpoint of its transferability across the rainfall inputs. To this end, the model is run with conventional input obtained from the raingauge network (RG), from the CML-based rainfall, and combination thereof (CML-RG). The model performance is evaluated over 12 flood events, four of which are low-rate events (maximum rain intensity below 15 mm/h), while maximum rainfall intensity exceeds 35 mm/h during four most extreme ones. The model performance significantly varies across the events. Although the RG model yields the highest performance in most instances, it is outperformed by the model forced with CML- and/or CML-RG data during the three most extreme events. There is no strong correlation between the peak rainfall intensity and model performance; however, the RG model outperforms the CML- and CML-RG models over the low-intensity events. The CML model is outperformed by the CML-RG and, especially, RG model according to Nash-Sutcliffe efficiency, relative error in peak flows and in runoff volume, however, CML model performs best in most cases according to the coefficient of determination, which suggests that this rainfall input best captures rainfall dynamics. This study clearly indicates a great potential of CML to improve hydrological model performance in high-flow range. It also suggests that further research is needed to reduce biases in rain depth estimation at a sub-catchment level. Further research is also needed to enable optimal combination of different rainfall inputs to hydrological model, as well as to improve spatial discretisation of hydrological models to better accommodate “linear” CML rainfall data.
| Are you an Early Career Scientist ? | No |
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