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Conference Paper

Continuous rainfall-runoff modelling in ungauged basins: steps forwards, bottlenecks and possible applications in early warning system design

Authors

Grimaldi,  Salvatore
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Volpi,  Elena
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Papalexiou,  Simon Michael
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Petroselli,  Andrea
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Cappelli,  Francesco
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Grimaldi, S., Volpi, E., Papalexiou, S. M., Petroselli, A., Cappelli, F. (2023): Continuous rainfall-runoff modelling in ungauged basins: steps forwards, bottlenecks and possible applications in early warning system design, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-1641


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017947
Abstract
The benefit of continuous modelling in hydrological studies is widely recognized, indeed it is particularly promising for estimating the design hydrological input for a variety of practical applications and there is a clear tendency to overcome the concept of design hydrograph in favor of the design runoff simulation. Recently, it was underlined the possibility and the opportunity to apply the continuous framework also in the challenging case of ungauged basins. However, while promising, this approach is still not commonly adopted in practice mainly because it needs as input a simulated rainfall time series, that still is not user-friendly task. In this contribution we summarize the recent steps forward of the continuous modelling in ungauged basins and the residual procedural bottlenecks, moreover we explore its use for designing flood early warning systems. We illustrate a framework based on hydrological-hydraulic synthetic scenarios for selecting the best-performing machine learning model for forecasting discharges. Finally, feature importance measures are introduced for discerning the most influential sub-basins where the measurement instrumentations should be installed enabling the implementation of a cost-effective flood early warning system.