Regional multi-objective calibration for distributed hydrological modelling: a decision tree based approach

Large scale modelling is becoming increasingly important in hydrology, particularly to characterize and quantify changes in the hydrological regime, whose drivers are typically large-scale phenomena, up to the global scale (e.g., climate change). This can be done with distributed models by estimatin...

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Vydáno v:Proceedings of the International Association of Hydrological Sciences Ročník 385; s. 65 - 69
Hlavní autoři: Pesce, Matteo, Viglione, Alberto, von Hardenberg, Jost, Tarasova, Larisa, Basso, Stefano, Merz, Ralf, Parajka, Juraj, Tong, Rui
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Gottingen Copernicus GmbH 18.04.2024
Copernicus Publications
Témata:
Calibration
Catchments
Climate change
Clouds
Datasets
Decision trees
Efficiency
Geomorphology
Hydrologic models
Hydrologic regime
Hydrology
Large-scale models
Machine learning
Mathematical functions
Modelling
MODIS
Mountain regions
Mountainous areas
Parameter estimation
Parameters
Precipitation
Scale models
Snow
Snow cover
Temperature
Watersheds
Italy
ISSN:2199-899X, 2199-8981, 2199-899X
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Shrnutí:Large scale modelling is becoming increasingly important in hydrology, particularly to characterize and quantify changes in the hydrological regime, whose drivers are typically large-scale phenomena, up to the global scale (e.g., climate change). This can be done with distributed models by estimating spatially consistent model parameters i.e. parameters having a functional relationship with catchment characteristics. In this study we adopt the newly developed PArameter Set Shuffling (PASS) approach, based on a machine learning decision tree algorithm, for the regional calibration of the TUWmodel over North-Western Italy. The method exploits observed patterns of locally calibrated parameters and catchment (climatic and geomorphological) descriptors, to derive functional relationships between the variables. The calibration procedure is performed by including snow cover information, as captured by MODIS datasets, in the model efficiency function. The results show that the PASS regionalization procedure allows to obtain very good regional model efficiencies, without significant loss of performance when moving from training to test catchments and from calibration to verification period, confirming the robustness of the methodology. We also highlight that using snow information in the calibration procedure is helpful to obtain spatially consistent model parameters for this study area. In the spirit of “obtaining good results for the right reasons”, this should be a preferred approach when performing the regional calibration of distributed hydrologic models over mountainous regions.
Bibliografie:ObjectType-Article-1
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ISSN:2199-899X
2199-8981
2199-899X
DOI:10.5194/piahs-385-65-2024