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Conference Paper

Investigating the hidden patterns: A data-driven approach for temporal correlation estimation of errors in rainfall-runoff models

Authors

El Ouahabi,  Taha-Abderrahman
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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

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

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

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Citation

El Ouahabi, T.-A., Andréassian, V., Bourgin, F., Perrin, C. (2023): Investigating the hidden patterns: A data-driven approach for temporal correlation estimation of errors in rainfall-runoff models, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3654


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020888
Abstract
Streamflow forecasts produced by hydrological models and post-processing approaches provide valuable information for water management and decision-making. Existing post-processing approaches rarely account for temporal correlation in error models and assume their statistical independence. Understanding this correlation is essential for developing robust post-processors of hydrological models, able to provide reliable forecasts across multiple lead times and aggregation timescales.The temporal correlation of errors is complex. It is often non-linear and dynamic, and influenced by many factors. Here, we use a probabilistic framework coupled with a few statistical methods (including among others, machine learning techniques) to estimate the temporal structure of error correlation. We aim to address several research questions: i) detecting and understanding situations that significantly affect the temporal characteristics of errors; ii) improving the reliability of aggregated forecasts by explicitly modelling the autocorrelation structure. We provide an analysis for a large set of French catchments and several reforecast experiments based on the lumped GR6J hydrological model.