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Skilful probabilistic predictions of UK floods months ahead using machine learning models trained on multimodel ensemble climate forecasts

Skilful probabilistic predictions of UK floods months ahead using machine learning models trained on multimodel ensemble climate forecasts

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint.

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Authors

Simon Moulds , Louise J. Slater , Louise Arnal, Andrew Wood

Abstract

Seasonal streamflow forecasts are an important component of flood risk management. Hybrid forecasting methods that predict seasonal streamflow using machine learning models driven by climate model outputs are currently underexplored, yet have some important advantages over traditional approaches using hydrological models. Here we develop a hybrid subseasonal to seasonal streamflow forecasting system to predict the monthly maximum daily streamflow up to four months ahead. We train a random forest machine learning model on dynamical precipitation and temperature forecasts from a multimodel ensemble of 196 members (eight seasonal climate ...  more

DOI

https://doi.org/10.31223/X5X405

Subjects

Climate, Hydrology, Physical Sciences and Mathematics

Keywords

floods, flood prediction, seasonal forecasting, machine learning

Dates

Published: 2024-07-12 02:26

Last Updated: 2024-07-23 08:36

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License

CC BY Attribution 4.0 International

Additional Metadata

Data Availability (Reason not available):
The input data and scripts that are needed to reproduce the results of this study will be uploaded to a research data repository under an MIT license upon acceptance for publication. They can be made available upon request.