Preprints
https://doi.org/10.5194/hess-2022-89
https://doi.org/10.5194/hess-2022-89
 
31 Mar 2022
31 Mar 2022
Status: this preprint is currently under review for the journal HESS.

The suitability of a hybrid framework including data driven approaches for hydrological forecasting

Sandra M. Hauswirth1, Marc F. P. Bierkens1,2, Vincent Beijk3, and Niko Wanders1 Sandra M. Hauswirth et al.
  • 1Utrecht University, Department of Physical Geography, Princetonlaan 8a, Utrecht, The Netherlands
  • 2Deltares, Daltonlaan 600, 3584 BK Utrecht, The Netherlands
  • 3Rijkswaterstaat, Water, Verkeer en Leefomgeving, Griffioenlaan 2, Utrecht, The Netherlands

Abstract. Hydrological forecasts are important for operational water management and near future planning, even more so in light of increased occurrences of extreme events such as floods and droughts. Having a flexible forecasting framework that can deliver this information in fast and computational efficient manner is critical. In this study, the suitability of a hybrid forecasting framework, combining data-driven approaches and seasonal (re)forecasting information to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at national scale with the Netherlands as focus. Five different ML models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation and sea water levels, were run with seasonal (re)forecast data (EFAS and SEAS5) of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics Anomaly Correlation Coefficient (ACC), Continuous Ranked Probability (Skill) Score (CRPS and CRPSS), and Brier Skill Score (BSS) in comparison to a climatological reference hindcast. Aggregating results of all stations and ML models revealed that the hindcasting framework outperformed the climatological reference forecasts by roughly 60 % for discharge predictions (80 % for surface water level predictions). Skilful prediction for the first lead month, independently of initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snow melt dynamics captured in the training phase of the model. Surface water levels hindcasts showed similar skill and skilful lead times. While the different ML models showed differences in performance during a testing and training phase using historical observations, running the ML framework in a hindcast setting showed only minor differences between the models, which is attributed to the uncertainty in seasonal forecasts. However, despite being trained on historical observations, the hybrid framework used in this study shows similar skilful predictions as previous large scale forecasting systems. With our study we show that a hybrid framework is able to bring location specific skilful seasonal forecast information with global seasonal forecast inputs. At the same time our hybrid approach is flexible and fast, and as such a hybrid framework could be adapted to make it even more interesting to water managers and their needs, for instance a part of a fast model-predictive control framework.

Sandra M. Hauswirth et al.

Status: open (until 17 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-89', Anonymous Referee #1, 05 May 2022 reply
  • RC2: 'Comment on hess-2022-89', Anonymous Referee #2, 18 May 2022 reply

Sandra M. Hauswirth et al.

Sandra M. Hauswirth et al.

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Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purpose, combined and rerun with global data is able to create valuable information similar to large scale forecasting models.