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

Abstract

Abstract. Hydrological forecasts are important for operational water management and near-future planning, even more so in light of the increased occurrences of extreme events such as floods and droughts. Having a forecasting framework, which is flexible in terms of input forcings and forecasting locations (local, regional, or national) 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 from dynamical models, to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at a national scale, with the Netherlands as the focus. Five different machine learning (ML) models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation, and seawater levels, were run with seasonal (re)forecast data, including the European Flood Awareness System (EFAS) and ECMWF seasonal forecast system (SEAS5), of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics, i.e. 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 the 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 the initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snowmelt dynamic captured in the training phase of the model. Surface water level 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 to 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, as part of a fast model-predictive control framework.

Hydrological regimes explain the seasonal predictability of streamflow extremes

“…On the one hand, improvements in accurately forecasting high-risk extreme events is critical for disaster risk reduction, which can be achieved through the application of new methods (e.g. machine learning, hybrid approaches, data assimilation) on the basis of better understanding the predictability (Troin et al 2021, van der Wiel et al 2021, Gordon et al 2022, Papacharalampous et al 2022, Torelló-Sentelles and Franzke 2022, Hauswirth et al 2023. Our study presents insights into the limitations of predictability for both floods and droughts and their discrepancies, including their spatial patterns and temporal deterioration.…”

“…In the 21st century, an increased frequency of hydrological extremes, both droughts and floods, has been observed over Europe, posing immediate socio-economic threats (Cammalleri et al 2020, Rakovec et al 2022. Early warning systems can reduce the societal vulnerability to these hydrological extremes, but demand for high-quality hydrological forecasts (Wanders andWood 2016, De Perez et al 2017). Against increasingly extreme weather and climate change, the United Nations initiated the 'Early Warnings for All' action plan 2023-2027 to boost the power of predictions and build the capacity to act to reduce disaster risk and support climate adaptation.…”

A Digital Twin Dam and Watershed Management Platform

“…Since we have to prepare dams and river basins for extreme rainfall conditions, the demand for more scientific and intelligent data-driven watershed management is rapidly increasing [19][20][21]. As a conventional approach, the utilization of numerical simulations for large-scale dams and river basins has gained significant popularity.…”