Abstract ID: 106
Improving global hydrological simulations through bias-correction and multi-model blending
Lead Author: Amulya Chevuturi
UK Centre for Ecology and Hydrology, UK
Keywords: Seasonal prediction, Hydrological forecasts, Bias-correction, Multi-model blending
Abstract: In light of the future vulnerability to hydrological hazards and water scarcity under a changing climate, it is imperative to develop accurate and reliable global hydrological forecasts. As a part of the World Meteorological Organization’s (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches for blending multi-model simulations for developing holistic operational forecasts. This study used the ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset; as ensemble of global seasonal forecasts and reforecasts of river discharge and related hydrological variables from four state-of-the-art land surface and hydrological models. As the global models are not calibrated for local conditions, the aim was to assess and investigate ways to improve the raw model simulations for providing best possible forecasts. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981—2019 for three variables: evapotranspiration, surface soil moisture and streamflow. We tested blending approaches based on (weighted) averaging of the multi-model simulations, using the catchment’s Kling-Gupta Efficiency (KGE) for the variable as the weight. A simple (arithmetic) multi-model averaging method was used as a benchmark to identify the added value of the weighted blended approach. The analysis also investigated improvements with and without bias-correction of simulations before applying the blending approaches. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. The results indicate that there is potential to successfully implement the bias-corrected weighted blending approach to improve operational forecasts. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions.
Co-authors:
Maliko Tanguy: UK Centre for Ecology & Hydrology, Wallingford, UK
Katie Facer-Childs: UK Centre for Ecology & Hydrology, Wallingford, UK
Alberto Martinez-de la Torre: UK Centre for Ecology & Hydrology, Wallingford, UK | Meteorological Surveillance and Forecasting Group, DT Catalonia, Agencia Estatal de Meteorolog ́ıa (AEMET), Barcelona, Spain
Sunita Sarkar: UK Centre for Ecology & Hydrology, Wallingford, UK
Stephan Thober: Department of Computational Hydrosystems, Helmholtz-Zentrum für Umweltforschung – UFZ, Germany
Luis Samaniego: Department of Computational Hydrosystems, Helmholtz-Zentrum für Umweltforschung – UFZ, Germany
Oldrich Rakovec: Department of Computational Hydrosystems, Helmholtz-Zentrum für Umweltforschung – UFZ, Germany | Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha-Suchdol 16500, Czech Republic
Matthias Kelbling: Department of Computational Hydrosystems, Helmholtz-Zentrum für Umweltforschung – UFZ, Germany
Edwin H. Sutanudjaja: Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
Niko Wanders: Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
Eleanor Blyth: UK Centre for Ecology & Hydrology, Wallingford, UK
