Post-processing large-scale river discharge forecasts at ungauged locations

Matthews, G. R. (2025) Post-processing large-scale river discharge forecasts at ungauged locations. PhD thesis, University of Reading. doi: 10.48683/1926.00127378

Abstract/Summary

Reliable river discharge forecasts are crucial for effective water resource management and flood risk mitigation. However, uncertainty in the forecasts is inevitable due to limitations in hydrological process understanding, errors in the meteorological forcings, observation uncertainty, and computational constraints. For flood forecasting, this forecast uncertainty can lead to ineffective or delayed preparatory actions. The primary aim of this thesis is to improve the actionable information from ensemble river discharge forecasts at gauged and ungauged locations using post-processing – a technique used to statistically correct the forecasts and reduce the uncertainty. Post-processing is already part of the forecasting system of the Copernicus Emergency Management Service’s European Flood Awareness System (EFAS). The skill of the EFAS operational at-gauge post-processing method is evaluated, finding that post-processing improves the skill of the forecasts particularly for large rivers for which hydrological errors dominate. Barriers to the use of the operational post-processed forecasts are identified via a co-production workshop, including lack of local relevance, and difficulty accessing the forecasts and associated documentation. A key limitation of the operational post-processing method is that it is only applicable at gauged locations. To overcome this limitation, a data-assimilation-inspired technique is developed to propagate observation information along the river network. Combined with the at-gauge post-processing method, this new information propagation technique allows the correction of river discharge forecasts at ungauged locations. This new post-processing method was evaluated and found to improve forecasts up to a 5-day lead-time. The new method is computationally efficient, adapts to the flow situation, and is applicable to any ensemble river discharge forecast. By improving the skill of the forecast at ungauged locations, this work aims to support more informed decision-making in flood risk management and water resource planning, ultimately helping to protect people, infrastructure, and economies from hydrological extremes.

Item Type	Thesis (PhD)
URI	https://centaur.reading.ac.uk/id/eprint/127378
Identification Number/DOI	10.48683/1926.00127378
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Download/View statistics	View download statistics for this item