Surface Water Flood forecasting using reasonable worst case scenarios from ensemble rainfall forecasts

Abstract

Surface water flooding (SWF) presents a significant risk to livelihoods, which is projected to increase under climate change. However, forecasting the intense convective rainfall that causes most SWF on the temporal and spatial scales required for effective flood forecasting remains extremely challenging. National scale flood forecasts are currently issued for England and Wales by the Flood Forecasting Centre (FFC). The forecasts are well regarded amongst flood responders, although they feel they would benefit from more location-specific information.We have developed an enhanced, regional-scale surface water flood forecast system driven by post-processed ensemble rainfall forecasts. We apply a neighbourhood post-processing method to generate percentile-based reasonable worst case rainfall scenarios from the UK operational Met Office Global and Regional Ensemble Prediction System (MOGREPS-UK), a 2.2km horizontal resolution, convection-permitting operational ensemble system that provides forecasts at up to 5 days lead time. Enhanced surface water flood forecasts are then generated by conducting look-ups of meteorological inputs against catchment-level hydrological reference data from the national Environment Agency Risk of SWF mapping database. In this manner the likely severity of flooding associated with forecast rainfall events is assessed by reference to the driving hyetographs for local-scale hydrological modelling, which is available nationally.Evaluation of the forecasts is informed by both quantitative assessment and qualitative user feedback. We tested the new forecast system over Northern England over summer 2022 and held a co-development workshop with professional and volunteer flood responders, in which we presented participants with existing and new forecasts for recent case-study flood events. We found that responders would routinely use the enhanced forecasts if they were offered as a complement to existing operational provision, with the enhanced information having the strongest impact on decision making for severe, high impact flood events. Responders valued having access to more localised forecast information, which was viewed as useful for decision making, despite the necessity of accepting a higher degree of forecast uncertainty.We evaluated the SWF forecasts over a historical 10-year period for days with observed SWF events across Northern England and, to assess false alarms, we verified them against SWF forecasts produced using radar observations for several summers’ continuous daily forecasts. The method is effective at forecasting impacts from higher impact flood events, although still generally over-estimates the extent of affected areas. The results of quantitative skill assessment will form a key basis for determining future operational deployment across England and Wales, which we will discuss the feasibility of and requisite next steps.