Evaluation of Global Flood Forecasts in Ungauged Catchments

Boelee, Leonore (2022). Evaluation of Global Flood Forecasts in Ungauged Catchments. PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.00014b78


Flood forecasting and warning is a non-structural measure which has proved to be efficient and cost effective to minimise negative impacts of flooding (WMO & GWP, 2013) (Mishra and Singh, 2011). Higher spatial and temporal resolution data from earth observations (EO) and the increase in post processing technology has opened opportunities for forecasting floods at continental and global scales (Emerton et al., 2016); Revilla-Romero et al., 2015). This means flood forecasts are available for regions where previously there were no forecasting capabilities from global forecasting systems like the Global Flood Awareness System (GloFAS). A major barrier in using GloFAS in ungauged catchments is a lack of knowledge on how well river flow was predicted for flood events due to a lack of observations. The aim of this thesis is to provide information on GloFAS forecast performance, modelling processes and uncertainty in ungauged catchments. This thesis provides analysis of GloFAS flood forecast performance for the African continent without relying on gauged data and introduces a total of 888 new locations for which performance information is now available. This thesis also provides new methods to assess global flood forecast performance on continental and catchment scales in ungauged catchments, using a combination of flood event databases and inundation data from EO. This combination of data is available globally and these methods could be repeated for any ungauged catchment. This thesis has quantified the performance, model processes and uncertainty of GloFAS flow forecasts in a wide range of ungauged catchments with the objective of unlocking the potential of using GloFAS forecasts in these and other ungauged locations. It is recommended that more research is undertaken in quantitative assessments of forecast performance in ungauged catchments to remove barriers for flood prone regions with no or limited gauged data to benefit from the increasing availability of global flood forecasts.

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