Mark V. Bernhofen

and 15 more

Over the last two decades, several datasets have been developed to assess flood risk at the global scale. In recent years, some of these datasets have become detailed enough to be informative at national scales. The use of these datasets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using global datasets and methodologies. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global datasets nationally. To assess national flood risk, we use 6 datasets of global flood hazard, 7 datasets of global population, and 3 different methods for calculating vulnerability that have been used in previous global studies of flood risk. We find that the datasets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global datasets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global datasets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.

Webster Gumindoga

and 4 more

This study investigates propagation effects of CMORPH rainfall estimation errors on streamflow simulation for a headwater catchment of the Zambezi River. Model simulations (2006-2012) by the Representative Elementary Watershed (REW) framework are carried out for uncorrected and for bias corrected CMORPH product (the Climate Prediction Center Morphing technique of the National Oceanic and Atmospheric Administration, or NOAA). As a benchmark to assessments, the model is run for in-situ observed rainfall obtained from 6 stations at a daily timestep. Analysis of CMORPH rainfall necessitates bias correction. A suite of performance indicators indicates that uncorrected CMORPH estimates show substantial augmentation of rainfall error to streamflow simulation mismatch whereas bias corrected estimates show attenuation of error. The ɛ-NSGAII algorithm is selected for single and multi-objective calibration to assess CMORPH error propagation to REW streamflow results. Improved hydrograph simulation is achieved by multi-objective calibration. Flow discharge simulation during the dry season shows more substantial error attenuation compared to wet season high flow discharge simulation. Further, this study shows that ratios of model based actual evapotranspiration over rainfall (ETa/R) and stream flow over rainfall (Qs/R) (runoff coefficient) at seasonal base change subject to selected uncorrected and corrected CMORPH. REW water storage (ΔS) is affected as well as calibrated model parameters. The paper provides new insights on propagation effects of satellite based rainfall errors in stream flow modelling.