Application of the WRF model rainfall product for the localized flood hazard modeling in a data-scarce environment

Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.

Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers

Deltas, estuaries, and wetlands are prone to frequent coastal flooding throughout the world. In addition, a large number of people in the United States have settled in these low-lying regions. Therefore, the ecological merit of wetlands for maintaining sustainable ecosystems highlights the importance of flood risk and hazard management in these regions. Typically, hydrodynamic models are used for coastal flood hazard mapping. The huge computational resources required for hydrodynamic modeling and the long-running time of these models (order of hours or days) are two major drawbacks that limit the application of these models for prompt decision-making by emergency responders. In the last decade, DEM-based classifiers based on Height Above Nearest Drainage (HAND) have been widely used for rapid flood hazard assessment demonstrating satisfactory performance for inland floods. The main limitation is the high sensitivity of HAND to the topography which degrades the accuracy of these methods in flat coastal regions. In addition, these methods are mostly used for a given return period and generate static hazard maps for past flood events. To cope with these two limitations, here we modify HAND and propose a composite hydrogeomorphic index for rapid flood hazard assessment in coastal areas. We also propose the development of hydrogeomorphic threshold operative curves for real-time flood hazard mapping. We select the Savannah river delta as a testbed, calibrate the proposed hydrogeomorphic index on Hurricane Matthew and validate the performance of the developed operative curves for Hurricane Irma. Validation results demonstrate that the operative curves can rapidly generate flood hazard maps with satisfactory accuracy. This indicates the high efficiency of our proposed methodology for fast and accurate estimation of hazard areas for an upcoming coastal flood event which can be beneficial for emergency responders and flood risk managers.

A combined approach for flood hazard assessment using hydrological, hydraulic and GIS modeling case study of El Hamiz Wadi, Algeria

Flood hazard is one of the most destructive natural phenomena causing a significant material damage and human losses in Algeria during the past decade. The aim of this study is to assess and characterize the flood hazard in El Hamiz watershed (Algeria). In addition, this study identifies areas and elements prone to this hazard to minimize the damages and losses. The methodology used in the present study is based on a combination of hydrological and hydraulic modeling using the Hec-Ras software and mapping techniques using a Geographic Information System (GIS). The flood hazard assessment model of El Hamiz wadi using different hydrological (rainfall / runoff transfer and return period), hydraulic (water level and flow velocity) and exposure (land use data and socio-economic data) parameters revealed that the downstream part of the studied area is the most susceptible to flood events. The mapping of flood-prone areas indicated the extent of this phenomena on the El Hamiz wadi banks, especially through heavy submersion of residential areas characterized by a high urban concentration including inhabitants, activities and infrastructures.

Integration of GIS-Based Multicriteria Decision Analysis and Analytic Hierarchy Process for Flood-Hazard Assessment in the Megalo Rema River Catchment (East Attica, Greece)

This study deals with the flood-hazard assessment and mapping in the catchment of Megalo Rema (East Attica, Greece). Flood-hazard zones were identified utilizing Multi-Criteria Decision Analysis (MCDA) integrated with Geographic Information System (GIS). Five factors were considered as the most influential parameters for the water course when high storm-water runoff exceeds drainage system capacity and were taken into account. These factors include slope, elevation, distance from stream channels, geological formations in terms of their hydro-lithological behavior and land cover. To obtain the final weights for each factor, rules of the Analytic Hierarchy Process (AHP) were applied. The final flood-hazard assessment and mapping of the study area were produced through Weighted Linear Combination (WLC) procedures. The final map showed that approximately 26.3 km2, which corresponds to 22.7% of the total area of the catchment, belongs to the high flood risk zone, while approximately 25 km2, corresponding to ~15% of the catchment, is of very high flood risk. The highly and very highly prone to flooding areas are located mostly at the southern and western parts of the catchment. Furthermore, the areas on both sides of the channel along the lower reaches of the main stream are of high and very high risk. The highly and very highly prone to flooding areas are relatively low-lying, gently sloping and extensively urbanized, and host the densely populated settlements of Rafina-Pikermi, Penteli, Pallini, Peania, Spata, Glika Nera, Gerakas and Anthousa. The accuracy of the flood-hazard map was verified by correlating flood events of the last 30 years, the Hydrologic Engineering Center’s River Analysis System (HEC–RAS) simulation and quantitative geomorphological analysis with the flood-hazard level. The results of our approach provide decision makers with important information for land-use planning at a regional scale, determining safe and unsafe areas for urban development.