Academic literature on the topic 'FLOOD INUNDATION MAPS'

Abstract. This study introduces a flood hazard assessment part of the global flood risk assessment (Part 2) conducted with a distributed hydrological Block-wise TOP (BTOP) model and a GIS-based Flood Inundation Depth (FID) model. In this study, the 20 km grid BTOP model was developed with globally available data on and applied for the Ganges, Brahmaputra and Meghna (GBM) river basin. The BTOP model was calibrated with observed river discharges in Bangladesh and was applied for climate change impact assessment to produce flood discharges at each BTOP cell under present and future climates. For Bangladesh, the cumulative flood inundation maps were produced using the FID model with the BTOP simulated flood discharges and allowed us to consider levee effectiveness for reduction of flood inundation. For the climate change impacts, the flood hazard increased both in flood discharge and inundation area for the 50- and 100-year floods. From these preliminary results, the proposed methodology can partly overcome the limitation of the data unavailability and produces flood~maps that can be used for the nationwide flood risk assessment, which is presented in Part 2 of this study.

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

To date, various methods of flood prediction using numerical analysis or machine learning have been studied. However, a methodology for simultaneously predicting the rainfall return period and an inundation map for observed rainfall has not been presented. Simultaneous prediction of the return period and inundation map would be a useful technique for responding to floods in real-time and could provide an expected inundation area by return period. In this study, return period estimation for observed rainfall was performed via PNN (probabilistic neural network). SVR (support vector regression) and a SOM (self-organizing map) were used to predict flood volume and inundation maps. The study area was the Gangnam area, which has experienced extensive urbanization. The database for training SVR and SOM was constructed by one- and two-dimensional flood analysis with consideration of 120 probable rainfall events. The probable rainfall events were composed with 2–100 year return periods and 1–3 hour durations. The SVR technique was used to predict flood volume according to the rainfall return period, and the SOM was used to cluster various expected flood patterns to be used for predicting inundation maps. The prediction results were compared with the simulation results of a two-dimensional flood analysis model. The highest fitness of the predicted flood maps in the study area was calculated at 85.94%. The proposed method was found to constitute a practical methodology that could be helpful in improving urban flood response capabilities.

Eco-hydraulic analyses begin with the arrangement of tributaries. This research aimed to minimize the discharge of flood run-off downstream and map the flood inundation by spatial analysis uses DEMNAS data and mapping of flood inundation areas using ArcGIS. Analysis of return period flood points using HEC-RAS version 5.0.7. The data needed is the cross section of the river, the distance between the sections, the Manning's roughness number, the return period flood discharge and the slope of the river. The integration between topographic maps, watersheds and flood water levels can display areas that are potentially affected by inundation floods, so that the flood inundation limits and flood inundation areas can be calculated. This research examined proper eco-hydraulics design so that it could reduce discharge, identify locations prone to flooding, and describe the magnitude of the flood impact quantitatively. The results eco-hydraulic method obtained the design border width of 100 m, the condition before the existing river border arrangement was carried out, the inundation height was 0.30 – 1.13 m and after the river border arrangement the discharge could be reduced to 113.09 – 209 m3/s and the inundation height is 0 – 0.31 m. Based on the research results, it is known that border arrangement can provide benefits for flood control measures.