12/5/2023 0 Comments Areal flood map![]() Between 19, 44% of all floods happened in Asian countries, causing 288 000 deaths and economic damage of $136 billion (U.S. ![]() 1) as a result of the severe floods that occurred in China. In the 1990s, approximately 100 000 people died and 1.4 billion people were affected ( Fig. There was a significantly increasing trend in number of floods occurring, number of deaths, number of people affected, and economic damage over the past half century. Figure 1, which was produced using data from the Center for Research on the Epidemiology of Disasters (CRED shows the worldwide flood statistics from 1950 to 2010. As a trade-off, the false alarm rate for the PERSIANN-CCS simulation (0.23) is better than that of the Stage 2 simulation (0.31).įloods are among the most devastating natural hazards in terms of the number of people affected and economic loss ( Ashley and Ashley 2008 Cook and Merwade 2009). Since the PERSIANN-CCS simulation slightly underestimated the discharge, the probability of detection (0.93) is slightly lower than that of the Stage 2 simulation (0.97). The simulation in both cases shows a good agreement (0.67 and 0.73 critical success index for Stage 2 and PERSIANN-CCS simulations, respectively) with the AWiFS flood extent. The results show that the PERSIANN-CCS simulation tends to capture the observed hydrograph shape better than Stage 2 (minimum correlation of 0.86 for PERSIANN-CCS and 0.72 for Stage 2) however, at most of the stream gauges, Stage 2 simulation provides more accurate estimates of flood peaks compared to PERSIANN-CCS (49%–90% bias reduction from PERSIANN-CCS to Stage 2). ![]() The model results were evaluated in two aspects: point comparison using USGS streamflow and areal validation of inundation maps using USDA’s flood extent maps derived from Advanced Wide Field Sensor (AWiFS) 56-m resolution imagery. The model was run using the a priori hydrologic parameters and hydraulic Manning n values from lookup tables. HiResFlood-UCI was forced with the near-real-time Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) and NEXRAD Stage 2 precipitation data. This research applied the high-resolution coupled hydrologic–hydraulic model from the University of California, Irvine, named HiResFlood-UCI, to simulate the historical 2008 Iowa flood. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties from such disasters. Floods are among the most devastating natural hazards in society.
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