Flood Forecasting and Inundation Mapping Using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data: The 2008 Iowa Flood

Abstract

Floods are among the most devastating natural hazards in society. Flood forecasting is crucially important in order to provide warnings in time to protect people and properties from such disasters. 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. HiResFlood-UCI was forced with the near-realtime Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) and NEXRAD Stage 2 precipitation data. The model was run using the a priori hydrologic parameters and hydraulic Manning n values from lookup tables. 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 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 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. 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). 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).

Applications of Satellite-Based Rainfall Estimates in Flood Inundation Modeling—A Case Study in Mundeni Aru River Basin, Sri Lanka

“…Among them are: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN [2]); the Tropical Rainfall Measuring Mission (TRMM [3]); and the Global Satellite Mapping of Precipitation (GSMaP [4]). These datasets have been applied to numerical hydrological models to simulate floods in various locations of the world [5][6][7][8]. Within South Asia, Nanda et al [9] used an SRE dataset to develop a real-time flood-forecasting model for a basin in eastern India.…”

“…Some employed multiple SREs and then tested their validity by comparing stream discharge [5,[14][15][16][17][18]. Flood inundation extent and depth have also been simulated by applying solo SREs such as PERSIANN and TRMM to distributional flood models [8,19]. In addition, the RRI model has been utilized with GSMaP to simulate the 2008 flood event in Pakistan [20].…”

The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data

“…Such resolutions are not suited to the representation of floodplains of small streams. On the other hand, detailed flood inundation mapping approaches are available at higher resolutions (Bradbrook et al, 2005;Sanders, 2007;Nguyen et al, 2015), but require large computational resources which limit the implementation possibility at a large scale. In both cases, most of the proposed mapping approaches would not be compatible with application in real time.…”

Methods and tools to support real time risk-based flood forecasting - a UK pilot application

“…A very recent study applied flood forecasting and Inundation mapping using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data for the 2008 flood in Iowa. This study does not only apply innovative sources of data, but also outputs a probabilistic inundation prediction [17]. These approaches have demonstrated opportunities in probabilistic flood risk analysis in operational forecasting, but have also shown limitations; specifically, the computational load and large datasets limit the number of ensemble simulations that can be carried out.…”