Simulation-based decision support system for flood damage assessment under uncertainty using remote sensing and census block information

Abstract

The level of damage of flood events does not solely depend on exposure to flood waters. Vulnerabilities due to various socio-economic factors such as population at risk, public awareness, and presence of early warning systems, etc. should also be taken into account. Federal and state agencies, watershed management coalitions, insurance companies, need reliable decision support system to evaluate flood risk, to plan and design flood damage assessment and mitigation systems. In current practice, flood damage evaluations are generally carried out based on results obtained from one dimensional (1D) numerical simulations. In some cases, however, 1D simulation is not able to accurately capture the dynamics of the flood events. The present study describes a decision support system, which is based on 2D flood simulation results obtained with CCHE2D-FLOOD. The 2D computational results are complemented with information from various resources, such as census block layer, detailed survey data, and remote sensing images, to estimate loss of life and direct damages (meso or micro scale) to property under uncertainty. Flood damage calculations consider damages to residential, commercial, and industrial buildings in urban areas, and damages to crops in rural areas. The decision support system takes advantage of fast raster layer operations in a GIS platform to generate flood hazard maps based on various user-defined criteria. Monte Carlo method based on an event tree analysis is introduced to account for uncertainties in various parameters. A case study illustrates the uses of the proposed decision support system. The results show that the proposed decision support system allows stake holders to have a better appreciation of the consequences of the flood. It can also be used for planning, design, and evaluation of future flood mitigation measures.

Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

“…Land/water discrimination is a valuable topic in terms of the services it provides to decision makers such as water area estimation [1,2], flood monitoring [3,4], flood disaster assessment [5][6][7], hydrological regulation and erosion control [8], and water resources management [9][10][11].…”

A Comparison of Land Surface Water Mapping Using the Normalized Difference Water Index from TM, ETM+ and ALI

“…LSW mapping, using remote sensing techniques, plays an important role in wetland monitoring [1][2][3], flood monitoring [4][5][6], flood disaster assessment [7][8][9][10], surface water area estimation [11][12][13][14], and water resources management [15][16][17]. Over three decades, multi-resource remote sensing data, such as data from the Advanced Very High Resolution Radiometer (AVHRR) [5,6,18], Moderate-resolution Imaging Spectroradiometer (MODIS) [16,19], Small Satellite Constellation For Environment and Disaster Monitoring And Forecasting A/B Satellites (HJ-1A/B) [12,20], Multispectral Scanner System (MSS) [6,11], Thematic Mapper (TM) [13,20,21], and Enhanced Thematic Mapper Plus (ETM+) [7,11] have been employed to extract information on land surface water bodies. One widely used remote sensing dataset for LSW mapping are the TM and ETM+ images provided by the Landsat series satellites [11].…”

Flood Damage Modeling on the Basis of Urban Structure Mapping Using High-Resolution Remote Sensing Data

“…Based on these land use/land cover maps that describe the spatial location of various elements at risk in the flooded area, flood losses are calculated. Often, only coarse land use classes like residential buildings, industry, and agriculture are derived from satellite images and integrated in flood loss modeling [10][11][12].…”