Inundation depth (or level) is the most basic information for flood risk assessment; however, its mapping suffers from lack of in situ data in many cases. The aim of this study is to propose a new method for estimating inundation depth and spatially distributed water level for a local-scale pluvial flood using a combination of flood extent information derived from remote sensing imagery and hydrodynamic simulations. The study assumes the location of the inundation area given by the remote sensing imagery is mostly, but not completely, reliable. The estimation error of ground surface area wetted by inundation water body (wetted area) is used as an index to determine the most likely distribution of inundation depth. The proposed method was applied to two study areas to examine the performance of the method for different topographic characteristics. It showed promising results with an estimation precision of 0.02 to 0.17 m. An additional experiment suggested that water level could not be correctly estimated without flood extent information, complementing errors of the ground elevation data, and furthermore, using different topographic datasets revealed that the performance was highly influenced by the ground elevation data.
Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions—all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998–2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.
Abstract:Flood damage functions form the core of flood risk assessment. This study proposes a method for establishing flood damage functions for agricultural crops in data-scarce regions. The method assumes that the flood damage ratio is a function of inundation depth only and utilizes inundation depth estimated from flood extent information and hydro dynamic simulations. The parameters of the damage functions are calibrated through the SCEUA method (Shuffled Complex Evolution method developed at The University of Arizona) so that the calculated flood damages match observations compiled in flood disaster statistics. The established three functions show good agreement with actual agricultural damages caused by a rainfall event in 2010 and are validated against another rainfall event in 2009. The results indicate that the established damage functions are capable of estimating flood damage at the district scale, while damage estimations at finer spatial resolution differ between the functions, suggesting that detailed statistical data need to be incorporated to reduce the estimation uncertainty at fine scales.
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