A Method to Assess Localized Impact of Better Floodplain Topography on Flood Risk Prediction

Guy J.-P Schumann,Konstantinos M Andreadis

doi:10.1155/2016/6408319

Guy J.-P Schumann, Konstantinos M Andreadis

Open Access

https://doi.org/10.1155/2016/6408319

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Abstract

Many studies have highlighted the need for a higher accuracy global digital elevation model (DEM), mainly in river floodplains and deltas and along coastlines. In this paper, we present a method to infer the impact of a better DEM on applications and science using the Lower Zambezi basin as a use case. We propose an analysis based on a targeted observation algorithm to evaluate potential data acquisition subregions in terms of their impact on the prediction of flood risk over the entire study area. Consequently, it becomes trivial to rank these subregions in terms of their contribution to the overall accuracy of flood prediction. The improvement from better topography data may be expressed in terms of economic output and population affected, providing a multifaceted assessment of the value of acquiring better elevation data. Our results highlight the notion that having higher resolution measurements would improve our current large-scale flood inundation prediction capabilities in the Lower Zambezi by at least 30% and significantly reduce the number of people affected as well as the economic loss associated with high magnitude flooding. We believe this procedure to be simple enough to be applied to other regions where high quality topographic and hydrodynamic data are currently unavailable.

Highlights

Topographic information in the form of a digital elevation model (DEM) is required for many environmental process models and applications
The model is LISFLOOD-FP [6] in subgrid channel (SGC) mode [12] forced with forecast flood flows for the Zambezi and Shire Rivers simulated by the Variable Infiltration Capacity (VIC) distributed hydrology model, which is conditioned on meteorological ensemble forecast (ENS) data from ECMWF
An important point of this study is that the targeted observation analysis can be used to assess the impact new measurements of important variables have on the prediction of flood hazard and we showed how inclusion of socioeconomic data can augment that analysis, there is the substantial caveat that the verification model in our case was only based on higher resolution (90 m) SRTM topography and not on high accuracy elevation data, for example, from LiDAR or new InSAR technology [1]

Summary

Introduction

Topographic information in the form of a digital elevation model (DEM) is required for many environmental process models and applications. The accuracy of a DEM is determining the performance of the model applied and the success of the application. Different technologies exist to acquire land elevation, often with varying degrees of accuracy and precision depending on the technology and resolution used. Accurate topography is desirable and in many cases a prerequisite for successful modeling, it is often not available at the required accuracy level and resolution for many areas around the world. As argued by Schumann et al [2], there is a need for a higher accuracy (LiDAR-type) global DEM than currently available, for mapping and predicting natural hazards such as flooding in developing countries that are often deprived but in need of a high quality DEM

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