利用越南中部高地的住户调查和遥感数据,用下一代数字高程模型 FABDEM 评估 LISFLOOD-FP

Laurence Hawker, J. Neal, J. Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, Pham Khanh Nam
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引用次数: 0

摘要

摘要洪水是一个普遍存在的全球性挑战,每年造成的损失总计数十亿美元。中低收入国家受到的影响最为严重,因为这些国家人口的快速变化导致洪灾风险增加。这些地区往往也缺乏高精度的灾害测绘数据,无法更好地了解或管理风险。为了填补这一信息空白,近年来开发了一些全球洪水模型。然而,这些数据产品的性能还存在很大的不确定性。可以说,全球洪水模型最重要的组成部分是数字高程模型(DEM),它必须代表没有森林和建筑物等地表人工痕迹的地形。在此,我们以最近发布的 FABDEM DEM 为基础,开发并评估了新一代全球水动力洪水模型。我们在越南中部高原的三个研究地点,使用基于家庭调查和近期洪水遥感观测的两个独立验证数据集,对该模型进行了评估,并将其与使用 MERIT DEM 的前一版本进行了比较。基于 FABDEM 的全球洪水模型始终优于基于 MERIT 的模型,模型与遥感数据之间的一致性大于两个验证数据集之间的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing LISFLOOD-FP with the next-generation digital elevation model FABDEM using household survey and remote sensing data in the Central Highlands of Vietnam
Abstract. Flooding is an endemic global challenge with annual damages totalling billions of dollars. Impacts are felt most acutely in low- and middle-income countries, where rapid demographic change is driving increased exposure. These areas also tend to lack high-precision hazard mapping data with which to better understand or manage risk. To address this information gap a number of global flood models have been developed in recent years. However, there is substantial uncertainty over the performance of these data products. Arguably the most important component of a global flood model is the digital elevation model (DEM), which must represent the terrain without surface artifacts such as forests and buildings. Here we develop and evaluate a next generation of global hydrodynamic flood model based on the recently released FABDEM DEM. We evaluate the model and compare it to a previous version using the MERIT DEM at three study sites in the Central Highlands of Vietnam using two independent validation data sets based on a household survey and remotely sensed observations of recent flooding. The global flood model based on FABDEM consistently outperformed a model based on MERIT, and the agreement between the model and remote sensing was greater than the agreement between the two validation data sets.
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