The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images
The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images
复制标题
DOI：
10.3389/feart.2015.00043
复制
发表时间：
2015-01-01
影响因子：
2.9
通讯作者：
Dance, Sarah L.
中科院分区：
文献类型：
--
作者：
Mason, David C.;Garcia-Pintado, Javier;Dance, Sarah L.
河流洪水淹没模型的基本数据要求是所研究河段的数字地形模型（DTM）。建模所需的比例决定了DTM所需的精度。对于城市地区的洪水建模，高分辨率DTM（如机载LiDAR（光探测和测距）产生的DTM）是最有用的，许多发达国家的大部分地区现在已经使用LiDAR进行了测绘。在偏远地区，可以使用较低分辨率的DTM在更大的范围内模拟洪水，在不久的将来，选择的DTM可能是从TanDEM-X数字高程模型（DEM）派生的DTM。通过结合现有的高分辨率和低分辨率数据集获得的可变分辨率全球DTM将有助于在全球范围内模拟洪水动态，尽可能以高分辨率进行，并在偏远地区的较大河流中以较低分辨率进行。洪水建模中使用的另一个重要数据来源是洪水范围，通常来自合成孔径雷达（SAR）图像。当洪水边界处的水位观测值（WLO）可以在河流河段沿着的各个点处进行估计时，如果使用DTM对洪水范围进行建模，则洪水范围将变得更加有用。为了说明这样一个全球DTM的效用，最近的研究涉及WLO在空间尺度的两端的两个例子进行了讨论。第一个需要高分辨率的空间数据，并涉及同化的WLO从一个真实的序列的高分辨率SAR图像到洪水模型更新模型状态与观测随着时间的推移，并估计河流流量和模型参数，包括河流水深和摩擦。结果表明，这种基于地球观测的洪水预报系统是可行的。第二个例子是在一个更大的规模，并使用SAR衍生的WLO，以改善低分辨率的TanDEM-X DEM的洪水范围所覆盖的地区。由此产生的随机高度误差的减少是显著的。
A basic data requirement of a river flood inundation model is a Digital Terrain Model (DTM) of the reach being studied. The scale at which modeling is required determines the accuracy required of the DTM. For modeling floods in urban areas, a high resolution DTM such as that produced by airborne LiDAR (Light Detection And Ranging) is most useful, and large parts of many developed countries have now been mapped using LiDAR. In remoter areas, it is possible to model flooding on a larger scale using a lower resolution DTM, and in the near future the DTM of choice is likely to be that derived from the TanDEM-X Digital Elevation Model (DEM). A variable-resolution global DTM obtained by combining existing high and low resolution data sets would be useful for modeling flood water dynamics globally, at high resolution wherever possible and at lower resolution over larger rivers in remote areas. A further important data resource used in flood modeling is the flood extent, commonly derived from Synthetic Aperture Radar (SAR) images. Flood extents become more useful if they are intersected with the DTM, when water level observations (WLOs) at the flood boundary can be estimated at various points along the river reach. To illustrate the utility of such a global DTM, two examples of recent research involving WLOs at opposite ends of the spatial scale are discussed. The first requires high resolution spatial data, and involves the assimilation of WLOs from a real sequence of high resolution SAR images into a flood model to update the model state with observations over time, and to estimate river discharge and model parameters, including river bathymetry and friction. The results indicate the feasibility of such an Earth Observation-based flood forecasting system. The second example is at a larger scale, and uses SAR-derived WLOs to improve the lower-resolution TanDEM-X DEM in the area covered by the flood extents. The resulting reduction in random height error is significant.