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Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale

机译:Flow-R,用于在区域范围内绘制泥石流和其他重力危害敏感性的模型

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摘要

The development of susceptibility maps for debris flows is of primary importance due to population pressure in hazardous zones. However, hazard assessment by process-based modelling at a regional scale is difficult due to the complex nature of the phenomenon, the variability of local controlling factors, and the uncertainty in modelling parameters. A regional assessment must consider a simplified approach that is not highly parameter dependant and that can provide zonation with minimum data requirements. A distributed empirical model has thus been developed for regional susceptibility assessments using essentially a digital elevation model (DEM). The model is called Flow-R for Flow path assessment of gravitational hazards at a Regional scale (available free of charge under http://www.flow-r.org) and has been successfully applied to different case studies in various countries with variable data quality. It provides a substantial basis for a preliminary susceptibility assessment at a regional scale. The model was also found relevant to assess other natural hazards such as rockfall, snow avalanches and floods.ududThe model allows for automatic source area delineation, given user criteria, and for the assessment of the propagation extent based on various spreading algorithms and simple frictional laws. We developed a new spreading algorithm, an improved version of Holmgren's direction algorithm, that is less sensitive to small variations of the DEM and that is avoiding over-channelization, and so produces more realistic extents.ududThe choices of the datasets and the algorithms are open to the user, which makes it compliant for various applications and dataset availability. Amongst the possible datasets, the DEM is the only one that is really needed for both the source area delineation and the propagation assessment; its quality is of major importance for the results accuracy. We consider a 10 m DEM resolution as a good compromise between processing time and quality of results. However, valuable results have still been obtained on the basis of lower quality DEMs with 25 m resolution.
机译:由于危险区域的人口压力,制定泥石流敏感性图至关重要。但是,由于现象的复杂性,局部控制因素的可变性以及建模参数的不确定性,很难在区域范围内通过基于过程的建模进行危害评估。区域评估必须考虑一种简化的方法,该方法不高度依赖参数,并且可以以最少的数据要求提供分区。因此,已经开发出了一种分布式经验模型,主要使用数字高程模型(DEM)进行了区域敏感性评估。该模型称为“ Flow-R”,用于在区域范围内评估重力危险的流路(可在http://www.flow-r.org下免费获得),并且已成功应用于可变国家中的不同案例研究数据质量。它为区域范围内的初步药敏性评估提供了坚实的基础。该模型还被发现与评估其他自然灾害有关,例如落石,雪崩和洪水。 ud ud该模型允许在给定用户标准的情况下自动划定源区域,并基于各种传播算法和评估传播程度。简单的摩擦定律。我们开发了一种新的扩展算法,这是Holmgren方向算法的改进版本,它对DEM的细微变化不太敏感,并且避免了过通道化,因此产生了更实际的范围。 ud ud用户可以使用各种算法,这使其适用于各种应用程序和数据集可用性。在可能的数据集中,DEM是源区域轮廓和传播评估真正需要的唯一数据集。其质量对于结果准确性至关重要。我们认为10 m DEM分辨率是处理时间和结果质量之间的良好折衷。但是,在分辨率为25 m的低质量DEM的基础上,仍可获得有价值的结果。

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