ABSTRACT

High-resolution modelling is needed to improve the understanding and management of storm water in cities. It requires data, which is not always available; hence the growing importance of handling missing data. Here, we use impervious areas in cities as case study. They are responsible for rapid runoff that can generate surface flooding. A methodology to handle such binary missing data relying on scale-invariant properties is presented. It uses a previous study, which showed in 10 peri-urban areas that imperviousness exhibits scale-invariant features from metres to kilometres, to generate realistic scenarios for the missing impervious data. More precisely, fractal fields are commonly simulated thanks to a simple binary multiplicative cascade process (β-model). Here we condition it to the available data. Numerical simulations are used to confirm theoretical expectations. They are then implemented to infill missing impervious data on a 3 km² catchment and the corresponding uncertainty is quantified.

摘要

需要高分辨率建模来提高对城市雨水的理解和管理。它需要数据，而数据并不总是可用的；因此处理缺失数据的重要性日益增加。在这里，我们使用城市中的不透水区域作为案例研究。它们负责导致地表洪水的快速径流。提出了一种依赖于尺度不变属性来处理此类二进制缺失数据的方法。它使用之前的一项研究，该研究表明，在 10 个城市周边地区，不透水表现出从米到公里的尺度不变特征，为缺失的不透水数据生成逼真的场景。更准确地说，分形场通常由于简单的二元乘法级联过程（β-模型）。在这里，我们将其条件化为可用数据。数值模拟用于确认理论预期。然后实施它们以填充 3 km ²集水区缺失的不透水数据，并量化相应的不确定性。