Abstract
AbstractA topographic index (flood descriptor) that combines the scaling of bankfull depth with morphology was shown to describe the tendency of an area to be flooded. However, this approach depends on the quality and availability of flood maps and assumes that outcomes can be directly extrapolated and downscaled. This work attempts to relax these problems and answer two questions: (1) Can functional relationships be established between a flood descriptor and geomorphic and climatic‐hydrologic catchment characteristics? (2) If so, can they be used for low‐complexity predictive modeling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high‐resolution flood modeling results as training benchmarks, and on catchment characteristics. Elementary catchments of four river basins in Europe (Thames, Weser, Rhine, and Danube) serve as training data set, while those of the Rhône river basin in Europe serve as testing data set. Two return periods are considered, the 10‐ and 10,000‐year. Prediction of envelope flood extents and flood‐prone areas show that both models achieve high hit rates with respect to testing benchmarks. Average values were found to be above 60% and 80% for the 10‐ and the 10,000‐year return periods, respectively. In spite of a moderate to high false discovery rate, the critical success index value was also found to be moderate to high. It is shown that by relating classification outcomes to catchment characteristics, the prediction of envelope flood extents may be achieved for a given region, including ungauged basins.
Highlights
Floods pose a serious threat to individuals and communities as shown by disaster data found, for example, in the International Disaster Database (EM-DAT) of the Centre for Research of the Epidemiology of Disasters (CRED)
This work attempts to relax these problems and answer two questions: 1) Can functional relationships be established between a flood descriptor and geomorphic and climatic-hydrologic catchment characteristics? 2) If so, can they be used for low-complexity predictive modelling of envelope flood extents? Linear stepwise and random forest regressions are developed based on classification outcomes of a flood descriptor, using high-resolution flood modelling results as training benchmarks, and on catchment characteristics
The classification results are evaluated with two specific metrics based on a 2 × 2 binary contingency matrix that is constituted by values of: tp or the number of raster cells marked as flood-prone in both the segmented GFI and the benchmark flood hazard maps; fn or the number of raster cells marked as flood-free in the segmented GFI but marked as flood-prone in the benchmark; tn or the number of raster cells marked as flood-free in both the segmented GFI and the benchmark; and, fp or the number of raster cells marked as flood-prone in the segmented GFI but marked as flood-free in the benchmark
Summary
Floods pose a serious threat to individuals and communities as shown by disaster data found, for example, in the International Disaster Database (EM-DAT) of the Centre for Research of the Epidemiology of Disasters (CRED). Regression models were objectively calibrated and optimized for the prediction of flood extents and flood-prone areas In this way, two major drawbacks found in previous applications are relaxed: 1) the complete dependence on benchmarks, since in this approach they are only needed to train the models and not for every location where flood-prone areas are to be delineated, as is the case in Degiorgis et al (2012) for example; and, 2) the assumption of transferability of the TH without any physical basis, as is the case in Degiorgis et al (2012) for example, since here catchment geomorphic and climatic-hydrologic characteristics are used to regress the TH. A brief description of the workflow used to obtain the benchmark flood maps is explained; in section 4, the results obtained for each part of the methodological workflow, i.e., classification and physical characterization of elementary catchments, model development and prediction, are presented; and, in section 5, the main conclusions are drawn and future work is addressed
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