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Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.
Humus forms are a distinctive morphological indicator of soil organic matter decomposition. The spatial distribution of humus forms depends on environmental factors such as topography, climate and vegetation. In montane and subalpine forests, environmental influences show a high spatial heterogeneity, which is reflected by a high spatial variability of humus forms. This study aims at examining spatial patterns of humus forms and their dependence on the spatial scale in a high mountain forest environment (Val di Sole/Val di Rabbi, Trentino, Italian Alps). On the basis of the distributions of environmental covariates across the study area, we described humus forms at the local scale (six sampling sites), slope scale (60 sampling sites) and landscape scale (30 additional sampling sites). The local variability of humus forms was analyzed with regard to the ground cover type. At the slope and landscape scale, spatial patterns of humus forms were modeled applying random forests and ordinary kriging of the model residuals. The results indicate that the occurrence of the humus form classes Mull, Mullmoder, Moder, Amphi and Eroded Moder generally depends on the topographical position. Local-scale patterns are mostly related to micro-topography (local accumulation and erosion sites) and ground cover, whereas slope-scale patterns are mainly connected with slope exposure and elevation. Patterns at the landscape scale show a rather irregular distribution, as spatial models at this scale do not account for local to slope-scale variations of humus forms. Moreover, models at the slope scale perform distinctly better than at the landscape scale. In conclusion, the results of this study highlight that landscape-scale predictions of humus forms should be accompanied by local- and slope-scale studies in order to enhance the general understanding of humus form patterns.
Humus forms are a distinctive morphological indicator of soil organic matter decomposition. The spatial distribution of humus forms depends on environmental factors such as topography, climate and vegetation. In montane and subalpine forests, environmental influences show a high spatial heterogeneity, which is reflected by a high spatial variability of humus forms. This study aims at examining spatial patterns of humus forms and their dependence on the spatial scale in a high mountain forest environment (Val di Sole/Val di Rabbi, Trentino, Italian Alps). On the basis of the distributions of environmental covariates across the study area, we described humus forms at the local scale (six sampling sites), slope scale (60 sampling sites) and landscape scale (30 additional sampling sites). The local variability of humus forms was analyzed with regard to the ground cover type. At the slope and landscape scale, spatial patterns of humus forms were modeled applying random forests and ordinary kriging of the model residuals. The results indicate that the occurrence of the humus form classes Mull, Mullmoder, Moder, Amphi and Eroded Moder generally depends on the topographical position. Local-scale patterns are mostly related to micro-topography (local accumulation and erosion sites) and ground cover, whereas slope-scale patterns are mainly connected with slope exposure and elevation. Patterns at the landscape scale show a rather irregular distribution, as spatial models at this scale do not account for local to slope-scale variations of humus forms. Moreover, models at the slope scale perform distinctly better than at the landscape scale. In conclusion, the results of this study highlight that landscape-scale predictions of humus forms should be accompanied by local- and slope-scale studies in order to enhance the general understanding of humus form patterns.