Sustainable flood risk management encompasses the implementation of nature-based solutions to mitigate flood risk. These measures include the establishment of land use types with a high (e.g., forest patches) or low (e.g., sealed surfaces) water retention and infiltration capacity at strategic locations in the catchment. This paper presents an approach for assessing the relative impact of such land use changes on economic flood damages and associated risk. This spatially explicit approach integrates a reference situation, a flood damage model, and a rainfall-runoff model considering runoff re-infiltration and propagation to determine relative flood risk mitigation or increment related to the implementation of land use change scenarios. The applicability of the framework is illustrated for a 4800 ha undulating catchment in the region of Flanders, Belgium, by assessing the afforestation of 187.5 ha (3.9 %), located mainly in the valleys, and sealing of 187.5 ha, situated mainly at higher elevations. These scenarios result in a risk reduction of 57 % (EUR 100 000) for the afforestation scenario and a risk increment of <1 % (EUR ∼ 500) for the sealing scenario.

Soil sealing increases surface runoff in a watershed and decreases infiltration into the soil. Consequently, urbanization poses a significant challenge for watershed management to mitigate faster runoff accumulation downstream and associated floods. Hydrological models are often employed to assess the impact of land-use dynamics on flood events. Alternatively, data-driven approaches combining time series of land use geodatasets and georeferenced flooded zones also allow to assess the relationship between soil sealing and flood severity. This study presents such data-driven analysis using a spatially explicit archive of flooded areas dating back to 1988 in the Flanders region of Belgium, which is characterized by urban sprawl. This archived data, along with time series of rainfall and land use, were analyzed for three middle-sized river subbasins using two machine learning methods: boosted regression trees and support vector regression. The machine learning methods were found suitable for this type of analysis, since their flexibility allows for spatially explicit models with larger sample sizes. However, the relationship between soil sealing and flood volume and extent could not be conclusively confirmed by our models. This may be due to data limitations, such as the limited number of recorded historical floods, inaccuracies in recorded historical flood polygons and inconsistencies in the land use classifications. It is therefore stressed that continued consistent monitoring of floods and land use changes is required.

Sustainable flood risk management encompasses the implementation of nature-based solutions to mitigate flood risk. These measures include the establishment of land use types with a high (e.g. forest patches) or low (e.g. sealed surfaces) water retention and infiltration capacity at strategic locations in the catchment. This paper presents an approach for assessing the relative impact of such land use changes on economic flood damages and associated risk. This spatially explicit approach integrates a reference situation, a flood damage model and a rainfall-runoff model, considering runoff re-infiltration and propagation, to determine relative flood risk mitigation or increment related to the implementation of land use change scenarios. The applicability of the framework is illustrated for a 4800 ha undulating catchment in the region of Flanders, Belgium by assessing afforestation of 187.5 ha (3.9 %), located mainly in the valleys, and sealing of 187.5 ha, situated mainly at higher elevations. These scenarios result in a risk reduction of 57 % (100 856 €) for the afforestation scenario and a risk increment of < 1 % (535 €) for the sealing scenario.

Sustainable flood risk management encompasses the implementation of nature-based solutions to mitigate flood risk. These measures include the establishment of land use types with a high (e.g. forest patches) or low (e.g. sealed surfaces) water retention and infiltration capacity at strategic locations in the catchment. This paper presents an approach for assessing the relative impact of such land use changes on economic flood damages and associated risk. This spatially explicit approach integrates a reference situation, a flood damage model and a rainfall-runoff model, considering runoff re-infiltration and propagation, to determine relative flood risk mitigation or increment related to the implementation of land use change scenarios. The applicability of the framework is illustrated for a 4800 ha undulating catchment in the region of Flanders, Belgium by assessing afforestation of 187.5 ha (3.9 %), located mainly in the valleys, and sealing of 187.5 ha, situated mainly at higher elevations. These scenarios result in a risk reduction of 57 % (100 856 €) for the afforestation scenario and a risk increment of

Afforestation of certain areas of a river catchment can reduce the outflow of sediment from that catchment. We developed algorithms and software, called CAMF, to minimize the sediment outflow, based on a) a model for local sediment production, b) some parameters such as retention capacity and saturation threshold, c) a raster geo-database containing elevation data and land use. The software can also be adapted to model the effect of other actions than afforestation. We implemented both Single Flow Direction (SFD) and Multiple Flow Direction (MFD) methods to simulate flow transport. We analyze the differences between the two approaches. With the use of MFD methods the spatial interaction increases. As a consequence, the flow simulation with CAMF-MFD, executed in each iteration of the minimization procedure, has a substantially higher computational cost. The total execution time of CAMF can be prohibitively expensive for large geo-databases, since in each iteration only the cell(s) with the maximum sediment outflow reduction are selected.In each iteration of the minimization procedure, a sediment flow simulation is performed for each candidate cell. Since these simulations are independent of each other, we parallelized CAMF for multi-core processors using Open Multi-Processing (OpenMP) directives. Each thread executes the simulations for a subset of the candidate cells. To distribute the simulations over threads, dynamic scheduling is used to handle the imbalance due to the varying execution time of the simulations for each candidate cell. We also adapted the algorithm in two ways to accelerate the execution. First, in each iteration several cells, that produce nearly the same sediment yield reduction at the outlet, are selected. A threshold T determines the number of selected cells. Second, a complete ranking of all cells, with respect to their potential for sediment yield reduction by afforestation, is only computed every K iterations, while in intermediate iterations only N cells are ranked, namely those at the top of the previous complete ranking. The values for T, K and N substantially reduce the computational cost, while the solution quality is typically only slightly lower.We evaluated the performance of the accelerated variant for minimizing sediment outflow by afforestation using a raster geo-database of the Tabacay catchment (Ecuador), with a cell size of 30m × 30m. From a total of 73 471 non-null cells, 27 246 are candidate cells for afforestation.A high speedup is obtained for up to 28 cores (≈22), leading to a substantial reduction of the execution time. The accelerated variant produces nearly the same yield reduction at the outlet and selects almost the same cells than the original CAMF-MFD. The difference between the set of cells selected by both algorithms is measured by the relative spatial coincidence RSC. Results show that in all considered cases RSC > 99%.