Government-led decontamination of agricultural land in the Fukushima accident (2011) region has lowered the on-site radiation risk considerably. From 2013 to early 2017, 11.9% of the land in the Fukushima disaster affected Niida watershed in Japan was remediated through topsoil removal. However, this resulted in a 237.1% increase in suspended sediment loads in the river for 2016 compared to 2013. In contrast, sediment loads decreased by 41% from 2016 to 2017; this can be attributed to the effect of natural vegetation restoration on sediment yield and transfer patterns (Bin et al., 2022). Since radiocaesium firmly binds to the clay minerals in the soil, it is inevitably transported along with the sediments downstream to the river systems. These observations confirm that rapid, spatially targeted interventions, such as revegetation, e.g., through afforestation, have the potential to decrease the magnitude and period of increased exports of contaminated sediments. The CAMF tool (Cellular Automata-based Heuristic for Minimizing Flow) (Vanegas et al., 2012) was originally designed to find the cells in a raster representation of a watershed for which afforestation would lead to a maximal reduction of sediment exports with minimal effort or cost while taking sediment flow from cell to cell into account. In our research, we adapted the CAMF tool to account for the radiocaesium budgets associated with the transported sediments. We applied the approach to the Niida catchment, where land-cover changes in upstream decontaminated regions are detected using drone imagery and linked to increased sediment loads in the Niida river using long-term river monitoring systems. For example In 2014, agricultural land (18.02 km2) was one of the major land uses in the regions where decontamination was ordered, resulting in increased sediment loads from 2014 to 2016. By recognizing both the on- and off-site impacts of the remediation interventions and their temporal dynamics, the modified CAMF tool offers scope for supporting the formulation of spatio-temporal schemes for the remediation of agricultural land. These schemes aim to decrease the radiation risk for downstream communities and minimize the potential recontamination of already decontaminated sites.

We present a method to select cells within a raster representation of a catchment for which an intervention, e.g. afforestation, maximizes the reduction of sediment at the catchment's outlet. This constrained binary optimization problem is solved in the CAMF (Cellular Automata based heuristic for Minimizing Flow) software by a steepest ascent hill-climbing method, safeguarding the spatial interaction between cells. Since its execution time can be prohibitively long for large data-sets, we present variants of the method to reduce the number of iterations and the cost of the iterations, and we exploit parallelization. We present a tuning algorithm for meta-parameters of the new algorithmic variants to balance the computational cost and the accuracy. Application to two catchments shows that the accelerations reduce the cost by a factor larger than 100 in comparison with the original CAMF. This approach is applicable to many types of interventions to control flow phenomena in catchments.

Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima prefecture, Japan, to identify 1,000 cells within decontaminated zones where immediate afforestation would have maximally reduced both sediment and residual ¹³⁷Cs export. The 1,000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and ¹³⁷Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.

Afforestation can mitigate the export of water, sediment, and dissolved or adsorbed contaminants to river systems, but identifying effective intervention sites requires accounting for multiple flow-related criteria and their spatial interactions. This paper presents a multi-criteria heuristic approach that extends CAMF (Cellular Automata-based Heuristic for Minimizing Flow), originally designed to select cells from a rasterized landscape for interventions that minimize sediment yield at target sites. We integrated the Distance-to-Ideal-Point (DIST2IP) algorithm in CAMF, enabling the selection of cells where intervention can minimize two or more flows simultaneously. The multi-criteria CAMF was applied ex post to the radioactively contaminated Niida river catchment, Fukushima Prefecture, Japan, to identify 1000 cells within decontaminated zones where the then immediate afforestation would have maximally reduced both sediment and residual ¹³⁷Cs export. The 1000 best cells selected by DIST2IP, representing 4% of the decontaminated cells, would have reduced sediment export by 22% and ¹³⁷Cs export by 6%. Selected cells are within the union of cells identified by the two single-criteria optimizations and are predominantly close to water bodies, confirming that blocking flow paths before they connect to the river system is most effective.