Search

In the context of elevated concerns related to nuclear accidents and warfare, the lessons learnt from the Fukushima Daiichi Nuclear Power Plant accident in 2011 are important. In particular, Japanese authorities implemented an ambitious decontamination program to reduce the air dose rate in order to facilitate the return of the local inhabitants to previously evacuated areas. This approach contrasts the strategy adopted in Chernobyl, where the most contaminated areas remain off limits. Nonetheless, the effectiveness of the Japanese decontamination strategy on the dispersion of radioactive contaminant fluxes across mountainous landscapes exposed to typhoons has not been quantified. Based on the unique combination of river monitoring and modeling in a catchment representative of the most impacted area in Japan, we demonstrate that decontamination of 16% of the catchment area resulted in a decrease of 17% of sediment-bound radioactive fluxes in rivers. Decontamination operations were therefore relatively effective, although they could only be conducted in a small part of the area due to the dominance of steep forested slopes. In fact, 67% of the initial radiocesium contamination was calculated to remain stored in forested landscapes, which may contribute to future downstream radiocesium dispersion during erosive events. Given that only a limited proportion of the initial population had returned in 2019 (~30%), it raises the question as to whether decontaminating a small percentage of the contaminated area was worth the effort, the price, and the amount of waste generated? [ABSTRACT FROM AUTHOR]

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Excessive sediment discharge in karstic regions can be highly disruptive to water treatment plants. It is essential for catchment stakeholders and drinking water suppliers to limit the impact of high sediment loads on potable water supply, but their strategic choices must be based on simulations integrating surface and groundwater transfers and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical descriptions for the modelling process, and they can be particularly complex to predict due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new cascade modelling approach with an erosion-runoff geographic information system (GIS) model (WaterSed) and a deep neural network. The model was used in the Radicatel hydrogeological catchment (106 km 2 in Normandy, France), where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five design storms under current land use and compared to four land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices, and coupling of eco-engineering/best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes (positive dye tracing) were used as input data for the deep neural network model. The model structure was found by a classical trial-and-error procedure, and the model was trained on 2 significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly backward-chaining nested cross-validation revealed that the model is able to generalize on new datasets. Simulations made for the four land use scenarios suggested that ploughing up 33 % of grasslands would increase sediment discharge at the water treatment plant by 5 % on average. By contrast, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the ranges of 10 %–44 % and 24 %–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 % to 80 %). The cascade modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plants under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers. [ABSTRACT FROM AUTHOR]

Excessive sediment discharge at karstic springs and thus, water treatment plants, can be highly disruptive. It is essential for catchment stakeholders and drinking water supplier to reduce the impact of sediment on potable water supply, but their strategic choices must be based on simulations, integrating surface and groundwater transfers, and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical description for the modelling process, and they can be seen as a black-box due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new coupled modelling approach with an erosion-runoff GIS model (WaterSed) and a deep neural network. The model was used in the Radicatel catchment (106 km2 in Normandy, France) where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five designed storm projects under current land use and compared to three land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes were used as input data for the deep neural network model. The model structure was found by a classical trial and error procedure, and the model was trained on two significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly-backward chaining nested cross validation revealed that the model is able to generalize on new datasets. Simulations made for the three land use scenarios suggested that ploughing up 33 % of grasslands would not increase significantly sediment discharge at the water treatment plant (5 % in average). In the opposite, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the range of 10-44 and 24-61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 to 80 %). The coupled modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plant under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers. [ABSTRACT FROM AUTHOR]