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Article ? AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button. Tier 2 ? Original research — experimental, observational, or case-control study. Direct primary evidence. Environmental Sources Marine & Wildlife Remediation Sign in to save

Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments

Frontiers in Earth Science 2019 3 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 30 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Janet C. Richardson, David M. Hodgson, Paul Kay David M. Hodgson, David M. Hodgson, Paul Kay David M. Hodgson, Paul Kay Paul Kay Paul Kay Paul Kay Paul Kay David M. Hodgson, Paul Kay Paul Kay Paul Kay David M. Hodgson, David M. Hodgson, David M. Hodgson, Ben Aston, David M. Hodgson, Andrew Walker, David M. Hodgson, Paul Kay Paul Kay David M. Hodgson, Paul Kay Paul Kay

Summary

Using satellite imagery and climate modeling, researchers mapped how sediment erodes and disperses through a managed river catchment in Yorkshire, England. Better forecasting of sediment and pollutant transport can help water managers reduce contamination of drinking water sources.

Study Type Environmental

Satellite imagery and climate change projections improve our ability to map and forecast sediment sources and transport pathways at high resolution, which is vital for catchment management. Detailed assessment of temporal and spatial changes in erosion risk are key to forecasting pollutant dispersal, which affects water treatment costs and ecology. Outputs from scenario modelling of the River Derwent catchment, Yorkshire, indicate clear spatial and temporal trends in erosion risk. These trends are not picked up by using traditional methods, which rely on static land use maps. Using satellite-derived maps show that lower resolution traditional land-use maps relatively underestimate erosion risk in terms of location of source areas and seasonal variation in erosion risk. Seasonal variation in agricultural practices can be assessed by incorporating bare land variation into models, which show that erosion risk is relatively overestimated if all agricultural land is assumed to have the same character. Producing seasonal land use maps also allows the assessment of temporal variation in rainfall, which in combination with climate change projections allows for adaptable management plans. The bias in gradient in modelling, which assumes that high gradients result in greater sediment erosion risk, show that traditional models underestimate the contribution of erosion risk in lowland areas. This is compounded by the absence of artificial drainages in topographic rasters, which increases connectivity in lowland areas. By producing end member scenarios, model outputs help to inform where catchment management should be targeted, and whether seasonal interventions should be implemented. This information is vital to communicate with landowners when they implement catchment management practices, such as sediment traps and earth bunds. Adaption of erosion risk modelling practices is urgently needed in order to quantify the impact of artificial interference in which human activity disrupts ‘natural’ sediment source-to sink configurations, such as integrating new pathways and stores due to land use change and management. Furthermore, integrating higher resolution catchment modelling and improved seasonal forecasts of pollutant flux to oceans will permit more effective interventions. This paper highlights single output erosion risk maps are not effective to inform catchment management.

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