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

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...

Full description

Bibliographic Details
Main Authors: Janet Cristine Richardson, David Mark Hodgson, Paul Kay, Benjamin J. Aston, Andrew C. Walker
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-11-01
Series:Frontiers in Earth Science
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/feart.2019.00277/full
id doaj-9d9ce780bacc42b7acf6fad4b4b3f9f4
record_format Article
spelling doaj-9d9ce780bacc42b7acf6fad4b4b3f9f42020-11-25T01:11:15ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632019-11-01710.3389/feart.2019.00277470726Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed CatchmentsJanet Cristine Richardson0David Mark Hodgson1Paul Kay2Benjamin J. Aston3Andrew C. Walker4Stratigraphy Group, School of Earth and Environment, University of Leeds, Leeds, United KingdomStratigraphy Group, School of Earth and Environment, University of Leeds, Leeds, United KingdomSchool of Geography, University of Leeds, Leeds, United KingdomYorkshire Water, Clean Water and Catchment Strategy, Bradford, United KingdomYorkshire Water, Clean Water and Catchment Strategy, Bradford, United KingdomSatellite 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 modeling 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 modeling, 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 modeling 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 modeling 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.https://www.frontiersin.org/article/10.3389/feart.2019.00277/fullerosion risksatellite imageryseasonalityland usesediment budgetdiffuse pollution
collection DOAJ
language English
format Article
sources DOAJ
author Janet Cristine Richardson
David Mark Hodgson
Paul Kay
Benjamin J. Aston
Andrew C. Walker
spellingShingle Janet Cristine Richardson
David Mark Hodgson
Paul Kay
Benjamin J. Aston
Andrew C. Walker
Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments
Frontiers in Earth Science
erosion risk
satellite imagery
seasonality
land use
sediment budget
diffuse pollution
author_facet Janet Cristine Richardson
David Mark Hodgson
Paul Kay
Benjamin J. Aston
Andrew C. Walker
author_sort Janet Cristine Richardson
title Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments
title_short Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments
title_full Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments
title_fullStr Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments
title_full_unstemmed Muddying the Picture? Forecasting Particulate Sources and Dispersal Patterns in Managed Catchments
title_sort muddying the picture? forecasting particulate sources and dispersal patterns in managed catchments
publisher Frontiers Media S.A.
series Frontiers in Earth Science
issn 2296-6463
publishDate 2019-11-01
description 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 modeling 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 modeling, 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 modeling 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 modeling 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.
topic erosion risk
satellite imagery
seasonality
land use
sediment budget
diffuse pollution
url https://www.frontiersin.org/article/10.3389/feart.2019.00277/full
work_keys_str_mv AT janetcristinerichardson muddyingthepictureforecastingparticulatesourcesanddispersalpatternsinmanagedcatchments
AT davidmarkhodgson muddyingthepictureforecastingparticulatesourcesanddispersalpatternsinmanagedcatchments
AT paulkay muddyingthepictureforecastingparticulatesourcesanddispersalpatternsinmanagedcatchments
AT benjaminjaston muddyingthepictureforecastingparticulatesourcesanddispersalpatternsinmanagedcatchments
AT andrewcwalker muddyingthepictureforecastingparticulatesourcesanddispersalpatternsinmanagedcatchments
_version_ 1725172031832457216