{"numFound":7,"page":1,"rows":20,"url":"http://catalogue.ceh.ac.uk/cmp/documents?facet=impWaterPollutant%7CPhosphorus&facet=impCaMMPIssues%7CConfidence+levels&facet=impCaMMPIssues%7CManagement+outcome&facet=impScale%7Ccatchment&facet=impScale%7CRiver+reach&facet=impTopic%7CRiver+flow+and+networks&facet=impTopic%7CWater+quality","results":[{"catalogue":"cmp","description":"Despite the uncertainty in future climate and land use, large-scale measures put in place today to improve water quality are likely to remain effective in the future in northeast Scotland. Dynamic models can provide useful indications of the likely lag time between the implementation of measures and improvements in water quality.","documentType":"IMP_DOCUMENT","identifier":"7f45279b-5818-4667-8a6e-3439f26c6e44","impCaMMPIssues":["Transferability","Pollution Source","Management outcome","Climate Change"],"impDataType":["Time series"],"impScale":["River reach","Catchment"],"impTopic":["Water quality","Non-agricultural pollution","Climate scenarios"],"impWaterPollutant":["Sediment","Phosphorus","Nitrogen"],"incomingCitationCount":0,"keyword":["Water quality","Transferability","Time series","Sediment","River reach","Pollution Source","Phosphorus","Non-agricultural pollution","Nitrogen","Management outcome","Climate scenarios","Climate Change","Catchment"],"recordType":"Case Study","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/7f45279b-5818-4667-8a6e-3439f26c6e44"],"resourceType":"Case Study","state":"published","title":"Case Study 4: Effectiveness of pollution control measures under scenarios of future climate and land cover change at the catchment scale.  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LUCI then simulates flow and nutrient accumulation over the landscape, with areas routing through these mitigating features mapped as “mitigated” i.e. not hydrologically connected to the stream. Hydrologically connected areas of the landscape with large accumulations of water or pollutants are mapped as having potential to benefit from changes inland cover or management which could provide mitigating services. Hydrological routing is also used to map annual flow and average loading and concentration of N and P at each point on the river network. For agricultural production and carbon, LUCI compares the services provided by the current utilisation of the landscape to estimates of its potential capability. For each service, the model then processes this data to identify areas where change might improve service provision, and where current service provision is good or lacks potential to improve. Trade-offs between outputs for the individual ecosystem services are modelled and mapped, with an option for the user to preferentially weight services of higher importance.\n","documentType":"IMP_DOCUMENT","identifier":"adead10b-a6a0-45fa-b350-acd16b23c3fe","impCaMMPIssues":["Transferability","Other Services","Management outcome","Confidence levels"],"impDataType":["Geospatial / Map"],"impScale":["Regional","Plot","National","Field","Farm","Catchment","catchment"],"impTopic":["Water quality","Soil","River flow and networks","Land use","Land cover","Greenhouse gas fluxes","Climate scenarios","Catchments and topography","Biodiversity"],"impWaterPollutant":["Sediment","Phosphorus","Nitrogen"],"incomingCitationCount":0,"keyword":["Water quality","Transferability","Soil","Sediment","River flow and networks","Regional","Plot","Phosphorus","Other Services","Nitrogen","National","Management outcome","Land use","Land cover","Greenhouse gas fluxes","Geospatial / Map","Field","Farm","Confidence levels","Climate scenarios","Catchments and topography","Catchment","Biodiversity"],"recordType":"Model (UKCEH)","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/adead10b-a6a0-45fa-b350-acd16b23c3fe"],"resourceType":"Model (UKCEH)","state":"published","title":"LUCI  (Land Utilisation & Capability Indicator)","version":1.0,"view":["public"],"shortenedDescription":"LUCI explores the capability of a landscape to provide a variety of ecosystem services, such as agricultural production, flood and diffuse pollutant mitigation, carbon sequestration, habitat provision etc. The model is a second generation extension and accompanying..."},{"catalogue":"cmp","description":"SAGIS was developed for UKWIR, with support from the Environment Agency, SEPA and Natural England, to analyse sources of chemicals at the river basin scale and identify measures to improve river, lake and estuary water quality. National data on the source of chemicals from a range of point and diffuse sectors are processed to create inputs to the water quality model SIMCAT, and the outputs used to create databases and visualisations of predicted apportioned water quality.","documentType":"IMP_DOCUMENT","identifier":"57c40da0-2dc7-43a1-8291-f9ae2fd0a18d","impCaMMPIssues":["Transferability","Pollution Source","Confidence levels"],"impScale":["River reach","Regional","National","Catchment","national"],"impTopic":["Water quality","Non-agricultural pollution","Land use"],"impWaterPollutant":["Sediment","Phosphorus","Nitrogen"],"incomingCitationCount":0,"keyword":["Water quality","Transferability","Sediment","River reach","Regional","Pollution Source","Phosphorus","Non-agricultural pollution","Nitrogen","National","Land use","Confidence levels","Catchment"],"recordType":"Model (UKCEH)","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/57c40da0-2dc7-43a1-8291-f9ae2fd0a18d"],"resourceType":"Model (UKCEH)","state":"published","title":"SAGIS (Source Apportionment GIS)","version":1.0,"view":["public"],"shortenedDescription":"SAGIS was developed for UKWIR, with support from the Environment Agency, SEPA and Natural England, to analyse sources of chemicals at the river basin scale and identify measures to improve river, lake and estuary water quality. National data on the source of chemicals..."},{"catalogue":"cmp","description":"INCA-P operates at a daily time step, tracking the stores and fluxes of water, sediment, dissolved and particulate P in both the land and in-stream phases of a river catchment. The model is spatially ‘semi-distributed’: the water course is split into reaches with associated sub-catchments. Two spatial set-ups are possible – the traditional set-up, in which there is a single main stem, or a ‘branched’ set-up. The latter allows in-stream processes, effluent inputs and residence times in tributaries to be simulated, and can be particularly useful in larger catchments or complex river networks. Each sub-catchment is split into landscape classes – there can be as many of these as desired or warranted by the data resolution or needs of the study. Landscape classes are “functional units” and within each unit P inputs, plant P uptake, soils and flow pathways should be similar, though for convenience classes are often based on land use and/or soil type. All land-based processes are calculated for a generic 1 km2 cell for each landscape class within each sub-catchment. Water, sediment, total dissolved P (TDP) and particulate P (PP) outputs from the 1 km2 cell for each land class are multiplied by the land class area, and summed to provide total inputs from the sub-catchment to the reach. These inputs are assumed to enter the stream reach directly, rather than being routed spatially from one land class to another. Reach inputs are therefore from the land phase and from any upstream reaches.","documentType":"IMP_DOCUMENT","identifier":"dc139812-d332-44fb-8fc4-f9943652d1ef","impCaMMPIssues":["Transferability","Pollution Source","Management outcome","Confidence levels","Climate Change"],"impDataType":["Time series"],"impScale":["River reach","Catchment","catchment"],"impTopic":["Water quality","River flow and networks","Non-agricultural pollution","Land use","Climate scenarios","Catchments and topography"],"impWaterPollutant":["Sediment","Phosphorus"],"incomingCitationCount":0,"keyword":["Water quality","Transferability","Time series","Sediment","River reach","River flow and networks","Pollution Source","Phosphorus","Non-agricultural pollution","Management outcome","Land use","Confidence levels","Climate scenarios","Climate Change","Catchments and topography","Catchment"],"recordType":"Model (UKCEH)","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/dc139812-d332-44fb-8fc4-f9943652d1ef"],"resourceType":"Model (UKCEH)","state":"published","title":"INCA-P  (Integrated Catchments Model - Phosphorus dynamics)","version":1.0,"view":["public","jaccos"],"shortenedDescription":"INCA-P operates at a daily time step, tracking the stores and fluxes of water, sediment, dissolved and particulate P in both the land and in-stream phases of a river catchment. The model is spatially ‘semi-distributed’: the water course is split into reaches with associated..."},{"catalogue":"cmp","description":"This case study examines the sensitivity / uncertainty in outputs of models at various scales resulting from differences or uncertainties in models and input driving data. Three cases will be considered: A) uncertainties arising from the model structure being used; B) uncertainties arising from the data being used; and C) uncertainties arising from drivers used in future scenarios.","documentType":"IMP_DOCUMENT","identifier":"bba51e3c-9808-4dbb-951a-30a127b8a4b3","impCaMMPIssues":["Confidence levels","Climate Change"],"impScale":["River reach","Catchment"],"impTopic":["Water quality","Soil","Climate scenarios","Biodiversity"],"impWaterPollutant":["Phosphorus","Nitrogen","Faecal Indicator Organisms"],"incomingCitationCount":0,"keyword":["Water quality","Soil","River reach","Phosphorus","Nitrogen","Faecal Indicator Organisms","Confidence levels","Climate scenarios","Climate Change","Catchment","Biodiversity"],"recordType":"Case Study","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/bba51e3c-9808-4dbb-951a-30a127b8a4b3"],"resourceType":"Case Study","state":"published","title":"Case Study 6: Effects of input data quality and quantity on evaluation of land management policies and agri-environment interventions at catchment to national scales.","version":1.0,"view":["public","jaccos"],"shortenedDescription":"This case study examines the sensitivity / uncertainty in outputs of models at various scales resulting from differences or uncertainties in models and input driving data. Three cases will be considered: A) uncertainties arising from the model structure being used;..."},{"catalogue":"cmp","description":"QUESTOR modelling shows that all the mitigation methods applied can reduce the amount of Chlorophyll-a predicted to occur in the river. The extent of the reduction is different depending on the data used to drive the QUESTOR model. The effect of increasing the shading (from 20-60%) declines downstream as the river becomes too wide for trees to provide shade.","documentType":"IMP_DOCUMENT","identifier":"edddc8d0-4389-4d6d-981b-e076fc9034f1","impCaMMPIssues":["Other Services","Confidence levels"],"impScale":["River reach"],"impTopic":["Water quality"],"impWaterPollutant":["Phosphorus","Nitrogen","Biological quality"],"incomingCitationCount":0,"keyword":["Water quality","River reach","Phosphorus","Other Services","Nitrogen","Confidence levels","Biological quality"],"recordType":"Case Study","resourceIdentifier":["https://catalogue.ceh.ac.uk/id/edddc8d0-4389-4d6d-981b-e076fc9034f1"],"resourceType":"Case Study","state":"published","title":"Case Study 5: Uncertainty in ecological responses to water quality control measures at the river basin scale. ","version":1.0,"view":["public","jaccos"],"shortenedDescription":"QUESTOR modelling shows that all the mitigation methods applied can reduce the amount of Chlorophyll-a predicted to occur in the river. The extent of the reduction is different depending on the data used to drive the QUESTOR model. The effect of increasing the shading..."},{"catalogue":"cmp","description":"QUESTOR represents a river as a series of river reaches within which physical, chemical and biological processes operate.. The initial selection of reaches is based on the location of \tconfluences, diffuse (catchment) sources, discharge points, abstraction points, monitoring sites, and weirs. Once this has been done, long reaches, say those greater than 10km, can be sub-divided. The flow and water quality out of a reach are calculated based on a mass balance of flows into the head of the reach plus contributions from point sources and diffuse sources minus abstractions. The determinands and processes modelled are not fixed within the QUESTOR modelling environment, but are tailored to the particular application. The QUESTOR model has sets of equations to simulates nitrogen and phosphorus species, pH and suspended sediment, the aspects of water quality of particular interest are those determinands indicative of the impacts on the river ecosystem, namely temperature, chlorophyll-a (Chl-a), BOD and DO. Chl-a is used as a surrogate for the algae using a fixed stoichiometry model whereby the ratio chl-a:C:N:P is 1:50:10:1. The growth of algae is limited by light, temperature and nutrient concentrations.  The model was developed to help catchment managers assess the impact of their actions e.g. changing discharges consents, abstracting more water from a river or building a flood relief channel. The aim was to produce a physically realistic representation of a number of important measures of river water quality while being practical to run using generally available datasets. \nQUESTOR is based on the earlier in-stream water quality models IHQM (Institute of Hydrology water Quality Model) and QUASAR (Quality Simulation Along Rivers). QUASAR was sold commercially for both VAX and PC systems, and is now available free of charge, but unsupported, as PC-QUASAR. 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