Integrated land-water risk analysis for the protection of sensitive catchments from diffuse pollution Reaney S M (1&2), Lane S N (1), Heathwaite A L (2) and Dugdale L (1&3) (1) Department of Geography, Durham University, UK (2) Centre for Sustainable Water Management, Lancaster Environment Centre, Lancaster, UK (3) Eden Rivers Trust, UK

What to do where? ? ? ? ??

The nature of diffuse pollution Diffuse pollution has some special characteristics: spatially-distributed spatially-structured time-varying above ground and below ground The source of a in-stream problem may be Extensive Hidden from view

The SCIMAP approach Based on the approach Risk + Connection = Problem Focus on the connectivity Integrates long term behaviour Based on a probabilistic framework Considers surface runoff and near surface flows Integrated consideration of uncertainty

Surface Flow Connectivity

Real World Example of Connected and Disconnected Areas

Example Application of SCIMAP – Fine Sediment The River Eden Catchment, UK

Calculation of a Fine Sediment Risk Map

Field scale problem identification

Testing of the approach River Eden catchment Electrofishing Annual sampling by Environment Agency and the Eden Rivers Trust Across 2,309 km sites per year Salmon parr and fry Trout parr and fry Spatial water quality sampling 211 samples collected within 3 hours Across 614 km 2 Analysed for Nitrogen, Phosphorus and Potassium Potassium results presented today

Acknowledgement: Eden Rivers Trust Electro Fishing Results

Fry and Risk

Potassium and Risk Using only the surface flow index No land use weighting

Assessment of land cover risk uncertainty Sensitivity of the approach to land cover risk parameterisation GLUE type framework 30,000 parameter sets investigated Uniform distribution No assumed relationships between parameters Assessed against the electro-fishing data for 2002 Spatial water quality sampling for NO 3

Uncertanity results presentation Determine an objective function (OF) Find the best OF values (minimum 10) and work out mean and standard deviation of parameter values that give best results Add in next best OF Plot the weightings against the objective function

Training land use weightings on salmonid fry

Training land use weightings on water quality (nitrate)

Expression of uncertainty in the risk maps The fittest 0.1% parameter sets used for the uncertainty analysis Mean and coefficient of variation calculated Colour of the in stream points determined by the mean Size of the points related to the variation in the sample results

Thin green lines = low risk but low certainty Wide red lines = high risk and high certainty

Conclusions SCIMAP offers a risk mapping framework Explicit handling of spatial risk connectivity Based on available data Simple to apply to new locations Low cost Integrated assessment of parameter uncertainty Currently being tested With physical and ecological data Uncertainty analysis of model structural options Flow routing, slope determination, rescaling of risk, etc Will be expanded to consider Nitrogen Phosphorus

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