integrated land-water risk analysis for the protection of sensitive catchments from diffuse...
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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?
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The nature of diffuse pollution Diffuse pollution has some special
characteristics:spatially-distributedspatially-structuredtime-varyingabove 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
Rainfall Pattern
Land Cover
Slope
Upslope Area
Classical Wetness Index
Surface Flow Index
(Connection Risk)
Channels
Point Scale Risk
Route risk through catchment (concn
and dilute)
Stream Power
Erodability
Risk Map
DEM
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 km2 280 sites per year Salmon parr and fry Trout parr and fry
Spatial water quality sampling 211 samples collected within 3 hours Across 614 km2 Analysed for Nitrogen, Phosphorus and Potassium
Potassium results presented today
Acknowledgement: Eden Rivers Trust
Electro Fishing Results
Fry and Risk
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
0-20% 20-40% 40-60% 60-80% 80-100%
Connectivity band
Sa
lmo
nid
fry
co
un
ts
Connectivity plus finesediment risk
Connectivity only
Potassium and Risk
Risk_1
ln (
K)
0.00900.00850.00800.00750.0070
3
2
1
0
-1
Scatterplot of ln (K) vs Risk_1
Using only the surface flow indexNo land use weighting
Assessment of land cover riskuncertainty
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 NO3
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
0.06 0.07 0.08 0.09 0.10
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Improved pasture
0.06 0.07 0.08 0.09 0.10
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Extensive grazing
0.06 0.07 0.08 0.09 0.10
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Moorland
0.06 0.07 0.08 0.09 0.10
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Peat
0.06 0.07 0.08 0.09 0.10
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Arable
0.06 0.07 0.08 0.09 0.10
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Woodland
Training land use weightings on water quality (nitrate)
0 0.05 0.1 0.15 0.2 0.25 0.30
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Improved pasture
0 0.05 0.1 0.15 0.2 0.25 0.30
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Extensive grazing
0 0.05 0.1 0.15 0.2 0.25 0.30
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Moorland
0 0.05 0.1 0.15 0.2 0.25 0.30
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Peat
0 0.05 0.1 0.15 0.2 0.25 0.30
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Arable
0 0.05 0.1 0.15 0.2 0.25 0.30
0.2
0.4
0.6
0.8
1
OF
Wei
ghtin
g
Woodland
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
