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The Macro-Ecological Model. A tool for addressing the challenges of integrated catchment management. Annelie Holzkaemper & Vikas Kumar. 3 rd annual conference of CSC. University of Sheffield. MEM Project team. Academics: - PowerPoint PPT Presentation
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The Macro-Ecological Model
A tool for addressing the challenges of integrated catchment management
3rd annual conference of CSC University of Sheffield
Annelie Holzkaemper & Vikas Kumar
MEM Project team
Academics: David N. Lerner, Lorraine Maltby, Philip Warren, John Wainwright, Clive W. Anderson, Mahesan Niranjan, Bob Harris
Researchers: Vikas Kumar, Ben Surridge, Achim Paetzold
EA Project Steering Group: Colin Gibson, Stuart Kirk, Hilary Aldridge, Aileen Kirmond, Viki Hirst, Mark Diamond, Craig Elliot, Paul Logan
Why do we need the MEM?
• to support high-level decision making in integrated catchment management
• to support communication among different planning functions within the EA
What is the MEM?
Model Inputsmanagement
scenarios
Model Outputschanges in status of
objectives
Management objectives
ChemicalEnvironment
BiologicalEnvironment
The MEM
PhysicalEnvironment
Social and economic
Environment
Ecol. StatusChem. Status
Flood risk
How do we develop the MEM?
Bayesian belief network (BBN)
RainSprinkler
Wet grass
S R P(W=F) P(W=T)
F F 1.0 0.0
T F 0.1 0.9
F T 0.1 0.9
T T 0.01 0.99
How do we develop the MEM?
Step 1: Identification of index variables to represent objectives
Ecological status
Biological quality Physico-chemical quality
Phosphate concentrationGQA biology Number of properties affected
Flood risk
How do we develop the MEM?
Step 2: Development of conceptual sub-model
AppliedP
PO4 load to river
Soil P
River flow
PO4
from CSO’s
PO4 in indu-strial effluent
PO4 inSTW effluent
PO4 conc.Transformation
PO4 from degrad.
Organicpollution
Riparian buffer
Agriculturaldrainage
Soil type
Sediment-bound PO4
DissolvedP04
Land erodability
Pathway
Rainfall
Bed&Bankerosion
Field buffers
Restored wetland
Sedi.,Adsorp., Precip.
Plant uptake
Channel vegetation
C-Fconnectivity
Secondary channels
Embankments
Channel maintenance
Urban storm-water runoff
Urban sealingThreshold rain-
fall events
CSO storage capacity
Microbial uptake
PO4 from septic tanks
PO4 in urban runoff
Precipitation Urban sealing
How do we develop the MEM?
Step 3: Simplification of conceptual sub-model
Livestock
PO4 load to river
River dischargePO4 load fromSTW effluent
PO4 conc.
Riparian buffer
Restored wetland
Embankments
PO4 load fromagriculture
Arable land
Managed grassland
PO4 load from CSO’s
SIMCATPSYCHIC
How do we develop the MEM?
Livestock
PO4 load to river
River dischargePO4 load fromSTW effluent
PO4 conc.
Riparian buffer
Restored wetland
Embankments
PO4 load fromagriculture
Arable land
Managed grassland
PO4 load from CSO’s
Step 4: Specification of sub-model
O2
Invertebrates
Microbialactivity
Algae
Light
Biological quality-module
How do we develop the MEM?
PO4 conc.PO4 load
Water quality-module
Discharge
Land use
Rainfall
Abstraction
Hydrology-module
Step 5: Merging of sub-models
How could the MEM be applied?
Baseline Scenario: • Current conditions
Number of water bodies
passing standards
GQ
Ab
io
PO
4
Number of properties
affected by 100-year
flood
Flood risk
MEM prediction:
Ecological status
How could the MEM be applied?
Management-Scenario 1: • 30% reduction of PO4 inputs from sewage treatment works• Introduction of embankments in 50% of lowland water bodies
Number of water bodies
passing standards
Number of properties
affected by 100-year
flood
Flood risk
MEM prediction:
GQ
Ab
io
PO
4
Ecological status
How could the MEM be applied?
Management-Scenario 2: • 20% reduction in number of livestock• Introduction of restored wetlands in 10% of lowland water bodies• Introduction of riparian buffers in 20% of lowland water bodies
GQ
Ab
io
PO
4MEM prediction:
Number of water bodies
passing standards
Number of properties
affected by 100-year
flood
Flood riskEcological status
Summary
• Objectives tools are required to assist in integrated catchment management
• Decision support for integrated management can be provided through integrated modelling
• The BBN is a suitable approach for integrating knowledge from different resources
• The MEM will predict impacts of management scenarios on multiple objectives
How do we develop the MEM?
GQA biology
Ammonia
Concrete substrate
Algae
Weirs
BOD
Light
Phosphate
Shading Turbidity
Flow variability
EmbankmentsRestored wetlands
River discharge
PO4 load to river
PO4 load fromSTW effluent
PO4 load from CSO’s
PO4 load fromagriculture
Livestock
Riparian buffer
Arable land
Managed grassland
Flood extentChannel
roughness
O2
Properties withinfloodplain
Properties affectedby flood
How do we develop the MEM?
GQA biology
O2
Ammonia
Concretesubstrate
Algae
Weirs
BOD
Light
Phosphate
Shading Turbidity
Flow variability
Step 1: Model identification
Example: PSYCHIC
Inputs:
Land useLivestock numbersArea of crop typesPrecipitationTemperatureSoil typeSlopeProximity to surface waters
Output:
P load delivered to riverLand useLivestock numbersArea of crop typesPrecipitationTemperatureSoil typeSlopeProximity to surface waters
Step 2: Multiple model runs
Run 0:
Run 1:
3.1…1015wbn
……………
5.2…6010wb1
PO4 load
…Livestock number
% Arable
Run 0
InputsOutput
2.5…515wbn
……………
3.9…3010wb1
PO4 load
…Livestock number
% Arable
Run 1
InputsOutput
Current conditions
50% reduction in number of livestock
Step 3: BBN structure definition
Arable land
Livestock
PO4 loadSoil type
Slope Proximity
PSYCHIC inputs
PSYCHIC output
Managed grassland
Precipitation
Temperature
Step 4: BBN specification
Arable Livestock Grassl. Temp Precip. … PO4 load
10 60 50 10 556 … 5.2
… … … … … … …
15 10 30 10.1 438 … 3.1
Arable land
Livestock
PO4 load
Managed grassland