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?

Engineering NewsReport 1993

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

Thank you for your attention!http://www.shef.ac.uk/mem.html

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

Step 5: BBN application

Arable land

Livestock

PO4 load

Managed grassland

C

B

A

Conditional probabilitiesfor PO4 conc. on water body scale:

C

B

A

C

B

A

C

B

A