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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:

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The macro ecological model

The Macro-Ecological Model

A tool for addressing the challenges of

integrated catchment management

Annelie Holzkaemper & Vikas Kumar

3rd annual conference of CSC

University of Sheffield


Mem project team
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
Why do we need the MEM?

Engineering News

Report 1993


Why do we need the mem1
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

Ecol. Status

Chem. Status

Flood risk

What is the MEM?

Model Inputs

management scenarios

Physical

Environment

Chemical

Environment

Management objectives

Social and economic Environment

Biological

Environment

The MEM

Model Outputs

changes in status of objectives


How do we develop the mem
How do we develop the MEM?

Bayesian belief network (BBN)

Sprinkler

Rain

Wet grass


How do we develop the mem1

Biological quality

Physico-chemical quality

GQA biology

Number of properties

affected

Phosphate concentration

How do we develop the MEM?

Step 1: Identification of index variables to represent objectives

Ecological status

Flood risk


How do we develop the mem2

Threshold rain-

fall events

Urban sealing

Secondary

channels

Organic

pollution

Embankments

Urban storm-

water runoff

Restored wetland

CSO storage

capacity

PO4 from

degrad.

Channel

maintenance

PO4

from CSO’s

C-F

connectivity

Soil P

Channel

vegetation

Riparian buffer

Land

erodability

Soil type

Field buffers

Plant uptake

Agricultural

drainage

Microbial

uptake

Sedi.,Adsorp.,

Precip.

Sediment-

bound PO4

Pathway

PO4 load

to river

Transformation

PO4 conc.

Dissolved

P04

PO4 in indu-

strial effluent

Applied

P

Bed&Bank

erosion

River flow

Rainfall

PO4 in

STW effluent

PO4 from

septic tanks

PO4 in

urban runoff

Urban sealing

Precipitation

How do we develop the MEM?

Step 2: Development of conceptual sub-model


How do we develop the mem3

Riparian buffer

PO4 load

from CSO’s

Restored

wetland

Embankments

Livestock

Arable land

PO4 load from

agriculture

PO4 load

to river

PO4 conc.

Managed

grassland

River discharge

PO4 load from

STW effluent

How do we develop the MEM?

Step 3: Simplification of conceptual sub-model


How do we develop the mem4

Riparian buffer

PO4 load

from CSO’s

Restored

wetland

Embankments

Livestock

Arable land

PO4 load from

agriculture

PO4 load

to river

PO4 conc.

Managed

grassland

River discharge

PO4 load from

STW effluent

PSYCHIC

How do we develop the MEM?

Step 4: Specification of sub-model

SIMCAT


How do we develop the mem5

Biological quality-module

Microbial

activity

Light

Algae

O2

Invertebrates

Discharge

Land use

Abstraction

Rainfall

Hydrology-module

How do we develop the MEM?

Step 5: Merging of sub-models

Water quality-module

PO4 load

PO4 conc.


How could the mem be applied
How could the MEM be applied?

  • Baseline Scenario:

  • Current conditions

MEM

MEM prediction:

Ecological status

Flood risk

Number of

properties affected by 100-year flood

Number of

water bodies passing

standards

GQAbio

PO4


How could the mem be applied1
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

MEM prediction:

Ecological status

Flood risk

Number of

properties affected by 100-year flood

Number of

water bodies passing

standards

GQAbio

PO4


How could the mem be applied2
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

MEM prediction:

Ecological status

Flood risk

Number of

properties affected by 100-year flood

Number of

water bodies passing

standards

GQAbio

PO4


Summary
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


The macro ecological model

Thank you for your attention!

http://www.shef.ac.uk/mem.html


How do we develop the mem6

Shading

Turbidity

PO4 load from

STW effluent

PO4 load

from CSO’s

Light

Livestock

Weirs

BOD

Algae

Phosphate

PO4 load

to river

PO4 load from

agriculture

Arable land

O2

Managed

grassland

Embankments

Restored

wetlands

Riparian buffer

River discharge

Flow variability

GQA biology

Concrete

substrate

Channel

roughness

Flood extent

Properties affected

by flood

Ammonia

Properties within

floodplain

How do we develop the MEM?


How do we develop the mem7
How do we develop the MEM?

Shading

Turbidity

Light

Weirs

BOD

Algae

Phosphate

O2

Flow variability

GQA biology

Concrete

substrate

Ammonia


Step 1 model identification
Step 1: Model identification

Example: PSYCHIC

Inputs:

Output:

Land use

Livestock numbers

Area of crop types

Precipitation

Temperature

Soil type

Slope

Proximity to surface waters

Land use

Livestock numbers

Area of crop types

Precipitation

Temperature

Soil type

Slope

Proximity to surface waters

P load delivered to river


Step 2 multiple model runs

Inputs

Output

Run 0

% Arable

Livestock number

PO4 load

wb1

10

60

5.2

wbn

15

10

3.1

Inputs

Output

Run 1

% Arable

Livestock number

PO4 load

wb1

10

30

3.9

wbn

15

5

2.5

Step 2: Multiple model runs

Run 0:

Current conditions

Run 1:

50% reduction in number of livestock


Step 3 bbn structure definition

Managed

grassland

Temperature

Precipitation

Arable land

Proximity

PO4 load

Soil type

Livestock

Slope

Step 3: BBN structure definition

PSYCHIC inputs

PSYCHIC output


Step 4 bbn specification

Arable land

Livestock

PO4 load

Step 4: BBN specification

Managed

grassland


Step 5 bbn application

Arable land

Livestock

PO4 load

A

B

C

Step 5: BBN application

Conditional probabilities

for PO4 conc. on

water body scale:

A

Managed

grassland

B

C

A

B

C

A

B

C