An Adaptive ManagementModel

for the

Red River Basin of the North

Adaptive Management

Elements of Adaptive Management

W orkshop andO rganizational

Techniques

M odelling style,philosophy, and

Techniques

Experim enta lM anagem ent

Theory and P ractice

Facilita ted m eetingsŸ Structured exerc ises

Ÿ Flip charts

Ÿ Silent generation

M ultip le S takeholdersŸ Public in terest groups

Ÿ Policy m akers

Ÿ Scientists

Ÿ M anagers

M odel TypesŸ Dynam ic s im ulation

Ÿ Conceptual

Style / ApproachŸ In terd isc ip linary

Ÿ Balanced levels ofdeta il

Ÿ Functionalrepresentation

Ÿ Focus on uncerta intiesand a lternatives

Focus on critica luncerta inties

Experim enta ldesign

Power Analysis

Workshop Sequence

Scoping W orkshop

Technical M eetings

M odel D evelopm entand D ata Analysis

Second W orkshop

...

Ÿ In itia l P roblem ReviewŸDefin ition of m odel scopeŸKey issuesŸReview of understanding

and possib le approaches

ŸDetailed m odel specification

Ÿ In itia l M odel developm entŸData analysisŸM odel analysis

ŸReview of progressŸRefinem ent of approachŸDiscussion of scenariosŸPlanning for next steps

Activ ities w ill depend onsituation but could include:ŸM odel gam ingŸScenario analysis

Bounding the Problem

• ActionsHuman activities and inputs we are interested in manipulating – the “levers”

• IndicatorsCritical system outputs that we will use to assess the effects of actions

• Driving VariablesFactors external to system such as weather, commodity prices

• SpaceExtent and resolution

• TimeExtent and resolution

• ActionsHuman activities and inputs we are interested in manipulating – the “levers”

• IndicatorsCritical system outputs that we will use to assess the effects of actions

• Driving VariablesFactors external to system such as weather, commodity prices

• SpaceExtent and resolution

• TimeExtent and resolution

W orkshopsand

ModelingData analysis

OptionAnalysis

AdaptiveManagement

Planning

ExperimentalDesign

Management andImplementation

Synthesis ofunderstanding andidentification and filteringof uncertainties

Critical analysis ofdata to resolvealternate hypothesesand uncertainties

Experimentaldesign integratedwith managementactivities

Develop experimentaldesigns that can beintegrated w ith managementand which w ill resolveuncertainties

Determine feasabilityof and/or approach tointegrating criticalexperiments intomanagement

Explore PotentialImplications of DifferentPolicy and M anagementOptions UnderUncertainty

A dapt ivemanagement

Adaptive Management Models

• Dynamic simulation models (not optimizations)• Intended to understand system dynamics not

generate detailed predictions.• Interdisciplinary – look at interactions between

hydrology, agriculture, crop diseases etc.• Policy relevant• “Aids to thinking” - Explore alternative

hypotheses and scenarios• What if …Climate change, policy change, etc.

• Dynamic simulation models (not optimizations)• Intended to understand system dynamics not

generate detailed predictions.• Interdisciplinary – look at interactions between

hydrology, agriculture, crop diseases etc.• Policy relevant• “Aids to thinking” - Explore alternative

hypotheses and scenarios• What if …Climate change, policy change, etc.

The Red River Basin of the North

Study area (Ecoregions)

Red River Basin(~125,000 km2)

THEN

NOW

Digital Elevation Model data

Population estimates for the Red River Basin on a 1 km2 grid

1997 Red River Flooding

1997 Flooding at Grand Forks

Flood and Fire Damage at Grand Forks

St Vincent in 1997 Flood

Commodity Price Trends

North Dakota Census Data

Red River Basin Model

Broad Project Objectives

• To distill from the multiplicity of causal factors a simple systems model that can be used to explore the natural, economic and social dynamics observed in the Red River Basin.

• To use the model to explore alternative policies with stakeholders and decision makers

• To distill from the multiplicity of causal factors a simple systems model that can be used to explore the natural, economic and social dynamics observed in the Red River Basin.

• To use the model to explore alternative policies with stakeholders and decision makers

Issues to explore with model

• Land use interactions with hydrology.

• Impacts of policies, incentive programs etc.

• Crop diseases in response to cropping patterns.

• Feasibility of alternate crops and crop rotations.

• Water quality issues.

• Impacts of climate change.

• Land use interactions with wildlife.

• Land use interactions with hydrology.

• Impacts of policies, incentive programs etc.

• Crop diseases in response to cropping patterns.

• Feasibility of alternate crops and crop rotations.

• Water quality issues.

• Impacts of climate change.

• Land use interactions with wildlife.

Detailed Objectives of first Phase

• Explore initial sub models for:• Farm economics• Crop diseases• Crop production• Driving weather/climate variables• Hydrology routine to generate inputs to other models• Etc …

• Framework for linking sub-models at the basin level.• Initial compilation of datasets at the basin level.• Plan for continued of model development and analysis.

• Explore initial sub models for:• Farm economics• Crop diseases• Crop production• Driving weather/climate variables• Hydrology routine to generate inputs to other models• Etc …

• Framework for linking sub-models at the basin level.• Initial compilation of datasets at the basin level.• Plan for continued of model development and analysis.

C ropD isease

C lim ate D riv ingVariables

C rop andVegetation G rowth

and Y ie ld

Econom icand Policy

D rivers

Econom ics

C rop System /Land U seSelection

C R P,C R EP , R IM

etc.

T illagePractices

C hem ica lU se

R ota tions

C ropVarie ties

H ydrology andSoils

L ivestock

Socia l Issues andD em ographics

10

65

7

8

11

W ater M anagem entActions

Land U se Actions

3 4

13

1

2

W ild life ,H unting

andF ishing

14

16

17

18

22

19

12

20

9

21

15

Conceptual Model

Draft (!) Balaton Conceptual Model

Lake Levels(Water Budget)

Jobs

Human Population(Demographics)

Weather/Climate- Wind- Temp- Precip

- Humidity

Fish andWildlife

Reed Beds Land Use- Forest

- Livestock- Vinyards

Lake LevelRegulatory

Policy

Tourism

WaterTemperature

Eel PopulationMortality

WaterQuality

Styles of Modelling

"Soft" Modelling - Social Responses and Behaviours

Modelling w ith Significant Uncertainties - Crop Disease

Hard Science - Hydrology

C ropD isease

C lim ate D riv ingVariables

C rop andVegetation G rowth

and Y ie ld

Econom icand Policy

D rivers

Econom ics

C rop System /Land U seSelection

C R P,C R EP , R IM

etc.

T illagePractices

C hem ica lU se

R ota tions

C ropVarie ties

H ydrology andSoils

L ivestock

Socia l Issues andD em ographics

10

65

7

8

11

W ater M anagem entActions

Land U se Actions

3 4

13

1

2

W ild life ,H unting

andF ishing

14

16

17

18

22

19

12

20

9

21

15

Current Model Status

Hydrologic Units

STATSGO and CANSIS Soils data

Administrative areas (counties, rural municipalities)

Land Cover for the Red River Basin

USGS Land Use/Land Cover System Legend (Modified Level 2)

Value Code Class Name

1 100 Urban and Built-Up Land2 211 Dryland Cropland and Pasture3 212 Irrigated Cropland and Pasture4 213 Mixed Dryland/Irrigated Cropland and Pasture5 280 Cropland/Grassland Mosaic6 290 Cropland/Woodland Mosaic7 311 Grassland8 321 Shrubland9 330 Mixed Shrubland/Grassland10 332 Savanna11 411 Deciduous Broadleaf Forest12 412 Deciduous Needleleaf Forest13 421 Evergreen Broadleaf Forest14 422 Evergreen Needleleaf Forest15 430 Mixed Forest16 500 Water Bodies17 620 Herbaceous Wetland18 610 Wooded Wetland19 770 Barren or Sparsely Vegetated20 820 Herbaceous Tundra21 810 Wooded Tundra22 850 Mixed Tundra23 830 Bare Ground Tundra24 900 Snow or Ice100 NO DATA

Ecoregions relevant to the Red River Basin

Locations of centers of VEMAP weather cells

Spatial Representation

1 km ^2 grid for whole basin(approxim ate ly 125,000 cells)

G rid cells aggregated by uniquecom binations of:

Ÿ Adm inistrative unitsŸ Soil unitsŸ W eather 's tations'Ÿ Land Cover TypeŸ EcoregionŸ Hydrologic Unit

(Approxim ate ly 5,000 units)

G rid Cell groups subdivideddynam ically to account for

d ifferent land use practices suchas rotations.

(approxim ate ly 20,000 units)

1 km ^2 grid for whole basin(approxim ate ly 125,000 cells)

G rid cells aggregated by uniquecom binations of:

Ÿ Adm inistrative unitsŸ Soil unitsŸ W eather 's tations'Ÿ Land Cover TypeŸ EcoregionŸ Hydrologic Unit

(Approxim ate ly 5,000 units)

G rid Cell groups subdivideddynam ically to account for

d ifferent land use practices suchas rotations.

(approxim ate ly 20,000 units)

Nested Time Steps

Annual Time Step

ŸEconom icsŸAnnual Indicators

Monthly Time Step

ŸIndicatorsŸM onthly F low accum ulation

Daily Time Step

ŸSoil m oistureŸPlant growthŸD isease dynam icsŸPlanting/H arvesting decisions

• Model developed for Windows PC

• Visual Basic

• Microsoft Access

• Uses ArcView 3.2 at present for mapping

• Uses Excel for chart and tabular output

• Model developed for Windows PC

• Visual Basic

• Microsoft Access

• Uses ArcView 3.2 at present for mapping

• Uses Excel for chart and tabular output

Implementation

• Various run time options

• Weather data options

• Can vary scope of model

• Critical to manage indicators …

• Various run time options

• Weather data options

• Can vary scope of model

• Critical to manage indicators …

Running the Model

• Multiple saved scenarios

• Indicators at various time steps and spatial resolutions

• View maps at a time

• View charts/tables for one or more spatial units or indicators

• Multiple saved scenarios

• Indicators at various time steps and spatial resolutions

• View maps at a time

• View charts/tables for one or more spatial units or indicators

Viewing outputs

Tension Zone

Snow Surface storage

Upper ZoneStorage

Precip ita tion

7 Perco lation

1

3 Snow M elt 4 R unoff

5 In filtra tionT ranspira tion 6 Evapotranspira tion

G round W ater8 Perco lation

Hydrology Sub-model

• Initial model implemented

• Penman Monteith Equation

• Some model analysis completed

• Model has not yet been refined based on observations

• Initial model implemented

• Penman Monteith Equation

• Some model analysis completed

• Model has not yet been refined based on observations

Example Model Output

Water contributed to flow from different hydrologic

units in June

Tension soil water in May

Tension soil water available in August

Flow at Fargo

Scenario: Scenario 1; Hydrologic Unit: 9020104

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1984/01 1985/01 1986/01 1987/01 1988/01 1989/01 1990/01 1991/01 1992/01 1993/01

Date

Forecast Flow

Measured Flow

Scenario: Historical 99 year base; Hydrologic Unit: 9020301

0

5000

10000

15000

20000

25000

30000

35000

40000

Date

FlowCFS

Observed

100 year base runFlows at Grand Forks

100 year base runFlows at Grand Forks

0

1000

2000

3000

4000

5000

6000

7000

Forecast

Observed

100 year base runFlows at Grand Forks

(Moving Average)

100 year base runFlows at Grand Forks

(Moving Average)

y = 1.6629x - 1811.4

R2 = 0.6891

0

1000

2000

3000

4000

5000

6000

0 1000 2000 3000 4000 5000 6000

Predicted flow

Ob

se

rve

d f

low

100 year base runFlows at Grand Forks

(Moving Average)

100 year base runFlows at Grand Forks

(Moving Average)

Flows at Grand Forks - Preliminary Model Runs (19/4/2002)

0

5000

10000

15000

20000

25000

1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

FlowCFS (LAI=3)

FlowCFS Standard Run

FlowCFS (LAI=0)

Actuals

Effects of Vegetation on flow (A crude analysis!)

Effects of Vegetation on flow (A crude analysis!)

Summary

• Simple integrated systems model

• Include both “hard science” such as hydrology with less certain aspects

• Developed in a stakeholder process

• Explicit recognition of uncertainties

• Focus is not on prediction

• Model used to explore different scenarios (What if …)

• Simple integrated systems model

• Include both “hard science” such as hydrology with less certain aspects

• Developed in a stakeholder process

• Explicit recognition of uncertainties

• Focus is not on prediction

• Model used to explore different scenarios (What if …)