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 …)