Models of Human-Environment Interaction Lecture-11 2015-07-01 · Overexploitation of marine...

p. 1

Integrated Models of Human-Environment Interaction:Multi-agent, network and spatial models

Jürgen ScheffranCliSAP Research Group Climate Change and Security

Institute of Geography, Universität Hamburg

Models of Human-Environment InteractionLecture 11, July 1, 2015

p. 2

Fishery

Technology

Policy

Fishery management in human-environment interaction

Human andSocial Systems

NaturalSystems

p. 3

Overexploitation of marine resources and fishery conflicts

70% of fish stocks worldwide heavily overexploited

Some of them collapsed or to be collapsed, e.g. NorthwestAtlantic or North Sea cod

Low quality of management strategies

High levels of subsidies

Collective-action problem in common pool resource (Tragedy of the commons)

Conflicts on scarce fish stocks

p. 4

Ecological and economic viability condition

Economic viability condition V = p h − C >= 0 withPrice p = a − b hHarvest h = g x C Source: Scheffran 2000

p. 5

Fishery and socio-ecological interaction

g CEffectiveefforts

hHarvest

TechnologyEnvironment

Policy

r (K – x)Fish productivity

p. 6

Compatibility of economic and ecological viability

•200 •400 •600 •800 •1000

•-200

•-100

•100

•200

Efforts C

Fish stock x

Ecological viabilityEconomic viability

V<0

V>0

V<0

Source: Bendor/Scheffran 2009

p. 7

Viability domains for various parameters

200 400 600 800 1000

-200

-100

100

200

200 400 600 800 1000

-200

-100

100

200

200 400 600 800 1000

-80-60-40-20

204060

200 400 600 800 1000

-200

200

400

600

800

1000

•a=1;=0.001; K=1000; b=1/2000; r=1/K

•a=1;=0.004; K=1000; b=1/2000; r=0.2/K•a=1;=0.004; K=1000; b=1/2000; r=1/K

•a=0.5;=0.001; K=1000; b=1/2000; r=0.2/K

p. 8

Multi-player competition for natural resources

Source: Scheffran 2000

p. 9

Sustainable competition among multiple actors


p. 10

Baseline parameter set

0.2


p. 11

Competitive fishery case6 fishing companies, 2 fish species


p. 12

Cooperative fishery case


p. 13

Co-management of fisheries

ScientificInstitution Fishery

CouncilFishing Firms

(group 1)

Fish Stock

other species

Fishing Firms(group 2)

ManagementAuthority

species interactionestimates, catch dataharvestrecommendationquota

p. 14

Watersecurity

Technology

Policy

Water management in human-environment interaction


NaturalSystems

p. 15

Multidimensional conflict constellation:

• increased upstream water consumption limits water availability downstream

• increased industrialization in urbanregions leads to higher water demandwhich increases scarcity for rural(agricultural) water use

• internal conflicts within the rural andurban populations concerningallocation issues

Additional threat to agricultural production in Nile Delta region from sealevel rise in the Eastern Mediterranean

The Nile River water conflict

p. 16

Systemic overview of the Nile water conflict

industrial use

agricultural use:irrigation

humanconsumption

agricultural use:irrigation

humanconsumption

industrial use

humanconsumption

water availability upstream

outflow to the ocean

water availability downstream

industrialproduction

agriculturalproduction: yield

agriculturalproduction: yield

industrialproduction

industriallabor

agriculturallabor

agriculturallabor

industriallabor

populationgrowth

migration

climate change

floods

precipitationpatterns

waterpollution

landdegradation

landerosion

sea level rise

salinization

humanwellbeing

humanwellbeing

humanwellbeing

upstream

downstream

ruralpopulation

urbanpopulation

land availability upstream

land availability downstream

conflict between geographic regions

internalconflict

internalconflict

conf

lict b

etw

een

rura

l and

urb

an p

opul

atio

n

negative feedback positive feedback neutral or ambivalent feedback

Source: Scheffran/Link/Schilling 2012

p. 17

• Countries attempt to expand their water use through investments

• For each country, the total change in water availability is

• To reach this goal, countries have to make investments. The target investments are

• Each country seeks to adjust its actual investments based on the target investment by

/c hH h C C c

, , ,

1

n

i k i c k i ik

H p h C

,* *

,

c ij ji i

i j c ii

h CC H

h

*, , ,i t i i t i tC C C

Basic features of the simulation model

p. 18

Countries invest into water availability to satisfy increased water demand

Change in water use and supply depends on total investments and on unit costs and fractions (priorities) of investments allocated to action paths:

1. National water consumption without exceeding actual water supply. 2. Increase national water supply.3. Invest into and collaborate on water supply in an upstream country to

benefit from increased external supply.4. Threaten or pressure an upstream country not to reduce transboundary

water supply or resist to threats by a downstream country.

Scenarios of climate change (20 year period):• Baseline scenario without climate change • Reduced water availability by 20%• Increased water availability by 20%

Action paths and scenarios applied in the simulation model


p. 19

Interactions between countries in the Nile river basin


p. 20

Interactions between countries in the simulation model

Source: P. Michael Link

p. 21

Symbol line: baseline; dashed line: reduced water; solid line: increased water

Investments into the expansion of water resources


p. 22


Development of water supply


p. 23


Development of water consumption


p. 24

• Distribution of Nile River water has been a contestedissue in the past, shaped by political pressure fromdownstream countries

• Upstream countries increasingly seek to develop and usetheir water potential – risk of conflict increases

• Cooperation of all users in the Nile River Basin isessential for cost-efficient water use and conflictprevention

• Cooperation is still preferred choice of countries• Climate change not only affects supply and demand

but also viability of water development projects

Main conclusions from the Nile model simulations

p. 25

Bio-energyFood

Technology

Policy

Food and bioenergy in human-environment interaction


NaturalSystems

p. 26

Spatial-agent model of bioenergy and land use(Scheffan/Bendor 2009)

Spatial modeling: Heterogeneous environmental factors create a non-uniform environment for growing and cultivating crops.

Agent networks: Farmers are responsive to market signals that depend on ability and willingness of other farmers to plant and cultivate biofuel crops.

Multi-agent modeling techniques can explain how actors can adapt to system constraints through learning and negotiation processes.

Local parameterization by GIS data to create a matrix of spatially relevant system dynamics models

Cells act as individual agents and can gain information and material from neighboring cells

p. 27

Agents in a spatial modeling environment

GIS Data Layers

Illinois Boundary Map

MM

MM

M MM

M

MM M M

MM

M

MM M M M

GISMaps

SME

M = Models placedin each cell

GIS Data Layers

Illinois Boundary Map

MM

MM

M MM

M

MM M M

MM

M

MM M M M

GISMaps

SME

M = Models placedin each cell

Scheffran/Bendor/Wang/Hannon, A Spatial-Dynamic Model of Renewable Energy Crop Introduction in Illinois, September 2007.

Spatial Boundary Map

Agent models in each cell

p. 28

The spatial farmer agent model

Farmer agents in spatial landscape, harvesting crops in cells and selling crops on a common market (no direct interaction among cells)

System dynamics model for each cell, including Crop YieldHarvestingCrop market pricePriority of land for several crop typesInvestment costs for cultivation and harvest Profit Land use dynamics changing crop mix

“Evolutionary game” among competing crops: farmer agents iteratively shift crop priority towards growing profits

Changing distribution of crops in time, depending on adaptation

p. 29

Multi-crop multi-agent model

Farmer1

Corn

Soybean

Switchgrass

MiscanthusMarket

Farmer2

Corn

Soybean

Switchgrass

Miscanthus

Physical, economicand political factors

p. 30

Miscanthus sector of the bioenergy model


p. 31

Farmer profits and priorities

hik Harvest of each crop k

pk market price of crop k

Ci invested cost to cultivate crops at per hectare unit cost cik

Si = sk hik : “political” revenue from biofuels subsidies and carbon credits both

of which are assumed to be proportionate to harvest (sk US$ per ton subsidy)

Individual farmer i net profit function (revenue minus cost):

Decision rule: Dynamics of shifting farmer decisions about crop mix given by fraction ri

k of land assigned to crop k (priority) , depending on value gradient:


p. 32

Spatial extension of ABM model

Equations from system dynamics model into Berkeley Madonna

Model simulates behavior of individual farmer agents and is arrayed in a 37 x 65 grid, with 1568 active grid cells

Scenarios over 50 years: increasing demands ($100 mio./year), policy change, subsidy/carbon credits $25 per ton biomass

Python scripts are used to give every cell a unique identification number within standard, non-spatial database.

Model outputs processed through Python scripts to GIS maps

ArcGIS for representation

p. 33

Data collection and processing

Geographically referenced agricultural data for the State of Illinois

Unit of analysis corresponding to the size of one township (6 x 6 mile)

Generate land use map for Illinois using USDA National Agricultural Statistics Service (NASS) Cropland Data Layer with satellite imagery

Layer aggregated to 13 standardized categories of land cover

Classification decisions based on extensive field observations collected during annual NASS June Agricultural Survey.

Aggregate from NASS map (30x30 meters) to county-size (6x6 miles), using ESRI ArcToolbox GIS software

Soybean and corn production data from Illinois Crop Yields Historical NASS Database (1972-2004) with five year yield average (1997-2001)

p. 34

Data collection and processing

Sparse data on switchgrass and miscanthus growth and harvesting costs

Potential miscanthus and switchgrass yield estimates, based on soil quality, climate, and other environmental conditions in Dhungana 2007 and Khanna et al. 2005 (2 km x 2 km resolution)

Miscanthus and switchgrass unit cost estimate by John and Watson (2007)

Switchgrass similar geographic production, but 26% yield of miscanthus; data in western Illinois incomplete

Production corn and soybeans: Crop Yields Historical NASS Database (2007)

Average value of production=amount sold multiplied by average price: average $2.604 bill. Soybeans, $4.072 bill. corn per year (2000-2006)

Harvest cost data for corn and soybeans from Illinois Farm Business Farm Management Association through the University of Illinois Farm Decision Outreach Council (FARMDOC 2007)

Cost factors: direct (fertilizer, pesticides, seed, storage, drying, crop insurance)power (machine use/lease/depreciation, utilities, fuel), and overhead (labor, building repair/rent/depreciation, insurance) costs.

p. 35

Yield of maize, soy and and miscanthus in Illinois

Madhu Khanna, Basant Dhungana, etal.2007

Based on MISCANMOD model

p. 36

Spatial priorities of corn and soybeans with energy crops (t = 50 years)

Corn priority Soybean priority


p. 37

Simulation of spatial priorities and profits of miscanthus in Illinois

Miscanthus priority


p. 38

Dynamics of harvest & prices of food and energy crops


p. 39

Bioenergy infrastructure: Transport, biorefineries, consumption

Refinery PlanningSite Selection based on:• Resources (e.g., water) • Farms• Transp. networks• Storage (optional)

Biorefinery

Objective: Integrate feedstocks, bioprocessing plants and consumer demands into a regional economic model• Logistics optimization• Lifecycle assessment• Emission reductions

Transp. networks (e.g., rail, highway)

Customers (e.g., gasstation)

Harvest

Feedstockfarm

Storage

Transport…

Feedstock Shipping• Transp. mode• Fleet design (e.g., vehicle capacity)

• Dispatch route & schedule• Handling at farm/storage

Fuel Distribution• Transp. mode & fleet• Dispatch route & schedule• Price and demand

uncertainty

Cost Analysis• Transp. • Facility• Inventory• Environmental externalities

p. 402022

Biomass production and biorefinery localisation in Illinois

Corn

Perennial grass

Kang, Önal, Ouyang, Scheffran, Tursun 2010

p. 41

Crop production for Jiangsu province in China

TitelUntertitel Year

20112012

20132014

20152016

20172018

20192020

20212022

20232024

20252026

20272028

20292030

Biomass output (103t)

0

5000

10000

15000

20000

25000

30000Shadow price (CNY/t)

100

150

200

250

300

350

400Wheat Oilseed rape Medium-indiea-rice Non-glutinous-rice Beans Corn Cotton Giantreed on arableland Giant reed on mudflat Biomass shadow price

Crop yield (wheat) Cultivation cost

Source: Kesheng Shu 2014

Cultivation area

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Models of Human-Environment Interaction Lecture-11 2015-07-01 · Overexploitation of marine...

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