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C.G.

Spatial analysis for integrated natural

resources management and decision making

2008 European Summer School in Resource and Environmental Economics

SPACE IN UNIFIED MODELS OF ECONOMY AND ECOLOGY

Carlo GiupponiUniversità Ca’ Foscari di Venezia, DSE-CEEMPhD Programme on Science and Management of Climate ChangeEuro-Mediterranean Centre for Climate ChangeFondazione Eni Enrico Mattei

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C.G.

Introduction

2008 European Summer School in Resource and Environmental Economics

C.G. 3

Topics

• Management of natural resource in socio-ecosystems

• Spatial analysis of natural vs. human variables• Integration of ecologic and socio-economic

variables: the case of environmental assessment of agricultural systems

• Various approaches for supporting policy/decision making: cartographic models; spatial dynamic models; spatial decision support systems

• Assessing the past or the present, vs. projecting into the future: scenario analysis in the climate change context In

trod

uctio

n

C.G. 4

Keywords

• policy/decision making• space• social-ecological systems• integration• communication• participation• multiple criteria• decision support

Intr

oduc

tion

C.G. 5

Unprecedented change in structure and functions

•More land was converted to cropland in the 30 years after 1950 than in the 150 years between 1700 and 1850.

Cultivated Systems in 2000 cover 25% of Earth’s terrestrial surface

(Defined as areas where at least 30% of the landscape is in croplands, shifting cultivation, confined livestock production, or freshwater aquaculture)

Intr

oduc

tion

C.G. 6

Patterns of change

• Ecosystems in some regions are returning to conditions similar to their pre-conversion states

• Rates of ecosystem conversion remain high or are increasing for specific ecosystems and regions

Intr

oduc

tion

2

C.G. 7

Water resources

• 15 - 35% of irrigation withdrawals exceed supply rates and are therefore unsustainable (low to medium certainty)

Intr

oduc

tion

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C.G.

The case of water resources management

2008 European Summer School in Resource and Environmental Economics

C.G. 9

The main challenges of WRM

• Securing water for people• Securing water for food production• Developing sustainable job creating

activities• Protecting vital ecosystems• Dealing with variability• Managing risks• Raising awareness and understanding• Forging the political will to act• Ensuring collaboration across sectors and

boundariesGWP-TAC, 2000

Intr

oduc

tion:

WR

M

C.G. 10

Definition of IWRM

• IWRM is a process which promotes the co-ordinated development and management of water, land and related resources, in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems.Not a single definitionIWRM practices depend on context

GWP-TAC, 2000

Intr

oduc

tion:

WR

M

C.G. 11

Integration in IWRM

• Integration is necessary but not sufficient, it cannot guarantee development of optimal strategies, nor the solution of conflicts;

• Two basic categories of –within and between – integration:–Natural system: resource availability

and quality–Human system: resource use and

depletion.

GWP-TAC, 2000

Intr

oduc

tion:

WR

M

C.G. 12

Natural System Integration

• Managing the continuum of water bodies (inland, coast, ocean);

• Managing water and land (river basin as a planning unit)

• Focus on “Green water”, not only on “Blue water”

• Managing surface and ground- waters• Managing quantity and quality• Managing up-stream and down-stream

GWP-TAC, 2000

Intr

oduc

tion:

WR

M

3

C.G. 13

Human System Integration

• Mainstreaming and involving institutions, the private sector and stakeholders

• Implementing cross-sectoral approach and evaluation of impacts

• Considering macroeconomic effects of development

• Designing operational methods and tools for stakeholders’ involvement and conflict management and resolution

GWP-TAC, 2000

Intr

oduc

tion:

WR

M

C.G. 14

Integration and Sustainability

• Overriding criteria:–Economic efficiency in water use

(scarce resources –water and finance) –Equity (equal rights to access to

water)–Environmental and ecological

sustainability (preservation of resources for future generations)

GWP-TAC, 2000

Intr

oduc

tion:

WR

M

C.G. 15

Sustainability Science and IWRM

• Focus on the dynamic interactions between nature and society, to learn how to:

1. integrate the effects of key processes across the full range of scales from local to global;

2. make society able to guide those interactions along sustainable trajectories

3. implement participatory procedures involving scientists, stakeholders, citizens, to transform knowledge claims into trustworthy, socially robust, usable knowledge, for the transition to sustainability

JFK School of Government, Harvard Univ., 2000

Intr

oduc

tion:

WR

M

C.G. 16

Core questions

1. How to integrate nature-society interactions?2. How evolving nature-society interactions will

influence long term trends in environment and development?

3. What determines vulnerability and resilience of nature-society systems?

4. Can scientifically meaningful “limits” be defined?5. What system (market, rules,…) can improve

more the social capacity to guide interactions with nature?

6. How to improve systems for monitoring, modelling and reporting?

7. How can today’s research activities be integrated into systems for adaptive management and societal learning?

JFK School of Government, Harvard Univ., 2000

Intr

oduc

tion:

WR

M

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C.G.

Spatial decision and policy making

2008 European Summer School in Resource and Environmental Economics

C.G. 18

Building (and sharing) knowledge

• Analysis (observations, hypotheses, etc.)• Modelling (mental, empirical, mechanistic,

mathematical, etc.)

Courtney, 2001

Dec

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akin

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oces

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Analysis

Modelling

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C.G.

Spatial analysis

C.G. 20

Effects of spatialisation methods

C.G. 21

Raster data model

Discretization

Spatial entities

Sampling

Spat

ial a

naly

sis

Hydrologic fluxes (x,z)

Spat

ial a

naly

sis

Hydrologic fluxes (x,y)

Spat

ial a

naly

sis

Hydrologic balance in agro-ecosystems (x,z)

Spat

ial a

naly

sis

5

C.G. 25

Geostatistical spatial analysis

[ ]2)(

)()()(2

1)(ˆ ∑≠

−=hN

ji

ji xzxzhN

hγ

Spat

ial a

naly

sis

C.G. 26

Spatial filters

Spat

ial a

naly

sis

C.G. 27

Criterion/factor maps

Aspect

Fractal

Hillshade

Segmentation

Elevation

Temperature

Land use

Rainfall

C.dorsatus

Spat

ial a

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sis

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C.G.

Processes, patterns, systems

C.G. 29

Fuzzy membership to suitability for C.dorsatus

0.0

0.2

0.4

0.6

0.8

1.0

15 20 25 30 35

Avg. T° Jul

0.0

0.2

0.4

0.6

0.8

1.0

-2.0 -1.0 0.0 1.0 2.0 3.0

Avg. T° Jan

0.0

0.2

0.4

0.6

0.8

1.0

0 45 90 135 180 225 270 315 360

Aspect

0.0

0.2

0.4

0.6

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1.0

0 500 1000 1500 2000 2500

Elevation

0.0

0.2

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0.8

1.0

100 110 120 130 140 150

Precip. Jul

0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50

Slope

Proc

esse

s, p

atte

rns,

sys

tem

s

C.G. 30

Suitability analysis withMCE-OWA for C. dorsatus

Proc

esse

s, p

atte

rns,

sys

tem

s

6

C.G. 31

Suitability map

Suitability C.dorsatus (Biomapper vs. MCE-OWA)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

FALSE POSITIVE (%)

TRU

E PO

SITI

VE (%

Biomapper (ROC = 0.853)

Constrained MCE (ROC = 0.879)

Proc

esse

s, p

atte

rns,

sys

tem

s

C.G. 32

Green: suitable without populations

Connectivity analysis

Proc

esse

s, p

atte

rns,

sys

tem

s

C.G. 33

Identification of protected areas

Proc

esse

s, p

atte

rns,

sys

tem

s

C.G. 34

Socio-ecosystem: definition

• Social-ecological systems (or socio-ecosystems; SES): complex adaptive systems where social and biophysical agents are interacting at multiple temporal and spatial scales;

→the concept emphasizes the adoption of a single integrated approach for the analysis of both social and economical agents and the natural components of the ecosystem

Soci

o-ec

osys

tem

s

C.G. 35

Socio-ecosystem governance

• The main challenge for the study of governance of social-ecological systems is improving our understanding of the conditions under which cooperative solutions are sustained, how social actors can make robust decisions in the face of uncertainty and how the topology of interactions between social and biophysical actors affect governanceBuild up adaptive capacity: the capacity

of a SES to manage resilience in relation to alternate regimes

Soci

o-ec

osys

tem

s

C.G. 36

Pixelizing vs. socializing

• Socializing the pixels: to take remote sensing and other geophysical data beyond their usual use in appliedsciences, to address the concerns ofsocial sciences (patterns → processes)

• Pixelizing the social: linking socio-economic infromation and models (e.g. S-ABM) with raster imagery (processes →patterns)

7

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C.G.

Modelling

C.G. 38

Conceptual model

Mod

ellin

g

C.G. 39

Relational diagrams and models

Once the relational diagram is finalised it can be used for building a mathematical model by implementing equations formalising the relations between external, state, auxiliary, and rate variablesM

odel

ling

C.G. 40

LUC scenario models

-100

-80

-60

-40

-20

00 1000 2000 3000 4000 5000 6000 7000 8000

distance (m)

loss

of o

pen

area

s (%

)

Distance from villages and loss of open areas

Cellular automata

Mod

ellin

g

C.G. 41

NO3_OUT

Nitrate transport in surface waters

ORGN_OUT

Organic nitrogen transport in surface waters

YLD

Crop production

Impact indicators

Mod

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C.G.

Decision making process

8

C.G. 43

Decision making process

Courtney, 2001

Dec

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g pr

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ses

C.G. 44

Knowledge based DM process

Dec

isio

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akin

g pr

oces

ses

Analysis

Modelling

Simulation

Problem recognition

Problem definition

Alternative generation Scenarioanalysis

Public

par

tici

pation

Choice /Decision

Implementation

C.G. 45

DM is and iterative process

Adapt. from Belton and Steward, 2002

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Scenarios and simulations

C.G. 47

Need for scenario analysis

• Finding #3 of MEA: The degradation of ecosystem services could grow significantly worse during the first half of this century and is a barrier to achieving the Millennium Development Goals

Scen

ario

ana

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d si

mul

atio

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C.G. 48

Scenarios

• Scenario: A plausible and often simplified description of how the future may develop, based on a coherent and internally consistent set of assumptions about key driving forces and relationships.

→neither predictions nor projections → “narrative storyline.”→derived from projections of models but often also

from additional information from other sources.→A small set (typically 3 or 4) of scenarios is

usually created and analyzed for investigations into possible/plausible futures.

Scen

ario

ana

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s an

d si

mul

atio

n

9

C.G. 49

IPCC SRES Scenarios

IPCC SRES

Scen

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s an

d si

mul

atio

n

C.G. 50

Potentials of scenario approach

• Scenarios can help evaluate different action steps and identify "robust" actions(decisions/policies) that make sense across a wide variety of future conditions.

• Scenarios development is a fundamental component of decision making

• Scenarios are especially important where there is high uncertainty about the future.

• A set of several significantly different scenarios helps "bound the uncertainty" of the future so that an organisation can systematically plan for future contingencies and clarify its preferred vision of the future.

Institute for Alternative Futures

Scen

ario

ana

lysi

s an

d si

mul

atio

n

C.G. 51

Suitability: current vs. HadA1-2020

Current suitability HadA2-2020 suitability

Change detection

Scen

ario

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s an

d si

mul

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Decision support

C.G. 53

The DPSIR meta-model and communication framework

• Driving forces = Underlying causes and origins of pressure on the environment

• Pressures = The variables which directly cause environmental problems

• State = The current condition of the environment

• Impact = The ultimate effects of changes of state, damage caused

• Response = Decisional option= Effort to solve the problem caused by the specific impact

Response

Impact

State

Pressures

DrivingForces

Dec

isio

n su

ppor

t

C.G. 54

Integrated Assessment Modelling

√ Integrated Assessment: a process of combining, interpreting, and communicating knowledge from diverse scientific disciplines in such a way that the whole set of cause-effect interactions of a problem can be evaluated from a synoptic perspective with two characteristics:1. It should have added value comparable to single

disciplinary oriented assessments2. It should provide useful information to decision

makers(Rothmans and van Asselt, 1996)

√ Integrated Assessment Modelling: computer based processes and tools to analyse and simulate the spatio-temporal behaviour of complex systems in relation to human planning and decision making

Dec

isio

n su

ppor

t

10

C.G. 55

Integrated Modelling

Dec

isio

n su

ppor

t

C.G. 56

Integrated Modelling and EIA in the DPSIR framework

Dec

isio

n su

ppor

t

C.G. 57

Effects of External Drivers

Dec

isio

n su

ppor

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C.G. 58

Problem solving approach

Dec

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n su

ppor

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C.G. 59

DPSIR framework as an IA [meta]model

Dec

isio

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ppor

t

C.G. 60

IAM in the DPSir framework

Dec

isio

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ppor

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11

C.G. 61

IAM in the DPSir framework

Dec

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C.G. 62

IAM in the DPSIR framework

Dec

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C.G. 63

A schematic DPSIR model for water resources management

DETERMINANTISTATO RISORSA

STOCK RISORSAPRESSIONI

IMPATTO

~

FORZANTI ESTERNES DPS

IR

Rinnovazione

Tasso rinnovazione

RISPOSTA

SISTEMA TERRITORIALE: RISORSE IDRICHE

PROGRAMMAMISURE

MISURA

LIMITE IMPATTACCETTABILE

10.33 mer 18 mag 200

Untitled

0.00 20.00 40.00 60.00 80.00 100.00Time

1:

1:

1:

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11: PROGRAMMA MISURE

1 1

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10.33 mer 18 mag 200

Untitled

0.00 25.00 50.00 75.00 100.00Time

1:

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1: FORZANTI ESTERNE 2: DETERMINANTI 3: PRESSIONI 4: STOCK RISORSA

1

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3 3

4

4

Dec

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C.G. 64

Planning and Decision Making in the DPSIR framework

Dec

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C.G. 65

Planning and Decision Making in the DPSIR framework

Dec

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C.G. 66

MCA in the DPSIR framework

Dec

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12

C.G. 67

MCA in the DPSIR framework

Dec

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C.G. 68

Spatial information in the DPSIR framework

Dec

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Spatial multi-criteria analysis

EVALUATION OF ALTERNATIVE LAND USE SCENARIOS

Scenario 2

Scenario 1

Protectionof

groundwater

RISK FORSURFACE WATER

Impact index forsurface water

Vulnerability of surface water

1/2 1/2

RISK FOR GROUNDWATER

Impact index forgroundwater

Vulnerability of groundwater

1/2 1/2

RDLr MTL

1/2 1/23/4 1/4

Landscape diversity

Distance towaterRDR MTR NT ER

1/4 1/4 1/4 1/4

Dec

isio

n su

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t

C.G. 70

Spatial multi-criteria evaluation

Vulnerability of ground water Scenario 1: Impacts on groundwater Scenario 2: Impacts on groundwater

Scenario 1: Risk for groundwater Scenario 2: Risk for groundwater Difference map

Dec

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Concluding remarks

2008 European Summer School in Resource and Environmental Economics

C.G. 72

Methodological remarks

• Spatial data analysis may represent a significant part of the theoretical background of ecological and economic analyses (assumptions, robustness, etc.);

• Analysing socio-ecosystems without robust spatial methods is like analysing time series without knowing the chronological order of data;

• Integrated models could contribute to improving decision/policy making processes;

• DSS’s based upon the DPSIR framework, in combination with GIS, IAM and MCA functionalities show great potential for NRM;

• Significant gaps do exist between scientific knowledge and policy making.

Con

clud

ing

rem

arks

13

C.G. 73

Filling the science-policy gap (1/2)

• Different priorities and objectives of stakeholders and researchers are the main causes of the existing gaps

• Key actors should be preliminary identified and involved all phases of the decision making process

• It is necessary to adapt approaches and tools to the users’ needs and not vice-versa

• Flexibility should be assured all along the development and implementation process

• Supporting the decision process also means making knowledge accessible and easy to understand

• The ability to implement expert knowledge (i.e. detained by qualified persons) in the process is of fundamental importance

Con

clud

ing

rem

arks

C.G. 74

Filling the science-policy gap (2/2)

• Indicators play a fundamental role in providing concise and targeted quantitative features of the various aspects to be considered in the choice

• A plethora of approaches is available for the assessment of alternative options

• Sensitivity and uncertainty analysis, and quality assurance should be carried out during all the development phases and the outputs associated with the results

• Capacity building and training of end-users (policy makers or consultants) are necessary to ensure that the process is not mismanaged or the tools misused

• The improvement of the quality of the decision process is the main indicator of success

Con

clud

ing

rem

arks