Spatial analysis for integrated natural resources management and

Transcript

Spatial analysis for
integrated natural
resources management and
decision making
Carlo Giupponi
Università Ca’ Foscari di Venezia, DSE-CEEM
PhD Programme on Science and Management of Climate Change
Euro-Mediterranean Centre for Climate Change
Fondazione Eni Enrico Mattei
C.G.
Center for Environmental Economics and Management
Dipartimento di Scienze Economiche
Università Ca’ Foscari di Venezia
2008 European Summer School in Resource and
Environmental Economics
SPACE IN UNIFIED MODELS OF ECONOMY AND ECOLOGY
Introduction
C.G.
•
•
•
Introduction
•
•
Management of natural resource in socioecosystems
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
C.G. 3
Keywords
Introduction
Topics
•
•
•
•
•
•
•
•
policy/decision making
space
social-ecological systems
integration
communication
participation
multiple criteria
decision support
C.G. 4
Unprecedented change in
structure and functions
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
Introduction
Introduction
•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
C.G. 5
(Defined as areas where at least 30% of the landscape is in croplands, shifting cultivation,
confined livestock production, or freshwater aquaculture)
C.G. 6
1
Introduction
• 15 - 35% of irrigation withdrawals exceed
supply rates and are therefore
unsustainable (low to medium certainty)
• 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
boundaries
• 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.
¾ Not a single definition
¾ IWRM practices depend on context
Introduction: WRM
Definition of IWRM
GWP-TAC, 2000
GWP-TAC, 2000
C.G. 10
Integration in IWRM
Natural System Integration
• 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.
• 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
Introduction: WRM
Introduction: WRM
The main challenges of WRM
C.G. 9
GWP-TAC, 2000
C.G. 11
The case of water
resources management
C.G.
C.G. 7
Introduction: WRM
Water resources
GWP-TAC, 2000
C.G. 12
2
• 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
Integration and Sustainability
• Overriding criteria:
Introduction: WRM
Introduction: WRM
Human System Integration
– 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
C.G. 13
GWP-TAC, 2000
C.G. 14
Core questions
C.G. 15
C.G.
JFK School of Government, Harvard Univ., 2000
Introduction: WRM
• 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
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
C.G. 16
Building (and sharing) knowledge
Spatial decision and policy
making
Decision making processes
Introduction: WRM
Sustainability Science and IWRM
C.G. 18
• Analysis (observations, hypotheses, etc.)
• Modelling (mental, empirical, mechanistic,
mathematical, etc.)
Analysis
Modelling
Courtney, 2001
3
Effects of spatialisation methods
Spatial analysis
C.G.
C.G. 20
Raster data model
Hydrologic fluxes (x,z)
Spatial entities
Sampling
Spatial analysis
Spatial analysis
Discretization
C.G. 21
Hydrologic balance in agroecosystems (x,z)
Spatial analysis
Spatial analysis
Hydrologic fluxes (x,y)
4
Geostatistical spatial analysis
Spatial filters
2
1 N (h)
∑ [z( xi) − z ( xj)]
2 N ( h) i ≠ j
Spatial analysis
Spatial analysis
γˆ (h) =
C.G. 26
C.G. 25
Criterion/factor maps
Rainfall
Spatial analysis
Land use
Temperature
Elevation
Segmentation
Hillshade
Processes, patterns, systems
Fractal
C.dorsatus
Aspect
C.G.
C.G. 27
Fuzzy membership to suitability
for C.dorsatus
Suitability analysis with
MCE-OWA for C. dorsatus
Avg. T° Jul
1.0
Avg. T° Jan
0.8
1.0
0.6
Aspect
0.8
1.0
0.4
0.6
Elevation
0.8
0.2
1.0
0.4
Precip. Jul
0.6
0.0
15
0.2
0.8
20
0.0
-2.0
1.0
30
25
0.4
0.6
0.2
-1.0
0.0
0
35
Slope
0.8
0.0
1.0
450.2 90
135
0.4
2.0
3.0
1.0
0.6
180
225
0.8 270
315
360
0.4
0.6
0.0
0
0.2
500
1000
1500
2000
2500
0.4
0.0
100
0.2
110
120
130
140
150
0.0
0
C.G. 29
10
20
30
40
50
C.G. 30
5
Suitability C.dorsatus (Biomapper vs. MCE-OWA)
90
80
70
60
50
40
Connectivity analysis
100
TRUE POSITIVE (%
Suitability map
30
Green: suitable
without populations
20
Biomapper (ROC = 0.853)
10
Constrained MCE (ROC = 0.879)
0
0
10
20
30
40
50
60
70
80
90
100
FALSE POSITIVE (%)
C.G. 31
C.G. 32
Socio-ecosystem: definition
Socio-ecosystems
Identification of protected areas
Socio-ecosystems
C.G. 33
C.G. 35
• Social-ecological systems (or socioecosystems; 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
C.G. 34
Socio-ecosystem governance
Pixelizing vs. socializing
• 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 governance
ªBuild up adaptive capacity: the capacity
of a SES to manage resilience in relation
to alternate regimes
• Socializing the pixels: to take remote
sensing and other geophysical data
beyond their usual use in applied
sciences, to address the concerns of
social sciences (patterns → processes)
• Pixelizing the social: linking socioeconomic infromation and models (e.g. SABM) with raster imagery (processes →
patterns)
C.G. 36
6
Conceptual model
Modelling
Modelling
C.G.
C.G. 38
LUC scenario 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 variables
Cellular automata
Distance from villages and loss of open areas
distance (m)
0
1000
2000
3000
4000
5000
6000
7000
8000
0
loss of open areas (%)
Modelling
Modelling
Relational diagrams and models
-20
-40
-60
-80
-100
C.G. 40
Impact indicators
YLD
Crop
production
NO3_OUT
Modelling
Nitrate transport in surface
waters
ORGN_OUT
C.G. 39
Decision making process
Organic nitrogen transport in
surface waters
C.G. 41
C.G.
7
Decision making process
Knowledge based DM process
Problem recognition
C.G. 43
DM is and iterative process
C.G. 45
Adapt. from Belton and Steward, 2002
Alternative generation
Simulation
Scenario
analysis
Choice /
Decision
Implementation
Scenarios and simulations
C.G.
Scenarios
Scenario analysis and simulation
C.G. 47
Problem definition
Modelling
C.G. 44
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
Public participation
Courtney, 2001
Analysis
• 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.
C.G. 48
8
Potentials of scenario
approach
IPCC SRES Scenarios
Suitability: current vs.
HadA1-2020
HadA2-2020 suitability
Current suitability
Change detection
C.G. 50
IPCC SRES
C.G. 49
The DPSIR meta-model and
communication framework
Decision support
Integrated Assessment
Modelling
• 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
Driving
state, damage caused
Response
Forces
• Response = Decisional option
= Effort to solve the problem Pressures
caused by the specific impact
State
√ Integrated Assessment: a process of combining,
Decision support
Decision support
Institute for Alternative Futures
C.G.
C.G. 51
Impact
C.G. 53
• 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.
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 assessments
2. 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
C.G. 54
9
Integrated Modelling and EIA in
the DPSIR framework
Decision support
Decision support
Integrated Modelling
C.G. 55
C.G. 56
Problem solving approach
Decision support
Decision support
Effects of External Drivers
C.G. 57
C.G. 58
C.G. 59
IAM in the DPSir framework
Decision support
Decision support
DPSIR framework as an IA
[meta]model
C.G. 60
10
IAM in the DPSIR framework
Decision support
Decision support
IAM in the DPSir framework
C.G. 61
C.G. 62
A schematic DPSIR model for
water resources management
1: FORZANTI ESTERNE
SISTEMA TERRITORIALE: RISORSE IDRICHE
2: DETERMINANTI
1,40
1,25
0,13
1,05
1:
2:
3:
4:
RISPOSTA
FORZANTI ESTERNE
3: PRESSIONI
4: STOCK RISORSA
1
S DPS
4
~
1,05
0,95
0,10
1,00
1:
2:
3:
4:
DETERMINANTI
2
2
3
3
3
1
STATO RISORSA
2
4
IMPATTO
1
0,70
0,65
0,07
0,96
Decision support
0.00
STOCK RISORSA
25.00
50.00
Time
Rinnovazione
75.00
10.33
100.00
mer 18 mag 200
Untitled
Tasso rinnovazione
1: PROGRAMMA MISURE
1:
PROGRAMMA
MISURE
1
1
IR
LIMITE IMPATT
ACCETTABILE
1
1:
1
1:
0
1
1
MISURA
0.00
20.00
40.00
60.00
Time
80.00
10.33
Decision support
1:
2:
3:
4:
PRESSIONI
Planning and Decision Making in
the DPSIR framework
100.00
mer 18 mag 200
Untitled
C.G. 63
C.G. 64
C.G. 65
MCA in the DPSIR framework
Decision support
Decision support
Planning and Decision Making in
the DPSIR framework
C.G. 66
11
Spatial information
in the DPSIR framework
Decision support
Decision support
MCA in the DPSIR framework
C.G. 67
C.G. 68
Spatial multi-criteria analysis
1/4
MTR
1/4
NT
ER
1/4
1/4
Impact index for
surface water
Distance to Landscape
water
diversity
3/4
Vulnerability of
surface water
Decision support
1/2
1/2
RISK FOR
SURFACE WATER
Scenario 1
1/4
RDLr
MTL
1/2
1/2
Impact index for
groundwater
Protection
of
groundwater
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
Vulnerability of
groundwater
1/2
1/2
RISK FOR
GROUNDWATER
Scenario 2
Decision support
RDR
Spatial multi-criteria evaluation
EVALUATION OF ALTERNATIVE LAND USE SCENARIOS
C.G.
Methodological remarks
Concluding remarks
Concluding remarks
C.G. 70
• 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.
C.G. 72
12
Filling the science-policy gap
(2/2)
• 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
• 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
Concluding remarks
Concluding remarks
C.G. 73
Filling the science-policy gap
(1/2)
C.G. 74
13

Spatial analysis for integrated natural resources management and

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