winter school on system analysis and integrated modelling...

1

Center forEnvironmentalEconomicsand Management

Dipartimento di Scienze Economiche

UniversitàCa’Foscari di Venezia

C.G.

Winter School on

System Analysis and Integrated Modelling in

Climate Change Research

Opening remarks

Carlo Giupponi

Venice, 3-6 February 2009

C.G. 2

• a consortium of three Italian universities (Ca' Foscari, Salento and Sassari), established in 2008 in collaboration with the Euro-Mediterranean Centre for Climate Change (CMCC), with the aim of promoting and coordinating advanced studies on climate change impacts and policy.

• The three universities joined the School through PhD Programmes in the fields of agro-forestry climatology, energy systems and technology, climate change impact, management and policies, assessment and valuation methods, and on advanced integrated modelling. The School is based at the Department of Economics of the Ca' Foscari University of Venice.

• The School supports and organises advanced training and research activities with emphasis on the development of innovative management strategies for both physical and socio-economic climate related phenomena.

Intr

od

uct

ion

2

C.G. 3

• Why a school about System Analysis and Integrated Modelling in Climate Change Research?

• Because of the interests in:

1. bridging of ecologic and economic modelling cultures;

2. innovative methods for integrated modelling of socio-ecosystems: multi-scale, hierarchical, etc.;

3. advances in – more focused – modelling tools and frameworks;

4. improved communication with the policy sphere.

Intr

od

uct

ion

C.G. 4

1. “global climate change […] cannot be considered in isolation from the many other stresses and tensions attending the rapid globalization of the socio-economic system”

2. “…a meaningful coupled climate-socio-economic model needs to encompass a daunting array of interrelated global and regional problems - representing, in effect, the evolution of the entire human civilization and the global ecological system on which it depends.”

3. “can the investigation of a few critical sub-problemsand an examination of their relation to the general problem of societal evolution contribute to a more realistic integrated ”world view”?”

4. “policymakers have received little explicit advice from the scientific community as to the most effective policies for avoiding dangerous climate change (in contrast to the intense lobbying activities of special interest groups).”

Intr

od

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ion

Hasselmann, 2008

3

C.G. 5

Issues and research needs:

• to approach the climate change issue within the broader perspective of sustainable development;

• to develop innovative methods for holistic modelling of socio-ecosystems by integrating different schools and traditions in modelling, exploiting synergies, and focusing on: hierarchies of multiple scales; different levels of details; emergent properties;…

• to select a limited number of critical model components within a comprehensive modelling framework;

• to bridge the gap between research and policy making to communicate policy scientific evidences

Intr

od

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ion




C.G.

The climate change issue in a globalised world: CC and SD

4

C.G. 7

Drivers of changeS

D in

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g w

orl

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C.G. 8

Sustainable development in a changing world

• Broadening the science-defined agenda for studying global change to engage more the social component;

• Linking research to action and reconciling scientific excellence with social relevance;

• Deepening a local-based integrated understanding of vulnerability to global change;

• Exploring the design and management of systems which integrate research, assessment and decision support.

JFK School of Government, Harvard Univ., 2000

SD

in a

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ing

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rld

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C.G. 9

SD and adaptation to climate change

I IPCC-AR4-WG2-Ch20

SD

in a

ch

ang

ing

wo

rld

C.G. 10

SD and adaptive capacity

• the factors that determine a country’s ability to promote (sustainable) development coincide with the factors that influence adaptive capacity relative to climate change, climate variability and climatic extremes:

• access to resources,

• entitlements (property rights),

• institutions and governance,

• human resources (human capital) and

• technology.

→Two way causality between SD and AC

IPCC-AR4-WG2-Ch20

SD

in a

ch

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ing

wo

rld

6

C.G. 11

SD and adaptive capacity

IPCC-AR4-WG2-Ch20 (Swart et al., 2003)

SD

in a

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ing

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C.G.

Holistic modelling of socio-ecosystems

7

C.G. 13

Socio-ecosystem: definition

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

→the concept emphasizes the adoption of an integrated approach for the analysis of both social and economic agents and the natural components of the ecosystem

So

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-eco

syst

ems

C.G. 14

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 governance

So

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-eco

syst

ems

8

C.G. 15

Resilience of socio-ecosystems

• measured by the capacity of a system to absorb disturbance and reorganizewhile undergoing change so as to still retain essentially the same function, structure and feedbacks - and therefore the same identity

• focused on persistence, adaptiveness, variability and unpredictability

→Building up resilience as a strategy preventing the system from moving into an un-desirable regime from which it is either difficult or impossible to recover

The Resilience Alliance

So

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-eco

syst

ems

C.G. 16

A "ball-in-the-basin" representation of resilience

• Both the state of the system and the shape of the basin can change, determining changes in regimes, i.e. in processes and system functions

• Alternate regimes are separated by thresholdsthat are marked by levels of controlling variables where there is a change in feedbacks. It is the changed feedbacks that lead to the changes in function and therefore structure.

The Resilience Alliance

a. b.

System state

Attractor

So

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-eco

syst

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9

C.G. 17

Resilience management

• Facing complex co-evolving systems for sustainability requires the ability to cope with, adapt to, and shape change without losing options for future adaptability (Folkeet al. 2003);

• Strengthening ecosystems’ capacity to provide ecosystem services requires at least three levels of analysis and understanding:

1. the ecosystem and its dynamics, including the processes, elements, and links to other ecosystems that can contribute to resilience.

2. the learning and generation of ecological knowledge, and how it is applied in management practices, including monitoring and responses to ecosystem dynamics

3. the institutional dynamics, the governance system, that underlies management strategies, and that can allow for adequate responses to uncertainty and change

(Berkes and Folke 1998, Berkes et al. 2003

So

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C.G.

Modelling frameworks and innovative modules

Scales, hierarchies, details and emergent properties

10

C.G. 19

Maize potential productionM

od

ellin

g f

ram

ewo

rks

DOYEM 134

AMX 41

EFF 0.45

KDF 0.65

Q10 2

TREF 25

MAINLV 0.03

MAINST 0.015

MAINRT 0.015

MAINSO 0.01

ASRQRT 1.444

ASRQLV 1.463

ASRQST 1.513

ASRQSO 1.49

FRTRL 0.2

RGRL 0.0294

LAICR 4

TBASE 10

CFST 0.494

DTMAX

DTMIN

DTR

1.03 CROP DEVELÉ?

1.03 CROP DEVELOPMENT

1.04 LEAF CO2 ASÉ?

1.04 LEAF CO2 ASSIM

subroutine GLA?

subroutine GLA

1.05 DAILY GROSSÉ?

1.05 DAILY GROSS CO2

ASSIMILATION

1.06 CARBOHYDRÉ?

1.06 CARBOHYDRATE

PRODUCTION1.07 MAINTAINANCE?

1.07 MAINTAINANCE

1.08 DRY MATTERÉ?

1.08 DRY MATTER

PARTITIONING

1.09 GROWTH OFÉ?

1.09 GROWTH OF PLANT ORGANS AND

TRASLOCATION

1.10 LEAF DEVELÉ?

1.10 LEAF DEVELOPMENT

1.11 DRY MATTERÉ?

1.11 DRY MATTER

PRODUCTION

1.12 WEATHER DAÉ?

1.12 WEATHER DATA

Gli studenti sono invitati a sperimentare l'effetto i variazioni dei

parametri ambientali (DTMAX, DTR, ecc.) e colturali (es. TBASE)

sul comportamento della coltura, osservando gli andamenti dei grafici e delle tabelle impostati nel livello sottostante.

Questo modello ¸ stato realizzato sulla

base del modello SUCROS1 (produzione potenziale) per la coltura

del mais, adottando alcune semplificazioni per alleggerire i calcoli

e renderlo pi¯ adatto a scopi didattici.�

�La versione originale in linguaggio FST ¸ stata pubblicata da van Laar et al.

(Eds.) "Simulation of crop growth for

potential and water limited production situation" 1992. I titoli dei moduli sono

tratti dal testo e dal listato originali.��La versione in Stella ¸ ancora in fase

di sviluppo ed ¸ stata realizzata da

Carlo Giupponi come materiale per le esercitazioni degli studenti del corso di

Agricultura e produzioni 1.

C.G. 20

Potential production model

DAVTMP

DVS

IDVS

IntgrlDVS

~DVRR

~DVRV

DVS

AMAX

AMX

~

AMDVS

~AMTMP ~

DTMAX

~

DTMIN

DDTMP

DVS & DVR

AMAX

DTGA & LAI

DLAI & GLAI

DVR

DOY

DTGA

DRY MATTER

IntgrlDVS

GROWTH

RDR

~FCOB

GLV

DOYEM

DVS

LAI

~SLA

GLV

DTEFF

DELT

RGRL

NPLLA0

GPHOT

~FSH

~FLV

~FST

FSO

FRT

GLAI

DTEFF

MAINT

Ea

GLAI

Rc

DLAI

~SLA

KDF

LAIDTGA

~DTR

EFF

DAVTMP

DRY MATTER

DVS

DVS

TEFF

TREF

Q10

~DTR

GPHOT

MAINT

MAINTS

MNDVS

MAINLV MAINRT

DAVTMP

MAINST MAINSO

WLV

WST

WRT

WSO

WLV WST WRT WSO

WLVG

WLVG

~FSH

IntgrlDVS

FRT

~FLV

~FST FSO

F's

ASRQ

ASRQLV

ASRQST

ASRQSO

ASRQRT

FRTRL

EMERG

DOYEM

DOY

TRANSL

DVS

WST

GTW

GRT

CFST

GST

GSO

GLV

LAI

RGRL

intgrlLAI

RDR RDRDV

RDRSL

~RDRSH

DLV

WLVG

DAVTMP~

RDRLT

LAICR

GRT

WLVD

WRTI

WSTI

WLVI

IntrgrlWRT

IntgrlWLVG

GLV

IntgrlWLVD

DLV

GST

IntgrlWST

IntgrlWSO

GSO

IntgrlWLV

TADRW

IntgrlTADRW

TDRW

IntgrlTDRW

HI

DOYDDTMP

TBASE

1.03 CROP DEVELOPMENT 1.04 LEAF CO2 ASSIM

subroutine GLA

1.05 DAILY GROSS CO2 ASSIMILATION

1.06 CARBOHYDRATE PRODUCTÉ

1.07 MAINTAINANCE

1.08 DRY MATTER PARTITIONING

1.09 GROWTH OF PLANT ORGANS AÉ 1.10 LEAF DEVELOPMENT

1.11 DRY MATTER PRODUCTION

1.12 WEATHER DATA

Mo

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C.G. 21

Modelling of river ecology

Haykock, 1999

ZF height in new section

Zero Freshwater

Qe

Cq

ho

q

Zero NO3N conc to saline section

ZF height poly estimate

Q input

Aw

~

prec mm

r

Retention N tonnes

Gate height

T10

Gate width

daily irrigation feed to area

Zero Pond Retention Ng per day

Export at Gate

~Julian Days

Air Temperature

Air Temperature

Freshwater river pond retention N

Mass NO3N Zero Pond

Q input

Aw Cavar cava

Cava Retention Ng per day 2

Zero NO3N concentration

Cava daily tonnes N

Mass NO3N Cava Cavalier

T10

Critical flow into Cava

pump wetland losses

~

Zucc ET95

Cava water volume

Cava outflow



Zero Freshwater

ZF height in current section

Aw current section

New management disturbance pulse

r for current section

T10

Retention Ng per day 3

New mgmt dist pulse smoothed

l per sec into Cava

Current section daily retention

Previous section NO3N tonnes retention

Current management

disturbance pulseCurrent mgmt dist pulse smoothed

Cava Inflow

~

Julian Days

Aw Cava

Graph 2 Phragmites Biomass

~

Julian Days

Scirpus lacustris Biomass

Unit N uptake in Phragmites Unit N uptake in Scirpus

Unit P uptake in Phragmites Unit P uptake in Scirpus

Q input


Graph 3

Mass in Freshwater Section tonnes

NO3N mass Input

Qe


Cava daily tonnes N

Unit N uptake in Phragmites

Area of Freshwater Terrace m sq

Table 1 input and output concentration

riparian N input tonnes per day

Riparian soil kg per day

Unit N uptake in Scirpus

Freshwater N vegetation uptakeFreshwater N vegetation uptake

N03N mass output

Total N Freshwater Retention

Graph 4

Aw saline wetland

overall summary

r saline wetland

saline wetland Retention Ng per day

saline wetland daily tonnes N

Mass NO3N saline wetland

NO3N mass Input

Noname 1

Mass NO3N input

T10


sw

soilw

inflow m per d

~

prec mm

evap m per day~

prec mm prec m per day

exch

outflow

hydra cond

outstotal wat

swmax

NO3 plantNNH4

outNO3

inno3

exch soilw

uptake 1uptake 2

detr N

decay

miner rate~

mort

miner

~

temp

~Dese station A ppt 93

uptake rate

light

~

temp

NO3surf

NO3surf

outNH4

exch NH4

soilw

inNH4

ads N

exch

NH4surf

NH4surf

outs

nitsurf

~

tempinflow m per d

denitsurf

insurfno3

sw

downfl

surfoutNO3

outflow

NO3

exch

wflnh4

denit

t

surfoutnh4

outflow

NH4

insurfnh4

~

temp

~prec mm

evap mm

~

prec mm evap mm

hydra cond

inflow mŠcu per second

Cava totalN retention

terrace daily

vegetation N uptake

inflow mŠcu per second

Zero NO3N conc to Lagoon

Q to lagoon

~

Dese NH4N conc in surface water

~

Dese NH4N conc in surface water

pump wetland losses

Total N Saline Retention


Aw pump wetland

Mass NO3N Zero Terrace

riparian soil % saturation

Aw zero lake

r zero lake

zero lake Retention Ng per day

zero lake daily tonnes N 3

Mass NO3N Zero Lake

T10



Mass NO3N Riparian Woodland

riparian N input tonnes per day

Saturated thinkness

denit

t 2

Input flow

Length of riparian edge

Riparian soil

evap

Saturated thinkness

ppt

Outflow 2

Saline section losses

Poplar ETpopKsat

water surface gradient

vertical elevation difference

width of riparian zone

Qmecs per metre of edge

Ksat

water surface gradient

N dynamics riparian


Q losses

Q at gate

Graph 6

Saline Section



Rip return flow

~

Zucc ET96


Input flow 2soil irrigation

evap maizeppt 2

Outflow 3

Irrigation water 25mm

evap mm~

prec mm


Input flow 3 irrigation soil no irrigation

evap 2ppt 3

Outflow 4

Irrigation water 0mm

Qe

~

Dese data coverted for Zero

~

Zucc Temp 95

Q input

irrigation idealtotal irrigated water used

irrigation area

Input flow 2

tonnes NO3N into irrigation landMass NO3N irrigation land

~Dese PO4P Concentration

tonnes per mq PO4P into irrigation land

total PO4P lost

to irrigation land

Input flow 2

tonnes NO3N into irrigation land

~

Dese PO4P Concentration

Aw pump wetlandMass NO3N pump wetland

evap mm

Cava Inflow

NO3 mass to lagoon

Export N03N to Lagoon


~Dese PO4P Concentration

~Dese NH4N conc in surface water

Air Temperature

Retention PO4P tonnes

Export PO4P at GateMass PO4P Freshwater

Section tonnes

PO4N mass Input

Qe

PO4P mass output

Total PO4P Freshwater Retention

Total PO4P Saline Retention

Saline section PO4P losses

PO4P mass to lagoon

Export PO4P to Lagoon

Cava InflowQmecs per metre of edge


irrigation ideal

Riparian inflow

~



Freshwater N vegetation uptake


pump wetland losseszero lake daily tonnes N 3

Ratio uptake PO4P

ration uptake PO4P 2

PO4N mass Input

Noname 2

Mass PO4P input

Total PO4P budgets

Noname 3

Freshwater N vegetation uptake


Mass PO4P Zero Pond


Noname 4

Mass PO4P Zero Lake

Noname 5

~

Zucc Temp 96

Mass PO4P Zero Terrace

pump wetland losses


Noname 6

Mass PO4P pump wetland

Noname 7

Mass PO4P saline wetland

Noname 8

~

Dese PO4P Concentration Mass PO4P irrigation

irrigation ideal

Noname 9

Cava Inflow

Mass PO4P Cava

~


Riparian inflow

~


Noname 10

Mass PO4P riparian

~

Zucc ppt 95

Previous section PO4P retention

~

Zucc ppt 96

~

Dese station A Temp 93

Zero Catchment General ET Eqn

~

t3

~rinaldo mcu per day yr 1

evap mm















Zero baseflow NO3N 2

t 3

Qmecs calc

NO3N storm calc

~

Qe baseflow

NO3N calc

rinaldo mcu per day

year 1 smooth


















NO3N calc

Qmecs calc

~

Dese NO3N Concentration

Q to lagoon

Graph 7

Zero PO4P conc to Lagoon

Graph 8

Graph 9

Graph 10

Graph 11

pump wetland nutrient budget model

Cava

Freshwater Pond

Freshwater Terrace Veg N uptake

Potential N for Zero Freshwater Lake

Saline Wetland (N)

N mass balances for Freshwater Section

Riparian Woodland

Irrigation of soil <10cm

Zero - New Freshwater Section

Biomass (gDW/m-sq) Equations

Input Variables for Brix unit wetland model

Surface rates pump wetland

Sub-surface rates pump wetland

Water balance for pump wetland m-sq

Input data files

basic sim data Meterological sim dataData used in Current Simulation

Hydro-chemical simulation data

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C.G. 22

Modelling of Carbon cycle

http://mvhs1.mbhs.edu/mvhsproj/carbon/carbon.html

6 stocksMo

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C.G. 23


http://www.shodor.org/Master/environmental/general/carbon/carboncs.html

5 stocksMo

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C.G. 24


http://wufs.wustl.edu/pathfinder/path201/notes_2008/notes_11_06_08.htm

Atmospheric C

Ocean C

Ocean Untake

Ocean Release

Fossil Fuel

Land Plants Soils

Respriation

Photosysthesis

Exhalation

Runoff

Sediments

Deforestation

Detrius

Unknown Sink

CO2 ppm

NH Photosynthesis

Season

Graph 1

4 stocksMo

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C.G. 25

ICES: Intertemporal Computable Equilibrium System

http://www.feem-web.it/ices/model2_fig2.html

Mo

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Nested tree structure forindustrial production

processes

The Demand Side




C.G.

Increased impacts on the policy-making sphere

14

C.G. 27

Modelling and policy/decision making

• “All models are wrong, but some are useful” (Box, 1979)

• …yes, but which ones?

�Some thoughts about bridging CC modelling and policy/decision making on Friday

Mo

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C.G. 28

Open issues

• Integrating CC issues in the SD debate and vice-versa

• Informing policy makers about possible futures → normative scenarios

• Identifying barriers

• Developing capabilities for understanding adaptive cycles

• Developing methods for SES analysis and management

• Developing operational resilience strategies

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C.G. 29

Research needs

• Expand understanding of the synergies in and/or obstacles to simultaneous progress in promoting enhanced adaptive capacity and sustainable development;

• Integrate more closely current work in the development and climate-change communities.

• Search for common ground between spatially explicit analyses of vulnerability and aggregated integrated assessment models.

• Recognise that uncertainties will continue to be pervasive and persistent, and develop or refine new decision-support mechanisms that can identify robust coping strategies even in the face of this uncertainty

• Characterise the full range of possible climate futures and the paths that might bring them forward.

IPCC-AR4-WG2-Ch20

Sci

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winter school on system analysis and integrated modelling...

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