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An integrated framework to assess plausible future
livelihood and poverty changes in deltas:
an application to coastal Bangladesh
AGU Fall meeting 2015
GC43G: Sustainable Deltas:
Multidisciplinary Analyses of Complex Systems II
17 December 2015, San Francisco
A.N. Lázár1, A. Payo1, R. J. Nicholls1, C. Hutton1, H. Adams2, M. Salehin3, A. Haque3, D. Clarke1, L. Bricheno4, J.A. Fernandes5, Mofizur Rahman6, Ali Ahmed6, P.K. Streatfield6
1 University of Southampton, United Kingdom, [email protected] University of Exeter, United Kingdom3 Bangladesh University of Engineering & Technology, Bangladesh4 National Oceanography Centre, Liverpool, United Kingdom5 Plymouth Marine Laboratory, United Kingdom6 International Centre for Diarrhoeal Disease Research, Bangladesh
ESPA Deltas: Overarching aim
To provide policy makers with the knowledge and tools to enable them to evaluate the effects of policy decisions on ecosystem services and
people's livelihoods
Vision: Link science to policy at the landscape scale
What are the key drivers?
How will these change with time and how do they interact?
What are the consequences of these changes for ecosystem services?
How will these affect the people, particularly the poor?
How can policy processes use this science?
The Consortium21 partners and about 100 members from a range of disciplines
UK (7 partners)
• University of Southampton- Lead Robert Nicholls PI
• University of Oxford
• Exeter University
• Dundee University
• Hadley Centre MET office
• Plymouth Marine Laboratories
• National Oceanography Centre Liverpool
Bangladesh (12 partners)
• Institute of Water and Flood Management, Bangladesh University of Engineering and Technology (BUET) – Prof Rahman Lead PI
• Bangladesh Institute of Development Studies (BIDS)
• Institute of Livelihood Studies (ILS)
• Ashroy Foundation
• Institute of International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B)
• Center for Environmental and Geographic Information Services (CEGIS)
• Bangladesh Agricultural University
• Bangladesh Agricultural Research Institute (BARI)
• Technological Assistance for Rural Advancement (TARA)
• International Union for Conservation of Nature (IUCN)
• University of Dhaka
• Water Resources Planning Organization (WARPO)
India (2 partners)
• Jadavpur University: Indian Lead
• IIT Kanpur 3
Study Area: Land UseComposite ESPA Deltas Project Analysis
Science Questions from National Policy MakersGeneral Economic Division, Planning Commission, Bangladesh
Sealevel rise?
(… cm/100y)
More /extreme
storms?More/intense
rainfall?
Spatial
developments
Subsidence?
More summer
Drought?
Salt
Intrusion?
Decreased
river
Discharge?
Increased
river
Discharge?
Increased
Erosion?
5
Impacts on ecosystem services and rural livelihoods, infrastructure, poverty disaster preparedness of the above.
Model Inputs
Climate-precipitation-temperature-evaporation
Bay of Bengal-mean sea level-(subsidence)
Economy-market price-cost of farm inputs-wages
Levees/Polders-location-height-drainage rate
Demography-life expectancy-fertility rate-migration rate
Hydrology-discharge-sediment
Ecosystem Services-agriculture-aquaculture-fisheries-mangroves
Governance-subsidies-land use planning-infrastructure planning
-cyclone-storm surge
Hazards
Outputs
• fish catches• net earnings from
- farming, - aquaculture & - fishing
Livelihoods
• river salinity• groundwater salinity• union-wise soil salinity • crop productivity
Salinisation
I. Household outputs: a) Bayesian statistical module:
• asset-based relative poverty indicator
b) Process-based module:• economics (income, costs/expenses, savings/assets)
• relative wealth-level• calories / protein intake / BMI• monetary poverty indicators
Household Wellbeing, Poverty & Health
• water elevation • inundated area
Coastal hydrology
II. Regional economic outputs • sectoral output (tons, BDT)• GINI• GDP/capita• income tax revenue• household debt level
Bio-physical environment emulation is based on high fidelity models
• Climate (Met Office Hadley Centre)
• Hydrology (INCA, Delft-3D, FVCOM, ModFlow-SeaWat)
• Bay of Bengal (POLCOMS-GCOMS, fisheries species model)
• Mangrove (SLAMM, Markov chain & cellular automata model)
Own development
• regional soil salinity model
• extended FAO CROPWAT model(with salinity, temperature, CO2, aquaculture)
Verification / Validation
Soil salinity conceptual model
Who are the participants in ESPA Deltas and ΔDIEM development?
50+ agencies
Stakeholders
Five main groups of participants
1586 surveyed households
Households Integrators UsersESPA Deltas team
~100 specialists
ClimateDemographyAgricultureOceanographyFisheriesMangrovesSocial scienceEconomicsWater resource management
Long iteration route that involves seeking advice from a broader team
v
10
Iterative learning with stakeholders
Stakeholder
Integrator
ESPA Deltas Team
Shorter iteration, running ΔDIEM with different inputs, SSPs,…
Users
Stakeholder EngagementWorkshop at General Economic Division, Dhaka16 September 2015
11
StakeholderScenarios for Bangladesh
IntegratedModel
Socio/EnvironmentalModels
Data
SimulationsS1, S2, …, Sn
AdaptationResponses
(e.g. coastal defence, irrigation projects, etc.)
Climate change, etc.
Qu
alit
ativ
e
Qu
anti
tati
ve
Sem
i-Q
ual
itat
ive
Qu
anti
tati
ve
ScenariosNarrative
Bas
elin
e
iterative learning loop
Participatory Modelling(an Iterative Learning Loop)
ExpertsStakeholder
Meeting
13
Stakeholder
User
ESPA Deltas Team
Planned Interventions & Governance
Bussiness as Usual
More Sustainable
Less Sustainable
Global Climate/Demographic/Economic system
Delta Hydrology
Bay of Bengal
Integrator
Fisheries&
AquacultureAgriculture
Mangrove
System startup
Human Wellbeing changes and responses
Household Health, Food & Income
Emulators
Governance Socio-EconomicPhysical & Ecological
nursery
spwaning areas
Floods protection
system
Historical dataHH Survey DataHousehold
DDIEMStructure Issues, scenarios,
interventions
14
ESPA Deltas: Components
Bay Bengal ModelGCOMS
GCM/ RCM
Catchment Models:GWAVA / INCA MODFLOW HydroTrend
Delta ModelFVCOM,Delft3D
Crop Model: CROPWAT
Coastal Fisheries ModelSize- & Species-based models
Temp, rainfall
Sea
leve
l, SL
P, S
ST, w
ind
s
Water, sediment, nutrients
Water flow, level, salinity, temp, sediment, nutrients
Primary productivity, T,S,O2, currents
Mangrove Model
Quantitative Physical/Ecological Models
Inland Fisheries Model
Morphology &
Land CoverAquaculture Model
Surg
e le
vel
Lan
d U
se
Laws, policies
Gaps, Conflicts, Implementation efficiencies
Key issues, Scenarios
Governance research
Know
ledge inte
gra
tion /
Scenarios (
ΔD
IEM
)
Populationprojections
Demographics, economics & poverty
Qualitative surveyrelationship b/t environment & social behaviour
Quantitative survey(consumption, assets, employment, migration, health, poverty, …)
Pro
cess
un
der
stan
din
g fo
r ea
ch s
oci
o-e
colo
gica
l gro
up
+ q
uan
tifi
ed b
ehav
iou
r
Spatial associative model b/t land use and poverty
Plausible futures
SRES
A1
B
(RC
P4
.5/6
-8
.5)
Development Scenarios
Less Sustainable
(LS)
Business As Usual
(BAU)
More Sustainable
(MS)
warmer (Q8)
moderately warmer &
wetter (Q0)
warmer & wetter(Q16)
by
20
50
16
Key factors associated with highest asset poverty (Census): Red is soil salinity, Green is waterlogging,
Yellow is access to market Amoaka Johnson et al. (in review) Sustainability Science.
[email protected]@soton.ac.uk
Q0BAUQ0LS Q0MS
Q8BAUQ8LS Q8MS
Q16BAUQ16LS Q16MS
2050Asset-poverty: Likelihood for being in the poorest quintal
i) Climate has minimal effect
ii) Changes in sustainability practices impacts around the Sundarbans fringe
iii) The stubborn poverty in the East is due to access and infrastructure
Less Sustainable Future 2050
More Sustainable Future 2050
Investment in Sustainability (water resource management, agricultural adaptations, coastal defence)
Requires investment in transport infrastructure
Sustainability levels identified by stakeholders
World Bank (national)Simulation (min/mean/max)
All 9 scenarios shows a decrease in the mean $1.9 poverty (consumption-based;
% population), but some unions remain very poor
The GINI coefficient measures the inequality. The HIES GINI coefficient for rural areas is captured well with the simulated mean value. The national average World Bank GINI coefficient is lower than our simulated value. However, we simulate remote rural areas, where we expect larger income differences. Spatially, the year 2014 is plotted. It seems that the Barisal division has higher GINI coefficients, thus the income-based inequality is larger there.
HIES (rural, 2000-2010), World Bank (national, 2013-14)Simulation (min/mean/max)
Q0 LS Q0 BAU Q0 MS
Q8 LS Q8 BAU Q8 MS
Q16 LS Q16 BAU Q16 MS
2050
Mean socio-economic inequality (GINI, % population) decreases, but some areas remain very inequal HIES (rural, 2000-2010),
World Bank (national, 2013-14)Simulation (min/mean/max)
ΔDIEM builds on simulators & observationsPrimary/secondary data and expert knowledge provide the basis of the Household module
ESPA Deltas seasonal household survey & HIES datasets:
• 21 household archetypes based on seasonality of livelihoods
• economic decisions
• poverty/health indicators
Archetypes Season 1 Season 2 Season 3
1 Small Business Small Business Small Business
2 Small scale Manufacturing Small scale Manufacturing Small scale Manufacturing
3 Farming Farming Farming
4 Fishing Fishing Fishing
5 Small scale Manufacturing Farm Labour Small scale Manufacturing
6 Small scale Manufacturing Small Business Small Business
7 Small Business Small Business Farming
8 Small Business Small Business Small scale Manufacturing
9 Farming no Job Farming
10 Farming Small scale Manufacturing Small scale Manufacturing
11 Farming Small Business Farming
12 Small scale Manufacturing Small Business Small scale Manufacturing
13 Farming Small Business Small Business
14 Small scale Manufacturing Fishing Small scale Manufacturing
15 Small Business Small scale Manufacturing Small Business
16 Farm Labour Farm Labour Small scale Manufacturing
17 Farm Labour Small scale Manufacturing Small scale Manufacturing
18 Small scale Manufacturing Small scale Manufacturing Small Business
19 Small Business Small scale Manufacturing Small scale Manufacturing
20 Forest Dependent in >1 season
21 All Other types
Observation (HIES)Simulation (min/mean/max)
Simulated mean household food expenditure (BDT/month) follows sparse observations, but shows large annual fluctuations pre-2020
Q0-BAU
Labsa (Satkhira)
Observations:IWM Annual Research Report (BARI 2009-2014)
Simulated soil salinity (dS/m)reproduces observed seasonality & magnitude
Q0 LS Q0 BAU Q0 MS
Q8 LS Q8 BAU Q8 MS
Q16 LS Q16 BAU Q16 MS
Inundated Area (% change 2050-2000) for non-protected land greatly increases under Q16 scenario, but not under other climates
Take home message
ΔDIEM:
o a transdisciplinary tool
o a holistic way for thinking and communication
Baseline projection results:
o Socio-economic scenarios are more important than climate scenarios at household level.
o Importance of Ecosystem Services is likely to decrease
o Poverty and Inequality are likely to decrease, but not in marginal areas