Assessing future environmental, livelihood & poverty changes
in coastal Bangladesh: an integrated framework
Social Simulation of Fisheries and Coastal ManagementManchester Metropolitan University
6-7 June 2016
Attila Lazar ([email protected])University of Southampton
on behalf of the ESPA Deltas consortium
http://www.espadelta.net/
The ESPA Deltas consortium
• University of Southampton - Robert Nicholls PI
• University of Oxford• Exeter University • Dundee University • Hadley Centre MET office • Plymouth Marine
Laboratories National Oceanography Centre Liverpool
• Jadavpur University• IIT Kanpur
• Institute of Water and Flood Management, Bangladesh University of Engineering & Technology
• Bangladesh Institute of Development Studies • Institute of Livelihood Studies • Ashroy Foundation• Institute of International Centre for Diarrhoeal Disease
Research, Bangladesh • Center for Environmental and Geographic Information
Services • Bangladesh Agricultural University• Bangladesh Agricultural Research Institute • Technological Assistance for Rural Advancement • International Union for Conservation of Nature • University of Dhaka• Water Resources Planning Organization
UK (7) Bangladesh (12) India (2)
3
• Why Deltas
• Introduce ESPA Deltas
• Introduce ΔDIEM
• Some plausible futures of coastal Bangladesh
Outline
Home to over half a billion (>7%) people (occupy only1% of the world’s land)
Economic hotspots with significant EcosystemServices (fertile land, agriculture, fisheries, aquaculture,forest products, flood/surge protection)
Upstream human actions reduce water (>40%) andsediment supply (~26%): subsidence, loss ofwetlands, accelerated erosion, salinization.
Sea level rise increases salinity & accelerates landloss
Tropical storms & cyclones cause devastatingflooding
High population density, intensive land use andpotential for future mass migration.
Deltas are Hotspots
4
Population potentially displaced by current sea level trends to 2050
Source: IPCC AR4 using data in Ericson et al. (2006)
Threatened Deltas
6
Science questions from policy makersGeneral Economic Division, Planning Comission, 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?
And assessment of the potential Impact of these drivers on ecosystemservices (agriculture, fisheries, forestry, fresh drinking water),
infrastructure, disaster preparedness, and rural livelihoods.
ESPA Deltas (2012-16): 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
Key Questions
• What are the key drivers of change?
• 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?
Bay of
Bengal
9
Study area and project elements
Exogenous drivers(upstream flow diversion, ocean circulation, climate change, macro-economics, …)
off-shore fisheries
fisheries
Sundarbans
agriculture
aquaculture
char land
Endogenous drivers(Bangladeshi policies, laws, subsidies, flood management, subsidence, …)
Live
liho
od
& la
nd
use
demography incl. migration
security (financial,
environmental)
markets
livelihood & poverty
10
ESPA Deltas: Components
Bay Bengal:GCOMS
ClimateHadRM3/PRECIS
Upstream Basin:INCA MODFLOW HydroTrend
Delta PlainFVCOM,Delft3DMODFLOW
AgricultureCROPWAT
Coastal FisheriesSize- & Species-based models
Temp, rainfall PE, etc.Sea level, SLP, SST, winds
Water, sediment, nutrients, salinity
water, salinity, sediment Mangrove
Quantitative Biophysical Models
MorphologyLand Cover Land Use
Aquaculture
Surg
e le
vel
Primary productivity, T,S,O2, currents
Soil salinity
flooding
Economic analysis & modelling
Socio-Economic Data Collection & Analysis
Quantitative household survey (consumption, assets, employment, migration, health, poverty, …)
Statistical Associative model(land use, environment, socio-
economy, census)
Population projections
Qualitative household survey(ES vs. livelihoods)
Laws, policies:Gaps, Conflicts, Implementation efficiencies
Stakeholder engagement: Key issues, Scenario development, Iterative learning
Governance Analysis & Stakeholder Engagement
Know
ledge inte
gra
tion (Δ
DIE
M)
Scenario d
evelo
pm
ent &
quantification
Delta Dynamic Integrated Emulator Model (ΔDIEM)
11
• A modelling tool to formally synthesise results AND
aid analysis and planning in coastal Bangladesh.
• Designed to test a range of future scenarios and quantify interdependencies of: • the bio-physical environment and ecosystem services;
• rural livelihoods, poverty & health;
• associated governance.
• PC-based metamodel• fully coupled
• harmonises scales & method
• fast run-time
• includes feedbacks
• operates at the (653) Union level AND
daily/weekly/monthly/yearly time step.
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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
Participant
Issues, scenarios,interventions
50+ agencies
1586 surveyed households
~100 specialists &
students
BUET
Organisation
ΔDIEM conceptual 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
Verification:
• programming bugs
• ΔDIEM emulator vs. high fidelity simulator outputs
Validation:
• ΔDIEM outputs vs. other datasets (spatially / temporally)
Verification / Validation
Bio-physical environment emulation is based on high fidelity models
• Climate (MetOffice Hadley Centre)
• Hydrology (INCA, Delft-3D, FVCOM, ModFlow-SeaWat)
• Bay of Bengal (POLCOMS-GCOMS, fisheries species model)
• Mangrove (SLAMM, Markov chain & cellular automata model)
Novel integration approaches
• emulation methodology
• regional soil salinity component
• extended FAO CROPWAT model(with salinity, temperature, CO2, aquaculture)
Verification / Validation
Soil salinity conceptual model
FVCOM results (Q0-BAU, year 2000) ΔDIEM emulation (Q0-BAU, year 2000)
Emulated river salinity (ppt) matches well the FVCOM results
𝑤𝑖 = 1 − 𝑑𝑖𝑟
2
Verification / Validation
Labsa (Satkhira)
Observations:IWM Annual Research Report (BARI 2009-2014)
Simulated soil salinity (dS/m) reproduces observed seasonality & magnitude
Verification / Validation
The novel process-based household module builds on
• primary data (ESPA Deltas household survey: qual. & quant.)
• secondary data (BBS, HIES)
• expert knowledge
Key features:
• Coupled with bio-physical changes
Verification / Validation
• 37 household archetypes (based on
seasonality of livelihoods)
• economic decisions (i.e. coping strategies including loans)
• poverty/health indicator outputs
Observation (HIES)Simulation (min/mean/max)
Household food expenditure (BDT/month) follows sparse observations, but shows large annual fluctuations pre-2020.
Q0-BAU
Verification / Validation
HIES (rural, 2000-2010), World Bank (national, 2013-14)Simulation (min/mean/max)
Income inequality (GINI, %
population) matches rural observations, but not national.
Verification / Validation
The novel geo-spatial (statistical) asset poverty module built on
• CENSUS, and other socio-economic indicators
• Land cover and land use maps
• Soil salinity, flooding indicators
Inputs:Land coverLand useSoil salinityWaterloggingAccess to marketEmployment rateChildren in schoolLiteracy rate
Amoako Johnson et al. Sustain Sci (2016) 11:423–439
Output:Asset poverty index:The likelihood for being in the poorest poverty quintal
BD Health Survey 2011
Census 2011 ΔDIEM (year 2011)
Simulated (relative) asset poverty indicator captures observed spatial variability
Verification / Validation
Amoako Johnson et al. Sustain Sci (2016) 11:423–439
Stakeholder Engagement
An ongoing process from the beginning
of the project:
• Issue Identification
• Scenario development
• Policy exploration
Participatory Modelling
the Iterative Learning Loop
Long iteration route that involves seeking advice from a broader team
v
26
Stakeholder
Integrator
ESPA Deltas Team
Shorter iteration, running ΔDIEM with different inputs, SSPs,…
Users
Participatory Modelling
the Iterative Learning Loop
Relevant management/policy questions for ΔDIEM analysis
today future
• renegotiated Farakka treaty
• changing polder heights
• land zoning policies
• new potential crops
• farming subsidies
• guaranteed crop prices
• fishing regulations
• groundwater use policies
• new loan types
28
ESPA Deltas’ scenario frameworkSR
ES A
1B
(RC
P 6
.0 -
8.5
)
By
20
50
Development Scenarios
Less Sustainable(LS)
Business As Usual(BAU)
More Sustainable(MS)
moderately warmer& wetter
(Q0)
warmer& wetter, but variable
(Q8)
much warmer& drier
(Q16)
Sea Level scenariosMean sea level rise (compared to 2000)
2050 2099
Q0 +0.25 m +0.73 m
Q8 +0.21 m +0.58 m
Q16 +0.23 m +0.56 m
Subsidence (2000 to 2100): 0.3 m
Kay et al 2015. Environ. Sci.: Processes Impacts, 2015, 17, 1311
Q16 might be the ‘worst’ climate – higher temperature &decreasing rainfall
Climate scenarios
2100 2100
5 – much below normal
4 – below normal
3 – near normal
2 – above normal
1 – much above normal
Getting wetter Getting dryerVery variable
An
nu
al m
ean
Dry
sea
son
• The number of days with low flows and high flows seem to gradually increase in most scenarios
• More extremes and large inter-annual valriabilityare likely
River flow scenariosDrought / Flood indices
32
Fishery scenariosBay of Bengal total catches and values
• Current rates of catches are not sustainable.
Fernandez et al 2015. ICES Journal of Marine Science; doi:10.1093/icesjms/fsv217
33
Demography scenarios
• Population is expected to decrease even under the MS scenario
Szabo et al 2015. ESRC Centre for Population Change; Working Paper 61; March 2015; ISSN 2042-4116
Economic scenariosPercentage change in ΔDIEM Economic Input Variables by 2030No further change after 2030
Economic input variable LS BAU MSCost of agriculture (seed, pesticide, fertiliser types) 0 10 20Cost of aquaculture (feed, post larvae, fishling) 20 10 0Cost to keep livestock/poultry, fishing, Forest collection 0 10 20Land rent cost (farming) 0 10 20Cost to do Services & Manufacturing business 20 0 -20Market (selling) price of agriculture crops 0 10 20Market (selling) price of fish 30 10 20Market (selling) price of aquaculture crops (shrimp) 0 10 20Income from forest goods (honey, fruits, timber, etc) -20 -10 0Income from Manufacturing, Services and Livestock/Poultry 65 110 165Remittances (BDT/month) 20 30 40Household expenses 0 10 20Daily wage (without food) (BDT/day) 0 10 30Cost of diesel (BDT/gallon) 0 10 20Employment rate (% population) 0 10 30Literacy rate (% population) 2 4 8Children in school (% population) 2 5 10Travel time to major cities -10 -30 -50USD/BDT exchange rate & PPP exchange rate 0 0 0 H
un
t, A
. 201
5. E
SPA
Del
tas:
Eco
no
mic
Po
licy
Dim
ensi
on
s. P
roje
ct R
epo
rt
35
Composite indicatorsNormalised to the minimum/maximum range.
Farming & fishing income:
• weighted average income from farming and fishing
Welfare:
• process-based poverty index
• statistical asset poverty likelihood
Food security:
• calorie intake,
• protein intake
• BMI
GINI:
• Income inequality in union
36
Composite indicatorsNormalised to the minimum/maximum range.
Drought:
• number of day below 20 percentile total inflow
• number of days with no precipitation (March-June)
• Australian drought watch index (March-June)
Flood:
• Number of days above 90 percentile total inflow
• Number of days with >25cm inundation depth
Soil salinity:
• Area averaged soil salinity (March-June)
37
Socio-Economy vs. Provisioning Ecosystem Services
• Q16LS: moderate improvement in poverty and food security, but collapse of rural income and enhanced off-farm sectors. Inequality gently rising after 2025.
• Q0MS: enhanced agriculture, fisheries and off-farm (diversity), large improvement in poverty and food security, but ES income slightly decline after 2025. Inequality gently improving after 2025.
1 – increase0 – decrease
38
Hazards vs. Provisioning Ecosystem Services
• Q16LS: drought with collapse of income and less flooding.
• Q0MS: increase in flood and water availability with rise in income.
• High inter-annual variability for many indicators.
1 – increase0 – decrease
Relative asset poverty index in 2050The likelihood for being poor
- very likely
- very unlikely
• Decline in relative poverty around the Sunderbans
• Stubborn (relative) poverty in the East connected to transport and access.
Q16LS
Q8BAU
Q0MS
40
Relative asset poverty index Key factors controlling poverty
Amoako Johnson et al. Sustain Sci (2016) 11:423–439
41
Dominance of livelihoods (Q0BAU)
• Importance of business and manufacturing incomes increases.
• Small farm owners and fishers rely less on Ecosystem Services.
42
Mean calorie Intake in 2050 (kcal/cap/day)
• Calorie intake, protein intake and BMI are mainly affected by the socio-economic scenarios.
• Magnitude depends on governance
Q16LS
Q8BAU
Q0MS
Crop yield in 2050 (fraction)Mean of all crops throughout the year
• Higher yield and more salt tolerant crops perform better.
• Crop variety depends on the development scenario.
- potential yield
- no yield
Q16LS
Q8BAU
Q0MS
Farming income in 2050 (BDT/month)
Q16LS
Q8BAU
Q0MS
• Most farmers are poor• Better varieties improve livelihood,
not enough for a step change in wellbeing
Intervention examples
• Increased Ganges baseflow
• Agriculture subsidies
46
Intervention: increased dry season flowDaily river salinity (ppt)
Riv
er s
alin
ity
(pp
t)
Q0MSQ8BAUQ16LS
Dry season Dry season Dry season Dry season
47
Intervention: better price for grains (rice, wheat, maize)Relative wealth of farmers in 2050
baseline after intervention
Q0MSQ0MS
Q16LS Q16LS
- least poor
- poorest
Key Conclusions
•Key Findings of relevance to policy
• Future is substantially impacted by policy interventions –more so than climate impacts (until 2050)
• Strong signal of off-farm economics exists within all scenarios
• Diversity of livelihoods is significant with substantial benefits from access to diverse range of economic inputs (agriculture, aquiculture, off farm)
• These are early findings based on the current data, models and assumptions. This needs to be owned and further developed in Bangladesh. This is the beginning of the process.
ESPA Deltas Legacy(Post-December 2016)
• Data at BUET, WARPO and the ESPA Deltas web sites
• Downloadable ΔDIEM from BUET and ESPA Deltas web sites
• Trained and experienced staff at BUET
• Journal papers (27+ journals published to date, with many more to come)
• Newsletters and Policy Briefs
• Established interest and collaboration with the Dutch-funded Delta Plan 2100 project and with the General Economic Division of the Planning Commission in Bangladesh.
DECCMA project (2014-18)
(DEltas, Vulnerability and Climate Change: Migration and Adaptation)
Overarching aims:
1. to assess migration as an adaptation option in deltaic environments under a changing climate;
2. to deliver policy support on sustainable gender-sensitive adaptation in deltaic areas.
http://www.geodata.soton.ac.uk/deccma/
DECCMA objectives
1. to understand the governance mechanisms that promote or hinder
migration of men and women in deltas;
2. to identify climate change impact hotspots in deltas where vulnerability
will grow and adaptation will be needed;
3. to understand the conditions that promote migration and its outcomes, as well as gender-specific adaptation options for trapped
populations, via surveys;
4. to understand how climate-change-driven global and national macro-economic processes impact on migration of men and women in deltas;
5. to produce an integrated systems-based bio-physical and socio-economic
model to investigate potential future migration under climate change;
6. to conceptualise and evaluate migration within a wide suite of
potential adaptation options at both the household and delta level;
7. to identify feasible and desirable adaptation options (planned and autonomous) and support implementation of stakeholder-led gender-
sensitive adaptation policy choices.
DECCMA Study sites
DECCMA integration
Key features:
• Spatial / temporal scenarios
• Adaptation (household/individual level) including migration
• ‘Evaluation’ of success considering gender -> research tem & stakeholder views
Using different modelling techniques:
1. Bayesian Belief Network modelling
2. Meta modelling: building on the ΔDIEM framework but with an adaptive agent-based social model
Lazar et al 2015, A method to assess migration and adaptation in deltas: A preliminary fast track assessment, DECCMA Working Paper
Social Simulation of Fisheries and Coastal ManagementManchester Metropolitan University
6-7 June 2016
Assessing future environmental, livelihood & poverty changes
in coastal Bangladesh: an integrated framework
Attila Lazar ([email protected])University of Southampton
on behalf of the ESPA Deltas consortium
http://www.espadelta.net/
