ESPA Deltas Project

Introduction to ESPA Deltas Project

Professor Munsur Rahman, IWFM, BUET Professor Roberts Nicholls, University of Southampton, UK

For ESPA Deltas (www.espadelta.net)

ESPA Scientific Review Meeting Nairobi, 17-18 November 2016 Change to Running Order  Munsur Rahman (BUET): “Introduction to ESPA Deltas Project”

 Robert Nicholls (University of Southampton): “Delta Dynamic Integrated Emulator Model (ΔDIEM): Development and Results”

 Craig Hutton (University of Southampton): “Deltas, ecosystem services and human well-being”

 Discussion (20 minutes) THE CONSORTIUM (100+ members)

UK (7) Bangladesh (12) India (2) • University of • Institute of Water and Flood Management, Southampton- Lead Bangladesh University of Engineering and • Jadavpur Robert Nicholls PI Technology (BUET) – Prof Rahman Lead PI University (Biophysical Modelling) (Physical Modelling) (MangroveMod • University of Oxford • Bangladesh Institute of Development elling): Indian Studies (BIDS) Institute of Livelihood Studies (Scenario Development) (ILS) Lead • Exeter University • Ashroy Foundation • IIT Kanpur (Ecosystem Services and • Institute of International Centre for (Hydrological Poverty) Diarrhoeal Disease Research, Bangladesh • Dundee University (Legal (ICDDR,B) Modelling) context) • Center for Environmental and Geographic • Hadley Centre MET office Information Services (CEGIS) • Bangladesh Agricultural University (Climate Change • Bangladesh Agricultural Research Institute Modelling) (BARI) • Plymouth Marine • Technological Assistance for Rural Laboratories (Fisheries Advancement (TARA) Modelling) • International Union for Conservation of Strategic• National Partner: Oceanography General EconomicNature (IUCN) Division, Planning Commission Centre Liverpool (Marine • University of Dhaka • Water Resources Planning Organization Modelling) (WARPO) Database/others Deltas are Vulnerability Hotspots Deltas are home to over half a billion (>7%) people (occupy only 1% of the world’s land) as they are economic hotspots, food baskets for many nations, supporting much of the world’s fisheries, forest products, and extensive agriculture.  Human actions from upstream deplete them from water and sediment : on a global scale >40% of river discharge and 26% of sediment are being intercepted by large reservoirs.  Local exploration contributes to subsidence, loss of Nile wetlands, and accelerated erosion  Sea level rise increases salinity and accelerates land loss  Tropical storms and cyclones cause devastating Ganges flooding 4 Ecosystem Services/Activities in GBM delta

Key Provisioning and Regulating Ecosystem Services:

 Riverine (Fisheries/Navigation)  Forestry (livelihood/soil conservation) Key Ecosystem  Agriculture/Aquaculture Services (livelihood)  Wetlands/Floodplains (Fisheries/flood protection)  Marine Fisheries (Livelihood)  Mangrove (protection from flooding /sediment trap/fisheries) Multiple Scales in the GB Delta HUMAN ‘PROCESSES’ River Floods/ Sediment Supply Changing Land Use/Catchment Management

People displaced by 2100 in Bangladesh 42 to 54 million (23% to 30% of total)

 Flooding/Erosion Climate  Sedimentation ‘Global Variability  Salinization Climate  Subsidence Change’  Water logging  Sea Level rise NATURAL Lecture 4. Climate change and the Cyclones/ integrated coastal system. Wednesday PROCESSES 25 July 2007 Marine Processes 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 Engagement: With national level policy processes that impact at a community level ESPA Delta: Objectives

In Coastal Bangladesh  Present relationship between ecosystem services (ES) and human well-being and health?  How ES might evolve in the next 50-100 years (~demographic, economic, social, environmental, climate change and variability)?  Policy influence? Study Area  Robust policies, effective across the range of uncertainty? ESPA Deltas: Project Structure Stakeholder Engagement (since 2010) Engagement process from the beginning of the project: • Issue Identification • Scenario development • Policy exploration Bangladesh Delta Plan 2100 www.bangladeshdeltaplan2100.org/ Historical Trends and Trade-Offs

 Since the 1950s, increasing gross domestic product and per capita income mirror rising levels of food and inland fish production (PS).  In contrast, non-food ecosystem services (RS) such as water availability, water quality and land stability (RS) have deteriorated.

Trade-off between use of environmental Hossain et al. (2014) resources and development Socio-Economic Data Collection and Analysis

• 7 socio-ecological systems based on:  Dominant livelihood type (~land use) Study Area: Land Use  Proximity to area of natural resources  Unique characteristics of land formation Biophysical Modelling Biophysical Modelling Climate models Hydrological modeling for GBM basins: Changes in river flow

Coastal modeling Coastal modeling Coastal modeling Groundwater modeling (inundation) (salinity) (cyclone/ storm surge) (salinity)

Numerical simulation of groundwater salinity

Sundarban modeling Area loss Species distribution Biophysical Modelling  Yield expected to decrease should soil salinity increase  Area losses in Sundarbans – 7-23%  Increasing dominance of salt tolerant species  decrease fish production in Bangladesh EEZ by up to 10%. ; Good management can achieve higher catches Fisheries

Sundarban Socio-Economic Data Analysis Poverty has spatially differentiated drivers Poverty has a spatial distribution (i.e. not random)

 Salinity intrusion is more important in the poorest unions in the Satkhira and Khulna  Environmental factors are important for unions in Bagerhat  Lack of access to markets and other facilities and human capital (education and employment) have a stronger impact in unions in Patuakhali and Bhola

o Ecosystem services are critical for income and well-being of poor people; o Diverse livelihoods are associated with lower levels of poverty o Those without ES are those most likely to be both materially poor and experience low satisfaction with life. o Richer people enhance well-being by diversifying livelihoods Integration: Delta Dynamic Integrated Emulator Model (ΔDIEM)

GCM/ RCM Laws, Temp, rainfall policies

Catchment Models: Gaps, Conflicts, GWAVA / INCA Implementation Key issues,

MODFLOW DIEM)

HydroTrend Δ efficiencies Scenarios Governance research Sea level, SLP, SST, winds Water, sediment, nutrients Crop Model: Delta Model Qualitative survey Morphology & CROPWAT FVCOM, relationship b/t Delft3D Land Cover environment & social Aquaculture behaviour Model

Water flow, level, salinity, Land Use Quantitative survey temp, sediment, Mangrove (consumption, assets, nutrients Model employment, migration, Surge level Surge ecological group

- health, poverty, …) Coastal Fisheries Bay Bengal Model Primary productivity, Model Spatial associative GCOMS T,S,O2, currents Size- & Species- model b/t land use based models Process understanding behaviour quantified + and poverty Knowledgeintegration / Scenarios ( for each socio

Quantitative Physical/ Inland Fisheries Population projections Ecological Models Model Demographics, economics & poverty Snap shot of Journal and Conference Papers

ESPA Deltas book TITLE: “Integrated Assessment for Policy Analysis: Rural Livelihoods in Deltas” EDITORS: Nicholls, Hutton, Hanson, Rahman, Salehin and others, Palgrave Publisher Web site hosted at: www.espadelta.net ESPA Deltas Final Events: Bangladesh 30/31 October and London 22/23 November Science-Policy interaction in adaptive delta planning Sharing key features of Bangladesh Delta Plan 2100 and ESPA Deltas Project

Goals: RiU, Linkage with BDP 2100, Potential use of ESPA deltas results in BDP Summary Outcomes:  Policy intervention can be assessed using the developed tool (▲DIEM)

 ▲DIEM can be adjusted to judge investment 21 decision both in the short and long term basis Key Outputs

. Biophysical and socio economic surveys, data, analysis and insights . Internal Reports (6) . Journal (40+) and Conference (20+) Papers, with more in the process of publication . Newsletters/Policy Briefs/ Newspaper Articles . A set of coupled biophysical models . The Delta Dynamic Integrated Emulator Model (ΔDIEM) including a livelihoods module . Application of ΔDIEM to the Bangladesh Delta Plan 2100 . National/Regional/Local Dissemination of results ESPA Deltas Legacy

. A new integrated framework for analysing ecosystem services and livelihoods in coastal Bangladesh . Data and models in Bangladesh at BUET, WARPO . Trained and experienced staff in Bangladesh at BUET . Numerous academic and policy relevant outputs . Good relationship with national stakeholders . General Economic Division (GED) of the Planning Commission and the Bangladesh Delta Plan 2100 (BDP2100) . Strong interest from national stakeholders (GED) in further application and development within BDP2100: . Detailed application of ΔDIEM to the Coastal Hotspot . Extending ΔDIEM to a National Coverage . Related projects – DECCMA on migration and adaptation . (http://www.geodata.soton.ac.uk/deccma/) Thank You Introduction to ESPA Deltas Project

Professor Munsur Rahman, IWFM, BUET Professor Roberts Nicholls, University of Southampton, UK

For ESPA Deltas (www.espadelta.net)

ESPA Scientific Review Meeting Nairobi, 17-18 November 2016 Delta Dynamic Integrated Emulator Model (ΔDIEM): Development and Results

Robert J. Nicholls, University of Southampton Mashfiqus Salehin, Institute of Water and Flood Management (IWFM), BUET Attila Lazar, University of Southampton

and the ESPA Deltas team

ESPA Scientific Review Meeting Nairobi, 17-18 November 2016 Plan

• Bio-physical and socio-economic/governance components and their integration

• Delta Dynamic Integrated Emulator Model (ΔDIEM)

• Illustrate some plausible futures for coastal Bangladesh

• Concluding remarks

2 The ESPA Deltas Approach

Analysis of ecosystem services and human livelihoods in coastal Bangladesh requires: • integration of the social, physical and ecological dynamics of deltas • identification and quantification of the mechanisms by which the system components interact to produce human well-being

Hence, the ESPA Delta Project has: • applied participatory techniques to engage stakeholders to identify issues, develop scenarios and discuss results • determined which physical and biological processes affect life, livelihoods, health and mobility • analysed and quantified these relationships • developed a predictive model to analyse scenarios and explore possible futures Study Area

4 Key biophysical factors and links to governance and socio-economic factors

5 ESPA Deltas: Components Study area of ESPA Deltas: • GBM river basin • Bay of Bengal • SW coastal zone of Bangladesh Delta Dynamic Integrated Emulator Model (ΔDIEM)

• Interdisciplinary tool - environment - socio-economy - demography - governance

• Builds on - high fidelity models, - secondary data - ESPA Deltas household survey (1486 households, sampled three times over a year) - expert knowledge (ESPA Deltas team, stakeholders)

• Meta-model - harmonises scales & methods - fully coupled - ‘quick’ running time 7 The participants in ΔDIEM development

Five main groups of participants

Stakeholders Households ESPA Deltas team Integrators Users

50+ agencies 1586 surveyed ~100 specialists & households students Climate Demography Agriculture Oceanography Fisheries Mangroves Social science Economics Water resource management Delta Dynamic Integrated Emulator Model (ΔDIEM)

Long iteration route that involves seeking advice from a broader team Iterative learning Shorter iteration, running ΔDIEM with different inputs, SSPs,…

Stakeholder ESPA Deltas Team

Users Integrator 9 ΔDIEM – Main Inputs

Climate Hazards Demography -precipitation -cyclone -life expectancy -temperature -storm surge -fertility rate -evaporation -migration rate

Hydrology Levees/Polders Economy -discharge -location -market price -sediment -height -cost of farm inputs -drainage rate -wages

Bay of Bengal Ecosystem Services Governance -mean sea level -agriculture -subsidies -(subsidence) -aquaculture -land use planning -fisheries -infrastructure -mangroves planning ΔDIEM – Main Outputs Coastal hydrology Household Wellbeing, Poverty & Health • water elevation • inundated area • Inundation depth

Salinisation I. Household outputs: • river salinity a) Bayesian statistical module: • asset-based relative poverty indicator • groundwater salinity b) Process-based module: • union-wise soil salinity • economics (income, costs/expenses, savings/assets) • crop productivity • relative wealth-level • calories / protein intake / BMI Livelihoods • monetary poverty indicators • fish catches II. Regional economic outputs • sectoral output (tons, BDT) • net earnings from • GINI - farming, • GDP/capita - aquaculture & • income tax revenue - fishing • household debt level ΔDIEM – Household Component

37 ΔDIEM Verification and Validation

Verification: • programming bugs • ΔDIEM outputs vs. high fidelity simulator outputs • coupled ΔDIEM outputs: make sense?

Validation: • ΔDIEM outputs vs. other datasets (spatially / temporally) Example validation: Novel Union-level salinity model. Simulated soil salinity (dS/m) follows seasonality & magnitude

Labsa (Satkhira)

Observations: IWM Annual Research Report (BARI 2009-2014) ESPA Delta Scenario Framework and Participatory Methods (Iterative Learning Loop)

Development Scenarios Less Business As More

8.5) Sustainable Usual Sustainable - (LS) (BAU) (MS)

Q0 X

(RCP 6.0 (RCP Q0MS

By 2050 By X Q8 Q8BAU

Q16 X

SRES A1B A1B SRES Q16LS

15 ESPA Deltas Scenarios -- Examples

• Climate, sea level and subsidence • Cropping patterns • Land cover • River flow and nutrient inputs (from INCA model) • Fisheries (from PML models) • Economic growth • Demography • Etc.

16 (i) Example scenarios – Fisheries – from PML models Bay of Bengal total catches and values

5 5 10 Hilsha 10 Bombay Duck Current catches are 6 2.5 not sustainable, but 2 4 significant fisheries 1.5 can be sustained with 1 2 appropriate 0.5 Total catches (tons) Total catches (tons) management 0 0 Q0MS 2000 2024 2050 2000 2024 2050 Q8BAU

11 10 Q16LS 10 Hilsha 10 Bombay Duck 3 2

1.5 2

1 0.5 Total value (BDT/year) Total value (BDT/year)

0 0 17 2000 2024 2050 2000 2024 2050 (ii) Example scenarios: demography of study site Population of study area is expected to decline under all scenarios

18 (iii) Economic scenarios Less Business As More Economic input variable Sustainable Usual Sustainable Cost of agriculture (seed, pesticide, fertiliser types) 0 10 20 • Percentage change in ΔDIEM Cost of aquaculture (feed, post larvae, fishling) 20 10 0 Economic Input Variables by 2030 Cost to keep livestock/poultry, fishing, Forest collection 0 10 20 • No further change after 2030 Land rent cost (farming) 0 10 20 Cost to do Services & Manufacturing business 20 0 -20 Market (selling) price of agriculture crops 0 10 20 Market (selling) price of fish 30 10 20 Market (selling) price of aquaculture crops (shrimp) 0 10 20 Income from forest goods (honey, fruits, timber, etc.) -20 -10 0 Income from Manufacturing, Services and Livestock/Poultry 65 110 165 Remittances (BDT/month) 20 30 40 Household expenses 0 10 20 Daily wage (without food) (BDT/day) 0 10 30 Cost of diesel (BDT/gallon) 0 10 20 Employment rate (% population) 0 10 30 Literacy rate (% population) 2 4 8 Children in school (% population)* 2 5 10 Travel time to major cities * -10 -30 -50 USD/BDT exchange rate & PPP exchange rate* 0 0 0 19 Illustrative Results

20 Inundated Area (% change 2050-2000) for non-protected land greatly increases under Q16 scenario, but not under other climate scenarios

Q8 LS Q8 BAU Q8 MS

Q0 LS Q0 BAU Q0 MS

Q16 LS Q16 BAU Q16 MS Crop yield in 2050 (fraction) mean across all crops and seasons o Higher yield and more salt tolerant crops perform better o Crop variety depends on the development scenario. Provisioning Ecosystem Services – Socio-economic situation

Less Sustainable (LS) Business As Usual (BAU) More Sustainable (MS)

Inter-annual variability Greatest for environmental hazards; Moderate for provisioning ecosystem services; Minimal for socio-economic indicators 23 Dominance of livelihoods (Q0BAU) Six examples from the 37 archetypes

• Ecosystem Service-based livelihoods are declining in Farming FarmLabour importance over time for all Livestock household types. Fishing • Small farm owners and fishers rely Forest Manufacturing less on Ecosystem Services. Business • Importance of business and Remittance manufacturing incomes increases.

Selected archetypes • Business-Business-Business (Land Owner Businessman). Fairly balanced income distribution; ends up relying heavily on off-farm business activities • Farming-Farming-Farming (Landless farmer). Pond-based aquaculture and farm labour provide the bulk of their income • Farming-Farming (Large Land Owner farmer). Large land to sustain wellbeing; extra income from occasional farm labour & business activities • Fishing-Fishing-Fishing (Landless traditional fisher). Fishing and livestock provides livelihood with increasing limited fishing income • Business-Manufacturing-Business (Landless Off-farm labourer). Only do off-farm activities, 24 • Forest Dependent. Does both ES-based and off-farm activities with no significant change in income dominance. Policy Analysis

Example management/policy strategies that could be analysed with ΔDIEM across multiple scenarios – exploring uncertainties

• renegotiated Farakka treaty • Ganges Barrage • changing polder heights • land zoning policies • new potential crops • subsidies for farming • guaranteed crop price for farmers • restrictions on off-shore fishing • new loan types today future

25 Example Policy Intervention: Ganges Barrage (presented in Bangladesh on 31 October 2016)

26 Policy intervention: Ganges Barrage

Proposed Ganges Barrage

More freshwater in dry season

27 Policy intervention: Ganges Barrage Policy intervention: Ganges Barrage

ΔDIEM Simulation Maximum Salinity (Year 2000) Policy intervention: Ganges Barrage

ΔDIEM Simulation Soil salinity Policy intervention: Ganges Barrage

ΔDIEM Simulation Boro (HYV) yield Concluding Thoughts • ESPA Deltas/ΔDIEM provides a new linked model and data framework for thinking about the future of coastal Bangladesh • It has a modular approach so can be incrementally improved, extended and applied in other deltas (transferable) • Development of the method is a process and the participatory approach adopted was key element to success – giving local ownership • Two key results for coastal Bangladesh (to 2050) • Future is more influenced by human choices/policy interventions than climate change • There is a strong signal from off-farm economic growth in all scenarios – ecosystem services diminish as a proportion of the economy with time, continuing historic trends Delta Dynamic Integrated Emulator Model (ΔDIEM): Development and Results

Robert J. Nicholls, University of Southampton Mashfiqus Salehin, Institute of Water and Flood Management (IWFM), BUET Attila Lazar, University of Southampton

and the ESPA Deltas team

ESPA Scientific Review Meeting Nairobi, 17-18 November 2016 Deltas, Ecosystem Services and Human Well-Being

C. Hutton1, A.N. Lázár1, A. Payo1, R. J. Nicholls1, H. Adams2, M. Salehin3, A. Haque3, D. Clarke1, L. Bricheno4, J.A. Fernandes5, Mofizur Rahman6, Ali Ahmed6, P.K. Streatfield6, M. Rahman3

1 University of Southampton, United Kingdom, [email protected] 2 University of Exeter, United Kingdom 3 Bangladesh University of Engineering & Technology, Bangladesh 4 National Oceanography Centre, Liverpool, United Kingdom 5 Plymouth Marine Laboratory, United Kingdom 6 International Centre for Diarrhoeal Disease Research, Bangladesh

ESPA Scientific Review Meeting Nairobi, 17-18 November 2016 ΔDIEM Socio-Economic Input Study Area: Land Use and Poverty Analysis Impacts of Salinity: Changing Land Use Livelihoods are Complex and Diversified; Dependence on ES for Income Varies From survey data: Seasonal dependence on provisioning ES 3.5% Exclusively in agriculture/fisheries (all three seasons) 75.9% One or two out of three seasons in agriculture/fisheries 20.6% Exclusively in non-agriculture/ fisheries sectors in three seasons No of livelihood activities 15.0% One livelihood type 2.4% Two livelihood types 82.6% Three or more livelihood types

Rounds 1 = Pre-Monsoon, Round 2= Monsoon, Round 3 = Post Monsoon – 1500 households in 64 Communities Ecosystem Services are Critical for Income and Well-Being of Poor People; Diverse Livelihoods are Associated with Lower Levels of Poverty Poorer Poorer  Those without ES are those most likely to be both materially poor and experience low satisfaction with life.  ES as a safety net for the poorest; rely on open access resources because of having no access to land or stable off-farm income sources.  The richest people can leverage land Less poor Less poor resources and enhance well-being by diversifying livelihoods (to access education and thus service-based jobs, or Greater use of ES high-end off-farm or agricultural business opportunities) Dominance of livelihoods (Q0BAU) Six examples from the 37 archetypes within SES

Selected archetypes • Business-Business-Business (Land Owner Businessman). Fairly balanced income distribution; ends up relying heavily on off-farm business activities • Farming-Farming-Farming (Landless farmer). Pond-based aquaculture and farm labour provide the bulk of their income • Farming-Farming (Large Land Owner farmer). Large land to sustain wellbeing; extra income from occasional farm labour & business activities • Fishing-Fishing-Fishing (Landless traditional fisher). Fishing and livestock provides livelihood with increasing limited fishing income • Business-Manufacturing-Business (Landless Off-farm labourer). Only do off-farm activities, 7 • Forest Dependent. Does both ES-based and off-farm activities with no significant change in income dominance. Health: Impact of High Salinity Conc. in Drinking Water Sources by Socio-Ecological System (SES)

People from Irrigated Agriculture (48%) and Marine & Coastal Periphery (11%) are more likely to be hypertensive.

Mean drinking water salinity varied within SES in coastal Bangladesh, 2014 explaining most of the variance in hypertension

8 Under-5 child Nutrition is Closely Associated with Calorie Intake and Household Wealth

o Clear association between overall under-5 child malnutrition and calorie intake o Clear relationship between households’ wealth index and under-5 child malnutrition

o 36-50% of children are malnourished in South- West coastal zone o High dependence on Socio-Ecological Systems (SESs)

9 Under-5 Child Nutrition is Associated with Fish Consumption; Diversity in Diet is Essential

o Diverse diet is an important driver for under-5 child malnutrition. o Associate with fish in diet and total calorie and as well as protein intake and poverty o There may be a non linear relationship after higher diversity

o Fish consumption and under-5 malnutrition status are broadly inversely associated o Proportion of fish consumption In total protein intake is lower among poorer household where malnutrition is highest 10 ΔDIEM Socio-Economic Outputs ΔDIEM Outputs Household Wellbeing, Poverty & Health Coastal hydrology • water elevation • inundated area

I. Household outputs: a) Bayesian statistical module: Salinisation • asset-based relative poverty indicator • river salinity b) Process-based module: • groundwater salinity • economics (income, costs/expenses, savings/assets) union-wise soil salinity • • relative wealth-level crop productivity • • calories / protein intake / BMI • monetary poverty indicators Livelihoods II. Regional economic outputs • fish catches • sectoral output (tons, BDT) • net earnings from • GINI - farming, • GDP/capita - aquaculture & • income tax revenue - fishing • household debt level Mean calorie Intake in 2050 (kcal/cap/day) • Calorie intake, protein intake and BMI are mainly affected by the socio-economic scenarios.

Q0MS

Q8BAU

Q16LS

13 Provisioning Ecosystem Services – Socio-Economics

Less Sustainable (LS) Business As Usual (BAU) More Sustainable (MS)

14 Q8LS Q8BAU Q8MS Relative Asset- poverty in 2050: Likelihood for being in the poorest quintal i) Climate has minimal effect Q0LS Q0BAU Q0MS ii) Changes in sustainability practices impacts around the Sundarbans fringe iii) The stubborn Q16LS Q16BAU Q16MS poverty in the East is due to access and infrastructure Less Sustainable Future 2050

Requires investment in transport infrastructure

More Sustainable Future 2050

Investment in Sustainability (water resource management, agricultural adaptations, coastal defence)

Sustainability levels identified by stakeholders 100 100 100

All 9 scenarios shows a 50 50 50 decrease in the mean $1.9 absolute poverty indicator 0 0 0 However some unions 2000 2024 2050 2000 2024 2050 2000 2024 2050 remain very poor – 100 100 100 particularly in the less sustainable world 50 50 50

0 0 0 2000 2024 2050 2000 2024 2050 2000 2024 2050

100 100 100

World Bank (national) Simulation (min/mean/max) 50 50 50

0 0 0 2000 2024 2050 2000 2024 2050 2000 2024 2050

Q8LS 80 80 80

60 60 60

40 40 40 HIES (rural, 2000-2010), World Bank (national, 2013-14) 20 20 20

0 0 0 Simulation (min/mean/max) 2000 2024 2050 2000 2024 2050 2000 2024 2050

80 80 80 The GINI coefficient 60 60 60 measures the 40 40 40 . 20 20 20 inequality 0 0 0 Model show good early 2000 2024 2050 2000 2024 2050 2000 2024 2050 o concurrence with HEIS 80 80 80 but not with WB 60 60 60 o In equality remains flat in 40 40 40 all scenarios 20 20 20

0 0 0 2000 2024 2050 2000 2024 2050 2000 2024 2050 The GINI coefficient measures the inequality – Spatial Outputs Ecosystem Services & Poverty Business As Usual Scenario: o Moderate increase in welfare and food security Government Intervention/Policy Matter o Highest inequality o Gradual downward trends in fishing o Decline in farming livelihood o Socio-economic scenarios are o High inter-annual variability in flooding more important than climate Less Sustainable Scenario scenarios at household level. o Moderate improvement in welfare and food security o But collapse of rural income (i.e. farming & fishing) Importance of Ecosystem Services o Enhanced off-farm sectors o o Income inequality gently rising after 2025 is likely to decrease o Minimal flooding due to low river flows. More Sustainable Scenario o Poverty and Inequality are likely to o Enhanced agriculture; sustainable fisheries decrease, but not in marginal o Intensified off-farm activities o Diversity -- big improvement in poverty & food security areas o Income inequality comparable with BAU o Increase in flood intensity; slight decrease in drought o Lowest soil salinity levels 20 Thank you