Foreseer: visualisation, decision support and an analytical framework for the water-land-energy nexus Keith Richards (with Julian Allwood, Bojana Bajželj, Dennis Konadu, Zenaida Sobral Mourão, Ying Qin)
Foreseer: visualisation, decision support and an analytical framework for the water-land-energy
nexus
Keith Richards (with Julian Allwood, Bojana Bajželj, Dennis Konadu, Zenaida Sobral
Mourão, Ying Qin)
Dr. Julian Allwood, Energy scenarios and materials processing
Dr. John Dennis, Biofuels, clean coal, LCA and land use
Dr. Richard Fenner, Civil Engineer, water & environmental mgt.
Prof. Chris Gilligan, Mathematical biology, statistics & uncertainty
Dr. Richard McMahon, Elec. Engineering and renewables
Prof. Danny Ralph, Energy modeling, Judge Business School
Prof. John Pyle, FRS Atmospheric science, IPCC lead author
Prof. Keith Richards, Geography, river and water management
Prof. Paul Linden, FRS Fluid mechanics, water in Himalayas
Dr. Liz Curmi, RA for Water
Bojana Bajzelj, RA for Land
Grant Kopec, RA for Energy
Investigators
Content
• Foreseer – an introduction – Analytical framework
• Material flow accounting – “source” to “service”
• Variables and coefficients – intermediate transformations
• Flexible mix of modelling and data (“data” > GIS > spreadsheet> Sankey)
– Visualisation
• Sankey Diagrams
• Multivariate – linked systems
• Spatially distributed, temporally dynamic
– Decision-support tool for policy appraisal
• Future scenario analysis (uncertainty)
• Examples • Global food production and GHG emissions; Bojana Bazjelj)
• UK energy policy appraisal (WholeSEM project; Zenaida Mourao, Dennis Konadu)
• Water-land(food)-energy in the Jing-Jin-Ji Region (Ying Qin) 3
Visualisation – Sankey diagrams
Charles Joseph Minard 1869 Napoleon’s army
Industrial processes (efficiency of material and energy use) Steelmaking reheating furnace
http://www.sankey-diagrams.com/steelmaking-reheating-furnace/
Visualisation – Sankey diagrams
Visualisation – Land to NPP to Land services
Visualisation - GHG emissions (i)
Bajželj, B, Allwood, JM and Cullen, JM (2013) Designing Climate Change Mitigation
Plans That Add Up. Environmental Science & Technology 47, 8062-8069
Visualisation - GHG emissions (ii)
Analytical framework (i) – global food
• Future food security (2050)
• Global population 9.6 bn (UN median estimate)
• How can we feed the future population?
• What factors are relevant?
– Scenarios - (a) Supply
• Continue current rates of yield improvement (CT)
• “Sustainable intensification” to close yield gaps (YG)
– Scenarios – (b) Demand
• Cut food waste by 50%
• Consider effects of dietary change
• Land Use change to meet increased demand
• Global land cover distribution Global land suitability
Analytical framework (i) – global food
IIASA and FAO (2010) Global Agro-Ecological Zones (GAEZ v3.0)
• Track NPP from service needs to production (material flow accounting)
Analytical framework (i) – global food
• Changes in population and diet
Analytical framework (i) – global food
Analytical framework (i) – global food
Analytical framework (i) – global food
Analytical framework (i) – global food
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2010 2050 - HRen 2050 - HNuc 2050 - HCCS 2050 - CMar
Pri
mar
y En
erg
y(TW
h)
Other
Imported Electricity
Wave & tidal
Hydro
Solar
Wind
Nuclear
Bioenergy crops - 2nd gen
Bioenergy crops - 1st gen
Waste
Imported bioenergy
Natural Gas
Coal
Oil
Primary Energy mix to meet UK energy demand in 2010, based on national energy statistics, and projections to 2050 under each of the Carbon Plan pathways (with HRen – “Higher Renewables, more energy efficiency”, HNuc – “Higher Nuclear, less energy efficiency”, HCCS – “Higher CCS, more bioenergy”, “CMar” – Core MARKAL).
Analytical framework (ii) – Energy policy appraisal
Primary energy composition of energy pathways - 2050
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Connection to land use systems
Connection to water system
UK energy-land-water system connections
Analytical framework (ii) – Energy policy appraisal
Land – energy relationships
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Analytical framework (ii) – Energy policy appraisal
Land – energy relationship
• Allocating land use
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Analytical framework (ii) – Energy policy appraisal
Water – energy relationship
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Analytical framework (ii) – Energy policy appraisal
Water – energy relationship
(a) Extraction
(b) Refining and electricity generation
Blue – freshwater
Red – Tidal water
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Analytical framework (ii) – Energy policy appraisal
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Comparison of current and projected impact of bioenergy cropping on land UK distributions by 2050 under different scenarios of crop yield and composition: (a) BAU Composition & BAU Yield; (b) BAU Composition & Increase Yield; (c) BAU Composition & Increase Yield (d) 50-50 Composition & Increase Yield
Land for bioenergy - some pathways cause land use stress
Analytical framework (ii) – Energy policy appraisal
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Resource Core
MARKAL
Higher
Renewable
Higher
CCS
Higher
Nuclear
Land
BAU Yield BAU crop composition
50/50 Crop composition
High Yield
improvement
BAU crop composition
50/50 Crop composition
Water
PAU
High Coastal
High Inland
Integrated CCS
Key: Impact designations
Land
Water
Low Maximum land for energy crops equal
or less than currently unused arable land Low
Lower than or up to current actual
abstractions level
Medium Up to 10% of UK land area Medium Up to 100% increase in 2010
abstraction for thermal generation
High Above 10% UK land area High Above 100% increase in 2010
abstractions for thermal generation
Land and water resource requirements lead to regrets
Analytical framework (ii) – Energy policy appraisal
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Analytical framework (ii) – Energy policy appraisal
Analytical Framework (iii) – the Jing-Jin-Ji nexus
The Haihe Basin and the Jing-Jin-Ji region • Growing pressures on energy, water and
land resources
• One of the most important food production regions in the country
• Capital region – continuing growth in urban areas and industry
• Intense competition for water – lowest water availability per capita out of the nine major river basins
• Key focus area for (more) sustainable development
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Methodology A spatially-explicit integrated resource model that integrates water, energy and land sub-models
Land Sub-model
Energy Sub-model
Water for agriculture
Energy for land
Water Sub-model
Energy for water
Models
Inputs
Population/ urbanisation
GDP/IVA Climate
Land for energy
Water demand Water supply Water for energy Water for agriculture
Output
Food demand Crop production Livestock Land for energy
Energy demand Energy supply Technology Energy for water Energy for land
Changes in diet
Water For energy
Analytical Framework (iii) – the Jing-Jin-Ji nexus
Managed water from source to sink in Hebei
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
Managed water in Beijing, Tianjin and Hebei
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
Land use in Hebei
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
Land use in Beijing, Tianjin and Hebei
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
Energy use in Hebei
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
Energy use in Beijing, Tianjin and Hebei
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
Energy use in the Jing-Jin-Ji region
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
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2500
Beijing_2010 Beijing_2020 Beijing_2030 Tianjin_2010 Tianjin_2020 Tianjin_2030 Hebei_2010 Hebei_2020 Hebei_2030
Wat
er w
ith
dra
wal
s (m
illio
n m
3)
Water for energy for 2010, 2020 and 2030 (BAU)
Coal extraction Oil extraction Gas extraction Coal washing
Oil refining Coal fired -OT cooling Coal fired - WT cooling Coal fired - Dry cooling
Oil fired power generation Gas fired power generation CSP power generation
Analytical Framework (iii) – the Jing-Jin-Ji nexus
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1000000
1500000
2000000
2500000
3000000
3500000
Beijing_2010 Beijing_2020 Beijing_2030 Tianjin_2010 Tianjin_2020 Tianjin_2030 Hebei_2020 Hebei_2020 Hebei_2030
Ener
gy u
se (
TCE)
Energy for water for 2010, 2020, 2030
Supply of local surface water Supply of local groundwater Recycled water
Desalination of water SNWTP Transfers (yellow river)
Analytical Framework (iii) – the Jing-Jin-Ji nexus
Recent and future work
• Analysis of policy consistency – energy futures and the “Three Red Lines” industrial water policy
• Future resource supply and demand under different scenarios
– Changes in Climate , Socio-economy (dietary habits), Technology
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Analytical Framework (iii) – the Jing-Jin-Ji nexus
Framework for whole/multivariate systems analysis
Water-land-energy nexus, with explicit links at critical nodes; flexible so can change
variables (area of land to NPP) and add extra “layers” (GHG, air pollution)
Adaptable to different spatial scales and resolutions The tool can be as simple or as complex as required (depends on objectives of study &
available global/national/regional/local data)
Visual, user-friendly representation
Visually and quantitatively compare the trade-offs between resources under specific
user-defined scenarios and different policies
Tool for policy analysis
Enables comparison of multi-dimensional scenarios for uncertainty appraisal; and can be
used to assess performance of different technology mixes, using GHG emissions, air pollution, water quality additions.
Foreseer - Conclusions
New Foreseer platform
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