foreseer: visualisation, decision support and an ... · ccs higher nuclear land bau yield bau crop...

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)

Decision support tool

www.foreseer.org

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Foreseer Demo Video with Explanations 02-07-12.wmv

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


IIASA and FAO (2010) Global Agro-Ecological Zones (GAEZ v3.0)

• Track NPP from service needs to production (material flow accounting)


• Changes in population and diet


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0

500

1000

1500

2000

2500

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


Land – energy relationships

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Land – energy relationship

• Allocating land use

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Water – energy relationship

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Water – energy relationship

(a) Extraction

(b) Refining and electricity generation

Blue – freshwater

Red – Tidal water

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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


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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


25


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


Managed water from source to sink in Hebei

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Managed water in Beijing, Tianjin and Hebei

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Land use in Hebei

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Land use in Beijing, Tianjin and Hebei

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Energy use in Hebei

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Energy use in Beijing, Tianjin and Hebei

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Energy use in the Jing-Jin-Ji region

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0

500

1000

1500

2000

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


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0

500000

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)


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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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.

[email protected]

Foreseer - Conclusions

New Foreseer platform

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foreseer: visualisation, decision support and an ... · ccs higher nuclear land bau yield bau crop...

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