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Na#onal Ins#tute for Environmental Studies
Integrated SMA/SSP: Research Experiments by the
AIM group Mikiko Kainuma, Shinichiro Fujimori, Naota Hanasaki and Yasuaki Hijioka
16-‐17 July 2011 CECO, Changwon, Korea
Workshop to Explore the New SMA/SSP Approach
Outline of the presenta#on
• Numerical examina#on of AIM’s SPA/SSPs – 2 scenarios for SSPs – Climate mi#ga#on according to RCP radia#ve forcing
• Global water resource assessment with SPA/SSPs
Purpose of Shared Policy Assump#ons (SPAs)/ Shared Socio-‐economic Pathways (SSP)
• Provide useful scenarios to IAV community by linking RCPs, ESM ensemble calcula#ons
• The IAV community needs to explore the implica#ons of climate change against the background of a variety of different circumstances that lead to greater or lesser mi#ga#ve and adap#ve capaci#es. (Jae)
• Provide regional and spa#al informa#on to beZer understand the mi#ga#on/adapta#on strategies
Socio-‐economic indicators and scenario development
IAM
GDP
Popula#on
Technology
Resources
Climate Policy
GHG GHG GHG GHG
Energy use
Land use
Preference Economy
Inputs Outputs
RCPs
SSPs
AIM’s SSPs
• Two types of SSPs are prepared – Standard scenario (SSP1) – High mi#ga#on capacity scenario (SSP2)
• Dematerializa#on, transport demand decrease, high technology
• 4 climate forcing target – RCP’s 2.6, 4.5, 6.0W/m2 – Addi#onally 3.7 W/m2
Scenario concepts SSP1 SSP2
Summary This scenario is what we call business as usual scenario. The technology and economic growth are just extension of the historical trend. The decision making is based on short-‐term. The equity would not improved.
Educa#onal and governance level is high. The decision making is based on long-‐term and comprehensive. As a results, high economic growth in developing countries and low carbon economy are achieved
Popula#on Medium Medium
Economy Medium Medium in OECD, high in non-‐OECD
Technology Medium High
Equity Not so improved Improved
Material usage
Medium Low
Globaliza#on Medium High
Environment Medium High
Scenario space
• SSP1 is the reference scenario • SSP2 has high mi#ga#on capacity. The equity improvement contributes to the adap#on challenge.
AIM’s SSP1
AIM’s SSP2
Shared Policy Assump#ons (SPAs)/ Shared Socio-‐economic Pathways (SSP)
• Both of SSP1 and SSP2 are below 8.5 W/m2 (shown later) • RCP climate forcing target and addi#onally 3.7W/m2 are
examined
Climate forcing SSP1 SSP2
8.5 W/m2
Non interven#on
6.0 W/m2
4.5 W/m2
3.7 W/m2
2.6 W/m2
Model used for the calcula#on
• Exchange informa#on among AIM models • AIM/CGE model is the main tool for SSP’s
– Last year the model was renewed
AIM/CGE AIM/Enduse
AIM/Impact[policy]
Technology informa#on
Climate informa#on Climate informa#on
Main features of a new CGE model New model Previous model
region 35 (Asian countries; 14) 24 (Asian countries; 8) Industry 38 (manufacture sector is
disaggregated in detail) 20
Emissions CO2, CH4, N2O, NH3, SOx, NOx, BC, OC
Ins#tu#on Household, government, Enterprise
Representa#ve household
Dynamics Recursive dynamic (1 year step)
Recursive dynamic (10 year step)
Base year 2005 2001
Base data Original energy balance and SAM (data reconcilia#on system, Fujimori et al., 2011)
GTAP and IEA energy balances
Program GAMS / MCP GAMS / MPSGE
Model Output + Assump#ons
• Popula#on • GDP
– GDP share by industry • Energy supply • Mi#ga#on cost (carbon price)
• Under prepara#on – Land use – Crop and food demand and supply
SSP without climate mi#ga#on -‐popula#on-‐
• Popula#on is from UN popula#on prospects medium case • It almost corresponds to the AR4’s median • SSP1 and SSP2 use the same popula#on scenario.
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Popu
latio
n (m
il)
AR4 range
AR4 Median
AIM_SSP
SSP without climate mi#ga#on -‐GDP-‐
• SSP1; almost middle of the AR4 range • SSP2; a liZle bit higher than SSP1. This is driven by non-‐OECD country’s high growth.
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GDP (tril USD
/year)
AR4 range
AR4 Median
SSP1
SSP2
Equity
• Compare GDP/cap and gap between OECD and non-‐OECD • Both expect improvements of the equity. • SSP2 has much higher improvement of inequity.
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0.45
Non
-‐OEC
D/OEC
D
SSP1
SSP2 OECD GDP /cap
Non-‐OECD GDP /cap
SSP without climate mi#ga#on -‐energy related emissions-‐
• Around the median of AR4 range • SSP2 has lower emission despite of high economic growth.
– Technology is high and material demand is low.
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CO2(GtC/year)
AR4 BaU range
AR4 Median
SSP1
SSP2
SSP without climate mi#ga#on -‐Primary Energy-‐
• Energy supply is similar. – The trend is extension of the historical trend.
• SSP2 has stronger energy intensity improvement (1.5 #mes).
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SSP1
SSP2
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EJ/tril US$
SSP1
SSP2
Energy supply Energy intensity
GHG emissions and its share
• SSP1’s GHG emissions • CO2 increases the share of GHG contribu#on.
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Mt C
O2-
eq/y
ear
BaU
N2O
CH4
CO2
_GHG emission share_WLD
Energy related CO2 emissions by sectors
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Mtoe/year
BaU
OTH
OEN
PWR
IND
_CO2_WLD
refinery Power Industry
Transport, Residen#al and Commercial
SSP and RCP Radia#ve Forcing
• The values of the radia#ve forcing of both SSP’s are between of 8.5 and 6.0 W/m2 in 2100 .
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diative Forcing (W
/m2) SSP1
SSP2
2.6W/m2
3.7W/m2
4.5W/m2
6.0W/m2
8.5W/m2
Emission pathways -‐ SSP with SPA -‐
• GHG emissions are forced to achieve climate target • The emission pathways follow RCPs
-‐20
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GtCO
2-‐eq
/ year
SSP1
SSP2
2.6W/m2
3.7W/m2
4.5W/m2
6.0W/m2
Climate mi#ga#on cost -‐SSP1-‐
• Tough mi#ga#on targets require high carbon prices
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emission price (2005U
S$/t-‐CO2)
2.6W/m2
3.7W/m2
4.5W/m2
6.0W/m2
Climate mi#ga#on cost -‐SSP2-‐
• The carbon prices are lower than SSP1
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20002010202020302040205020602070208020902100
emission price (2005U
S$/t-‐CO2)
2.6W/m2
3.7W/m2
4.5W/m2
6.0W/m2
Mi#ga#on cost comparison 2100’s carbon price
• SSP2’s mi#ga#on costs are lower than SSP1
SSP2
SSP10200
400
600
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1000
1200
1400
2.6W/m2 3.7W/m2 4.5W/m26.0W/m2
Emission price (2005U
S$/t-‐CO2)
SSP2
SSP1
Further steps and discussions
• Food and land related indicators will be prepared
• How can we describe adapta#on capability or challenges? – Equity?
• Here we proposed only two scenarios and they are not so extremely different. Impact studies could require us much different scenarios.
Storyline
• Popula#ons are same • Equity is improved in SSP2.
– The economic growth in developing countries is rela#vely high
• Technology is more advanced in SSP2
populaFon Economy Technology
Equity Environment
GlobalizaFon
SSP1 Middle Middle Middle Middle Middle Middle
SSP2 Middle High High High Middle High
U#lizing SSPs: From the view point of global water resources modeling
Naota Hanasaki, Shinichiro Fujimori, Toshihiko Masui, and Mikiko Kainuma Na#onal Ins#tute for Environmental
Studies
26
Outline
• Background and objec#ve • Review of water resources assessments • Methodology of AIM • Direc#ons and challenges • Scenarios • Results
Outline
27
Background and objec#ve
• SRES based climate change impact assessment – Big success – Less interac#on between IAM and IAV communi#es – Less linkage between adapta#on and mi#ga#on studies
• Recent advances – Shared Socioeconomic Pathways (SSPs) – Improvements in IAV model
• Ac#vity of AIM team – Climate change impact assessment on global water resources u#lizing SSPs and latest water resources model
– Discussing expected advances and challenges
Background
28
Review of water resources assessments
• IPCC AR4 (Fresh water chapter, Kundzewicz et al., 2006)
– Hydrologists of all over the world – Emphasis on physical aspects of climate change – Limited availability of socio economic scenarios (i.e. SRES published only basic variables for four regions)
Water resources Water stressed population
Review
29
Review of water resources assessments • Millennium Ecosystem Assessment (Alcamo et al., 2005)
– IAM specialists (i.e. authors of the chapter) – Focusing on socio-‐economic comprehensiveness (popula#on, food, trade, land use, technology…)
– (possibly) Limited details in climate change and hydrology.
SSPs would bridge AR4 type and MEA type assessments: Comprehensiveness both in physical & socio-‐economic aspects
Areas under severe water stress Water withdrawal Water resources
Review
30
Global water resources model H08
Human AcFvity
Natural Water Cycle
0.5°×0.5°
• Characteris#cs 1. Simulate both water availability (streamflow)
and water use at sub-‐annual basis 2. Deal with interac#on between natural
hydrological cycle and anthropogenic ac#vi#es
Hanasaki et al., 2006, J. of Hydrol. Hanasaki et al. ,2008a,b, Hydrol. Earth Sys. Sci.
31
Methods
Provision of clear storylines helps IAV community to prepare their own scenario by themselves
Input data requirement of H08
Geographical (0.5°×0.5°) Popula#on SSPs provide Agricultural water
Irrigated area
SSPs provide? If not, refer to published reports that is consistent with storylines.
Crop intensity
Irriga#on efficiency
Industrial water Domes#c water Reservoirs Crop type
Meteorological (0.5°×0.5°, daily) Temperature Climate model
community provides
Rela#ve humidity Air pressure Wind speed Short-‐wave radia#on Long-‐wave radia#on Precipita#on
32
Methods
SSPs of AIM group and scenarios used for this analysis
• SSPs proposed by AIM
Methods
33
Climate forcing SSP1 SSP2
8.5 W/m2
Non interven#on
6.0 W/m2
4.5 W/m2
3.7 W/m2
2.6 W/m2
reference
mi#ga#on
1. IAM assists IAV: Regional/Sector-‐wise details
• Dras#c difference in power genera#on
• Advanced research topics for water resources modeling – Cooling water demand and technology change
(So far, hydrologists es#mated industrial water by GDP and electricity consump#on, e.g. Alcamo et al., 2003; Shen et al., 2008)
– Manufacturing water demand and technology change
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Mto
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ar
other renewable
biomass with CCS
biomass wo CCS
nuclear
hydro
Gas with CCS
Oil with CCS
Coal with CCS
Gas wo CCS
Oil wo CCS
Coal wo CCS0
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Mto
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ar
other renewable
biomass with CCS
biomass wo CCS
nuclear
hydro
Gas with CCS
Oil with CCS
Coal with CCS
Gas wo CCS
Oil wo CCS
Coal wo CCS
Reference 550ppm
Research direction
34
2. IAV assists IAM: Advanced water availability info
• Water availability considering seasonality
年単位（従来型） Required water
Sustainably available water (stream flow)
Jan Dec
Deficit
Available water
Apr Aug
Concept of seasonal water deficit Availability of water (percentage of green part in the left figure)
Hanasaki et al., 2008
Research direction
35
50% 100% 80% 90% 0%
2. IAV assists IAM: Advanced water availability info
• Irrigated cropland produce 40% of all crops in the world
• Source of irriga#on water – Precipita#on (green water) – Irriga#on (blue water)
• River • Stored water • Non sustainable water (ground water overuse)
• Different in – Opportunity cost – Sustainability
Source of irrigation water
Hanasaki et al., 2010
Outline
36
3. Integrated study: Detailed analysis on hydropower
• Change in hydropower poten#al – IAV provides hydro power poten#al – IAM provides hydro power demand
• Addi#onal reservoir capacity? – Reservoirs benefit both water availability and power genera#on. Reservoirs play an important role for projected increase in precipitaFon variaFon.
Pokhrel, 2008 Hanasaki et al., 2006
Research direction
37
Challenges
– Fill the gaps of #me and space between IAM and H08 models
• AIM/CGE : Regional and annual basis • H08: Grid and daily basis (0.5deg) • e.g. Northern and southern China (or eastern and western USA)
show quite different hydrological condi#on – Collect cost and technology data in water sector
• Poor in quality and spa#al coverage • Time series data hardly available
– Link IAM and IAV communi#es • Discussion and mutual understanding
Discussion
38
Not to be too ambitious. Feasible research plans and steady research efforts are needed.
Concluding remarks
• Request to SSPs ac#vity from IAV modeling – Comprehensiveness both in physical & socio-‐economic aspects
– Provision of clear storylines helps IAV community to prepare their own scenario by themselves
• Advances and challenges of modeling – More interac#on between IAM and IAV communi#es
– Not to be too ambi#ous. Feasible research plans and steady research efforts are needed
39
Conclusion
References cited in the presenta#on for global water resource assessment part
1. Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rosch, T. and Siebert, S., (2003) Development and tes#ng of the WaterGAP 2 global model of water use and availability, Hydrolog. Sci. J. 48, 317-‐337.
2. Alcamo, J., et al.(2005) Changes in Ecosystem Services and Their Drivers across the Scenarios. In Ecosystems and Human Well-‐being: Scenarios (The Millennium Ecosystem Assessment). Hassan, R., Scholes, R. and Ash, N. (eds). Washington, DC: Island Press, pp. 299-‐373
3. Hanasaki, N., Kanae, S. and Oki, T., (2006) A reservoir opera#on scheme for global river rou#ng models, J. Hydrol. 327, 22-‐41.
4. Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., Shen, Y. and Tanaka, K., (2008) An integrated model for the assessment of global water resources -‐ Part 1: Model descrip#on and input meteorological forcing, Hydrol. Earth Syst. Sci. 12, 1007-‐1025.
5. Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., Shen, Y. and Tanaka, K., (2008) An integrated model for the assessment of global water resources -‐ Part 2: Applica#ons and assessments, Hydrol. Earth Syst. Sci. 12, 1027-‐1037.
6. Hanasaki, N., Inuzuka, T., Kanae, S. and Oki, T., (2010) An es#ma#on of global virtual water flow and sources of water withdrawal for major crops and livestock products using a global hydrological model, J. Hydrol. 384, 232-‐244.
7. Kundzewicz, Z.W., et al., (2007) Freshwater resources and their management. In Climate Change 2007: Impacts, Adapta#on and Vulnerability. Contribu#on of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Parry, M.L., Canziani, O.F., Palu#kof, J.P., van der Linden, P.J. and Hanson, C.E. (eds). Cambridge, UK: Cambridge University Press, pp. 173-‐210.
8. Oki, T. and Kanae, S., (2006) Global hydrological cycles and world water resources, Science 313, 1068-‐1072. 9. Pokhrel, Y., (2008) Global Hydropower Poten#al: Assessment and Analysis of the Impact of Climate Change. Master’s
thesis, Faculty of Engineering, The University of Tokyo. 10. Shen, Y., Oki, T., Utsumi, N., Kanae, S. and Hanasaki, N., (2008) Projec#on of future world water resources under SRES
scenarios: Water withdrawal, Hydrolog. Sci. J., 11-‐33.
References
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