Simon Parkinson1,2, Volker Krey1, Daniel Huppmann1, Taher Kahil1, David McCollum1,3, Oliver Fricko1, Edward A Byers1, Matthew Gidden1, Beatriz Mayor1, Zarrar Khan1,4, Catherine Raptis5, Narasimha D Rao1, Nils Johnson1, Yoshihide Wada1,6, Ned Djilali2, Keywan Riahi1,7,8
1 International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
2 University of Victoria, PO Box 3055 STN CSC, V8W 3P6 Victoria BC, Canada
3 University of Tennessee,1640 Cumberland Avenue, 37996-3340 Knoxville TN, United States
4 Universidad Pontificia Comillas, Calle de Santa Cruz de Marcenado 26, 28015 Madrid, Spain
5 ETH Zurich, John-von-Neumann-Weg 9, 8093 Zurich, Switzerland
6 Utrecht University, PO Box 80.115, 3508 Utrecht, The Netherlands
7 TU Graz, Inffeldgasse 21, 8010 Graz, Austria
8 Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, USA
Table of Contents
Figures Page
Figure S1: Regional representation of the MESSAGE IAM. 3
Figure S2: Models for water access and treatment. 3
Figure S3: Global distribution of per capita urban and rural domestic withdrawals. 4 Figure S4: Delineation of static water-scarce basin ecological regions. 4 Figure S5: Historical build out of desalination capacity estimated in each MESSAGE region. 5 Figure S6: Lower bounds on recycling and desalination production in 2030. 5 Figure S7: Stylized water conservation curve and linear parameterization. 6 Figure S8: Regional irrigation withdrawal trajectories (after conservation). 6 Figure S9: Regional urban withdrawal trajectories (after conservation). 7 Figure S10: Regional urban return flow trajectories (after conservation). 7 Figure S11: Regional rural withdrawal trajectories (after conservation). 8 Figure S12: Regional rural return flow trajectories (after conservation). 8 Figure S13: Power plant cooling system shares estimated in 2010 at the river basin-scale. 9 Figure S14: Representation of the power plant cooling technologies in the MESSAGE IAM. 9 Figure S15: Water supply sector representation incorporated into the MESSAGE IAM. 10 Figure S16: Water sector costs in 2030 in each MESSAGE region and globally. 12 Figure S17: Water sector costs in 2070 in each MESSAGE region and globally. 13 Figure S18: Water supply mixture in 2030 for each MESSAGE region and globally. 14 Figure S19: Water supply mixture in 2070 for each MESSAGE region and globally. 15 Figure S20: Power generation mixture in 2070 for each MESSAGE region and globally. 16 Figure S21: Power generation mixture in 2070 for each MESSAGE region and globally. 17 Tables
Table S1: Countries included in the MESSAGE regions. 2
Table S2: Parameterization of cooling technology costs and electric efficiency. 10 Table S3: Parameterization of water supply and treatment technologies in MESSAGE. 11
Supplementary References 18
2
Table S1 Countries included in the MESSAGE macro-regions.
Region Definition (list of countries)
NAM North America
(Canada, Guam, Puerto Rico, United States of America, Virgin Islands)
WEU
Western Europe
(Andorra, Austria, Azores, Belgium, Canary Islands, Channel Islands, Cyprus, Denmark, Faeroe Islands, Finland, France, Germany, Gibraltar, Greece, Greenland, Iceland, Ireland, Isle of Man, Italy, Liechtenstein, Luxembourg, Madeira, Malta, Monaco, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom)
PAO Pacific OECD
(Australia, Japan, New Zealand)
EEU
Central and Eastern Europe
(Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, The former Yugoslav Rep. of Macedonia, Hungary, Poland, Romania, Slovak Republic, Slovenia, Estonia, Latvia, Lithuania)
FSU
Former Soviet Union
(Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Republic of Moldova, Russian Federation, Tajikistan, Turkmenistan, Ukraine, Uzbekistan)
CPA Centrally Planned Asia and China
(Cambodia, China (incl. Hong Kong), Korea (DPR), Laos (PDR), Mongolia, Viet Nam) SAS South Asia
(Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka)
PAS
Other Pacific Asia
(American Samoa, Brunei Darussalam, Fiji, French Polynesia, Gilbert-Kiribati, Indonesia, Malaysia, Myanmar, New Caledonia, Papua, New Guinea, Philippines, Republic of Korea, Singapore, Solomon Islands, Taiwan (China), Thailand, Tonga, Vanuatu, Western Samoa)
MEA
Middle East and North Africa
(Algeria, Bahrain, Egypt (Arab Republic), Iraq, Iran (Islamic Republic), Israel, Jordan, Kuwait, Lebanon, Libya/SPLAJ, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria (Arab Republic), Tunisia, United Arab Emirates, Yemen)
LAM
Latin America and the Caribbean
(Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bermuda, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, French Guyana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Netherlands Antilles, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Santa Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela)
AFR
Sub-Saharan Africa
(Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cameroon, Cape Verde, Central African Republic, Chad, Comoros, Cote d'Ivoire, Congo, Democratic Republic of Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia, Zimbabwe)
3
Figure S1: Regional representation of the MESSAGE integrated assessment model (IAM).
Figure S2: Models for water access and treatment. Logistic models fit between estimated national incomes for 2010 from Dellink et al. (2017) [1], and connection rates from Baum et al. (2013) [2]. In the baseline scenario, countries converge along an exponential path from the estimated historical level towards the modeled connection rate obtained with the future SSP-based income projections. The SDG6 scenarios feature explicit narratives used to set the connection rates directly (i.e., 100% piped water access and 50% wastewater treated by 2030). It is assumed that once a given segment of the population has access to piped water, that they also have access to wastewater collection.
AFR
Per Capita Income [ USD2010 ]
Population with Piped Water Access [ % ]
●
Per Capita Income [ USD2010 ]
Population with Wastewater Treatment [ % ]
●
4
Figure S3: Global distribution of per capita delivered water volumes in 2030 estimated for the urban and rural domestic sectors at 7.5 arc-minutes using the approach described in Parkinson et al. (2016) [3]. Domestic water demands are adjusted in the SDG6 pathways to ensure there is enough water allocated for decent living standards in all locations, which in this paper is translated to a minimum of 100 liters per day in urban areas and 50 liters per day in rural areas [4-6]. Lacking explicit data, the withdrawals and return-flows are adjusted assuming an average efficiency of 75% from source to end-user.
Figure S4: Delineation of water-stressed basin units (ecoregions) following Hoekstra et al. (2010) [7]. Water stress is calculated as the ratio of withdrawals to renewable water availability, and is estimated previously for each ecoregion using data from the WaterGAP global hydrological model [8].
0 100 300 500
0.00.20.40.60.81.0
Urban − 2030
Liters per day
Cumulative Population Distribution
0 100 300 500
0.00.20.40.60.81.0
Rural − 2030
Liters per day
Cumulative Population Distribution
Baseline SDG6
Low-stress Medium-stress High-stress
5
Figure S5: Historical build out of desalination capacity estimated in each MESSAGE region [9].
Figure S6: Lower bounds on recycling and desalination production in 2030 at 7.5 arc-minutes in the SSP2 scenario.
1970 1980 1990 2000 2010 Reverse Osmosis
Installed Capacity [ km3 per year ] 02468
1970 1980 1990 2000 2010 Thermal Process
02468
MESSAGE Region Sub−Saharan Africa Centrally Planned Asia Eastern EU Former Soviet Union
Latin America Middle East & N. Africa North America Pacific Oceanic
Pacific Asia South Asia Western EU
6
Figure S7: Water conservation curve and linear parameterization used to represent demand response and anticipated implementation costs for end-use conservation measures in MESSAGE. The curve is implemented in the linear program using the indicated step functions. Previous work quantified the impact of diverse conservation options and implementation costs, and generally show that conservation costs increase non-linearly and offset a limited fraction of water demand [10–13]. A maximum conservation potential in each sector representing 30 % of the baseline withdrawals is assumed in this study, and is a somewhat conservative interpretation of previous assessments that focus specifically on water conservation potentials for specific sectors [11-14]. Extending previous work estimating regional-scale water supply expansion costs, we use 0.3 USD per m3 to represent the average cost for conservation measures because this approximates the point at which it can be expected that investment switches to expanding yield from conventional raw surface and groundwater sources [15].
Figure S8: Regional irrigation withdrawal trajectories (after conservation).
𝑐𝑏
𝜃𝑛
𝑐3
𝑐2 𝑐1
𝜃2
𝜃3
𝜃1 𝑐3=4
5∙ 𝑐𝑏 𝑐2=2
5∙ 𝑐𝑏 𝑐1=1
5∙ 𝑐𝑏
𝜃3=1 6∙ 𝜃𝑛 𝜃2=1
3∙ 𝜃𝑛 𝜃1=1
2∙ 𝜃𝑛
Water Conservation
Cost
2020 2040 2060 2080
02004006008001000
Baseline
Year
Irrigation Withdrawal [ million cubic meters ]
2020 2040 2060 2080
02004006008001000
SDG6−Supply
Year
2020 2040 2060 2080
02004006008001000
SDG6−Efficiency
Year
AFR CPA EEU FSU LAM MEA NAM PAO PAS SAS WEU
7
Figure S9: Regional urban (municipal + manufacturing) withdrawal trajectories (after conservation).
Figure S10: Regional urban (municipal + manufacturing) return-flow trajectories (after conservation).
2020 2040 2060 2080
050100150200250
Baseline
Year
Urban Withdrawal [ million cubic meters ]
2020 2040 2060 2080
050100150200250
SDG6−Supply
Year
2020 2040 2060 2080
050100150200250
SDG6−Efficiency
Year
AFR CPA EEU FSU LAM MEA NAM PAO PAS SAS WEU
2020 2040 2060 2080
050100150200250
Baseline
Year
Urban Return Flow [ million cubic meters ]
2020 2040 2060 2080
050100150200250
SDG6−Supply
Year
2020 2040 2060 2080
050100150200250
SDG6−Efficiency
Year
AFR CPA EEU FSU LAM MEA NAM PAO PAS SAS WEU
8
Figure S11: Regional rural (municipal) withdrawal trajectories (after conservation).
Figure S12: Regional rural return-flow trajectories (after conservation).
2020 2040 2060 2080
01020304050
Baseline
Year
Rural Withdrawal [ million cubic meters ]
2020 2040 2060 2080
01020304050
SDG6−Supply
Year
2020 2040 2060 2080
01020304050
SDG6−Efficiency
Year
AFR CPA EEU FSU LAM MEA NAM PAO PAS SAS WEU
2020 2040 2060 2080
01020304050
Baseline
Year
Rural Return [ million cubic meters ]
2020 2040 2060 2080
01020304050
SDG6−Supply
Year
2020 2040 2060 2080
01020304050
SDG6−Efficiency
Year
AFR CPA EEU FSU LAM MEA NAM PAO PAS SAS WEU
9
Figure S13: Cooling system shares (fraction of total installed capacity) across all power plants for different cooling system types identified for spatial units representing the intersection of river basins and countries with the facility-level dataset presented in Raptis and Pfister (2016) [16]. Shares are computed for each type of power plant in MESSAGE, and used to define the historical cooling technology capacity. Where data does not exist, the average across all plant types depicted here is used.
Figure S14: Representation of the power plant cooling technologies in the MESSAGE framework. Power plant cooling is treated as a commodity in the model that must be supplied by specific cooling technologies with unique cost and efficiency impacts.
Once through cooling - freshwater
Once through cooling – sea water
Recirculating - freshwater
Air cooling
Seawater thermal pollution Seawater withdrawal
Other operational water requirements Parasitic electricity demand
Power Plant Technology
10
Table S2 Data for advanced cooling technology costs and electric efficiency. A range of data is identified from the indicated literature sources. Data labelled ‘Efficient’ is used to parameterize technologies in the SDG6-Efficiency scenario. All other scenarios use mid-range data labelled ‘Baseline’.
Cooling Technology Cycle Type Parameter Units Efficient Baseline Source(s)
Recirculating
1. Air cooling not included for nuclear power plants and carbon capture and storage technologies due to perceived implementation challenges.
2. Other operational costs for cooling systems are accounted for through its energy and water use and the fixed costs for power plants.
3. Efficiency penalty is applied to represent parasitic electricity consumption from additional cooling equipment.
Figure S15: Reduced-form water supply sector representation incorporated into the MESSAGE IAM. The depicted technologies or processes transform water into different qualities. Sectoral water withdrawals and (waste) return-flows are input to the model, excluding energy-related water use, which is accounted for and optimized in MESSAGE at the technology-level. Additional energy inputs and waste outputs for the technologies are included and link the water supply sector to the energy system modeled in MESSAGE. Saline Water Resources Freshwater Resources Urban Potable
Rural
Urban Waste Rural Waste Manuf. Waste Manuf. Collected
Rural Collected
Urban Collected
Recycling
11
Table S3: Parameterization of the water supply and wastewater treatment technologies. A range of data is identified from the indicated literature sources. Data labelled ‘Efficient’ is used to parameterize technologies in the SDG6-Efficiency scenario. All other scenarios use mid-range data labelled ‘Baseline’.
Technology / Process Parameter Units Efficient Baseline Source(s)
Thermal Desalination1
Urban / Manufacturing Water Distribution
1. For simplification, thermal desalination technologies are parameterized to represent multi-stage flash and distillation options.
2. Representative of a common septic system. Additional energy requirements are expected to be negligible.
3. For simplification, the same technology is used to represent manufacturing and urban municipal wastewater recycling.
4. Electricity intensities for pumping reflect averages for surface and groundwater systems and do not account for long-distance transfers.
5. Electricity intensities for wastewater pumping are allocated to the treatment and recycling technologies and reflect averages.
12
Figure S16: Water sector costs in 2030 in each MESSAGE region and globally.
(1) (2) (3) (4) (5) (6)
AFR
020406080
(1) (2) (3) (4) (5) (6)
CPA
0100200300
(1) (2) (3) (4) (5) (6)
EEU
05101520
(1) (2) (3) (4) (5) (6)
FSU
020406080
(1) (2) (3) (4) (5) (6)
LAM
050100150
(1) (2) (3) (4) (5) (6)
MEA
050100150
(1) (2) (3) (4) (5) (6)
NAM
050100150
(1) (2) (3) (4) (5) (6)
PAO
01020304050
(1) (2) (3) (4) (5) (6)
PAS
050100150
(1) (2) (3) (4) (5) (6)
SAS
0100200300
(1) (2) (3) (4) (5) (6)
WEU
020406080120
(1) (2) (3) (4) (5) (6)
World
050010001500
Water Infrastructure Storage (excl. Hydro) Pumps & Distribution Wastewater Collection Wastewater Treatment
Wastewater Recycling Desalination Conservation
Water Sector Costs in 2030 [ billion USD per year ]
Infrastructure Transformation Pathway
Infrastructure Transformation Pathway (1) NoWatPol | NoClimPol (2) NoWatPol | 1.5 °C
(3) SDG6−Supply | NoClimPol (4) SDG6−Supply | 1.5 °C
(5) SDG6−Efficiency | NoClimPol (6) SDG6−Efficiency | 1.5 °C
13
Figure S17: Water sector costs in 2070 in each MESSAGE region and globally.
(1) (2) (3) (4) (5) (6)
AFR
050100150200
(1) (2) (3) (4) (5) (6)
CPA
0100200300
(1) (2) (3) (4) (5) (6)
EEU
05101520
(1) (2) (3) (4) (5) (6)
FSU
020406080100
(1) (2) (3) (4) (5) (6)
LAM
050100150200
(1) (2) (3) (4) (5) (6)
MEA
050100150200
(1) (2) (3) (4) (5) (6)
NAM
050100150
(1) (2) (3) (4) (5) (6)
PAO
010203040
(1) (2) (3) (4) (5) (6)
PAS
050100150
(1) (2) (3) (4) (5) (6)
SAS
0100200300400500
(1) (2) (3) (4) (5) (6)
WEU
050100150
(1) (2) (3) (4) (5) (6)
World
0500100015002000
Water Infrastructure Storage (excl. Hydro) Pumps & Distribution Wastewater Collection Wastewater Treatment
Wastewater Recycling Desalination Conservation
Water Sector Costs in 2070 [ billion USD per year ]
Infrastructure Transformation Pathway
Infrastructure Transformation Pathway (1) NoWatPol | NoClimPol (2) NoWatPol | 1.5 °C
(3) SDG6−Supply | NoClimPol (4) SDG6−Supply | 1.5 °C
(5) SDG6−Efficiency | NoClimPol (6) SDG6−Efficiency | 1.5 °C
14
Figure S18: Water supply mixture in 2030 for each MESSAGE region and globally.
(1) (2) (3) (4) (5) (6)
AFR
050100150
(1) (2) (3) (4) (5) (6)
CPA
0200400600800
(1) (2) (3) (4) (5) (6)
EEU
051015202530
(1) (2) (3) (4) (5) (6)
FSU
050100150200
(1) (2) (3) (4) (5) (6)
LAM
050150250350
(1) (2) (3) (4) (5) (6)
MEA
0100200300400
(1) (2) (3) (4) (5) (6)
NAM
0100200300400
(1) (2) (3) (4) (5) (6)
PAO
020406080120
(1) (2) (3) (4) (5) (6)
PAS
0100200300400
(1) (2) (3) (4) (5) (6)
SAS
0200400600800
(1) (2) (3) (4) (5) (6)
WEU
050100150200250
(1) (2) (3) (4) (5) (6)
World
01000200030004000
Water Source Conventional Recycling
Desalination Conservation
Freshwater Supply in 2030 [ km3 per year ]
Infrastructure Transformation Pathway
Infrastructure Transformation Pathway (1) NoWatPol | NoClimPol (2) NoWatPol | 1.5 °C
(3) SDG6−Supply | NoClimPol (4) SDG6−Supply | 1.5 °C
(5) SDG6−Efficiency | NoClimPol (6) SDG6−Efficiency | 1.5 °C
15
Figure S19: Water supply mixture in 2070 for each MESSAGE region and globally.
(1) (2) (3) (4) (5) (6)
AFR
050150250350
(1) (2) (3) (4) (5) (6)
CPA
0200400600
(1) (2) (3) (4) (5) (6)
EEU
05101520
(1) (2) (3) (4) (5) (6)
FSU
050100150200
(1) (2) (3) (4) (5) (6)
LAM
0100200300400
(1) (2) (3) (4) (5) (6)
MEA
0100200300400
(1) (2) (3) (4) (5) (6)
NAM
0100200300
(1) (2) (3) (4) (5) (6)
PAO
020406080100
(1) (2) (3) (4) (5) (6)
PAS
0100200300400
(1) (2) (3) (4) (5) (6)
SAS
02006001000
(1) (2) (3) (4) (5) (6)
WEU
050100150200
(1) (2) (3) (4) (5) (6)
World
01000200030004000
Water Source Conventional Recycling
Desalination Conservation
Freshwater Supply in 2070 [ km3 per year ]
Infrastructure Transformation Pathway
Infrastructure Transformation Pathway (1) NoWatPol | NoClimPol (2) NoWatPol | 1.5 °C
(3) SDG6−Supply | NoClimPol (4) SDG6−Supply | 1.5 °C
(5) SDG6−Efficiency | NoClimPol (6) SDG6−Efficiency | 1.5 °C
16
Figure S20: Power generation mixture in 2030 for each MESSAGE region and globally.
(1) (2) (3) (4) (5) (6) (6) SDG6−Efficiency | 1.5 °C
17
Figure S21: Power generation mixture in 2070 for each MESSAGE region and globally.
(1) (2) (3) (4) (5) (6) (6) SDG6−Efficiency | 1.5 °C
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