Criticality of Water: Aligning Water and Mineral Resources Assessment

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Criticality of water – aligning water and mineral resources assessment Thomas Sonderegger, Stephan Pfister, and Stefanie Hellweg Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b02982 • Publication Date (Web): 21 Sep 2015 Downloaded from http://pubs.acs.org on September 22, 2015

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Environmental Science & Technology

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Criticality of water – aligning water and mineral

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resources assessment

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Thomas Sonderegger*, Stephan Pfister, Stefanie Hellweg

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ETH Zurich, Institute of Environmental Engineering, Chair of Ecological Systems Design, John-

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von-Neumann-Weg 9, 8093 Zurich, Switzerland

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KEYWORDS: Criticality, Resources, Water

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ABSTRACT

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The concept of criticality has been used to assess whether a resource may become a limiting

10

factor to economic activities. It has been primarily applied to non-renewable resources, in

11

particular to metals. However, renewable resources such as water may also be overused and

12

become a limiting factor. In this paper, we therefore developed a water criticality method that

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allows for a new, user-oriented assessment of water availability and accessibility. Comparability

14

of criticality across resources is desirable, which is why the presented adaptation of the criticality

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approach to water is based on a metal criticality method, whose basic structure is maintained.

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With respect to the necessary adaptations to the water context, a transparent water criticality

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framework is proposed that may pave the way for future integrated criticality assessment of

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metals, water and other resources. Water criticality scores were calculated for 159 countries

ACS Paragon Plus Environment

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subdivided into 512 geographic units for the year 2000. Results allow for a detailed analysis of

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criticality profiles, revealing locally specific characteristics of water criticality. This is useful for

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the screening of sites and their related water criticality, for indication of water related problems

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and possible mitigation options and water policies, and for future water scenario analysis.

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TOC ART

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Introduction

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Driven by growing demand and technological advances, consumption of natural resources has

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increased rapidly since the 20th century.1 The expansion of resource extraction has profound

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environmental impacts and raises concerns about future resource availability.1,2 The concept of

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criticality has been used to assess whether a resource may become a limiting factor in the future,

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and it has been primarily applied to non-renewable resources, in particular metals. However, also

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renewable resources such as water may be overused and become a limiting factor to economic

32

activities. To account for the criticality of water, an adapted criticality framework is needed.

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While the use of the term ‘criticality’ in the context of raw materials dates back to 19393, the

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baseline concept of today’s criticality approaches is based on a study called Minerals, Critical

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Minerals, and the U.S. Economy, which introduced an assessment on two axes: ‘Supply Risk’

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and ‘Impact of Supply Restriction’.4 Pioneered by this study, several criticality assessment

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methodologies have recently been developed.5 An overview, in-depth analysis and discussion of

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the most prominent of these methods can be found in Erdmann and Graedel6, Achzet and Helbig3

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and Sonnemann et al.7.

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The history of water use assessment on a global scale dates back to the Virtual Water concept

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introduced by Allan in the early 1990s.8 In 2002, the Water Footprint Network translated the

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virtual water concept into the Water Footprint method.8 While these concepts considered water

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quantities in the supply chain of products without impacts, Life Cycle Assessment (LCA)

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approaches to water footprint have been suggested to account for the environmental relevance of

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water use.9,10 Impact assessment is now an integral part of the water footprint, which is

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standardized in the ISO 14046 standard on water footprinting.11 Kounina et al.12 provide a review

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and discussion of available LCA methods for water consumption as well as guidelines for further


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developments. Other water use assessments like the Falkenmark Index mainly focus on scarcity,

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considering minimal water needs or water demand and water availability (which is also the

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baseline for LCA approaches). A review of water scarcity indices and methods is presented in

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Brown and Matlock13. Some of these methods like the Water Poverty Index by Sullivan14 include

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aspects of vulnerability. However, none of these methods is based on a criticality approach as

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introduced above.

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While LCA and the water footprint aim at quantifying the impacts of human use on the

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environment, criticality methods have a user perspective and assess whether a resource may

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become limiting for a specific user. This perspective is also used in existing water risk

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assessments and tools. Some of these focus on physical risks (scarcity, floods)15 while others

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already include some measurements of regulatory and reputational (from a corporate

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perspective) risks.16,17 The criticality approach allows for a systematic integration of such supply

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risks. Furthermore, aspects of vulnerability (e.g. dependency on water for economic production)

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and adaptation to supply restrictions (e.g. production of desalinated water and water storage) of

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affected economies are taken into account, which has not previously been done. This is

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especially interesting for future water scenario analysis. A water criticality method therefore

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adds new aspects to water use assessments and broadens their scope.

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In this paper, we developed such a water criticality method. Comparability of criticality across

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resources is desirable to allow for a joint application and assessing of trade-offs. This is

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especially true for technologies for which water as well as metals may be limiting factors, e.g.

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concentrating solar thermal power (CSP), where suitable areas are often semi-arid18 and

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therefore water used for operation might be as critical as the metals used for construction19. A

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water criticality method should therefore be similar to other criticality assessments, which is why


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the presented adaptation of the criticality approach to water is based on the Methodology of

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Metal Criticality Determination by Graedel et al.20. The basic structure of the methodology is

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maintained with its three dimensions of supply risk, vulnerability to supply restriction, and the

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newly introduced environmental implications. However, the peculiarities of water as a resource

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make modifications of indicators necessary. For instance, water is not a globally traded good but

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rather mainly a locally used one, which is why spatial differentiation is needed: Instead of global

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availability and the political situation in the countries supplying the global market, as assessed

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for metals, local water availability and the political situation in a country and in its water

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supplying neighbors is assessed. Furthermore, while metals can sometimes be substituted with

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other metals, water has either to be conserved or treated for reuse in order to reduce pressure on

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the resource and reduce vulnerability to supply restriction. Due to data limitations the method is

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limited to an assessment of quantitative criticality, while water quality aspects were disregarded.

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The new water criticality framework proposed in this paper addresses these adaptations and

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paves the way for future integrated criticality assessment of metals and water.

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Materials and Methods

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The framework for water criticality is adapted from the Methodology of Metal Criticality

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Determination by Graedel et al.20, later referred to as metal criticality. This method fulfills the

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criteria of having a national perspective and it appeared to be the most elaborated and widely

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quoted method in the literature at the time of the beginning of our work. The structure of metal

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criticality is based on three axes: supply risk (SR), environmental implications (EI) and

91

vulnerability to supply restrictions (VSR). The SR and VSR axes consist of two to three

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components and each component contains one to four indicators, which are adapted to the water

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context (Figure 1, Table S1). As for metals, each indicator is assessed on a scale of 0-100 with


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higher scores indicating higher criticality. The component score is a weighted sum of the

95

indicators, whereas the axis score is a weighted sum of the component scores (Figure 1).

96

97 98

Figure 1. Diagram of the supply risk axis and the vulnerability to supply risk axes, their

99

components, and their constituent indicators (framework based on Graedel et al.20 and adapted to

100

water resources).

101 102

Criticality scores are provided at country level. Large countries are divided into the largest

103

administrative units within the country (see Figure S1), and countries including islands are

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divided into mainland and islands. If data are only available on the country level, country scores

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are assigned to administrative units. See Table S2 for detailed information on the data sources.

106

SR: Supply Risk

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The three components of the supply risk (SR) axis cover several aspects of water availability

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and accessibility. The hydrological, technological and economic component (HTE) evaluates

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physical availability. The governance component (GOV) assesses the risk of restricted

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accessibility of the resource because of poor water governance. The geopolitical component (GP)


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evaluates supply risks related to the situation in supplying countries. The HTE component is

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given half the weight because it represents physical availability, which cannot be changed but

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only mitigated by water governance and the geopolitical situation – the more resources are

114

within the country, the more control and management options are within the country. These two

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components together are given the other half weight. (Figure 1).

116

HTE: Hydrological, Technological, and Economic Indicators

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The HTE component should be “a useful relative indicator of the contemporary balance

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between supply and demand”.20 For water, this balance may be represented by water stress

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indicators such as the Water Stress Index (WSI) by Pfister et al.9, which ranges from 0.01 to 1.

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The WSI was calculated for more than 10’000 individual watersheds based on withdrawal

121

(driven by demand) to availability (limiting supply) ratios (WTA) and accounts for variation of

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precipitation and water storage capacity.9 WSI is multiplied with 100 for calculation of the HTE

123

component (Equation 1)

124 125 126 127 128

= ∙ 100

(1)

The second indicator used for metals, companion metal fraction, is not adaptable to the water context because water is not a byproduct of other activities.

129

GOV: Governance Indicators

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Since neither of the metal indicators were appropriate for measuring effects of regulation and

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social attitudes on water accessibility, new governance (GOV) indicators were considered.

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According to Ostrom21,22, the issue of how to best govern natural resources is settled neither in

133

academia nor in politics. While there are two poles either promoting private/market or


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public/state solutions, the many options in between may be much more effective: “Many

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successful [… common-pool resources] institutions are rich mixtures of ‘private-like’ and

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‘public-like’ institutions defying classification in a sterile dichotomy”.21 The idea here is to grasp

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this argumentation with three indicators: Out of the six Worldwide Governance Indicators (WGI)

138

used in various criticality assessments,3 government effectiveness (GE) and regulatory quality

139

(RQ) best represent the two poles of public and private governance (see supporting information).

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The third indicator, attempting to capture the various solutions in between, is the UN indicator

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for access to drinking water (ADW). Since GE and RQ correlate (Figure S1), which could be an

142

indication that they are measuring the same thing, they are given half the weight of ADW, which

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is the only water-related indicator and therefore given more weight for calculating the

144

governance component (GOV) (Equation 2). Scores for GE, RQ and ADW are subtracted from

145

100 because low GE and RQ indicate high risk and high ADW indicates low risk.

146 147 148

= ∙ 100 − + ∙ 100 − + ∙ 100 −

(2)

149

GP: Geopolitical Indicators

150

The first geopolitical indicator for metals reflects that the risk of mineral supply restrictions

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from nations that are politically unstable is higher than from those that are not. The second

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indicator quantifies global supply concentration with the Herfindahl−Hirschman Index (HHI). It

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evaluates the risk of having “all of your eggs in one basket”.20 Both indicators analyze the supply

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situation of the global market. Since water is not an internationally traded but rather locally used

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resource, the two indicators accounting for political stability (PS) and supply concentration (SC)

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as well as a new indicator accounting for upstream water stress (UWS) are applied only on water

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supplying neighbors. Two scaling factors (sf1, sf2) are applied to all three indicators: Since


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internally available water is not subject to risks, the ratio of inflow to total renewable water

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resources (TRWR) is used as a first scaling factor (Equation 3). Additionally, since data does not

160

show whether inflow or internal renewable water resources (IRWR) are used, the ratio of total

161

withdrawals (Wtot) minus desalinated water (D) to IRWR is used as a second scaling factor

162

(Equation 4) assuming that the more the internal resources are used, the more important inflows

163

become. A maximum of 1 is set for both scaling factors.

164 165

!

=

"#$#

1

%

%

!

"#$# ! "#$#

≤1

≥1

, = *+,- +

(3)

166 167

$./. 01

=

#$#

1

%

%

$./. 01

#$# $./. 01 #$#

≤1 ≥1

(4)

168 169 170 171 172

Political stability (PS) of water supplying neighbors is calculated with Equation 5, whereby PV is the WGI indicator for political stability and absence of violence. 2 = 3 ∙ ∙ ∑ ABCDE > 5 ! ∙ 2 ; ? @ ./. GHHHHHHHHHHIHHHHHHHHHHJ ; BAF !6/78.9:

(5)

K B=B;L F=LEBB=? ABCDE > ; BAF !ABCD=AM E? FDL>A B !

173 174

The risk of water supply restrictions from neighbor countries may depend as much on water

175

availability within these countries as on their political stability: If water resources are scarce in

176

an upstream country, this may indicate an increased risk of restricted inflow for the downstream

177

country. Therefore, upstream water stress (UWS) is calculated the same way as PS, using the

178

Water Stress Index (WSI) instead of PV (Equation 6).


9


179 180

N = 31 ∙ 2 ∙ ∑ ABCDE

>5 ; BAF

181

!6/78.9: !./.

∙ ;

? @

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(6)

182

The Herfindahl−Hirschman Index (HHI) is a commonly used measure of market

183

concentration. HHI for external water suppliers is calculated and transformed to the scale of 0-

184

100 as it has been done for metals20 (for details see supporting information). HHItransformed is used

185

for calculation of supply concentration (SC) (Equation 7).

186 187 188 189 190 191 192 193

P = 31 ∙ 2 ∙ =>LF

>QAM

(7)

The geopolitical component (GP) is calculated as the equally weighted sum of its three indicators (Equation 8). 2 = R ∙ 2 + R ∙ N + R ∙ P

(8)

194

EI: Environmental Implications

195

The evaluation of environmental implications (EI) “should be viewed as indicating to

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designers, governmental officials, and nongovernmental agencies the potential environmental

197

implications of utilizing a particular metal”.20 For the regionally used resource water, the

198

evaluation should indicate the potential environmental implications of using it in a particular

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location. In addition to location, source and specific use also influence environmental impacts:

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Depending on the user and the quality of available water, different treatments causing different

201

environmental impacts are needed to provide required water quality. Therefore, we evaluate EI

202

of water use in each sector separately. Sector scores are transformed to a scale of 0-100 based on


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203

efficiency and economic productivity of water use and aggregated to total country scores, as

204

described below. All in all, EI evaluation considers water use in a particular location from a

205

particular source for a particular purpose with a particular efficiency.

206

As for metals, the ReCiPe23 end point method is used and only the damage categories ‘Human

207

Health’ and ‘Ecosystems’ are calculated. The third damage category, ‘Resource Availability’,

208

does not include direct environmental impacts and is already addressed in the supply risk

209

assessment. To account for the location of water use, ReCiPe points per cubic meter water

210

consumption in different geographic units were calculated with data based on Pfister et al.24

211

(some examples are shown in Table S3). Water consumption or consumptive water use denotes

212

the part of withdrawals that is not released back into the original watershed. Concerning water

213

supply, Ecoinvent 2.225 inventory data is used for calculation of ReCiPe points per cubic meter

214

water withdrawn and is provided for domestic, agricultural, and industrial use (Table S3).

215

Graedel et al. compare EI of metals on a per kilogram basis.20 This makes sense if equal

216

quantities are used or if one metal can be substituted with the same mass of another metal. An

217

evaluation as described above, however, has to be based on water use at the regional level.

218

Therefore, (regional) impact from (regional) consumption (C) per use (domestic, agricultural,

219

industrial) is calculated by multiplying the damage per region (as shown in Table S3) with

220

volumes of water consumption (Equation 9). Since desalinated water is supplementary to

221

naturally available water, its consumption is not accounted for. Impact from withdrawals (W) per

222

use is calculated by multiplying damage per source (as shown in Table S3) with volumes of

223

withdrawals by source for each use (Equation 10, f = fraction).

224 225 226

STUVWX,ACB

(9)


11


227 228

STUVW$, 20

302

Compensation measures improve criticality performance by either increasing water availability

303

or reducing withdrawals. As a consequence, water stress and dependency on supplying neighbors

304

are reduced. Furthermore, environmental impacts of water use change. Similarly to the metal

305

criticality method, these effects are measured in ratios: the water stress ratio (WR), the

306

dependency ratio (DR), and the environmental impact ratio (ER). For all three ratios, the

307

indicator score is calculated for the compensation scenario and compared to the original indicator

308

score (Equations S23-S25).

309 310 311 312 313

For calculation of the compensation component (C), CP is weighted equally as the average of the three ratios (Equation 14). P = ∙ P2 + ∙

$#r1#ri# R

(14)

314

SU: Susceptibility Indicators

315

Graedel et al. assess the susceptibility component (SU) with a measure of innovation, referred

316

to here as ‘adaptive capacity’ (AC), and a measure of import reliance, referred to here as ‘supply

317

dependency’ (SD).20 Both indicators are adaptable to the water context. Since water availability


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318

differs temporally, water storage reduces susceptibility to supply restrictions. Therefore, a third

319

indicator called ‘dam capacity’ (DC) is introduced. SU is calculated as the equally weighted sum

320

of its indicators (Equation 15).

321 322 323

N = R ∙ P + R ∙ P + R ∙

(15)

324

There are indicators that have already been used to assess the ability to adapt to water supply

325

restrictions: Boulay28 uses the Gross National Income and the Social Water Stress Index and

326

Sullivan29 uses the Human Development Index (HDI) as measure for adaptive capacity (AC).

327

These indicators focus more on economic strength than on innovation, and this is reasonable

328

because economic strength facilitates access to advanced technologies for water compensation

329

such as wastewater treatment and desalination. For water criticality, HDI has been chosen as the

330

AC indicator because it includes economic strength and education, which are both important for

331

adaptation. HDI values on a 0-1 scale were downloaded from the UNDP website.30 Since a high

332

HDI indicates high AC and low vulnerability, HDI values are subtracted from 1 and are

333

multiplied with 100 to calculate AC scores on a scale of 0-100 (Equation 16).

334 335 336

P = 1 − ∙ 100

(16)

337

The potential for water storage in order to adapt to temporal variations of water availability is

338

assessed with the dam capacity (DC). According to the Falkenmark Water Stress Indicator, water

339

availability of more than 1700 m3 per capita and year indicates no water stress.13 For a storage

340

capacity per capita of half this amount, the DC score is set to 0. The reasoning behind this is a

341

simple assumption of a rainy and a dry season, whereby half of the water used in a year has to be


16

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342

stored in the rainy season for use in the dry season. Accordingly, DC is calculated with Equation

343

17.

344 345

P = 100 −

s./9tuc v/w7xc 6tyz.t {|}}x~

∙ 100

(17)

346 347

The supply dependency indicator (SD) corresponds to the scaling used for indicators in the

348

geopolitical component: Supply dependency is high if inflow in relation to total water resources

349

is high and if a high fraction of internal water resources is used. The scaling factors introduced in

350

Equations 3 and 4 are used for calculation of SD as shown in Equation 18.

351 352 353

= 3 ∙ ∙ 100 (18)

354

Overall Criticality

355

Overall criticality is calculated as the normalized (0-100 scale) length of the criticality vector

356

in the criticality space (Equation 19).

357 358

P%W%VU+%W =

359

√# ri r# √R

(19)

360

Results

361

Criticality scores were calculated for 159 countries subdivided into 512 geographic units for

362

the year 2000 (the year for which the most consistent water data and most data across different

363

sources were available). Results for criticality, supply risk (SR), environmental implications (EI),

364

and vulnerability to supply restrictions (VSR) are shown in Figure 2. The criticality scores range


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from 9 (Finland) to 78 (Afghanistan), the SR scores range from 2 (New Zealand) to 80

366

(Turkmenistan), the EI scores range from 0 (e.g. Republic of Congo) to 93 (Nebraska, US), and

367

the VSR scores range from 12 (Iceland) to 72 (Afghanistan).

368

369 370

Figure 2. Results for (A) aggregated Criticality, (B) Supply Risk, (C) Environmental

371

Implications, and (D) Vulnerability to Supply Restrictions.

372 373

The supply risk (SR) map in Figure 2 shows relatively high scores for well-known regions of

374

water scarcity: Northern Africa, the Middle East, Central and Southern Asia, as well as parts of

375

the US, Chile, South Africa and Northern China. The reason for the absolute scores being

376

moderately high (40-60) in many countries of these highly water scarce regions is a dampening

377

effect caused by linear summation with the other two components of the SR axis. Graedel et al.

378

“acknowledge that linear summation has inherent challenges”.20 Their as well as our answers to

379

these challenges are provision of values for each indicator for detailed analysis and transparency

380

and flexibility of the method (alternative aggregation and weighting are discussed below).

381

Therefore, interpretation of scores should be done carefully and with respect to relative


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382

differences and other indicators: Libya has a higher water supply risk than Saudi Arabia because

383

of poor water governance although both countries have similar HTE scores and a GP

384

(geopolitical component) score of 0.

385

A comparison of maps in Figure 2 shows that the global distribution of low and high scores is

386

different for each axis. Results are therefore further discussed below by analyzing three different

387

country/state profiles with the same resulting criticality scores clearly above average: Colorado

388

(US), Iraq, and Sudan (including South Sudan). Results for these three geographic units are

389

shown in Table 1 (median criticality is 27, the 90%-percentile is 56, and the maximum is 77).

390 391

Table 1. Results for criticality, its three axes (SR, EI, VSR), their components (upper part) and

392

detailed results for the VSR axis (lower part); all results are shown for Colorado (US), Iraq, and

393

Sudan (including South Sudan); explanation of acronyms can be found in the text and Table S1 Criticality SR

HTE GOV GP EI

VSR I

C

SU

Colorado 57

40

74

11

0

89

13

15

23

2

Iraq

56

74

93

46

63

41

50

43

67

39

Sudan

56

44

34

58

50

54

66

82

48

68

394 VSR I

NE C

CP Ratios WR DR ER SU AC DC SD

Colorado 13

15

15

23

0

46

48

50

42

2

5

0

0

Iraq

50

43

43

67

82

52

50

48

58

39

40

0

78

Sudan

66

82

82

48

50

46

48

45

45

68

47

63

95

395 396 397

The main contributing axes are EI for Colorado, SR for Iraq, and VSR for Sudan. A detailed

398

analysis of the EI score reveals that Colorado is highly inefficient in agricultural and domestic


19


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399

water use regarding withdrawals and consumption. Colorado gets higher scores for water

400

consumption per production/capita although impacts per cubic meter water consumed are lower

401

than for the other two countries. Since water consumption for agriculture has the largest

402

environmental impact globally (68%, Table S4) compared to other consumption and withdrawals

403

and is weighted accordingly (Equation 11), the total EI score is highest for Colorado. More

404

efficient water use would also lower water stress and thus the SR. The main reason for the high

405

SR score for Iraq is high water stress (high HTE score). Furthermore, Iraq depends on inflows

406

from Iran, Turkey and Syria, countries with high water stress and low political stability. This

407

results in a GP score of 63. The GOV score is not as high, but 46 is considerably above average

408

(median: 32; mean: 29). Although water governance and the geopolitical situation are similarly

409

critical in Sudan, the much lower water stress leads to a considerably lower SR. However, Sudan

410

has the highest VSR score of the three geographic units and therefore a similar criticality score.

411

The high importance score for Sudan is due to about 80% of its labor force working in

412

agriculture, which is responsible for 96% of water withdrawals. Additionally, about a third more

413

water is used per produced value than the global average. Colorado and Iraq have a much smaller

414

percentage of agricultural working force and slightly smaller shares of agricultural water

415

withdrawals. This results in lower importance scores even though water intensity of production is

416

almost four times higher in Colorado than in Sudan. Compensation potential (CP) is very limited

417

in Iraq and Sudan. Whereas water intensity of agricultural production is below average in Iraq,

418

and therefore no compensation with savings in this sector is possible, it is above average in

419

Sudan. An estimated 10% of total water use could be conserved in agriculture. This is the only

420

substantial compensation option for Sudan. Because agricultural production is very water

421

intensive in Colorado, conservation potential is high (about a third of total water use), and the CP


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422

score is low. Furthermore, about two thirds of the wastewater is treated in Colorado, which

423

creates the option of reusing it. The estimated compensation potential from reuse is 3% of total

424

water use, which is about the same as the total compensation potential for Iraq. Effects of water

425

compensation are accounted for in the ratio scores (50: no effect, 50:

426

negative effect). Although Colorado has considerable potential to reduce water use, the effect on

427

water stress is minimal because withdrawals still exceed renewable resources in large parts of the

428

state. There are negative environmental effects for compensation in Iraq because of desalination,

429

which accounts for a fourth of the total compensation potential and has higher environmental

430

impacts than use of water from other sources. Sudan has the highest susceptibility (SU) score

431

because of limited adaptive capacity (AC), lacking dam capacity (DC) and a high dependency on

432

inflow from other countries (SD). While dependency is determined by geography and

433

topography, there is still the opportunity to take action in building storage capacity and thereby

434

decreasing susceptibility to supply restrictions.

435

The presented results show that the three axes and adapted components and indicators from the

436

Methodology of Metal Criticality Determination by Graedel et al.20 are useful for creation and

437

analysis of country/regional profiles in regards to water criticality.

438

Discussion

439

Practical Implications

440

The water criticality method allows for a new, user-oriented assessment of water availability

441

and accessibility by creating water criticality profiles for countries and smaller geographic units.

442

This allows for the screening of sites and their related water criticality, which is useful for every

443

company or organization relying on water resources. The vulnerability axis and its assessment of

444

vulnerability and adaptive capacity identify water related problems and possible mitigation


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445

options and water policies. This is especially interesting for future water scenario analysis.

446

Criticality scores should be interpreted carefully and with respect to relative differences between

447

geographic units and to other indicators. Since comparison with the criticality of metals is not yet

448

possible by comparing aggregated criticality scores, it could be done by comparing water

449

criticality profiles and metal criticality profiles in detail. This is especially interesting for

450

technologies for which water as well as metals may be limiting factors, e.g. concentrating solar

451

thermal power (CSP), where suitable areas are often semi-arid18 and therefore water used for

452

operation might be as critical as the metals used for construction19. However, a fully integrated

453

assessment is not possible.

454

Alternative aggregation and weighting

455

The dampening effect caused by linear summation of components as shown in the Results

456

section for the supply risk axis is reducing discriminating power. While physical availability is

457

limited and can be seen as given, water governance and dependency on foreign resources may

458

have an additional effect on water availability to users. The effect of governance can be positive

459

or negative while dependency can only have a negative effect by adding another risk. However,

460

countries that are independent of foreign water resources currently get a score of 0, which

461

reduces a high HTE score to a low SR score. Even for Iraq, where dependency on foreign

462

resources is rather high (63), the SR score is lower than the HTE score because of linear

463

aggregation (Table 1). This problem has partly been addressed by using a higher weight for the

464

HTE component (Figure 1). Another possibility to handle this is a threshold-based assessment as

465

also mentioned by Graedel et al.20. For water criticality this could mean that if the HTE score is

466

critical (>50), SR is also considered to be critical and GOV and GP components are only

467

included if they do not reduce the SR score below 50. A further possibility is to derive a factor


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468

from the GOV and GP component to adjust HTE as explained in the supporting information. In

469

order to do this, the assumption of no effect for a certain score has to be made, and the

470

magnitude of the effect has to be determined. The case of the ratio indicators used in the

471

compensation component – indicating positive and negative effects of compensation – is similar:

472

The point of no effect for the different ratios is currently a score of 50, which may increase or

473

decrease the compensation potential score (CP), even though there is no effect. This is counter-

474

intuitive, and it would be easier to interpret the results if compensation effects on water stress,

475

dependency and environmental impact ratios would be added to or subtracted from the CP

476

scores. The results of the alternative aggregation shown in the SI are showing a more distinct

477

picture of low and high criticality. However, the correlation of the analyzed countries/regions is

478

very high for the two methods (Pearson’s r = 0.969, Spearman’s rho = 0.969 for overall

479

criticality) and therefore it does not change the direction of the results in general. The alternative

480

aggregation method has similar challenges as linear summation, but it facilitates interpretation

481

and communication of results, which is considered to be an advantage.

482

For reasons of comparability with metal criticality, the presented approach is the one based on

483

the aggregation mechanism chosen by Graedel et al..20 However, since the alternative

484

aggregation has higher discriminating power and is facilitating the interpretation and

485

communication of results, it is recommended to use the alternative aggregation for a stand-alone

486

water criticality assessment.

487

Limitations and further development

488

The presented method includes various indicators from different data sources and many

489

modeling and scaling choices. While in general, indicators are a simplification and modeling and

490

scaling assumptions are debatable, these simplifications and assumptions are needed to evaluate


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491

such a complex issue as water criticality on the global scale. The presented framework is

492

transparent, and the reasoning behind the choices made is explained. For further improvement of

493

the method, however, quantification and inclusion of uncertainties are essential. We also see

494

further developmental potential for some of the indicators.

495

On the supply risk axis, the concept of depletion time as used for metals could be used for

496

groundwater and large lakes, which can be seen as water stocks. However, data for these stocks

497

are not yet available with high quality on the global scale. This might change with increasing

498

availability of data from the GRACE (Gravity Recovery and Climate Experiment) satellites,

499

which are used in various recent studies.31,32 Furthermore, the hydrological, technological and

500

economic component (HTE) considers technological and economic factors only indirectly by

501

comparing availability and demand. If more detailed groundwater data become available,

502

increasing pumping costs due to decreasing groundwater tables could be included in the HTE

503

component.

504

An important missing aspect is water quality. Depending on the end-use purpose, different

505

water qualities – describing physical, biological and chemical water conditions – are needed.33 A

506

quantitative assessment can therefore overestimate water availability, and an integration of water

507

quality into the criticality assessment is desirable. However, Lustenberger has shown that data

508

availability on the global scale is a major challenge.33

509

The calculation of the geopolitical component is based on inflow/outflow-tables that were

510

created for this assessment. They lack dependencies on flows from countries other than those

511

that are neighboring (e.g. flows from China through Laos and Cambodia to Vietnam).

512

Furthermore, it is debatable as to what extent political stability has an influence on trans-


24

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513

boundary flows. The geopolitical analysis could therefore be improved by focusing on

514

international river basins and including studies such as those done by Kalbhenn and Bernauer34.

515

A major modification was needed for the environmental implications axis (EI), where impacts

516

are calculated per value produced or per person supplied (instead of per kg used as done for

517

metals) in order to be able to compare impacts in different countries. On the global scale,

518

environmental impacts of water and metal use are comparable: Precious metals are typically used

519

in lower quantities and therefore environmental impact of their total global use is lower than for

520

extensively used metals such as copper and iron. The environmental impact of total global water

521

use is between the total impact of iron and the total impact of other metals (Figure S6). For

522

comparability of environmental impacts across resources within a criticality method, it would be

523

helpful to evaluate the EI of metals also per value produced or per service provided.

524

Criticality is a supply oriented assessment, whereas Life Cycle Assessment (LCA) focuses on

525

the impacts of human use on the environment. Even though the two approaches have a

526

“complementary nature”7, they have different perspectives, which raises questions about how

527

and to what extent an integration of criticality into LCA – as for example suggested by

528

Sonnemann et al.7 – is possible. However, there is potential for mutual learning, which might

529

help to improve resource indicators in LCA.


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530

ASSOCIATED CONTENT

531

Supporting Information

532

Detailed results, maps for all indicators, and additional method descriptions. This material is

533

available free of charge via the Internet at http://pubs.acs.org.

534

AUTHOR INFORMATION

535

Corresponding Author

536

*phone: +41 44 633 60 14; e-mail: [email protected]

537

ACKNOWLEDGMENT

538

The authors thank Thomas E. Graedel (Yale University) for his collaboration and enabling a

539

research stay at Yale University, Nedal Nassar (Yale University) for his feedback to a previous

540

version of this paper, and Justin Boucher (ETH Zurich) for English proofreading. We also thank

541

two anonymous reviewers for their constructive comments.


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Criticality of Water: Aligning Water and Mineral Resources Assessment

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