Water Accounting and Vulnerability Evaluation (WAVE) Risk of Freshwater Depletion in Water Footprinting

Transcription

1 Supporting Information for Water Accounting and Vulnerability Evaluation (WAVE) Considering Atmospheric Evaporation Recycling and the Risk of Freshwater Depletion in Water Footprinting Markus Berger 1*, Ruud van der Ent 2, Stephanie Eisner 3, Vanessa Bach 1, Matthias Finkbeiner 1 1 Technische Universität Berlin, Chair of Sustainable Engineering, Office Z1, Strasse des 17. Juni 135, Berlin, Germany 2 Delft University of Technology, Department of Water Management, Postbus 5, 2600 AA Delft, The Netherlands 3 University of Kassel, Center for Environmental Systems Research, Wilhelmshöher Allee 47, Kassel, Germany * Corresponding author: markus.berger@tu-berlin.de; phone: +49.(0) ; fax: +49.(0) Content SI text Figures S1-S12 Table S1-S2 References Environmental Science and Technology March 31, 2014 S1

2 Uncertainties and sensitivity analysis Uncertainties in the accounting and in the vulnerability evaluation model of WAVE are difficult to assess as predicted evaporation recycling rates and the risk of freshwater depletion can hardly be calibrated with reality. As mentioned before, methodological choices are made from a conservative point of view. Hence, rather low effective depths of lakes and wetlands, a long availability time horizon of surface water stocks, and low scarcity reduction rates considering groundwater stocks have been selected. In line with the vulnerability approach applied in this work, this is meant to avoid too high scarcity reductions due to the inclusion of ground and surface water stocks. Nevertheless, the influence of these methodological choices can be analyzed by means of sensitivity analyses. As shown in Table S2, eight sensitivity scenarios are considered. First, the values of the effective depths, time horizons, and scarcity reductions are doubled and halved in a separate scenario at a time. Second, a combination of the above-mentioned settings leading to a minimum and a maximum scarcity reduction is analyzed (Table S2). As it can be seen in Figures S9a and S10b respectively, a bisection of the effective depths or a doubling of the availability time horizon leads to an increase in WDI of 1-10% in some basins located mainly in the USA, Canada, and Russia. Vice versa, a doubling of the effective depths and a bisection of the time horizon causes a scarcity reduction in the same order of magnitude (Figures S9b and S10a). Hence, the settings of the effective depths of lakes and wetlands as well as the choice of the availability time horizon influence WDI only in those basins in which the annually usable fraction of surface water stocks is relevant compared to runoff. When the scarcity reduction rates which consider groundwater stocks are halved or doubled, WDI increases by 1-10% (Figure S11a) or decreases by 1-20% (Figure S11b) in many drainage basins in Europe and in few basins in south-east Asia, Brazil, and the USA. Since the parameter settings of groundwater stocks influence different drainage basins than those of surface water stocks, the combined minimum and maximum sensitivity scenarios lead to altered S2

3 WDI results in many basins around the globe (Figure S12). Especially the combined doubling of effective depths and bisection of availability time horizons leads to an amplification of differences between WDI results obtained in these scenarios and the default WDI. As it can be seen in Figures S9-S12, the parameter variation does not influence the WDI results in many (semi-)arid drainage basins as they are set to the highest value (1.00) per se due to the consideration of absolute freshwater shortage. The basis on which absolute water shortage is defined (degree of aridity) influences the number of basins affected significantly (Figure S4). Changes in WDI resulting from the consideration of absolute freshwater scarcity in semi-arid and arid basins are shown in Figure S8. As mentioned in the discussion, the default values of BIER, BIER hydrol-eff, and WDI are provided on a country level in addition to the level of river basins and can be downloaded free of charge from the internet: Obviously, uncertainties can be relevant when determining country averages especially in countries with regions of different water scarcity like the United States or China. Therefore, the minimum and maximum factors of basins within the country are provided in addition to the average factors. They can be used to evaluate whether a difference between alternatives is significant or within the uncertainty range. It should be noted that there are many sources of uncertainty in water footprinting. Starting from inventory databases, significant differences can be found in the water consumption figures of materials and processes 1-2. Further uncertainties are added in the top-down regionalization required to determine geographically explicit water inventories of complex industrial product systems 3, which are a prerequisite for impact assessment. Thus, providing quantitative uncertainty figures in the WAVE model alone might pretend a level of precision which does not exist in practice. Therefore, it is recommended to additionally discuss potential uncertainties on a qualitative level by considering the methodological limitations addressed in the discussion. S3

4 The quality corrected risk of freshwater depletion In order to consider quality aspects of the water flows entering and leaving the product system (Figure 1), the implementation of water quality indicators (Q i ) is proposed according to the methodology of the Water Impact Index 4. A quality corrected effective water consumption (WC q,eff,n ) can serve as the basis for calculating the quality corrected risk of freshwater depletion (QRFD). ( ) (S1) WC q,eff,n considers the quality of each freshwater input (FW i ), waste water output (WW i ), evaporation recycling flow (ER i ), and synthetically created vapor recycling (VR i ) in each drainage basin n. ( ) (S2) As suggested in ref. 4, water quality indicators can be determined by relating a target concentration of a pollutant (C target,p ) to its actual concentration in the water flow (C actual,p ). When several pollutants are of concern, Q is determined based on the pollutant leading to the highest target concentration exceedance. ( ) (S3) Target concentrations can be determined based on legal thresholds, actual concentrations would have to be measured for each water flow entering or leaving the product system. Hence, a detailed water quality consideration which overcomes the limitations of simplified approaches like LCA or gray water causes high efforts and data demands. S4

5 Figure S1. Runoff fraction (α) which denotes the ratio of the long-term average runoff (blue water) and precipitation within a drainage basin. Figure S2. Hydrologically effective basin internal evaporation recycling (BIER hydrol-eff ) ratios denoting the fractions of evaporated water returning to the originating basin as blue water S5

6 Figure S3. Logistic function determining WDI based on CTA * ; S-curve leads to larger spreading of WDI in medium scarcity ranges 0.05<CTA * <0.20 and turns 1 at CTA * =0.25 Figure S4. Basins classified as dry subhumid, semi-arid, and arid according to UNEP 5 S6

7 Figure S5. Basin internal evaporation recycling (BIER) ratios, determined for a basin side length of 100 km BIER100 <1% 1%- 5%- 10%- >15% Wind x x x A) B) C) Figure S6. Relation between BIER and wind direction/basin shape leading to: A) BIER as modeled in Fig. 1; B) lower BIER as modeled due to shorter evaporation recycling distance x (equation 4); C) higher BIER as modeled due to longer evaporation recycling distance x (equation 4) S7

8 Figure S7. Relative changes in CTA scarcity ratio due to the consideration of ground and surface water stocks Figure S8. Changes in WDI due to the consideration of absolute freshwater shortage in relation to relative scarcity S8

9 A) B) Figure S9. Relative increase of default WDI results when A) the effective depths of lakes and wetlands are halved (2.5 m for lakes, 1 m for wetlands); B) the effective depths of lakes and wetlands are doubled (10 m for lakes, 4 m for wetlands) S9

10 A) B) Figure S10. Relative decrease of default WDI results when A) the availability time horizon of surface water stocks is halved (50 years); B) the availability time horizon of surface water stocks is doubled (200 years) S10

11 A) B) Figure S11. Relative increase of default WDI results when A) the scarcity reduction rates accounting for the presence of groundwater are halved compared to the default settings shown in Table 1; B) the scarcity reduction rates accounting for the presence of groundwater are doubled compared to the default settings shown in Table 1 S11

12 A) B) Figure S12. Relative decrease of default WDI results when A) the parameter settings leading to minimum scarcity are combined (double effective depths, half availability time horizon, double scarcity reduction rates, as shown in Table S2); B) the parameter settings leading to maximum scarcity are combined (half effective depths, double availability time horizon, half scarcity reduction rates, as shown in Table S2) S12

13 1 2 Table S1. Analysis of water consumption and effective water consumption required to produce 1 GJ of bioethanol and the resulting risk of freshwater depletion (RFD, WAVE), freshwater deprivation (Pfister et al. 2009) 6, and ecological scarcity (Frischknecht et al. 2009) 7 Water inventory WAVE Pfister et al. (2009) Frischknecht et al. (2009) Country WC [m³] BIER hydroleff WC eff [m³] WDI [m³ depleted / m³ consumed ] RFD [m³ depleted ] Water stress index [m³ deprived / m³ consumed ] Freshwater deprivation [m³ deprived ] Eco-factor [eco-points/ m³ consumed ] Ecological scarcity [eco-points] Colombia % Mexico % Thailand % Australia % Zambia % S1

14 7 Table S2. Scenarios examined in the sensitivity analysis including description and parameter settings Sensitivity scenario Description Parameter setting d 0.5x d 2x T 0.5x T 2x GW 0.5x GW 2x combined min combined max Bisection of effective depths for lakes and wetlands Doubling of effective depths of lakes and wetlands Bisection of availability time horizons of surface water stocks Doubling of availability time horizons of surface water stocks Bisection of scarcity reduction rates accounting for the presence of groundwater stocks Doubling of scarcity reduction rates accounting for the presence of groundwater stocks Combination of parameter settings leading to minimum scarcity Combination of parameter settings leading to minimum scarcity d eff, lakes = 2.5 m / d eff, wetlands = 1 m d eff, lakes = 10 m / d eff, wetlands = 4 m T = 50 years T = 200 years Bisection of reduction rates shown in Table 1 Doubling of reduction rates shown in Table 1 d 2x / T 0.5x / GW 2x d 0.5x / T 2x / GW 0.5x 8 9 S1

15 References (1) Ecoinvent centre Ecoinvent 3 LCI databse. Available at (accessed July 30, 2013). (2) PE International GaBi LCI database. Available at (accessed March 8, 2013). (3) Berger, M.; Warsen, J.; Krinke, S.; Bach, V.; Finkbeiner, M. Water Footprint of European Cars: Potential Impacts of Water Consumption along Automobile Life Cycles. Environmental Science and Technology 2012, 46 (7), (4) Veolia The Water Impact Index and the First Carbon-Water Analysis of a Major Metropolitan Water Cycle. Available at (accessed July 05, 2013). (5) United Nations Environment Programme, World Atlas of Desertification. 2 ed.; Arnold: London, 1997; p 192. (6) Pfister, S.; Koehler, A.; Hellweg, S. Assessing the environmental impacts of freshwater consumption in LCA. Environmental Science and Technology 2009, 43 (11), (7) Frischknecht, R.; Steiner, R.; Jungbluth, N., The Ecological Scarcity Method - Eco-Factors A method for impact assessment in LCA. Federal Office for the Environment: Bern, Swizerland, S2