prince Theory and methods

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1 prince CASE STUDY, APRIL Extending the PRINCE water use results with water scarcity weighting Water extraction in water-rich and water-scarce areas can have very different impacts on ecosystems and the services they offer local people. This case study describes work done under the PRINCE project to explore ways of reflecting water scarcity in the basic PRINCE water use macro-indicators. The water use statistics in the PRINCE accounts provide information on the volume of water use (in Mm 3 ) embedded in goods and services consumed in Sweden. But they are not calculated in a way that can reflect the differentiated impacts extraction of water is likely to have on ecosystem services in regions of production. In order to assess the potential localised socio-environmental effects of water extraction, water scarcity (i.e. the relative availability of sustainable freshwater resources) is an important consideration. In this case study, two methods are used to connect indices of water scarcity to the PRINCE water consumption outputs. Theory and methods Water scarcity is often presented as a ratio between demand and supply (a water-toavailability, WTA, ratio), which in turn can be assessed against scarcity thresholds for example, moderate water stress has been previously defined as a WTA ratio above 0.2 and severe water stress as a WTA ratio above 0.4 (Vorosmarty 2000) and thus converted into water scarcity indices (see e.g. Pfister et al. 2009). The present case study adopts two methods for linking water scarcity indices (WSIs) with PRINCE data. The first method relies on water scarcity data compiled for the EU-FP7 CREEA project and described in Lutter et al. (2016). In this study, national agricultural production and associated blue water (irrigation) consumption was disaggregated to river-basin level. This was combined with water availability information to provide a blue water scarcity index (based on Hoekstra et al. 2012) associated with different crop groups. We used an allocation matrix to allocate fractions of blue water used in each basin, per crop sector, to EXIOBASE to create a nationally or regionally scaled WSI estimate which accounts for the underlying spatial landscape of crop production, water extraction and water scarcity. The second method is conceptually more simplistic, and is not tied to sector-specific water usage or basin-level water availability. WTA ratios (freshwater withdrawal, divided by freshwater availability at national level) are drawn from FAO Aquastat, adopting the following equation from Pfister et al. (2009) where WTA* is an adjusted water-to-availability ratio. For aggregate (RoW) regions in EXIOBASE, the (highly simplified) assumption is made that

2 The PRINCE project The PRINCE project is developing a system of macro-level consumption-based indicators for Sweden for a wide range of environmental pressures, including greenhouse gas emissions, chemical pollutants and use of resources such as land, water and fish. This system will help monitor progress towards Sweden s Generational Goal: to hand over to the next generation a society in which the major environmental problems in Sweden have been solved, without increasing environmental and health problems outside Sweden s borders. PRINCE responds to a call from the Swedish Environmental Protection Agency (EPA) and Swedish Water and Marine Management Agency (SwAM). The framework developed by PRINCE should be able to produce policy-relevant indicators that can be updated regularly without substantial new research each time, and using the latest techniques. The system should also be compatible with Sweden s national statistics. PRINCE is being implemented by a research consortium led by Statistics Sweden. It is supported by the Swedish Environmental Protection Agency and the Swedish Agency for Marine and Water Management under a Swedish Environmental Protection Agency research grant (Environmental Research Appropriation 1:5). the WTA represents the sum of total regional freshwater extraction across countries in the region, divided by total water availability. No attempt is made to scale regional WTA values to regions of high or low agricultural productivity. WSI values resulting from each method are multiplied by blue water use results (all for 2011) from each region/agricultural product group combination to provide water-scarcity adjusted consumption metrics. Green water use is not considered as it is not linked to irrigation, and therefore scarcity is not contextually relevant. Analysis is restricted to agricultural products as data availability constrains the first method to these sectors. Finally, recent work has identified crop-country combinations associated with high levels of groundwater depletion (Dalin et al. 2017). Groundwater depletion presents an alternative perspective on the potential impact associated with water use (the assumption being that depletion of non-renewable resources is likely to be unsustainable). For individual crop groups, selected groundwater extraction per unit production (GWC, expressed in l/kg values for key producing regions are summarised as a complement to the results. Results Detailed results are presented for the first method only, reserving a short comparative discussion of results from the second method for the end of this section. A results spreadsheet accompanies this case study document. Method one Table 1 shows the top 20 crop-country combinations by total blue water consumption, and their adjusted rankings, for These account for 63.3% of Sweden s total unadjusted blue water consumption, and 69.8% of WSIadjusted consumption. While wheat from RoW Asia and Pacific 1 remains at the top of the list after adjustment for water scarcity, there are, significant changes lower down the ranking. The crop groups that account for 10% or more of Sweden s total embedded blue water consumption are discussed in more detail below. Wheat of wheat associated with Swedish consumption is Mm 3 (20.5% of the total across all crops, which is Mm 3 ). After adjustment for WSI, wheat s share of total blue water use for Swedish consumption rises to 27.0% ( Mm 3 of Mm 3 ). This indicates that production of wheat for Swedish consumption is more associated with water-stressed areas than the average crop. 1 An explanation of the country groupings used in the PRINCE accounts is available at

3 Table 1. Top 20 geographic sources of blue water embedded in Swedish consumption (for crop products), and values calculated using Lutter et al. (2016) data Crop use (WSI adjusted) RoW Asia and Pacific Wheat RoW Asia and Pacific Rice RoW Middle East Fruit China Wheat RoW Middle East Wheat RoW Asia and Pacific Other crops India Other crops United States Fodder crops RoW Middle East Vegetables India Sugar crops Spain Vegetables RoW Asia and Pacific Sugar crops Mexico Fodder crops RoW Middle East Nuts RoW Asia and Pacific Fodder crops China Rice RoW Middle East Other cereals Spain Fruits India Rice Australia Fodder crops Table 2 shows the top 10 producer regions (accounting for 98.9% of total blue water use). As can be seen, the relatively high WSIs for RoW Middle East and RoW Africa push these regions up the ranking after adjustment. Notably, Denmarksourced wheat drops several places. Data on groundwater depletion from Dalin et al. (2017) indicates that wheat production in countries in the RoW Middle East and RoW Africa regions is also associated with high groundwater extraction per unit of production (e.g. Kuwait l/kg, Qatar 9389 l/kg). Rice of rice associated with Swedish consumption is Mm3 (12.7% of the total across all crops). total water use for rice is Mm3 (11.7%), close to the average crop water stress profile. Table 3 shows the top 10 producer regions (accounting for 98.8% of total blue water use). linked to production in China is relatively high (ranked second), but drops when adjusted for water scarcity. In contrast, rice from RoW Africa is associated with higher water stress after the adjustment. Within the RoW Asia and Pacific region, which ranks first with both metrics, production in Pakistan (GWC = l/kg) is associated with a particularly high groundwater extraction rate. Fruit of fruit associated with Swedish consumption is Mm3 (12.1% of the total across all crops). Total water consumption is Mm3 (14.0%), indicating that fruit production for Swedish consumption is slightly more associated with water-stressed areas than the average crop. Table 4 shows the top 10 fruit-producing regions for Swedish consumption (accounting for 95.4% of total embedded blue water use). Unadjusted and

4 Table 2. Top 10 geographic sources of embedded blue water use for production of wheat associated with Swedish consumption, totals and values calculated using Lutter et al. (2016) data use (WSI adjusted) RoW Asia and Pacific China RoW Middle East India United States RoW Africa Russia Turkey Mexico Denmark Table 3. Top 10 geographic sources of embedded blue water use for production of rice associated with Swedish consumption, totals and values calculated using Lutter et al. (2016) data use (Mm 3 ) (WSI adjusted) RoW Asia and Pacific China India RoW Africa RoW Middle East RoW America United States Italy Russia Turkey Table 4. Top 10 geographic sources of embedded blue water use for production of fruit associated with Swedish consumption, totals and and values calculated using Lutter et al. (2016) data use (Mm 3 ) (WSI adjusted) RoW Middle East Spain RoW Africa RoW America RoW Asia and Pacific India United States Italy Greece China

5 Table 5. Top 10 geographic sources of embedded blue water use for production of other crops associated with Swedish consumption, totals and values calculated using Lutter et al. (2016) data use (WSI adjusted) RoW Asia and Pacific India Mexico Turkey RoW Middle East RoW Africa United States China RoW America Brazil Table 6. Top 10 geographic sources of embedded blue water use for production of fodder crops associated with Swedish consumption, totals and values using Lutter et al. (2016) data use (Mm 3 ) (WSI adjusted) United States Mexico RoW Asia and Pacific Australia Netherlands Sweden South Africa RoW America RoW Middle East Poland rankings are relatively consistent across the top 10, with Italy decreasing and China increasing. Within the GWC data, the group fruit is represented by three groups: citrus, dates and grapes, so an average GWC is calculated for these. This highlights the relatively high GWC in countries such as Qatar ( l/kg) and Kuwait ( l/ kg). Spanish fruit production is associated with a relatively low level of groundwater use (6 l/kg). Other crops of other crops 2 associated with Swedish consumption is Mm3 (10.5% of the total across all crops). Total blue water use is Mm 3 (6.4%) indicating that other crops consumption has a lower association with water-stressed areas than expected on average. Table 5 shows the top 10 producing regions for Swedish consumption (accounting for 98.8% of 2 Other crops is all crops excluding wheat, rice, other cereals, roots and tubers, sugar crops, pulses, nuts, oil crops, vegetables, fruits, fibres, and fodder crops.

6 Table 7. values calculated using Lutter et al. (2016) data and FAO Aquastat data. Aggregated regions are removed and data re-ranked to show top 20 country/crop combinations (using WSI-Lutter). Crop use Lutter et al use Mm3) Aquastat WSI Lutter WSI Aquastat China Wheat India Other crops Spain Vegetables United States Fodder crops India Sugar crops Mexico Fodder crops Spain Fruits India Rice India Wheat Australia Fodder crops India Oil crops India Fruits China Rice Turkey Other crops Netherlands Fodder crops China Other cereals China Oil crops Spain Other cereals United States Other cereals United States Oil crops total embedded blue water use). Mexico and Brazil move lower in the adjusted rankings, while India, RoW Middle East and China move up. GWC data is not summarised for this classification as there is not an equivalent, comprehensive, classification in the Dalin et al. (2017) dataset. Fodder crops of fodder crops associated with Swedish consumption is Mm3 (10.9% of the total across all crops). Total blue water use is Mm3 (9.1% of the total), indicating a slightly lower than average water scarcity profile. Table 6 shows the top 10 fodder-producing regions for Swedish consumption (accounting for 96.7% of total embedded blue water use). RoW Asia and Pacific rises to the top of the WSIadjusted rankings, with Sweden notably dropping. GWC data is not summarised for this classification as there is not an equivalent, comprehensive, classification. in the Dalin et al (2017) dataset. Method two This method was included in the study because accounts prepared in PRINCE are designed to be readily updateable, and it is thus appropriate to assess whether a less complex method provides an adequate proxy for more powerful methodologies. However, comparing the results from the first method and that based on FAO Aquastat data, it is clear that there are significant differences between rankings obtained from the more sophisticated method of Lutter et al These differences are not surprisingly more pronounced when we compare results for aggregated regions, due to the fact that WTA ratios are aggregated over broad areas with no allowance made for geographic crop-production patterns. However, even when aggregate RoW regions are removed and the data are re-ranked, significant discrepancies remain, as can be seen in Table 7.

7 Potential future work The results from the first method (based on Lutter et al. 2016) could be adopted to provide an interesting scarcity-based extension to blue water use accounts. These indices are designed for use in MRIO modelling and provide a robust mechanism for accounting for sub-national variability in water consumption and scarcity. A caveat here is that future updates to scarcity estimates based on the methods in Lutter et al. (2016) depend on a relatively complex methodology and are thus likely to rely on thirdparty compilation. While the second method is simpler, and values would be relatively easy to compile, the results do not correspond well to those from the first (presumably more reliable) method. Thus, it is not recommended that the latter values are adopted. References Dalin, C., Wada, Y., Kastner, T. and Puma, M. J. (2017). Groundwater depletion embedded in international food trade. Nature, 543(7647) DOI: / nature21403 Flach, R., Ran, Y., Godar, J., Karlberg, L. and Suavet, C. (2016). Towards more spatially explicit assessments of virtual water flows: linking local water use and scarcity to global demand of Brazilian farming commodities. Environmental Research Letters, 11(7) DOI: / /11/7/ Hoekstra, A. Y., Mekonnen, M. M., Chapagain, A. K., Mathews, R. E. and Richter, B. D. (2012). Global monthly water scarcity: blue water footprints versus blue water availability. PLoS ONE, 7(2). e DOI: /journal.pone One limitation of the Lutter et al. (2016) data is that it is only currently applicable to agricultural production, so work to extend such indicators to other sectors would be beneficial. Furthermore, recent efforts to provide linkages between subnational trade and local water scarcity (e.g. Flach et al. 2016) offer interesting opportunities for extending this work and providing increasingly robust connections between country supply chains and regions of sub-national impact. Lutter, S., Pfister, S., Giljum, S., Wieland, H. and Mutel, C. (2016). Spatially explicit assessment of water embodied in European trade: a productlevel multi-regional input-output analysis. Global Environmental Change, DOI: /j. gloenvcha Pfister, S., Koehler, A. and Hellweg, S. (2009). Assessing the environmental impacts of freshwater consumption in LCA. Environmental Science & Technology, 43(11) DOI: / es802423e Vӧrӧsmarty, C. J. (2000). Global water resources: vulnerability from climate change and population growth. Science, 289(5477) DOI: / science Read PRINCE news, blogs, publications and more at: This brief was written by Chris West, SEI. For more information contact: viveka.palm@scb.se