Impacts of River Water Consumption on Aquatic Biodiversity in Life Cycle

Impacts of River Water Consumption on Aquatic

Biodiversity in Life Cycle Assessment – a proposed

method, and a case study for Europe

Danielle M. Tendall*,†,‡, Stefanie Hellweg‡, Stephan Pfister‡, Mark A.J. Huijbregts§, Gérard Gaillard†.

† Biodiversity and Environmental Management, Research Station Agroscope Reckenholz- Tänikon ART, 8046 Zürich, Switzerland

‡ Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland

§ Department of Environmental Science, Radboud University, 6500 GL Nijmegen, The Netherlands

Supporting information

Pages: 14 Figures: 4 Tables: 5

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

1. Average characterization factor The EF we suggest (Eq. 3) assumes a linear behavior of species richness according to discharge at a given location, by using the derivative of the SDR. This assumption may be considered valid only for marginal changes in discharge1. However the SDRs used are not linear over large ranges of discharge. Therefore if a river water consumption affects a zone in a non-marginal way, an average CF (Eq. S1) should be used: the species loss per unit of discharge reduction is estimated as the slope between the original species richness and zero (rather than the slope of the SDR derivative at the original discharge, as for the marginal CF), and is therefore the average species loss over the entire discharge available. This can be interpreted as the maximum potential species loss prediction (upper boundary), whereas the marginal characterization factor can be interpreted as the minimum potential species loss prediction (lower boundary). The “real” prediction would lie somewhere between. Used together, the potential impact calculated using both the average and the marginal approach provide an indication of uncertainty of the prediction related to the choice of approach.

∑ ( ) (S1)

Where CFnm,j is the average characterization factor for a non-marginal water consumption in zone j, aggregating impacts on all subsequent downstream zones which are also non-marginally affected (GSE * 3 y/m ), SRQ0,i is the original species richness in zone i predicted using the SDR with original discharge Q0,i in zone i, and RFi and TFi are the zone rarity and threat factors respectively for zone i. This average CF always gives a higher impact than the corresponding marginal CF.

2. Eco-regions and watershed-level SDR results We present here the detailed results of the SDRs that we developed at the watershed-level: (1) a SDR for fish for an entire region (i.e. for Switzerland and for Europe). In the case of Switzerland, this resulted in the use of 769 watersheds, with annual average discharges ranging from 0.24 to 576 m3/s. The watersheds are partly nested, with smaller watersheds often part of larger watersheds. For Europe, watersheds consist of basins with outlet to the sea and are therefore entirely non-nested; the 399 European watersheds have average annual discharges ranging from 0.1 to 6434 m3/s. (2) Specific SDRs for fish for individual eco-regions (defined as an area with relatively homogeneous ecological conditions, within which comparisons and assessments of biodiversity are meaningful2), in particular for the Swiss Lowlands eco-region (Figure S1), and for the European Iberic, Central Plains and United Kingdom eco- regions (Figure S2).

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

Figure S1: Switzerland, with river network and eco-regions considered (adapted from Szerenczits et al. (2009)3)

Figure S2: The European river basins and eco-regions used (adapted from the Water Framework Directive (2000)2 and CCM project (2007)4). Other eco-regions are not displayed here and were not used, since the number of data points available was too limited (we therefore recommend using the SDR developed for all of Europe for these regions).

When using SDRs specific to eco-regions, goodness of fit (measured by Pearson’s R2) increases from 0.69 for whole of Switzerland to 0.9 for the lowland region (where most agricultural irrigation activity is located). The R2 for the alpine region is on the contrary reduced to 0.58, suggesting that influence of other environmental parameters on species richness may dominate over discharge. It can be noted that

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

for small discharges, there is a higher variability among the observed species richness, which may be interpreted as causing a higher uncertainty in the prediction of an effect of changes in discharge for the case of smaller rivers. This higher variability for small watersheds is an effect commonly observed in species-area relationships5. R2 is further improved to 0.93 by using the cumulative Weibull function, capable of reproducing the asymptotical behavior of the SDR in Switzerland. Although the improvement is marginal, this function visibly fits the data better. Accounting for over-fitting, the cumulative Weibull function is significantly better (F=54.44, p<0.001) using the F-test (which assesses goodness of fit while accounting for increase in degree of freedom), and the AIC is also lower (1076 for Weibull, resp. 1120 for power function). As a further statistical validation, having calibrated the fit on a random subset containing 70% of the data, goodness of fit (R2 = 0.92) remained constant when applying this parameterization to the remaining 30% of the data (data split validation). EPT species richness in Switzerland shows a relationship to discharge similar to that of fish, although maximum EPT species richness is higher than maximum fish species richness in Switzerland (317 resp. 45 species observed). Therefore according to the SDR principle, it may be expected that EPTs will be affected similarly to fish in the event of reductions in discharge due to increased water consumption. In other words, fish may be an appropriate indicator to represent impacts of water consumption on both fish and macro-invertebrates, if using relative species loss (as in the pre-existing method). However, in the method we advocate, absolute species loss is used. Therefore it is desirable to include as many taxa groups as possible, and if only one taxa group (such as fish) is considered, it must be kept in mind that this is a lower estimate, since further species from other taxa groups may also be affected.

The SDR at the continental scale only achieves an R2 of 0.35, which is further reduced to 0.23 when the outlying Danube basin is not considered. This is significantly lower than the goodness of fit achieved for the Swiss SDR. This supports the principle that variability is reduced when using spatial subsets of data (eg. Eco-regions). However it may also be due to inhomogeneous data from different sources with varying coverage and quality in the case of Europe. Similarly to Switzerland, when developing SDRs per eco-region, certain regions benefit from an improvement in goodness of fit (such as the Iberic and Central Plains regions), whereas others show no significant relationship (such as the United Kingdom). Differing ranges of discharge, species present, data quality and coverage may cause a part of these differences, however this may also indicate, as in Switzerland, that species richness is more clearly influenced by discharge in certain regions than in others: for the latter, the prediction of changes in species richness due to changes in discharge is therefore accompanied by higher uncertainty.

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

3. Estimation of habitat area and area affected

Figure S3: a) schematic representation of the estimation of habitat area from occurrence point data; b) schematic representation of the estimation of affected area from river segment data.

4. Threat status conversion from IUCN to scale 1 to 5 Table S1: Conversion of IUCN threat status of species6 to the scale used for threat status weighting in this paper. IUCN threat status Threat status weighting factor Least concern (LC) 1 Near threatened (NT) 2 Vulnerable (VU) 3 Endangered (EN) 4 Critically endangered (CR) 5

5. Case study: details of region and scenarios The Broye watershed has a catchment area of approx. 600 km2, an average discharge at outlet of 11.73 m3/s, an average altitude of 700 masl (range 400 to 1500 masl) and a pluvio-nival regime. The dominant land use in the region is agricultural. The location of the Broye within the Rhine basin, as well as the assumed locations of the consumptive withdrawals and the delimitation of the longitudinal zones used in the case study can be seen in Figure S4. Other tributaries of the Rhine would likewise be attributed with a similar zonation (here we only represent the zonation strictly downstream of the withdrawal locations assumed).

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

Figure S4: Map of Rhine basin with Broye sub-basin, river network, locations of consumptive withdrawals and longitudinal fish assemblage zones for case study (based on Huet et al.7): trout, grayling, barbel and bream.

The scenarios used in our case study8 consider 2050 as a time horizon, and assume a constant agricultural land use area. The agricultural land management (crop mix and spatial distribution, irrigation, tillage and fertilization) is optimized for climatic conditions in 2050, assuming an extreme climate change signal (using the ETH CML regional climate model and the A1B emissions scenario of IPCC, as provided in the ENSEMBLES project9). CropSyst is used to model crop growth10. Scenarios which maximize yield production in the region for the climate in 2050 would imply a drastic increase in irrigation water demand, from 1.13 mio m3/y currently to 46.24 mio m3/y in 2050. Assuming an efficiency of 70%, the river water consumption is therefore 32.23 mio m3/y.

6. Zones used to develop the Swiss zone-level SDR Table S2 provides the discharge, area, attributed zone class and species richness of the zones used to develop the Swiss zone-level SDR for fish.

Table S2: Yearly average discharge, area, attributed zone class according to Huet et al. (1949)7 and fish species richness (number of species observed) for the zones used to develop the zone-level SDR of fish for Switzerland. Zone ID Discharge Area (m2) Attributed zone Fish species richness (m3/s) Laire 4.12E-01 4.35E+07 Trout 9 S5

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

Lion 1.07E+00 1.15E+08 Trout 9 Versoix 6.08E-02 8.45E+06 Barbel 3 Promenthouse 4.28E+00 8.64E+07 Trout 4 Aubonne 2.72E+00 8.26E+07 Trout 17 Venoge 2.91E+00 3.09E+07 Grayling 15 Boiron 5.73E-01 2.41E+07 Trout 3 Orbe 9.40E-01 5.51E+06 Grayling 8 Orbe 9.40E-01 2.77E+07 Trout 11 Veyron 1.26E-01 7.98E+06 Grayling 3 Venoge 4.21E+00 7.60E+07 Trout 4 Morges 6.69E-01 3.85E+07 Trout 3 Senoge 3.14E-01 2.34E+07 Trout 5 Molombe 1.20E-01 1.01E+07 Trout 1 Sorge 2.21E-01 7.10E+06 Grayling 2 Mèbre 3.39E-01 2.20E+07 Trout 2 Talent 9.71E-01 6.15E+07 Trout 12 Nozon 5.90E-01 6.35E+07 Trout 8 Orbe 1.23E+01 9.40E+07 Trout 10 Tièhle 1.16E+01 4.47E+07 Barbel 22 Mujon 1.50E-01 1.28E+07 Trout 1 Brine 2.08E-01 1.40E+07 Trout 11 Arnon 2.52E+00 5.19E+07 Trout 11 Areuse 5.88E+00 1.98E+07 Grayling 12 Merdasson 3.26E-01 1.45E+07 Trout 1 Seyon 9.07E-01 5.20E+07 Trout 3 Bied du Locle 2.70E-01 2.50E+07 Trout 1 Rhône 1.84E+02 2.23E+08 Grayling 22 Thielle 2.71E+00 8.24E+07 Trout 24 Gryonne 9.28E-01 3.44E+07 Trout 23 Grande Eau 3.89E+00 1.44E+08 Trout 25 Vièze 3.74E+00 1.42E+08 Trout 16 Trient 6.11E+00 1.45E+08 Trout 25 Borgne 1.22E+01 3.85E+08 Trout 2 Navisence 7.98E+00 2.17E+08 Trout 2 Turtmänna 2.56E+00 9.34E+07 Trout 2 Rhine 1.05E+03 2.36E+07 Bream 42 Zihl 4.07E+01 6.59E+06 Bream 24 Gryonne 2.37E-01 1.74E+07 Barbel 23 Grande Eau 2.12E-01 4.36E+07 Barbel 25 Vièze 1 2.03E+00 1.45E+08 Trout 16 Aare 1.32E+02 7.79E+05 Bream 25 Aare 1.27E+02 3.67E+07 Grayling 29 Aare 1.32E+02 1.50E+08 Trout 25 Liène 3.56E+00 7.59E+07 Trout 3 Töss 9.96E+00 2.84E+07 Grayling 24 Aare 5.38E+02 1.15E+07 Barbel 32 Limmat 1.03E+02 4.16E+07 Grayling 30 Aare 2 3.26E+02 7.72E+07 Grayling 32 Suhre 4.18E+00 2.69E+07 Grayling 21 Rhine 1.56E+02 4.45E+09 Trout 20 Reuss 4.47E+01 8.31E+08 Trout 14 Giessbach 1.47E+00 2.62E+07 Trout 3

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

7. Rarity and vulnerability weighting of species Table S3 shows the rarity and threat status of the species considered in the case study (species occurring in the affected zones).

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

Table S3: Rarity factors, threat status and zone attribution of the species considered in the case study (the zones are depicted in Figure S4). Species Rarity factor Threat status Attributed zone Abramis brama 2.62E-03 1 Bream Alburnus alburnus 2.73E-03 1 Bream Alburnoides bipunctatus 1.34E-04 1 Grayling Alosa alosa 6.26E-03 1 Bream Alosa fallax 5.22E-03 1 Bream Ambloplites rupestris 1.41E-04 1 Barbel Ameiurus melas 8.94E-04 1 Bream Ameiurus nebulosus 1.47E-03 1 Bream Anguilla anguilla 1.23E-03 5 Bream Aspius aspius 1.43E-02 1 Bream Barbatula barbatula 4.98E-05 1 Grayling Barbus barbus 4.54E-04 1 Barbel Blicca bjoerkna 3.75E-03 1 Bream Carassius auratus 6.17E-05 1 Barbel Carassius carassius 2.53E-04 1 Barbel Carassius gibelio 4.48E-04 1 Grayling Pseudochondrostoma nasus 7.61E-04 1 Barbel Cobitis taenia 3.78E-03 1 Bream Coregonus lavaretus 1.08E-02 3 Bream Cottus gobio 1.64E-04 1 Trout Ctenopharyngodon idella 1.16E-03 1 Bream Cyprinus carpio 4.11E-04 3 Bream Esox lucius 7.26E-05 1 Barbel Gasterosteus aculeatus 8.88E-04 1 Bream Gobio gobio 2.80E-04 1 Barbel Gymnocephalus cernua 2.89E-03 1 Bream Hypophthalmichthys molitrix 7.22E-05 2 Grayling Lampetra fluviatilis 8.44E-05 1 Grayling Lampetra planeri 1.56E-04 1 Trout Lepomis gibbosus 1.14E-03 1 Bream Leucaspius delineatus 5.89E-03 1 Bream Leuciscus idus 4.18E-03 1 Bream Leuciscus leuciscus 5.95E-05 1 Grayling Lota lota 1.23E-04 1 Barbel Micropterus salmoides 4.80E-04 1 Bream Misgurnus fossilis 2.33E-02 1 Bream Oncorhynchus mykiss 5.53E-04 1 Bream Osmerus eperlanus 6.03E-03 1 Bream Perca fluviatilis 1.79E-04 1 Barbel Petromyzon marinus 1.87E-03 1 Bream Phoxinus phoxinus 4.45E-05 1 Grayling Platichthys flesus 2.81E-03 1 Bream Poecilia reticulata 4.56E-05 1 Grayling Pseudorasbora parva 2.64E-04 1 Barbel Pungitius pungitius 1.19E-03 1 Bream Rhodeus amarus 2.89E-02 1 Bream

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

Romanogobio albipinnatus 5.17E-03 1 Barbel Rutilus rutilus 2.35E-04 1 Barbel Salvelinus alpinus 1.11E-04 1 Trout Salmo salar 4.34E-05 1 Trout Salvelinus fontinalis 1.01E-04 1 Trout Salvelinus namaycush 6.71E-05 1 Trout Salmo trutta fario 8.23E-05 1 Trout Salmo trutta lacustris 8.35E-05 1 Trout Salmo trutta trutta 8.35E-05 1 Trout Sander lucioperca 3.32E-03 1 Bream Scardinius erythrophthalmus 1.73E-03 1 Bream Silurus glanis 5.46E-04 1 Barbel Squalius cephalus 2.61E-04 1 Barbel Telestes souffia 1.42E-03 1 Barbel Thymallus thymallus 6.93E-05 1 Grayling Tinca tinca 1.46E-03 1 Bream Vimba vimba 1.13E-02 1 Bream

Species without a threat status defined by the IUCN were given a factor 1. The attribution of species to zones was based on several literature sources, in order of preference: explicit literature11, 12, maps of occurrences13, 14, or deduced from descriptions of habitat15; species occurring in several zones were attributed to the zone were they dominantly occur; if they occurred in all zones equally, species were attributed to the largest zone they occur in (causing a "conservative" estimate).

8. Contribution of zones to total impact

Table S4: Impacts and weighting factors for each zone affected in the case study, contributing to the total impact. ∆w = 32.23 mio m3/y (amount of water consumed, Eq. 1 in Fig. 1). The local species loss is calculated according to Eq. 1 (Fig. 1) for each zone separately (without aggregation of all zones affected), but without weighting species loss by rarity and threat status factors, thus using only the black part of Eq. 4 (Fig. 1). The impacts in GSE*y provide the fully weighted impacts (using the entire Eq. 4, Fig. 1) in each zone. Zone SDR Local species Average threat Average rarity Impact in zone Figure loss in zone [-] factor TF (Eq. 6) factor RF (Eq. 5) [GSE*y] Grayling 2c 3.95·10-1 0.22 1.12·10-4 9.12·10-6 Barbel 2c 1.25·10-2 0.20 6.82·10-4 1.71·10-6 Bream 2c 4.22·10-3 0.25 6.72·10-3 7.18·10-6

9. Quantitative comparison of case study results according to SDR choice (tiered approach) The potential impacts calculated for the case study, using different SDRs, are shown in Table S5. All calculations use the same withdrawal location assumption (in the Broye watershed), aggregation of impacts in downstream zones according to the method proposed in Figure 1, and the same species vulnerability weighting provided in Table S4. Only different SDRs are used in each case, giving an

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

indication of the error that occurs in this case study at least if using an inadequate SDR (in the event of such an SDR being unavailable). Within each row, the same SDR was used to estimate species loss in all zones affected (e.g. Swiss Lowlands SDR was also used for the bream zone, although the latter extends beyond Switzerland). The first row represents the only case where the full method proposed is applied, i.e. the adequate zone- level SDR is used and the location of withdrawal is considered.

Table S5: Total potential impacts estimated for the case study, using different SDRs (tiered approach). The corresponding figures where the SDRs are displayed are indicated. The corresponding equations can be found in Table 1. Aggregated local species loss here is calculated for all zones affected, according to Eq. 1 (Fig. 1), but without the subsequent weighting by rarity and threat status (thus using only the black part of Eq. 4, Fig. 1), whereas the impacts in GSE*y provide the fully weighted impacts (using the entire Eq. 4, Fig. 1). Tier SDR used SDR Location of Aggregated Impacts figure withdrawal ∆w local species [GSE*y] loss [-] Switzerland, fish, 2c Broye 4.12·10-1 1.80·10-5 Tier 1: zone-level power SDR Switzerland, fish, 2c Netherlands 4.22·10-3 7.18·10-6 power Switzerland, fish, 2a Broye 4.69·10-1 4.75·10-5 power Tier 2: regionalized -1 -5 Swiss Lowlands, fish, 2a Broye 6.24·10 3.58·10 watershed-level power SDR Swiss Lowlands, fish, 2f Broye 8.52·10-1 2.16·10-5 CWF Tier 3: only broadly Europe, fish, power 2a Broye 5.88·10-1 3.58·10-5 regionalized SDR Tier 3: non- Non-regionalized, 2a Broye 5.41·10-1 4.57·10-5 regionalized SDR fish, power

Using watershed-level SDRs such as the non-regionalized SDR16, rather than a specifically developed zone-level SDR (e.g. if the latter is unavailable) overestimates the potential impacts as GSE*y by a maximum factor ~2.6 (Table S5, rows 1 and 7) in this case, reflecting the double-counting of species loss mentioned in the methods section. Indeed all tiers using SDRs at the watershed level (rows 3 to 7, Table S5) result in higher impacts than tier 1 using the zone-level SDR: estimates of potential species loss using watershed-level SDRs refer to total species richness in the upstream watershed, and not only in the zone concerned, thus aggregating them for each zone results in some double-counting due to overlaps of the upstream watersheds, and no distinction in the species lost. The choice of region-specific SDR causes differences in results of a factor 1.3 (Table S5, rows 3, 4, 5, and 6). Using the non-regionalized SDR (e.g. if a region-specific SDR is not available) could therefore slightly over- or underestimate the impact, depending on the region. Potential impacts using the cumulative Weibull function are lower than if using the power function (Table S5, rows 4 and 5) in this case study, due to the asymptotical behavior of the CWF. However due to the restricted number of observations for large discharges, the assumption that the SDR in Switzerland truly follows an asymptotical behavior remains uncertain.

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Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

The inclusion of a further taxon (EPT) could not be estimated as GSE*y because the global extent of occurrence of these species was not available. Nevertheless, it increases the absolute species loss by a factor 7.2 (from 0.5 to 3.4 species). Indeed using an SDR for both fish and EPT species (e.g. as shown in Figure 2d) results in a higher total species loss, since it sums the loss of EPT species as well as fish species.

10. Aggregation of different taxa The approach in this paper differs from the previously used PDF*m3*y: we take vulnerability-weighted equivalents of global extinction [GSE*y] as an indicator of potential impacts on biodiversity, in the form of biodiversity loss. This uses the absolute number of species potentially lost, weighted by the fraction of their habitat affected and threat status, which implies that the more taxa considered, the higher the impact. A normalization of different taxa, for example by the global characteristic species richness of the taxa, may be a way of addressing this issue if loss of species in different taxa are to be aggregated (Eq. S2):

∑ ( )

(S2)

Where ∆SRweighted represents the average fraction of global species richness lost (different from PDF, which provides the fraction of species lost in a local ecosystem), NT is the number of taxa considered,

∆SRi is the potential species loss in taxa i, and SRi is the total species richness of taxa i (known or estimated). Such a weighting is optional; if used, potential species loss in each taxa should also be provided separately without weighting, in order to respect current guidelines for LCA (e.g. the ISO 14044 norm). As an illustration of the weighting between taxa proposed in Eq. S2, the potential local loss of fish species amounts to 0.87 species in the case study basin, whereas the potential local loss of EPT species amounts to 5.08 species (using the SDRs “Swiss Lowlands, fish, CWF” and “Swiss Lowlands, EPT, CWF” respectively, for consistency between both taxa; species losses here are not weighted by their vulnerability). Assuming global freshwater fish species richness is 12’740 and global freshwater EPT species richness is 18’07217, this results in an average fraction of global species richness lost of 1.75*10-4.

11. Comparison between GSE*y and PDF*m3*y The impact units used by Hanafiah et al. are PDF*m3*y, which is the fraction lost of total species in a local ecosystem (e.g. a river basin), weighted by the total volume of the local ecosystem. This method cannot relate the loss to an equivalent of global species extinction, and it does not weight species by their rarity or vulnerability. It also gives more weight to species-poor ecosystems (they obtain a high PDF even if only a small number of species is actually lost). In summary, it shows the relative species loss (as a fraction) and the absolute amount of habitat affected. If we re-write the units with a common denomination (Eq. S3):

(S3)

With LSL = local species loss (in the entire river), LSR = local species richness (in the entire river) and LV = local volume (entire river), t = duration of impact. GSE*y is the local species lost weighted by their areal rarity (the fraction of their habitat which is affected, or area of their habitat which is affected divided by their total habitat area), and their threat S11

Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

status. A global extinction occurs if all of their habitat is affected. This approach gives more weight to species-rich ecosystems (where more species can disappear). It also reflects location of withdrawal within the basin by aggregating the impacts for all downstream longitudinal zones. We however assume also (for comparison’s sake) that only one zone is concerned in this example: re-written in the common denomination (Eq. S4):

∑( ) (S4)

With LSH = local species habitat area affected (per species), TSH = total species habitat area (per species), the ratio of which is summed for all species in the local ecosystem affected, TS = threat status. A conversion factor X from GSE*y to PDF*m3*y would thus be (Eq. S5 to S7):

∑( ) (S5)

∑ ( ) (S6)

(S7) ∑( )

This conversion factor for our example is the total volume of the river divided by the sum of "area of habitat affected, divided by total area of habitat, times the threat status" for each species in the river. (Note that this would be more complex for an ecosystem composed of several zones).

12. Estimating impacts of withdrawals which are returned to the river River water may also be withdrawn, used for a certain purpose, and returned to the river further downstream (e.g. diversion for a “run of the river” hydraulic power station). Such a withdrawal cannot be strictly considered as water consumption, since it returns to the river. However, a certain stretch of the river, between the point of withdrawal and the point of return, is deprived of the diverted water: in effect, this is equivalent to water consumption for this stretch of river. The method proposed in our paper can be used to estimate the impacts of this stretch-specific consumption: instead of aggregating impacts in all zones from the point of withdrawal to the river mouth (as for a consumptive withdrawal addressed in the main paper), only the impacts in zones between the point of withdrawal and the point of return would be aggregated. If the points of withdrawal and of return are within a same zone, only the impacts in that zone would be considered. The area of habitat affected (used to calculate the rarity factor in Eq. 5) can be likewise adapted to the area between the points of withdrawal and of return. Note that the impacts of water that is returned to the same point of withdrawal, but at a later time, cannot be estimated with the present method, since it is spatially explicit, but not temporally explicit (as it concerns changes in the average discharge). Furthermore, the impacts on aquatic biodiversity of non- consumptive withdrawals for cooling purposes have been addressed by Verones, et al. (2010)18.

13. Inventory requirements for zone-level assessments In order for the proposed method to be fully applicable in LCAs, inventory data of water consumption should be available with the following information:

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- Specification of river water as source type. - Specification of the location of withdrawal. Ideally, the latter information could be provided and recorded in the form of a geo-referenced location (e.g. coordinates), which can then be matched with any future available map of river zones and their characterization factors. Alternatively, a common zone identification database could be provided within LCIA software (and continuously extended as further regions are included): the location of withdrawal could thus be specified with a common zone “ID”. In our proposed method, knowing the exact location of withdrawal is not required: the zone in which it occurs is sufficient. If the zone is not known but only a larger region, the location of crop production may be estimated from Pfister, et al. (2011)19 for agricultural products and, if necessary, a weighted average of different potential locations used. Spatially explicit data may however be more difficult to obtain for other water-consuming activities.

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Tendall et al. 2013. Impacts of River Water Use on Aquatic Biodiversity in Life Cycle Assessment: Supporting information

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