Impacts of River Water Consumption on Aquatic Biodiversity in Life Cycle
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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). S1 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 S2 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. S3 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). S4 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.