The format and interpretation of this method differ from previous scarcity methods used in LCA (see methods comparison, section V in the ESM). It presents a concept similar to a previous ecosystem assessment characterization method (Pfister et al. 2009), and which is also used in a recently developed scarcity method for water footprint (Yano et al. 2015). However, the previous methods did not account for the water available after deducting current demand. These similarities demonstrate that water use impact assessment methods have evolved in the same direction.
While the AWARE modeling requires an intrinsic cut-off, it does not include additional modeling, such as a logistic curve as done previously in other methods (Pfister et al. 2009; Boulay et al. 2011b; Berger et al. 2014). This avoids the additional arbitrary choice of curve fitting and thresholds (different than cutoffs, discussed below), which were originally used to define water scarcity or stress, based on WTA (Alcamo et al. 2000; Vorosmarty et al. 2000). Since the meaning of this method is chosen to answer an LCA-oriented question “What is the potential to deprive another user when using water in this region?”, no reliable thresholds or definitions exist to justify such modeling with a logistic, or other, function. In addition, when modeling from the original fraction results to an index, the physical meaning is lost and rather adjusted to a perception of what is believed the results should look like. Here, since no such adjustment is performed, a value of 10 directly represents a region where 10 times less water is remaining per unit of surface in comparison to the reference flow, i.e., the world average. It can also be interpreted as a region where 10 times more area time is required to generate the same amount of unused water in comparison to the reference flow. These interpretations are taken as representative of the potential of users to be deprived, once multiplied with the inventory. In the context of water scarcity footprints which are reported in the public domain (e.g., in relation to products or organizational performance), the result has a relatively simple intuitive meaning. Since water scarcity affects some regions more so than others, the results are reported relative to water consumption at the global average location of water consumption.
The CF results present a larger distribution above one than below one (see Fig. 2). This is because the world average is calculated on the AMDi, whereas the CF is based on the inverse (1/AMDi), resulting in mathematically more weight being given to smaller values of AMDi (i.e., larger CF). This results in a larger discriminatory power for regions where AMDi is lower than AMDworldavg (and where conversely CF
is larger), which can be considered desirable since it is in the distinction of most scarce regions that the method is the most useful. These regions are also where the method is the most sensitive to data uncertainty, as discussed below.
The span of this new CF is chosen to be three orders of magnitude, between 0.1 and 100. In previous midpoint methods, this varied from two orders of magnitude (0.01–1) for Pfister et al. (2009) and Berger et al. (2014), and up to 5, 7, and 9 orders of magnitude for Boulay et al. (2011b), Hoekstra et al. (2012), and Swiss Eco-Scarcity method (Frischknecht et al. 2008), respectively, excluding the zero values. This was an important choice which was made with the thresholds placed along the distribution curve in order to keep as much of the natural distribution as possible yet fix the tailing values to the maximum and minimum (see Fig. S4 in ESM). This normative choice of a maximal range of three orders of magnitude was based on expert judgment. The analysis of the preliminary results revealed that three orders of magnitude was comparable to the variation within inventories and therefore meaningful for a balance between LCI and LCIA. Moreover, this range allowed for both the choice of geographical location (i.e., the water scarcity) and the improvement in water efficiency (i.e., the inventory) to play a significant role in the impact score and hence be improved for reducing impacts. This was not the case with a method spreading over four orders of magnitude, as initially considered. Reducing it to two orders of magnitude (cutoff of 10) was also considered but the cutoff affected an additional 17% of the world water consumption, and up to 43% of the world area for at least 1 month (compared to values in the ESM, Tables S2 and S3). This was judged too influential and resulting in too large of a loss of discriminatory power for regions with less than 10 times remaining water than the world average.
The quantification of ecosystem demand involves some choices and uncertainty which are inherent to the method chosen (Pastor et al. 2013). State of the art, monthly assessment was used in this method which is based on a fraction of the available flow being reserved for freshwater ecosystems. This approach was thought to be the most robust because it is the only global method that evaluates EWR on a monthly basis validated with several case studies across five different freshwater ecoregions (Pastor et al. 2013). However, there are also some limitations. The EWRs used vary monthly as a function of flow patterns but not as a function of other environmental aspects and the algorithm calculating EWR at global scale does not account for specific local aspects due to limited data access at global scale (river width, global aquatic fauna, etc.). Moreover, although the underpinning data includes information about the location of dams, there is variation and uncertainty about the ways in which these infrastructures are managed. In some cases, the management of dams includes specific water releases to meet environmental flow requirements.
Sensitivity to EWR
Considering the points mentioned above, the sensitivity of the method is tested using 150% of the value of EWR. The resulting values of the factors (of AWARE_EWR150%) show a 97.8% ranking consistency with the original values, using the rank correlation coefficient as in Boulay et al. (2015c). Only 3% of the world area passes beyond point of “demand > availability” on an annual level, corresponding to 10% of the world water consumption when an upper value of EWR150% is used (see Tables S2 and S3 in the ESM).
If another method was selected for the assessment of EWR, such as Richter et al.(2012) allocating 80% of natural flows for environmental requirements at all times, the fraction of the world consumption occurring in regions where “demand > availability” would be 21% (5% of world area) on an annual scale (compared to 3.8 and 0.8%, respectively, with current method) and 50% on a monthly scale (25.5% of area) for at least 1 month (compared to 33 and 12%, respectively with current method). This difference is explained by the fact that the current method does take into account flow seasonality in EWR, which will indeed lower the demand for some months and when compared to an assessment using a constant and maximal fraction of the flow all year round like Richter et al. (2012). Moreover, Richter et al. (2012) only allows 20% of daily flow to be used, which is limited in comparison to actual consumption levels in some regions of the world. As per the authors, this is based on a precautionary approach which is not the reference normally used in LCA. The annual average of Pastor et al. (2013) amounts to approximately half of the 80% value of Richter et al. (2012). This choice was made not only because it is the most recent method but also it introduces the monthly variation of flow requirement and allows a higher discrimination power due to the lower EWR share. This choice of water requirements, describing “fair conditions of ecosystems with respect to pristine environment” (taken as proxy for current state), is, however, different in different methods and based on expert assessment. AWARE therefore sets this proxy for current aquatic ecosystem demand as being equivalent in terms of the definition of demand, to current human water consumption. In reality, humans need more water than just consumption, to make up for withdrawals and uses that are not consumptive, and ecosystems may also use more water if it is available. This choice may be debatable and other approaches could have been used (assessing only essential human water consumption for example, or defining all water availability to ecosystem requirements), but it was judged to be the best considering existing models and data.
Aggregation of CFs
Another important new aspect introduced with this method is the choice of different values at the country and/or annual level, representing different types of aggregation for different uses. This provides an additional level of information in order to better bridge the gap between the spatial and temporal resolutions of the inventory in LCA with the relevant resolutions for water use impact assessment. While data on exact location within a country or exact timing during the year may not be known, the choice of selecting whether the water use is for agricultural purpose or not is already valuable information which can be used to enhance the representativeness of the aggregated CF.
At the watershed level, the difference between monthly and annual values has shown a rank correlation 65–97% (when compared to annual non-agri average) and 67–87% (when compared to agri average), with July being the lowest correlated month, and non-agri year average showing in general a higher correlation. The largest difference between monthly and annual are shown in Figs. S5 and S6 (ESM). About 50% of the world consumption is located in regions where the variation between the most different month (high or low) and the annual value is larger than 50, enforcing the importance of temporal resolution. Differences are especially seen in central Asia, Spain, North and South Africa, Western Australia, Middle East, and parts of China.
Spatial variability was analyzed by comparing annual values for each watershed with the annual value for this country. Maps illustrating this difference are shown in the ESM (Figs. S7 and S8). Here, differences are seen in Sub-Saharan Africa, Australia, Central Asia, and USA (specifically for agri CF). Lastly, the rank correlation between agri and non-agri annual values at the annual watershed level showed a rank correlation of 97% and is shown in Fig. S9 (ESM), meaning that although absolute values may differ significantly, the ranking of the watersheds is relatively well maintained. Largest differences are observed in central USA, Chile, Spain, North Africa, and Central Asia.
Global hydrological models are uncertain, especially at the monthly level (Scherer et al. 2015) and this is propagated to water scarcity measures (Pfister and Hellweg 2011; Núñez et al. 2015; Boulay et al. 2015c). This is mainly affecting not only water availability but also the demand side. Human demand is mainly estimated using model estimates and proxy data, until complete and consistent water consumption data is available worldwide. The point at which demand is higher than renewable availability, and hence the resulting value reaches the limit at which the factor is set to maximal (i.e., 100), is linked to EWR which is the most uncertain value. Due to the nature of the method, the largest differences in absolute terms are seen in regions where demand is close to availability (and the CF is large). For these regions, the relative significance of uncertainty in EWR increases (see example in the ESM, Fig. S13).
No analytical uncertainty propagation is possible due to the discrete steps in the function but stochastic uncertainty assessment should be done in the future. The model relies on inputs which are in turn outputs of models for which uncertainty distributions are not yet provided.
As per standard LCA practices, the AWARE factors are meant for use in LCA under the assumption of the marginality of the intervention (i.e., water consumption) in comparison to the background. Further discussion on this can be found in ESM. Regionalized assessment is still a challenge with the current databases and software, but is nonetheless possible. A Simapro-compatible csv file with AWARE CFs at the country level can be downloaded from the WULCA website, and OpenLCA is working on the applicability of regionalized impact assessment with AWARE. Furthermore, while inventory databases do not yet provide completely regionalized data, referring to a “global” region when no geographic information is specified, ecoinvent already focused on introducing the possibility of regionalization of water use flows into version 3 (Pfister et al. 2016).