ISO 14046 water footprint pdf 19: An overview of the International Standard and its applications

Martínez-Arce, A.; Chargoy, J.P.; Puerto, M.; Rojas, D.; Suppen, N. Water Footprint (ISO 14046) in Latin America, State of the Art and Recommendations for Assessment and Communication. Environments 2018, 5, 114.

iso 14046 water footprint pdf 19

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Martínez-Arce A, Chargoy JP, Puerto M, Rojas D, Suppen N. Water Footprint (ISO 14046) in Latin America, State of the Art and Recommendations for Assessment and Communication. Environments. 2018; 5(11):114.

Martínez-Arce, Andrés, Juan Pablo Chargoy, Maly Puerto, Diana Rojas, and Nydia Suppen. 2018. "Water Footprint (ISO 14046) in Latin America, State of the Art and Recommendations for Assessment and Communication" Environments 5, no. 11: 114.

Life cycle assessment (LCA) has been used to assess freshwater-related impacts according to a new water footprint framework formalized in the ISO 14046 standard. To date, no consensus-based approach exists for applying this standard and results are not always comparable when different scarcity or stress indicators are used for characterization of impacts. This paper presents the outcome of a 2-year consensus building process by the Water Use in Life Cycle Assessment (WULCA), a working group of the UNEP-SETAC Life Cycle Initiative, on a water scarcity midpoint method for use in LCA and for water scarcity footprint assessments.

The recommended method, based on 1/AMD, is based on the inverse of the difference between availability per area and demand per area instead of the ratio (Eq. (1) and Eq. (2)). It quantifies the potential of water deprivation, to either humans or ecosystems, and serves in calculating the impact score of water consumption at midpoint in LCA or to calculate a water scarcity footprint as per ISO 14046. It is based on the available water remaining (AWARE) per unit of surface in a given watershed relative to the world average, after human and aquatic ecosystem demands have been met. This method builds on the assumption that the potential to deprive another user of water (resulting from the multiplication of the inventory with the characterization factor, CFAWARE, Eq. (5)) is directly proportional to the amount of water consumed (inventory) and inversely proportional to the available water remaining per unit of surface and time in a region (watershed) (cf. Eq. (5) and Fig. 1). Additional discussion and justification on this assumption can be found in the following publications: the first one justifying the use of the available water minus environmental demand in the denominator (Hoekstra 2016) and the answer to this paper explaining the reason for including the human demand as well (Pfister et al. 2017). When the value of the demand is equal to or larger than the availability (negative AMD), the characterization factor (CF) is set to be maximal since the equation would no longer be continuous nor hold the same meaning (Eq. (4a)). The CF is normalized (Eq. (3)) and cutoffs are applied (Eq. (4a) and Eq. (4b)). This is further described below.

where demand refers to the sum of human water consumption (HWC) and environmental water requirements (EWR) and availability is the actual runoff (including human impacts on flow regulation), all calculated in m3/month and area in m2. AMD i is calculated in m3/m2month and the remaining volume of water available for use once demand has been met, per unit area and time (m3/m2month). Since this factor is expressed relative to the area, comparability across region is ensured. Its inverse, STe i , is expressed in m2month/m3, can be interpreted as the surface-time equivalent required to generate one cubic meter of unused water in this region. The value of AMDworldavg is the consumption-weighted average of AMDi over the whole world (0.0136 m3/m2month). Units of the CF are dimensionless, expressed in m3 world eq./m3 i(Eq. (3)).

Current human water consumption (the fraction of water withdrawal that does not return into the watershed after use) is used to represent human demand, with data obtained from the WaterGAP model (which is based on statistical data for consumption of freshwater withdrawals (Florke et al. 2013). This includes domestic, industrial, agricultural, livestock, and energy production sectors modeled for the year 2010. The WaterGAP consumption data is provided on a scale of 0.5 0.5, worldwide, on an annual time step except for agricultural use which is on a monthly time step.

Additional modeling choices are relevant to the design of this method, namely, (1) span and use of thresholds, (2) normalization with a reference flow, and (3) scaling modeling. The first describes the setting of thresholds, below and above which the value will be set as minimal and maximal. This choice limits the span of the CF to a maximal range that sets the difference between the lowest and highest value to three orders of magnitude, eliminating the tailing values where the meaning of the CF would be lost. Here, cutoff values of 0.1 and 100 are applied (Eq. (4a) and Eq. (4b), see ESM for details). The second choice is the normalization to a reference flow such as the potential impact of world average consumption, or a specific region, following similar reasoning as in global warming potential where CO2 is taken as a reference flow. Although both differ in the type of reference (a region versus a substance), the role of the reference is to translate an inventory of different flows into an equivalent reference flow. While this choice changes the absolute value of the CF, it does not change the relative results and may enhance the communicability, for example, by providing units of m3-equivalent, i.e., water consumption that are equivalent to the selected reference flow. Normalized CFs facilitate communication on a scale where the value of 1 correspond to the potential impact of the reference flow; values below and above 1 are respectively better and worse than the reference flow. Here, the world average is used as a reference flow (Eq. (3)). The third choice is the use of a scaling function to fit the obtained values within a defined range, such as a logistic curve ranging between 0 and 1 used in previous methods (Pfister et al. 2009; Boulay et al. 2011c). This provided a distribution of values with the hypothesis that impacts have a logistic relationships with the original fraction calculated (WTA, CTA, etc.) and can prevent the use of cutoffs. However, such modeling implies normative choices for the curve tuning parameters and little information is available to support this. For this reason, unlike previous methods, no such additional modeling was performed on the designed method in addition to the choice of cutoffs.

Results are available at www.wulca-waterlca.org and shown in Fig. 2 at the annual level. Curves describing the behavior of the CF in specific watersheds over the year are shown in the Electronic Supplementary Material (ESM; Fig. S5).

AWARE characterization factors for water scarcity footprint in m3 world eq./m3 consumed in region i (represented at annual level for non-agricultural use, i.e., equal contribution of each month)

Similar to Global Warming Potential (GWP) from IPCC (Myhre et al. 2013), the CF represents a relative value of the impact score of a water consumption in comparison with a reference expressed in terms of m3world eq. per m3 consumed in region i (see Eq. (3)). For the purpose of comparing water consumptions in different regions and months, the units m2month are considered equivalent everywhere. This assumes that consuming water in two regions with the same amount of regional remaining water per m2month after human and aquatic ecosystem demands were met is considered equal, as no other regional specification is included.

The CF is limited to a range from 0.1 to 100, with a value of 1 corresponding to a region with the same amount of remaining water per area within a certain period of time as the world average, values 0.99 of max). The lower cutoff of 0.1 has a negligible effect of less than 1% of world water consumption.

The CF is calculated at the sub-watershed level and monthly time step and available on that scale (see the ESM, Fig. S11 for monthly maps). Values are also aggregated to country level and/or annual time step for use with current inventory data at the respective resolutions. This aggregation can be done in different ways to better represent the time of year and location within the country of an agricultural use or a domestic/industrial use. Country annual average characterization factors for agricultural and non-agricultural use are therefore provided, based on water consumption-weighted averages at monthly and watershed level. The rationale for using country annual averages for agriculture vs non-agricultural users is only relevant when monthly and watershed values cannot be used because of lack of inventory information on the exact month or location of the water consumption. In order to aggregate from a smaller scale (month or sub-watershed) to a larger one (year or country), we need the time and space distribution of the water consumption (available from WaterGAP on the WULCA website). Agricultural water use (for irrigation) will occur more in certain regions and certain months (for example, in summer months for Mediterranean areas), so it is possible to aggregate the values to better represent agricultural use. The same is true for non-agricultural water consumption, although more evenly distributed. It is therefore simply a way to reduce the error from spatial and temporal variability when using large scale aggregated factors. 2ff7e9595c