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AWARE characterization factor samples

Authors: Lesage, Pascal; Boulay, Anne-Marie; Pfister, Stefan;

AWARE characterization factor samples

Abstract

Files contain 5000 samples of AWARE characterization factors, as well as sampled independent data used in their calculations and selected intermediate results. AWARE is a consensus-based method development to assess water use in LCA. It was developed by the WULCA UNEP/SETAC working group. Its characterization factors represent the relative Available WAter REmaining per area in a watershed, after the demand of humans and aquatic ecosystems has been met. It assesses the potential of water deprivation, to either humans or ecosystems, building on the assumption that the less water remaining available per area, the more likely another user will be deprived. The code used to generate the samples can be found here: https://github.com/PascalLesage/aware_cf_calculator/ Samples were updated from v1.0 in 2020 to include model uncertainty associated with the choice of WaterGap as the global hydrological model (GHM). The following datasets are supplied: 1) AWARE_characterization_factor_samples.zip Actual characterization factors resulting from the Monte Carlo Simulation. Contains 4 zip files: * monthly_cf.zip: contains 116,484 arrays of 5000 monthly characterization factor samples for each of 9707 watershed and for each month, in csv format. Names are cf_<BAS34S_ID>_<MONTH>.csv, where <BAS34S_ID> is the watershed id and <MONTH> is the first three letters of the month ('jan', 'feb', etc.). * average_agri_cf.zip: contains 9707 arrays of 5000 annual average, agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_agri_<BAS34S_ID>.csv. * average_non_agri_cf.zip: contains 9707 arrays of 5000 annual average, non-agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_non_agri_<BAS34S_ID>.csv. * average_unknown_cf.zip: contains 9707 arrays of 5000 annual average, unspecified use, characterization factor samples for each watershed, in csv format. Names are cf_average_unknown_<BAS34S_ID>.csv.. 2) AWARE_base_data.xlsx Excel file with the deterministic data, per watershed and per month, for each of the independent variables used in the calculation of AWARE characterization factors. Specifically, it includes: Monthly irrigation Description: irrigation water, per month, per basin Unit: m3/month Location in Excel doc: Irrigation File name once imported: irrigation.pickle table shape: (11050, 12) Non-irrigation hwc: electricity, domestic, livestock, manufacturing Description: non-irrigation uses of water Unit: m3/year Location in Excel doc: hwc_non_irrigation File name once imported: electricity.pickle, domestic.pickle, livestock.pickle, manufacturing.pickle table shape: 3 x (11050,) avail_delta Description: Difference between "pristine" natural availability reported in PastorXNatAvail and natural availability calculated from "Actual availability as received from WaterGap - after human consumption" (Avail!W:AH) plus HWC. This should be added to calculated water availability to get the water availability used for the calculation of EWR Unit: m3/month Location in Excel doc: avail_delta File name once imported: avail_delta.pickle table shape: (11050, 12) avail_net Description: Actual availability as received from WaterGap - after human consumption Unit: m3/month Location in Excel doc: avail_net File name once imported: avail_net.pickle table shape: (11050, 12) pastor Description: fraction of PRISTINE water availability that should be reserved for environment Unit: unitless Location in Excel doc: pastor File name once imported: pastor.pickle table shape: (11050, 12) area Description: area Unit: m2 Location in Excel doc: area File name once imported: area.pickle table shape: (11050,) It also includes: * information (k values) on the distributions used for each variable (uncertainty tab) * information (k values) on the model uncertainty (model uncertainty tab) * two filters used to exclude watersheds that are either in Greenland (polar filter) or without data from the Pastor et al. (2014) method (122 cells), representing small coastal cells with no direct overlap (pastor filter). (filters tab) 3) independent_variable_samples.zip Samples for each of the independent variables used in the calculation of characterization factors. Only random variables are contained. For all watershed or watershed-months without samples, the Monte Carlo simulation used the deterministic values found in the AWARE_base_data.xlsx file. The files are in csv format. The first column contains the watershed id (BAS34S_ID) if the data is annual or the (BAS34S_ID, month) for data with a monthly resolution. the other 5000 columns contain the sampled data. The names of the files are <variable_name.csv>. 4) intermediate_variables.zip Contains results of intermediate calculations, used in the calculation of characterization factors. The zip file contains 3 zip files: * AMD_world_over_AMD_i.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the ratio between the AMD (Availability Minus Demand) for the watershed-month and AMD_glo, the world weighted AMD average. Format is csv. * AMD_world.zip: contains one array of 5000 calculated values of the world average AMD. Format is csv. * HWC.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the total Human Water Consumption. Format is csv. 5) watershedBAS34S_ID.zip Contains the GIS files to link the watershed ids (BAS34S_ID) to actual spatial data.

Updated from version 1.0: added model uncertainty.

Country
Canada
Related Organizations
Keywords

characterization factors, life cycle assessment, water use impacts, Life Cycle Impact Assessment, uncertainty, AWARE

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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