Although it is not yet current practice in life cycle assessment, it is recommended that impact assessment methods be accompanied by their uncertainty data to better guide the decision maker. This work uses the best available information to assess uncertainty of the AWARE model for water scarcity and corresponding sensitivities of input parameters. An uncertainty estimate for the AWARE characterization factors (CFs) is provided via (1) arrays (5000 values per CF) with statistics, (2) dispersion analysis, and (3) distribution best fit and parameters. Results show that uncertainty, represented by the dispersion of the values, varies significantly around the world and tends to be more important in regions of higher scarcity and low in most regions around the world (area based) in terms of absolute spread. Globally, values of 18.8 and 66.28 are found for the spread, represented by the interpercentile range (95%) and interquartile range (25–75%), respectively. The lognormal distribution shows the best fit for most regions around the world and could be used as a default distribution. Two parameters come out as influential: actual water availability (because of precipitation uncertainty) and the global hydrological model itself (because of the variability of results obtained from different models). When compared with uncertainty associated with spatio-temporal variability, uncertainties found in this work are generally lower, and hence improving resolution in water scarcity assessments (to monthly and watershed levels) should remain the priority. Finally, required data for software integration of AWARE uncertainty are provided. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

中文翻译：

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量化 AWARE 表征因素的不确定性

虽然目前还没有生命周期评估的实践，但建议影响评估方法附有其不确定性数据，以更好地指导决策者。这项工作使用最佳可用信息来评估 AWARE 模型对水资源短缺和相应输入参数敏感性的不确定性。AWARE 特征因子 (CF) 的不确定性估计通过 (1) 阵列（每个 CF 5000 个值）与统计数据、(2) 离散分析和 (3) 分布最佳拟合和参数提供。结果表明，以值的分散为代表的不确定性在世界范围内变化很大，在绝对分布方面，在世界上大多数地区（基于区域）的稀缺性较高的地区往往更为重要。在全球范围内，值为 18.8 和 66。发现了 28 个价差，分别由百分位间距 (95%) 和四分位间距 (25–75%) 表示。对数正态分布显示出最适合世界上大多数地区的分布，可用作默认分布。有两个参数具有影响力：实际可用水量（由于降水的不确定性）和全球水文模型本身（由于从不同模型获得的结果的可变性）。与与时空变异相关的不确定性相比，这项工作中发现的不确定性通常较低，因此提高水资源短缺评估的分辨率（到月度和流域水平）应仍然是优先事项。最后，提供了软件集成 AWARE 不确定性所需的数据。这篇文章满足了金-金的要求http://jie.click/badges 中描述的JIE数据开放徽章。