GNS Science, Wairakei Research Center, Taupo, New Zealand.
NIWA, Wellington, New Zealand; Ministry for the Environment, Wellington, New Zealand.
Sci Total Environ. 2021 Feb 10;755(Pt 2):143292. doi: 10.1016/j.scitotenv.2020.143292. Epub 2020 Nov 5.
To effectively manage sustainably groundwater bodies, it is essential to establish what the naturally occurring ranges of chemical concentrations in groundwaters are and how they change over time. We defined baseline trends for New Zealand groundwaters using: 1) pattern recognition techniques to deal with inconsistent monitoring suites between the national (110 sites) and the denser regional network (>1000 sites), and 2) multivariate statistics to identify and remove impacted sites from the enhanced dataset. Rates of changes were calculated for 13 parameters between January 2005 and December 2014 at more than 1000 groundwater quality monitoring sites. The resulting dataset included 262 complete cases (CC), which was enhanced using Machine-Learning (ML) techniques to a total of 607 sites. Hierarchical cluster analysis was used to identify trend clusters that were consistent between the CC, ML-enhanced datasets and a 2006 study based on solely on the national network. The largest cluster (WR) consisted of low magnitude changes across all parameters and was attributed to water-rock interaction processes. The second largest cluster (I) exhibited fast changes particularly for parameters linked to human-induced impact. The third largest cluster (D) comprised decreases of all parameters and was associated with dilution processes. Trend clusters were further refined using groundwater quality state information, enabling the identification of impacted sites outside of Cluster I in the ML-enhanced and CC datasets. Corresponding trend baselines were subsequently derived at unimpacted sites using univariate quantile distribution (5th and 95th percentile thresholds). Finally, we developed classifications combining baselines (state and trend) and natural variability to enhance state of the environment reporting. This allowed the new identification of deteriorating trends at sites where groundwater quality state is not yet affected in addition to trend reversals. These classifications can be adapted to incorporate new knowledge or align with surface water quality reporting.
为了有效地可持续管理地下水体，必须确定地下水自然存在的化学浓度范围及其随时间的变化。我们使用以下方法为新西兰地下水定义了基准趋势：1）模式识别技术，用于处理国家（110 个站点）和更密集的区域网络（>1000 个站点）之间不一致的监测套件；2）多元统计，用于从增强数据集中识别和去除受影响的站点。在 2005 年 1 月至 2014 年 12 月期间，在 1000 多个地下水质量监测站点计算了 13 个参数的变化率。由此产生的数据集包括 262 个完整案例（CC），并使用机器学习（ML）技术增强到总共 607 个站点。层次聚类分析用于识别 CC、ML 增强数据集和仅基于国家网络的 2006 年研究之间一致的趋势聚类。最大的聚类（WR）由所有参数的低幅度变化组成，归因于水-岩相互作用过程。第二大聚类（I）表现出快速变化，特别是与人为影响相关的参数。第三大聚类（D）由所有参数的减少组成，与稀释过程有关。使用地下水质量状态信息进一步细化趋势聚类，从而在 ML 增强和 CC 数据集中识别聚类 I 之外的受影响站点。随后，在未受影响的站点使用单变量分位数分布（第 5 和第 95 百分位数阈值）得出相应的趋势基线。最后，我们结合基线（状态和趋势）和自然变异性开发了分类，以增强环境状况报告。这允许在地下水质量状态尚未受到影响的站点中识别恶化趋势，以及趋势逆转。这些分类可以进行调整，以纳入新知识或与地表水质量报告保持一致。
