Bayesian inference of uncertainty in freshwater quality caused by low-resolution monitoring.
Bayesian inference of uncertainty in freshwater quality caused by low-resolution monitoring.
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DOI：
10.1016/j.watres.2017.02.061
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发表时间：
2017-05
期刊：
影响因子：
12.8
通讯作者：
T. Krueger
中科院分区：
文献类型：
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作者：
T. Krueger
监管、低时间分辨率的淡水水质监测不能完全捕捉必要参数的频率分布，特别是那些高度倾斜和重尾的参数。因此，最终与环境标准相比较的汇总统计数据是不确定的。量化这种不确定性对于强有力的水质评估和可能的补救措施至关重要，但需要强有力的假设。本文使用英格兰西南部添马河流域的多年/多地点正磷酸盐(算术平均标准)、溶解氧(DO；10%标准)和氨(90%标准)数据，比较了三种方法来对缺失数据进行建模，以充分描述贝叶斯框架中的频率分布。首先，对假设的频率分布参数模型(对数正态分布或威布尔模型)进行拟合，对于“最佳”模型拟合存在明显的不确定性。其次，贝叶斯模型平均在适应关于最佳模型的数据不明确的情况下更一般，但不考虑可能丢失的数据。第三，对缺失的数据赋予一定权重的监测过程的准非参数多项式模型产生更广泛和更重的尾部频率分布。一次一次的灵敏度分析表明，平均正磷酸盐的多项式模型对支持范围的选择和对缺失数据的先验加权很敏感。10%DO和90%氨氮的敏感度较低。根据欧盟水框架指令，生态状态的综合概率密度跨越了几个状态类别，这意味着生态状态比之前承认的更不确定。对于正磷酸盐来说，对生态状况的监管和经验确定不仅过于精确，而且存在偏见。
Regulatory, low temporal resolution monitoring of freshwater quality does not fully capture the frequency distributions of the requisite parameters, particularly those that are highly skewed and heavy-tailed. Hence the summary statistics ultimately compared to environmental standards are uncertain. Quantifying this uncertainty is crucial for robust water quality assessment and possible remediation, but requires strong assumptions. This paper compares three ways to model the missing data needed to fully characterise a frequency distribution in a Bayesian framework using multi-year/multi-location orthophosphate (arithmetic mean standard), dissolved oxygen (DO; 10th percentile standard) and ammonia (90th percentile standard) data from the Tamar catchment in Southwest England. First, fitting an assumed parametric model of the frequency distribution (lognormal or Weibull), there is appreciable uncertainty around the “best” model fit. Second, Bayesian Model Averaging is more general in accommodating cases where the data are ambiguous with regard to the best model, but does not take into account possibly missing data. Third, a quasi-nonparametric multinomial model of the monitoring process that places some weight on those missing data yields wider and heavier-tailed frequency distributions. One-at-a-time sensitivity analysis suggests that the multinomial model for mean orthophosphate is sensitive to the choice of support range and the prior weights given to the missing data. Sensitivity is lower for 10th percentile DO and 90th percentile ammonia. The resultant probability densities of ecological status under the EU Water Framework Directive span several status classes, meaning ecological status is more uncertain than previously acknowledged. For orthophosphate, the regulatory, empirical determination of ecological status is not only overly precise but also biased.