Abstract Groundwater quality in many regions with intense agriculture has deteriorated due to the leaching of nitrate and other agricultural pollutants. Modified agricultural practices can reduce the input of nitrate to groundwater bodies, but it is crucial to determine the time span over which these measures become effective at reducing nitrate levels in pumping wells. Such estimates can be obtained from hydrogeological modeling or lumped-parameter models (LPM) in combination with environmental tracer data. Two challenges in such tracer-based estimates are (i) accounting for the different modes of transport in the unsaturated zone (USZ), and (ii) assessing uncertainties. Here we extend a recently published Bayesian inference scheme for simple LPMs to include an explicit USZ model and apply it to the Dunnerngau aquifer, Switzerland. Compared to a previous estimate of travel times in the aquifer based on a 2D hydrogeological model, our approach provides a more accurate assessment of the dynamics of nitrate concentrations in the aquifer. We find that including tracer measurements (3H/3He, 85Kr, 39Ar, 4He) reduces uncertainty in nitrate predictions if nitrate time series at wells are not available or short, but does not necessarily lead to better predictions if long nitrate time series are available. Additionally, the combination of tracer data with nitrate time series allows for a separation of the travel times in the unsaturated and saturated zone.

中文翻译：

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使用环境示踪剂确定不饱和区和饱和区旅行时间对硝酸盐减少措施延迟的相对重要性

摘要 由于硝酸盐和其他农业污染物的浸出，许多农业密集地区的地下水水质恶化。改进的农业实践可以减少硝酸盐进入地下水体，但确定这些措施在减少抽水井中硝酸盐含量方面有效的时间跨度至关重要。这种估计可以从水文地质建模或集总参数模型 (LPM) 与环境示踪数据相结合中获得。这种基于示踪剂的估计中的两个挑战是 (i) 考虑非饱和区 (USZ) 中的不同运输方式，以及 (ii) 评估不确定性。在这里，我们扩展了最近发布的简单 LPM 的贝叶斯推理方案，以包括明确的 USZ 模型并将其应用于瑞士 Dunnerngau 含水层。与之前基于 2D 水文地质模型对含水层中旅行时间的估计相比，我们的方法提供了对含水层中硝酸盐浓度动态的更准确评估。我们发现，如果井中的硝酸盐时间序列不可用或较短，则包括示踪剂测量（3H/3He、85Kr、39Ar、4He）会降低硝酸盐预测的不确定性，但如果有长的硝酸盐时间序列可用，则不一定会导致更好的预测。此外，将示踪数据与硝酸盐时间序列相结合，可以将非饱和区和饱和区的旅行时间分开。我们发现，如果井中的硝酸盐时间序列不可用或较短，则包括示踪剂测量（3H/3He、85Kr、39Ar、4He）可降低硝酸盐预测的不确定性，但如果有较长的硝酸盐时间序列可用，则不一定会导致更好的预测。此外，将示踪数据与硝酸盐时间序列相结合，可以将非饱和区和饱和区的旅行时间分开。我们发现，如果井中的硝酸盐时间序列不可用或较短，则包括示踪剂测量（3H/3He、85Kr、39Ar、4He）可降低硝酸盐预测的不确定性，但如果有较长的硝酸盐时间序列可用，则不一定会导致更好的预测。此外，将示踪数据与硝酸盐时间序列相结合，可以将非饱和区和饱和区的旅行时间分开。