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Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions

机译:输入和参数不确定性对集水区地下水行程分布预测的影响

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摘要

Groundwater travel time distributions (TTDs) provide a robust description of the subsurface mixing behavior and hydrological response of a subsurface system. Lagrangian particle tracking is often used to derive the groundwater TTDs. The reliability of this approach is subjected to the uncertainty of external forcings, internal hydraulic properties, and the interplay between them. Here, we evaluate the uncertainty of catchment groundwater TTDs in an agricultural catchment using a 3-D groundwater model with an overall focus on revealing the relationship between external forcing, internal hydraulic properties, and TTD predictions. Eight recharge realizations are sampled from a high-resolution dataset of land surface fluxes and states. Calibration-constrained hydraulic conductivity fields (K-s fields) are stochastically generated using the null-space Monte Carlo (NSMC) method for each recharge realization. The random walk particle tracking (RWPT) method is used to track the pathways of particles and compute travel times. Moreover, an analytical model under the random sampling (RS) assumption is fit against the numerical solutions, serving as a reference for the mixing behavior of the model domain. The StorAge Selection (SAS) function is used to interpret the results in terms of quantifying the systematic preference for discharging young/old water. The simulation results reveal the primary effect of recharge on the predicted mean travel time (MTT). The different realizations of calibration-constrained K-s fields moderately magnify or attenuate the predicted MTTs. The analytical model does not properly replicate the numerical solution, and it underestimates the mean travel time. Simulated SAS functions indicate an overall preference for young water for all realizations. The spatial pattern of recharge controls the shape and breadth of simulated TTDs and SAS functions by changing the spatial distribution of particles' pathways. In conclusion, overlooking the spatial nonuni
机译:地下水行程分布(TTD)提供了稳健的描述,对地下系统的地下混合行为和水文响应提供了稳健的描述。拉格朗日粒子跟踪通常用于导出地下水TTD。这种方法的可靠性受到外部强制,内部液压性能的不确定性和它们之间的相互作用。在这里,我们使用三维地下水模型评估农业集水区内流域地下水TTD的不确定性,整体侧重于揭示外部迫使,内部液压性能和TTD预测之间的关系。八个充电实现是从陆地助势和状态的高分辨率数据集中取样的。校准约束的液压导电场(K-S字段)使用空空间蒙特卡罗(NSMC)方法随机产生,用于每个充电实现。随机步行粒子跟踪(RWPT)方法用于跟踪粒子的途径和计算行程时间。此外,随机采样(RS)假设下的分析模型适用于数值解决方案,用作模型域的混合行为的参考。存储选择(SAS)函数用于解释量化对卸下年轻/旧水的系统偏好的结果。仿真结果揭示了充电对预测的平均行程时间(MTT)的主要影响。校准受约束的K-S字段的不同实现中度放大或衰减预测的MTT。分析模型不正确复制数值解决方案,并且低估平均行程时间。模拟SAS功能表示对所有实现的年轻水的整体偏好。通过改变粒子途径的空间分布,充电空间模式控制模拟TTD和SAS功能的形状和广度。总之,俯瞰空间诺瑞

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    UFZ Helmholtz Ctr Environm Res Dept Computat Hydrosyst Permoserstr 15 D-04318 Leipzig Germany;

    UFZ Helmholtz Ctr Environm Res Dept Computat Hydrosyst Permoserstr 15 D-04318 Leipzig Germany;

    UFZ Helmholtz Ctr Environm Res Dept Computat Hydrosyst Permoserstr 15 D-04318 Leipzig Germany;

    UFZ Helmholtz Ctr Environm Res Dept Environm Informat Permoserstr 15 D-04318 Leipzig Germany;

    UFZ Helmholtz Ctr Environm Res Dept Environm Informat Permoserstr 15 D-04318 Leipzig Germany;

    UFZ Helmholtz Ctr Environm Res Dept Computat Hydrosyst Permoserstr 15 D-04318 Leipzig Germany;

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  • 正文语种 eng
  • 中图分类 水文科学(水界物理学);
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