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Too Many Streams and Not Enough Time or Money? Analytical Depletion Functions for Streamflow Depletion Estimates
Ground Water ( IF 2.0 ) Pub Date : 2021-07-28 , DOI: 10.1111/gwat.13124
Qiang Li 1, 2 , Tom Gleeson 1 , Samuel C Zipper 3 , Ben Kerr 2
Affiliation  

Groundwater pumping can cause streamflow depletion by reducing groundwater discharge to streams and/or inducing surface water infiltration. Analytical and numerical models are two standard methods used to predict streamflow depletion. Numerical models require extensive data and efforts to develop robust estimates, while analytical models are easy to implement with low data and experience requirements but are limited by numerous simplifying assumptions. We have pioneered a novel approach that balances the shortcomings of analytical and numerical models: analytical depletion functions (ADFs), which include empirical functions expanding the applicability of analytical models for real-world settings. In this paper, we outline the workflow of ADFs and synthesize results showing that the accuracy of ADFs compared against a variety of numerical models from simplified, archetypal models to sophisticated, calibrated models in both steady-state and transient conditions over diverse hydrogeological landscapes, stream networks, and spatial scales. Like analytical models, ADFs are rapidly and easily implemented and have low data requirements but have significant advantages of better agreement with numerical models and better representation of complex stream geometries. Relative to numerical models, ADFs have limited ability to explore nonpumping related impacts and incorporate subsurface heterogeneity. In conclusion, ADFs can be used as a stand-alone tool or part of decision-support tools as preliminary screening of potential groundwater pumping impacts when issuing new and existing water licenses while ensuring streamflow meets environmental flow needs.

中文翻译:

流太多而没有足够的时间或金钱?流耗竭估计的分析耗竭函数

地下水抽水可通过减少向溪流的地下水排放和/或诱导地表水入渗而导致溪流枯竭。分析模型和数值模型是用于预测径流枯竭的两种标准方法。数值模型需要大量数据和努力来开发稳健的估计,而分析模型易于实施,数据和经验要求低,但受到众多简化假设的限制。我们开创了一种平衡分析模型和数值模型缺点的新方法:分析耗尽函数 (ADF),其中包括扩展分析模型在现实环境中的适用性的经验函数。在本文中,我们概述了 ADF 的工作流程并综合结果表明 ADF 的准确性与各种数值模型相比,从简化的原型模型到复杂的校准模型,在不同的水文地质景观、河流网络和空间的稳态和瞬态条件下秤。与分析模型一样,ADF 可以快速、轻松地实现,并且对数据的要求较低,但具有与数值模型更好的一致性和更好地表示复杂流几何形状的显着优势。相对于数值模型,ADF 探索非泵送相关影响和纳入地下异质性的能力有限。综上所述,
更新日期:2021-07-28
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