Quantifying Streamflow Depletion from Groundwater Pumping: A Practical Review of Past and Emerging Approaches for Water Management. (15th February 2022)

• Record Type: Journal Article
• Authors: Zipper, Samuel C.; Farmer, William H.; Brookfield, Andrea; Ajami, Hoori; Reeves, Howard W.; Wardropper, Chloe; Hammond, John C.; Gleeson, Tom; Deines, Jillian M.
• Is Part Of: Journal of the American Water Resources Association. Volume 58:Number 2(2022)
• Page Start: 289
• Page End: 312
• Publication Date: 2022-02-15
• Subjects: streamflow depletion -- stream–aquifer interactions -- watershed management -- decision support systems; Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/1752-1688.12998
• Languages: English
• ISSNs: 1093-474X

Abstract

Abstract: Groundwater pumping can cause reductions in streamflow ("streamflow depletion") that must be quantified for conjunctive management of groundwater and surface water resources. However, streamflow depletion cannot be measured directly and is challenging to estimate because pumping impacts are masked by streamflow variability due to other factors. Here, we conduct a management‐focused review of analytical, numerical, and statistical models for estimating streamflow depletion and highlight promising emerging approaches. Analytical models are easy to implement, but include many assumptions about the stream and aquifer. Numerical models are widely used for streamflow depletion assessment and can represent many processes affecting streamflow, but have high data, expertise, and computational needs. Statistical approaches are a historically underutilized tool due to difficulty in attributing causality, but emerging causal inference techniques merit future research and development. We propose that streamflow depletion‐related management questions can be divided into three broad categories (attribution, impacts, and mitigation) that influence which methodology is most appropriate. We then develop decision criteria for method selection based on suitability for local conditions and the management goal, actionability with current or obtainable data and resources, transparency with respect to process and uncertainties, and reproducibility.