Quantifying Streamflow Depletion for Science-Based Water Management: Challenges and Emerging Approaches
Abstract
Quantifying reductions in streamflow caused by groundwater pumping (streamflow depletion) is essential for managing groundwater and surface water resources. However, direct measurements of streamflow depletion are not feasible at scales greater than a single stream reach and estimating depletion is challenging because pumping impacts are hidden by streamflow variability from other causes. Here, we compare and contrast the strengths and weaknesses of three approaches for estimating streamflow depletion (analytical, numerical, and statistical models) with respect to common water management decisions. Management decisions related to streamflow depletion often include attributing changes in streamflow to different causes, estimating and predicting impacts of groundwater pumping, and designing mitigation strategies when groundwater and surface water systems are impaired. Analytical models are simple and 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 different drivers of streamflow variability, but have high data and computational needs. Statistical approaches are flexible to different data sources and hydrological targets, but struggle with causal attribution. Historically, statistical approaches have not been as frequently used for streamflow depletion estimation as analytical or numerical models, but there are several emerging data-driven approaches that hold promise. While there is no one-size-fits-all approach for streamflow depletion estimation, the approach used should be well-suited to local conditions, actionable with current or obtainable data and resources, transparent with respect to process and uncertainties, and reproducible.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFMSY25E0616Z