Leveraging Groundwater Dynamics to Improve Predictions of Summer Low‐Flow Discharges

Summer streamflow predictions are critical for managing water resources; however, warming‐induced shifts from snow to rain regimes impact low‐flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing relia...

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Veröffentlicht in:Water resources research 2023-08, Vol.59 (8), p.n/a
Hauptverfasser: Johnson, Keira, Harpold, Adrian, Carroll, Rosemary W. H., Barnard, Holly, Raleigh, Mark S., Segura, Catalina, Li, Li, Williams, Kenneth H., Dong, Wenming, Sullivan, Pamela L.
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Sprache:eng
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Zusammenfassung:Summer streamflow predictions are critical for managing water resources; however, warming‐induced shifts from snow to rain regimes impact low‐flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. We quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low‐flow prediction compared to snow metrics at a rain‐dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep‐storage‐dominated systems. Overall, including RLGW improved low‐flow prediction across diverse watersheds. Plain Language Summary Water managers across the western United States depend on accurate streamflow prediction models for water planning and allocation during summer months. Historically, these models use snow metrics to predict summer flows, but increasing temperatures across the western US are decreasing snow input and accumulation and increasing early snow melt rates leading to changes in streamflow generation mechanisms. Here, we seek to understand how stream flows will respond under warmer climates in three watersheds with distinct climate and underlying bedrock in the western US. We expanded upon commonly used low‐flow model snow metrics to include snow and streamflow metrics from the previous year as well as groundwater dynamics during the annual rec
ISSN:0043-1397
1944-7973
DOI:10.1029/2023WR035126