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In forested, seasonally dry watersheds, winter rains commonly replenish moisture deficits in the vadose zone before recharging underlying hillslope groundwater systems that sustain streamflow. However, the relative inaccessibility of the subsurface has hindered efforts to include the role of storage deficits, primarily generated by plant-water uptake, in moderating groundwater recharge. Here, we compare groundwater recharge inferred from the storage-discharge relationship with independent, distributed estimates of vadose zone storage deficits across 12 undisturbed California watersheds, thereby tracking the evolution of the deficit-recharge relationship without intensive field instrumentation. We find accrued deficits during the dry season alone insufficiently explain differences in the wet season partitioning of rainfall due to the non-monotonic behavior of the deficit during the subsequent wet season. Tracking the deficit at the storm event-scale within the wet season, however, reveals a characteristic response in groundwater to increasing rainfall not captured in the seasonal analysis, and may improve estimates of the rainfall required to generate recharge and streamflow on a per-storm basis. Our findings demonstrate the potential for existing public datasets to better capture water partitioning within the subsurface using a combined deficit-recharge approach, though our analysis is currently limited to basins with select characteristics. CODE AVAILABLE ON GITHUB: https://github.com/noah-beniteznelson/recharge_deficit
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A Streamflow Permanence Classification Model for Forested Streams That Explicitly Accounts for Uncertainty and Extrapolation
Accurate mapping of headwater streams and their flow status has important implications for understanding and managing water resources and land uses. However, accurate information is rare, especially in rugged, forested terrain. We developed a streamflow permanence classification model for forested lands in western Oregon using the latest light detection and ranging‐derived hydrography published in the National Hydrography Dataset. Models were trained using 2,518 flow/no flow field observations collected in late summer 2019–2021 across headwaters of 129 sub‐watersheds. The final model, the Western Oregon WeT DRy model, used Random Forest and 13 environmental covariates for classifying every 5‐m stream sub‐reach across 426 sub‐watersheds. The most important covariates were annual precipitation and drainage area. Model output included probabilities of late summer surface flow presence and were subsequently categorized into three streamflow permanence classes—Wet, Dry, and Ambiguous. Ambiguous denoted model probabilities and associated prediction intervals that extended over the 50% classification threshold between wet and dry. Model accuracy was 0.83 for sub‐watersheds that contained training data and decreased to 0.67 for sub‐watersheds that did not have observations of late summer surface flow. The model identified where predictions extrapolated beyond the domain characterized by the training data. The combination of spatially continuous estimates of late summer streamflow status along with uncertainty and extrapolation estimates provide critical information for strategic project planning and designing additional field data collection.
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- Award ID(s):
- 2025755
- PAR ID:
- 10644594
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 61
- Issue:
- 7
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2025WR040478
