Articles | Volume 30, issue 15
https://doi.org/10.5194/hess-30-5145-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-5145-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Aug 2026
Research article |
| 14 Aug 2026
| 14 Aug 2026
Groundwater hysteresis increasingly decouples flowing network length from streamflow as snow shifts to rain
Elijah N. Boardman
CORRESPONDING AUTHOR
Mountain Hydrology LLC, Reno, Nevada, 89503, USA
Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, Nevada, 89557, USA
Mark S. Wigmosta
Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
Nicole M. Fernandez
Department of Earth and Planetary Sciences, ETH Zürich, 8092 Zürich, Switzerland
John A. Whiting
Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, Nevada, 89557, USA
Adrian A. Harpold
Graduate Program of Hydrologic Sciences, University of Nevada, Reno, Reno, Nevada, 89557, USA
Department of Natural Resources and Environmental Science, University of Nevada, Reno, Reno, Nevada, 89557, USA
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Alpine snow drifts are an important component of the mountain water cycle, but these extreme environments are underrepresented by automatic monitoring networks and extrapolated spatial datasets. Our field surveys, statistical modeling, and remote sensing synthesis quantifies snow drifts heterogeneity across scales, from the vertical structure at a single location to the kilometer-scale patterning of snow within mountain watersheds.
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Environmental changes can cause hydrological model biases that vary over time (nonstationarity). We demonstrate a new calibration framework to detect and correct nonstationary streamflow biases after a large wildfire, which reduces predictive uncertainty and constrains parameter equifinality.
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Watersheds on the downwind side of a mountain range have deeper seasonal snow and more abundant glaciers due to topographic controls that favor wind drifting. Despite receiving less total snow, these drift-prone watersheds produce relatively more late-summer streamflow due to a combination of slow-melting snow drifts and mass loss from glaciers (and other perennial snow/ice features).
Elijah N. Boardman, Karen L. Boardman, Christopher A. Jones, Sean D. Shipman, John A. Whiting, Joseph W. Boardman, and Adrian A. Harpold
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Alpine snow drifts are an important component of the mountain water cycle, but these extreme environments are underrepresented by automatic monitoring networks and extrapolated spatial datasets. Our field surveys, statistical modeling, and remote sensing synthesis quantifies snow drifts heterogeneity across scales, from the vertical structure at a single location to the kilometer-scale patterning of snow within mountain watersheds.
Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
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Soil moisture helps control how plants grow and how land exchanges water and energy with the atmosphere. We evaluated how well 16 Earth System Models represent soil moisture and its links to plant activity using a benchmarking system and observational data. Many models simulate surface soil moisture well but show larger errors deeper in the soil and in colder regions. Models that represent plant–soil water interactions more directly better capture how ecosystems respond to water availability.
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Simulating the snowpack is challenging, as there are several sources of uncertainty due to e.g. the meteorological forcing. Using data assimilation techniques, it is possible to improve the simulations by fusing models and snow observations. However in forests, observations are difficult to obtain, because they cannot be retrieved through the canopy. Here, we explore the possibility of propagating the information obtained in forest clearings to areas covered by the canopy.
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Environmental changes can cause hydrological model biases that vary over time (nonstationarity). We demonstrate a new calibration framework to detect and correct nonstationary streamflow biases after a large wildfire, which reduces predictive uncertainty and constrains parameter equifinality.
Elijah N. Boardman, Andrew G. Fountain, Joseph W. Boardman, Thomas H. Painter, Evan W. Burgess, Laura Wilson, and Adrian A. Harpold
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Watersheds on the downwind side of a mountain range have deeper seasonal snow and more abundant glaciers due to topographic controls that favor wind drifting. Despite receiving less total snow, these drift-prone watersheds produce relatively more late-summer streamflow due to a combination of slow-melting snow drifts and mass loss from glaciers (and other perennial snow/ice features).
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Sebastian A. Krogh, Lucia Scaff, James W. Kirchner, Beatrice Gordon, Gary Sterle, and Adrian Harpold
Hydrol. Earth Syst. Sci., 26, 3393–3417, https://doi.org/10.5194/hess-26-3393-2022, https://doi.org/10.5194/hess-26-3393-2022, 2022
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We present a new way to detect snowmelt using daily cycles in streamflow driven by solar radiation. Results show that warmer sites have earlier and more intermittent snowmelt than colder sites, and the timing of early snowmelt events is strongly correlated with the timing of streamflow volume. A space-for-time substitution shows greater sensitivity of streamflow timing to climate change in colder rather than in warmer places, which is then contrasted with land surface simulations.
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Short summary
Distributed simulations and geochemical data indicate that groundwater hysteresis dampens the elasticity of flowing stream networks. This effect is expected to become more important as intense rainfall events replace gradual snowmelt in a warmer climate.
Distributed simulations and geochemical data indicate that groundwater hysteresis dampens the...
