Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3393-2022
© Author(s) 2022. 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-26-3393-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jul 2022
Research article |
| 05 Jul 2022
| 05 Jul 2022
Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does
Sebastian A. Krogh
CORRESPONDING AUTHOR
Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada 89557, USA
Global Water Center, University of Nevada, Reno, Nevada 89557, USA
Water Resources Department, Faculty of Agricultural Engineering,
University of Concepción, Chillán 3780000, Chile
Lucia Scaff
Global Water Futures, Canada First Research Excellence Fund (CFREF), University of Saskatchewan, Saskatoon, SK S7N 3H5, Canada
James W. Kirchner
Department of Environmental Systems Science, ETH Zurich, 8092
Zurich, Switzerland
Mountain Hydrology Research Unit, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Beatrice Gordon
Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada 89557, USA
Gary Sterle
Global Water Center, University of Nevada, Reno, Nevada 89557, USA
Adrian Harpold
Department of Natural Resources and Environmental Science, University of Nevada, Reno, Nevada 89557, USA
Global Water Center, University of Nevada, Reno, Nevada 89557, USA
Related authors
No articles found.
Cristina Prieto, Dmitri Kavetski, Fabrizio Fenicia, James Kirchner, David McInerney, Mark Thyer, and César Ãlvarez
EGUsphere, https://doi.org/10.5194/egusphere-2026-483, https://doi.org/10.5194/egusphere-2026-483, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Hourly streamflow data are increasingly available and can improve streamflow predictions. We tested simple ways to describe uncertainty by transforming flow values and by accounting for how errors persist from hour to hour, using seven catchments in Spain, Switzerland and the United States. Simple transformations and short-term error memory improve the reliability of probabilistic predictions and help combine hourly results into longer time scales for practical operational contexts.
Guilhem Türk, Christoph J. Gey, Bernd R. Schöne, Marius G. Floriancic, James W. Kirchner, Loic Leonard, Laurent Gourdol, Richard F. Keim, and Laurent Pfister
Hydrol. Earth Syst. Sci., 30, 343–369, https://doi.org/10.5194/hess-30-343-2026, https://doi.org/10.5194/hess-30-343-2026, 2026
Short summary
Short summary
How landscape features affect water storage and release in catchments remains poorly understood. Here we used water stable isotopes in 12 streams to assess the fraction of precipitation reaching streamflow in less than 2 weeks. More recent precipitation was found when streamflow was high and the fraction was linked to the geology (i.e. high when impermeable, low when permeable). Such information is key for better anticipating streamflow responses to a changing climate.
Esteban Alonso-González, Adrian Harpold, Jessica D. Lundquist, Cara Piske, Laura Sourp, Kristoffer Aalstad, and Simon Gascoin
The Cryosphere, 20, 209–225, https://doi.org/10.5194/tc-20-209-2026, https://doi.org/10.5194/tc-20-209-2026, 2026
Short summary
Short summary
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.
Elijah N. Boardman, Mark S. Wigmosta, Nicole M. Fernandez, John A. Whiting, and Adrian A. Harpold
EGUsphere, https://doi.org/10.5194/egusphere-2025-6294, https://doi.org/10.5194/egusphere-2025-6294, 2026
Short summary
Short summary
Distributed simulations and geochemical data suggest 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.
Huibin Gao, Laurent Pfister, and James W. Kirchner
Hydrol. Earth Syst. Sci., 29, 6529–6547, https://doi.org/10.5194/hess-29-6529-2025, https://doi.org/10.5194/hess-29-6529-2025, 2025
Short summary
Short summary
Some streams respond to rainfall with flow that peaks twice: a sharp first peak followed by a broad second peak. We analyzed data from a catchment in Luxembourg to better understand the processes behind this phenomenon. Our results show that the first peak is mostly driven directly by rainfall, and the second peak is mostly driven by rain that infiltrates to groundwater. We also show that the relative importance of these two processes depends on how wet the landscape is before the rain falls.
Quentin Duchemin, Maria Grazia Zanoni, Marius G. Floriancic, Hansjörg Seybold, Guillaume Obozinski, James W. Kirchner, and Paolo Benettin
Geosci. Model Dev., 18, 8663–8678, https://doi.org/10.5194/gmd-18-8663-2025, https://doi.org/10.5194/gmd-18-8663-2025, 2025
Short summary
Short summary
We introduce GAMCR (Generalized Additive Models for Catchment Responses), a data-driven model that estimates how catchments respond to individual precipitation events. We validate GAMCR on synthetic data and demonstrate its ability to investigate the characteristic runoff responses from real-world hydrologic series. GAMCR provides new data-driven opportunities to understand and compare hydrological behavior across different catchments.
Elijah N. Boardman, Gabrielle F. S. Boisramé, Mark S. Wigmosta, Robert K. Shriver, and Adrian A. Harpold
Hydrol. Earth Syst. Sci., 29, 6333–6352, https://doi.org/10.5194/hess-29-6333-2025, https://doi.org/10.5194/hess-29-6333-2025, 2025
Short summary
Short summary
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.
Cansu Culha, Sarah Godsey, Shawn Chartrand, Melissa Lafreniere, James McNamara, and James Kirchner
EGUsphere, https://doi.org/10.5194/egusphere-2025-4275, https://doi.org/10.5194/egusphere-2025-4275, 2025
Short summary
Short summary
We study how Arctic rivers respond to rainfall in a warming climate. We show that runoff response can increase more than 5x under wetter conditions, and Active Layer Detachments amplify water and material runoff response to rainfall. Increasing subsurface storage can reduce runoff sensitivity to rainfall. Our results inform the flashiness of rainfall-runoff predictions based on expected weather and erosion conditions.
Zahra Eslami, Hansjörg Seybold, and James W. Kirchner
Hydrol. Earth Syst. Sci., 29, 5121–5130, https://doi.org/10.5194/hess-29-5121-2025, https://doi.org/10.5194/hess-29-5121-2025, 2025
Short summary
Short summary
We used a new method to measure how streamflow responds to precipitation across a network of watersheds in Iran. Our analysis shows that streamflow is more sensitive to precipitation when groundwater levels are shallower, climates are more humid, topography is steeper, and drainage basins are smaller. These results are a step toward more sustainable water resource management and more effective flood risk mitigation.
Johannes J. Fürst, David FarÃas-Barahona, Thomas Bruckner, Lucia Scaff, Martin Mergili, Santiago Montserrat, and Humberto Peña
Nat. Hazards Earth Syst. Sci., 25, 3559–3579, https://doi.org/10.5194/nhess-25-3559-2025, https://doi.org/10.5194/nhess-25-3559-2025, 2025
Short summary
Short summary
The 1987 Parraguirre ice–rock avalanche developed into a devastating debris flow, causing the loss of many lives and inflicting severe damage near Santiago, Chile. Here, we revise this event, combining various observational records with modelling techniques. In that year, important snow cover coincided with persistent warm periods in spring. We also put forward upward corrections for the trigger and flood volumes involved in this debris flow. Finally, temporary river damming was key for the flow ferocity.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3517, https://doi.org/10.5194/egusphere-2025-3517, 2025
Short summary
Short summary
We studied how well Earth System Models simulate soil moisture and its connection to plant growth and water use. Using a model evaluation tool and real-world data, we found that models generally perform well at the surface but struggle deeper in the soil. These issues vary by region, especially in colder regions. Our results can help improve future model development and support better predictions of how ecosystems respond to a changing environment.
Elijah N. Boardman, Andrew G. Fountain, Joseph W. Boardman, Thomas H. Painter, Evan W. Burgess, Laura Wilson, and Adrian A. Harpold
The Cryosphere, 19, 3193–3225, https://doi.org/10.5194/tc-19-3193-2025, https://doi.org/10.5194/tc-19-3193-2025, 2025
Short summary
Short summary
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).
Julia L. A. Knapp, Wouter R. Berghuijs, Marius G. Floriancic, and James W. Kirchner
Hydrol. Earth Syst. Sci., 29, 3673–3685, https://doi.org/10.5194/hess-29-3673-2025, https://doi.org/10.5194/hess-29-3673-2025, 2025
Short summary
Short summary
This study explores how streams react to rain and how water travels through the landscape to reach them, two processes rarely studied together. Using detailed data from two temperate areas, we show that streams respond to rain much faster than rainwater travels to them. Wetter conditions lead to stronger runoff by releasing older stored water, while heavy rainfall moves newer rainwater to streams faster. These findings offer new insights into how water moves through the environment.
Zhuoyi Tu, Taihua Wang, Juntai Han, Hansjörg Seybold, Shaozhen Liu, Cansu Culha, Yuting Yang, and James W. Kirchner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3018, https://doi.org/10.5194/egusphere-2025-3018, 2025
Preprint archived
Short summary
Short summary
This study provides the first event-scale observational evidence that runoff sensitivity to precipitation decreases significantly in degrading permafrost regions of the Tibetan Plateau. Data-driven analysis reveals that permafrost thaw enhances infiltration and subsurface storage, reducing peak runoff and runoff coefficients, especially during heavy rainfall. These results are important for drought and flood risk management under climate change.
SofÃa Segovia, Pablo A. Mendoza, Miguel Lagos-Zúñiga, LucÃa Scaff, and Andreas Prein
EGUsphere, https://doi.org/10.5194/egusphere-2025-3061, https://doi.org/10.5194/egusphere-2025-3061, 2025
Short summary
Short summary
High-resolution climate simulations can improve our understanding of precipitation and temperature patterns in regions with complex terrain. We evaluate a new climate dataset against in-situ observations, and its potencial for hydrological modeling. Results show that, despite some limitations in dry areas, high-resolution climate models can provide information of a quality comparable to that of observation-based products, supporting their use in water resources planning and decision-making.
James W. Kirchner
Hydrol. Earth Syst. Sci., 28, 4427–4454, https://doi.org/10.5194/hess-28-4427-2024, https://doi.org/10.5194/hess-28-4427-2024, 2024
Short summary
Short summary
Here, I present a new way to quantify how streamflow responds to rainfall across a range of timescales. This approach can estimate how different rainfall intensities affect streamflow. It can also quantify how runoff response to rainfall varies, depending on how wet the landscape already is before the rain falls. This may help us to understand processes and landscape properties that regulate streamflow and to assess the susceptibility of different landscapes to flooding.
Marius G. Floriancic, Scott T. Allen, and James W. Kirchner
Hydrol. Earth Syst. Sci., 28, 4295–4308, https://doi.org/10.5194/hess-28-4295-2024, https://doi.org/10.5194/hess-28-4295-2024, 2024
Short summary
Short summary
We use a 3-year time series of tracer data of streamflow and soils to show how water moves through the subsurface to become streamflow. Less than 50% of soil water consists of rainfall from the last 3 weeks. Most annual streamflow is older than 3 months, and waters in deep subsurface layers are even older; thus deep layers are not the only source of streamflow. After wet periods more rainfall was found in the subsurface and the stream, suggesting that water moves quicker through wet landscapes.
Marius G. Floriancic, Michael P. Stockinger, James W. Kirchner, and Christine Stumpp
Hydrol. Earth Syst. Sci., 28, 3675–3694, https://doi.org/10.5194/hess-28-3675-2024, https://doi.org/10.5194/hess-28-3675-2024, 2024
Short summary
Short summary
The Alps are a key water resource for central Europe, providing water for drinking, agriculture, and hydropower production. To assess water availability in streams, we need to understand how much streamflow is derived from old water stored in the subsurface versus more recent precipitation. We use tracer data from 32 Alpine streams and statistical tools to assess how much recent precipitation can be found in Alpine rivers and how this amount is related to catchment properties and climate.
Gary Sterle, Julia Perdrial, Dustin W. Kincaid, Kristen L. Underwood, Donna M. Rizzo, Ijaz Ul Haq, Li Li, Byung Suk Lee, Thomas Adler, Hang Wen, Helena Middleton, and Adrian A. Harpold
Hydrol. Earth Syst. Sci., 28, 611–630, https://doi.org/10.5194/hess-28-611-2024, https://doi.org/10.5194/hess-28-611-2024, 2024
Short summary
Short summary
We develop stream water chemistry to pair with the existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset. The newly developed dataset, termed CAMELS-Chem, includes common stream water chemistry constituents and wet deposition chemistry in 516 catchments. Examples show the value of CAMELS-Chem to trend and spatial analyses, as well as its limitations in sampling length and consistency.
Shaozhen Liu, Ilja van Meerveld, Yali Zhao, Yunqiang Wang, and James W. Kirchner
Hydrol. Earth Syst. Sci., 28, 205–216, https://doi.org/10.5194/hess-28-205-2024, https://doi.org/10.5194/hess-28-205-2024, 2024
Short summary
Short summary
We study the seasonal and spatial patterns of soil moisture in 0–500 cm soil using 89 monitoring sites in a loess catchment with monsoonal climate. Soil moisture is highest during the months of least precipitation and vice versa. Soil moisture patterns at the hillslope scale are dominated by the aspect-controlled evapotranspiration variations (a local control), not by the hillslope convergence-controlled downslope flow (a nonlocal control), under both dry and wet conditions.
Tobias Nicollier, Gilles Antoniazza, Lorenz Ammann, Dieter Rickenmann, and James W. Kirchner
Earth Surf. Dynam., 10, 929–951, https://doi.org/10.5194/esurf-10-929-2022, https://doi.org/10.5194/esurf-10-929-2022, 2022
Short summary
Short summary
Monitoring sediment transport is relevant for flood safety and river restoration. However, the spatial and temporal variability of sediment transport processes makes their prediction challenging. We investigate the feasibility of a general calibration relationship between sediment transport rates and the impact signals recorded by metal plates installed in the channel bed. We present a new calibration method based on flume experiments and apply it to an extensive dataset of field measurements.
Nikos Theodoratos and James W. Kirchner
Earth Surf. Dynam., 9, 1545–1561, https://doi.org/10.5194/esurf-9-1545-2021, https://doi.org/10.5194/esurf-9-1545-2021, 2021
Short summary
Short summary
We examine stream-power incision and linear diffusion landscape evolution models with and without incision thresholds. We present a steady-state relationship between curvature and the steepness index, which plots as a straight line. We view this line as a counterpart to the slope–area relationship for the case of landscapes with hillslope diffusion. We show that simple shifts and rotations of this line graphically express the topographic response of landscapes to changes in model parameters.
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data
set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci, 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. 
Barnhart, T. B., Molotch, N. P., Livneh, B., Harpold, A. A., Knowles, J. F.,
and Schneider, D.: Snowmelt rate dictates streamflow, Geophys. Res. Lett., 43, 8006–8016, https://doi.org/10.1002/2016GL069690, 2016. 
Baroni, G., Facchi, A., Gandolfi, C., Ortuani, B., Horeschi, D., and van Dam,
J. C.: Uncertainty in the determination of soil hydraulic parameters and its
influence on the performance of two hydrological models of different complexity, Hydrol. Earth Syst. Sci., 14, 251–270,
https://doi.org/10.5194/hess-14-251-2010, 2010. 
Berghuijs, W. R., Woods, R. A., and Hrachowitz, M.: A precipitation shift
from snow towards rain leads to a decrease in streamflow, Nat. Clim. Change,
4, 583–586, https://doi.org/10.1038/nclimate2246, 2014. 
Blois, J. L., Williams, J. W., Fitzpatrick, M. C., Jackson, S. T., and Ferrier, S.: Space can substitute for time in predicting climate-change
effects on biodiversity, P. Natl. Acad. Sci. USA, 110, 9374–9379,
https://doi.org/10.1073/pnas.1220228110, 2013. 
Bowling, D. R., Logan, B. A., Hufkens, K., Aubrecht, D. M., Richardson, A.
D., Burns, S. P., Anderegg, W. R. L., Blanken, P. D., and Eiriksson, D. P.:
Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest, Agr. Forest Meteorol., 252, 241–255, https://doi.org/10.1016/j.agrformet.2018.01.025, 2018. 
Brooks, P. D., Chorover, J., Fan, Y., Godsey, S. E., Maxwell, R. M., McNamara, J. P., and Tague, C.: Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle dynamics, Water Resour. Res., 51, 6973–6987, https://doi.org/10.1002/2015WR017039, 2015. 
Broxton, P. D., Harpold, A. A., Biederman, J. A., Troch, P. A., Molotch, N.
P., and Brooks, P. D.: Quantifying the effects of vegetation structure on
snow accumulation and ablation in mixed-conifer forests, Ecohydrology, 8(,
1073–1094, https://doi.org/10.1002/eco.1565, 2015. 
Christiaens, K. and Feyen, J.: Analysis of uncertainties associated with
different methods to determine soil hydraulic properties and their propagation in the distributed hydrological MIKE SHE model, J. Hydrol., 246, 63–81, https://doi.org/10.1016/S0022-1694(01)00345-6, 2001. 
Cline, D. W.: Snow surface energy exchanges and snowmelt at a continental,
midlatitude Alpine site, Water Resour. Res., 33, 689–701,
https://doi.org/10.1029/97WR00026, 1997. 
Clow, D. W.: Changes in the timing of snowmelt and streamflow in Colorado: A
response to recent warming, J. Climate, 23, 2293–2306,
https://doi.org/10.1175/2009JCLI2951.1, 2010. 
Cooper, A. E., Kirchner, J. W., Wolf, S., Lombardozzi, D. L., Sullivan, B.
W., Tyler, S. W., and Harpold, A. A.: Snowmelt causes different limitations
on transpiration in a Sierra Nevada conifer forest, Agr. Forest Meteorol., 291, 108089, https://doi.org/10.1016/j.agrformet.2020.108089, 2020. 
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S.
L., Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W.,
Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague,
C., Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, Pelletier, J., Perket, J., Rouholahnejad-Freund, E., Wagener, T., Zeng, X.,
Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B.,
Safeeq, M., Shen, C., Verseveld, W., Volk, J., and Yamazaki, D.: Hillslope
Hydrology in Global Change Research and Earth System Modeling, Water Resour.
Res., 55, 1737–1772, https://doi.org/10.1029/2018WR023903, 2019. 
Foster, L., Bearup, L., Molotch, N., Brooks, P., and Maxwell, R.: Energy
budget increases reduce mean streamflow more than snow–rain transitions:
using integrated modeling to isolate climate change impacts on Rocky Mountain hydrology, Environ. Res. Lett., 11, 044015, https://doi.org/10.1088/1748-9326/11/4/044015, 2016. 
Gochis, D. J., Barlage, M., Cabell, R., Casali, M., Dugger, A., FitzGerald,
K., McAllister, M., McCreight, J., RafieeiNasab, A., Read, L., Sampson, K.,
Yates, D., and Zhang, Y.: The WRF-Hydro modeling system technical
description, (Version 5.1.1), https://ral.ucar.edu/sites/default/files/public/projects/wrf_hydro/technical-description-user-guide/wrf-hydro-v5.1.1-technical-description.pdf
(last access: 20 June 2022), 2020. 
Godsey, S. E., Aas, W., Clair, T. A., de Wit, H. A., Fernandez, I. J., Kahl,
J. S., Malcolm, I. A., Neal, C., Neal, M., Nelson, S. J., Norton, S. A.,
Palucis, M. C., Skjelkvåle, B. L., Soulsby, C., Tetzlaff, D., and
Kirchner, J. W.: Generality of fractal 1/f scaling in catchment tracer time
series, and its implications for catchment travel time distributions, Hydrol. Process., 24, 1660–1671, https://doi.org/10.1002/hyp.7677, 2010. 
Gordon, B. L., Brooks, P. D., Krogh, S. A., Boisrame, G. F. S., Carroll, R.
W. H., McNamara, J. P., and Harpold, A. A.: Why does snowmelt-driven streamflow response to warming vary? A data-driven review and predictive
framework, Environ. Res. Lett., 111, 1–20, https://doi.org/10.1088/1748-9326/ac64b4, 2022. 
Goulden, M. L. and Bales, R. C.: Mountain runoff vulnerability to increased
evapotranspiration with vegetation expansion, P. Natl. Acad. Sci. USA, 111, 14071–14075, https://doi.org/10.1073/pnas.1319316111, 2014. 
Harpold, A., Brooks, P., Rajagopal, S., Heidbuchel, I., Jardine, A., and
Stielstra, C.: Changes in snowpack accumulation and ablation in the
intermountain west, Water Resour. Res., 48, W11501, https://doi.org/10.1029/2012WR011949, 2012. 
Harpold, A. A. and Brooks, P. D.: Humidity determines snowpack ablation under a warming climate, P. Natl. Acad. Sci. USA, 115, 1215–1220,
https://doi.org/10.1073/pnas.1716789115, 2018. 
Harpold, A. A., Rajagopal, S., Crews, J. B., Winchell, T., and Schumer, R.:
Relative Humidity Has Uneven Effects on Shifts From Snow to Rain Over the
Western U.S., Geophys. Res. Lett., 44, 9742–9750, https://doi.org/10.1002/2017GL075046, 2017. 
He, C., Chen, F., Barlage, M., Liu, C., Newman, A., Tang, W., Ikeda, K., and
Rasmussen, R.: Can Convection-Permitting Modeling Provide Decent Precipitation for Offline High-Resolution Snowpack Simulations Over
Mountains?, J. Geophys. Res.-Atmos., 124, 12631–12654, https://doi.org/10.1029/2019JD030823, 2019. 
Hidalgo, H. G., Das, T., Dettinger, M. D., Cayan, D. R., Pierce, D. W., Barnett, T. P., Bala, G., Mirin, A., Wood, A. W., Bonfils, C., Santer, B. D.,
and Nozawa, T.: Detection and Attribution of Streamflow Timing Changes to
Climate Change in the Western United States, J. Climate, 22, 3838–3855,
https://doi.org/10.1175/2009JCLI2470.1, 2009. 
Houze, R. A.: Orographic effects on precipitating clouds, Rev. Geophys., 50, RG1001, https://doi.org/10.1029/2011RG000365, 2012. 
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch, T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M., Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink,
P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter,
T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A.
B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.: Importance and vulnerability of the world's water towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020. 
Jasechko, S., Kirchner, J. W., Welker, J. M., and McDonnell, J. J.: Substantial proportion of global streamflow less than three months old, Nat.
Geosci., 9, 126–129, https://doi.org/10.1038/ngeo2636, 2016. 
Jennings, K. S., Winchell, T. S., Livneh, B., and Molotch, N. P.: Spatial
variation of the rain–snow temperature threshold across the Northern
Hemisphere, Nat. Commun., 9, 1148, https://doi.org/10.1038/s41467-018-03629-7, 2018. 
Jepsen, S. M., Molotch, N. P., Williams, M. W., Rittger, K. E., and Sickman,
J. O.: Interannual variability of snowmelt in the Sierra Nevada and Rocky
Mountains, United States: Examples from two alpine watersheds, Water Resour.
Res., 48, 1–15, https://doi.org/10.1029/2011WR011006, 2012. 
Jepsen, S. M., Harmon, T. C., Ficklin, D. L., Molotch, N. P., and Guan, B.:
Evapotranspiration sensitivity to air temperature across a snow-influenced
watershed: Space-for-time substitution versus integrated watershed modeling,
J. Hydrol., 556, 645–659, https://doi.org/10.1016/j.jhydrol.2017.11.042, 2018. 
Kirchner, J. W.: A double paradox in catchment hydrology and geochemistry,
Hydrol. Process., 17, 871–874, https://doi.org/10.1002/hyp.5108, 2003. 
Kirchner, J. W.: Getting the right answers for the right reasons: Linking
measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, 1–5, https://doi.org/10.1029/2005WR004362, 2006. 
Kirchner, J. W., Godsey, S. E., Solomon, M., Osterhuber, R., McConnell, J.
R., and Penna, D.: The pulse of a montane ecosystem: coupling between daily
cycles in solar flux, snowmelt, transpiration, groundwater, and streamflow
at Sagehen Creek and Independence Creek, Sierra Nevada, USA, Hydrol. Earth
Syst. Sci., 24, 5095–5123, https://doi.org/10.5194/hess-24-5095-2020, 2020. 
Klesse, S., DeRose, R. J., Babst, F., Black, B. A., Anderegg, L. D. L.,
Axelson, J., Ettinger, A., Griesbauer, H., Guiterman, C. H., Harley, G., Harvey, J. E., Lo, Y., Lynch, A. M., O'Connor, C., Restaino, C., Sauchyn, D., Shaw, J. D., Smith, D. J., Wood, L., Villanueva-DÃaz, J., and Evans, M. E. K.: Continental-scale tree-ring-based projection of Douglas-fir growth: Testing the limits of space-for-time substitution, Global Change Biol., 26, 5146–5163, https://doi.org/10.1111/gcb.15170, 2020. 
Klos, P. Z., Link, T. E., and Abatzoglou, J. T.: Extent of the rain-snow
transition zone in the western U.S. under historic and projected climate,
Geophys. Res. Lett., 41, 4560–4568, https://doi.org/10.1002/2014GL060500, 2014. 
Kopp, S., Cline, D., Miniat, C., Lucero, C., Rothlisberger, J., Levinson, D., Evett, S., Brusberg, M., Lowenfish, M., Strobel, M., Tschirhart, W., Rindahl, B., Holder, S., and Ables, M.: Perspectives on the National Water Model, Water Resour. Impact, 20, 10–11, 2018. 
Krogh, S. A., Broxton, P. D., Manley, P. N., and Harpold, A. A.: Using Process Based Snow Modeling and Lidar to Predict the Effects of Forest Thinning on the Northern Sierra Nevada Snowpack, Front. Forest Global Change,
3, 21, https://doi.org/10.3389/ffgc.2020.00021, 2020. 
Leroux, N. R. and Pomeroy, J. W.: Modelling capillary hysteresis effects on
preferential flow through melting and cold layered snowpacks, Adv. Water
Resour., 107, 250–264, https://doi.org/10.1016/j.advwatres.2017.06.024, 2017. 
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J. A. J. A. J. A.
J., Prein, A. F. A. F., Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J.,
Eidhammer, T., Gochis, D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.: Continental-scale convection-permitting modeling of the current and future climate of North America, Clim. Dynam., 49, 71–95,
https://doi.org/10.1007/s00382-016-3327-9, 2017. 
Loheide, S. P. and Lundquist, J. D.: Snowmelt-induced diel fluxes through
the hyporheic zone, Water Resour. Res., 45, 1–9, https://doi.org/10.1029/2008WR007329, 2009. 
Lundquist, J. D. and Cayan, D. R.: Seasonal and Spatial Patterns in Diurnal
Cycles in Streamflow in the Western United States, J. Hydrometeorol., 3,
591–603, https://doi.org/10.1175/1525-7541(2002)003<0591:SASPID>2.0.CO;2, 2002. 
Lundquist, J. D. and Dettinger, M. D.: How snowpack heterogeneity affects
diurnal streamflow timing, Water Resour. Res., 41, 1–14,
https://doi.org/10.1029/2004WR003649, 2005. 
Lundquist, J. D., Cayan, D. R., and Dettinger, M. D.: Spring Onset in the
Sierra Nevada: When Is Snowmelt Independent of Elevation?, J. Hydrometeorol., 5, 327–342, https://doi.org/10.1175/1525-7541(2004)005<0327:SOITSN>2.0.CO;2, 2004. 
Lundquist, J. D., Dickerson-Lange, S. E., Lutz, J. A., and Cristea, N. C.:
Lower forest density enhances snow retention in regions with warmer winters:
A global framework developed from plot-scale observations and modeling, Water Resour. Res., 49, 6356–6370, https://doi.org/10.1002/wrcr.20504, 2013. 
Luo, L., Robock, A., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., Lohmann, D., Cosgrove, B., Wen, F., Sheffield, J., Duan, Q., Higgins, R. W., Pinker, R. T., and Tarpley, J. D.: Validation of the North American Land Data Assimilation System (NLDAS) retrospective forcing over the southern Great Plains, J. Geophys. Res.-Atmos., 108, 8843, https://doi.org/10.1029/2002JD003246, 2003. 
Marks, D. and Dozier, J.: Climate and Energy Exchange at the Snow Surface in
the Alpine Region of the Sierra Nevada 2. Snow Cover Energy Balance, Water
Resour. Res., 28, 3043–3054, 1992. 
McCabe, G. J. and Clark, M. P.: Trends and Variability in Snowmelt Runoff in
the Western United States, J. Hydrometeorol., 6, 476–482, https://doi.org/10.1175/JHM428.1, 2005. 
Meira Neto, A. A., Niu, G., Roy, T., Tyler, S., and Troch, P. A.: Interactions between snow cover and evaporation lead to higher sensitivity of streamflow to temperature, Commun. Earth Environ., 1, 56,
https://doi.org/10.1038/s43247-020-00056-9, 2020. 
Milly, P. C. D. and Dunne, K. A.: Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation, Science, 367, 1252–1255, https://doi.org/10.1126/science.aay9187, 2020. 
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M., and Engel, R.: Dramatic
declines in snowpack in the western US, npj Clim. Atmos. Sci., 1, 2,
https://doi.org/10.1038/s41612-018-0012-1, 2018. 
Müller, M. D. and Scherer, D.: A Grid- and Subgrid-Scale Radiation
Parameterization of Topographic Effects for Mesoscale Weather Forecast Models, Mon. Weather Rev., 133, 1431–1442, https://doi.org/10.1175/MWR2927.1, 2005. 
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.: Slower
snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219,
https://doi.org/10.1038/nclimate3225, 2017. 
Musselman, K. N., Lehner, F., Ikeda, K., Clark, M. P., Prein, A. F., Liu, C., Barlage, M., and Rasmussen, R.: Projected increases and shifts in rain-on-snow flood risk over western North America, Nat. Clim. Change, 8, 808–812, https://doi.org/10.1038/s41558-018-0236-4, 2018. 
Mutzner, R., Weijs, S. V., Tarolli, P., Calaf, M., Oldroyd, H. J., and Parlange, M. B.: Controls on the diurnal streamflow cycles in two subbasins of an alpine headwater catchment, Water Resour. Res., 51, 3403–3418,
https://doi.org/10.1002/2014WR016581, 2015. 
Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A.,
Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan,
Q.: Development of a large-sample watershed-scale hydrometeorological data
set for the contiguous USA: data set characteristics and assessment of
regional variability in hydrologic model performance, Hydrol. Earth Syst.
Sci., 19, 209–223, https://doi.org/10.5194/hess-19-209-2015, 2015. 
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, 1–19, https://doi.org/10.1029/2010JD015139, 2011. 
Ohmura, A.: Physical Basis for the Temperature-Based Melt-Index Method, J.
Appl. Meteorol., 40, 753–761, https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2, 2001. 
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G., Williams, Z., Brunke, M. A, and Gochis, D.: A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, J. Adv. Model. Earth Syst., 8, 41–65, https://doi.org/10.1002/2015MS000526, 2016. 
Pomeroy, J. W., Parviainen, J., Hedstrom, N., and Gray, D. M.: Coupled modelling of forest snow interception and sublimation, Hydrol. Process., 12, 2317–2337, https://doi.org/10.1002/(SICI)1099-1085(199812)12:15<2317::AID-HYP799>3.0.CO;2-X, 1998. 
Rasmussen, R., Dai, A., Liu, C., and Ikeda, K.: CONUS (Continental U.S.) II High Resolution Present and Future Climate Simulation, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/49SN-8E08, 2021. 
Regonda, S. K., Rajagopalan, B., Clark, M., and Pitlick, J.: Seasonal Cycle
Shifts in Hydroclimatology over the Western United States, J. Climate, 18,
372–384, https://doi.org/10.1175/JCLI-3272.1, 2005. 
Safa, H., Krogh, S. A., Greenberg, J., Kostadinov, T. S., and Harpold, A. A.:
Unraveling the Controls on Snow Disappearance in Montane Conifer Forests Using Multi-Site Lidar, Water Resour. Res., 57, 1–20, https://doi.org/10.1029/2020WR027522, 2021. 
Safeeq, M., Grant, G. E., Lewis, S. L., and Tague, C. L.: Coupling snowpack
and groundwater dynamics to interpret historical streamflow trends in the
western United States, Hydrol. Process., 27, 655–668, https://doi.org/10.1002/hyp.9628, 2013. 
Scaff, L., Prein, A. F., Li, Y., Liu, C., Rasmussen, R., and Ikeda, K.:
Simulating the convective precipitation diurnal cycle in North America's
current and future climate, Clim. Dynam., 55, 369–382,
https://doi.org/10.1007/s00382-019-04754-9, 2020. 
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.: Characteristics of the western United States snowpack from
snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160,
https://doi.org/10.1029/1999WR900090, 1999. 
Sivapalan, M., Yaeger, M. A., Harman, C. J., Xu, X., and Troch, P. A.:
Functional model of water balance variability at the catchment scale: 1.
Evidence of hydrologic similarity and space-time symmetry, Water Resour. Res., 47, 1–18, https://doi.org/10.1029/2010WR009568, 2011. 
Skamarock, W. C., Klemp, J. B., Dudhi, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3, NCAR/UCAR, Boulder, Colorado, USA, https://doi.org/10.5065/D68S4MVH, 2008. 
Stewart, I. T., Cayan, D. R., and Dettinger, M. D.: Changes in Snowmelt
Runoff Timing in Western North America under a `Business as Usual' Climate
Change Scenario, Climatic Change, 62, 217–232, https://doi.org/10.1023/B:CLIM.0000013702.22656.e8, 2004. 
Stewart, I. T., Cayan, D. R., and Dettinger, M. D.: Changes toward Earlier
Streamflow Timing across Western North America, J. Climate, 18, 1136–1155,
https://doi.org/10.1175/JCLI3321.1, 2005. 
Sturm, M., Goldstein, M. A., and Parr, C.: Water and life from snow: A
trillion dollar science question, Water Resour. Res., 53, 3534–3544,
https://doi.org/10.1002/2017WR020840, 2017. 
Sumargo, E. and Cayan, D. R.: The Influence of Cloudiness on Hydrologic
Fluctuations in the Mountains of the Western United States, Water Resour. Res., 54, 8478–8499, https://doi.org/10.1029/2018WR022687, 2018. 
Tague, C. and Grant, G. E.: Groundwater dynamics mediate low-flow response to global warming in snow-dominated alpine regions, Water Resour. Res., 45, 1–12, https://doi.org/10.1029/2008WR007179, 2009. 
Urióstegui, S. H., Bibby, R. K., Esser, B. K., and Clark, J. F.: Quantifying annual groundwater recharge and storage in the central Sierra
Nevada using naturally occurring 35∘ S, Hydrol. Process., 31, 1382–1397, https://doi.org/10.1002/hyp.11112, 2017. 
US Geological Survey: National Water Information System data available on the World Wide Web (USGS Water Data for the Nation), http://waterdata.usgs.gov/nwis/ (last access: 20 June 2022), 2016. 
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., and Weingartner, R.: Mountains of the world, water towers for humanity: Typology, mapping, and global significance, Water Resour. Res., 43, 1–13, https://doi.org/10.1029/2006WR005653, 2007. 
Viviroli, D., Archer, D. R., Buytaert, W., Fowler, H. J., Greenwood, G. B.,
Hamlet, A. F., Huang, Y., Koboltschnig, G., Litaor, M. I., López-Moreno,
J. I., Lorentz, S., Schädler, B., Schreier, H., Schwaiger, K., Vuille, M., and Woods, R.: Climate change and mountain water resources: overview and
recommendations for research, management and policy, Hydrol. Earth Syst. Sci., 15, 471–504, https://doi.org/10.5194/hess-15-471-2011, 2011. 
Wayand, N. E., Lundquist, J. D., and Clark, M. P.: Modeling the influence of
hypsometry, vegetation, and storm energy on snowmelt contributions to basins
during rain-on-snow floods, Water Resour. Res., 51, 8551–8569,
https://doi.org/10.1002/2014WR016576, 2015. 
Weiler, M., Seibert, J., and Stahl, K.: Magic components-why quantifying rain, snowmelt, and icemelt in river discharge is not easy, Hydrol. Process., 32, 160–166, https://doi.org/10.1002/hyp.11361, 2018.
 
Wilby, R., Dawson, C., and Barrow, E.: Sdsm – a Decision Support Tool for the Assessment of Regional Climate Change Impacts, Environ. Model. Softw., 17, 145–157, https://doi.org/10.1016/S1364-8152(01)00060-3, 2002. 
Winchell, T. S., Barnard, D. M., Monson, R. K., Burns, S. P., and Molotch, N.
P.: Earlier snowmelt reduces atmospheric carbon uptake in midlatitude subalpine forests, Geophys. Res. Lett., 43, 8160–8168, https://doi.org/10.1002/2016GL069769, 2016. 
Woelber, B., Maneta, M. P., Harper, J., Jencso, K. G., Gardner, W. P., Wilcox, A. C., and López-Moreno, I.: The influence of diurnal snowmelt
and transpiration on hillslope throughflow and stream response, Hydrol.
Earth Syst. Sci., 22, 4295–4310, https://doi.org/10.5194/hess-22-4295-2018, 2018. 
Wood, A. W. and Lettenmaier, D. P.: A Test Bed for New Seasonal Hydrologic
Forecasting Approaches in the Western United States, B. Am. Meteorol. Soc., 87, 1699–1712, https://doi.org/10.1175/BAMS-87-12-1699, 2006. 
Xia, Y., et al.: NCEP/EMC (2009), NLDAS Primary Forcing Data L4 Hourly 0.125×0.125 degree V002, edited by: Mocko, D., NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/6J5LHHOHZHN4, 2009. 
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L., Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy
flux analysis and validation for the North American Land Data Assimilation
System project phase 2 (NLDAS-2): 1. Intercomparison and application of
model products, J. Geophys. Res.-Atmos., 117, 1–27,
https://doi.org/10.1029/2011JD016048, 2012. 
Short summary
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.
We present a new way to detect snowmelt using daily cycles in streamflow driven by solar...
