Articles | Volume 29, issue 18
https://doi.org/10.5194/hess-29-4539-2025
© Author(s) 2025. 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-29-4539-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Sep 2025
Research article |
| 22 Sep 2025
| 22 Sep 2025
Characterizing the spatial distribution of field-scale snowpack using unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry
Ingram School of Engineering, Texas State University, San Marcos, TX, USA
Megan Verfaillie
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Jennifer M. Jacobs
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Adam G. Hunsaker
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Franklin B. Sullivan
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Michael Palace
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Department of Earth Sciences, University of New Hampshire, Durham, NH, USA
Cameron Wagner
Department of Civil and Environmental Engineering, University of New Hampshire, Durham, NH, USA
Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
present address: U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH, USA
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Quantifying how much snowmelt feeds mountain rivers is hard. In a Missouri Headwater watershed, we estimated the region's water cycle, focusing on snowmelt generation. Well-validated hydrological data were more accurate individually, but the land surface model better balanced the annual water budget. It also underestimates evaporation and sublimation, inflating runoff estimates. This approach shows snowmelt supplies only about 32 % of runoff, well below earlier estimates near 65 %.
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This study evaluated how different parameterization schemes in the Noah-MP land surface model affect snow and runoff processes in the Missouri River Basin. By comparing simulations with USGS streamflow and University of Arizona snow data across 50 subbasins, we found that alternative schemes improve groundwater, evapotranspiration, frozen soil, and snowmelt-runoff representation, supporting better flood prediction and water management.
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Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
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Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
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An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
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This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
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While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
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Manuscript not accepted for further review
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This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
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Seasonal snow is a key water source, yet its amount and timing are hard to estimate in shallow, wet regions. Using a drone with ground-penetrating radar and laser scanner, alongside field measurements in New Hampshire, we tracked snow depth, density, and wetness. The snow shifted rapidly from dry to wet, and a physically based mixing model best matched the radar response. These observations help study spatially variable snowpacks and melt transitions that challenge field and remote sensing data.
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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Quantifying how much snowmelt feeds mountain rivers is hard. In a Missouri Headwater watershed, we estimated the region's water cycle, focusing on snowmelt generation. Well-validated hydrological data were more accurate individually, but the land surface model better balanced the annual water budget. It also underestimates evaporation and sublimation, inflating runoff estimates. This approach shows snowmelt supplies only about 32 % of runoff, well below earlier estimates near 65 %.
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Eunsaem Cho, Eunsang Cho, Carrie M. Vuyovich, Bailing Li, and Jennifer M. Jacobs
EGUsphere, https://doi.org/10.5194/egusphere-2025-5557, https://doi.org/10.5194/egusphere-2025-5557, 2025
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This study evaluated how different parameterization schemes in the Noah-MP land surface model affect snow and runoff processes in the Missouri River Basin. By comparing simulations with USGS streamflow and University of Arizona snow data across 50 subbasins, we found that alternative schemes improve groundwater, evapotranspiration, frozen soil, and snowmelt-runoff representation, supporting better flood prediction and water management.
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Maxime Thomas, Thomas Moenaert, Julien Radoux, Baptiste Delhez, Eléonore du Bois d'Aische, Maëlle Villani, Catherine Hirst, Erik Lundin, François Jonard, Sébastien Lambot, Kristof Van Oost, Veerle Vanacker, Matthias B. Siewert, Carl-Magnus Mörth, Michael W. Palace, Ruth K. Varner, Franklin B. Sullivan, Christina Herrick, and Sophie Opfergelt
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This study examines the rate of permafrost degradation, in the form of the transition from intact well-drained palsa to fully thawed and inundated fen at the Stordalen mire, Abisko, Sweden. Across the 14 hectares of the palsa mire, we demonstrate a 5-fold acceleration of the degradation in 2019–2021 compared to previous periods (1970–2014) which might lead to a pool of 12 metric tons of organic carbon exposed annually for the topsoil (23 cm depth), and an increase of ~1.3%/year of GHG emissions.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
Short summary
Short summary
Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
Short summary
Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
Short summary
Short summary
An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere, 17, 3435–3442, https://doi.org/10.5194/tc-17-3435-2023, https://doi.org/10.5194/tc-17-3435-2023, 2023
Short summary
Short summary
This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
Short summary
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
Short summary
Short summary
This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
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Short summary
Unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry effectively map high-resolution snow depths. Our study found that UAS lidar outperformed SfM, particularly in capturing stable snow distribution patterns. Vegetation type was the primary factor influencing snow depth across forest and field areas, reflecting soil variables, such as organic matter. When analyzed separately, slope and forest canopy shadowing played key roles.
Unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry effectively...
