Articles | Volume 19, issue 3
https://doi.org/10.5194/gmd-19-1193-2026
© 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/gmd-19-1193-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
09 Feb 2026
Model evaluation paper |
| 09 Feb 2026
| 09 Feb 2026
Runoff evaluation in an Earth System Land Model for permafrost regions in Alaska
Xiang Huang
CORRESPONDING AUTHOR
Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos, NM, USA
now at: Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, Canada
Yu Zhang
Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Charles J. Abolt
Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Science and Analytics Team, The Freshwater Trust, Portland, OR, USA
Ryan L. Crumley
Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Cansu Demir
Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Richard P. Fiorella
Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Bob Busey
University of Alaska Fairbanks, Fairbanks, AK, USA
Bob Bolton
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Scott L. Painter
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Earth and Environmental Science Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, USA
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Jonathan A. Bachman, John Lamb, Craig Ulrich, Neslihan Taş, and Baptiste Dafflon
The Cryosphere, 19, 5499–5508, https://doi.org/10.5194/tc-19-5499-2025, https://doi.org/10.5194/tc-19-5499-2025, 2025
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We studied how Arctic landscapes change as the ground thaws by comparing measurements taken ten years apart. We found that some areas sank and new ponds formed, with different patterns depending on the shape of the land. These changes affect how water and carbon flow and cycle through the environment. The results help understand how and where the Arctic is shifting, and highlight the need for repeated observations to track long-term changes.
Ashley Brereton, Zelalem A. Mekonnen, Bhavna Arora, William J. Riley, Kunxiaojia Yuan, Yi Xu, Yu Zhang, Qing Zhu, Tyler L. Anthony, and Adina Paytan
Geosci. Model Dev., 18, 8157–8173, https://doi.org/10.5194/gmd-18-8157-2025, https://doi.org/10.5194/gmd-18-8157-2025, 2025
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Wetlands absorb carbon dioxide (CO2), helping slow climate change, but they also release methane, a potent warming gas. We developed a collection of AI-based models to estimate magnitudes of CO2 and methane exchanged between the land and the atmosphere, for wetlands on a regional scale. This approach helps to inform land-use planning, restoration, and greenhouse gas accounting, while also creating a foundation for future advancements in prediction accuracy.
Tao Liu, Luke A. McGuire, Ann M. Youberg, Charles J. Abolt, and Adam L. Atchley
Nat. Hazards Earth Syst. Sci., 25, 4135–4151, https://doi.org/10.5194/nhess-25-4135-2025, https://doi.org/10.5194/nhess-25-4135-2025, 2025
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Wildfires increase flood risk by making it harder for soil to absorb water. We studied how this risk changes over time as the landscape recovers and how it will be affected by more intense rainfall due to climate change. Using a computer model of a burned watershed in Arizona, we found that while the soil's ability to soak up water recovers over a few years, future rainfall is predicted to be so intense that the period of high flood danger will last longer, making severe floods much more common.
Alexandra Hamm, Erik Schytt Mannerfelt, Aaron A. Mohammed, Scott L. Painter, Ethan T. Coon, and Andrew Frampton
The Cryosphere, 19, 3693–3724, https://doi.org/10.5194/tc-19-3693-2025, https://doi.org/10.5194/tc-19-3693-2025, 2025
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The fate of thawing permafrost carbon is essential for understanding the permafrost–climate feedback and projections of future climate. Here we study transport of organic carbon by groundwater in the active layer of a hillslope model. We find that carbon transport velocities and microbial mineralization rates are strongly dependent on liquid saturation in the seasonally thawed active layer. In a warming climate, the rate at which permafrost thaws determines how fast carbon can be transported.
James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regier, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad F. Patel
Biogeosciences, 22, 995–1034, https://doi.org/10.5194/bg-22-995-2025, https://doi.org/10.5194/bg-22-995-2025, 2025
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The loss and gain of surface water (variable inundation) are common processes across Earth. Global change shifts variable inundation dynamics, highlighting a need for unified understanding that transcends individual variably inundated ecosystems (VIEs). We review the literature, highlight challenges, and emphasize opportunities to generate transferable knowledge by viewing VIEs through a common lens. We aim to inspire the emergence of a cross-VIE community based on a proposed continuum approach.
Claire L. Bachand, Chen Wang, Baptiste Dafflon, Lauren N. Thomas, Ian Shirley, Sarah Maebius, Colleen M. Iversen, and Katrina E. Bennett
The Cryosphere, 19, 393–400, https://doi.org/10.5194/tc-19-393-2025, https://doi.org/10.5194/tc-19-393-2025, 2025
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Temporally continuous snow depth estimates are important for understanding changing snow patterns and impacts on frozen ground in the Arctic. In this work, we developed an approach to predict snow depth from variability in snow–ground interface temperature using small temperature sensors that are cheap and easy to deploy. This new technique enables spatially distributed and temporally continuous snowpack monitoring that has not previously been possible.
Kavya Sivaraj, Kurt Solander, Charles Abolt, and Elizabeth Hunke
EGUsphere, https://doi.org/10.5194/egusphere-2024-3315, https://doi.org/10.5194/egusphere-2024-3315, 2024
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Melt ponds are seasonal water bodies whose presence affect the rate of Arctic sea ice loss by increasing the absorption of solar radiation. Despite their importance, large-scale observational datasets of Melt Pond Fraction (MPF) are inadequate due to low-resolution sensors and spectral misclassifications caused by different ice types. Our novel ML-based workflow overcomes these limitations by leveraging morphological operators, resulting in an improved Sentinel-2-based mean MPF of 11% from 20%.
Nathan Alec Conroy, Jeffrey M. Heikoop, Emma Lathrop, Dea Musa, Brent D. Newman, Chonggang Xu, Rachael E. McCaully, Carli A. Arendt, Verity G. Salmon, Amy Breen, Vladimir Romanovsky, Katrina E. Bennett, Cathy J. Wilson, and Stan D. Wullschleger
The Cryosphere, 17, 3987–4006, https://doi.org/10.5194/tc-17-3987-2023, https://doi.org/10.5194/tc-17-3987-2023, 2023
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This study combines field observations, non-parametric statistical analyses, and thermodynamic modeling to characterize the environmental causes of the spatial variability in soil pore water solute concentrations across two Arctic catchments with varying extents of permafrost. Vegetation type, soil moisture and redox conditions, weathering and hydrologic transport, and mineral solubility were all found to be the primary drivers of the existing spatial variability of some soil pore water solutes.
Ian Shirley, Sebastian Uhlemann, John Peterson, Katrina Bennett, Susan S. Hubbard, and Baptiste Dafflon
EGUsphere, https://doi.org/10.5194/egusphere-2023-968, https://doi.org/10.5194/egusphere-2023-968, 2023
Preprint archived
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Snow depth has a strong impact on soil temperatures and carbon cycling in the arctic. Because of this, we want to understand why snow is deeper in some places than others. Using cameras mounted on a drone, we mapped snow depth, vegetation height, and elevation across a watershed in Alaska. In this paper, we develop novel techniques using image processing and machine learning to characterize the influence of topography and shrubs on snow depth in the watershed.
Xiang Huang, Charles J. Abolt, and Katrina E. Bennett
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-8, https://doi.org/10.5194/tc-2023-8, 2023
Manuscript not accepted for further review
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Near-surface humidity is a sensitive parameter for predicting snow depth. Greater values of the relative humidity are obtained if the saturation vapor pressure was calculated with over-ice correction compared to without during the winter. During the summer thawing period, the choice of whether or not to employ an over-ice correction corresponds to significant variability in simulated thaw depths.
Bo Gao and Ethan T. Coon
The Cryosphere, 16, 4141–4162, https://doi.org/10.5194/tc-16-4141-2022, https://doi.org/10.5194/tc-16-4141-2022, 2022
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Representing water at constant density, neglecting cryosuction, and neglecting heat advection are three commonly applied but not validated simplifications in permafrost models to reduce computation complexity at field scale. We investigated this problem numerically by Advanced Terrestrial Simulator and found that neglecting cryosuction can cause significant bias (10%–60%), constant density primarily affects predicting water saturation, and ignoring heat advection has the least impact.
Katrina E. Bennett, Greta Miller, Robert Busey, Min Chen, Emma R. Lathrop, Julian B. Dann, Mara Nutt, Ryan Crumley, Shannon L. Dillard, Baptiste Dafflon, Jitendra Kumar, W. Robert Bolton, Cathy J. Wilson, Colleen M. Iversen, and Stan D. Wullschleger
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In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations in snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because deeper snow will insulate the ground from the cold winter. In this paper, we use modeling to characterize snow to better understand the drivers of snow distribution. Eventually, this work will be used to improve models used to study future changes in Arctic and sub-Arctic snow patterns.
Carlotta Brunetti, John Lamb, Stijn Wielandt, Sebastian Uhlemann, Ian Shirley, Patrick McClure, and Baptiste Dafflon
Earth Surf. Dynam., 10, 687–704, https://doi.org/10.5194/esurf-10-687-2022, https://doi.org/10.5194/esurf-10-687-2022, 2022
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This paper proposes a method to estimate thermal diffusivity and its uncertainty over time, at numerous locations and at an unprecedented vertical spatial resolution from soil temperature time series. We validate and apply this method to synthetic and field case studies. The improved quantification of soil thermal properties is a cornerstone for advancing the indirect estimation of the fraction of soil components needed to predict subsurface storage and fluxes of water, carbon, and nutrients.
Baptiste Dafflon, Stijn Wielandt, John Lamb, Patrick McClure, Ian Shirley, Sebastian Uhlemann, Chen Wang, Sylvain Fiolleau, Carlotta Brunetti, Franklin H. Akins, John Fitzpatrick, Samuel Pullman, Robert Busey, Craig Ulrich, John Peterson, and Susan S. Hubbard
The Cryosphere, 16, 719–736, https://doi.org/10.5194/tc-16-719-2022, https://doi.org/10.5194/tc-16-719-2022, 2022
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This study presents the development and validation of a novel acquisition system for measuring finely resolved depth profiles of soil and snow temperature at multiple locations. Results indicate that the system reliably captures the dynamics in snow thickness, as well as soil freezing and thawing depth, enabling advances in understanding the intensity and timing in surface processes and their impact on subsurface thermohydrological regimes.
Haruko M. Wainwright, Sebastian Uhlemann, Maya Franklin, Nicola Falco, Nicholas J. Bouskill, Michelle E. Newcomer, Baptiste Dafflon, Erica R. Siirila-Woodburn, Burke J. Minsley, Kenneth H. Williams, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 26, 429–444, https://doi.org/10.5194/hess-26-429-2022, https://doi.org/10.5194/hess-26-429-2022, 2022
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This paper has developed a tractable approach for characterizing watershed heterogeneity and its relationship with key functions such as ecosystem sensitivity to droughts and nitrogen export. We have applied clustering methods to classify hillslopes into
watershed zonesthat have distinct distributions of bedrock-to-canopy properties as well as key functions. This is a powerful approach for guiding watershed experiments and sampling as well as informing hydrological and biogeochemical models.
Jiancong Chen, Baptiste Dafflon, Anh Phuong Tran, Nicola Falco, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 25, 6041–6066, https://doi.org/10.5194/hess-25-6041-2021, https://doi.org/10.5194/hess-25-6041-2021, 2021
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The novel hybrid predictive modeling (HPM) approach uses a long short-term memory recurrent neural network to estimate evapotranspiration (ET) and ecosystem respiration (Reco) with only meteorological and remote-sensing inputs. We developed four use cases to demonstrate the applicability of HPM. The results indicate HPM is capable of providing ET and Reco estimations in challenging mountainous systems and enhances our understanding of watershed dynamics at sparsely monitored watersheds.
Qina Yan, Haruko Wainwright, Baptiste Dafflon, Sebastian Uhlemann, Carl I. Steefel, Nicola Falco, Jeffrey Kwang, and Susan S. Hubbard
Earth Surf. Dynam., 9, 1347–1361, https://doi.org/10.5194/esurf-9-1347-2021, https://doi.org/10.5194/esurf-9-1347-2021, 2021
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We develop a hybrid model to estimate the spatial distribution of the thickness of the soil layer, which also provides estimations of soil transport and soil production rates. We apply this model to two examples of hillslopes in the East River watershed in Colorado and validate the model. The results show that the north-facing (NF) hillslope has a deeper soil layer than the south-facing (SF) hillslope and that the hybrid model provides better accuracy than a machine-learning model.
Ryan L. Crumley, David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, Christopher Cosgrove, and Community Snow Observations Participants
Hydrol. Earth Syst. Sci., 25, 4651–4680, https://doi.org/10.5194/hess-25-4651-2021, https://doi.org/10.5194/hess-25-4651-2021, 2021
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In this study, we use a new snow data set collected by participants in the Community Snow Observations project in coastal Alaska to improve snow depth and snow water equivalence simulations from a snow process model. We validate our simulations with multiple datasets, taking advantage of snow telemetry (SNOTEL), snow depth and snow water equivalence, and remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.
Dylan R. Harp, Vitaly Zlotnik, Charles J. Abolt, Bob Busey, Sofia T. Avendaño, Brent D. Newman, Adam L. Atchley, Elchin Jafarov, Cathy J. Wilson, and Katrina E. Bennett
The Cryosphere, 15, 4005–4029, https://doi.org/10.5194/tc-15-4005-2021, https://doi.org/10.5194/tc-15-4005-2021, 2021
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Polygon-shaped landforms present in relatively flat Arctic tundra result in complex landscape-scale water drainage. The drainage pathways and the time to transition from inundated conditions to drained have important implications for heat and carbon transport. Using fundamental hydrologic principles, we investigate the drainage pathways and timing of individual polygons, providing insights into the effects of polygon geometry and preferential flow direction on drainage pathways and timing.
Robert Ladwig, Paul C. Hanson, Hilary A. Dugan, Cayelan C. Carey, Yu Zhang, Lele Shu, Christopher J. Duffy, and Kelly M. Cobourn
Hydrol. Earth Syst. Sci., 25, 1009–1032, https://doi.org/10.5194/hess-25-1009-2021, https://doi.org/10.5194/hess-25-1009-2021, 2021
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Using a modeling framework applied to 37 years of dissolved oxygen time series data from Lake Mendota, we identified the timing and intensity of thermal energy stored in the lake water column, the lake's resilience to mixing, and surface primary production as the most important drivers of interannual dynamics of low oxygen concentrations at the lake bottom. Due to climate change, we expect an increase in the spatial and temporal extent of low oxygen concentrations in Lake Mendota.
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Short summary
Predicting hydrological runoff in Arctic permafrost regions is difficult due to limited observations and complex terrain. We used a detailed physics-based model simulations to improve runoff estimates in a Earth system land model. Our method improved runoff accuracy and worked well across two different Arctic regions. This helps make runoff parameterization schemes more reliable for understanding water flow in permafrost areas under a changing climate.
Predicting hydrological runoff in Arctic permafrost regions is difficult due to limited...
