Articles | Volume 13, issue 12
https://doi.org/10.5194/tc-13-3337-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-13-3337-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Dec 2019
Research article |
| 18 Dec 2019
| 18 Dec 2019
Changing characteristics of runoff and freshwater export from watersheds draining northern Alaska
Department of Geosciences, University of Massachusetts, Amherst, MA 01003, USA
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Svetlana L. Stuefer
Civil and Environmental Engineering, College of Engineering and Mines, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Dmitry Nicolsky
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Related authors
Michael A. Rawlins and Ambarish V. Karmalkar
The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, https://doi.org/10.5194/tc-18-1033-2024, 2024
Short summary
Short summary
Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
P. Dass, M. A. Rawlins, J. S. Kimball, and Y. Kim
Biogeosciences, 13, 45–62, https://doi.org/10.5194/bg-13-45-2016, https://doi.org/10.5194/bg-13-45-2016, 2016
Short summary
Short summary
Productivity of the vegetation of northern Eurasia has been found to be increasing over the last few decades. Using statistical tools we investigate major factors driving the increase in photosynthetic activity. Most of this change is explained by rising temperatures, which drive an increase in productivity. However, the contribution of changing patterns of rainfall and cloudiness is also significant, especially in the southern parts of the region which exhibit higher drought vulnerability.
Y. Yi, J. S. Kimball, M. A. Rawlins, M. Moghaddam, and E. S. Euskirchen
Biogeosciences, 12, 5811–5829, https://doi.org/10.5194/bg-12-5811-2015, https://doi.org/10.5194/bg-12-5811-2015, 2015
Short summary
Short summary
We found that regional warming promotes widespread deepening of soil thaw in the pan-Arctic area; continued warming will most likely promote permafrost degradation in the warm permafrost areas. We also found that deeper snowpack enhances soil respiration from deeper soil carbon pool more than temperature does, particularly in the cold permafrost areas, where a large amount of soil carbon is stored in deep perennial frozen soils but is potentially vulnerable to mobilization from climate change.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
Short summary
Short summary
We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
Short summary
Short summary
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
Annett Bartsch, Xaver Muri, Markus Hetzenecker, Kimmo Rautiainen, Helena Bergstedt, Jan Wuite, Thomas Nagler, and Dmitry Nicolsky
EGUsphere, https://doi.org/10.5194/egusphere-2024-2518, https://doi.org/10.5194/egusphere-2024-2518, 2024
Short summary
Short summary
We developed a robust freeze/thaw detection approach, applying a constant threshold on Copernicus Sentinel-1 data, that is suitable for tundra regions. All global, coarser resolution products, tested with the resulting benchmarking dataset, are of value for freeze/thaw retrieval, although differences were found depending on seasons, in particular during spring and autumn transition.
Michael A. Rawlins and Ambarish V. Karmalkar
The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, https://doi.org/10.5194/tc-18-1033-2024, 2024
Short summary
Short summary
Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
Kelsey Spehlmann, Eugénie Euskirchen, and Svetlana Stuefer
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-153, https://doi.org/10.5194/tc-2023-153, 2023
Revised manuscript under review for TC
Short summary
Short summary
Sublimation is the hidden portion of the water cycle where snow changes phase directly to water vapor, skipping the liquid state. Though sublimation is difficult to measure, especially in remote regions such as arctic and subarctic Alaska where this study took place, our measurements confirm that sublimation is a substantial component of the annual water cycle. Results from this research contribute to knowledge of how site conditions affect sublimation rates and the winter hydrologic cycle.
Thomas Schneider von Deimling, Hanna Lee, Thomas Ingeman-Nielsen, Sebastian Westermann, Vladimir Romanovsky, Scott Lamoureux, Donald A. Walker, Sarah Chadburn, Erin Trochim, Lei Cai, Jan Nitzbon, Stephan Jacobi, and Moritz Langer
The Cryosphere, 15, 2451–2471, https://doi.org/10.5194/tc-15-2451-2021, https://doi.org/10.5194/tc-15-2451-2021, 2021
Short summary
Short summary
Climate warming puts infrastructure built on permafrost at risk of failure. There is a growing need for appropriate model-based risk assessments. Here we present a modelling study and show an exemplary case of how a gravel road in a cold permafrost environment in Alaska might suffer from degrading permafrost under a scenario of intense climate warming. We use this case study to discuss the broader-scale applicability of our model for simulating future Arctic infrastructure failure.
Lei Cai, Hanna Lee, Kjetil Schanke Aas, and Sebastian Westermann
The Cryosphere, 14, 4611–4626, https://doi.org/10.5194/tc-14-4611-2020, https://doi.org/10.5194/tc-14-4611-2020, 2020
Short summary
Short summary
A sub-grid representation of excess ground ice in the Community Land Model (CLM) is developed as novel progress in modeling permafrost thaw and its impacts under the warming climate. The modeled permafrost degradation with sub-grid excess ice follows the pathway that continuous permafrost transforms into discontinuous permafrost before it disappears, including surface subsidence and talik formation, which are highly permafrost-relevant landscape changes excluded from most land models.
Lei Cai, Hanna Lee, Sebastian Westermann, and Kjetil Schanke Aas
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-230, https://doi.org/10.5194/tc-2019-230, 2019
Preprint withdrawn
Short summary
Short summary
We develop a sub-grid representation of excess ground ice in the Community Land Model (CLM) by adding three landunits to the original CLM sub-grid hierarchy, in order to prescribe three different excess ice conditions in one grid cell. Single-grid simulations verify the potential of the model development on better projecting excess ice melt in a warming climate. Global simulations recommend the proper way of applying the model development with the existing excess ice dataset.
Lei Cai, Vladimir A. Alexeev, Christopher D. Arp, Benjamin M. Jones, Anna Liljedahl, and Anne Gädeke
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2016-31, https://doi.org/10.5194/essd-2016-31, 2016
Preprint withdrawn
Short summary
Short summary
This study produced a high-resolution dynamical downscaling data set for the Alaskan North Slope and surrounding areas. It helps to resolve the problem of the sparse observation over this region, where routinely and accurately measuring climatic variables is extremely difficult. This data set boosts up multiple research projects that explore the various climatic impacts over the Alaskan North Slope of the past and the future.
Lei Cai, Vladimir A. Alexeev, Christopher D. Arp, Benjamin M. Jones, Anna Liljedahl, and Anne Gädeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-87, https://doi.org/10.5194/tc-2016-87, 2016
Preprint withdrawn
Short summary
Short summary
This paper introduces the development process of a data set that specifically made for climatic impacts research over the Alaskan North Slope. This data set can offset to some extent the sparseness of observation on spatial and temporal scales, retrieving high-resolution climatic backgrounds that enable various studies in the fields of climatology, hydrology, ecology, etc.
P. Dass, M. A. Rawlins, J. S. Kimball, and Y. Kim
Biogeosciences, 13, 45–62, https://doi.org/10.5194/bg-13-45-2016, https://doi.org/10.5194/bg-13-45-2016, 2016
Short summary
Short summary
Productivity of the vegetation of northern Eurasia has been found to be increasing over the last few decades. Using statistical tools we investigate major factors driving the increase in photosynthetic activity. Most of this change is explained by rising temperatures, which drive an increase in productivity. However, the contribution of changing patterns of rainfall and cloudiness is also significant, especially in the southern parts of the region which exhibit higher drought vulnerability.
Y. Yi, J. S. Kimball, M. A. Rawlins, M. Moghaddam, and E. S. Euskirchen
Biogeosciences, 12, 5811–5829, https://doi.org/10.5194/bg-12-5811-2015, https://doi.org/10.5194/bg-12-5811-2015, 2015
Short summary
Short summary
We found that regional warming promotes widespread deepening of soil thaw in the pan-Arctic area; continued warming will most likely promote permafrost degradation in the warm permafrost areas. We also found that deeper snowpack enhances soil respiration from deeper soil carbon pool more than temperature does, particularly in the cold permafrost areas, where a large amount of soil carbon is stored in deep perennial frozen soils but is potentially vulnerable to mobilization from climate change.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
Short summary
Short summary
We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
Short summary
Short summary
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
Related subject area
Discipline: Snow | Subject: Numerical Modelling
Exploring the decision-making process in model development: focus on the Arctic snowpack
Exploring the potential of forest snow modeling at the tree and snowpack layer scale
Microstructure-based modelling of snow mechanics: experimental evaluation of the cone penetration test
Snow redistribution in an intermediate-complexity snow hydrology modelling framework
Analyzing the sensitivity of a blowing snow model (SnowPappus) to precipitation forcing, blowing snow, and spatial resolution
Multi-physics ensemble modelling of Arctic tundra snowpack properties
Regime shifts in Arctic terrestrial hydrology manifested from impacts of climate warming
Modelling snowpack on ice surfaces with the ORCHIDEE land surface model: Application to the Greenland ice sheet
Snow cover prediction in the Italian central Apennines using weather forecast and land surface numerical models
A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting
Land–atmosphere interactions in sub-polar and alpine climates in the CORDEX flagship pilot study Land Use and Climate Across Scales (LUCAS) models – Part 1: Evaluation of the snow-albedo effect
Elements of future snowpack modeling – Part 1: A physical instability arising from the nonlinear coupling of transport and phase changes
Elements of future snowpack modeling – Part 2: A modular and extendable Eulerian–Lagrangian numerical scheme for coupled transport, phase changes and settling processes
Assessment of neutrons from secondary cosmic rays at mountain altitudes – Geant4 simulations of environmental parameters including soil moisture and snow cover
A seasonal algorithm of the snow-covered area fraction for mountainous terrain
Snow cover duration trends observed at sites and predicted by multiple models
Deep ice layer formation in an alpine snowpack: monitoring and modeling
Multi-physics ensemble snow modelling in the western Himalaya
Micromechanical modeling of snow failure
Winter tourism under climate change in the Pyrenees and the French Alps: relevance of snowmaking as a technical adaptation
A simulation of a large-scale drifting snowstorm in the turbulent boundary layer
Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain
Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan
Cecile B. Menard, Sirpa Rasmus, Ioanna Merkouriadi, Gianpaolo Balsamo, Annett Bartsch, Chris Derksen, Florent Domine, Marie Dumont, Dorothee Ehrich, Richard Essery, Bruce C. Forbes, Gerhard Krinner, David Lawrence, Glen Liston, Heidrun Matthes, Nick Rutter, Melody Sandells, Martin Schneebeli, and Sari Stark
The Cryosphere, 18, 4671–4686, https://doi.org/10.5194/tc-18-4671-2024, https://doi.org/10.5194/tc-18-4671-2024, 2024
Short summary
Short summary
Computer models, like those used in climate change studies, are written by modellers who have to decide how best to construct the models in order to satisfy the purpose they serve. Using snow modelling as an example, we examine the process behind the decisions to understand what motivates or limits modellers in their decision-making. We find that the context in which research is undertaken is often more crucial than scientific limitations. We argue for more transparency in our research practice.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
Short summary
Short summary
As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Clémence Herny, Pascal Hagenmuller, Guillaume Chambon, Isabel Peinke, and Jacques Roulle
The Cryosphere, 18, 3787–3805, https://doi.org/10.5194/tc-18-3787-2024, https://doi.org/10.5194/tc-18-3787-2024, 2024
Short summary
Short summary
This paper presents the evaluation of a numerical discrete element method (DEM) by simulating cone penetration tests in different snow samples. The DEM model demonstrated a good ability to reproduce the measured mechanical behaviour of the snow, namely the force evolution on the cone and the grain displacement field. Systematic sensitivity tests showed that the mechanical response depends not only on the microstructure of the sample but also on the mechanical parameters of grain contacts.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
Short summary
Short summary
Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Ange Haddjeri, Matthieu Baron, Matthieu Lafaysse, Louis Le Toumelin, César Deschamps-Berger, Vincent Vionnet, Simon Gascoin, Matthieu Vernay, and Marie Dumont
The Cryosphere, 18, 3081–3116, https://doi.org/10.5194/tc-18-3081-2024, https://doi.org/10.5194/tc-18-3081-2024, 2024
Short summary
Short summary
Our study addresses the complex challenge of evaluating distributed alpine snow simulations with snow transport against snow depths from Pléiades stereo imagery and snow melt-out dates from Sentinel-2 and Landsat-8 satellites. Additionally, we disentangle error contributions between blowing snow, precipitation heterogeneity, and unresolved subgrid variability. Snow transport enhances the snow simulations at high elevations, while precipitation biases are the main error source in other areas.
Georgina Jean Woolley, Nick Rutter, Leanne Wake, Vincent Vionnet, Chris Derksen, Richard Essery, Philip Marsh, Rosamund Tutton, Branden Walker, Matthieu Lafaysse, and David Pritchard
EGUsphere, https://doi.org/10.5194/egusphere-2024-1237, https://doi.org/10.5194/egusphere-2024-1237, 2024
Short summary
Short summary
Parameterisations of Arctic snow processes were implemented into the multi-physics ensemble version of SVS2-Crocus and evaluated using density and SSA measurements at an Arctic tundra site. Optimal combinations of parameterisations that improved the simulation of density and SSA were identified. Top performing ensemble members featured modifications that raise wind speeds to increase compaction in surface layers, prevent snowdrift and increase viscosity in basal layers.
Michael A. Rawlins and Ambarish V. Karmalkar
The Cryosphere, 18, 1033–1052, https://doi.org/10.5194/tc-18-1033-2024, https://doi.org/10.5194/tc-18-1033-2024, 2024
Short summary
Short summary
Flows of water, carbon, and materials by Arctic rivers are being altered by climate warming. We used simulations from a permafrost hydrology model to investigate future changes in quantities influencing river exports. By 2100 Arctic rivers will receive more runoff from the far north where abundant soil carbon can leach in. More water will enter them via subsurface pathways particularly in summer and autumn. An enhanced water cycle and permafrost thaw are changing river flows to coastal areas.
Sylvie Charbit, Christophe Dumas, Fabienne Maignan, Catherine Ottlé, Nina Raoult, and Xavier Fettweis
EGUsphere, https://doi.org/10.5194/egusphere-2024-285, https://doi.org/10.5194/egusphere-2024-285, 2024
Short summary
Short summary
The evolution of the Greenland ice sheet is highly dependent on surface melting and therefore on the processes operating at the snow-atmosphere interface and within the snow cover. Here we present new developments to apply a snow model to the Greenland ice sheet. The performance of this model is analysed in terms of its ability to simulate ablation processes. Our analysis shows that the model performs well when compared with the MAR regional polar atmospheric model.
Edoardo Raparelli, Paolo Tuccella, Valentina Colaiuda, and Frank S. Marzano
The Cryosphere, 17, 519–538, https://doi.org/10.5194/tc-17-519-2023, https://doi.org/10.5194/tc-17-519-2023, 2023
Short summary
Short summary
We evaluate the skills of a single-layer (Noah) and a multi-layer (Alpine3D) snow model, forced with the Weather Research and Forecasting model, to reproduce snowpack properties observed in the Italian central Apennines. We found that Alpine3D reproduces the observed snow height and snow water equivalent better than Noah, while no particular model differences emerge on snow cover extent. Finally, we observed that snow settlement is mainly due to densification in Alpine3D and to melting in Noah.
Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
Short summary
Short summary
We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
Short summary
Short summary
Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Konstantin Schürholt, Julia Kowalski, and Henning Löwe
The Cryosphere, 16, 903–923, https://doi.org/10.5194/tc-16-903-2022, https://doi.org/10.5194/tc-16-903-2022, 2022
Short summary
Short summary
This companion paper deals with numerical particularities of partial differential equations underlying 1D snow models. In this first part we neglect mechanical settling and demonstrate that the nonlinear coupling between diffusive transport (heat and vapor), phase changes and ice mass conservation contains a wave instability that may be relevant for weak layer formation. Numerical requirements are discussed in view of the underlying homogenization scheme.
Anna Simson, Henning Löwe, and Julia Kowalski
The Cryosphere, 15, 5423–5445, https://doi.org/10.5194/tc-15-5423-2021, https://doi.org/10.5194/tc-15-5423-2021, 2021
Short summary
Short summary
This companion paper deals with numerical particularities of partial differential equations underlying one-dimensional snow models. In this second part we include mechanical settling and develop a new hybrid (Eulerian–Lagrangian) method for solving the advection-dominated ice mass conservation on a moving mesh alongside Eulerian diffusion (heat and vapor) and phase changes. The scheme facilitates a modular and extendable solver strategy while retaining controls on numerical accuracy.
Thomas Brall, Vladimir Mares, Rolf Bütikofer, and Werner Rühm
The Cryosphere, 15, 4769–4780, https://doi.org/10.5194/tc-15-4769-2021, https://doi.org/10.5194/tc-15-4769-2021, 2021
Short summary
Short summary
Neutrons from secondary cosmic rays, measured at 2660 m a.s.l. at Zugspitze, Germany, are highly affected by the environment, in particular by snow, soil moisture, and mountain shielding. To quantify these effects, computer simulations were carried out, including a sensitivity analysis on snow depth and soil moisture. This provides a possibility for snow depth estimation based on the measured number of secondary neutrons. This method was applied at Zugspitze in 2018.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
Short summary
Short summary
The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
Short summary
Short summary
Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Louis Quéno, Charles Fierz, Alec van Herwijnen, Dylan Longridge, and Nander Wever
The Cryosphere, 14, 3449–3464, https://doi.org/10.5194/tc-14-3449-2020, https://doi.org/10.5194/tc-14-3449-2020, 2020
Short summary
Short summary
Deep ice layers may form in the snowpack due to preferential water flow with impacts on the snowpack mechanical, hydrological and thermodynamical properties. We studied their formation and evolution at a high-altitude alpine site, combining a comprehensive observation dataset at a daily frequency (with traditional snowpack observations, penetration resistance and radar measurements) and detailed snowpack modeling, including a new parameterization of ice formation in the 1-D SNOWPACK model.
David M. W. Pritchard, Nathan Forsythe, Greg O'Donnell, Hayley J. Fowler, and Nick Rutter
The Cryosphere, 14, 1225–1244, https://doi.org/10.5194/tc-14-1225-2020, https://doi.org/10.5194/tc-14-1225-2020, 2020
Short summary
Short summary
This study compares different snowpack model configurations applied in the western Himalaya. The results show how even sparse local observations can help to delineate climate input errors from model structure errors, which provides insights into model performance variation. The results also show how interactions between processes affect sensitivities to climate variability in different model configurations, with implications for model selection in climate change projections.
Grégoire Bobillier, Bastian Bergfeld, Achille Capelli, Jürg Dual, Johan Gaume, Alec van Herwijnen, and Jürg Schweizer
The Cryosphere, 14, 39–49, https://doi.org/10.5194/tc-14-39-2020, https://doi.org/10.5194/tc-14-39-2020, 2020
Pierre Spandre, Hugues François, Deborah Verfaillie, Marc Pons, Matthieu Vernay, Matthieu Lafaysse, Emmanuelle George, and Samuel Morin
The Cryosphere, 13, 1325–1347, https://doi.org/10.5194/tc-13-1325-2019, https://doi.org/10.5194/tc-13-1325-2019, 2019
Short summary
Short summary
This study investigates the snow reliability of 175 ski resorts in the Pyrenees (France, Spain and Andorra) and the French Alps under past and future conditions (1950–2100) using state-of-the-art climate projections and snowpack modelling accounting for snow management, i.e. grooming and snowmaking. The snow reliability of ski resorts shows strong elevation and regional differences, and our study quantifies changes in snow reliability induced by snowmaking under various climate scenarios.
Zhengshi Wang and Shuming Jia
The Cryosphere, 12, 3841–3851, https://doi.org/10.5194/tc-12-3841-2018, https://doi.org/10.5194/tc-12-3841-2018, 2018
Short summary
Short summary
Drifting snowstorms that are hundreds of meters in depth are reproduced using a large-eddy simulation model combined with a Lagrangian particle tracking method, which also exhibits obvious spatial structures following large-scale turbulent vortexes. The horizontal snow transport flux at high altitude, previously not observed, actually occupies a significant proportion of the total flux. Thus, previous models may largely underestimate the total mass flux and consequently snow sublimation.
Franziska Gerber, Nikola Besic, Varun Sharma, Rebecca Mott, Megan Daniels, Marco Gabella, Alexis Berne, Urs Germann, and Michael Lehning
The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, https://doi.org/10.5194/tc-12-3137-2018, 2018
Short summary
Short summary
A comparison of winter precipitation variability in operational radar measurements and high-resolution simulations reveals that large-scale variability is well captured by the model, depending on the event. Precipitation variability is driven by topography and wind. A good portion of small-scale variability is captured at the highest resolution. This is essential to address small-scale precipitation processes forming the alpine snow seasonal snow cover – an important source of water.
Edward H. Bair, Andre Abreu Calfa, Karl Rittger, and Jeff Dozier
The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, https://doi.org/10.5194/tc-12-1579-2018, 2018
Short summary
Short summary
In Afghanistan, almost no snow measurements exist. Operational estimates use measurements from satellites, but all have limitations. We have developed a satellite-based technique called reconstruction that accurately estimates the snowpack retrospectively. To solve the problem of estimating today's snowpack, we used machine learning, trained on our reconstructed snow estimates, using predictors that are available today. Our results show low errors, demonstrating the utility of this approach.
Cited articles
ACIA: Arctic Climate Impact Assessment, Cambridge
University Press, New York, 1042 pp., 2005. a
Amon, R., Rinehart, A., Duan, S., Louchouarn, P., Prokushkin, A., Guggenberger,
G., Bauch, D., Stedmon, C., Raymond, P., Holmes, R., McClelland, J. W., Peterson, B. Walker, S. A., and Zhulidovk, V.: Dissolved
organic matter sources in large Arctic rivers, Geochim. Cosmochim. Ac., 94, 217–237, https://doi.org/10.1016/j.gca.2012.07.015, 2012. a
Arnborg, L., Walker, H. J., and Peippo, J.: Water discharge in the Colville
River, 1962, Geogr. Ann. A, 48, 195–210,
1966. a
Bense, V., Ferguson, G., and Kooi, H.: Evolution of shallow groundwater flow
systems in areas of degrading permafrost, Geophys. Res. Lett., 36,
L22401, https://doi.org/10.1029/2009GL039225, 2009. a, b, c
Bense, V. F., Kooi, H., Ferguson, G., and Read, T.: Permafrost degradation as
a control on hydrogeological regime shifts in a warming climate, J.
Geophys. Res.-Earth, 117, F03036, https://doi.org/10.1029/2011JF002143, 2012. a, b
Bintanja, R. and Selten, F. M.: Future increases in Arctic precipitation
linked to local evaporation and sea-ice retreat, Nature, 509, 479–482,
2014. a
Boisvert, L. N., Webster, M. A., Petty, A. A., Markus, T., Bromwich, D. H., and
Cullather, R. I.: Intercomparison of Precipitation Estimates over the Arctic
Ocean and Its Peripheral Seas from Reanalyses, J. Climate, 31,
8441–8462,
https://doi.org/10.1175/JCLI-D-18-0125.1, 2018. a
Bowling, L. C., Kane, D. L., Gieck, R. E., Hinzman, L. D., and Lettenmaier,
D. P.: The role of surface storage in a low-gradient Arctic watershed, Water
Resour. Res., 39, , 1087, https://doi.org/10.1029/2002WR001466, 2003. a, b
Bring, A., Fedorova, I., Dibike, Y., Hinzman, L., Mård, J., Mernild, S.,
Prowse, T., Semenova, O., Stuefer, S. L., and Woo, M.-K.: Arctic terrestrial
hydrology: A synthesis of processes, regional effects, and research
challenges, J. Geophys. Res.-Biogeo., 121, 621–649,
https://doi.org/10.1002/2015JG003131, 2016. a, b, c, d
Brodzik, M. J. and Knowles, K.: EASE-Grid: A Versatile Set of Equal-Area
Projections and Grids, in: Discrete Global Grids, Santa
Barbara, edited by: Goodchild, M., CA, USA, National Center for Geographic Information and
Analysis, 2002. a
Brown, J. and Romanovsky, V. E.: Report from the International Permafrost
Association: State of permafrost in the first decade of the 21st century,
Permafrost Periglac., 19, 255–260, 2008. a
Cai, L., Alexeev, V. A., Arp, C. D., Jones, B. M., Liljedahl, A. K., and
Gädeke, A.: The Polar WRF Downscaled Historical and Projected Twenty-First
Century Climate for the Coast and Foothills of Arctic Alaska, Front. Earth Sci., 5, 111,
https://doi.org/10.3389/feart.2017.00111,
2018. a
Chadburn, S., Burke, E., Cox, P., Friedlingstein, P., Hugelius, G., and
Westermann, S.: An observation-based constraint on permafrost loss as a
function of global warming, Nat. Clim. Change, 7, 340–344, https://doi.org/10.1038/nclimate3262, 2017. a
Clilverd, H. M., White, D. M., Tidwell, A. C., and Rawlins, M. A.: The
Sensitivity of Northern Groundwater Recharge to Climate Change: A Case Study
in Northwest Alaska, J. Am. Water Resour. As.,
47, 1228–1240, https://doi.org/10.1111/j.1752-1688.2011.00569.x, 2011. a
Dean, J., van der Velde, Y., Garnett, M. H., Dinsmore, K. J., Baxter, R.,
Lessels, J. S., Smith, P., Street, L. E., Subke, J.-A., Tetzlaff, D., Washbourne, I., Wookey, P. A., and Billett, M. F.:
Abundant pre-industrial carbon detected in Canadian Arctic headwaters:
implications for the permafrost carbon feedback, Environ. Res. Lett., 13,
034024, https://doi.org/10.1088/1748-9326/aaa1fe, 2018. a
Dee, D. P., Uppala, S. M., Simmons, A., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M., Balsamo, G., Bauer, D. P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge‐Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, P. de., Tavolato, C., Thépaut, J. N., and Vitart, F.: The
ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Du, J., Kimball, J. S., Jones, L., and Watts, J. D.: Implementation of
satellite based fractional water cover indices in the pan-Arctic region using
AMSR-E and MODIS, Remote Sens. Environ., 184, 469–481, 2016. a
Du, J., Kimball, J. S., Duguay, C., Kim, Y., and Watts, J. D.: Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015, The Cryosphere, 11, 47–63, https://doi.org/10.5194/tc-11-47-2017, 2017. a
Food and Agriculture Organization/UNESCO: Digital Soil Map of the World
and Derived Properties, version 3.5, November, 1995, Original scale
1:5 000 000, UNESCO, Paris, France, 1995.
Francis, J. A., Cassano, J. J., Gutowski Jr., W. J., Hinzman, L. D.,
Holland, M. M., Steele, M. A., White, D. M., and Vörösmarty, C. J.:
An Arctic Hydrologic System in Transition: Feedbacks and Impacts on
Terrestrial, Marine, and Human Life, J. Geophys. Res.-Biogeo., 114, G04019,
https://doi.org/10.1029/2008JG000902, 2009. a
Frey, K. E. and Smith, L. C.: Amplified carbon release from vast West Siberian
peatlands by 2100, Geophys. Res. Lett., 32,
L09401,
https://doi.org/10.1029/2004GL022025, 2005. a
Frey, K. E., McClelland, J. W., Holmes, R. M., and Smith, L. C.: Impacts of
climate warming and permafrost thaw on the riverine transport of nitrogen and
phosphorus to the Kara Sea, J. Geophys. Res.-Biogeo., 112, G04S58, https://doi.org/10.1029/2006JG000369, 2007. a
Gautier, E., Dépret, T., Costard, F., Virmoux, C., Fedorov, A., Grancher,
D., Konstantinov, P., and Brunstein, D.: Going with the flow: Hydrologic
response of middle Lena River (Siberia) to the climate variability and
change, J. Hydrol., 557, 475–488, https://doi.org/10.1016/j.jhydrol.2017.12.034, 2018. a
Hinkel, K. and Nelson, F.: Spatial and temporal patterns of active layer
thickness at Circumpolar Active Layer Monitoring (CALM) sites in northern
Alaska, 1995–2000, J. Geophys. Res.-Atmos., 108, , 8168, https://doi.org/10.1029/2001JD000927, 2003. a
Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., and Kuhry, P.: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps, Biogeosciences, 11, 6573–6593, https://doi.org/10.5194/bg-11-6573-2014, 2014. a
Jones, M. K. W., Pollard, W. H., and Jones, B. M.: Rapid initialization of
retrogressive thaw slumps in the Canadian high Arctic and their response to
climate and terrain factors, Environ. Res. Lett., 14,
055006, https://doi.org/10.1088/1748-9326/ab12fd, 2019. a
Jorgenson, M., Yoshikawa, K., Kanevskiy, M., Shur, Y., Romanovsky, V.,
Marchenko, S., Grosse, G., Brown, J., and Jones, B.: Permafrost
characteristics of Alaska, in: Proceedings of the Ninth International
Conference on Permafrost, 3, University of Alaska,
Fairbanks, 121–122, 2008. a
Kaiser, K., Canedo-Oropeza, M., McMahon, R., and Amon, R. M.: Origins and
transformations of dissolved organic matter in large Arctic rivers,
Sci. Rep.-UK, 7, 13064, https://doi.org/10.1038/s41598-017-12729-1, 2017. a
Kane, D. and Stuefer, S.: Reflecting on the status of precipitation data
collection in Alaska: a case study, Hydrol. Res., 46, 478–493, https://doi.org/10.2166/nh.2014.023, 2015. a
Kattsov, V. M., Walsh, J. E., Chapman, W. L., Govorkova, V. A., Pavlova, T. V.,
and Zhang, X.: Simulation and Projection of Arctic Freshwater Budget
Components by the IPCC AR4 Global Climate Models, J. Hydrometeorol., 8,
571–589, https://doi.org/10.1175/JHM575.1, 2007. a, b
Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Woolen, J.,
Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H.,
Jenne, R., and Fiorino, M.: The NCEP-NCAR 50-year reanalysis: Monthly
means CD-ROM and documentation, B. Am. Meteorol. Soc., 82, 247–267,
https://doi.org/10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2, 2001. a
Lamontagne-Hallé, P., McKenzie, J. M., Kurylyk, B. L., and Zipper, S. C.:
Changing groundwater discharge dynamics in permafrost regions, Environ.
Res. Lett., 13, 084017, https://doi.org/10.1088/1748-9326/aad404, 2018. a, b, c, d
Mann, P. J., Eglinton, T. I., McIntyre, C. P., Zimov, N., Davydova, A., Vonk,
J. E., Holmes, R. M., and Spencer, R. G.: Utilization of ancient permafrost
carbon in headwaters of Arctic fluvial networks, Nat. Commun., 6,
6, 7856, https://doi.org/10.1038/ncomms8856, 2015. a
McClelland, J. W., Stieglitz, M., Pan, F., Holmes, R. M., and Peterson, B. J.:
Recent changes in nitrate and dissolved organic carbon export from the upper
Kuparuk River, J. Geophys. Res.-Biogeo., 112, g04S60,
https://doi.org/10.1029/2006JG000371, 2007. a
McClelland, J. W., Townsend-Small, A., Holmes, R. M., Pan, F., Stieglitz, M.,
Khosh, M., and Peterson, B. J.: River export of nutrients and organic matter
from the North Slope of Alaska to the Beaufort Sea, Water Resour. Res.,
50, 1823–1839, https://doi.org/10.1002/2013WR014722, 2014. a, b, c
Miller, J. R., Russell, G. L., and Caliri, G.: Continental-scale river flow in
climate models, J. Climate, 7, 914–928, https://doi.org/10.1175/1520-0442(1994)007<0914:CSRFIC>2.0.CO;2, 1994. a
Muskett, R. and Romanovsky, V.: Alaskan permafrost groundwater storage changes
derived from GRACE and ground measurements, Remote Sens., 3, 378–397,
https://doi.org/10.3390/rs3020378, 2011. a
Neilson, B. T., Cardenas, M. B., O'Connor, M. T., Rasmussen, M. T., King,
T. V., and Kling, G. W.: Groundwater Flow and Exchange Across the Land
Surface Explain Carbon Export Patterns in Continuous Permafrost Watersheds,
Geophys. Res. Lett., 15,
7596–7605, https://doi.org/10.1029/2018GL078140, 2018. a, b
Nicolsky, D. J., Romanovsky, V., Panda, S., Marchenko, S., and Muskett, R.:
Applicability of the ecosystem type approach to model permafrost dynamics
across the Alaska North Slope, J. Geophys. Res.-Earth, 122, 50–75, https://doi.org/10.1002/2016JF003852, 2017. a, b, c, d
Peterson, B. J., Holmes, R. M., McClelland, J. W., Vörösmarty, C. J.,
Lammers, R. B., Shiklomanov, A. I., Shiklomanov, I. A., and Rahmstorf, S.:
Increasing river discharge to the Arctic Ocean, Science, 298, 2171–2173,
https://doi.org/10.1126/science.1077445, 2002. a
Peterson, B. J., McClelland, J., Curry, R., Holmes, R. M., Walsh, J. E., and
Aagaard, K.: Trajectory shifts in the Arctic and sub-Arctic freshwater
cycle, Science, 313, 1061–1066, https://doi.org/10.1126/science.1122593,
2006. a
Rawlins, M. A.: Beaufort Lagoon Ecosystems LTER, Model simulated hydrological estimates for the North Slope drainage basin, Alaska, 1980–2010, Environmental Data Initiative, https://doi.org/10.6073/pasta/2c1cd0c1d3f0f257d9dac659314bc777, 2019. a
Rawlins, M. A.: Permafrost Water Balance Model, v3 source code, available at: https://blogs.umass.edu/csrc/pwbm, last access: 14 December 2019. a
Rawlins, M., Nicolsky, D., McDonald, K., and Romanovsky, V.: Simulating soil freeze/thaw dynamics with an improved pan-Arctic water balance model, J. Adv. Model. Earth Sy., 5, 659–675, https://doi.org/10.1002/jame.20045, 2013. a, b, c, d
Rawlins, M. A., Lammers, R. B., Frolking, S., Fekete, B. M., and
Vörösmarty, C. J.: Simulating Pan-Arctic Runoff with a Macro-Scale
Terrestrial Water Balance Model, Hydrol. Process., 17, 2521–2539, https://doi.org/10.1002/hyp.1271, 2003. a, b
Rawlins, M. A., Fahnestock, M., Frolking, S., and Vörösmarty, C. J.:
On the Evaluation of Snow Water Equivalent Estimates over the Terrestrial
Arctic Drainage Basin, Hydrol. Process., 21, 1616–1623, https://doi.org/10.1002/hyp.6724, 2007. a
Rawlins, M. A., Serreze, M. C., Schroeder, R., Zhang, X., and McDonald, K. C.:
Diagnosis of the Record Discharge of Arctic-Draining Eurasian Rivers in
2007, Environ. Res. Lett., 4, 045011, https://doi.org/10.1088/1748-9326/4/4/045011,
2009. a
Rawlins, M. A., Steele, M., Holland, M. M., Adam, J. C., Cherry, J. E.,
Francis, J. A., Groisman, P. Y., Hinzman, L. D., Huntington, T. G., Kane,
D. L., Kmball, J. S., Kwok, R., Lammers, R. B., Lee, C. M. Lettenmaier, D. P., McDonald, K. C., Podest, E., Pundsack, J. W., Rudels, B., Serreze, M. C., Shiklomanov, A., Skagseth, Ø., Troy, T. J., Vörösmarty, C. J., Wensnahan, M., Wood, E. F., Woodgate, R., Yang, D., Zhang, K., and Zhang, T.: Analysis of the Arctic System for Freshwater Cycle
Intensification: Observations and Expectations, J. Climate, 23, 5715–5737,
https://doi.org/10.1175/2010JCLI3421.1,
2010. a, b, c, d
Rember, R. D. and Trefry, J. H.: Increased concentrations of dissolved trace
metals and organic carbon during snowmelt in rivers of the Alaskan Arctic,
Geochim. Cosmochim. Ac., 68, 477–489, https://doi.org/10.1016/S0016-7037(03)00458-7, 2004 a
Rennermalm, A. K., Wood, E. F., and Troy, T. J.: Observed changes in pan-arctic
cold-season minimum monthly river discharge, Clim. Dynam., 35, 923–939,
https://doi.org/10.1007/s00382-009-0730-5, 2010. a
Rienecker, M., Suarez, M., Gelaro, R., Todling, R., Bacmeister, J., Liu, E.,
Bosilovich, M., Schubert, S., Takacs, L., Kim, G., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C. Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M., and Woollen, J.: MERRA-NASA's
Modern-Era Retrospective Analysis for Research and Applications, J. Climate, 24, 3624–3648, https://doi.org/10.1175/JCLI-D-11-00015.1, 2011. a
Romanovsky, V. E., Smith, S. L., and Christiansen, H. H.: Permafrost thermal
state in the polar Northern Hemisphere during the international polar year
2007–2009: a synthesis, Permafrost Periglac., 21, 106–116,
https://doi.org/10.1002/ppp.689,
2010. a
Scaff, L., Yang, D., Li, Y., and Mekis, E.: Inconsistency in precipitation measurements across the Alaska–Yukon border, The Cryosphere, 9, 2417–2428, https://doi.org/10.5194/tc-9-2417-2015, 2015. a
Schroeder, R., McDonald, K. C., Zimmerman, R., Podest, E., and Rawlins, M.:
North Eurasian Inundation Mapping with Passive and Active Microwave Remote
Sensing, Environ. Res. Lett., 5, 015003, https://doi.org/10.1088/1748-9326/5/1/015003, 2010. a, b
Schuur, E., Vogel, J. G., Crummer, K. G., Lee, H., Sickman, J. O., and
Osterkamp, T. E.: The effect of permafrost thaw on old carbon release and
net carbon exchange from tundra, Nature, 459, 556–559, https://doi.org/10.1038/nature08031, 2009. a
Serreze, M. C., Barrett, A. P., Slater, A. G., Woodgate, R. A., Aagaard, K.,
Lammers, R. B., Steele, M., Moritz, R., Meredith, M., and Lee, C. M.: The
large-scale freshwater cycle of the Arctic, J. Geophys. Res.-Oceans, 111, C11010,
https://doi.org/10.1029/2005JC003424,
2006. a, b
Serreze, M. C., Barrett, A. P., and Stroeve, J.: Recent changes in tropospheric
water vapor over the Arctic as assessed from radiosondes and atmospheric
reanalyses, J. Geophys. Res.-Atmos., 117,
D10104, https://doi.org/10.1029/2011JD017421, 2012. a
Shiklomanov, I. A., Shiklomanov, A. I., Lammers, R. B., Peterson, B. J., and
Vörösmarty, C. J.: The dynamics of river water inflow to the
Arctic Ocean, Kluwer Academic Press, Dordrecht, in:
The Freshwater Budget of the Arctic Ocean, edited by: Lewis, E. I.,
Jones, E. P., Lemke, P., Prowse, T. D., and Wadhams, P., 281–296, 2000. a
Smith, L. C., Pavelsky, T. M., MacDonald, G. M., Shiklomanov, A. I., and
Lammers, R. B.: Rising minimum daily flows in northern Eurasian rivers: A
growing influence of groundwater in the high-latitude hydrologic cycle, J.
Geophys. Res.-Biogeo., 112, G04S47, https://doi.org/10.1029/2006JG000327, 2007. a
Smith, S., Romanovsky, V., Lewkowicz, A., Burn, C., Allard, M., Clow, G.,
Yoshikawa, K., and Throop, J.: Thermal state of permafrost in North America:
a contribution to the international polar year, Permafrost Periglac., 21,
117–135, https://doi.org/10.1002/ppp.690, 2010. a
Spencer, R. G., Mann, P. J., Dittmar, T., Eglinton, T. I., McIntyre, C.,
Holmes, R. M., Zimov, N., and Stubbins, A.: Detecting the signature of
permafrost thaw in Arctic rivers, Geophys. Res. Lett., 42,
2830–2835, https://doi.org/10.1002/2015GL063498, 2015. a
St. Jacques, J. M. and Sauchyn, D. J.: Increasing winter baseflow and mean
annual streamflow from possible permafrost thawing in the Northwest
Territories, Canada, Geophys. Res. Lett., 36,
L01401, https://doi.org/10.1029/2008GL035822, 2009. a, b, c
Striegl, R. G., Aiken, G. R., Dornblaser, M. M., Raymond, P. A., and Wickland,
K. P.: A decrease in discharge-normalized DOC export by the Yukon River
during summer through autumn, Geophys. Res. Lett., 32,
L21413, https://doi.org/10.1029/2005GL024413, 2005. a
Stuefer, S., Kane, D. L., and Liston, G. E.: In situ snow water equivalent
observations in the US Arctic, Hydrol. Res., 44, 21–34,
https://doi.org/10.2166/nh.2012.177, 2013. a
Stuefer, S. L., Arp, C. D., Kane, D. L., and Liljedahl, A. K.: Recent Extreme
Runoff Observations From Coastal Arctic Watersheds in Alaska, Water Resour.
Res., 53, 9145–9163,
https://doi.org/10.1002/2017WR020567, 2017. a, b, c, d
Vonk, J. E., Tank, S. E., Mann, P. J., Spencer, R. G. M., Treat, C. C., Striegl, R. G., Abbott, B. W., and Wickland, K. P.: Biodegradability of dissolved organic carbon in permafrost soils and aquatic systems: a meta-analysis, Biogeosciences, 12, 6915–6930, https://doi.org/10.5194/bg-12-6915-2015, 2015. a
Vörösmarty, C. J., Fekete, B. M., Maybeck, M., and Lammers, R. B.:
Gloabl System of Rivers: Its Role in Organizing Continental Land Mass and
Defining Land-to-Ocean Linkages, Global Biogeochem. Cy., 14, 599–621,
https://doi.org/10.1029/1999GB900092, 2000. a
Walvoord, M. A. and Kurylyk, B. L.: Hydrologic impacts of thawing
permafrost – A review, Vadose Zone J., 15, https://doi.org/10.2136/vzj2016.01.0010, 2016. a, b, c
Walvoord, M. A. and Striegl, R. G.: Increased groundwater to stream discharge
from permafrost thawing in the Yukon River basin: Potential impacts on
lateral export of carbon and nitrogen, Geophys. Res. Lett., 34,
L12402, https://doi.org/10.1029/2007GL030216, 2007. a, b, c, d
Wickland, K. P., Waldrop, M. P., Aiken, G. R., Koch, J. C., Jorgenson, M. T.,
and Striegl, R. G.: Dissolved organic carbon and nitrogen release from boreal
Holocene permafrost and seasonally frozen soils of Alaska, Environ. Res.
Lett., 13, 065011,
https://doi.org/10.1088/1748-9326/aac4ad, 2018. a, b
Willmott, C. J. and Matsuura, K.: Advantages of the mean absolute error (MAE)
over the root mean square error (RMSE) in assessing average model
performance, Clim. Res., 30, 79–82, 2005. a
Willmott, C. J., Robeson, S. M., Matsuura, K., and Ficklin, D. L.: Assessment
of three dimensionless measures of model performance, Environ. Modell.
Softw., 73, 167–174, https://doi.org/10.1016/j.envsoft.2015.08.012, 2015. a
Wrona, F. J., Johansson, M., Culp, J. M., Jenkins, A., Mård, J.,
Myers-Smith, I. H., Prowse, T. D., Vincent, W. F., and Wookey, P. A.:
Transitions in Arctic ecosystems: Ecological implications of a changing
hydrological regime, J. Geophys. Res.-Biogeo., 121,
650–674, https://doi.org/10.1002/2015JG003133, 2016. a
Wu, P., Wood, R., and Stott, P.: Human influence on increasing Arctic river
discharges, Geophys. Res. Lett., 32, L02703, https://doi.org/10.1029/2004GL021570,
2005. a, b
Yang, D., Goodison, B. E., Ishida, S., and Benson, C. S.: Adjustment of Daily
Precipitation Data at 10 Stations in Alaska: Application of World
Meteorological Organization Intercomparison Results, Water Resour.
Res., 34, 241–256, https://doi.org/10.1029/97WR02681, 1998. a
Yang, D., Kane, D. L., Hinzman, L. D., Zhang, X., Zhang, T., and Ye, H.:
Siberian Lena River hydrologic regime and recent change, J. Geophys.
Res.-Atmos., 107, 4694, https://doi.org/10.1029/2002JD002542, 2002. a
Yang, D., Kane, D., Zhang, Z., Legates, D., and Goodison, B.: Bias corrections
of long-term (1973–2004) daily precipitation data over the northern
regions, Geophys. Res. Lett., 32, L19501, https://doi.org/10.1029/2005GL024057,
2005. a
Yi, Y., Kimball, J. S., Jones, L. A., Reichle, R. H., Nemani, R., and Margolis,
H. A.: Recent climate and fire disturbance impacts on boreal and arctic
ecosystem productivity estimated using a satellite-based terrestrial carbon
flux model, J. Geophys. Res.-Biogeo., 118, 606–622,
https://doi.org/10.1002/jgrg.20053, 2013. a
Yi, Y., Kimball, J. S., Rawlins, M. A., Moghaddam, M., and Euskirchen, E. S.: The role of snow cover affecting boreal-arctic soil freeze–thaw and carbon dynamics, Biogeosciences, 12, 5811–5829, https://doi.org/10.5194/bg-12-5811-2015, 2015.
a, b, c
Yi, Y., Kimball, J. S., Chen, R. H., Moghaddam, M., Reichle, R. H., Mishra, U., Zona, D., and Oechel, W. C.: Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska, The Cryosphere, 12, 145–161, https://doi.org/10.5194/tc-12-145-2018, 2018. a, b
Yue, S., Pilon, P., and Cavadias, G.: Power of the Mann–Kendall and Spearman's
rho tests for detecting monotonic trends in hydrological series, J.
Hydrol., 259, 254–271, https://doi.org/10.1016/S0022-1694(01)00594-7, 2002. a
Zhang, X., He, J., Zhang, J., Polyakov, I., Gerdes, R., Inoue, J., and Wu, P.:
Enhanced poleward moisture transport and amplified northern high-latitude
wetting trend, Nat. Clim. Change., 3, 47–51,
https://doi.org/10.1038/nclimate1631, 2013. a, b
Short summary
We investigate the changing character of runoff, river discharge and other hydrological elements across watershed draining the North Slope of Alaska over the period 1981–2010. Our synthesis of observations and modeling reveals significant increases in the proportion of subsurface runoff and cold season discharge. These and other changes we describe are consistent with warming and thawing permafrost, and have implications for water, carbon and nutrient cycling in coastal environments.
We investigate the changing character of runoff, river discharge and other hydrological elements...
