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Title: Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape

Abstract

Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to the atmosphere under warming climate scenarios. Ice-wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. This microtopography plays a critical role in regulating the fine-scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behavior under the current as well as changing climate. Here, we present an end-to-end effort for high-resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We also develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites near Barrow, Alaska, spanning across low to transitional to high-centered polygons, representing a broad polygonal tundra landscape. A multiphase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using a high-resolution lidar digital elevation model (DEM), microtopographic features of the landscape were characterized and represented in the high-resolution model mesh. The best available soil data from field observations and literature were utilizedmore » to represent the complex heterogeneous subsurface in the numerical model. Simulation results demonstrate the ability of the developed modeling approach to capture – without recourse to model calibration – several aspects of the complex thermal regimes across the sites, and provide insights into the critical role of polygonal tundra microtopography in regulating the thermal dynamics of the carbon-rich permafrost soils. Moreover, areas of significant disagreement between model results and observations highlight the importance of field-based observations of soil thermal and hydraulic properties for modeling-based studies of permafrost thermal dynamics, and provide motivation and guidance for future observations that will help address model and data gaps affecting our current understanding of the system.« less

Authors:
 [1];  [2];  [3];  [4];  [1];  [1];  [5]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Intel Corporation, Hillsboro, OR (United States)
  5. Univ. of Alaska, Fairbanks, AK (United States). Geophysical Inst.
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1327597
Grant/Contract Number:
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
The Cryosphere (Online)
Additional Journal Information:
Journal Name: The Cryosphere (Online); Journal Volume: 10; Journal Issue: 5; Journal ID: ISSN 1994-0424
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; hydrology; permafrost; tundra; modeling; PFLOTRAN

Citation Formats

Kumar, Jitendra, Collier, Nathan, Bisht, Gautam, Mills, Richard T., Thornton, Peter E., Iversen, Colleen M., and Romanovsky, Vladimir. Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape. United States: N. p., 2016. Web. doi:10.5194/tc-10-2241-2016.
Kumar, Jitendra, Collier, Nathan, Bisht, Gautam, Mills, Richard T., Thornton, Peter E., Iversen, Colleen M., & Romanovsky, Vladimir. Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape. United States. doi:10.5194/tc-10-2241-2016.
Kumar, Jitendra, Collier, Nathan, Bisht, Gautam, Mills, Richard T., Thornton, Peter E., Iversen, Colleen M., and Romanovsky, Vladimir. 2016. "Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape". United States. doi:10.5194/tc-10-2241-2016. https://www.osti.gov/servlets/purl/1327597.
@article{osti_1327597,
title = {Modeling the spatiotemporal variability in subsurface thermal regimes across a low-relief polygonal tundra landscape},
author = {Kumar, Jitendra and Collier, Nathan and Bisht, Gautam and Mills, Richard T. and Thornton, Peter E. and Iversen, Colleen M. and Romanovsky, Vladimir},
abstractNote = {Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to the atmosphere under warming climate scenarios. Ice-wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. This microtopography plays a critical role in regulating the fine-scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behavior under the current as well as changing climate. Here, we present an end-to-end effort for high-resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We also develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites near Barrow, Alaska, spanning across low to transitional to high-centered polygons, representing a broad polygonal tundra landscape. A multiphase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using a high-resolution lidar digital elevation model (DEM), microtopographic features of the landscape were characterized and represented in the high-resolution model mesh. The best available soil data from field observations and literature were utilized to represent the complex heterogeneous subsurface in the numerical model. Simulation results demonstrate the ability of the developed modeling approach to capture – without recourse to model calibration – several aspects of the complex thermal regimes across the sites, and provide insights into the critical role of polygonal tundra microtopography in regulating the thermal dynamics of the carbon-rich permafrost soils. Moreover, areas of significant disagreement between model results and observations highlight the importance of field-based observations of soil thermal and hydraulic properties for modeling-based studies of permafrost thermal dynamics, and provide motivation and guidance for future observations that will help address model and data gaps affecting our current understanding of the system.},
doi = {10.5194/tc-10-2241-2016},
journal = {The Cryosphere (Online)},
number = 5,
volume = 10,
place = {United States},
year = 2016,
month = 9
}

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  • This Modeling Archive is in support of an NGEE Arctic discussion paper under review and available at http://www.the-cryosphere-discuss.net/tc-2016-29/. Vast carbon stocks stored in permafrost soils of Arctic tundra are under risk of release to atmosphere under warming climate. Ice--wedge polygons in the low-gradient polygonal tundra create a complex mosaic of microtopographic features. The microtopography plays a critical role in regulating the fine scale variability in thermal and hydrological regimes in the polygonal tundra landscape underlain by continuous permafrost. Modeling of thermal regimes of this sensitive ecosystem is essential for understanding the landscape behaviour under current as well as changing climate.more » We present here an end-to-end effort for high resolution numerical modeling of thermal hydrology at real-world field sites, utilizing the best available data to characterize and parameterize the models. We develop approaches to model the thermal hydrology of polygonal tundra and apply them at four study sites at Barrow, Alaska spanning across low to transitional to high-centered polygon and representative of broad polygonal tundra landscape. A multi--phase subsurface thermal hydrology model (PFLOTRAN) was developed and applied to study the thermal regimes at four sites. Using high resolution LiDAR DEM, microtopographic features of the landscape were characterized and represented in the high resolution model mesh. Best available soil data from field observations and literature was utilized to represent the complex hetogeneous subsurface in the numerical model. This data collection provides the complete set of input files, forcing data sets and computational meshes for simulations using PFLOTRAN for four sites at Barrow Environmental Observatory. It also document the complete computational workflow for this modeling study to allow verification, reproducibility and follow up studies.« less
  • Existing land surface models (LSMs) describe physical and biological processes that occur over a wide range of spatial and temporal scales. For example, biogeochemical and hydrological processes responsible for carbon (CO 2, CH 4) exchanges with the atmosphere range from the molecular scale (pore-scale O 2 consumption) to tens of kilometers (vegetation distribution, river networks). Additionally, many processes within LSMs are nonlinearly coupled (e.g., methane production and soil moisture dynamics), and therefore simple linear upscaling techniques can result in large prediction error. In this paper we applied a reduced-order modeling (ROM) technique known as "proper orthogonal decomposition mapping method" thatmore » reconstructs temporally resolved fine-resolution solutions based on coarse-resolution solutions. We developed four different methods and applied them to four study sites in a polygonal tundra landscape near Barrow, Alaska. Coupled surface–subsurface isothermal simulations were performed for summer months (June–September) at fine (0.25 m) and coarse (8 m) horizontal resolutions. We used simulation results from three summer seasons (1998–2000) to build ROMs of the 4-D soil moisture field for the study sites individually (single-site) and aggregated (multi-site). The results indicate that the ROM produced a significant computational speedup (> 10 3) with very small relative approximation error (< 0.1%) for 2 validation years not used in training the ROM. We also demonstrate that our approach: (1) efficiently corrects for coarse-resolution model bias and (2) can be used for polygonal tundra sites not included in the training data set with relatively good accuracy (< 1.7% relative error), thereby allowing for the possibility of applying these ROMs across a much larger landscape. By coupling the ROMs constructed at different scales together hierarchically, this method has the potential to efficiently increase the resolution of land models for coupled climate simulations to spatial scales consistent with mechanistic physical process representation.« less
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  • Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid watermore » content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological–thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface–subsurface, deterministic–stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological–thermal dynamics.« less
  • Liquid water is scarce across the landscape of the McMurdo Dry Valleys (MDV), Antarctica, a 3800 km 2 ice-free region, and is chiefly associated with soils that are adjacent to streams and lakes (i.e. wetted margins) during the annual thaw season. However, isolated wetted soils have been observed at locations distal from water bodies. The source of water for the isolated patches of wet soil is potentially generated by a combination of infiltration from melting snowpacks, melting of pore ice at the ice table, and melting of buried segregation ice formed during winter freezing. In this paper, high resolution remotemore » sensing data gathered several times per summer in the MDV region were used to determine the spatial and temporal distribution of wet soils. The spatial consistency with which the wet soils occurred was assessed for the 2009–10 to 2011–12 summers. The remote sensing analyses reveal that cumulative area and number of wet soil patches varies among summers. The 2010–11 summer provided the most wetted soil area (10.21 km 2) and 2009–10 covered the least (5.38 km 2). Finally, these data suggest that wet soils are a significant component of the MDV cold desert land system and may become more prevalent as regional climate changes.« less