A radiative–conductive–convective approach to calculate thaw season ground surface temperatures for modelling frost table dynamics

Williams, Tyler J.; Pomeroy, John W.; Rasouli, Kabir; Carey, Sean K.; Quinton, William L.

doi:10.1002/hyp.10573

Cited by 26 publications

(22 citation statements)

References 45 publications

(83 reference statements)

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“…This n ‐factor can exhibit interannual variability (Juliussen and Humlum, 2007) and is expected to change with climate warming and concomitant snowpack changes. Thus, others have proposed quasiempirical means to determine ground surface temperatures in permafrost regions from meteorological data and an understanding of the surface energy balance (Hwang, 1976; Williams et al, 2015).…”

Section: Surface Hydrology Modeling In Permafrost Regionsmentioning

confidence: 99%

Hydrologic Impacts of Thawing Permafrost—A Review

Walvoord¹,

Kurylyk

2016

Vadose Zone Journal

679

646

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Where present, permafrost exerts a primary control on water fluxes, flowpaths, and distribution. Climate warming and related drivers of soil thermal change are expected to modify the distribution of permafrost, leading to changing hydrologic conditions, including alterations in soil moisture, connectivity of inland waters, streamflow seasonality, and the partitioning of water stored above and below ground. The field of permafrost hydrology is undergoing rapid advancement with respect to multiscale observations, subsurface characterization, modeling, and integration with other disciplines. However, gaining predictive capability of the many interrelated consequences of climate change is a persistent challenge due to several factors. Observations of hydrologic change have been causally linked to permafrost thaw, but applications of process-based models needed to support and enhance the transferability of empirical linkages have often been restricted to generalized representations. Limitations stem from inadequate baseline permafrost and unfrozen hydrogeologic characterization, lack of historical data, and simplifications in structure and process representation needed to counter the high computational demands of cryohydrogeologic simulations. Further, due in part to the large degree of subsurface heterogeneity of permafrost landscapes and the nonuniformity in thaw patterns and rates, associations between various modes of permafrost thaw and hydrologic change are not readily scalable; even trajectories of change can differ. This review highlights promising advances in characterization and modeling of permafrost regions and presents ongoing research challenges toward projecting hydrologic and ecologic consequences of permafrost thaw at time and spatial scales that are useful to managers and researchers.Abbreviations: AEM, airborne electromagnetic; ALT, active layer thickness; CALM, Circumpolar Active Layer Monitoring; EMI, electromagnetic induction; ERT, electrical resistivity tomography; GPR, ground-penetrating radar; InSAR, Interferometric Synthetic Aperture Radar; NMR, nuclear magnetic resonance; SR, seismic refraction; TDEM, timedomain electromagnetics.Permafrost hydrology is a rapidly progressing research field, and a number of new discoveries and questions have emerged in recent years. Research interest in cold regions has been spurred in part by surface temperature warming rates in high latitudes (McBean et al., 2005) and high altitudes (Pepin et al., 2015) that are greater than the global average. This warming has produced changes to the cryosphere, including permafrost (ground that is £0°C year round), that impact hydrologic processes and conditions (ACIA, 2005;Hinzman et al., 2013). Despite increased attention, there are still critical limitations in hydrologic data coverage, subsurface characterization, process-level understanding, and integrated modeling approaches. Due to its low hydraulic conductivity (K), permafrost strongly affects the movement, storage, and exchange of surface and subsurface water. In...

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Section: Surface Hydrology Modeling In Permafrost Regionsmentioning

confidence: 99%

Hydrologic Impacts of Thawing Permafrost—A Review

Walvoord¹,

Kurylyk

2016

Vadose Zone Journal

679

646

View full text Add to dashboard Cite

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“…The n-factor has been shown to exhibit interannual variability in mountainous regions due to interannual changes in snowpack (Juliussen and Humlum, 2007). However, alternative quasi-empirical approaches exist to determine surface temperature or the ground heat flux by balancing the surface energy fluxes (e.g., Hwang, 1976;Williams et al, 2015).…”

Section: Limitationsmentioning

confidence: 99%

Improved Stefan Equation Correction Factors to Accommodate Sensible Heat Storage during Soil Freezing or Thawing

Kurylyk

2015

Permafrost & Periglacial

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In permafrost regions, the thaw depth strongly controls shallow subsurface hydrologic processes that in turn dominate catchment runoff. In seasonally freezing soils, the maximum expected frost depth is an important geotechnical engineering design parameter. Thus, accurately calculating the depth of soil freezing or thawing is an important challenge in cold regions engineering and hydrology. The Stefan equation is a common approach for predicting the frost or thaw depth, but this equation assumes negligible soil heat capacity and thus exaggerates the rate of freezing or thawing. The Neumann equation, which accommodates sensible heat, is an alternative implicit equation for calculating freeze-thaw penetration. This study details the development of correction factors to improve the Stefan equation by accounting for the influence of the soil heat capacity and non-zero initial temperatures. The correction factors are first derived analytically via comparison to the Neumann solution, but the resultant equations are complex and implicit. Explicit equations are obtained by fitting polynomial functions to the analytical results. These simple correction factors are shown to significantly improve the performance of the Stefan equation for several hypothetical soil freezing and thawing scenarios.

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“…affected by solar radiation, land surface properties (e.g., vegetation type and snow cover), and soil properties (Williams et al, 2015). By using the n factor, the degree-day sum of ground surface temperature can be reasonably estimated from that of air temperature (Qin et al, 2016).…”

Section: Water Resources Researchmentioning

confidence: 99%

Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model

Qin

Chen

Yang

et al. 2018

Water Resources Research

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We develop a Bayesian model to predict the maximum thickness of seasonally frozen ground (MTSFG) using historical air temperature and precipitation observations. We use the Stefan solution and meteorological data from 11 stations to estimate the MTSFG changes from 1961 to 2016 in the Yellow River source region of northwestern China. We employ an antecedent precipitation index model to estimate changes in the liquid soil water content. The marginal posterior probability distributions of the antecedent precipitation index parameters are estimated using Markov chain Monte Carlo sampling methods. We compare the results of our stochastic method with those obtained from the traditional deterministic method and find that they are consistent in general. The stochastic approach is effective for estimating the historical changes in the frozen ground depth (root‐mean‐square errors = 0.13–0.35 m), and it provides more information on model uncertainty regarding soil moisture variations. Additionally, simulation shows that the MTSFG has decreased by 0.31 cm per year over the last 56 years on the northeastern Qinghai‐Tibet Plateau. This decrease in frost depth accelerated in the 1990s and 2000s. Considering the lack of data on seasonally frozen soil monitoring, the Bayesian method provides a pragmatic approach to statistically model frozen ground changes using available meteorological data.

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A radiative–conductive–convective approach to calculate thaw season ground surface temperatures for modelling frost table dynamics

Cited by 26 publications

References 45 publications

Hydrologic Impacts of Thawing Permafrost—A Review

Hydrologic Impacts of Thawing Permafrost—A Review

Improved Stefan Equation Correction Factors to Accommodate Sensible Heat Storage during Soil Freezing or Thawing

Estimating Seasonally Frozen Ground Depth From Historical Climate Data and Site Measurements Using a Bayesian Model

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