Simulated Soil Temperature Research Articles

Development and validation of the frozen soil parameterization scheme in Common Land Model

In this paper, a frozen soil parameterization scheme is developed based on the Common Land Model. We modify a frozen soil parameterization scheme using soil matric potential to define maximum liquid water content when soil temperature is below the freezing point; the simulation performance is validated using the data from Maqu station on the Tibetan Plateau from 2005 to 2006. The simulated results indicate that the modified frozen soil parameterization scheme allows the liquid water to exist when the soil temperature is below the freezing point, and the simulated soil liquid water content is significantly improved. The simulated soil temperature is also improved because of the successful simulation of the soil liquid water content. The distribution of energy from the modified model is closer to the observed data. Also, the simulated latent heat flux from the modified model increases and the simulated soil heat flux descends compared with the original model.

Evaluation of two process-based models to estimate soil N2O emissions in Eastern Canada

Process-based models play an important role in the estimation of soil N2O emissions from regions with contrasting soil and climatic conditions. A study was performed to evaluate the ability of two process-based models, DAYCENT and DNDC, to estimate N2O emissions, soil nitrate- and ammonium-N levels, as well as soil temperature and water content. The measurement sites included a maize crop fertilized with pig slurry (Quebec) and a wheat-maize-soybean rotation as part of a tillage-fertilizer experiment (Ontario). At the Quebec site, both models accurately simulated soil temperature with an average relative error (ARE) ranging from 0 to 2%. The models underpredicted soil temperature at the Ontario site with ARE from −5 to −7% for DNDC and from −5 to −13% for DAYCENT. Both models underestimated soil water content particularly during the growing season. The DNDC model accurately predicted average seasonal N2O emissions across treatments at both sites whereas the DAYCENT model underpredicted N2O emissions by 32 to 58% for all treatments excluding the fertilizer treatment at the Quebec site. Both models had difficulty in simulating the timing of individual emission events. The hydrology and nitrogen transformation routines need to be improved in both models before further enhancements are made to the trace gas routines. Key words: Nitrous oxide, process-based model, DNDC, greenhouse gas emissions, soil

Modelling soil temperatures in northern peatlands

SummaryMethane emissions from northern peatlands are strongly dependent on soil temperatures. Therefore, to predict methane emissions from northern peatlands under future climatic conditions, it is important to simulate the effect of these changing climatic conditions on peat temperatures. This article reports on the development and testing of two one‐dimensional (1D) models used to simulate soil temperatures at shallow depths in a northern peatland. First, the HIP‐Dlet (Heat in Peat – Dirichlet) model applies measured temperatures to the surface boundary of a conduction‐based temperature model. Secondly, the HIP‐Nmann (Heat in Peat – von Neumann) model simulates the surface boundary from standard meteorological measurements. The HIP‐Dlet model provides a reasonable to good approximation of measured temperatures showing that heat transport processes within the soil are adequately simulated. The model does not simulate an advective liquid heat flux. However, only substantial rainfall events (> 70 mm over 3 days) during the study period had any significant effect on model error. Errors in the HIP‐Nmann model were of a similar magnitude to the HIP‐Dlet model. Errors in the HIP‐Dlet model resulted predominantly from errors in the measured soil temperatures. Errors in the HIP‐Nmann were due to errors in the measured soil temperatures and the inaccurate simulation of the surface boundary condition. The development of future peatland temperature models should, therefore, focus on the simulation of the surface boundary condition, particularly the parameterisation of the surface resistance that is shown here to produce significant errors in the modelled soil temperatures.

Evaluation of Community Climate System Model soil temperatures using observations from Russia

Soil temperatures simulated by the fully coupled Community Climate System Model (CCSM) version 3.0 are evaluated using three gridded climatologies (1951–1980, 1961–1990, 1971–2000) based on data from more than 400 Russian sites. CCSM captures the annual phase of the soil temperature cycle well, but not the amplitude. It provides slightly too high (low) soil temperatures in winter (summer). Root mean square errors, on average, are less than 5 K. Simulated near-surface air temperatures agree well, on average, with near-surface air temperatures from reanalysis data. Errors in simulated atmospheric-temperature forcing correlate statistically significantly (95% or higher confidence level) with soil temperature errors, i.e. contribute to discrepancy in soil temperature simulation. Comparison to International Satellite Cloud Climatology project data shows that errors in simulated cloud fraction explain some soil and near-surface air temperature and precipitation discrepancies. Evaluation by means of Global Precipitation Climatology Centre data identifies inaccurately-simulated precipitation as a contributor to underestimating summer soil temperatures. Comparison to snow-depth observations shows that overestimating snow depth yields winter soil-temperature overestimation. Sensitivity studies show that uncertainty in mineral-soil composition notably, and differences between the vegetation in CCSM and nature marginally contribute to discrepancies between simulated and observed soil-temperature climatology.

Impact of snow cover on soil temperature and its simulation in a boreal aspen forest

Abstract Snow cover has low thermal conductivity and high albedo, which have large impacts on the soil thermal regime and microclimate. Accurate simulations of snow and soil processes are critical to ecosystem and climate modelling. In this study, we developed a detailed snow and soil scheme in an ecosystem model EALCO to (1) improve the simulations of snow and soil thermal regimes and (2) investigate the effects of different model configurations and parameterizations including snow and soil layering resolutions, fractional snow cover parameterization, water table and total soil column depths, and the inclusion/exclusion of a surface organic layer and unfrozen water in frozen soil. The model includes dynamic layering of snow, user-adjustable layering of soil, snow cover compaction and destructive metamorphisms, implicit solutions of the soil heat and water transfer equations using a finite difference method, distinct parameterizations of the surface organic layer and unfrozen water in frozen soil. The model was tested using nine years (1994–2002) of winter field measurements obtained in a boreal aspen forest. The tested variables included snow depth, snow temperature, soil heat flux, and soil temperatures at various depths. The results showed that the model well reproduced the snow and soil thermal regimes and their interactions observed at the site. The results also indicated that the simulation of snow cover duration was affected by the snow layering resolution and fractional snow cover parameterization. The simulation of soil temperature was affected by the soil layering resolution, the depths of water table and soil column, and the inclusion of a soil organic layer. Inclusion of unfrozen water in frozen soil was found to have only a small effect on soil temperature simulation at this site.

Improved modeling of permafrost dynamics in a GCM land‐surface scheme

Global climate models (GCM) are frequently used to understand and predict future climate change, but most GCMs do not attempt to represent permafrost dynamics and its potentially critical feedbacks on climate. In this paper, we evaluate the Community Land Model (CLM3), which is a land‐surface scheme, against observations and identify potential modifications to this model that improve fidelity of permafrost and soil temperature simulations. These modifications include increasing the total soil depth by adding new layers, incorporating a surface organic layer, and modifying the numerical scheme to include unfrozen water dynamics and more realistic phase change representation.

An evaluation of the performance of the soil temperature simulation algorithms used in the PRZM model

Soil temperature simulation is an important component in environmental modeling since it is involved in several aspects of pollutant transport and fate. This paper deals with the performance of the soil temperature simulation algorithms of the well-known environmental model PRZM. Model results are compared and evaluated based on the basis of its ability to predict in situ measured soil temperature profiles in an experimental plot during a 3-year monitoring study. The evaluation of the performance is based on linear regression statistics and typical model statistical errors such as the root mean square error (RMSE) and the normalized objective function (NOF). Results show that the model required minimal calibration to match the observed response of the system. Values of the determination coefficient R2 were found to be in all cases around the value of 0.98 indicating a very good agreement between measured and simulated data. Values of the RMSE were found to be in the range of 1.2 to 1.4°C, 1.1 to 1.4°C, 0.9 to 1.1°C, and 0.8 to 1.1°C, for the examined 2, 5, 10 and 20 cm soil depths, respectively. Sensitivity analyses were also performed to investigate the influence of various factors involved in the energy balance equation at the ground surface on the soil temperature profiles. The results showed that the model was able to represent important processes affecting the soil temperature regime such as the combined effect of the heat transfer by convection between the ground surface and the atmosphere and the latent heat flux due to soil water evaporation.

Using APSIM-soiltemp to simulate soil temperature in the podding zone of peanut

Measurement or accurate simulation of soil temperature is important for improved understanding and management of peanuts (Arachis hypogaea L.), due to their geocarpic habit. A module of the Agricultural Production Systems Simulator Model (APSIM), APSIM-soiltemp, which uses input of ambient temperature, rainfall and solar radiation in conjunction with other APSIM modules, was evaluated for its ability to simulate surface 5 cm soil temperature in 35 peanut on-farm trials conducted between 2001 and 2005 in the Burnett region (25°36′S to 26°41′S, 151°39′E to 151°53′E). Soil temperature simulated by the APSIM-soiltemp module, from 30 days after sowing until maturity, closely matched the measured values (R2 ≥ 0.80) in the first three seasons (2001–04). However, a slightly poorer relationship (R2 = 0.55) between the observed and the simulated temperatures was observed in 2004–05, when the crop was severely water stressed. Nevertheless, over all the four seasons, which were characterised by a range of ambient temperature, leaf area index, radiation and soil water, each of which was found to have significant effects on soil temperature, a close 1 : 1 relationship (R2 = 0.85) between measured and simulated soil temperatures was observed. Therefore, the pod zone soil temperature simulated by the module can be generally relied on in place of measured input of soil temperature in APSIM applications, such as quantifying climatic risk of aflatoxin accumulation.

Development of Frozen Soil Model

In this paper,firstly, new version of frozen soil model is presented, in which the enthalpy and total volumetric water equivalent content in soil is used as predictive variables to replace the predictive variables of soil temperature and volumetric liquid water content in the current models respectively. It makes the required estimation of liquid-ice phase change rate in the current models unnecessary, and therefore avoids the introduction of the error in estimation of the phase change rate into the new model and in turn will improve the new version model in both speeding convergence of numerical iterative process and enhancing the model performance. At the same time, a numerical scheme is also developed for the diagnostic equations in the new version, which is very efficient for finding the true solution with a few iteration times. It's proved the numerical simulation results of the new version model are in good agreement with two field observed data. Besides, based on analyzing three schemes for soil freezing-thaw from theoretical view and comparing the numerical simulation results, it is found that the parameterization scheme, which takes the relation between moisture potential and temperature in soil derived from the equilibrium thermodynamics theory into consideration, can describe the freezing-thaw processes well. Numerical results have shown that different freezing-thaw parameterizations have greater influence on simulated soil temperature, water content, and latent and sensible heat fluxes at soil surface.

The impact of simulated soil temperatures on the estimation of snow depth over Siberia from SSM/I compared to a multi-model climatology

The snowpack is a key variable of the hydrological cycle. In recent years, numerous studies have demonstrated the importance of long-term monitoring of the Siberian snowpack on large spatial scales owing to evidence of increased river discharge, changes in snow fall amount and alterations with respect to the timing of ablation. This can currently only be accomplished using remote sensing methods. The main objective of this study is to take advantage of a new land surface forcing and simulation database in order to both improve and evaluate the snow depths retrieved using a dynamic snow depth retrieval algorithm. The dynamic algorithm attempts to account for the spatial and temporal internal properties of the snow cover. The passive microwave radiances used to derive snow depth were measured by the Special Sensor Microwave/ Imager (SSM/I) data between July 1987 and July 1995. The evaluation of remotely sensed algorithms is especially difficult over regions such as Siberia which are characterized by relatively sparse surface measurement networks. In addition, existing gridded climatological snow depth databases do not necessarily correspond to the same time period as the available satellite data. In order to evaluate the retrieval algorithm over Siberia for a recent multi-year period at a relatively large spatial scale, a land surface scheme reanalysis product from the Global Soil Wetness Project-Phase 2 (GSWP-2) is used in the current study. First, the high quality GSWP-2 input forcing data were used to drive a land surface scheme (LSS) in order to derive a climatological near-surface soil temperature. Four different snow depth retrieval methods are compared, two of which use the new soil temperature climatology as input. Second, a GSWP-2 snow water equivalent (SWE) climatology is computed from 12 state-of-the-art LSS over the same time period covered by the SSM/I data. This climatology was compared to the corresponding fields from the retrievals. This study reaffirmed the results of recent studies which showed that the inclusion of ancillary data into a satellite data-based snow retrieval algorithm, such as soil temperatures, can significantly improve the results. The current study also goes a step further and reveals the importance of including the monthly soil temperature variation into the retrieval, which improves results in terms of the spatial distribution of the snowpack. Finally, it is shown that further improved predictions of SWE are obtained when spatial and temporal variations in snow density are accounted for.

Modeling a wheat–maize double cropping system in China using two plant growth modules in RZWQM

Agricultural system models are potential tools for evaluating soil-water–nutrient management in intensive cropping systems. In this study, we calibrated and validated the Root Zone Water Quality Model (RZWQM) with both a generic plant growth module (RZWQM-G) and the CERES plant growth module (RZWQM-C) for simulating winter wheat ( Triticum aestivum L.) and maize ( Zea mays L.) double cropping systems in the Northern China Plain (NCP), China. Data were obtained from an experiment conducted at Yucheng Integrated Agricultural Experimental Station (36°57′N, 116°36′E, 28 m asl) in the North China Plain (NCP) from 1997 to 2001 (eight crop seasons) with field measurements of evapotranspiration, soil water, soil temperature, leaf area index (LAI), biomass and grain yield. Using the same soil water and nutrient modules, both plant modules were calibrated using the data from one crop sequence during 1998–1999 when detailed measurements of LAI and biomass growth were available. The calibrated models were then used to simulate maize and wheat production in other years. Overall simulation runs from 1997 to 2001 showed that the RZWQM-C model simulated grain yields with a RMSE of 0.94 Mg ha −1 in contrast to a RMSE of 1.23 Mg ha −1 with RZWQM-G. The RMSE for biomass simulation was 2.07 Mg ha −1 with RZWQM-G and 2.26 Mg ha −1 with RZWQM-C model. The RMSE values of simulated evapotranspiration, soil water, soil temperature and LAI were 1.4 mm, 0.046 m 3 m −3, 1.75 °C and 1.0 for RZWQM-G and 1.4 mm, 0.047 m 3 m −3, 1.84 °C and 1.1 for RZWQM-C, respectively. The study revealed that both plant models were able to simulate the intensive cropping systems once they were calibrated for the local weather and soil conditions. Sensitivity analysis also showed that a reduction of 25% of current water and N applications reduced N leaching by 24–77% with crop yield reduction of 1–9% only.

A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO 2 and H 2O vapor exchange

We utilized eddy-covariance observations of carbon dioxide (CO 2) and water vapor exchange at three AmeriFlux mid-latitude forest stands to evaluate IBIS, a Dynamic Global Vegetation Model (DGVM). Measurements of leaf area index (LAI), soil moisture and temperature, runoff, soil carbon (C), and soil respiration (R) were also compared with model output. An experimental approach was designed to help attribute model errors to the vegetation dynamics and phenology formulations versus simulated biological processes. Continental scale phenology sub-models poorly represented the timing of budburst and evolution of canopy LAI in deciduous forests. Biases of vegetation green-up of 6 weeks and delayed senescence were noted. Simulated soil temperatures were overestimated (underestimated) during the summer (winter) on average by 2–5 °C. Ecosystem R was overestimated during the growing season, on average, by 20–60 g C m −2 month −1, and underestimated during the winter by 10–20 g C m −2 month −1 at all sites. Simulated soil R failed to capture observed mid-summer peak rates and was generally lower than observed in winter. The overall comparison of simulated net ecosystem production (NEP) to observations showed a significant underestimate of growing season NEP of 25–100 g C m −2 month −1, and an overall positive bias of 10–40 g C m −2 month −1 during the winter. Excellent agreement between annual average NEP observations and IBIS simulations in “fixed vegetation” mode resulted from offsetting seasonal model biases. The magnitude of simulated variation in seasonal and inter-annual C exchange was generally dampened with respect to observations. The parameterization, and in some cases the formulations (e.g., ecosystem R and phenology) limited model capacity to capture the seasonal fluctuations of C and water exchange. Model parameterizations and formulations were originally constrained and generalized for application to a wide range of global climate and soil conditions and plant functional types (PFTs), likely contributing to model biases. This problem potentially applies to other DGVMs and biosphere models, and will likely become increasingly relevant if investigators apply their models at higher spatial resolution. We suggest that revisions to DGVMs should focus on advancing the capabilities of current phenology formulations to account for photoperiod, soil moisture and frost in addition to temperature. Model representations of PFTs and formulations of ecosystem R need to be rethought, particularly with respect to use of Q 10 temperature functions as modifiers. Surface energy balance, C allocation, soil R, and plant response to nutrient stress deserve attention as well.

Interannual Variability of Deep-Layer Hydrologic Memory and Mechanisms of Its Influence on Surface Energy Fluxes

Abstract The characteristics of deep-layer terrestrial memory are explored using observed soil moisture data and simulated soil temperature from the Illinois Climate Network stations. Both soil moisture and soil temperature are characterized by exponential decay in amplitude, linear lag in phase, and increasing persistence with depth. Using spectral analysis, four dominant low-frequency modes are identified in the soil moisture variability. These signals have periods of about 12, 17, 34, and 60 months, which correspond to annual cycle, (4/3) ENSO, quasi-biennial (QB) ENSO, and quasi-quadrennial (QQ) ENSO signals, respectively. For deep layers, the interannual modes are dominant over the annual cycle, and vice versa for the near-surface layer. There are inherently two mechanisms by which deep-layer moisture impacts the surface fluxes. First, its temporal variability sets the lower boundary condition for the transfer of moisture and heat fluxes from the surface. Second, this temporal variability influences the uptake of moisture by plant roots, resulting in the variability of the transpiration and, therefore, the entire energy balance. Initial results suggest that this second mechanism may be more predominant.

Spatial and temporal simulation of soil CO2concentrations in a small forested catchment in Virginia

The question of how to extrapolate point measurements of soil CO2 processes to coarser scales remains unanswered because we know little about the spatial and temporal variability in the CO2 concentration of soil air. In this work, we describe a series of simple physically-based models that simulate soil temperature, soil tension, and soil CO2 processes. We apply these models to simulate the spatial and temporal dynamics of soil CO2 concentrations throughout a small catchment in the Virginia Blue Ridge. Output from the simulations is compared with field measurements. We find that despite some model deficiencies, we are able to simulate the gross patterns through space and time of soil air CO2 concentration. During the growing season when soil temperature is high, we find that soil water status is the limiting control on soil respiration and CO2 concentration. We also find that soil CO2 concentration can be high despite low respiration values due to decreased soil diffusivity as moisture fills pore spaces.

A simple model for predicting soil temperature in snow-covered and seasonally frozen soil: model description and testing

Abstract. Microbial processes in soil are moisture, nutrient and temperature dependent and, consequently, accurate calculation of soil temperature is important for modelling nitrogen processes. Microbial activity in soil occurs even at sub-zero temperatures so that, in northern latitudes, a method to calculate soil temperature under snow cover and in frozen soils is required. This paper describes a new and simple model to calculate daily values for soil temperature at various depths in both frozen and unfrozen soils. The model requires four parameters: average soil thermal conductivity, specific heat capacity of soil, specific heat capacity due to freezing and thawing and an empirical snow parameter. Precipitation, air temperature and snow depth (measured or calculated) are needed as input variables. The proposed model was applied to five sites in different parts of Finland representing different climates and soil types. Observed soil temperatures at depths of 20 and 50 cm (September 1981–August 1990) were used for model calibration. The calibrated model was then tested using observed soil temperatures from September 1990 to August 2001. R2-values of the calibration period varied between 0.87 and 0.96 at a depth of 20 cm and between 0.78 and 0.97 at 50 cm. R2-values of the testing period were between 0.87 and 0.94 at a depth of 20cm, and between 0.80 and 0.98 at 50cm. Thus, despite the simplifications made, the model was able to simulate soil temperature at these study sites. This simple model simulates soil temperature well in the uppermost soil layers where most of the nitrogen processes occur. The small number of parameters required means that the model is suitable for addition to catchment scale models. Keywords: soil temperature, snow model

Adaptation of the Integrated Nitrogen Model for Catchments (INCA) to seasonally snow-covered catchments

Abstract. Testing of the Integrated Nitrogen model for Catchments (INCA) in a wide range of ecosystem types across Europe has shown that the model underestimates N transformation processes to a large extent in northern catchments of Finland and Norway in winter and spring. It is found, and generally assumed, that microbial activity in soils proceeds at low rates at northern latitudes during winter, even at sub-zero temperatures. The INCA model was modified to improve the simulation of N transformation rates in northern catchments, characterised by cold climates and extensive snow accumulation and insulation in winter, by introducing an empirical function to simulate soil temperatures below the seasonal snow pack, and a degree-day model to calculate the depth of the snow pack. The proposed snow-correction factor improved the simulation of soil temperatures at Finnish and Norwegian field sites in winter, although soil temperature was still underestimated during periods with a thin snow cover. Finally, a comparison between the modified INCA version (v.1.7) and the former version (v.1.6) was made at the Simojoki river basin in northern Finland and at Dalelva Brook in northern Norway. The new modules did not imply any significant changes in simulated NO3- concentration levels in the streams but improved the timing of simulated higher concentrations. The inclusion of a modified temperature response function and an empirical snow-correction factor improved the flexibility and applicability of the model for climate effect studies. Keywords: inorganic N leaching, degree-day snow model, snow pack, catchment scale model

A two‐directional freeze and thaw algorithm for hydrologic and land surface modelling

This paper presents a two‐directional Stefan's solution for computing the freeze and thaw of soil columns. Use of an inverted set of equations to drive the soil freeze‐thaw from the bottom is a significant improvement to the original freeze‐thaw model. Simulations using the algorithm compared well with the 0°C isotherm interpolated from observed data at Arctic, Subarctic and cool temperate sites with different frost conditions that range from permafrost to seasonal frost across a north‐south gradient in North America. The incorporation of explicit freeze‐thaw parameterization in hydrologic and land surface models would enable realistic simulation of soil temperature, soil moisture and runoff/infiltration and hence energy, water and greenhouse gas exchange processes in high latitudes.

SOIL TEMPERATURE UNDER A DORMANT BERMUDAGRASS MULCH: SIMULATION AND MEASUREMENT

The ENergy and WATer BALance (ENWATBAL) model is a mechanistic, numerical model that simulates soil waterand temperature profiles, evaporation from soil, and transpiration from crops, but it does not simulate the effects of a mulchlayer. Surface vegetative mulches are becoming more common, especially in reduced -tillage systems, limiting the modelsapplicability. Our objective was to modify ENWATBAL to enable physically based simulation of the effects of a dense mulch.As a preliminary evaluation of the model, soil temperatures simulated with the modified model were compared with thosemeasured at Watkinsville, Georgia, in Cecil sandy loam (clayey, kaolinitic, thermic, Typic Kanhapludult) under a dense,thatchy layer of dormant bermudagrass (Cynodon dactylon, [L.] Pers.) that acted as a mulch during the simulation period.Measured daily soil temperature amplitudes at 0.04 m depth were about 2.5.C during an 8-day period in December 1995.Simulated amplitudes were 12.C with the original ENWATBAL model (configured for a bare soil) and 3.5.C with themulch-enhanced model. The root mean square error between hourly measured and simulated soil temperatures was 4.1.Cusing the original ENWATBAL model and 1.1.C using the mulch-enhanced model. Measured soil temperatures lagged behindthose simulated, indicating that conduction may be an important process of heat transfer through the mulch. Two solutionmethods were tested: an iterative solution for mulch and soil surface temperatures implicit in the energy balance equations,and a linearized explicit solution of the energy balances. The latter method was 50 times faster than the iterative methodwithout compromising accuracy; the largest linearization error was only 0.01.C. The capability to simulate mulch effectsincreases the scope of problems where ENWATBAL is applicable.

APPLICATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) TO SIMULATE SOIL TEMPERATURE

A new and flexible regression model, Multivariate Adaptive Regression Splines (MARS), is introduced and appliedto simulate soil temperature at three depths. MARS uses a divide-and-conquer approach to automatically classify the trainingdata into several groups. In each group, a regression line or hyperplane is generated. Compared to other intelligent computingtechnologies, MARS is fast, flexible, and capable of determining the important inputs to the model. The inputs to the modelinclude the day of the year, the maximum and minimum air temperatures, rainfall, and potential evapotranspiration. The outputscontain the soil temperatures at depths of 100, 500, and 1500 mm. The performance of MARS was compared to that ofartificial neural networks (ANNs). The correlation coefficients of linear regression from both MARS and ANNs were alwayshigher than 0.950. MARS also indicated that the day of the year is the input that is most significant to the output, followedby the minimum air temperature. The results demonstrate the potential of MARS to be used as a regression technology in agriculturalapplications.

Changes in soil temperature and active layer thickness during the twentieth century in a region in western Canada

Increases in active layer thickness and permafrost degradation induced by climate warming may have profound socio‐economic and eco‐environmental consequences. Using a process‐based model of Northern Ecosystem Soil Temperature (NEST) and data from climate records, remote sensing vegetation parameters, and soil features, we simulated soil temperature and active layer thickness (ALT) for a region in western Canada during the twentieth century. The results showed that the region‐averaged annual mean soil temperatures at different depths responded to air temperature forcing consistently during the twentieth century, but with reduced magnitudes in both long‐term trend and interannual variation. From the 1900s to 1986–1995, ALT increased 124 cm (or 79%) in the isolated and sporadic discontinuous permafrost zones, 59 cm (or 37%) in the extensive discontinuous permafrost zone, and 20 cm (or 21%) in the continuous permafrost zone on the basis of the current permafrost distribution map. The simulated results also indicated the disappearance of 17% of the permafrost in the discontinuous permafrost zone from the 1900s to the 1940s, and another 22% from the 1940s to 1986–1995. General agreement was found when comparing the simulated results with soil temperature records at climate stations, ALT at survey sites, and rates of permafrost degradation interpreted from aerial photographs. Owing to differences in spatial scales, spatial coverage, and time periods, many of the comparisons were not strict 1‐to‐1 comparisons and should instead be viewed as indirect supporting evidence.