Simulation of soil temperature and moisture under different snow and frost conditions with COUP model (original) (raw)
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Cold Regions Science and Technology, 2015
A physically-based heat and mass transfer model, CoupModel, is calibrated to simulate wintertime soil temperature, soil frost depth, and snow depth for a 14-year period in a highland area of Iran. A Monte Carlo based approach is used for calibration process based on subjective performance criteria. Sensitivity and uncertainty analyses of the model were performed by selecting 30 parameters and the model was run using 22,000 samples taken from the uncertainty range of the parameters. By using the Nash-Sutcliffe Index to evaluate the performance of the model and applying a cutoff threshold for the performance to snow depth and soil temperature, 161 behavioral simulations were recognized and considered as the accepted ensemble to represent the field conditions. Sensitivity analysis of the model revealed some parameters associated with soil evaporation, soil hydraulic properties, and snow modeling as sensitive and highly important parameters. Uncertainty analysis of the model for wintertime soil temperatures showed a reasonable agreement between simulations and observations in most cases. However, a systematic error occurred at some periods because of high uncertainty of the actual snow density and details of snow melting. Uncertainties were also due to the simplified model assumptions regarding snow thermal properties and temperature within snow cover. The snow depth at the accumulation and melting stages were described well by the model in most cases.
Applied Engineering in Agriculture, 2009
Soil freezing and thawing processes and soil moisture redistribution play a critical role in the hydrology and microclimate of seasonally frozen agricultural soils. Accurate simulations of the depth and timing of frost and the redistribution of soil water are important for planning farm operations and choosing rotational crops. The Simultaneous Heat and Water (SHAW) model was used to predict soil temperature, frost depth, and soil moisture in agricultural soils near Carman, Manitoba. The model simulations were compared with three years of field data collected from summer 2005 to the summer 2007 in four cropping system treatments (oats with berseem clover cover crop, oats alone, canola, and fallow). The simulated soil temperatures compared well with the measured data in all the seasons (R 2 =0.96-0.99). The soil moisture simulations were better during the summer (RMSE=9.1-12.0% of the mean) compared to the winter seasons (RMSE=17.5-19.7% of the mean). During the winter, SHAW over-predicted by 0.02 to 0.10 m 3 m-3 the amount of total soil moisture below the freeze front and under-predicted by 0.02 to 0.05 m 3 m-3 the soil moisture in the upper frozen layers. The model was revised to account for the reduction in effective pore space resulting from frozen water to improve the winter soil moisture predictions. After this revision, the model performed well during the winter (RMSE=14.4% vs. 17.5%; R 2 =0.74 vs. 0.67 in vegetated treatments, and RMSE=12.9% vs. 19.7%; R 2 =0.73 vs. 0.52 in fallow treatments). The modified SHAW model is an enhanced tool for predicting the soil moisture status as a function of depth during spring thawing, and for assessing the availability of soil moisture at the beginning of the subsequent growing season.
Model of the influence of snow cover on soil freezing
A mathematical model of snow-cover influence on soil freezing, taking into account the phase transition layer, water migration in soil, frost heave and ice-layer formation, has been developed. The modeled results are in good agreement with data observed in natural conditions. The influence of a possible delay between the time of negative temperature establishment in the air and the beginning of snow accumulation, and possible variations of the thermophysical properties of snow cover in the wide range previously reported were investigated by numerical experiments. It was found that the delay could change the frozen-soil depth up to 2^3 times, while different thermophysical characteristics of snow changed the resulting freezing depth 4^5 times.
Simulation of Soil Temperature Dynamics with Models Using Different Concepts
The Scientific World Journal, 2012
This paper presents two soil temperature models with empirical and mechanistic concepts. At the test site (calcaric arenosol), meteorological parameters as well as soil moisture content and temperature at 5 different depths were measured in an experiment with 8 parcels realizing the combinations of the fertilized, nonfertilized, irrigated, nonirrigated treatments in two replicates. Leaf area dynamics was also monitored. Soil temperature was calculated with the original and a modified version of CERES as well as with the HYDRUS-1D model. The simulated soil temperature values were compared to the observed ones. The vegetation reduced both the average soil temperature and its diurnal amplitude; therefore, considering the leaf area dynamics is important in modeling. The models underestimated the actual soil temperature and overestimated the temperature oscillation within the winter period. All models failed to account for the insulation effect of snow cover. The modified CERES provided ...
The numerical scheme development of a simplified frozen soil model
Advances in Atmospheric Sciences, 2009
In almost all frozen soil models used currently, three variables of temperature, ice content and moisture content are used as prognostic variables and the rate term, accounting for the contribution of the phase change between water and ice, is shown explicitly in both the energy and mass balance equations. The models must be solved by a numerical method with an iterative process, and the rate term of the phase change needs to be pre-estimated at the beginning in each iteration step. Since the rate term of the phase change in the energy equation is closely related to the release or absorption of the great amount of fusion heat, a small error in the rate term estimation will introduce greater error in the energy balance, which will amplify the error in the temperature calculation and in turn, cause problems for the numerical solution convergence. In this work, in order to first reduce the trouble, the methodology of the variable transformation is applied to a simplified frozen soil model used currently, which leads to new frozen soil scheme used in this work. In the new scheme, the enthalpy and the total water equivalent are used as predictive variables in the governing equations to replace temperature, volumetric soil moisture and ice content used in many current models. By doing so, the rate terms of the phase change are not shown explicitly in both the mass and energy equations and its pre-estimation is avoided. Secondly, in order to solve this new scheme more functionally, the development of the numerical scheme to the new scheme is described and a numerical algorithm appropriate to the numerical scheme is developed. In order to evaluate the new scheme of the frozen soil model and its relevant algorithm, a series of model evaluations are conducted by comparing numerical results from the new model scheme with three observational data sets. The comparisons show that the results from the model are in good agreement with these data sets in both the change trend of variables and their magnitude values, and the new scheme, together with the algorithm, is more efficient and saves more computer time. Key words: simplified frozen soil model, variable transformation, enthalpy and total water equivalent, numerical algorithm, model validation Citation: Li, Q., S. Sun, and Q. Dai, 2009: The numerical scheme development of a simplified frozen soil model. Adv. Atmos. Sci., 26(5), 940-950,
Biosystems Engineering, 2017
Agro-ecosystem models, such as the DNDC (DeNitrification and DeComposition) model are useful tools when assessing the sustainability of agricultural management. Accuracy in soil temperature estimations is important as it regulates many important soil biogeochemical processes that lead to greenhouse gas emissions (GHG). The objective of this study was to account for the effects of snow cover in terms of the measured snow depth (mm of water), soil texture and crop management in temperate latitudes in order to improve the surface soil temperature mechanism in DNDC and thereby improve GHG predictions. The estimation of soil temperature driven by the thermal conductivity and heat capacity of the soil was improved by considering the soil texture under frozen and unfrozen conditions along with the effects of crop canopy and snow depth. Calibration of the developed model mechanisms was conducted using data from Alfred, ON under two contrasting soil textures (sandy loam vs. clay). Independent validation assessments were conducted using soil temperatures at different depths for contrasting managements for two field sites located in Canada (Guelph, ON and Glenlea, MB). The validation results indicated high model accuracy (R 2 > 0.90, EF ! 0.90, RMSE < 3.00 C) in capturing the effects of management on soil temperature. These developments in soil heat transfer mechanism improved the performance of the model in estimating N 2 O emissions during spring thaw and provide a foundation for future studies aimed at improving simulations in DNDC for better representations of other biogeochemical processes.
Journal of Applied Meteorology, 2005
The Hydro–Thermodynamic Soil–Vegetation Scheme (HTSVS) coupled in a two-way mode with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Meteorological Model (MM5) is evaluated for a typical snowmelt episode in the Baltic region by means of observations at 25 soil temperature, 355 snow-depth, and 344 precipitation sites that have, in total, 1000, 1775, and 1720 measurements, respectively. The performance with respect to predicted near-surface meteorological fields is evaluated using reanalysis data. Snow depth depends on snow metamorphism, sublimation, and snowfall. Because in the coupled model these processes are affected by the predicted surface radiation fluxes and cloud and precipitation processes, sensitivity studies are performed with two different cloud microphysical schemes and/or radiation schemes. Skill scores are calculated as a quality measure for the coupled model’s performance for a typical forecast range of 120...
Water, 2016
Water and energy processes in frozen soils are important for better understanding hydrologic processes and water resources management in cold regions. To investigate the water and energy balance in seasonally frozen soils, CoupModel combined with the generalized likelihood uncertainty estimation (GLUE) method was used. Simulation work on water and heat processes in frozen soil in northern China during the 2012/2013 winter was conducted. Ensemble simulations through the Monte Carlo sampling method were generated for uncertainty analysis. Behavioral simulations were selected based on combinations of multiple model performance index criteria with respect to simulated soil water and temperature at four depths (5 cm, 15 cm, 25 cm, and 35 cm). Posterior distributions for parameters related to soil hydraulic, radiation processes, and heat transport indicated that uncertainties in both input and model structures could influence model performance in modeling water and heat processes in seasonally frozen soils. Seasonal courses in water and energy partitioning were obvious during the winter. Within the day-cycle, soil evaporation/condensation and energy distributions were well captured and clarified as an important phenomenon in the dynamics of the energy balance system. The combination of the CoupModel simulations with the uncertainty-based calibration method provides a way of understanding the seasonal courses of hydrology and energy processes in cold regions with limited data. Additional measurements may be used to further reduce the uncertainty of regulating factors during the different stages of freezing-thawing.
Development of an enthalpy-based frozen soil model and its validation in a cold region in China
Journal of Geophysical Research: Atmospheres, 2016
An enthalpy-based frozen soil model was developed for the simulation of water and energy transfer in cold regions. To simulate the soil freezing/thawing processes stably and efficiently, a three-step algorithm was applied to solve the nonlinear governing equations: (1) a thermal diffusion equation was implemented to simulate the heat conduction between soil layers; (2) a freezing/thawing scheme used a critical temperature criterion to judge the phase status and introduced enthalpy and total water mass into freezing depression equation to represent ice formation/melt and corresponding latent heat release/absorption; and (3) a water flow scheme was employed to describe the liquid movement within frozen soil. In addition, a parameterization set of hydraulic and thermal properties was updated by considering the frozen soil effect. The performance of the frozen soil model was validated at point scale in a typical mountainous permafrost basin of China. An ice profile initialization method is proposed for permafrost modeling. Results show that the model can achieve a convergent solution at a time step of hourly and a surface layer thickness of centimeters that are typically used in current land surface models. The simulated profiles of soil temperature, liquid water content, ice content and thawing front depth are in good agreement with the observations and the characteristics of permafrost. The model is capable of continuously reproducing the diurnal and seasonal freeze-thaw cycle and simulating frozen soil hydrological processes.