Improved snow-cover model for multi-annual simulations with the STICS crop model under cold, humid continental climates (original) (raw)
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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.
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.
2000
The aim was to use the COUP model to simulate soil heat and water processes under different snow and soil frost conditions. The calibration data for the study was collected in a snow manipulation study in a 47-year old stand of Norway spruce located in the eastern part of Finland. The COUP model simulations were able to illustrate the typical effects of freezing and thawing in terms of soil temperature, snow cover and water content. The model was capable to produce good predictions for the control plots without any interference of the snow cover as well for artifi cial plots with snow cover removed. However, simulated soil frost depth and water balance had some lacks in their predictions. One source of error relied in the meteorological input data, because the precipitation and relative humidity time series were not considered to be well representative of the study area. In addition, the model results were likely affected by the net radiation and cloudiness data, which were measured away from the study site. More measurement points in each plot and at different depths could aid in a more detailed calibration of the model. Despite these defi ciencies the dataset as a whole met quite well the model input requirements. Essential parameters that the model needs to run can be readily determined and easily introduced in the model. The simulations can be further improved through some changes in the calibration process and/or through validating the model with additional independent data. In conclusion, the COUP model proved to be a functional tool for the simulation of heat and water soil processes. It is easy to manage, well organized and capable to simulate a range of soil situations by defi ning only few parameters and conditions.
Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada
Canadian Journal of Soil Science, 2010
The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) is a widely used modeling package that often simulates wheat yield and biomass well. However, some previous studies reported that its simulation on soil moisture was not always satisfactory. On the other hand, the Simultaneous Heat and Water (SHAW) model, a more sophisticated, hourly time step soil microclimate model, needs inputs of plant canopy development over time, which are difficult to measure in the field especially for a long-term period (longer than a year). The SHAW model also needs information on surface residue, but treats them as constants. In reality, however, surface residue changes continuously under the effect of tillage, rotation and environment. We therefore proposed to use DSSAT-CSM to simulate dynamics of plant growth and soil surface residue for input into SHAW, so as to predict soil water dynamics. This approach was tested using three conventionally tilled wheat rotations...
Acta Agriculturae Scandinavica, Section B - Plant Soil Science, 2009
Modelling of ecosystem processes often requires soil temperature as a driving variable. Since soil temperature measurements are seldom available for regional applications, they have to be estimated from standard meteorological data. The objective of this paper is to present a general, simple empirical approach for estimating daily depth profiles of soil temperature from air temperature and a surface cover index (LAI; leaf area index) mainly focusing on agricultural soils in cold temperate regions. Air and soil temperature data measured daily or every fifth day at one to six different depths was acquired from all meteorological stations in Sweden where such records are available. The stations cover latitudes from 55.65 to 68.42 N and mean annual air temperatures from +8.6 to -0.6 o C. The time series spanned between two and ten years. The soils at the stations cover a wide range of soil textures, including two organic soils. We calibrated the model first for each station and then for all stations together and the general parameterization only slightly decreased the goodness of fit. This general model then was applied to two treatments in a field experiment: bare soil and a winter rape crop. The parameters governing the influence of LAI on heat fluxes were optimized using this experiment. Finally, the model was validated using soil temperature data from two barley treatments differing in LAI taken from another field experiment. In general, the model predicted daily soil temperature profiles well. For all soils and depths at the meteorological stations, 95% of the simulated daily soil temperatures differed by less than 2.8 o C from measurements. The corresponding differences were somewhat higher for the validation data set (3.9 o C), but bias was still low. The model explained 95% of the variation in the validation data. Since no site-specific adjustments were made in the validation simulations, we conclude that the application of the general model 3 presented here will result in good estimates of soil temperatures under cold temperate conditions. The very limited input requirements (only air temperature and LAI) that are easily obtainable from weather stations and from satellites make this model suitable for spatial applications at catchment or regional scales.
Field Crops Research, 2014
Modelling the production and N uptake of spring wheat (Triticum aestivum L.) according to climate and N fertilization in Eastern Canada is important for estimating efficient N application rates and evaluating the sustainability of agricultural practices. The objective of this paper was to examine the response of observed yield, biomass, and plant N to fertilization rates and climate variations and to compare the performance of the STICS (Simulateur mulTIdisciplinaire pour des Cultures Standard), DNDC (DeNitrification and DeComposition), and DayCent (daily version of CENTURY) models for predicting these outcomes.
Simulation of Daily Mean Soil Temperatures for Agricultural Land Use Considering Limited Input Data
Atmosphere, 2021
A one-dimensional simulation model that simulates daily mean soil temperature on a daily time-step basis, named AGRISOTES (AGRIcultural SOil TEmperature Simulation), is described. It considers ground coverage by biomass or a snow layer and accounts for the freeze/thaw effect of soil water. The model is designed for use on agricultural land with limited (and mostly easily available) input data, for estimating soil temperature spatial patterns, for single sites (as a stand-alone version), or in context with agrometeorological and agronomic models. The calibration and validation of the model are carried out on measured soil temperatures in experimental fields and other measurement sites with various climates, agricultural land uses and soil conditions in Europe. The model validation shows good results, but they are determined strongly by the quality and representativeness of the measured or estimated input parameters to which the model is most sensitive, particularly soil cover dynamic...
Journal of Hydrometeorology, 2006
The energy and water balances at the earth's surface are dramatically influenced by the presence of snow cover. Therefore, soil temperature and moisture for snow-covered and snow-free areas can be very different. In computing these soil state variables, many land surface schemes in climate models do not explicitly distinguish between snow-covered and snow-free areas. Even if they do, some schemes average these state variables to calculate grid-mean energy fluxes and these averaged state variables are then used at the beginning of the next time step. This latter approach introduces a numerical error in that heat is redistributed from snow-free areas to snow-covered areas, resulting in a more rapid snowmelt. This study focuses on the latter approach and examines the sensitivity of soil moisture and streamflow to the treatment of the soil state variables in the presence of snow cover by using WATCLASS, a land surface scheme linked with a hydrologic model. The model was tested for t...
Journal of Arid Environments, 1997
Temperature conditions and the availability of moisture in the near-surface soil environment drives many important plant and other biological processes. Vegetation, which can be controlled by management, affects the spatial and temporal variability of heat and water in the soil. Land managers need to address the interactions between physical, chemical and biological factors in the near surface, but lack the necessary information. The ability to predict temperature and water within the soil-plant-atmosphere system enhances our ability to evaluate management options and enables better understanding of interactions between surface processes and the atmosphere. The Simultaneous Heat and Water (SHAW) Model, a detailed model of heat and water movement in a plant-snow-residue-soil system, was applied to 2 full years of data on semi-arid sagebrush rangeland to simulate vegetation effects on the spatial and temporal variation of soil temperature and water. Minor calibration was necessary to match the drop in measured soil water potential as the soil dried in the late spring and early summer. The model accounted for over 93% of the variation in average daily soil temperature for a sagebrushcovered area and over 96% of the variation in temperature for a bare soil surface for 2 years. Rapid changes in surface water potential and drying of the soil profile as simulated by the model closely tracked measured observations.
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.