MACROCLIMATIC MODEL FOR ESTIMATING MONTHLY SOIL TEMPERATURES UNDER SHORT-GRASS COVER IN CANADA

1973 ◽  
Vol 53 (3) ◽  
pp. 263-274 ◽  
Author(s):  
C. E. OUELLET
Keyword(s):  
Soil Temperature ◽  
Potential Evapotranspiration ◽  
Important Variable ◽  
Climatic Variables ◽  
Standard Errors ◽  
Climatic Data ◽  
Regression Equations ◽  
Grass Cover ◽  
Temperature Variations ◽  
Soil Temperatures

A macroclimatic model was developed to estimate monthly soil temperatures under short-grass cover. It involved multiple regression equations for each month and for each of six depths (1, 10, 20, 50, 100, and 150 cm). Data used were obtained from published records of soil temperature and corresponding climatic variables. They were from 41 stations over several years with station-years per regression varying from 88 to 226 according to depths and months. The climatic variables were related to air temperature, rainfall, snowfall, and potential evapotranspiration. An additional important variable was the estimated soil temperature of the previous month. The equations explain 70–96% of the soil temperature variations and the standard errors of estimate varied from 0.7 to 2.2 C. Temperatures estimated for 1 yr and eight stations with climatic data not used in the development of the equations departed from the observed values by less than 0.5, 1.0, and 2.0 C in 34, 62, and 92% of the cases, respectively. Errors resulting from the estimation of monthly normals by this model are expected to be generally less than 1.0 degree C.

COMPARISON OF OBSERVATIONS AND MACROCLIMATIC MODEL ESTIMATES OF MONTHLY WINTER SOIL TEMPERATURES AT OTTAWA

1985 ◽  
Vol 65 (1) ◽  
pp. 109-122 ◽  
Author(s):  
L. M. DWYER ◽  
H. N. HAYHOE
Keyword(s):  
Snow Cover ◽  
Predictive Models ◽  
Regression Models ◽  
Stepwise Regression ◽  
Regression Coefficients ◽  
Data Sets ◽  
Regression Equations ◽  
Grass Cover ◽  
Partial Regression ◽  
Soil Temperatures

Estimates of monthly soil temperatures under short-grass cover across Canada using a macroclimatic model (Ouellet 1973a) were compared to monthly averages of soil temperatures monitored over winter at Ottawa between November 1959 and April 1981. Although the fit between monthly estimates and Ottawa observations was generally good (R for all months and depths 0.10, 0.20, 0.50, 1.00 and 1.50 m was 0.90), it was noted that midwinter estimates were generally below observed temperatures at all soil depths. Data sets used in the development of the original Ouellet (1973a) multiple regression equations were collected from stations across Canada, many of which have reduced snow cover. It was found that the buffering capability of the snow cover accumulated at Ottawa during the winter months was underestimated by the pertinent partial regression coefficients in these equations. The coefficients were therefore modified for the Ottawa station during the winter months. The resultant regression models were used to estimate soil temperature during the winters of 1981–1982 and 1982–1983. Although the Ottawa-based models included fewer variables because of the smaller data base available from a single site, comparisons of model estimates and observations were good (R = 0.84 and 0.91) and midwinter estimates were not consistently underestimated as they were using the original Ouellet (1973a) model. Reliable monthly estimates of soil temperatures are important since they are a necessary input to more detailed predictive models of daily soil temperatures. Key words: Regression model, snowcover, stepwise regression, variable selection

scholarly journals Specifics of soil temperature under winter wheat canopy

2013 ◽  
Vol 43 (3) ◽  
pp. 209-223 ◽  
Author(s):  
Jana Krčmáŕová ◽  
Hana Stredová ◽  
Radovan Pokorný ◽  
Tomáš Stdŕeda
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Soil Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Soil Surface ◽  
Regression Equations ◽  
Experimental Plot ◽  
Near Surface ◽  
Soil Temperatures

Abstract The aim of this study was to evaluate the course of soil temperature under the winter wheat canopy and to determine relationships between soil temperature, air temperature and partly soil moisture. In addition, the aim was to describe the dependence by means of regression equations usable for phytopathological prediction models, crop development, and yield models. The measurement of soil temperatures was performed at the experimental field station ˇZabˇcice (Europe, the Czech Republic, South Moravia). The soil in the first experimental plot is Gleyic Fluvisol with 49-58% of the content particles measuring < 0.01 mm, in the second experimental plot, the soil is Haplic Chernozem with 31-32% of the content particles measuring < 0.01 mm. The course of soil temperature and its specifics were determined under winter wheat canopy during the main growth season in the course of three years. Automatic soil temperature sensors were positioned at three depths (0.05, 0.10 and 0.20 m under soil surface), air temperature sensor in 0.05 m above soil surface. Results of the correlation analysis showed that the best interrelationships between these two variables were achieved after a 3-hour delay for the soil temperature at 0.05 m, 5-hour delay for 0.10 m, and 8-hour delay for 0.20 m. After the time correction, the determination coefficient reached values from 0.75 to 0.89 for the depth of 0.05 m, 0.61 to 0.82 for the depth of 0.10 m, and 0.33 to 0.70 for the depth of 0.20 m. When using multiple regression with quadratic spacing (modeling hourly soil temperature based on the hourly near surface air temperature and hourly soil moisture in the 0.10-0.40 m profile), the difference between the measured and the model soil temperatures at 0.05 m was −2.16 to 2.37 ◦ C. The regression equation paired with alternative agrometeorological instruments enables relatively accurate modeling of soil temperatures (R2 = 0.93).

scholarly journals Specifics of soil temperature under winter oilseed rape canopy

2014 ◽  
Vol 44 (3) ◽  
pp. 205-218
Author(s):  
Jana Krčmářová ◽  
Tomáš Středa ◽  
Radovan Pokorný
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Oilseed Rape ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Regression Equations ◽  
Winter Oilseed Rape ◽  
Determination Coefficient ◽  
Soil Temperatures ◽  
Dwarf Variety

Abstract The aim of this study was to evaluate the course of soil temperature under the winter oilseed rape canopy and to determine relationships between soil temperature, air temperature and partly soil moisture. In addition, the aim was to describe the dependence by means of regression equations usable for pests and pathogens prediction, crop development, and yields models. The measurement of soil and near the ground air temperatures was performed at the experimental field Žabiče (South Moravia, the Czech Republic). The course of temperature was determined under or in the winter oilseed rape canopy during spring growth season in the course of four years (2010 - 2012 and 2014). In all years, the standard varieties (Petrol, Sherpa) were grown, in 2014 the semi-dwarf variety PX104 was added. Automatic soil sensors were positioned at three depths (0.05, 0.10 and 0.20 m) under soil surface, air temperature sensors in 0.05 m above soil surfaces. The course of soil temperature differs significantly between standard (Sherpa and Petrol) and semi-dwarf (PX104) varieties. Results of the cross correlation analysis showed, that the best interrelationships between air and soil temperature were achieved in 2 hours delay for the soil temperature in 0.05 m, 4 hour delay for 0.10 m and 7 hour delay for 0.20 m for standard varieties. For semi-dwarf variety, this delay reached 6 hour for the soil temperature in 0.05 m, 7 hour delay for 0.10 m and 11 hour for 0.20 m. After the time correction, the determination coefficient (R2) reached values from 0.67 to 0.95 for 0.05 m, 0.50 to 0.84 for 0.10 m in variety Sherpa during all experimental years. For variety PX104 this coefficient reached values from 0.51 to 0.72 in 0.05 m depth and from 0.39 to 0.67 in 0.10 m depth in the year 2014. The determination coefficient in the 0.20 m depth was lower for both varieties; its values were from 0.15 to 0.65 in variety Sherpa. In variety PX104 the values of R2 from 0.23 to 0.57 were determined. When using multiple regressions with quadratic spacing (modelling of hourly soil temperature based on the hourly near surface air temperature and hourly soil moisture in the 0.10-0.40 m profile), the difference between the measured and modelled soil temperatures in the depth of 0.05 m was -3.92 to 3.99°C. The regression equation paired with alternative agrometeorological instruments enables relatively accurate modelling of soil temperatures (R2 = 0.95).

An artificial neural network to estimate soil temperature

1997 ◽  
Vol 77 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Chun-Chieh Yang ◽  
Shiv O. Prasher ◽  
Guy R. Mehuys
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Temperature ◽  
Potential Evapotranspiration ◽  
Daily Rainfall ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Soil Temperatures ◽  
Ann Models ◽  
Artificial Neural

This study was undertaken to develop an artificial neural network (ANN) model for transient simulation of soil temperature at different depths in the profile. The capability of ANN models to simulate the variation of temperature in soils was investigated by considering readily available meteorologic parameters. The ANN model was constructed by using five years of meteorologic data, measured at a weather station at the Central Experimental Farm in Ottawa, Ontario, Canada. The model inputs consisted of daily rainfall, potential evapotranspiration, and the day of the year. The model outputs were daily soil temperatures at the depths of 100, 500 and 1500 mm. The estimated values were found to be close to the measured values, as shown by a root-mean-square error ranging from 0.59 to 1.82 °C, a standard deviation of errors from 0.61 to 1.81 °C, and a coefficient of determination from 0.937 to 0.987. Therefore, it is concluded that ANN models can be used to estimate soil temperature by considering routinely measured meteorologic parameters. In addition, the ANN model executes faster than a comparable conceptual simulation model by several orders of magnitude. Key words: Artificial neural networks, soil temperature, precipitation, potential evapotranspiration

ESTIMATION OF MANITOBA SOIL TEMPERATURES FROM ATMOSPHERIC METEOROLOGICAL MEASUREMENTS

1980 ◽  
Vol 60 (2) ◽  
pp. 299-309 ◽  
Author(s):  
A. REIMER ◽  
C. F. SHAYKEWICH
Keyword(s):  
Soil Temperature ◽  
Nuclear Research ◽  
Soil Surface ◽  
Damping Factor ◽  
Regression Equations ◽  
Estimation Equations ◽  
Surface Temperatures ◽  
Soil Temperatures ◽  
Explained Variance ◽  
The Difference

Soil-temperature studies were conducted under forage and zero tillage conditions at the Whiteshell Nuclear Research Establishment (WNRE), Pinawa, Manitoba, as part of the plant radiation ecology research program. The objective was to develop estimation equations for monthly mean and daily mean soil surface temperatures from atmospheric meteorological measurements. Subsoil temperatures were estimated from predicted soil surface temperatures by applying an appropriate damping factor. Monthly mean soil surface temperatures were estimated for summer and winter months from regression equations with meteorological predictors. Daily mean soil surface temperatures were predicted from regression equations with meteorological predictors combined with best-fit Fournier-series seasonal curves. Daily mean subsoil temperatures at 10 cm were estimated from predicted soil surface temperatures by applying an appropriate damping factor. The standard deviation of the difference between predicted and observed temperatures was generally less than 1 °C for daily and monthly estimates. A good estimate of the seasonal subsoil temperature at 10, 50, 100 and 200 cm was found from a periodic function with damping and phase paramaters. The explained variance was 95% or more. With appropriate assumptions regarding soil thermal properties and mean annual soil temperature, accurate results were obtained quickly and economically.

An Australian comparison of Penman's potential evapotranspiration estimates and class A evaporation pan data

Soil Research ◽  
1992 ◽  
Vol 30 (1) ◽  
pp. 101 ◽  
Author(s):  
FHS Chiew ◽  
TA Mcmahon
Keyword(s):  
Linear Regression ◽  
Potential Evapotranspiration ◽  
Actual Evapotranspiration ◽  
Climatic Data ◽  
Regression Equations ◽  
Climatic Regions ◽  
Class A ◽  
Time Periods ◽  
Land Surfaces ◽  
Evaporation Pan

Estimates of potential evapotranspiration are required to serve as an aid for estimating actual evapotranspiration. Penman's combination equation is generally accepted as an appropriate method for estimating potential evapotranspiration. However, as all the climatic data required to calculate Penman's potential evapotranspiration are seldom available, potential evapotranspiration is more commonly approximated as a factor times standard evaporation pan reading. In this paper, linear regression equations relating Penman's potential evapotranspiration for land surfaces to Class A evaporation pan data over several time periods are developed for various climatic regions throughout Australia. The analyses indicate that the correlations between daily estimates of Penman's potential evapotranspiration and pan data are poor, and therefore, pan data should be treated with caution if used to approximate daily potential evapotranspiration. The correlations improve over longer time periods, and the equations developed for three-day and weekly totals may be used as a last resort to approximate potential evapotranspiration in areas where climatic data required to calculate potential evapotranspiration are not available.

Crop yield, soil temperature and sensitivity of EPIC under central-eastern Canadian conditions

1998 ◽  
Vol 78 (3) ◽  
pp. 431-439 ◽  
Author(s):  
G. Roloff ◽  
R. Dejong ◽  
M.C. Nolin
Keyword(s):  
Soil Temperature ◽  
Crop Yields ◽  
Potential Evapotranspiration ◽  
Relative Sensitivity ◽  
Weather Data ◽  
Corn Yield ◽  
Eastern Canada ◽  
Nitrate N ◽  
Soil Temperatures ◽  
Central Eastern

The Environmental Policy Integrated Climate (EPIC) model can be an important tool for agricultural and environmental management. Application of EPIC in central-eastern Canada requires testing to evaluate its performance and to indicate eventual changes that ultimately can lead to a more accurate model. We compared crop yields and soil temperatures estimated using EPIC (version 5300) with measured data from two sites — Barrhaven, Ontario and St. Antoine, Quebec — where corn (Zea mays L.) and soybean (Glycine max L.) were grown during 4 yr. The sensitivity of several EPIC outputs to variations in selected input parameters was also determined. EPIC was run with either the Penman-Monteith (PM) or the Baier-Robertson (BR) potential evapotranspiration (PET) method. Mean estimated and measured soybean yields were not different, independent of PET method and location. Mean corn yields were underestimated at Barrhaven with the PM method, whereas they were overestimated by the BR method at St. Antoine. Using the PM method resulted in no difference between estimated and measured yields in 2 out of 5 corn years, while the BR method ensued in no difference in 3 out of 5 years. For soybean, only the BR method resulted in 1 yr of yield different than measured. Performance of the model in relation to experimental error and efficiency of the model varied with crop and location, and indicated that EPIC caused less error and was more efficient in estimating soybean than corn yields. Comparing limited measured soil temperature data with EPIC estimates reveled that EPIC tended to underestimate soil temperature; however no effect on estimated yield was observed. The relative sensitivity of EPIC outputs was least for yield and greatest for leached nitrate-N. The latter also displayed the highest variability in sensitivity. EPIC studies focusing on leached nitrate-N should require the most accurate crop, soil, hydrological and weather data in order to minimize errors. Key words: Environmental modelling, soybean yield, corn yield, sensitivity analysis

scholarly journals Vegetation Cover Change Associated With Changes in Hydro-Climatic Variables in Northern Sub-Saharan Africa in Recent Decades.

2021 ◽  
Author(s):  
Faustin Katchele Ogou ◽  
Tertsea Igbawua
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Potential Evapotranspiration ◽  
Kendall Test ◽  
Sub Saharan Africa ◽  
Climatic Variables ◽  
Climatic Data ◽  
High Coverage ◽  
Adaptation Capacity ◽  
Sub Saharan

Abstract The environmental change in Northern Sub-Saharan Africa (NSSA) remains a challenge in relation with hydro-climatic variations and the low adaptation capacity of the region. The present study investigates the vegetation cover (NDVI) change associated with variations in hydro-climatic indicators over the period 1982–2015. The conventional statistical techniques such as the linear and multiple regressions, Mann-Kendall test, Sen’slope and the Pearson’s correlation were employed. The vegetation cover based on vegetation (NDVI) and hydro-climatic data were used. Trends in vegetation cover and hydro-climatic variables had monotonically increased except for the soil moisture that had monotonically decreased in the region. The proportion of significant positive (negative) changes were 46.78% (8.10%), 38.13% (0.34%), 52.12% (0.10%), 82.86% (0.00%) and 10.54% (38.27%) for NDVI, precipitation, potential evapotranspiration, temperature and soil moisture, respectively. The low vegetation dominated the NSSA region with a proportion of about 32% of the total area coverage. The vegetation classes including low coverage, very high coverage, and extreme high coverage exhibited increasing trends. Meanwhile, moderate coverage and high coverage exhibited decreasing trends. The area-averaged precipitation and temperature were positively correlated with the NDVI; however, the area-averaged soil moisture showed negative association with NDVI. Except the precipitation and Significant positive (negative) correlations of NDVI with the precipitation, temperature and soil moisture at the 5% level occupied 1.67% (11.59%), 3.37%(26.19%) and 10.24% (6.75%), respectively. However, the combine effects of hydro-climatic variables are better for the monitoring of vegetation cover. This confirms that the vegetation cover is influenced by many factors.

scholarly journals Simulation of Daily Mean Soil Temperatures for Agricultural Land Use Considering Limited Input Data

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 441
Author(s):  
Philipp Grabenweger ◽  
Branislava Lalic ◽  
Miroslav Trnka ◽  
Jan Balek ◽  
Erwin Murer ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Input Data ◽  
Agricultural Land ◽  
Soil Column ◽  
Soil Conditions ◽  
Snow Layer ◽  
Time Step ◽  
Soil Temperatures ◽  
Dimensional Simulation ◽  
Limited Input Data

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 dynamics (biomass and snow cover), soil pore volume, soil texture and water content over the soil column.

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

2004 ◽  
Vol 8 (4) ◽  
pp. 706-716 ◽  
Author(s):  
K. Rankinen ◽  
T. Karvonen ◽  
D. Butterfield
Keyword(s):  
Heat Capacity ◽  
Simple Model ◽  
Soil Temperature ◽  
Specific Heat ◽  
Specific Heat Capacity ◽  
Frozen Soil ◽  
Model Description ◽  
Freezing And Thawing ◽  
Catchment Scale ◽  
Soil Temperatures

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

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