Analytical Model of the Soil Temperature Distribution Based on Weather Data

2021 ◽  
Author(s):  
M. Naser Reda ◽  
M. Spinnler ◽  
R. Mahamud ◽  
Thomas Sattelmayer
Keyword(s):  
Temperature Distribution ◽  
Soil Temperature ◽  
Analytical Model ◽  
Weather Data

Analytical Model of the Soil Temperature Distribution Based on Weather Data

2020 ◽  
Author(s):  
M. Naser Reda ◽  
Markus Spinnler ◽  
Rajib Mahamud ◽  
Thomas Sattelmayer
Keyword(s):  
Temperature Distribution ◽  
Soil Temperature ◽  
Analytical Model ◽  
Numerical Models ◽  
Computational Cost ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Data Sets ◽  
Temperature Profiles

Abstract The measurement of soil temperature profiles for different locations or climates is essential for calculating the thermal performance of applications connected with the soil, e.g., underground heat storage systems. Estimating soil temperature profiles is identified as crucial knowledge for plant and crop growth as well as for germination in all agricultural tasks. The ground temperature depends on weather conditions (ambient temperature, solar irradiation, wind velocity, sky radiation, etc.) that contribute to the resulting temperature distribution within the soil close to the surface. In literature, several approaches have been discussed to predict soil temperature in different climates and locations, such as data-driven models, wavelet transform artificial neural networks, statistical models, etc. However, these models require extensive data sets from literature and high computational efforts. In the present study, a one-dimensional analytical model will be presented, which is based on the Green’s Function (GF) method. The model can estimate the daily and annual variation of the soil temperature distribution at different depths from real-time weather data sets. The model was experimentally validated with an accuracy of more than 96%. The significant advantage of the presented analytical method is the low computational cost, which is lower than that of numerical models by approximately two orders of magnitude.

Analytical Model to Predict Nonhomogeneous Soil Temperature Variation

1995 ◽  
Vol 117 (2) ◽  
pp. 100-107 ◽  
Author(s):  
M. Krarti ◽  
D. E. Claridge ◽  
J. F. Kreider
Keyword(s):  
Thermal Properties ◽  
Soil Temperature ◽  
Temperature Variation ◽  
Analytical Model ◽  
Soil Surface ◽  
Deep Soil ◽  
Soil Thermal Properties ◽  
Order Of Magnitude ◽  
Soil Surface Temperature ◽  
Annual Time

This paper presents an analytical model to predict the temperature variation within a multilayered soil. The soil surface temperature is assumed to have a sinusoidal time variation for both daily and annual time scales. The soil thermal properties in each layer are assumed to be uniform. The model is applied to two-layered, three-layered, and to nonhomogeneous soils. In case of two-layered soil, a detailed analysis of the thermal behavior of each layer is presented. It was found that as long as the order of magnitude of the thermal diffusivity of soil surface does not exceed three times that of deep soil; the soil temperature variation with depth can be predicted accurately by a simplified model that assumes that the soil has constant thermal properties.

Analytical Model to Predict Annual Soil Surface Temperature Variation

1995 ◽  
Vol 117 (2) ◽  
pp. 91-99 ◽  
Author(s):  
M. Krarti ◽  
C. Lopez-Alonzo ◽  
D. E. Claridge ◽  
J. F. Kreider
Keyword(s):  
Surface Temperature ◽  
Analytical Model ◽  
Annual Variation ◽  
Soil Surface ◽  
Weather Data ◽  
Phase Lag ◽  
Model Predictions ◽  
Soil Thermal Properties ◽  
Soil Surface Temperature ◽  
Surface Temperature Variation

An analytical model is developed to predict the annual variation of soil surface temperature from readily available weather data and soil thermal properties. The time variation is approximated by a first harmonic function characterized by an average, an amplitude, and a phase lag. A parametric analysis is presented to determine the effect of various factors such as evaporation, soil absorptivity, and soil convective properties on soil surface temperature. A comparison of the model predictions with experimental data is presented. The comparative analysis indicates that the simplified model predicts soil surface temperatures within ten percent of measured data for five locations.

Coping with soil and climatic hazards during crop establishment in the semi-arid tropics

1996 ◽  
Vol 36 (8) ◽  
pp. 971 ◽  
Author(s):  
DG Abrecht ◽  
KL Bristow
Keyword(s):  
Soil Temperature ◽  
Water Deficit ◽  
Weather Data ◽  
Simulation Studies ◽  
Management Options ◽  
Crop Species ◽  
Crop Establishment ◽  
High Soil ◽  
High Soil Temperature ◽  
Semi Arid

Climatic induced hazards (e.g. water deficit, high soil temperature and high soil strength) that adversely affect seedling emergence and establishment of annual crops on red earth soils (Kandsols) at Katherine in the Daly basin of the Northern Territory are reviewed and results of some recent simulation studies and experiments are presented. Simulation studies, using 100 years of historical weather data, have shown that maize and sorghum density at Katherine is rarely reduced by water deficit during crop establishment. However, the median number of days between 1 December and 20 January during which seedlings may be exposed to damagingly high soil temperature (>55�C between 2 and 7 days after sowing) was 5.5, out of an estimated 21 days suitable for sowing. While the exposure of a crop to inclement conditions during establishment may have immediate and dramatic effects on the mortality of pre-emergent and post-emergent seedlings, there may also be longer-term and less evident adverse effects on crop growth and development. The responses of developing seedlings to inclement conditions following sowing are described and management options (eg adjusting planting dates, changing crop species, changing seedbed configurations, using surface mulch) for the amelioration of the seedbed environment are discussed. Of the possible management options for ameliorating adverse seedbed conditions during crop establishment in the semi-arid tropics (SAT), it appears that the best practice is to maintain a soil surface cover (mulch) in close proximity to the emerging seedlings. The presence of surface mulch extends the window of opportunity for establishing crops by slowing soil drying, delaying the onset of high soil temperatures and high soil impedance, and by improving the availability of water to the young seedlings at this critical stage.

An analytical model for the prediction of temperature distribution and evolution in hybrid laser-waterjet micro-machining

2017 ◽  
Vol 47 ◽  
pp. 33-45 ◽  
Author(s):  
Shaochuan Feng ◽  
Chuanzhen Huang ◽  
Jun Wang ◽  
Hongtao Zhu ◽  
Peng Yao ◽  
...  
Keyword(s):  
Temperature Distribution ◽  
Analytical Model ◽  
Micro Machining

scholarly journals A Site-Specific, Weather-Based Disease Regression Model for Sclerotinia Blight of Peanut

Plant Disease ◽  
2007 ◽  
Vol 91 (11) ◽  
pp. 1436-1444 ◽  
Author(s):  
D. L. Smith ◽  
J. E. Hollowell ◽  
T. G. Isleib ◽  
B. B. Shew
Keyword(s):  
Relative Humidity ◽  
Soil Temperature ◽  
Air Temperature ◽  
Partial Resistance ◽  
Production Costs ◽  
Weather Data ◽  
Weather Variables ◽  
Site Specific ◽  
Sclerotinia Blight ◽  
Blight Disease

In North Carolina, losses due to Sclerotinia blight of peanut, caused by the fungus Sclerotinia minor, are an estimated 1 to 4 million dollars annually. In general, peanut (Arachis hypogaea) is very susceptible to Sclerotinia blight, but some partially resistant virginia-type cultivars are available. Up to three fungicide applications per season are necessary to maintain a healthy crop in years highly favorable for disease development. Improved prediction of epidemic initiation and identification of periods when fungicides are not required would increase fungicide efficiency and reduce production costs on resistant and susceptible cultivars. A Sclerotinia blight disease model was developed using regression strategies in an effort to describe the relationships between modeled environmental variables and disease increase. Changes in incremental disease incidence (% of newly infected plants of the total plant population per plot) for the 2002–2005 growing seasons were statistically transformed and described using 5-day moving averages of modeled site-specific weather variables (localized, mathematical estimations of weather data derived at a remote location) obtained from SkyBit (ZedX, Inc.). Variables in the regression to describe the Sclerotinia blight disease index included: mean relative humidity (linear and quadratic), mean soil temperature (quadratic), maximum air temperature (linear and quadratic), maximum relative humidity (linear and quadratic), minimum air temperature (linear and quadratic), minimum relative humidity (linear and quadratic), and minimum soil temperature (linear and quadratic). The model explained approximately 50% of the variability in Sclerotinia blight index over 4 years of field research in eight environments. The relationships between weather variables and Sclerotinia blight index were independent of host partial resistance. Linear regression models were used to describe progress of Sclerotinia blight on cultivars and breeding lines with varying levels of partial resistance. Resistance affected the rate of disease progress, but not disease onset. The results of this study will be used to develop site- and cultivar-specific spray advisories for Sclerotinia blight.

scholarly journals Generation of Microclimates From the Differential Decomposition of Textiles in North Central Florida

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Raphaela M Meloro ◽  
Breeanne Linnea Nastav ◽  
Valerie B DeLeon
Keyword(s):  
Soil Temperature ◽  
Weather Data ◽  
Soil Environment ◽  
Decomposition Rates ◽  
North Central ◽  
Central Florida ◽  
Post Mortem Interval ◽  
Software Analysis ◽  
Different Types ◽  
Subsequent Decomposition

The analysis of textiles as trace evidence is an important area of focus in the field of forensic science, because enhanced understanding of the decomposition of textiles may point to more accurate methods for estimating the post mortem interval (PMI) of remains found in association with these materials. This research is especially crucial in areas with unique climates, like the state of Florida. This study examines the generation of microclimates from the differential decomposition of various textile types. This study hypothesized that the decomposition of textiles will generate microclimates with soil properties that differ from those of the surrounding environment, and that different types of textiles will create different microclimates as they decompose. Samples of cotton, UV-proofed cotton, polyester, cotton-polyester blended fabric, ripstop, and wool were buried at four sites on a property in North Central Florida for thirteen weeks, with measures of soil temperature, pH, and moisture level, and weather data collected weekly. Following burial, decomposition of each textile type was scored. Data collected were analyzed in R statistical software. Analysis indicated that the level of degradation differed by textile type but not by site. Textile presence, type of textile, and subsequent decomposition significantly impacted soil pH and moisture at all sites, but did not have a significant effect on soil temperature. The results of this study demonstrate that the decomposition of textiles can create diverse and unique microclimates in the soil environment. When found in association with human remains, presence and type of textile should be considered when estimating decomposition rates and the postmortem interval.   

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