Hourly Soil Temperature Research Articles

A Convolutional Neural Network Model for Soil Temperature Prediction under Ordinary and Hot Weather Conditions: Comparison with a Multilayer Perceptron Model

Soil temperature is a critical parameter in soil science, agriculture, meteorology, hydrology, and water resources engineering, and its accurate and cost-effective determination and prediction are very important. Machine learning models are widely employed for surface, near-surface, and subsurface soil temperature predictions. The present study employed a properly designed one-dimensional convolutional neural network model to predict the hourly soil temperature at a subsurface depth of 0–7 cm. The annual input dataset for this model included eight hourly climatic features. The performance of this model was assessed using a wide range of evaluation metrics and compared to that of a multilayer perceptron model. A detailed sensitivity analysis was conducted on each feature to determine its importance in predicting the soil temperature. This analysis showed that air temperature had the greatest impact and surface thermal radiation had the least impact on soil temperature prediction. It was concluded that the one-dimensional convolutional model performed better than the multilayer perceptron model in predicting the soil temperature under both normal and hot weather conditions. The findings of this study demonstrated the capability of the model to predict the daily maximum soil temperature.

Spatiotemporal variation characteristics of hourly soil temperature in different layers in the low-latitude plateau of China

Soil temperature change has considerable impact on land surface energy and water balances, and hence on changes in weather/climate, surface/subsurface hydrology, and ecosystems. However, little is known regarding the spatiotemporal variations and influencing factors of changes in hourly soil temperature (depth: 5–320 cm) in low-latitude highland areas. This study analyzed the hourly soil temperature at each hour during 2004–2020 and at 08:00, 14:00, and 20:00 (Beijing Time) during 1961–2020. The results revealed the following. 1) As soil depth increased, average soil temperature increased in autumn and winter, and decreased annually and in spring and summer. It exhibited significant increase during 00:00–23:00 annually, seasonally, and monthly, especially at depths of 40–320 cm during 2004–2020. Average soil temperature increased at 08:00 and decreased at 14:00 and 20:00 with increasing soil depth, but the opposite trend was found annually, seasonally, and monthly at 08:00, 14:00, and 20:00 during 1961–2020. 2) With increasing elevation, average soil temperature decreased at 08:00, 14:00, and 20:00 at depths of 5–20 cm, and showed significant increase trend at 08:00 and 14:00 at depths of 10–20 cm (except at 14:00 at 10-cm depth). 3) At 5-cm depth, the critical accumulated soil temperature of ≥12°C and 14°C extended the potential growing season during 1961–2020. 5) Significant uptrend of hourly soil temperature annually, seasonally, and monthly potentially leads to additional release of carbon to the atmosphere and increased soil respiration, reinforcing climate warming. These findings contribute to better understanding of the variation of shallow soil temperatures and land–atmosphere interactions in low-latitude highland areas.

Soil Freeze-Thaw and Water Transport Characteristics Under Different Vegetation Types in Seasonal Freeze-Thaw Areas of the Loess Plateau

In the arid and semi-arid regions of the Loess Plateau, seasonal freezing and thawing influence soil water movement, and water movement directly influences vegetation growth. However, currently, research with regard to freezing and thawing processes under various vegetation types and the mechanism of soil water movement is lacking. Therefore, the present study explored soil water migration characteristics of two typical vegetation types [arbor land (AL) and shrub land (SL)] on the Loess Plateau during seasonal freezing and thawing processes using bare land (BL) as a control. We used field measured data for hourly soil temperature (ST) and soil water content (SWC) at a depth of 100 cm below the soil surface from November 2017 to March 2018. Freezing and thawing process was divided into three stages based on ST change (initial freezing period, stable freezing period, and thawing period). Compared with previous studies in this area, ST is lower than expected, and SWC migration characteristics are also different. The results revealed that: 1) the maximum freezing depth of AL and SL was 60 cm, which was 30 cm less than that of BL. The freezing date of each soil layer in BL was the earliest and average ST value was the lowest. BL had the highest degree of freezing. The freezing of all soil layers in AL occurred at a later date than that of SL. ST and the minimum soil freezing temperatures were higher than those of SL, and the capacity of AL to resist freezing was higher; 2) the SWCs in AL and BL at depths of 0–10 cm and 10–30 cm decreased, whereas SWCs of AL and BL at a depth of 60 cm increased by 152 and 146%, respectively. The SWCs of SL at soil depths of 0–10 cm, 10–30 cm, and 30–60 cm increased by 46.3, 78.4 and 205%, respectively. The amount and distribution of soil moisture in SL were optimum when compared to those of AL and BL. The results of the present study could provide a scientific basis for vegetation restoration in arid and semi-arid areas of the Loess Plateau.

Artificial Neural Network (ANN) based Soil Temperature model of Highly Plastic Clay

ABSTRACT Artificial neural networks (ANNs) are one of the popular methods of artificial intelligence that seek to follow the human mind function and nervous system with its successful application increased in many areas of engineering. The current study is focused to develop an ANN-based predictive model of the soil temperature of Yazoo clay using based on field instrumentation data. To provide an acceptable dataset for developing the predictive model, the investigation was carried out in six instrumented slopes within the 25 miles (40.2 km) radius from metropolitan Jackson in Mississippi. The six selected slopes were instrumented with soil moisture sensors, automated rain gauge, air, and soil temperature sensors starting from mid-August 2018. Volumetric moisture content, precipitation, air, and soil temperature values at 1.5 m (5 ft) depth at the crest of the six slopes were collected using automated data loggers and observed for more than seventeen months. The established database consisting of 13650 datasets was implemented for ANN intelligent system and multiple-degree Fourier series non-linear regression technique for predicting the hourly soil temperature. The total hourly natural rainfall and time, average previous soil temperature, and average hourly air temperature were set to be the inputs of the model and the hourly soil temperature was set to be the output of the model. These datasets were used as the training data and validated with each target slope. Sensitivity analysis was also conducted, and the most influential input parameters on the data output were determined. In this study, the change of soil temperature with atmospheric temperature was investigated, and a predictor model was developed by adopting the Levenberg-Marquardt (LM) algorithm method and the Tan-sigmoid transfer function. The developed ANNs model showed an excellent fit with the observed field values.

Prediction Model for Hourly Soil Temperature by Soil Depth to Irrigated Sandy Loam Soil in Greenhouse

Prediction Model for Hourly Soil Temperature by Soil Depth to Irrigated Sandy Loam Soil in Greenhouse

Long‐term effects of tree expansion and reduction on soil climate in a semiarid ecosystem

AbstractIn sagebrush ecosystems, pinyon and juniper tree expansion reduces water available to perennial shrubs and herbs. We measured soil water matric potential and temperatures at 13–30 and 50–65 cm soil depths in untreated and treated plots across a range of environmental conditions. We sought to determine the effects of tree expansion, tree reduction treatments, and expansion phase at time of treatment over 12–13 yr post‐treatment. Because the effects of tree reduction on vegetation can vary with the soil temperature/moisture regime, we also analyzed differences in soil climate variables between the mesic/aridic‐xeric and frigid/xeric regime classifications for our sites. Growing conditions during all seasons except spring were greatly limited by lack of available water, low temperatures, or both. Advanced tree expansion reduced wet days (total hours per 24 hr when hourly average soil water matric potential >−1.5 MPa), especially in early spring. Fire and mechanical tree reduction increased wet days and wet degree days (sum of hourly soil temperatures >0°C when soil is wet per 24 hr) compared with no treatment for most seasons. Burning resulted in higher soil temperatures than untreated or mechanically treated woodlands. Tree reduction at advanced expansion phases increased wet days in spring more than when implemented at earlier phases of expansion. Added wet days from tree reduction were negatively associated with October through June precipitation and vegetation cover, rather than time since treatment, with more wet days added on drier sites and years. The longer period of water availability in spring supports increased growth and cover of not only shrubs and perennial herbs, but also invasive weeds on warmer and drier sites, for many years after tree reduction. We found that sites classified as mesic/aridic‐xeric had warmer soil temperatures all seasons and were drier in spring and winter than sites classified as frigid/xeric. Land managers should consider reducing trees at earlier phases of expansion or consider revegetation when treating at advanced phases on these warmer and drier sites that lack perennial herb potential.

Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network

Soil temperature (ST) plays a key role in the processes and functions of almost all ecosystems, and is also an essential parameter for various applications such as agricultural production, geothermal development, and their utilization. Although numerous machine learning models have been used in the prediction of ST, and good results have been obtained, most of the current studies have focused on daily or monthly ST predictions, while hourly ST predictions are scarce. This paper presents a novel scheme for forecasting the hourly ST using weather forecast data. The method considers the hourly ST prediction to be the superposition of two parts, namely, the daily average ST prediction and the ST amplitude (the difference between the hourly ST and the daily average ST) prediction. According to the results of correlation analysis, we selected nine meteorological parameters and combined two temporal parameters as the input vectors for predicting the daily average ST. For the task of predicting the ST amplitude, seven meteorological parameters and one temporal parameter were selected as the inputs. Two submodels were constructed using a deep bidirectional long short-term memory network (BiLSTM). For the task of hourly ST prediction at five different soil depths at 30 sites, which are located in 5 common climates in the United States, the results showed the method proposed in this paper performs best at all depths for 30 stations (100% of all) for the root mean square error (RMSE), 27 stations (90% of all) for the mean absolute error (MAE), and 30 stations (100% of all) for the coefficient of determination (R2), respectively. Moreover, the method adopted in this study displays a stronger ST prediction ability than the traditional methods under all climate types involved in the experiment, the hourly ST produced by it can be used as a driving parameter for high-resolution biogeochemical models, land surface models and hydrological models and can provide ideas for an analysis of other time series data.

Diurnal Variability of Soil and Air temperature as a function of time and depth, A case study of the cold wave that hit Zawia region during the period 1-5 February 1999

Soil temperature is one of the important variables in agricultural studies, which is necessary to be measured and analyze. There are some major challenges facing a lot of agricultural studies focus on soil temperature in Libya, such as, the soil temperature is measured only at Agro meteorological stations in the country, the lack of stations, the measurement of soil temperature is a cumbersome business and not as simple as air temperature be noted by single thermometer, Separate thermometers are required to measure the temperature of the soil at different depths, the installation of thermometer sensor correctly in the soil is another very complicated technical matter. However, in Libya there are no enough studies into the pattern of hourly variation in soil temperature over twenty-four hours due to the lack of such measurements at agricultural lands. So that, this attempt has been made to diagnose the behavior of soil temperature and air temperature as a case study, This work was carried out by using the daily and hourly data recorded for the temperature in the standard depths of soil (5,10,20,30,50,100 cm), and the surface air temperature at heights of (5, 10, 15, 50, 100, 150, 200 cm), Data were measured, Reported and collected every three hours interval during the period from 01 to 05 February 1999, at Zawia Agro-Meteorological Station where the location of the station was under the effect of cold wave. The data were measured on a daily basis at 0000, 0300, 0600, 0900, 1200, 1500, 1800, 2100 UTC. The correlation coefficient between the hourly soil temperature and hourly air temperature at certain depths and heights were calculated using the Pearson’s Formula, then the matrix of correlation factors have been derived, The vertical profile for the hourly variation of temperature in the layer between 100 cm under the surface of soil and 200 cm above the surface of soil have been described, Graphs and tables were done to show more explanation. The analysis of the observation shows that soil temperature variability decreases with depth, the highest temperature variability in top soil, and the lowest one is deeper than 50 cm. These results were affected due to the prevailing weather situation because at night time there is no enough solar radiation and the ambient temperature is lower than soil temperature. As a result, the soil temperature transferred heat from soil to atmosphere above. In addition, the low thermal conductivity of the soil is also one important factor that affects heat storage. These results demonstrated the importance of monitoring the soil temperature as an important element for agricultural studies. The results of the present paper could fill in some of the gap related to the soil temperature studies in Libya, and can be used for planning agronomic and plant protection practices.

Warming effects on soil respiration in moss-dominated crusts in the Tengger Desert, northern China

Despite the important role of biological soil crusts in the soil carbon cycles of desert ecosystems, the responses of soil respiration in biological soil crust-dominated areas to warming are not well understood. The goal of this study was to investigate the expected increases in temperature on soil respiration both diurnally and seasonally in biological soil crust-dominated areas. We used open-top chambers to simulate warming in the Shapotou region in the Tengger Desert, northern China. An automated soil respiration system was used to measure the soil respiration rates in moss-dominated crusts. The measured environmental variables included the precipitation, volumetric soil water content, air temperature and soil temperature at depths of 0, 5, 10, 20, and 50 cm. The response of soil respiration to warming is a function of soil moisture following rainfall in desert ecosystems. Our results showed that 1.5 °C of simulated warming significantly decreased soil respiration, indicating that the inhibition of soil respiration was likely due to the reduction in soil water content at a relatively high temperature. Over daily cycles, hourly soil respiration rates have commonly been related to hourly temperatures. The observed diel hysteresis between hourly soil respiration and temperature resulted in semielliptical hysteresis loops, and the temperature often lagged behind soil respiration for several hours. The lag times between soil respiration and temperature were significantly and positively related to the depth of the soil temperature measurements. The proximate reason for the diel hysteresis between soil respiration and temperature was likely a mismatch between the depth of CO2 production and the depth of the temperature measurements. Our results indicate that warming increases the response of soil respiration to soil water availability in biological soil crust-dominated desert ecosystems. Therefore, the accelerated drying effect of warming on soil respiration and diel soil respiration patterns between soil respiration and temperature at different depths should be considered in future soil carbon cycle models for biological soil crust-dominated desert ecosystems.

Catchment Hydrological Modeling with Soil Thermal Dynamics during Seasonal Freeze-Thaw Cycles

To account for the seasonal changes in the soil thermal and hydrological dynamics, the soil moisture state physical process defined by the Richards Equation is integrated with the soil thermal state defined by the numerical model of phase change based on the quasi-linear heat conductive equation. The numerical model of phase change is used to compute a vertical soil temperature profile using the soil moisture information from the Richards solver; the soil moisture numerical model, in turn, uses this temperature and phase, information to update hydraulic conductivities in the vertical soil moisture profile. Long-term simulation results from the test case, a head water sub-catchment at the peak of the Caribou Poker Creek Research Watershed, representing the Alaskan permafrost active region, indicated that freezing temperatures decreases infiltration, increases overland flow and peak discharges by increasing the soil ice content and decaying the soil hydraulic conductivity exponentially. Available observed and the simulated soil temperature comparison analysis showed that the root mean square error for the daily maximum soil temperature at 10-cm depth was 4.7 °C, and that for the hourly soil temperature at 90-cm and 300-cm was 0.17 °C and 0.14 °C, respectively.

Testing and application of simple semi-analytical models for soil temperature estimation and prediction in environmental assessments

ABSTRACTThe ability of various semi-analytical models to predict soil temperature profiles in an experimental plot during a 16-year monitoring study for soil depths up to 120 cm is evaluated. The models are developed from an analytical model by replacing the steady-state soil temperature with easily obtained hourly and daily average temperature values. Such values include the hourly air temperature, the daily average air temperature, the hourly soil temperature of selected soil depths from three daily observations, the daily average of the soil temperature profile and the hourly soil temperature for the bottom depth. The performance evaluation results show that, in principle, all models exhibit high correlation (R2 values in the range 0.85–0.97), indicating a very good agreement between measured and predicted values. In addition, error statistics reveal that the best performance in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) is the model based on the daily average of the soil temperature profile with MAE values in the range of 0–0.4°C and RMSE values in the range of 0.1–1.5°C.

Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter?

The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. Continental-scale climate and microclimate layers are now widely available. However, most terrestrial organisms exist within microclimate spaces that are very small, relative to the spatial resolution of those layers. We examined the effects of multi-resolution, multi-extent topographic and climate inputs on the accuracy of hourly soil temperature predictions for a small island generated at a very high spatial resolution (<1 m2) using the mechanistic microclimate model in NicheMapR. Achieving an accuracy comparable to lower-resolution, continental-scale microclimate layers (within about 2–3°C of observed values) required the use of daily weather data as well as high resolution topographic layers (elevation, slope, aspect, horizon angles), while inclusion of site-specific soil properties did not markedly improve predictions. Our results suggest that large-extent microclimate layers may not provide accurate estimates of microclimate conditions when the spatial extent of a habitat or other area of interest is similar to or smaller than the spatial resolution of the layers themselves. Thus, effort in sourcing model inputs should be focused on obtaining high resolution terrain data, e.g., via LiDAR or photogrammetry, and local weather information rather than in situ sampling of microclimate characteristics.

Impact of diurnal freeze–thaw cycles on the soil nematode Scottnema lindsayae in Taylor Valley, Antarctica

Global climate change scenarios predict not only higher temperatures, but also increased climatic variability. In cold regions, these changes may bring about a shift in the frequency of soil freeze–thaw cycles (FTCs), which represent a significant physiological challenge, especially for small, poikilothermic animals with limited mobility. To assess the impact of FTCs on cold-adapted soil biota, we evaluated freeze–thaw dynamics (i.e., 0 °C crossings) and demographics of the dominant nematode Scottnema lindsayae (proportion of adults, population size) over 20 years in soils at two locations in Taylor Valley, Antarctica. Based on hourly soil temperature data, we demonstrate that FTCs are a frequent feature in Taylor Valley, but with high inter-annual and spatial variability. Valley topography and soil moisture were found to impact FTC frequency, suggesting that basins within Taylor Valley have different susceptibilities to environmental variability. Increased FTC frequency in 1999–2001 coincided with a shift in S. lindsayae populations, with fewer juveniles reaching maturity. In the years following decreased adult proportions, overall S. lindsayae numbers were reduced, implying a strong negative effect of FTCs on in situ recruitment. Our results suggest that increased FTC frequency in the Dry Valleys slows S. lindsayae development, reducing reproductive success, and may take years to impact population size, which demonstrates the importance of long-term research to accurately predict the consequences of climate change on soil biota and biogeochemical cycling in the cold regions.

Specifics of soil temperature under winter oilseed rape canopy

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).

Microclimate modelling at macro scales: a test of a general microclimate model integrated with gridded continental‐scale soil and weather data

Summary The microclimate experienced by organisms is determined by local weather conditions. Yet the environmental data available for predicting the effect of climate on the distribution and abundance of organisms are typically in the form of long‐term average monthly climate measured at standardized heights above the ground. Here, we demonstrate how hourly microclimates can be modelled mechanistically over decades at the continental scale with biologically suitable accuracy. We extend the microclimate model of the software package niche mapper to capture spatial and temporal variation in soil thermal properties and integrate it with gridded soil and weather data for Australia at 0·05° resolution. When tested against historical observations of soil temperature, the microclimate model predicted 85% of the variation in hourly soil temperature across 10 years from the surface to 1 m deep with an accuracy of 2–3·3 °C (c. 10% of the temperature range at a given depth) across an extremely climatically diverse range of sites. This capacity to accurately and mechanistically predict hourly local microclimates across continental scales creates new opportunities for understanding how organisms respond to changes in climate.

Validation of Noah-Simulated Soil Temperature in the North American Land Data Assimilation System Phase 2

AbstractSoil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North American Land Data Assimilation phase 2 (NLDAS-2) has generated 31 years (1979–2009) of simulated hourly soil temperature data with a spatial resolution of ⅛°. This dataset has not been comprehensively evaluated to date. Thus, the purpose of this paper is to assess Noah-simulated soil temperature for different soil depths and time scales. The authors used long-term (1979–2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0–10, 10–40, and 40–100 cm) for annual and monthly time scales. Short-term (1997–99) observed soil temperatures from 72 Oklahoma Mesonet stations were used to validate simulated soil temperatures for three soil layers and for daily and hourly time scales. The results showed that the Noah land surface model generally matches observed soil temperature well for different soil layers and time scales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season that are due to small downward longwave radiation and issues related to model parameters.

Models for estimation of hourly soil temperature at 5cm depth and for degree-day accumulation from minimum and maximum soil temperature

A model has been developed for the daily variation in soil temperature at 5 cm depth, for use where both the minimum and maximum temperatures are known or can be estimated. The model is based on data from three Australian sites with minute-by-minute data over 3–7 years. The model uses two sine curves; one for the increase from minimum to maximum and another for the relatively rapid decrease in temperature immediately after the maximum. An exponential decay function is used for the slower decrease in temperature until the minimum is reached. The time of the minimum soil temperature is primarily determined by the time of sunrise and therefore varies depending on the day of the year, whereas the time of the maximum temperature is influenced primarily by the time of the middle of the day (midpoint between sunrise and sunset). The time of the transition point between the maximum and the next minimum is related to the time of sunset. Therefore, the model uses latitude, longitude, and the day of the year to determine the time of sunrise and sunset to adjust the shape of the temperature profile throughout the day. The model has been validated using 3-hourly soil temperature data for 35 other sites in Australia, with a correlation of 0.993 between actual 3-hourly temperatures and those predicted. Its use for degree-day calculations has been validated using hourly data from a site in Victoria, where the model’s estimates of degree-days differ &lt;0.7% from the value based on individual hourly temperatures, whereas methods that assume a symmetrical change from maximum to minimum temperature overestimate degree-days by 6–7%.

Haar wavelet in estimating depth profile of soil temperature

A Haar wavelet based method for the estimation of soil temperature at different depths is described in this paper. Diurnal variation in the hourly soil temperature is estimated at different depths varying from 0 to 45 cm. This estimation is compared with observed data available for Trombay site at the depths 5, 10, and 20 cm. This Haar technique can be interpreted from incremental and multi-resolution viewpoint. The estimated values show and excellent agreement with the observed values. The Haar based estimated values are found to be more accurate than the values obtained by FDM approach. More accurate solutions can be obtained by changing the time scale in Haar wavelet; at the same time main features of the solution are preserved. Sensitivity analyses indicate that soil temperature was no so sensitive to changes of soil thermal parameters. Moreover the use of Haar wavelets is found to be accurate, simple, fast, flexible, convenient, small computation costs and computationally attractive.

Ground temperature estimations using simplified analytical and semi-empirical approaches

In this work, subsurface ground temperature profiles are estimated by exploiting two different approaches. In the first one, an analytical model is examined which, considering a quasi steady state system, implements the superposition of annual and daily sinusoidal fluctuations. In the second one, semi-empirical models are developed based on the general formula of the preceding, by replacing the steady state soil temperature with easily obtained daily average temperatures. Various subsets of soil temperature were used for model development, in order to explore the possibility of minimizing data requirements. Comparison of observational data with model results reveals that the observational patterns of hourly soil temperature are fairly well approximated by both by the analytical and the semi-empirical models. All models seem to capture the main characteristics of the annual course of soil temperature, with the results obtained from the semi-empirical models fluctuating in a much more realistic way than those of the analytical model. It is concluded that the proposed models may serve as useful tools for estimating and predicting soil temperatures to be used as practical reference in various environmental and energy applications.

Temperature‐dependent development and distribution in the soil profile of pupae of greyback canegrub Dermolepida albohirtum (Waterhouse) (Coleoptera: Scarabaeidae) in Queensland sugarcane

Abstract The temperature‐dependent development rate of pupae of greyback canegrub, Dermolepida albohirtum (Waterhouse) (Coleoptera: Scarabaeidae), a major pest of sugarcane in central and northern Queensland, was determined under six constant temperature regimes: 18, 20, 23, 25, 27 and 30°C, and for four geographically separated populations. Development rate increased significantly with increasing temperature. Parameters of the linear regression equation did not differ among populations and common coefficients were calculated. Developmental zero, at and below which no development occurs, for pupae was 12.0°C and the thermal constant was 476 day‐degrees (D°). Minimum and maximum periods for pupal development were 26 days at 30°C and 75 days at 18°C, respectively. The phenology of pupae was determined in soil‐filled cubicles in a shade house and in the field at Ayr (19°35′S, 147°25′E), north Queensland, using D. albohirtum field‐collected as late‐stage third‐instar larvae and kept in containers. Pupation of D. albohirtum began in late August or early September and eclosion was complete by mid‐ to late October. The phenology data were used to validate the development model. Eclosion was predicted by summing hourly fractions of day‐degrees until 476 D° was reached and was close to actual eclosion. As temperature, and hence pupal development rate, varies with soil depth, the distribution of the third instars in pupal cells in the soil profile was determined in recently harvested fields of sugarcane in the Burdekin sugarcane district centred on Ayr. Numbers of late third instars in pupal cells peaked at 300–400 mm, with pupae found from 30 to 700 mm. Pupal development was simulated using hourly soil temperatures measured at depths of 200 and 400 mm at Ayr and at Sarina (21°22′S, 149°13′E). The pupal stage was predicted to take up to 2–10 days longer at 200 mm deep than at 400 mm depending on pupation site and date. When pupation was simulated in late August, as is likely in the field, pupal development at 400 mm deep took 48–56 days at Ayr and 58–62 days at Sarina.