Predict Soil Temperature Research Articles

An attention-aware LSTM model for soil moisture and soil temperature prediction

Accurate prediction of soil moisture (SM) and soil temperature (ST) plays an important role in Earth system science, helping to forecast and understand ecosystem changes. They present great challenges because land-atmospheric interactions are complex and diverse in space and time. Although deep learning methods have excellent performance for land surface variables’ prediction such as SM and ST, they are often questioned due to their over-parameterized black-box nature and neglect of physical knowledge and interpretability. From this, we propose an attention-aware LSTM Model (ILSTM_Soil) by taking multi-feature attention, predictor attention and temporal attention into account. We first used LSTM to generate multi-feature vectors of all predictors, and then the three attention mechanisms were designed to summarize these feature vectors for SM and ST prediction. Experiment results for SM and ST prediction at the lead time of 1 and 7 days on ten FLUXNET sites suggest that the proposed ILSTM_Soil model outperforms Random Forest (RF), Support Vector Regression (SVR), Elastic-Net (ENET), original Long Short-Term Memory (LSTM), and attention LSTM (A-LSTM) models in most cases. The interpretation of attention weights verifies that the proposed model can capture physical knowledge over SM and ST. The code of ILSTM_Soil is made publicly available and we hope it can encourage researchers to develop effective DL models in land surface variables’ prediction conveniently.

Exploration of Soil and Weather Factors on Mulberry Root Rot Incidence in the Western Zone of Tamil Nadu, India

Aims: To record the occurrence of mulberry root rot disease, epidemiology, interaction of weather and soil parameters with the soil-borne pathogens in Western zone of Tamil Nadu during 2019-2020.
 Study Design: Survey.
 Place and Duration of Study: Surveyed in Coimbatore, Tiruppur, Erode, Dharmapuri and Krishnagiri districts of Tamil Nadu. Laboratory experiments were carried out at Department of Sericulture & Department of Plant Pathology, Tamil Nadu Agricultural University (TNAU), Coimbatore between July 2019 and Jan 2021.
 Methodology: Per cent disease incidence of root rot was recorded in all surveyed gardens. To analyze the soil and weather parameters, the composite soil samples were subjected to textural analysis and weather data were collected from TNAU Agro Climate Research Centre. To predict soil temperature for all surveyed locations, the model regression equations were derived. The correlation analysis was done between per cent disease incidence, weather and soil parameters.
 Results: The highest disease incidence was recorded in Nallampalli block of Dharmapuri district (54 per cent) whereas the lowest in Udumalaipettai block of Tiruppur district (0.06 per cent). The infected mulberry root samples yielded complex of soil-borne pathogens including Macrophomina phaseolina, Lasiodiplodia theobromae, Fusarium sp., and pathogenicity was proved. The results revealed that root rot incidence was recorded in all types of cultivars, significantly in ruling variety V1 irrespective of its age, soil type, spacing, and irrigation method. Soil parameters like texture, temperature and moisture content were found to augment the disease. Per cent disease incidence had significantly positive correlation with the weather factors like air and soil temperature whereas negative correlation with relative humidity and rainfall.
 Conclusion: Synergism of abiotic stress factors hinders the mulberry plant health and increases its susceptibility to the soil-borne pathogens.

A new model to predict subsoil-thermal profiles based on seasonal rain conditions and soil properties

In the present work, a novel way to simulate the soil temperature profiles is developed. Soil temperature profiles are essential to know the Stabilized Temperature (ST). In topics related to low depth geothermal energy, sustainable building, renewable energies, and energy efficiency is essential to know both soil temperature profiles and the ST in the design phase. The first way to predict soil temperature profiles was by solving the heat conduction equation by Fourier series. The main problem with the analytical solution is the environmental factors. By omitting these conditions, results agree only with some regions where environmental conditions have less influence on the subsoil temperature. The presented simulator allows using variables used in the analytical solution, likewise environmental factors like relative humidity and rain duration. Besides, the change of thermo-physical properties along the year is considered. The results of the simulator are compared against experimental data in situ and the analytical solution used previously. The comparison results point out that the new simulator reproduces up to 80% better the soil temperature profiles than the analytical solution by Fourier series.

Knowledge‐guided machine learning for improving daily soil temperature prediction across the United States

AbstractData‐driven models used for predicting soil temperature usually have increasing errors with increasing depth. By exploring the integration of knowledge‐based and machine learning approaches, this study used a novel transformation of meteorological variables to increase prediction accuracy of soil temperature with increasing soil depth. Using datasets for two soil textures (silty clay loam and loamy coarse sand) at two locations with different climates, predictive models were developed for five depths (5, 10, 20, 50, and 100 cm) as a function of meteorological features using an adaptive neuro‐fuzzy inference system (ANFIS). For each depth, soil temperature was predicted with nontransformation (NT), autocorrelation (AC), moving average (MA), and a combination of transformations (NT‐ACMA) of meteorological features. Across all depths, the predictive accuracy of NT‐ACMA models was significantly higher than that of NT, AC, and MA models for both soil textures (R2 = .99, RMSE = 1 °C for silty clay loam; R2 = .99, RMSE = 1.2 °C for loamy coarse sand), with increasing prediction accuracy as soil depth increases. Results for different soil textures and climates in 11 locations across the contiguous United States show that, except for 100‐cm depth, there seems to be significant positive linear relationships between the best moving average and the solar inclination. This makes our NT‐ACMA technique transferable to any location in the contiguous United States irrespective of the location, climate, and soil texture. We conclude that integrating lag times and moving averages of meteorological features can lead to better prediction of soil temperature at different soil depths.

A New Embedded Estimation Model for Soil Temperature Prediction

With the continuous development of Earth science, soil temperature has received more and more attention in Earth system research as an important parameter. The change of soil temperature (Ts) in different regions and related time series is affected by many factors, which bring certain difficulties to the accuracy of soil temperature prediction and the robustness of the algorithm. In this paper, an embedded network prediction model based on the gated recurrent unit (GRU) model is proposed to learn the local and global features of historical temperature for improving the prediction performance of soil temperature. We input different steps into the GRU model, and the output is weighted to obtain the final prediction result. In order to obtain the global characteristics of soil temperature, we connect the previous steps to the output layer directly, and the local characteristics of soil temperature are obtained through the following steps. This paper uses the soil temperature data from two meteorological stations (Laegern and Fluehli) in Switzerland as the input data to predict the soil temperature for different soil depths (5 cm, 10 cm, and 15 cm) at different time points (6 hrs, 12 hrs, and 24 hrs), using RMSE, MAE, MSE, and R 2 performance indicators as evaluation criteria to verify the accuracy of prediction. As the experimental results show, our method has the best performance compared to the others (artificial neural networks (ANN), extreme learning machine model (ELM), long short-term memory network (LSTM), gated recurrent unit network (GRU)). In particular, we estimated the soil temperature at the soil depth of 10 cm of the Fluehli station in the coming 6 hrs; our method achieved the best performance; and, meanwhile, our model achieved the maximum value of R 2 (0.9914) and the minimum values of RMSE (0.4668), MAE (0.2585), and MSE (0.2214) compared with the other four models. Therefore, our model can not only predict the soil temperature at different depths but also improve the accuracy.

Improving the simulation of soil temperature within the EPIC model

Soil temperature is a key driver of several physical, chemical, and biological processes. The Environmental Policy Integrated Climate (EPIC) is a comprehensive ecosystem model that simulates soil temperature dynamics using a cosine function approach driven by daily air temperature and average annual soil temperature at damping depth, which may erroneously predict lower soil temperatures in winter. A new cosine model and a pseudo-heat-transfer model were therefore developed and implemented for simulating soil temperature. The two methods were evaluated by comparing simulated daily soil temperatures with observed data at 24 study sites. Results showed that the two new methods had similar performance and the better statistical results obtained with these new methods demonstrated the ability to better predict the soil temperature for a wide range of pedoclimatic conditions, land management, and land uses. The main reason for the improved performance was due to a better prediction of soil temperature during the winter period.

GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables

Accurate prediction of soil temperature (Ts) is critical for efficient soil, water and field crop management. In this study, hourly Ts variations at 5, 10, and 30 cm soil depth were predicted for an arid site (Sirjan) and a semi-humid site (Sanandaj) in Iran. Existing machine learning models have high performance, but suffer from uncertainty and instability in prediction. Therefore, GLUE approach was implemented to quantify model uncertainty, while wavelet coherence was used to assess interactions between Ts and meteorological parameters. Standalone machine learning models (adaptive neuron fuzzy interface system (ANFIS), support vector machine model (SVM), radial basis function neural network (RBFNN), and multilayer perceptron (MLP)) were hybridized with four optimization algorithms (sunflower optimization (SFO), firefly algorithm (FFA), salp swarm algorithm (SSA), particle swarm optimization (PSO)) to improve Ts prediction accuracy and reduce model uncertainty. For both arid and semi-humid sites, ANFIS-SFO produced the most accurate performance at studied soil depths. At best, hybridization with SFO (ANFIS-SFO, MLP-SFO, RBFNN-SFO, SVM-SFO) decreased RMSE by 5.6%, 18%, 18.3%, and 18.2% at 5 cm, 11.8%, 10.4%, 10.6%, and 12.5% at 10 cm, and 9.1%, 12.1%, 13.9%, and 14.2% at 30 cm soil depth compared with the respective standalone models. GLUE analysis confirmed the superiority of hybrid models over the standalone models, while the hybrid models decreased the uncertainty in Ts predictions. ANFIS-SFO covered 95%, 94%, and 96% observation data at 5, 10, and 30 cm soli depths, respectively. Wavelet coherence analysis demonstrated that air temperature, relative humidity, and solar radiation, but not wind speed, had high coherence with Ts at different soil depths at both sites, and meteorological parameters mostly influenced Ts in upper soil layers. In conclusion, uncertainty analysis is a necessary and powerful technique to obtain an accurate and realistic prediction of Ts. In contrast, wavelet coherence analysis is a useful tool to investigate the most effective variables that strongly affect predictions.Graphic abstract

Development of a predictive model for soil temperature and its application to species distribution modeling of ant species in South Korea

Soil temperature is an important factor for determining ant species inhabitation, but it has rarely been applied in species distribution modeling due to limited data sources. Hence, this study aimed to develop a predictive model for soil temperature and evaluate the potential distribution of a few ant species in South Korea. The monthly maximum and minimum soil temperatures were predicted by linear regression as a function of the monthly maximum and minimum air temperatures. Then, the developed model was used to predict the potential distribution of the tropical fire ant (Solenopsis geminata Fabricius; TFA), red imported fire ant (Solenopsis invicta Buren; RIFA), and yellow crazy ant (Anoplolepis gracilipes Smith; YCA). Three ant species were selected because they have globally exerted negative impacts on the ecology of the invading area. The developed model could predict soil temperatures at 5, 10, 20, and 30 cm deep with an R2 value of 0.95. The climate suitability of TFA, RIFA, and YCA, predicted by soil temperature in the CLIMEX model, was found to be up to 25, 12, and 65 times higher than the climate suitability predicted by air temperature, respectively. This study showed that the potential distribution predicted based on air and soil temperature could be different for a species inhabiting the ground, suggesting that it is necessary to consider not only the outside environment, but also the soil environment, for an accurate prediction.

Artificial neural network-assisted glacier forefield soil temperature retrieval from temperature measurements

Soil temperature is one of the most important glacio-meteorological parameters that play a critical role in glacier energy and mass balance dynamics, surface hydrological processes, and glacier-atmosphere interaction. However, the availability of the data is acutely scarce in the Himalayan glaciated region. In this study, we applied artificial neural network (ANN) models for the prediction of soil temperature of glacial forefield region of the Pindari Glacier (Central Himalaya). Three-layer feed-forward ANN models were developed and tested for estimating multi-depth soil temperatures using concurrent and antecedent air-soil temperature data for one complete annual cycle as inputs for the models. Models with different combinations of input variables were tested, and best sets of variables were selected based on the prediction accuracy. Rigorous statistics were further employed to compare the performances of different models. High concurrence was obtained between ANN-estimated and measured soil and air temperatures as evident by various correlation coefficients and error ranges. In a boarder perspective, our results point toward the applicability of developed ANN models to provide robust soil temperature prediction for the glacial forefield regions of the Central Himalaya.

SOIL TEMPERATURE PROFILE INVESTIGATION UNDER ARID CLIMATE OF KUWAIT USING MECHANISTIC AND MIXED MODELS

Soil temperature prediction at several depths is crucial for environmental, engineering, and management applications. In this study, two approaches, namely the mechanistic model and the mixed model, are used to estimate the soil temperature at depths from 0.05 m to 8 m and compared with measured data. The Carslaw-Jaeger equation s parameters, the average, the amplitude, and the phase lag, that suit an arid environment such as Kuwait are determined by the two techniques. Heat flux analyses of the soil are presented. The generated models are tested against the measured data with different statistical tests such as R2, RMSE, and MAPE. Values of R2 for the mechanistic and the mixed models range from 0.9119 to 0.9315 and 0.9068 to 0.9268 respectively, for the depths from 0.05 m to 1 m.

Soil Temperature Prediction Using Convolutional Neural Network Based on Ensemble Empirical Mode Decomposition

Soil temperature plays an important role in agriculture, industry and other fields. Accurate soil temperature prediction can help improve productivity and avoid risks in many fields. At present, many machine learning methods have been applied to soil temperature prediction such as support vector regression (SVR), artificial neural network (ANN), long short-term memory neural network (LSTM) and others. In this article, we propose a machine learning model called convolutional neural network based on ensemble empirical mode decomposition (EEMD-CNN) to predict soil temperature. In this model, ensemble empirical mode decomposition (EEMD) is used to decompose original soil temperature series into several intrinsic mode functions (IMFs). After decomposition, the original series are combined with IMFs to get new two-dimension input data as the input of the convolutional neural network (CNN). By comparing the results which is predicted by the trained model with the original soil temperature series and other four models of persistence forecast (PF), backpropagation neural network (BPNN), LSTM and EEMD-LSTM these, the result shows that EEMD-CNN has the better performance than other four models. EEMD-CNN shows good performance not only on predicting next day’s soil temperature but also on predicting several days delay’s temperature also has good performance. It is concluded that the proposed EEMD-CNN model in this study is a suitable tool for soil temperature prediction.

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

The temperature of the soil at different depths is one of the most important factors used in different disciplines, such as hydrology, soil science, civil engineering, construction, geotechnology, ecology, meteorology, agriculture, and environmental studies. In addition to physical and spatial variables, meteorological elements are also effective in changing soil temperatures at different depths. The use of machine-learning models is increasing day by day in many complex and nonlinear branches of science. These data-driven models seek solutions to complex and nonlinear problems using data observed in the past. In this research, decision tree (DT), gradient boosted trees (GBT), and hybrid DT–GBT models were used to estimate soil temperature. The soil temperatures at 5, 10, and 20 cm depths were estimated using the daily minimum, maximum, and mean temperature; sunshine intensity and duration, and precipitation data measured between 1993 and 2018 at Divrigi station in Sivas province in Turkey. To predict the soil temperature at different depths, the time windowing technique was used on the input data. According to the results, hybrid DT–GBT, GBT, and DT methods estimated the soil temperature at 5 cm depth the most successfully, respectively. However, the best estimate was obtained with the DT model at soil depths of 10 and 20 cm. According to the results of the research, the accuracy rate of the models has also increased with increasing soil depth. In the prediction of soil temperature, sunshine duration and air temperature were determined as the most important factors and precipitation was the most insignificant meteorological variable. According to the evaluation criteria, such as Nash-Sutcliffe coefficient, R, MAE, RMSE, and Taylor diagrams used, it is recommended that all three (DT, GBT, and hybrid DT–GBT) data-based models can be used for predicting soil temperature.

Development of a linear based stochastic model for daily soil temperature prediction: One step forward to sustainable agriculture

Many variables in our environment are impacted by alternations in soil temperature (Tsoc). Fluctuations in Tsoc alter gas absorption and emission capacity of the soil, which mutually influence climate changes. Hence, providing viable methodologies of modeling Tsoc is of great importance. Since thorough investigations of time series structure are mostly neglected, and appropriate pre-processing methods are not applied to them, the direct use of nonlinear methods for soil temperature forecasting has become more common than other approaches. In this study, unlike most of the existing studies that estimate Tsoc as a variables based approach, soil temperature is forecasted using a linear stochastic based methodology with sufficient knowledge of time series structure. With this methodology, the components of the Tsoc time series are determined in order to perform stochastic modeling using Holt-Winters advanced exponential smoothing. The results of this procedure are compared with two stochastic techniques based on seasonal standardization (stdω) and spectral analysis (sf) in terms of six Tsoc time series at each station. The Tsoc. time series applied in the current study were measured at Bandar Abbas and Kerman synoptic stations, Iran, at depths of 5, 10, 20, 30, 50, and 100 cm. Owing to the comparison of the applied techniques in different Tsoc time series analysis, the stochastic model with Holt-Winters advanced exponential smoothing (Bandar Abbas station: (R2% = 96.649, RMSE = 0.977, MAPE% = 1.963, AICc = −733.006; Kerman station: R2% = 92.416, RMSE = 1.806, MAPE% = 7.793, AICc = 360.410) outperformed the stdω and sf methods. In addition to comparing linear stochastic methods, the results of Tsoc modeling are compared with two powerful nonlinear methods. In an antecedent study on the mentioned sites, Nahvi et al. (2016) employed extreme learning machine (ELM) and self-adaptive evolutionary ELM (SaE-ELM) nonlinear techniques. A comparison of results indicates that the proposed methodology outperformed the nonlinear models (ELM and SaE-ELM). Indeed, the proposed linear based stochastic model not only enhanced forecasting accuracy but also demonstrated the capability of linear models in Tsoc time series forecasting. Concerning the comprehensibility and generality of the framework, it can apply to other stations with different climatological conditions.

Application of Random Forest and ICON Models Combined with Weather Forecasts to Predict Soil Temperature and Water Content in a Greenhouse

Climate change might potentially cause extreme weather events to become more frequent and intense. It could also enhance water scarcity and reduce food security. More efficient water management techniques are thus required to ensure a stable food supply and quality. Maintaining proper soil water content and soil temperature is necessary for efficient water management in agricultural practices. The usage of water and fertilizers can be significantly improved with a precise water content prediction tool. In this study, we proposed a new framework that combines weather forecast data, numerical models, and machine learning methods to simulate and predict the soil temperature and volumetric water content in a greenhouse. To test the framework, we performed greenhouse experiments with cherry tomatoes. The numerical models and machine learning methods we selected were Newton’s law of cooling, HYDRUS-1D, the random forest model, and the ICON (inferring connections of networks) model. The measured air temperature, soil temperature, and volumetric water content during the cultivation period were used for model calibration and validation. We compared the performances of the models for soil temperature and volumetric water content predictions. The results showed that the random forest model performed a more accurate prediction than other methods under the limited information provided from greenhouse experiments. This approach provides a framework that can potentially learn best water management practices from experienced farmers and provide intelligent information for smart greenhouse management.

Improving hydrologic model to predict the effect of snowpack and soil temperature on carbon dioxide emission in the cold region peatlands

Peatlands cover only about 3% of the Earth’s surface and store 15–30% of the Global soil carbon as a peat. However, human intervention and climate change threatens the stability of peatlands, owing to deforest, wildfire, mining, drainage, glacial retreat, and permafrost. In our study, we modified the SWAT model to couple snow, soil temperature and carbon dioxide emission. Then the modified SWAT was used for predicting snow depth, soil temperature at different depths and carbon dioxide emission from peatlands and other land uses at Athabasca river basin, Canada. The results of the study indicated that SWAT model estimated the daily snow depth with R2, NSE, RMSE and PBIAS values of 0.83, 0.76, 0.52 and −2.3 in the calibration period (2006–2007) and 0.79, 0.71, 0.97 and −3.6 for the validation period (2008–2009), respectively. The SWAT model also predicted soil temperature very well at three depths (5 cm, 10 cm and 30 cm). The simulation model results also confirmed that the modified SWAT model estimates the CO2 emission at Athabasca river basin with good model fit during calibration (R2 = 0.71, NSE = 0.67, RMSE = 2.6 and PBIAS = 3.2) and during validation (R2 = 0.63, NSE = 0.58, RMSE = 3.1 and PBIAS = 9.3). Overall, our result confirmed that SWAT model performed well in representing the dynamics of snow depth, soil temperature and CO2 emissions in the peatlands at the Athabasca river basin.

Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach

Soil temperature plays an important role in understanding hydrological, ecological, meteorological, and land surface processes. However, studies related to soil temperature variability are very scarce in various parts of the world, especially in the Indian Himalayan Region (IHR). Thus, this study aims to analyze the spatio-temporal variability of soil temperature in two nested hillslopes of the lesser Himalaya and to check the efficiency of different machine learning algorithms to estimate soil temperature in the data-scarce region. To accomplish this goal, grassed (GA) and agro-forested (AgF) hillslopes were instrumented with Odyssey water level and decagon soil moisture and temperature sensors. The average soil temperature of the south aspect hillslope (i.e., GA hillslope) was higher than the north aspect hillslope (i.e., AgF hillslope). After analyzing 40 rainfall events from both hillslopes, it was observed that a rainfall duration of greater than 7.5 h or an event with an average rainfall intensity greater than 7.5 mm/h results in more than 2 °C soil temperature drop. Further, a drop in soil temperature less than 1 °C was also observed during very high-intensity rainfall which has a very short event duration. During the rainy season, the soil temperature drop of the GA hillslope is higher than the AgF hillslope as the former one infiltrates more water. This observation indicates the significant correlation between soil moisture rise and soil temperature drop. The potential of four machine learning algorithms was also explored in predicting soil temperature under data-scarce conditions. Among the four machine learning algorithms, an extreme gradient boosting system (XGBoost) performed better for both the hillslopes followed by random forests (RF), multilayer perceptron (MLP), and support vector machine (SVMs). The addition of rainfall to meteorological and meteorological + soil moisture datasets did not improve the models considerably. However, the addition of soil moisture to meteorological parameters improved the model significantly.

Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model

An enhanced hybrid artificial intelligence model was developed for soil temperature (ST) prediction. Among several soil characteristics, soil temperature is one of the essential elements impacting the biological, physical and chemical processes of the terrestrial ecosystem. Reliable ST prediction is significant for multiple geo-science and agricultural applications. The proposed model is a hybridization of adaptive neuro-fuzzy inference system with optimization methods using mutation Salp Swarm Algorithm and Grasshopper Optimization Algorithm (ANFIS-mSG). Daily weather and soil temperature data for nine years (1 of January 2010 - 31 of December 2018) from five meteorological stations (i.e., Baker, Beach, Cando, Crary and Fingal) in North Dakota, USA, were used for modeling. For validation, the proposed ANFIS-mSG model was compared with seven models, including classical ANFIS, hybridized ANFIS model with grasshopper optimization algorithm (ANFIS-GOA), salp swarm algorithm (ANFIS-SSA), grey wolf optimizer (ANFIS-GWO), particle swarm optimization (ANFIS-PSO), genetic algorithm (ANFIS-GA), and Dragonfly Algorithm (ANFIS-DA). The ST prediction was conducted based on maximum, mean and minimum air temperature (AT). The modeling results evidenced the capability of optimization algorithms for building ANFIS models for simulating soil temperature. Based on the statistical evaluation; for instance, the root mean square error (RMSE) was reduced by 73%, 74.4%, 71.2%, 76.7% and 80.7% for Baker, Beach, Cando, Crary and Fingal meteorological stations, respectively, throughout the testing phase when ANFIS-mSG was used over the standalone ANFIS models. In conclusion, the ANFIS-mSG model was demonstrated as an effective and simple hybrid artificial intelligence model for predicting soil temperature based on univariate air temperature scenario.

Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths

This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST5cm and ST 20cm of Tabriz station and ST10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.

Monthly Soil Temperature Modeling Using Gene Expression Programming

Soil temperature is a critical variable controlling below-ground processes for global and continental carbon budgets. However, there are an insufficient number of climatic stations monitoring soil temperature. In this study, GEP model was used for estimation of monthly soil temperature using air temperature, depth, relative humidity and solar radiation data for the Antalya, Isparta, and Burdur in Turkey. This model was tested using measured meteorological data. The values of R2 between observed and predicted soil temperatures ranged from 0.95 to 0.97. Predictions with GEP model show good agreement with actual soil temperature measurements. New equations are presented for calculation of soil temperatures at different depths. The GEP-based formulations are very practical to predict soil temperature. Soil temperature prediction with GEP model is helpful in various processes, including agricultural decision, heating or cooling of buildings and ground-source heat pump applications.

A Big Root Approximation of Site‐Scale Vegetation Water Uptake

AbstractLand surface model (LSM) predictions of soil moisture and transpiration under water‐limited conditions suffer from biases due to a lack of mechanistic process description of vegetation water uptake. Here, I derive a “big root” approach from the porous pipe equation for root water uptake and compare its predictions of soil moistures during the 2010 summer drought at the Wind River Crane site to two previously used Ohm's law analog plant hydraulic models. Due to a fuller representation of pressure gradients and flows within a complex root system architecture, the new formulation achieves somewhat improved fit and significantly lower bias compared to the Ohm's law analog models. A key advantage of the improved physical representation is the increased robustness of fits and predictions, making it less liable to overfitting. This new mechanistic model advances our understanding of vegetation water limitation at site scale with potential to improve LSM predictions of soil moisture, temperature and surface heat, water, and carbon fluxes.