Energy Balance Studies Research Articles

Characterizing energy balance closure over a heterogeneous ecosystem using multi-tower eddy covariance

Single point eddy covariance measurements of the Earth’s surface energy budget frequently identify an imbalance between available energy and turbulent heat fluxes. While this imbalance lacks a definitive explanation, it is nevertheless a persistent finding from single-site measurements; one with implications for atmospheric and ecosystem models. This has led to a push for intensive field campaigns with temporally and spatially distributed sensors to help identify the causes of energy balance non-closure. Here we present results from the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19)—an observational experiment designed to investigate how the Earth’s surface energy budget responds to scales of surface spatial heterogeneity over a forest ecosystem in northern Wisconsin. The campaign was conducted from June–October 2019, measuring eddy covariance (EC) surface energy fluxes using an array of 20 towers and a low-flying aircraft. Across the domain, energy balance residuals were found to be highest during the afternoon, coinciding with the period of surface heterogeneity-driven mesoscale motions. The magnitude of the residual varied across different sites in relation to the vegetation characteristics of each site. Both vegetation height and height variability showed positive relationships with the residual magnitude. During the seasonal transition from latent heat-dominated summer to sensible heat-dominated fall the magnitude of the energy balance residual steadily decreased, but the energy balance ratio remained constant at 0.8. This was due to the different components of the energy balance equation shifting proportionally, suggesting a common cause of non-closure across the two seasons. Additionally, we tested the effectiveness of measuring energy balance using spatial EC. Spatial EC, whereby the covariance is calculated based on deviations from spatial means, has been proposed as a potential way to reduce energy balance residuals by incorporating contributions from mesoscale motions better than single-site, temporal EC. Here we tested several variations of spatial EC with the CHEESEHEAD19 dataset but found little to no improvement to energy balance closure, which we attribute in part to the challenging measurement requirements of spatial EC.

Hypothalamic free fatty acid receptor-1 regulates whole-body energy balance

ObjectiveFree fatty acid receptor-1 (FFAR1) is a medium- and long-chain fatty acid sensing G protein-coupled receptor that is highly expressed in the hypothalamus. Here, we investigated the central role of FFAR1 on energy balance. MethodsCentral FFAR1 agonism and virogenic knockdown were performed in mice. Energy balance studies, infrared thermographic analysis of brown adipose tissue (BAT) and molecular analysis of the hypothalamus, BAT, white adipose tissue (WAT) and liver were carried out. ResultsPharmacological stimulation of FFAR1, using central administration of its agonist TUG-905 in diet-induced obese mice, decreases body weight and is associated with increased energy expenditure, BAT thermogenesis and browning of subcutaneous WAT (sWAT), as well as reduced AMP-activated protein kinase (AMPK) levels, reduced inflammation, and decreased endoplasmic reticulum (ER) stress in the hypothalamus. As FFAR1 is expressed in distinct hypothalamic neuronal subpopulations, we used an AAV vector expressing a shRNA to specifically knockdown Ffar1 in proopiomelanocortin (POMC) neurons of the arcuate nucleus of the hypothalamus (ARC) of obese mice. Our data showed that knockdown of Ffar1 in POMC neurons promoted hyperphagia and body weight gain. In parallel, these mice developed hepatic insulin resistance and steatosis. ConclusionsFFAR1 emerges as a new hypothalamic nutrient sensor regulating whole body energy balance. Moreover, pharmacological activation of FFAR1 could provide a therapeutic advance in the management of obesity and its associated metabolic disorders.

Comparative study of energy balance in urban and forest areas in Central Amazonia

Data from two experimental sites in central Amazonia were used, one located in a forested region and the other in an urban region. The values of the radiation and energy balance components were measured at both sites. The observed components of the radiation balance in the forest, and urban areas were quite different. The city, the radiative (albedo and emissivity) and thermal (absorptivity) parameters of the surface produced greater reflection of solar radiation and emission of longwave radiation. Urban pollution reduced the incident solar radiation and increased the longwave radiation emitted by the atmosphere. The energy balance presented marked differences in the partition between sensible and latent heat flux between forest and city. In the forest, much of the available energy is converted into latent heat flux, due to the process of evapotranspiration. Whereas, in the city, energy is equally divided into sensible and latent heat fluxes.

Metabolic and fecal microbial changes in adult fetal growth restricted mice

BackgroundFetal growth restriction (FGR) increases risk for development of obesity and type 2 diabetes. Using a mouse model of FGR, we tested whether metabolic outcomes were exacerbated by high-fat diet challenge or associated with fecal microbial taxa.MethodsFGR was induced by maternal calorie restriction from gestation day 9 to 19. Control and FGR offspring were weaned to control (CON) or 45% fat diet (HFD). At age 16 weeks, offspring underwent intraperitoneal glucose tolerance testing, quantitative MRI body composition assessment, and energy balance studies. Total microbial DNA was used for amplification of the V4 variable region of the 16 S rRNA gene. Multivariable associations between groups and genera abundance were assessed using MaAsLin2.ResultsAdult male FGR mice fed HFD gained weight faster and had impaired glucose tolerance compared to control HFD males, without differences among females. Irrespective of weaning diet, adult FGR males had depletion of Akkermansia, a mucin-residing genus known to be associated with weight gain and glucose handling. FGR females had diminished Bifidobacterium. Metabolic changes in FGR offspring were associated with persistent gut microbial changes.ConclusionFGR results in persistent gut microbial dysbiosis that may be a therapeutic target to improve metabolic outcomes.ImpactFetal growth restriction increases risk for metabolic syndrome later in life, especially if followed by rapid postnatal weight gain.We report that a high fat diet impacts weight and glucose handling in a mouse model of fetal growth restriction in a sexually dimorphic manner.Adult growth-restricted offspring had persistent changes in fecal microbial taxa known to be associated with weight, glucose homeostasis, and bile acid metabolism, particularly Akkermansia, Bilophilia and Bifidobacteria.The gut microbiome may represent a therapeutic target to improve long-term metabolic outcomes related to fetal growth restriction.

Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multi-angle Imaging Spectroradiometer (MISR), and Advanced Himawari Imager (AHI) AOD products by comparing their retrievals to the daily AERONET AOD values and explored the ability of AHI and AERONET to capture daily aerosol variability at 54 sites in Asia and Oceania from July 2015 to December 2019. For polar-orbiting satellites, based on collocations with AERONET measurements, MISR-derived AOD showed high consistency with daily average AERONET AOD, with a correlation coefficient (R) of 0.94 and 80.85% matchups falling into the envelopes of expected error (EE, ± (0.05 + 0.15 × AODAERONET)). Compared with single MODIS products, a combination of satellite data from the Terra and Aqua MODIS dark target (DT) algorithm might obtain a dataset with better DTR (R = 0.94 and %Within EE = 72.10). The satellite-retrieved AOD did a good job to represent the daily mean AERONET AOD, because AERONET AOD averaged within a ± 30 min time window of different satellite overpasses closely mirrored the daily mean AERONET AOD. Furthermore, the seasonal average diurnal variations (SADV) of AERONET AOD were changing insignificant, except for the urban coverage area in the Southern Hemisphere and equatorial climate. For geostationary satellite, the DTR of AHI was relatively poorer in comparison to the other sensors (R = 0.84, %Within EE = 58.00), and AHI AOD was seriously underestimated in South Asia and overestimated in Oceania. Besides, the SADVs of AHI AOD presented several patterns summarized as wave type, unimodal type, stable fluctuation type, and other types, which meant that AHI AOD retrievals did not accurately capture the diurnal variability of aerosols. This study provides a reference for the aerosol community to select satellite AOD products with appropriate DTR.

Energy Balance Studies of Weed Management Practices in Mustard (Brassica juncea (L.) Czerj and Cosson)

An experiment was conducted at College Farm, College of Agriculture, Rajendranagar, Hyderabad during rabi 2020-21 on loamy sand soils to study energy balance of weed management practices in mustard. The energy balance studies were determined by using direct and indirect energy. Among the different weed management practices, Stomp 30 % EC as PE fb straw mulch 5 t ha-1 recorded higher energy input. This treatment was followed by Goal 23.5 % EC as PE fb straw mulch 5 t ha-1 and Raft 6 % EC as PE fb straw mulch 5 t ha-1. Maximum energy output, net energy, energy use efficiency and energy productivity were noticed under intercultivation and hand weeding at 15 and 30 DAS and it was statistically on par with Raft 6 % EC as PE fb intercultivation at 30 DAS, Goal 23.5 % EC as PE fb intercultivation at 30 DAS and Stomp 30 % EC as PE fb intercultivation at 30 DAS.

Analysis of outlet temperature of parabolic trough collector solar water heater using machine learning techniques

The green sources of energy are ocean, hydro, solar, tidal, wave, wind, biomass, etc. Among all these wind, solar and hydro are mainly used. Particularly, solar energy has various applications such as atmospheric energy balance studies, solar energy collecting systems, analysis of the thermal load on buildings, etc. Parabolic trough collector (PTC) based solar water heater (SWH) gains a significant role in water heating systems. Parabolic trough collector is a concentrating type collector which collects the solar radiation in copper tube placed in the focal point of the parabolic trough. It also generates a high temperature which is suitable for steam generation. The main goal of this paper is to predict the outlet temperature of parabolic trough collector solar water heater with time and temperature for different days using various machine learning techniques.

Mammalian models of diabetes mellitus, with a focus on type 2 diabetes mellitus.

Although no single animal model replicates all aspects of diabetes mellitus in humans, animal models are essential for the study of energy balance and metabolism control as well as to investigate the reasons for their imbalance that could eventually lead to overt metabolic diseases such as type 2 diabetes mellitus. The most frequently used animal models in diabetes mellitus research are small rodents that harbour spontaneous genetic mutations or that can be manipulated genetically or by other means to influence their nutrient metabolism and nutrient handling. Non-rodent species, including pigs, cats and dogs, are also useful models in diabetes mellitus research. This Review will outline the advantages and disadvantages of selected animal models of diabetes mellitus to build a basis for their most appropriate use in biomedical research.

Life cycle assessment for evaluating the energy balance of the multi-feedstock biodiesel production process in Indonesia

This study aims to compare the energy balance of the production of biodiesel from multiple feedstocks to the production of biodiesel from palm oil in Indonesia. The energy balance study indicates that the biodiesel multi-feedstock plant is feasible to operate. The renewability of biodiesel from multiple feedstocks is 3.31 and that of palm oil biodiesel is 6.23. It is due to the fact that the energy content and demand of multi-feedstock biodiesel are much lower than palm oil biodiesel. The energy content of biodiesel from multiple feedstocks (and its co-products) is less than that of biodiesel from palm oil (and its co-products), 109,041 MJ vs 130,370 MJ, respectively. The energy demands for the production of biodiesel from multiple feedstocks and biodiesel from palm oil are 35 × 103 MJ and 23 × 103 MJ, respectively. Energy demands, including fossil and biomass, are attributable to plantation operations, including the usage of fertilisers. Plantations of multiple crops use more energy than plantations of oil palm alone because they require more fertilisers. It is not suggested to produce multi-feedstock biodiesel from sunflower, canola, and soybean since its energy balance is not better than biodiesel from palm oil.

Associations between the Dietary Inflammatory Index and Sleep Metrics in the Energy Balance Study (EBS).

(1) Background: Sleep, a physiological necessity, has strong inflammatory underpinnings. Diet is a strong moderator of systemic inflammation. This study explored the associations between the Dietary Inflammatory Index (DII®) and sleep duration, timing, and quality from the Energy Balance Study (EBS). (2) Methods: The EBS (n = 427) prospectively explored energy intake, expenditure, and body composition. Sleep was measured using BodyMedia's SenseWear® armband. DII scores were calculated from three unannounced dietary recalls (baseline, 1-, 2-, and 3-years). The DII was analyzed continuously and categorically (very anti-, moderately anti-, neutral, and pro-inflammatory). Linear mixed-effects models estimated the DII score impact on sleep parameters. (3) Results: Compared with the very anti-inflammatory category, the pro-inflammatory category was more likely to be female (58% vs. 39%, p = 0.02) and African American (27% vs. 3%, p < 0.01). For every one-unit increase in the change in DII score (i.e., diets became more pro-inflammatory), wake-after-sleep-onset (WASO) increased (βChange = 1.00, p = 0.01), sleep efficiency decreased (βChange = -0.16, p < 0.05), and bedtime (βChange = 1.86, p = 0.04) and waketime became later (βChange = 1.90, p < 0.05). Associations between bedtime and the DII were stronger among African Americans (βChange = 6.05, p < 0.01) than European Americans (βChange = 0.52, p = 0.64). (4) Conclusions: Future studies should address worsening sleep quality from inflammatory diets, leading to negative health outcomes, and explore potential demographic differences.

Development of a dynamic model for prediction of energy in milk protein, lactose, fat, and enteric methane emissions in goats based on energy balance and indirect calorimetry studies.

Feed costs are overwhelmingly the largest expense for dairy producers. Thus, improving milk production efficiency (milk fat and protein are the main incomes for farmers) is of great economic importance in the dairy industry. The main objective of this study was to develop a dynamic energy partitioning model to describe and quantify how dietary energy from carbohydrate, protein, and fat is transferred to milk (protein, lactose, and fat) in dairy goats. In addition, due to increasing worldwide concerns regarding livestock contribution to global warming, methane (CH4) emission was quantified. For modeling purposes, 158 individual goat observations were used and randomly split into 2/3 for model development and 1/3 for internal evaluation. For external evaluation, 20 different energy balance studies from the literature (77 observations) were evaluated. The Root Mean Square Prediction Error (RMSPE) was 13.2% for loss of energy in CH4, 16.8% for energy in fat, 19.4% for energy in protein, and 22.3 energy in lactose. Mean bias was around zero for all variables and the slope bias was zero for milk energy in lactose, close to 1% for milk fat (1.01%), and around 3% and 10% for protein and CH4, respectively. Random bias was greater than 85% for energy in CH4 and milk energy components indicating non-systematic errors and that the equation in the model fitted the data properly. Analyses of residuals appeared to be randomly distributed around zero. Slopes of regression lines for residuals vs. predicted were positive for milk fat energy, zero for lactose, and negative for milk energy in protein and CH4. This model suggested for use with mixed diets and by-products to obtain balanced macronutrient supply, methane emissions, and milk performance during mid lactation could be an interesting tool to help farmers simulate scenarios that increase milk fat and protein, evaluate CH4 emissions, without the costs of running animal trials.

A Case Study of Energy Balance of Castor Bean Cultivation in Iran

The production of biofuel from farm products has been promoted as a replacement for fossil fuels. Castor bean (Ricinus communis L.) is one of the best crops used to produce biodiesel. The first step toward the introduction and widespread cultivation of the crop is to investigate the energy balance and economic analysis for its production in the field. To do so, we studied the comparison of the energy balance sheet on three different planting dates and plant densities of Castor beans in the Urmia. All data, such as input and output, were converted to equal values of the consumed and produced energy, by using special formulas and indexes, after which the energy efficiency was calculated. The results of analysis of variance showed that the effect of different planting dates on energy output of biomass, grain, oil, protein yield, and their energy efficiency was significant. Energy efficiency of oil and protein yield was also affected by plant density per unit area. The interaction effect of planting date and plant density was also significant on biomass energy output and its efficiency, energy efficiency of oil, and protein yield. According to the results, in order to achieve maximum energy efficiency, it is suggested that the most desirable planting date is May 20 and the most suitable density of 16,000 plants per hectare in West Azerbaijan.

Evaporation and energy balance studies of Lake Kasumigaura

Evaporation and energy balance studies of Lake Kasumigaura

Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems.

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple timescales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study AmeriFlux core site cluster in the upper Great Lakes region of the USA from 1997 to 2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying levels of canopy sheltering and vegetation (US-Los and US-ALQ), and a very tall (400m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became weaker with time as non-climatic factors such as local disturbances likely dominated flux time series. Average decadal NEE at the tall tower transitioned from carbon source to sink to near neutral over 24years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. Declining snowfall offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. Higher CO2 increased maximum net assimilation parameters but not total gross primary productivity. Stand-scale sites were larger net sinks than the landscape tower. Clustered, long-term carbon flux observations provide value for understanding the diverse links between carbon and climate and the challenges of upscaling these responses across space.

The Response of Land Surface Temperature Changes to the Vegetation Dynamics in the Yangtze River Basin

Land surface temperature (LST) is a key parameter in the study of surface energy balance and climate change from local through to global scales. Vegetation has inevitably influenced the LST by changing the surface properties. However, the thermal environment pattern in the Yangtze River Basin (YRB) still remains unclear after the implementation of large-scale ecological restoration projects. In this study, the temporal and spatial variation characteristics of LST were analyzed based on the Theil–Sen estimator, Mann–Kendall trend analysis and Hurst exponent from 2003 to 2021. The relationships between vegetation and LST were further revealed by using correlation analysis and trajectory-based analysis. The results showed that the interannual LST was in a state of fluctuation and rise, and the increasing rate at night time (0.035 °C·yr−1) was faster than that at day time (0.007 °C·yr−1). An obvious cooling trend could be identified from 2007 to 2012, followed by a rapid warming. Seasonally, the warming speed was the fastest in summer and the slowest in autumn. Additionally, it was found that autumn LST had a downward trend of 0.073 °C·yr−1 after 2015. Spatially, the Yangtze River Delta, Hubei province, and central Sichuan province had a significant warming trend in all seasons, except autumn. The northern Guizhou province and Chongqing showed a remarkable cooling trend only in autumn. The Hurst exponent results indicated that the spring LST change was more consistent than the other three seasons. It was found by studying the effect of land cover types on LST changes that sparse vegetation had a more significant effect than dense vegetation. Vegetation greening contributed 0.0187 °C·yr−1 to the increase in LST in winter, which was spatially concentrated in the central region of the YRB. For the other three seasons, vegetation greening slowed the LST increase, and the degree of the effect decreased sequentially in autumn, summer, spring and winter. These results improve the understanding of past and future variations in LST and highlight the importance of vegetation for temperature change mitigation.

Permanent Emergency LED Lamp Based on a Series Single-Switch Resonant Converter With Battery Clamp

Permanent emergency lighting is a safety device whose lamp is always on and continues to operate when a mains failure occurs. The circuit has to charge the battery and drive the lamp while the grid is present and keep the lamp running from the battery in case of mains failure. This might require up to three different converters: front-end converter, electronic ballast, and battery charger. Since an emergency lamp must be on for as long as the local regulations require during a grid failure, efficiency is key. This article presents a permanent emergency lamp based on a series resonant current regulator that operates both as a ballast and as a battery charger. In this circuit, the power converter is placed in series with the LED array, operating as a controllable nondissipative impedance. A methodology based on the study of energy balances during resonant operation intervals is presented, aiming to design and build a laboratory prototype with low component count and high efficiency.

Daily Evapotranspiration Estimations by Direct Calculation and Temporal Upscaling Based on Field and MODIS Data

Daily evapotranspiration (ET) integration is essential to various applications of agricultural water planning and management, ecohydrology, and energy balance studies. The constant reference evaporative fraction (EFr) temporal upscaling method has been proven to be efficient in extrapolating instantaneous ET to a daily timescale. Unlike upscaling methods, the direct calculation (DC) method developed in our previous study directly estimates daily ET without calculating instantaneous ET. The present study aimed to compare daily estimations of ET using the EFr and DC methods based on field and MODIS data at a site from the ChinaFLUX network. The estimation results were validated by eddy covariance (EC) ET both with and without the correction of energy imbalance. Based on field data, the results show that (i) the DC method performed with higher accuracy when compared to uncorrected EC measurements, while daily ET from both methods was overestimated; (ii) the DC method still performed better after the EC ET was corrected by the Residual Energy scheme, and the overestimations were significantly decreased; (iii) both methods performed best when compared with corrected ET by the Bowen Ratio scheme. The results from satellite data reveal that (i) the constant EFr method overestimated daily ET by a mean-bias-error (MBE) of 5.6 W/m2, and a root-mean-square error (RMSE) of 18.6 W/m2; and (ii) the DC method underestimated daily ET by a smaller MBE of −4.8 W/m2 and an RMSE of 22.5 W/m2. Therefore, the DC method has similar or better performance than the widely used constant EFr upscaling method and can estimate daily ET directly and effectively.

Energy balance in cancer survivors at risk of weight gain: a review.

The study of energy balance [i.e., energy intake (EI) and energy expenditure (EE)] is a powerful tool for understanding body weight regulation and may contribute to our understanding of rapid weight gain risk in certain cancer survivors post-diagnosis. The purpose of this review was to summarize studies that assessed longitudinal, prospective changes in components of energy balance from diagnosis/start of treatment to any duration of follow-up in cancer survivors with prior evidence of weight gain (breast, prostate, thyroid, gynecologic, testicular, and acute lymphoblastic leukemia) RESULTS: The available literature suggests that energy balance components may be altered in cancer survivors who have a heightened risk of weight gain post-diagnosis. The evidence for EI was overall inconsistent. Conversely, decreases in resting and physical activity EE during the active phases of treatment (e.g., chemotherapy, hypothyroid state) were commonly noted, which then slowly rebounded towards baseline levels at the end of treatment and during follow-up assessments. Much of this evidence is based on data collected from breast cancer survivors, which highlights a paucity of data currently available on other cancer types. While there is growing acknowledgement that weight management interventions in cancer survivors are needed, it is important to recognize that changes in both behavioral (EI, physical activity EE) and passive (resting EE, thermic effect of food) components of energy balance may occur post-diagnosis. This information can help to inform weight management interventions which often entail modifications in diet and/or physical activity.

Evaluation of ERA5-Land and HARv2 Reanalysis Data at High Elevation in the Upper Dudh Koshi Basin (Everest Region, Nepal)

Abstract We present a multisite evaluation of meteorological variables in the Everest region (Nepal) from ERA5-Land and High Asian Refined Analysis, version 2 (HARv2), reanalyses in comparison with in situ observations, using classical statistical metrics. Observation data have been collected since 2010 by seven meteorological stations located on or off glacier between 4260 and 6352 m MSL in the upper Dudh Koshi basin; 2-m air temperature, specific and relative humidities, wind speed, incoming shortwave and longwave radiations, and precipitation are considered successively. Overall, both gridded datasets are able to resolve the mesoscale atmospheric processes, with a slightly better performance for HARv2 than that for ERA5-Land, especially for wind speed. Because of the complex topography, they fail to reproduce local- to microscale processes captured at individual meteorological stations, especially for variables that have a large spatial variability such as precipitation or wind speed. Air temperature is the variable that is best captured by reanalyses, as long as an appropriate elevational gradient of air temperature above ground, spatiotemporally variable and preferentially assessed by local observations, is used to extrapolate it vertically. A cold bias is still observed but attenuated over clean-ice glaciers. The atmospheric water content is well represented by both gridded datasets even though we observe a small humid bias, slightly more important for ERA5-Land than for HARv2, and a spectacular overestimation of precipitation during the monsoon. The agreement between reanalyzed and observed shortwave and longwave incoming radiations depends on the elevation difference between the station site and the reanalysis grid cell. The seasonality of wind speed is only captured by HARv2. The two gridded datasets ERA5-Land and HARv2 are applicable for glacier mass and energy balance studies, as long as either statistical or dynamical downscaling techniques are used to resolve the scale mismatch between coarse mesoscale grids and fine-scale grids or individual sites.

Comparative analysis of seven machine learning algorithms and five empirical models to estimate soil thermal conductivity

Soil thermal conductivity (λ) is an important thermal property that is crucial for surface energy balance and water balance studies. 1602 measured soil thermal conductivity values representing 189 soils were used to evaluate five empirical models (i.e., de Vries (1963) model (de Vries 1963), Campbell (1985) model (Campbell1985), Johansen (1975) model (Johansen 1975), Côté and Konrad (2005) model (Côté and Konrad 2005), and Lu et al. (2007) model (Lu 2007)) and seven machine learning (ML) algorithms (i.e., Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Linear Regression (LR), K-Nearest Neighbors (KNN), Neural Network (NN), and Gaussian Process (GP)) to estimate λ. Our results demonstrated that the average root mean squared error (RMSE) values of ML were 66% and 82% of the empirical model values on validation and test sets respectively. The three best ML algorithms (GBDT, NN, RF) performed significantly better than the three best empirical models (Lu 2007, Côté and Konrad 2005, Johansen 1975): 0.183 < RMSE < 0.259 (W m−1 K−1) for ML algorithms and 0.293 < RMSE < 0.320 (W m−1 K−1) for empirical models. For ML, we recommend the GBDT, NN and RF algorithms. For empirical models, we recommend to use three normalized models (Lu 2007, Côté and Konrad 2005, Johansen 1975) over the physically-based model (DV1963) and the regression model (CG1985). The feature importance rankings performed by the RF and GBDT algorithms show that soil moisture content and soil bulk density are the most critical factors affecting λ. Soil moisture content and soil bulk density together account for more than 80% of the influence importance value of λ. RF gives more consistent feature importance ranking results than GBDT, therefore, we recommend the use of RF for selecting features.