Soil Temperature Measurements Research Articles

Temporal variability and predictability predict alpine plant community composition and distribution patterns.

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species-environment associations. In this study, we evaluated how two components of temporal environmental variation-variability and predictability-impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long-Term Ecological Research site, we used in situ, high-resolution temporal measurements of soil moisture and temperature from 13 locations ("nodes") distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within-day) and seasonal (2- to 4-month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes.

Microtopographic effect on soil temperature during a burn by shifting cultivation in a tropical rain forest

Fire is an important component of shifting cultivation systems. Although its effects on soil properties depend on its severity and duration, few studies have measured soil temperature during burns at different depths and in relation to topography in tropical forests. Frequent rain keeps the soil moist in these ecosystems, but soil moisture does not only depend on precipitation but also on relief position. Here we monitor soil temperature at 1 and 10 cm depth before the burn along elevation gradients of three logged hillsides to establish the daily temperature variation, and to compare it with the soil temperature under forested sites and during the burn. Additionally, moisture content and physicochemical properties of the soil were measured before and after the burn.Results before the burn indicate a larger daily temperature amplitude in logged versus forested areas, and specially at crests and shoulder positions in comparison to backslopes and toeslopes. The fire increased soil temperature only in 1 cm depth, and for less than 40 degrees at most of the sites. Soil moisture content was negatively correlated with maximum temperature during the burn, however, also fuel loads and litter thickness determined fire intensity, with the spatial distribution of these variables being unrelated to topography. Nutrient contents changed only in the litter layer, in which C and N diminished by 35 % and 24 %, respectively, while total Ca, Mg, K and P increased by 210, 202, 318, 235 % respectively. We conclude that soil moisture contents contributed to maintain fire intensity low at the studied site.

Supralittoral isopod (Oniscidea) diversity at three ecoregions along the Colombian Caribbean: useful data for environmental management

Introduction: Terrestrial isopods (Oniscidea) play important roles in the ecological processes of the soil in tropical ecosystems and are employed as indicators of environmental impact. Despite their importance, studies on the ecology of this suborder in the Neotropics are scarce. Objective: To assess spatial changes in alpha and beta diversity of Oniscidea in the supralittoral zone across Archipiélagos Coralinos (ARCO), Magdalena (MAG), and Morrosquillo (MOR) ecoregions in the Colombian Caribbean. Methods: We conducted Direct Intuitive Searches of specimens and measured soil temperature and moisture in 19 transects with logs, leaf litter, coral remains, and decomposing plant material. Results: A total of 1 970 individuals representing 17 species were collected, with Tylidae, Halophilosciidae, and Ligiidae being the most abundant families. Alpha diversity orders were higher in ARCO than MAG and MOR ecoregions. MAG and MOR differed in observed richness. The structure of the assemblage varied in dominant species and abundances. In ARCO, the indicator species were Tylos niveus, Littorophiloscia denticulata, Halophiloscia trichoniscoides, and Ligia baudiniana; in MOR, it was Tylos marcuzzii, and in MAG, it was Littorophiloscia tropicalis. High beta diversity (> 60 %), with significant differences in the assemblage structure among ecoregions, was confirmed by the NMDS, which distinctly separated each group. CCA analysis revealed a negative relationship between most species with soil temperature and moisture, with a positive relationship observed with T. marcuzzii. Conclusions: This is the first effort to describe spatial changes in the diversity of oniscideans in the supralittoral zone of the Neotropical region, providing a baseline for future studies. This information could be instrumental in identifying priority areas for conservation efforts.

Results of the CO2 diffuse degassing survey from the 2017 IAVCEI CCVG 13th volcanic gas workshop: Pululahua Dome Complex, Ecuador

Pululahua is a potentially active andesite and dacite lava dome complex. This paper presents the results of a survey focused on carbon dioxide (CO2) diffuse degassing at Pululahua, which was conducted during the 2017 International Association of Volcanology and Chemistry of the Earth's Interior (IAVCEI) Commission of the Chemistry of Volcanic Gases (CCVG) 13th Gas Workshop. Our objective was to conduct a comprehensive investigation of CO2 diffuse degassing by employing standard methods for measuring CO2 flux and temperature, and data processing. These methods were applied to map the spatial distribution of the measured parameters, investigate the origin of CO2, and quantify the volcanic CO2 output within the surveyed area of Pululahua. We carried out a total of 350 soil CO2 flux and 329 soil temperature measurements and collected 12 gas samples for carbon isotopic composition analysis, surrounding the three youngest domes in the complex. In addition, seventeen CO2 flux measurements over a thermal water pool were performed. Our findings indicate that the diffuse emission at Pululahua's crater floor is fed by both biogenic and volcanic CO2. Fluxes from each source are similar in magnitude, with approximately 90% of the measurements falling into an intermediate flux range. The occurrence of volcanic CO2 emissions is supported by the carbon isotopic composition. Diffuse degassing distribution highlights a CO2 anomaly surrounding the younger domes within the crater. We estimated the CO2 diffuse emission using both statistical and geostatistical approaches over area of 3.36 km2, resulting in values of 154.2 t d−1 and 126.2 t d−1 respectively. Based on the geostatistical quantification of the total CO2 emission from soil degassing, Pululahua's crater volcanic CO2 contribution is estimated between 59 and 97 t d−1. Finally, the potential hazards associated with the release of cold CO2 at Pululahua's crater are also discussed.

Study on the effect of ground stratification on heat transfer of vertical ground heat exchangers in lateritic clay areas

The potential effect of ground stratification on the efficiency of ground heat exchangers (GHEs) in lateritic clay regions remains unclear. Model experiments were conducted to investigate the effects of soil dry density, water content, and ground stratification on the heat exchange between GHEs and the surrounding lateritic clay. A 3D numerical model accounting for ground stratification was proposed and validated using experimental data. The model further examined the influence of soil moisture on the dynamic heat exchange process. The findings of the model experiments indicate that the heat from buried pipes increase soil temperature by 12.8 % when the initial water content of lateritic clay (with dry density of 1.1 g/cm3) rises from 5 % to 28 %. Raising the dry density from 1.1 to 1.3 g/cm3 at 28 % water content results in a 7.5 % temperature increase. The initial soil moisture and density have negligible effects on the temperature gradient within 18 cm of the geothermal pipes. The temperature increased faster in the sand layer than in the lateritic clay layer owing to the difference in thermal properties. The impact of heat produced by GHEs on the temperature field in homogeneous soil layers differs considerably from that in stratified soil layers. The 3D numerical model provided a good fit for the measured soil temperature in the stratified soil layer, including the soil temperature variation at the layered interface. The simulation results showed that for dry and saturated strata, the temperature of the lateritic clay around the GHEs increased with increasing initial stratum moisture. For unsaturated lateritic clay strata, because of the possible impacts of moisture and heat coupling migration, the temperature of the stratum around the GHEs first increased and subsequently decreased as the initial stratum moisture was enhanced.

Evaluation of soil temperature in CMIP6 multimodel simulations

Soil temperature plays a crucial role in understanding land-atmosphere interactions. The Sixth Phase of Coupled Model Intercomparison Project (CMIP6) provides valuable information on soil temperature, however, the performance of these models in capturing soil temperature variations remains unclear. To comprehensively assess the performance of CMIP6 in simulating soil temperature, we employed a set of in-situ observation data, observation-derived gridded data (TS-GCB) and reanalysis data (ERA5L) to build various evaluating metrics for the surface (0–5 cm) and subsurface (5–15 cm) soils. At the global scale, the multimodel ensemble mean (MME) of CMIP6 generally captured the spatial, annual and seasonal variations of soil temperature, but overestimated measured soil temperature by 1.86 °C (vs. TS-GCB, surface) and 2.16 °C (vs. TS-GCB, subsurface), indicating more heat accumulated in soils than the reality. Surface soil temperature was better represented by MME compared to the subsurface soil layer (vs. TS-GCB). In addition, we found the largest simulation bias in the tropical zone, and both positive and negative bias in the arid regions. The large model spread of the individual models in representing soil temperatures in cold regions or periods highlights the needs of improved understanding of how snow and freeze-thaw affect soil thermal dynamics. Overall, the performance of MME is superior to that of the majority of individual models (vs. TS-GCB). Locally, the large discrepancy among observation-derived data, reanalysis data and CMIP6 simulations suggested that it is imperative to acquire more ground-truth soil temperature to better inform model simulation and forecasting.

Artificial intelligence to predict soil temperatures by development of novel model.

Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10cm depth. More importantly, the models developed here can predict temperature levels at 10cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.

A distributed-temperature-sensing-based soil temperature profiler

Abstract. Storage change in heat in the soil is one of the main components of the energy balance and is essential in studying the land–atmosphere heat exchange. However, its measurement proves to be difficult due to (vertical) soil heterogeneity and sensors easily disturbing the soil. Improvements in the precision and resolution of distributed temperature sensing (DTS) equipment has resulted in its widespread use in geoscientific studies. Multiple studies have shown the added value of spatially distributed measurements of soil temperature and soil heat flux. However, due to the spatial resolution of DTS measurements (∼30 cm), soil temperature measurements with DTS have generally been restricted to (horizontal) spatially distributed measurements. This paper presents a device which allows high-resolution measurements of (vertical) soil temperature profiles by making use of a 3D-printed screw-like structure. A 50 cm tall probe is created from segments manufactured with fused-filament 3D printing and has a helical groove to guide and protect a fiber-optic (FO) cable. This configuration increases the effective DTS measurement resolution and will inhibit preferential flow along the probe. The probe was tested in the field, where the results were in agreement with the reference sensors. The high vertical resolution of the DTS-measured soil temperature allowed determination of the thermal diffusivity of the soil at a resolution of 2.5 cm, many times better than what is feasible using discrete probes. A future improvement in the design could be the use of integrated reference temperature probes, which would remove the need for DTS calibration baths. This could, in turn, support making the probes “plug and play” into the shelf instruments without the need to splice cables or experience in DTS setup design. The design can also support the integration of an electrical conductor into the probe and allow heat tracer experiments to derive both the heat capacity and the thermal conductivity over depth at high resolution.

Evaluation of soil water content and bulk electrical conductivity across the U.S. Climate Reference Network using two electromagnetic sensors

AbstractSoil bulk electrical conductivity (BEC) was evaluated alongside soil volumetric water content (VWC) and soil temperature measurements using the HydraProbe (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP) with accuracy range of BEC ≤ 0.3 S m−1, and the time domain reflectometry (TDR)‐315L Probe (model TDR‐315L, Acclima, Inc.) (hereafter called AP) suitable for BEC up to 0.6 S m−1, at 23 stations of the U.S. Climate Reference Network. Previous evaluations revealed inconsistent performance of both sensors in some clay soils using manufacturer‐recommended calibrations in converting dielectric permittivity measurements to VWC. Here, we found that hourly values of BEC reached 0.6 S m−1 in high clay content soils and exceeded 2 S m−1 in high saline soils, and these high values of BEC were associated with poor performance and failures of both HP and AP sensors. Large values of BEC occurred in predominantly saturated soils where VWC values reached about 0.5 m3 m−3 for saline soils and about 0.7 m3 m−3 for clay soils, while low magnitudes of BEC were associated with low soil water content and seldomly saturated soils. Low hourly BEC values of less than 0.1 S m−1 were observed in wide variety of soil types, where sensor performance was typically excellent. The most influential factor on BEC was high soil water content conditions. Although dielectric permittivity measurements in estimating the soil water content were sensitive to BEC as some high clay content and high salinity soils increased BEC, the impact of large BEC on dielectric permittivity measurements was smaller in the well‐drained top soil layers than in deep soil layers that remained near saturation. Soil temperature had only a small impact on BEC. With high clay content and high salinity, the specific area of clay minerals was also associated with the magnitude of BEC.

Comparison of Met Office regional model soil moisture with COSMOS‐UK field‐scale in situ observations

AbstractThe UK Met Office state‐of‐the‐art, deterministic, convection‐permitting, coupled land‐atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in‐situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km2 gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw‐Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field‐scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS‐UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS‐UK soil moisture observations is generally good, and that changes made to the land data assimilation approach during a recent operational upgrade had a generally beneficial impact on UKV soil moisture analyses under very dry conditions.

Soil warming-induced reduction in water content enhanced methane uptake at different soil depths in a subtropical forest

Global warming can significantly impact soil CH4 uptake in subtropical forests due to changes in soil moisture, temperature sensitivity of methane-oxidizing bacteria (MOB), and shifts in microbial communities. However, the specific effects of climate warming and the underlying mechanisms on soil CH4 uptake at different soil depths remain poorly understood. To address this knowledge gap, we conducted a soil warming experiment (+4 °C) in a natural forest. From August 2020 to October 2021, we measured soil temperature, soil moisture, and CH4 uptake rates at four different soil depths: 0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm. Additionally, we assessed the soil MOB community structure and pmoA gene (with qPCR) at the 0–10 and 10–20 cm depths. Our findings revealed that warming significantly enhanced soil net CH4 uptake rate by 12.28 %, 29.51 %, and 61.05 % in the 0–10, 20–40, and 40–60 cm soil layers, respectively. The warming also led to reduced soil moisture levels, with more pronounced reductions observed at the 20–40 cm depth compared to the 0–20 cm depth. At the 0–10 cm depth, warming increased the relative abundance of upland soil cluster α (a type of MOB) and decreased the relative abundance of Methylocystis, but it did not significantly increase the pmoA gene copies. Our structural equation model analysis indicated that warming directly regulated soil CH4 uptake rate through the decrease in soil moisture, rather than through changes in the pmoA gene and MOB community structure at the 0–20 cm depth. In summary, our results demonstrate that warming enhances soil CH4 uptake at different depths, with soil moisture playing a crucial role in this process. Under warming conditions, the drier soil pores allow for better CH4 penetration, thereby promoting more efficient activity of MOB.

Ground Heat Flux Reconstruction Using Bayesian Uncertainty Quantification Machinery and Surrogate Modeling

AbstractGround heat flux (G0) is a key component of the land‐surface energy balance of high‐latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G0 data (measured or modeled) for snow‐free period as a reference. When observed G0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state‐of‐the‐art uncertainty quantification methods, the developed G0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation.

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture.

Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site

Abstract. Estimates of winter (snow-covered non-growing season) CO2 fluxes across the Arctic region vary by a factor of 3.5, with considerable variation between measured and simulated fluxes. Measurements of snow properties, soil temperatures, and net ecosystem exchange (NEE) at Trail Valley Creek, NWT, Canada, allowed for the evaluation of simulated winter NEE in a tundra environment with the Community Land Model (CLM5.0). Default CLM5.0 parameterisations did not adequately simulate winter NEE in this tundra environment, with near-zero NEE (< 0.01 gCm-2d-1) simulated between November and mid-May. In contrast, measured NEE was broadly positive (indicating net CO2 release) from snow-cover onset until late April. Changes to the parameterisation of snow thermal conductivity, required to correct for a cold soil temperature bias, reduced the duration for which no NEE was simulated. Parameter sensitivity analysis revealed the critical role of the minimum soil moisture threshold of decomposition (Ψmin) in regulating winter soil respiration. The default value of this parameter (Ψmin) was too high, preventing simulation of soil respiration for the vast majority of the snow-covered season. In addition, the default rate of change of soil respiration with temperature (Q10) was too low, further contributing to poor model performance during winter. As Ψmin and Q10 had opposing effects on the magnitude of simulated winter soil respiration, larger negative values of Ψmin and larger positive values of Q10 are required to simulate wintertime NEE more adequately.

Development of Microclimate Data Recorder on Coffee-Pine Agroforestry Using LoRaWAN and IoT Technology

Microclimate monitoring in agroforestry is very important to understand the complex interactions between vegetation, soil, and the environment. Microclimate parameters include air and soil temperature, air humidity, soil moisture, and light intensity. This research aims to develop a new microclimate data recording system for coffee-pine agroforestry, utilizing LoRaWAN and IoT technology to capture real-time microclimate parameters. Unlike traditional data loggers that require manual download on-site, this innovative system enables instant data download from IoT servers, thereby increasing data efficiency and accessibility. The system proved effective, significantly improving the precision of air temperature and humidity, as well as soil temperature measurements, with an average accuracy of 100%. However, soil moisture and light intensity recorded lower accuracies of 81.23% and 82.56%, respectively, indicating potential areas for future research and system refinement. The system maintains a 15-minute sampling period, aligning with conventional datalogger intervals. This represents an advancement in precision agriculture for microclimate monitoring, enabling the data to be utilized in decision-making for agroforestry management, which involves complex interactions between the local microclimate and the broader ecological system. It underscores the significance of sustainable land use as a response to global climate change.

Radiometric method of soil temperature and moisture measurement

Radiometric method of soil temperature and moisture measurement

Assessment of soil microclimate in an urban park of Budapest, Hungary

Investigation of urban parks is a particular research section in the frame of urban geology, with increasing social importance. Both natural and anthropogenic factors affect these green sites, giving special scientific importance to their investigation. Thus, the behavior of such a ’composite’ system is expected to be quite complex. Based on the general circumstances, a research project was introduced in 2016 in the former Mining and Geological Survey of Hungary (now: Supervisory Authority for Regulatory Affairs). The ongoing project was focused on the behavior of urban parks. Within the frame of this research, between 2016 and 2019, field studies were started in 4 parks of Budapest. This paper is targeting one of these parks, namely Honvéd square. Our objective is to understand the behavior of urban parks under special conditions (e.g., during heat island effects) by getting information from well-defined positions. This can be essential to perform more sustainable water management in an urban park. Within the frame of the field works soil temperature and soil moisture measurements were being recorded manually every week in every park, at four different locations within a park. Observation points were selected to describe the different microclimates of the parks. A statistical analysis of the data reveals that the urban heat island (UHI) effect is reflected in soil temperatures at a citywide scale and that by moderating urban soil surface temperature extremes, trees and shrubs may help to reduce the adverse impacts of urbanization on microclimate, soil processes, and human health. It can be stated, that beside the manual measurements, the automated soil temperature detection was significantly influenced by soil depth at the Honvéd square park. At 100 cm below the surface, the soil temperature is relatively constant. It was approved that not only do deeper soil layers undergo less drastic seasonal temperature fluctuations but also the changes taking place lag further behind those of shallower soil layers.

Noble gases in groundwater reflect wet-season temperature in an arid, monsoonal, and mountainous environment

Comparing directly measured soil temperatures with noble gas recharge temperatures (NGTs) inferred from noble gas concentrations indicates that the infiltrating soil water equilibrates with soil air near the soil surface during the rainy season. Therefore, NGTs of groundwater recently recharged by the Indian Summer Monsoon (ISM) in the Dhofar Mountains in southern Oman reflect the soil temperatures of the 3-month period and do not represent an annual mean. This finding highlights the need to account for seasonality when interpreting NGT data in regions with pronounced dry and wet seasons.We extend the observations from the southern flank of the Dhofar Mountains to three wells situated on the northern flank of the Dhofar Mountains. Two of these wells yield water of Holocene age that was recharged by the monsoon, their NGT signals are therefore classified as seasonal. The NGT calculated from a third well for recharge conditions during the Last Glacial Maximum (LGM), when the ISM was absent, is approximately 3 °C lower than that of the two Holocene wells. The lower LGM noble gas temperature corresponds well with the lower annual Sea Surface Temperature (SST) in the nearby Arabian Sea.NGTs from published studies from northern Oman are 1–3 °C higher when compared with our data of the same period in the southern Oman. We explain this regional difference of reconstructed temperatures for the LGM and Holocene groundwater with a more continental climatic influence on the infiltration conditions further to the north. The published NGTs from northern Oman show a large temperature difference between the late Holocene and the LGM. In view of our finding of seasonal NGT signals under monsoonal climate, part of this difference may reflect a change in the precipitation regime rather than in air temperature.

The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model

AbstractThis synthetic study explores the value of near‐surface soil moisture and soil temperature measurements for the estimation of soil moisture and soil temperature profiles, soil hydraulic and thermal parameters, and latent heat and sensible heat fluxes using data assimilation (ensemble Kalman filter) in combination with unsaturated zone flow modeling (HYDRUS‐1D), for 12 United States Department of Agriculture soil textures in a homogeneous and bare soil scenario. The soil moisture profile is estimated with a root mean square error (RMSE) of 0.04 cm3/cm3 for univariate soil temperature assimilation and 0.01 cm3/cm3 for univariate soil moisture assimilation. Soil temperature assimilation performs better for soils with higher clay content compared to soils with higher sand content. The latent and sensible heat fluxes are estimated with smaller RMSE for univariate soil temperature assimilation compared to univariate soil moisture assimilation for 8 out of 12 soil types. As the climate condition changes from hot semi‐arid to sub‐humid climate, the soil moisture assimilation performs better for high permeable soil but worse for low permeable soil. In summary, the findings suggest that for most soil texture classes, assimilating soil temperature in vadose zone models is skillful to improve latent heat flux, soil moisture profile, and soil hydraulic parameters. Joint assimilation with soil moisture can further enhance the accuracy of the model outputs for all range of soil texture and climate conditions.

High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

AbstractPermafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter‐scale maps of near‐surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near‐surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k‐fold cross validation, and the modeled extents of near‐surface permafrost were compared to the observed extents at each site. At‐a‐site near‐surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at‐a‐site prediction (70%–83%), yet they had greater accuracy when transferred to the non‐training site (62%–78%). These models demonstrate the potential for integrating high‐resolution spatial data and machine learning models to develop maps of near‐surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.