Soil Heat Flux Research Articles

Physical mechanism-based simulation methods for soil and ground heat flux from observations at multi-sites

It is important to obtain the soil heat flux at a specific depth (Gz) and at the ground’s surface (G0) to understand the surface energy balance and climate change. However, previous evaluations of Gz and G0 simulations based on multiple sites have been relatively insufficient. We evaluated the accuracy of using physical mechanism-based gradient and calorimetry methods to simulate Gz and changes in heat storage (ΔS) based on observations collected at 28 sites. Three methods for simulating G0 were also evaluated using triple collocation (TC) and extended triple collocation (ETC) metrics. The ability of the gradient method to simulate Gz was strong, particularly for the lower soil layer, with a median Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), and index of agreement for the evaluated sites of 0.76, 0.86, and 0.91, respectively. The accuracy of the calorimetry method for simulating ΔS was generally lower than that of the gradient method for determining Gz. Overall, the combination of gradient analysis and calorimetry showed the best performance in simulating G0. The TC evaluation showed that the combination of gradient and calorimetry had a strong ability to simulate G0 in most land cover types (root mean square error (RMSE) <4 W/m2) but this ability was lost in the snow cover type (RMSE > 10 W/m2). When evaluated by ETC, the three methods had strong effects only on alpine meadow, floodland, and shrub types (CC > 0.70). Appropriate evaluation indices were recommended based on the application of the model.

Warming-induced soil moisture stress threatens food security in India.

Soil moisture (SM) interconnects various components of the Earth system and drives the land-atmosphere feedbacks and food production. However, around 40% of global vegetated land experiences SM drying. India is one of the global hotspots of land-atmosphere interactions and an extensively agrarian economy, but underexplored in terms of SM dynamics and its ramifications on food security. Here, we examine the mechanism of SM drying and its implications on cropland productivity in India based on remote sensing measurements and land surface model simulations in recent decades (2000-2019) and future projection of the 21st century. We find SM reduction predominantly in monsoon (4.5%) and winter (3%) seasons that areinthemajor agricultural seasonsofKharif and Rabi, respectively. Machine learning (ML)-based random forest (RF) reveals that temperature (T, 30.76%) is the dominant driver of SM variability, and then precipitation (P, 26.34%), evapotranspiration (ET, 26.08%) and surface greenness (16.82%). Concurrently, India experiences severe warming in terms of land (0.59 ℃/dec), soil (0.48 ℃/dec) and soil heat flux (SHF, 0.16 W/m2/dec) during 2000-2019. Partial correlation analysis between SM and T limiting the influence of P reveals a strong negative (> - 0.5) relationship in the agriculture intensive regions of Indo-Gangetic Plain (IGP) and South India (SI). Drying owing to warming and increased SHF, termed as warming-induced moisture stress, reduces gross primary productivity (GPP) (i.e. browning) and yield of major food crops of wheat, rain-fed rice, maize and soyabean, predominantly in SI and eastern IGP. Granger Causality shows that warming-induced soil moisture stress has a maximum temporal lag of 1month. In a warming world, the ever-growing population demands more food, and therefore, the warming-induced soil moisture stress is a serious threat to food security in India and similar agro-climatic regions of the world. This calls for climate-resilient agriculture, better agronomic management, improved irrigation and adoption of water-efficient crops.

Effects of different mulching technologies on rainfed maize field energy exchange and evapotranspiration partitioning in northwest China

Artificial mulching, a prevalent water-saving tool in arid and semiarid agroecosystems, complicated energy exchange and water transport process between land surface and atmosphere. The field energy and evapotranspiration (ET) partitioning within a soil-mulch-plant-atmosphere continuum have not been comprehensively examined, which limited cognition of the underlying mechanisms that control hydrological processes and achieve yield improvement. Here, a four-season experiment in a rainfed summer maize field with four mulching treatments (NM: non-mulching, SM: straw mulching, RPBF: plastic-mulched ridge with bare furrow, and RPSF: plastic mulched ridge with straw-mulched furrow) was conducted to study the dynamics of surface energy and ET components and their regulation mechanisms. Results indicated that the effects of mulching on field energy partitioning were most evident at the maize initial stage, during which net radiation was significantly decreased under PM treatments (RPBF and RPSF), while soil heat flux was significantly reduced in SM treatments (p < 0.05). The average latent heat flux (LE) was 3.55 and 5.65 MJ·d−1 for NM maize at sowing and seedling stages, but decreased by 9.01 %, 27.04 %, 29.58 % at sowing, and 7.08 %, 9.56 % and 16.46 % at seedling stages for SM, RPBF and PRSF, respectively. The energy component differences among different treatments were minimal during the middle and late stages with high leaf coverage, and total LE and sensible heats were not significantly different. Mulch exerted a notable impact on soil evaporation (E) and crop transpiration (T), characterized by the average E of 0.96, 0.82, 0.57 and 0.46 mm·d−1 in NM, SM, RPBF and RPSF, while the value for T was 1.67, 1.88, 2.03 and 2.10 mm·d−1, respectively. PM conserved soil water by reducing E during the initial growing season and in dry periods, but also enhancing T and deepening the depth of soil water consumption under higher leaf cover. Energy exchange and ET partitioning were found to be more sensitive to soil water content (SWC) during periods characterized by lower leaf coverage, while the role of underlying canopy coverage characteristics overwhelmed soil properties in controlling energy and ET partitioning along with the leaf expansion. These findings contributed to a more comprehensive understanding of different mulching management on rainfed maize energy exchange and water use in Northwest China.

MONTPEL: A multi-component Penman-Monteith energy balance model

Mechanistic modelling is gradually replacing empiricism in crop models, focusing on leaf-level physiological processes. This shift necessitates simulating crop surface temperature at infra-canopy sub-daily scales but many crop models still rely on empirical formulations for canopy temperature estimation, typically on a daily basis. We developed MONTPEL, a multi-component Penman-Monteith model that allows simulating the crop energy balance with flexible canopy representations (“BigLeaf” vs. “Layered”, “Lumped” vs. “Sunlit-Shaded”) and accounts for atmospheric stability conditions. We analyzed the model behavior, sensitivity and accuracy, using measurements from four wheat (Triticum aestivum L.) experiments conducted under varying pedoclimatic and water stress conditions. Measurements included hourly energy balance terms (total net radiation, soil heat flux, sensible and latent energy fluxes), hourly temperature of the canopy surface or of leaves at different depths inside the canopy, and sunlit and shaded leaf temperatures around solar noon at different dates. MONTPEL reproduced the measured energy balance terms with a root mean square error (RMSE) between 21 and 87 Wm-2 and a coefficient of determination (R²) exceeding 0.65. The model's accuracy in simulating canopy temperature, with RMSE ≤ 2.2 °C and R² ≥ 0.92, remained consistent regardless of measurement scale. Adjusting the aerodynamic resistance for atmospheric stability minimized simulated canopy temperature errors, notably in semi-arid conditions. Crop latent energy flux and temperature were most sensitive to the maximal stomatal conductance (gs,max) parameter. However, using a single gs,max value across the simulated experiments yielded satisfactory results, suggesting a weak sensitivity to the temporal and site-to-site variability of gs,max. Distinguishing sunlit from shaded canopy fractions systematically resulted in lower latent energy fluxes compared to “Lumped” canopy representation results. Analysis identified limitations in the multi-component approach, particularly an unrealistic uniform temperature shift across leaf layers when soil surface temperature changes.

A preliminary observational study on the characteristics of surface turbulent fluxes over the South China Sea Islands

AbstractIn recent years, there has been a rise in human activities in oceanic areas, making the land–atmosphere interactions over islands a major scientific concern on a global scale. Examining the observation data from offshore areas enables a more comprehensive understanding of the turbulent fluxes in offshore atmospheric environments, patterns of momentum, energy and material exchange between the atmosphere and underlying surface in an oceanic boundary layer, and development of a heterogeneous atmospheric boundary layer. The related findings will assist in developing theoretical models and parameterization schemes to simulate the influence of heterogeneous surfaces on land–atmosphere interactions on the South China Sea Islands. Existing studies on the turbulent fluxes over the South China Sea Islands were mainly conducted on the Nansha Islands, whereas studies on the waters of the South China Sea are scarce. In this study, we used 10 Hz high‐frequency turbulence measurements to calculate the latent and sensible heat fluxes over the South China Sea Islands using the eddy correlation method. These findings were then compared with data from the Dunhuang Gobi, Ordos desert, and Xilingol grassland regions in inland China, along with the observed net radiation and surface heat fluxes. The findings indicate that the energy fluxes over the South China Sea in summer exhibit prominent diurnal variations. The magnitude of either latent or soil heat flux is low, and the net radiation is predominantly transformed into sensible heat flux, which warms the atmosphere. Furthermore, the daily variation curves of sensible and latent heat fluxes are influenced by intermittent turbulence on the islands and reefs, resulting in a less smooth pattern compared with soil heat flux. Although the South China Sea Islands have small land areas and are surrounded by the sea, the land–atmosphere interactions over the underlying surface of this region are similar to those over the underlying surface of grasslands in inland China during summer. The daily mean sensible heat flux on the islands is higher than that in an inland area, and the time lag in its response to sunrise is longer than that in inland areas by approximately 1 h. The overall energy balance ratio is approximately 0.75, c which is in line with the average level, but an energy balance residual of approximately 25% still exists. Furthermore, extreme weather conditions, such as typhoons, can disrupt the diurnal variations of sensible and latent heat fluxes, and the cyclical patterns are subsequently restored.

A dataset of solar radiation and soil heat fluxes for agro-ecosystems in the hilly area of Central Sichuan Province (2005&amp;ndash;2021)

A dataset of solar radiation and soil heat fluxes for agro-ecosystems in the hilly area of Central Sichuan Province (2005&amp;ndash;2021)

Surface energy fluxes in a drip-irrigated agroecosystem: Unique advection effect of oasis

Surface energy fluxes, mainly encompassing the net radiation (Rn), latent heat flux (LE), sensible heat flux (Hs), and soil heat flux (Gs), play an important role in the land-atmosphere interactions. However, almost all sites face the problem of energy imbalance, and advection fluxes associated with large inhomogeneous surfaces have been ignored, especially in arid oasis areas. In this study, a three-year continuous measurement of energy fluxes with an eddy covariance system was conducted in a drip-irrigated oasis agroecosystem in Northwest China. Reanalysis data including air temperature (Ta), soil moisture (θ), and leaf area index (LAI) in our cropland and surrounding deserts were also collected. The results showed that multi-year mean turbulent fluxes (LE+Hs) accounted for 75 ± 8 % (mean ± standard deviation) of available energy (Rn–Gs). To be precise, LE took up 72 ± 10 % of available energy, and 7.8 ± 2.8 % of it was induced by higher sensible heat advection, proving a pronounced advection effect in this study. When advection was present, most likely during the heading stage, the threshold value for the Priestley–Taylor parameter α, an indicator to reflect the advection effect, fell in the range of 0.88–1.34. Additionally, after a significant irrigation event, α showed a good linear relationship with differences in air temperature (ΔTa), soil moisture (Δθ), and leaf area index (ΔLAI) between our cropland and surrounding deserts. It's worth mentioning that Δθ was the most significant factor, showing a negative correlation with the advection effect. This study has deepened our understanding of the energy balance in oasis agriculture, emphasizing that the advection effect should not be overlooked.

A Machine Learning-Driven Approach to Uncover the Influencing Factors Resulting in Soil Mass Displacement

For most landslides, several destabilising processes act simultaneously, leading to relative sliding along the soil or rock mass surface over time. A number of machine learning approaches have been proposed recently for accurate relative and cumulative landside displacement prediction, but researchers have limited their studies to only a few indicators of displacement. Determining which influencing factors are the most important in predicting different stages of failure is an ongoing challenge due to the many influencing factors and their inter-relationships. In this study, we take a data-driven approach to explore correlations between various influencing factors triggering slope movement to perform dimensionality reduction, then feature selection and extraction to identify which measured factors have the strongest influence in predicting slope movements via a supervised regression approach. Further, through hierarchical clustering of the aforementioned selected features, we identify distinct types of displacement. By selecting only the most effective measurands, this in turn informs the subset of sensors needed for deployment on slopes prone to failure to predict imminent failures. Visualisation of the important features garnered from correlation analysis and feature selection in relation to displacement show that no one feature can be effectively used in isolation to predict and characterise types of displacement. In particular, analysis of 18 different sensors on the active and heavily instrumented Hollin Hill Landslide Observatory in the north west UK, which is several hundred metres wide and extends two hundred metres downslope, indicates that precipitation, atmospheric pressure and soil moisture should be considered jointly to provide accurate landslide prediction. Additionally, we show that the above features from Random Forest-embedded feature selection and Variational Inflation Factor features (Soil heat flux, Net radiation, Wind Speed and Precipitation) are effective in characterising intermittent and explosive displacement.

Water fluxes and energy partitioning over a Larix principis-rupprechtii plantation forest in a dryland mountain ecosystem, Northwest China

Plantations make up a sizable component of forest ecosystems in China's drylands and play an important role controlling the energy balance and water cycle. However, there is limited knowledge regarding the exchange of water and energy in dryland plantations, particularly those located within the intricate and unique environments of dryland mountain regions. The focus of this study was the investigation of a Larix principis−rupprechtii plantation within a dryland mountain ecosystem located in Northwest China. The eddy covariance method was employed to calculate the water and energy fluxes, while the thermal dissipation probe (TDP) method was utilized to measure forest overstory transpiration (T) from 2018 to 2021. The links between energy allocation and various components in water flux were investigated, as well as the variable dynamics of water and energy flux and their relationship with environmental conditions. The findings demonstrated that the annual mean cumulative evapotranspiration (ET) was about 510 mm, accounting for over 82.8 % of the precipitation, which was mainly from soil evaporation and understory transpiration, rather than overstory transpiration (T). The average value of T/ET was 0.25 for the growing season, and surface conductance (Gs) was always higher than canopy conductance (Gc) on both daily and monthly scales. The evaporative fraction (EF) and Priestley-Taylor model coefficient (α) in the growing season were significantly higher than those in the non-growing season, and α>1 from June to September, indicating that the ecosystem in the study area had sufficient water supply. Furthermore, during the growing season, the ratio of latent heat flux (LE) to net radiation (Rn) was larger than that in the non-growing season, while the proportion of sensible heat flux (H) was opposite, and soil heat flux (G) accounted for the smallest proportion of net radiation. This study provides valuable insights into the energy fluxes and water fluxes within the ecosystem of dryland mountain plantations, and it serves as a guide for choosing afforestation tree species in the future.

Can changes in land use in a semi-arid region of Brazil cause seasonal variation in energy partitioning and evapotranspiration?

Changes to forests due to deforestation, or their replacement by agricultural areas, alter evapotranspiration and the partitioning of available energy. This study investigated seasonal variations in the energy balance and evapotranspiration in landscapes under different levels of anthropogenic intervention in the semi-arid region of Brazil. Micrometeorological data was obtained from September 2020 to October 2022 for three areas of the semi-arid region: preserved Caatinga (CAA, native vegetation), Caatinga under regeneration (REGE) and a deforested area (DEFA). Here, we use the Bowen ratio energy balance method. Measurements were taken of global solar radiation, air temperature, relative humidity, vapour pressure deficit, rainfall, net radiation, latent heat flux, sensible heat flux, soil heat flux, evapotranspiration, volumetric soil water content and Normalised Difference Vegetation Index. Sensible heat flux was the dominant flux in both areas with 66% for preserved Caatinga vegetation, 63% for Caatinga under regeneration and 62% deforested area. The latent heat flux was equivalent to 28% of the net radiation for preserved Caatinga vegetation, Caatinga under regeneration and deforested area. The evapotranspiration in turn responded as a function of water availability, being higher during the rainy seasons, with average values of 1.82 mm day−1 for preserved Caatinga vegetation, 2.26 mm day−1 for Caatinga under regeneration and 1.25 mm day−1 for deforested area. The Bowen ratio presented values > 1 in deforested area, preserved Caatinga vegetation and Caatinga under regeneration. Thus, it can be concluded that the change in land use alters the energy balance components, promoting reductions in available energy and latent and sensible heat fluxes during the rainy-dry transition in the deforested area. In addition, the seasonality of energy fluxes depends on water availability in the environment.

Ensemble machine learning for interpretable soil heat flux estimation

Soil heat flux (SHF) is a key component of the surface energy balance and driver of soil physiochemical and biological processes. Accurate estimation of soil heat flux is challenging due to variations in soil composition, overlying vegetation density and phenology, and highly variable environmental forcings. Existing SHF process-based and data-driven estimation methods have focused on midday landscape scale estimates that correspond to satellite acquisitions, despite the high variability that SHF displays at diurnal scales and throughout the growing season. Recently developed data-driven techniques have emphasized pre-determined predictor variables, with model complexity and predictor selection not carefully evaluated, leaving a gap in our understanding of what information is required to accurately predict SHF. Here we developed and evaluated a suite of ensemble machine learning (ML) models to quantify the ability of meteorological and remote sensing data to predict SHF variability at half-hourly temporal resolution throughout a growing season, producing a comprehensive evaluation of the importance of predictor set composition for SHF estimation at high temporal resolution. We compared this suite of machine learning models to six semi-empirical models and found that the machine learning models broadly outperformed the existing models in capturing diurnal variability across the growing season for four agro-ecosystems (soybean, corn, sorghum, and miscanthus). Crop-specific ML models were able to capture over 86% of the variability in SHF using only two predictor variables, pointing to the need for careful evaluation of predictor sets to identify synergistic combinations. ML models developed using pooled data across all crops captured almost 80% of SHF variability using three predictors, demonstrating the power and generalizability of these methods independent of crop type. Shapley additive explanations (SHAP) were used to examine model interpretability, providing insights into the typically opaque ML modelling process and interaction of predictor variables. Models trained with fewer input variables tended to display more linear and interpretable feature attribution, motivating the use of interpretability as an important consideration in parsimonious model selection. These results provide a robust demonstration of the ability of ML to capture the variability in SHF at sub-hourly resolution across growing seasons spanning a wide range of phenological variation for unique agricultural systems. This study provides a comprehensive evaluation of predictor requirements for model performance, guiding future applications that will take advantage of the next generation of satellite-based observing systems, or in-situ proximal observations of vegetation status and meteorological conditions.

Evapotranspiration estimation using high-resolution aerial imagery and pySEBAL for processing tomatoes

AbstractThe use of high-resolution aerial imagery for assessing actual crop evapotranspiration $$ \left({ET}_{a}\right)$$ holds the potential to optimize the use of limited water resources in agriculture. Despite this potential, there is a shortage of information regarding the effectiveness of energy balance algorithms, initially designed for satellite remote sensing in estimating $$ {ET}_{a}$$ using aerial imagery. This study addresses this gap by employing the remote sensing model pySEBAL (Surface Energy Balance Algorithm for Land) in conjunction with high-resolution aerial imagery to estimate $$ {ET}_{a}$$ for processing tomatoes. Throughout the 2021 growing season, an aircraft captured multispectral and thermal imagery over a processing tomato field near Esparto, California, USA. Simultaneously, an eddy covariance flux tower within the field measured high-frequency turbulent fluxes and low-frequency biometeorology variables essential for evaluating the energy balance. The comprehensive assessment of energy balance components, including $$ {ET}_{a}$$, yielded compelling evidence that pySEBAL accurately estimated $$ {ET}_{a}$$ at high spatial resolution. The root mean square error (RMSE) and normalized RMSE for various energy balance components were as follows: 33 W m− 2 (12%) for latent heat flux, 29 W m− 2 (35%) for sensible heat flux, 24 W m− 2 (4%) for net radiation, and 10 W m− 2 (15%) for soil heat flux. Additionally, $$ {ET}_{a}$$ exhibited an RMSE and NRMSE of 0.26 mm d− 1 (6%). Moreover, the spatial mapping of $$ {ET}_{a}$$ across the processing tomato field visually depicted the spatial variability associated with irrigation scheduling, crop development, areas affected by disease, and soil heterogeneity. This research underscores the value of high resolution spatial aerial imagery and pySEBAL algorithm for estimating $$ {ET}_{a}$$ variability in the field, a crucial aspect for guiding precision irrigation management and ensuring the optimal use of limited water resources in agriculture.

Dynamics, plant physiological and environmental controls of energy exchange in a grapevine greenhouse in Northeast China

The dynamics of latent heat flux (LE) in greenhouse agricultural production, an integral component of water and energy balance, are yet to be comprehensively understood. A four-year experiment (2017–2019, and 2021) was conducted in a solar greenhouse with grapevines to examine the patterns of energy and water vapor and their underlying mechanisms. The results indicated that LE was responsible for most of the net radiation (Rn), comprising 55.13–73.62 %. Sensible heat flux (H) accounted for 16.42–22.80 % of Rn, whereas the ratio of soil and wall heat flux to Rn was relatively minor, ranging from -7.3 % to 16.5 % during the four growing seasons. The order of LE/Rn in the different growth stages (I, II, and III) of grapevines was: II > III > I. The average value of Bowen ratio (β = H /LE) in the entire growth period was 0.34–0.44. The close relationship between β and leaf area index (LAI) demonstrated that LAI significantly influenced the energy distribution of the greenhouse, with a larger impact in stage I than in stages II and III (R2 at 0.19–0.50). The high values of the decoupling coefficient (0.69–0.82) and the Priestley–Taylor coefficient (α, 0.90–1.02) suggested that Rn was the main factor determining LE during the growing seasons. However, throughout stages I and III, LE was primarily influenced by vapor pressure deficit (VPD) and crop surface conductance (Gc), and the controlling effect of Rn on LE weakened. Gc was closely related to α (R2 = 0.59–0.90), suggesting that Gc significantly affected daily LE. In addition, changes in normalization of VPD by photosynthetically active radiation and LAI affected Gc, which in turn affected LE and energy distribution in the greenhouse. These findings are important for studying water vapor transport in greenhouses and for developing energy-driven models, that may improve water management for irrigation in greenhouses.

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.

Assessment of satellite-based water requirements for a drip-irrigated apple orchard in Mediterranean agroclimatic conditions

Accurate assessment of evapotranspiration (ETa) and crop coefficient (Kc) is crucial for optimizing irrigation practices in water-scarce regions. While satellite-based surface energy balance models offer a promising solution, their application to sparse canopies like apple orchards requires specific validation. This study investigated the spatial and temporal dynamics of ETa and Kc in a drip-irrigated ‘Pink Lady’ apple orchard under Mediterranean conditions over three growing seasons (2012/13, 2013/14, 2014/15). The METRIC model, incorporating calibrated sub-models for leaf area index (LAI), surface roughness (Zom), and soil heat flux (G), was employed to estimate ETa and Kc. These estimates were validated against field-scale Eddy Covariance data. Results indicated that METRIC overpredicted Kc and ETa with errors less than 10 %. These findings highlight the potential of the calibrated METRIC model as a valuable decision-making tool for irrigation management in apple orchards.

An Assessment of the Coupled Weather Research and Forecasting Hydrological Model on Streamflow Simulations over the Source Region of the Yellow River

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m3/m3. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography.

Energy partitioning over an irrigated vineyard in arid northwest China: Variation characteristics, influence degree, and path of influencing factors

In this study, a two-year experiment in an irrigated vineyard was conducted to investigate the variations in energy fluxes (net radiation, Rn; latent heat flux, LE; sensible heat flux, H; and soil heat flux, G) and quantify the influence of arid advection and environmental factors (vapor pressure deficit (VPD), Rn, air temperature, wind speed, precipitation, and volumetric soil water content) on energy partitioning in arid Northwest China. For the diurnal variation, the peak values of the energy fluxes appeared at approximately 14:00, except for G (which appeared at approximately 16:00). LE was the main consumer of daytime (Rn>0) energy during the growing season (average LE/(Rn–G) was 87% and 89% in the two years), even during the shooting and leaf-fall stages. Arid advection, which affects energy partitioning mainly by increasing atmospheric evaporative demand and providing heat energy, contributed 6–60% and 1–56% of the average daytime LE during the two growing seasons, especially at the fruiting stage. Moreover, advection accounted for more than half of average daytime energy imbalances. We found that the arid advection induced by irrigation could be attenuated by selecting a reasonable irrigation time. According to the structural equation modeling (SEM) analysis, Rn had the strongest direct and positive regulation effect on LE at the half-hourly and daily scales, indicating that LE was limited by energy rather than water in the study area. The influence of Rn on LE gradually weakened with an increase in the time scale, whereas the effect of VPD increased, which may have been due to the smaller time-lag effect between VPD and LE. The diurnal variations in LE at approximately 08:00–19:30 and 19:30–08:00 were mainly controlled by the direct positive effects of Rn and VPD, respectively. The results obtained in this study will provide a better understanding of surface processes and help improve water resource management in arid agricultural areas.

A dataset of water vapor-heat-carbon fluxes and meteorological observations in croplands of Hai River Basin from 2013 to 2020

The present study area was located in the cropland ecosystem near Guanting Reservoir in Haihe River Basin. We conducted long-term observations of the multi-scale fluxes and meteorological elements, using various observation systems, including lysimeter, eddy covariance, large aperture scintillometer and automatic weather station/gradient of meteorological elements. This dataset consists of multi-scale fluxes (meter, hundred-meter, and kilometer-meter) and meteorological elements data. Observation items include ecosystem net carbon exchange, latent heat flux/evapotranspiration, sensible heat flux, air temperature, relative humidity, wind speed, wind direction, downward/upward shortwave radiation, downward/upward longwave radiation, net radiation, atmospheric pressure, precipitation, infrared radiation temperature, photosynthetic effective radiation, soil temperature, soil moisture, soil heat flux, and average soil temperature, etc. This dataset is processed carefully and can be used to study the effects of reservoir expansion and shrinkage on the circulation of carbon and water or other substances in the surrounding ecosystem. Additionally, it can serve as a powerful data basis for the research on relevant remote sensing estimation and model simulation.

Response of Land Surface Temperature to Heatwave-Induced Bio-Geophysical Changes in Tropical Forests on Hainan Island from 2010 to 2022

Land surface temperature plays an important role in the water cycle and surface energy balance. Using data collected by a vorticity covariance tower from 2010 to 2022, the relative threshold method and TRM method were employed to study the land–atmosphere exchange of water and the heat flux of rubber forest ecosystems under heatwave and non-heatwave conditions. The results show that the latent heat flux, sensible heat flux, and incoming and outgoing radiation increase from non-heatwave to heatwave conditions. In addition, the multi-year average LST was 6.7 °C higher under HW conditions than under non-HW conditions at the 99% confidence level. Further attribution analysis demonstrates that heatwave-induced land surface temperature change is mainly governed by atmospheric factors rather than by land surface factors. Specifically, radiative forcing shows the largest positive contribution, which is partly offset by the negative contributions of air temperature and relative humidity. In particular, the contributions of radiative forcing, air temperature, relative humidity, and atmospheric pressure to LST were 14.70 K, −4.76 K, −5.86 K, and −0.04 K, respectively. Moreover, surface resistance contributed to LST by 2.42 K, aerodynamic resistance by −0.23 K, and soil heat flux by −0.91 K.

An Analytical Model of Active Layer Depth Under Changing Ground Heat Flux

AbstractImproved modeling of permafrost active layer freeze‐thaw plays a crucial role in understanding the response of the Arctic ecosystem to the accelerating warming trend in the region over the past decades. However, modeling the dynamics of the active layer at diurnal time scale remains challenging using the traditional models of freeze‐thaw processes. In this study, a physically based analytical model is formulated to simulate the thaw depth of the active layer under changing boundary conditions of soil heat flux. Conservation of energy for the active layer leads to a nonlinear integral equation of the thaw depth using a temperature profile approximated from the analytical solution of the heat transfer equation forced by ground heat flux. Temporally variable ground heat flux is estimated using non‐gradient models when field observations are not available. Validation of the proposed model conducted against field data obtained from three Arctic forest and tundra sites demonstrates that the model is able to simulate both thaw depth and soil temperature profiles accurately. The model has the potential to estimate regional variability of the thaw depth for permafrost related applications.