Simulated Soil Temperature Research Articles

Improved thermal conductivity parameterization of SHAW model in permafrost regions on the Qinghai-Tibet Plateau

Soil thermal conductivity is critical for determining the hydrological and energy transfer processes in permafrost regions and simulating hydrological and thermal dynamic processes with land surface models. In this study, improved thermal conductivity parameterizations of the Simultaneous Heat and Water model (SHAW) were assessed using observation data from permafrost regions on the Qinghai-Tibet Plateau (QTP). The results indicated that the SHAW could precisely describe soil hydrological and thermal processes at the Tanggula (TGL) site. In general, the performances of Johansen's, Luo's, and the ORCHIDEE-MICT model's soil organic matter (SOM) parameterizations were superior to the original De Vries's parameterization. The SOM parameterization performed the best. The root mean square error (RMSE) of the active layer's soil temperature was 0.42 °C lower than that of the original parameterization at the Xidatan (XDT) site and 0.2 °C lower at the TGL site. In particular, the RMSE of the soil temperature at 3 m depth at TGL was 0.8 °C lower than that of the original parameterization. The average Nash-Sutcliffe Efficiency (NSE) for simulating the active layer soil temperature using the SOM parameterization was 0.915 at TGL site and 0.806 at XDT site. While De Vries parameterization yielded average NSE values of 0.771 and 0.627 for soil temperature simulations at the two sites, respectively. The results indicate that the soil organic carbon cannot be ignored in the simulation of hydrological and thermal processes in permafrost areas with a high soil organic carbon content. Notably, the simulated soil temperatures during the freezing period and the soil liquid water content during the thawing period improved significantly after modifying the thermal conductivity parameterization of SHAW. Therefore, the revised SHAW more accurately reflects the water and heat transfer processes of permafrost and can be used to analyze land-atmosphere interactions.

Modified DNDC model to improve performance of soil temperature simulation under plastic film mulching and snow cover

Soil temperature plays a fundamental role in climatological and hydrological modeling as well as in simulating environmental processes and climate change. There are many models to simulate soil temperature, and the denitrification decomposition (DNDC) model is a widely used process-based biogeochemical model among them. However, this model does not simulate soil temperatures well under seasonal snow cover and plastic film mulching in arid and semi-arid regions. In this study, the DNDC model was calibrated and validated using a 9 years (2012–2020) soil temperature dataset from a typical semi-arid region of Gansu province, China (Yuzhong). Then, the model was tested in two other experimental sites in semi-arid regions (Dingxi and Tongwei in Gansu province, China). Model sensitivity analysis demonstrated that the simulated soil temperature was mainly related to air temperature. Model validation results indicated that the measured and simulated soil temperatures were in agreement with each other. Compared with the original model, the average RMSE of the soil temperature in the modified model decreased by 125.3 % and the average R2 increased by 52.1 % in winter under conditions of plastic film mulching and snow cover. For the test dataset, the R, R2, and RMSE of the modified DNDC model were 0.96–0.97, 0.89–0.94, and 2.01–2.76 in the Dingxi experiment site, and 0.94–0.95, 0.88–0.93, and 1.86–3.16, in the Tongwei experiment site, respectively. The performance of the modified model simulation was better than that of the original model. The modified DNDC model can be used as an effective simulation tool to predict soil temperature. The modified model also had good expandability and applicability in different experimental sites in different semi-arid regions. It can be used as an alternative scheme for future soil temperature predictions, and it can provide theoretical input for the formulation of local agricultural strategies.

Hybrid Assimilation of Snow Cover Improves Land Surface Simulations over Northern China

Abstract Ensemble data assimilation (DA) is an efficient approach to reduce snow simulation errors by combining observation and land surface modeling. However, there is a small spread between ensemble members of simulated snowpack, which typically occurs for a long time with 100% snow cover fraction (SCF) or snow-free conditions. Here, we apply a hybrid DA method, in which direct insertion (DI) is a supplement of the ensemble square root filter (EnSRF), to assimilate the spaceborne SCF into a land surface model, driven by China Meteorological Administration Land Data Assimilation System high-resolution climate forcings over northern China during the snow season in 2021/22. Compared to the open-loop experiment (without SCF assimilation), the root-mean-square error (RMSE) of SCF is reduced by 6% through the original EnSRF and is even lower (by 14%) in the combined DI and EnSRF (EnSRFDI) experiment. The results reveal the ability of both EnSRF and EnSRFDI to improve the SCF estimation over regions where the snow cover is low, while only EnSRFDI is able to efficiently reduce the RMSE over areas with high SCF. Moreover, the SCF assimilation is also observed to improve the snow depth and soil temperature simulations, with the Kling–Gupta efficiency (KGE) increasing at 60% and 56%–70% stations, respectively, particularly under conditions with near-freezing temperature, in which reliable simulations are typically challenging. Our results demonstrate that the EnSRFDI hybrid method can be applied for the assimilation of spaceborne observational snow cover to improve land surface simulations and snow-related operational products. Significance Statement Due to the small spread between the seasonal snowpack of ensemble simulations, ensemble snow cover fraction (SCF) data assimilation (DA) proves to be ineffective. Therefore, we apply a hybrid method that combines the direct insertion (DI) and ensemble square root filter (EnSRF) to assimilate the spaceborne SCF into a land surface model (LSM) driven by high-resolution climate forcings. Our results reveal the applicability of the EnSRFDI to further improve snow cover simulations over regions with high SCF. Furthermore, the DA experiments were validated through a large number of in situ observations from the China Meteorological Administration. The uncertainties of snow depth and soil temperature simulations are also slightly reduced by the SCF DAs, particularly over regions with a poor LSM performance.

Effects of complex environment on soil thermal regime alteration under crushed-rock embankment structures in the permafrost region on the Roof of the World

The thermal stability of permafrost under complex environment (climate scenarios, permafrost types and regional air temperatures) directly affects the long-term service performance of highway or railway. This study uses a large amount of valuable soil temperature monitoring and simulation data to examine the stability of typical crushed-rock embankments (CREs) along Qinghai-Tibet Railway, which is located in the permafrost region on the Roof of the World. Firstly, a novel numerical model for CREs considering a complex heat transfer environment is established and verified. Then, alteration characteristics of recent-term thermal regime of permafrost across time and space under three CREs are revealed based on a decade of field monitoring data. Finally, long-term thermal regime of permafrost under three CREs under complex environment is analyzed by numerical simulation. Results include: 1) Soil temperature near the ground surface under the three CREs shows a decreasing trend, whereas the overall temperature around the deep permafrost increases over time under a recent climate warming. 2) The warming rate of permafrost under CREs rises with the acceleration of climate scenarios and regional air temperature and the decrease of regional ground temperatures. 3) U-shaped crushed-rock embankment is the most suitable CRE for managing complex environment, especially when the mean annual ground temperature is 0 ∼ − 1 °C or climate scenarios are RCP 2.6 and RCP 4.5. 4) Transition from low-temperature permafrost to warm permafrost is a warning signal of permafrost degradation under climate warming. 5) With an increase of permafrost degradation rate (the thawing and warming rates of permafrost), embankment stability becomes worse. These findings will not only serve as a scientific basis for the embankment damage prevention of the Qinghai-Tibet Railway, but also provide important technical supports for the successful building of infrastructure in permafrost regions under complex environment.

Simulation of Soil Temperature under Plateau Zokor's (Eospalax baileyi) Disturbance in the Qinghai Lake Watershed, Northeast Qinghai-Tibet Plateau.

The soil temperature is a key factor affecting the fragile terrestrial ecosystems on the Qinghai-Tibet Plateau, and has been remarkably altered by the soil mammal's disturbance. This study first analyzed the soil temperature variation in grassland, mound, and bald patch under the disturbance of plateau zokor (Eospalax baileyi) from October 2018 to July 2020 in the Qinghai Lake watershed. Then, the SHAW (simultaneous heat and water) model was used to simulate the soil temperature change of three land surface types, and the sensitivity of soil temperature to environmental parameters before and after the disturbance was explored. The results showed the following: (1) The daily range of soil temperature was mound > bald patch > grassland, which became smaller as the depth increased, due to the co-influence of vegetation coverage and soil bulk density. There was an obvious hysteresis of soil heat transfer for grassland, as compared with mound and bald patch, especially at 5 and 15 cm depths. (2) The SHAW model was applicable for the simulation of soil temperature under the plateau zokor's disturbance, especially during the growing season, and had better simulation accuracy for deep soil. (3) Air-entry potential and pore-size distribution index obviously affected soil temperature change, because of the change in root system and soil pores under the plateau zokor's disturbance. With the evolution of disturbance process, the response of soil temperature to the leaf area index weakened gradually, owing to the different duration of disturbance and restoration. In general, the plateau zokor's disturbance alters the soil properties and vegetation characteristics, and further, distinctly affects heat transfer and soil temperature.

Evaluation of Parameterization Schemes for Matric Potential in Frozen Soil in Land Surface Models: A Modeling Perspective

AbstractSoil matric potential (SMP) is an important variable in land surface models (LSMs) for regulating soil hydrological processes and highly influenced by soil ice in frozen ground. In this study, four SMP parameterization schemes that incorporate different effects of soil pore ice were separately integrated into the Simultaneous Heat and Water (SHAW) model and evaluated for their ability to simulate heat and water transport at a permafrost monitoring site at the Tanggula Mountain Pass on the inner Qinghai‐Tibet Plateau based on the same data and model settings. The schemes differed in their treatment of pore ice effects, including effective porosity and cryosuction. An identifiability analysis of SMP‐related parameters for each scheme was performed, based on 1,000‐fold random parameter sampling. The scheme that considered both effects performed best, accurately simulating both soil moisture content and temperature in the active layer. The schemes that considered only one effect of pore ice showed limitations, with the effective porosity scheme underestimating unfrozen water content and the cryosuction scheme seriously overestimating it, and both schemes leading to large discrepancies in simulated soil temperature. The scheme that ignored both effects performed better than the partially considered schemes due to a compensating effect of reduced effective porosity and increased cryosuction in the presence of pore ice, but not as well as the scheme that considered both effects, particularly in simulating soil temperature during freezing periods and estimating unfrozen water content near the mid‐depth of the active layer.

Thermal simulations on periglacial soils of the Central Andes, Argentina

AbstractRock glaciers are the most common landforms of the Andean periglacial landscape in the Central Andes of San Juan, Argentina. Their active layer is gravelly with a typical openwork structure. The upper parts of these rock glaciers are coarse‐grained Turbic Cryosols, with no vegetation cover. Since March 2018, coarse soils in the active layer of the “Candidato” rock glacier have been monitored (31.9°S, 70.18°W). Three trenches, 4,000 m a.s.l. and down to a depth of 90 cm, were equipped with sensors to measure soil temperature and volumetric water content. We also measured particle size distributions and calculated thermal properties from soil samples. The mean thermal conductivities for unfrozen and frozen soils were 0.69 and 0.54 W m−1 K−1, respectively, and the mean thermal diffusivities were 2.05 × 10−7 and 1.64 × 10–7 m2 s−1, respectively. Analysis of the seasonal thermal and hydrological fluxes in the active layer is challenging, as the physical properties change cyclically, thus controlling processes such as water storage, infiltration and seepage, heat balance, mechanical behavior, and kinematic response. We used the Coupled Heat and Mass Transfer Model for the Soil–Plant–Atmosphere System (COUP) numerical computerized model, performing a site‐specific calibration, to simulate soil temperatures, active layer thicknesses, and seasonal freezing–thawing depths. The model implemented, in combination with a reanalysis of the meteorological data series, performed very well to reproduce the data from thermo‐sensors placed in the ground. This proposed methodology is viable for areas with limited instrumentation or low accessibility. The “Candidato” rock glacier can be used as a pilot model for thermal modeling purposes on rhyolitic rock glaciers in the region.

Modelling the Influence of Vegetation on the Hydrothermal Processes of Frozen Soil in the Qinghai–Tibet Plateau

Climate changes and vegetation conditions are key factors affecting the hydrothermal processes of frozen soil in the Qinghai–Tibet Plateau. Due to the complex relationship between climate factors, vegetation conditions and hydrothermal processes, few studies analyze the individual influences of climate changes and vegetation conditions on hydrothermal processes. Compared to changes in climate, it is easier to control other influential factors of vegetation change, especially human activities. Thus, it is necessary to analyze the possible influence of vegetation change on hydrothermal processes in specific climate conditions; this analysis could provide technical support to inform future human activities on frozen soil. This study uses a vertical hydrothermal process model, the SHAW model, based on meteorological and soil observation data from 2020 to 2021, to model the influence of vegetation changes on the soil temperature and moisture simulations at each layer of frozen soil by changing the key input values that represent vegetation conditions from −100% to 100% at 10% intervals. The results show that: (1) the simulated values have a certain credibility since the simulated soil temperature and moisture are basically consistent with the observed values over time; (2) the performance of soil temperature simulations in the deep layer is better than that in the shallow layer, while the performances of both soil temperature and moisture simulations in the warm season are better than those in the cold season; (3) among the LAI, dry biomass and surface albedo, the LAI is the main vegetation factor that affects the soil temperature and moisture simulations of the SHAW model in the frozen soil; (4) both the soil temperature and moisture simulations show declining trends when the LAI decreases by a large extent (larger than 60%) or increases, and show increasing trends when the LAI decreases by a small extent (smaller than 50%); (5) the warm period and the freeze–thaw alternating period are, respectively, the key periods when the soil temperature and moisture are affected by vegetation changes. The results of this study can provide theoretical supports for the prediction of the hydrothermal processes of frozen soil under a changing vegetation environment in the future.

Performance of Different Crop Models in Simulating Soil Temperature

Soil temperature is one of the key factors to be considered in precision agriculture to increase crop production. This study is designed to compare the effectiveness of a land surface model (Noah Multiparameterization (Noah-MP)) against a traditional crop model (Environmental Policy Integrated Climate Model (EPIC)) in estimating soil temperature. A sets of soil temperature estimates, including three different EPIC simulations (i.e., using different parameterizations) and a Noah-MP simulations, is compared to ground-based measurements from across the Central Valley in California, USA, during 2000-2019. The main conclusion is that relying only on one set of model estimates may not be optimal. Furthermore, by combining different model simulations, i.e., by taking the mean of two model simulations to reconstruct a new set of soil temperature estimates, it is possible to improve the performance of the single model in terms of different statistical metrics against the reference ground observations. Containing ratio (CR), Euclidean distance (dist), and correlation co-efficient (R) calculated for the reconstructed mean improved by 52%, 58%, and 10%, respectively, compared to both model estimates. Thus, the reconstructed mean estimates are shown to be more capable of capturing soil temperature variations under different soil characteristics and across different geographical conditions when compared to the parent model simulations.

Numerical simulation and evaluation of soil temperature on the Qinghai–Xizang Plateau by the CLM4.5 model

Numerical simulation and evaluation of soil temperature on the Qinghai–Xizang Plateau by the CLM4.5 model

Evaluation of CLM5.0 for simulating surface energy budget and soil hydrothermal regime in permafrost regions of the Qinghai-Tibet Plateau

Surface energy budget and soil hydrothermal regime are crucial for understanding the interactions between the atmosphere and land surface. However, large uncertainties in current land surface process models exist, especially for the permafrost regions in the Qinghai-Tibet Plateau. In this study, observed soil temperature, moisture, and surface energy fluxes at four sites in permafrost regions are chosen to evaluate the performance of CLM5.0. Furthermore, the soil property data, different thermal roughness length schemes, and dry surface layer (DSL) scheme are investigated. The results show that the soil property data is important for CLM5.0. The default scheme in CLM5.0 yields large errors for surface energy fluxes. The combination of the thermal roughness length and DSL scheme significantly improved the simulation of surface energy fluxes, especially for latent heat flux. The optimization of DSL scheme significantly improved soil temperature simulation and decreased the RMSE from 1.95 °C, 2.07 °C, 2.02 °C, and 2.95 °C to 1.34 °C, 1.35 °C, 1.35 °C and 2.29 °C in TGL site, respectively. The combination of the thermal roughness length and DSL scheme performed the best in shallow soil moisture, decreasing the RMSE from 0.136 m3 m−3 to 0.049 m3 m−3 in the XDT site but slightly enhancing the errors in middle soil. The interactions between surface energy and soil hydrothermal regime also discussed. However, the thermal roughness length and the DSL schemes are highly dependent on the condition of the underlying surface. Different schemes should be selected for different regions.

Study on water and heat transport of different types of vegetations and fields in Pengbo alpine irrigation district of Qinghai Tibet Plateau

Many water-saving projects have been initiated in the typical alpine irrigation district in Tibet, which resulted in dramatic changes to the hydrothermal conversion process. There is an urgent need to fully understand the hydrothermal transport process and to clarify the mechanism under which agricultural hydrology evolves in the alpine irrigation district of Tibet. This study selected highland barley field, oat field, wasteland (Natural herbage field without irrigation) in Pengbo irrigation district of Tibet as study objects to study soil water and heat dynamics and soil water balance. Observed water and temperature data were used to calibrate and validate simulations of the HYDRUS_1D model for 2019 and 2020, respectively. Simulations of soil water and temperature were in good agreement with the measured values, with determination coefficient of 0.69–0.91.This study proposes a set of hydrothermal parameters to fill the gap in the hydrothermal research of Tibet. The results showed that the variation of soil temperature was approximately sinusoidal, with a lag effect. The temperatures of the 0–20 cm and 30–50 cm soil layers lagged by about 1–2 h and 3–5 h, respectively. The highest rate of soil temperature increase occurred from the initial growth period to the rapid growth period during which the soil temperature of each layer increased by an average of 2 °C, however, the soil temperature in this period was not the most suitable for crop growth. Soil evaporation of highland barley field, oat field, and wasteland during the growth period accounted for 20 % – 29 %, 20 % – 29 %, 10 % – 18 % of the rainfall and irrigation, whereas transpiration accounted for 33 % – 43 %, 42 % – 50 %, and 36 % – 57 %, and percolation accounted for 42 %, 35–39 %, and 16–45 %, respectively. Implementation of water-saving projects resulted in reductions in irrigation volume and percolation rate. This study can provide a reference for clarifying the hydrothermal transport process and allocating agricultural water resources in the alpine irrigation district of Tibet.

Modeling heat-water-salt transport, crop growth and water use in arid seasonally frozen regions with an improved coupled SPAC model

The soil–plant-atmosphere continuum (SPAC) that is a dynamic, interactive and coupled system is particularly complicated in arid seasonally frozen regions (ASF regions) with the effects of soil freeze–thaw process and salinity. A quantitative understanding of heat-water-salt transport and crop growth in the SPAC over an entire agricultural year (agr-year) is an importance of promoting best practices for water use and crop production. In this paper, an improved coupled SPAC model (SHAW-SC) is presented and applied for continuously simulating the SPAC system of ASF regions over an agr-year. The model is developed based on modifying the SHAW model (version 3.0) and incorporating the EPIC crop growth module. The main new features of the SHAW-SC include extending the model capability for canopy growth and yield formation, improving the description of soil solute transport in the shallow groundwater systems, and providing functions to describe the effects of surface mulching on soil evaporation and salinity stress on crop growth and transpiration. Model testing and application were conducted with two agr-year data at the sunflower experimental field in irrigated areas of the upper Yellow River basin, northwest China (with an arid and cold climate). Simulations of soil water content, temperature, and crop growth indicators fitted well with the observations both in calibration and validation (with NSE > 0.64 and sufficiently small RMSE); meanwhile, simulation of salinity concentration was also satisfactory (NSE: 0.32 and 0.83; RMSE: 1.09 and 2.44 g L−1), despite the complexity of salt transport and the measurement difficulty. In addition, the comparison with previous experimental results also showed the reasonability of modeling results. Then, a comprehensive and quantitative analysis of the SPAC dynamics was conducted over an agr-year, with identifying five characteristic periods (i.e. pre-freezing, soil-freezing, soil-thawing, pre-planting and crop growth periods). Results provided a fuller understanding of the characteristics of water-salt dynamics and balance components, and the related issues of crop stress, water use and salinity control on an entire agr-year scale. Model testing and application proved that the proposed SHAW-SC was an efficient tool to simulate coupled processes of heat-water-salt transport and crop growth for ASF regions, and as well an enhancement of current SPAC models for practical field applications.

Sustainable water and nitrogen optimization to adapt to different temperature variations and rainfall patterns for a trade-off between winter wheat yield and N2O emissions

Optimizing irrigation and nitrogen (N) fertilizer applications is essential to ensure crop yields and lower environmental risks under climate change. The DeNitrification-DeComposition (DNDC) model was employed to investigate the impacts of irrigation regime (RF, rainfed; MI, minimum irrigation; CI, critical irrigation; FI, full irrigation) and N fertilizer rate (N60, N90, N120, N150, N180, N210, N240, N270, and N300 kg ha−1) on yield and nitrous oxide (N2O) emissions from winter wheat growing season under different temperature rise levels (+0, +0.5, +1.0, +1.5, and +2.0 °C scenarios) and precipitation year types (wet, normal, and dry seasons) in the North China Plain. Model evaluations demonstrated that simulated soil temperature, soil moisture, daily N2O flux, yield, and cumulative N2O emissions were generally in close agreement with measurements from field experiment over three growing seasons. By adopting simulation scenarios analysis, the model was then used to explore the effects of irrigation and N fertilizer inputs to balance yield and N2O emissions from winter wheat growing season. Based on reduced water and fertilizer inputs and N2O emissions with little yield penalty, recommended management practices included application of MI-N150 in wet season, CI-N120 in both normal and dry seasons, and CI-N150 for +0 to +2.0 °C scenarios. Recommended practices in different precipitation year types reduced irrigation amount by 75–150 mm, N rate by 75–105 kg N ha−1, yield by 0.16–0.86 t ha−1, cumulative N2O emissions by 0.13–0.64 kg ha−1, and yield-scaled N2O emissions by 15.5–85.0 mg kg−1 compared with current practices. The corresponding metrics for different elevated temperature levels decreased by 75 mm, 75 kg N ha−1, 0.09–0.50 t ha−1, 0.12–0.52 kg ha−1, and 13.7–72.3 mg kg−1, respectively. The proposed management practices can help to maintain high agronomic productivity and alleviate environmental pollution from agricultural ecosystems, thereby providing an important basis for mitigation strategies to adapt to climate change.

Modelling water and energy fluxes with an explicit representation of irrigation under mulch in a maize field

Globally, water-saving irrigation plays a vital role in agricultural ecosystems to achieve sustainable food production under climate change. Irrigation under mulch (IUM) system has been widely used in modern agricultural ecosystems due to its high water use efficiency, but it remains unclear how each component of the water and energy processes responds to this agricultural management practice. Current modeling approaches are inadequate in investigating the impacts of IUM management on water-energy balance, which have shown more complicated than non-mulched management. Therefore, this study provided an explicit simulation of water and energy fluxes in IUM system using a process-oriented ecosystem model—CoupModel and the three years of the eddy covariance (EC) measurements. Based on Monte Carlo and the multiple model performance evaluation criteria, most of the model sensitive parameters were well constrained and 32 potentially important parameters, e.g., iscovevap, the fraction of mulch coverage, were identified to characterize the impacts of plastic mulching on energy balance and water transport. After proper calibration, the coefficient of determination (R2) for measured and simulated soil temperature (T) and soil water content (SWC) was 0.79 and 0.60, respectively, and the R2 for T and SWC during the validation period were 0.91 and 0.71, respectively. Furthermore, we found that there was a strong coupling between the parameters of the water and energy processes, which would restrict the simulation results due to the correlation between the parameters and the evaluation indices. This study presented a systematic model parameters calibration in the agricultural ecosystem implemented with IUM and provided with a more comprehensive understanding of the water and energy balance in cropland. These results would help agricultural model development with more detailed considerations of the water-saving management.

A Modified SWAT Model to Simulate Soil Water Content and Soil Temperature in Cold Regions: A Case Study of the South Saskatchewan River Basin in Canada

Soil water content (SWC) and soil temperature are important hydrologic state variables. Accurate model simulation is critical in hydrologic regimes in cold regions dominated by spring snowmelt. In this study, we developed a combined physically-based soil temperature and energy-balance rain-on-snow (ROS) module for the Soil and Water Assessment Tool (SWAT) model and applied it to the South Saskatchewan River Basin (SSRB). We calibrated the SWAT base (SWAT-B) model and the SWAT modified (SWAT–M) model using daily measured soil temperature and SWC by hydrological response unit (HRU) for the years 2015 to 2020. The results of sensitivity analysis using the SUFI-2 technique in SWAT-CUP indicated that eight parameters have the most significant (p < 0.5) effect on streamflow, soil moisture, and snowmelt. Statistics for the SWAT-B and SWAT-M streamflow models revealed that the new module improved the Nash-Sutcliffe efficiency (NSE) from 0.39 to 0.71 and 0.42 to 0.76 for calibration and validation, respectively. The statistics for SWAT-simulated daily SWC showed that the measured data were a better fit with SWAT-M versus the SWAT-B output. Furthermore, SWAT-B values exceeded SWAT-M output and field measurements, and thus, the range of SWAT-M results was a better fit with observations. SWAT-B tended to underestimate soil temperature in the cold season, while SWAT-M significantly improved soil temperature simulation for winter. This new SWAT module simulated freeze-thaw cycles and captured the influence of snow cover on surface soil ice-water content. Spatial analysis of SWC and soil temperature across the SSRB showed that the SWAT-M model predicted more SWC and lower soil temperature in the western part of SSRB than SWAT-B, with higher soil temperature and lower SWC in the eastern region.

Evaluation of simulated soil moisture and temperature for a Canadian boreal forest

• CLASS well simulated soil temperature. • Simulated soil water was underestimated during winter, while its seasonal variation corresponded well with observations. • Thickness of organic layer and soil water freezing point influenced both soil temperature and soil water. • Percentage sand and clay, and drainage parameter had effect on soil water, but not on soil temperature. • Simulated soil temperature and soil water was improved after calibration, especially during winter. Soil temperature ( T soil ) and soil water ( θ soil ) are fundamental variables that have an essential role in many processes in forest ecosystems, as well as influencing the tree species distribution and forest composition over time. We tested the Canadian Land Surface Scheme (CLASS) capacity to simulate T soil and θ soil in the boreal forest using a sixteen-year data set of daily measurements. Sensitivity analyses were also carried out to evaluate the impact of the thickness of organic layer (TOL), soil texture (percentage sand and clay: PS and PC), drainage parameter (DP), and water freezing point (FP) on simulated T soil and θ soil . Finally, the model was also calibrated with a combination of model parameters. CLASS well simulated T soil while its performance for θ soil varied by soil horizon and season. In winter particularly, soil liquid water was greatly underestimated because simulated T soil was below 0 °C. Nevertheless, simulated θ soil seasonal variation corresponded well with observations. Based on sensitivity analyses, TOL had an important effect on both T soil and θ soil . Although PS, PC and DP had almost no effect on T soil , their effects on θ soil were substantial. T soil increased throughout the year and θ soi l increased during the winter with decreasing FP, yet the match between modelled θ soil and observations was not substantially improved. In general, T soil was well simulated by CLASS, except for the freezing during winter. Model calibration improves greatly both simulated T soil and θ soil , especially during winter in all soil layers. However, despite the model calibration, CLASS still requires improvement for modelling T soil and θ soil , hence emphasizing the need to review the equations governing these variables in CLASS.

Comparison of the Forecast Performance of WRF Using Noah and Noah-MP Land Surface Schemes in Central Asia Arid Region

The land surface scheme (LSS) plays a very important role in the forecast of surface meteorological elements in the Central Asia arid region. Therefore, two LSSs viz. Noah and Noah-MP were evaluated over the Central Asia arid region in January and July 2017 using the Weather Research and Forecasting (WRF) model at a 3-km horizontal grid resolution. The objective was to assess the performance of WRF LSSs by calculated the mean error (ME) and the root mean squared error (RMSE) of simulated hourly meteorological elements, such as 2-m air temperature, 10-m wind speed, soil temperature, soil moisture at 5 and 25 cm thickness, and surface soil heat flux at 5 cm thickness. The results showed that, compared to Noah, Noah-MP modeled less surface sensible heat flux in the northern Xinjiang (15~20 W∙m−2) and surface latent heat flux in most areas of Xinjiang (<10 W∙m−2) in January, and mainly generated less sensible heat flux in most areas of north Xinjiang and the mountainous regions of southern Xinjiang (≤20 W·m−2) which on the contrary, generated more surface latent heat flux in most parts of Xinjiang (15~20 W·m−2) in July. Meanwhile, the surface soil heat flux generated from Noah-MP was closer to the observations at Hongliuhe and Kelameili stations in January, the ME increased by 17.5% and reduced by 80.7%, respectively, the RMSE decreased by 44.4% and 61.7%, respectively, and closer to the observations at Xiaotang station in July, the ME and RMSE reduced by 19.1% and 20.5%, respectively. Compared to Noah, Noah-MP improved the overall simulation of soil temperature and soil moisture over the northern and eastern Xinjiang (at 10 cm thickness), the ME and RMSE of simulated soil temperature reduced by 85.0% and 13.4% in January, decreased by 78.6% and increased by 6.2% in July, respectively, and the ME and RMSE of simulated soil moisture reduced by 67.2% and 14.9% in January, reduced by 33.3% and 2.8% in July, respectively. Compared to Noah, Noah-MP’s results were lowered for the simulated 10-m wind speed and 2-m air temperature, especially the simulated 2-m air temperature over the cold climate regions of northern Xinjiang, was improved significantly, the ME and RMSE of simulated 10-m wind speed reduced by 0.8% and 4.9% in January, decreased by 6.7% and 2.8% in July, respectively, the ME and RMSE of simulated 2-m air temperature reduced by 2.8% and 1.0% in July, respectively. This study demonstrated the advantage of coupled Noah-MP over the Central Asia arid region, providing the basis for WRF/Noah-MP in future operational applications in the Central Asia arid region.

Impacts of Land Surface Parameterizations on Simulations over the Arid and Semiarid Regions: The Case of the Loess Plateau in China

Abstract The arid and semiarid areas of the Loess Plateau are extremely sensitive to climate change. Land–atmosphere interactions of these regions play an important role in the regional climate. However, most present land surface models (LSMs) are not reasonable and accurate enough to describe the surface characteristics in these regions. In this study, we investigate the effects of three key land surface parameters including surface albedo, soil thermal conductivity, and additional damping on the Noah LSM in simulating the land surface characteristics. The observational data from June to September from 2007 to 2009 collected at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) station in northwest China are used to validate the Noah LSM simulations. The results suggest that the retrieved values of surface albedo, soil thermal conductivity, and additional damping based on observations are in closer agreement with those of the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping, respectively. Furthermore, the model performance is not obviously affected by surface albedo parameterization schemes, while the scheme of soil thermal conductivity is vital to simulations of latent heat flux and soil temperature and the scheme of additional damping is crucial for simulating net radiation flux, sensible heat flux, and surface soil temperature. A set of optimal parameterizations is proposed for the offline Noah LSM at the SACOL station when the MULT scheme for surface albedo, the J75_NOAH scheme for soil thermal conductivity, and the Y08 scheme for additional damping are combined simultaneously, especially in the case of sensible heat flux and surface soil temperature simulations.

Numerical Simulation of Soil Temperature With Sand Mulching During the Growing Season of Spring Wheat

The thermal conditions of soil are important in practical agricultural production. The characteristics of heat flux, moisture content, thermal conductivity, and other soil parameters vary with temperature. This study uses VADOSE/W to create a model of heat transmission between soil and atmosphere, simulating daily changes in soil temperature using sand mulching. By using the published data to verify the model, the results show that the fitting effect is good and the reliability of the model is verified. We also used this model to determine the temporal and spatial distributions of soil temperature, temperature differences, and a temperature gradient for sand mulching and bare soil during the growing season of spring wheat. These results indicated that the sand mulching preserved heat at night. The difference in temperature at each depth and the temperature gradient became negative with the gradual increase in solar altitude. The difference in soil temperature at each depth and the temperature gradient tended to become positive by 22:00. These results indicated that the sand mulch effectively maintained the soil temperature in the morning and night during the growing season and impeded the transfer of heat at mid-day. This study provides a new method for determining the transfer of heat in sand-mulched soil, which can guide the effective regulation of soil temperature.