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The near‐surface soil water content (SWC) can reflect the agricultural drought levels. In addition, understanding the historical trends of near‐surface SWC change in China is an important step in combating climate change. The monthly near‐surface SWC data set during 1988–2016 published by the European Space Agency Climate Change Initiative were used in this study to analyse the variations of SWC in whole China and its seven natural divisions. Results indicated South and Central China had higher SWC than others because of their larger precipitation amounts. Significant (p <.05) decreasing trend in temporal SWC was found in Northeast China, which is related to the significant (p <.05) decreasing trend in the aridity index. However, significant (p <.01) increasing trends were found in Northwest China and Qing‐Tibet, which may be associated with the climate change (e.g., the ice and snow melting caused by increase of air temperature) and human activities (e.g., tillage and vegetation restoration). The monthly SWC in all divisions and whole China showed a main period scale (corresponding to peak values of wavelet variance) of about 225 months and 2 dry‐wet cycles. From 2016, the next 5–6 years are likely to be the wet years. The findings are of great significance to the management of soil water resources in China under global change. [ABSTRACT FROM AUTHOR]

Variations in and control of soil total organic carbon (TOC), total nitrogen (TN) and total phosphorus (TP) and their stoichiometric ratios (C:N, C:P and N:P) are critical in agricultural nutrient management. This study examined the scale‐dependent effects of environmental factors on these soil properties under two contrasting land‐use types (tea versus. bamboo) at plot scale (70 × 70 m) and landscape scale (2 × 2 km). Results showed that at both scales, the tea garden generally had higher TOC and nutrient concentrations and lower stoichiometric ratios than the bamboo forest, because of fertilizer and green manure applications in the tea garden. Influences of topography and soil texture on TOC, nutrients and stoichiometric ratios were scale‐dependent. In the tea garden plot, as scale increased, influences of topography and soil texture on TOC, TN and C:N ratio increased, whereas their influence on TP, C:P ratio and N:P ratio decreased. Similar patterns were observed in the forest plot. At the landscape scale, the effects of influences of topography and soil texture on TOC and nutrients were not obvious. The spatial variations in TOC, nutrients and stoichiometric ratios were controlled by both topography and soil texture at the plot scale but were mainly influenced by soil texture at the landscape scale. The influences of topography and soil texture on TOC, nutrients and stoichiometric ratios were generally weaker at landscape than at plot scale. This study confirmed that the effect of scale must be considered when predicting the spatial variations in TOC and nutrients. [ABSTRACT FROM AUTHOR]

The development of a theoretical method for estimating the wetting branch of the soil water retention curve (SWRC) is required for knowledge of the soil infiltration process. The aim of this study was to derive the theoretical functions to represent the wetting branch of the SWRC based on the Jensen method, and then compare the Jensen method and Kool & Parker (KP87) model for estimating the wetting branch of the SWRC. Fifteen soil samples with varying basic properties (e.g., grain‐size distribution and bulk density (BD)) were selected from the Unsaturated Soil Hydraulic Database (UNSODA) to test these two methods. Results showed that the Jensen method (root mean squared error (RMSE) = 0.057 cm3 cm−3) produced a substantially better performance in predicting the wetting branch of the SWRC than the KP87 (RMSE = 0.089 cm3 cm−3) for the 15 samples. The range of the scaled mean bias error (SMBE) between the Jensen method‐predicted and measured soil water contents at all pressure heads was −0.529 to 0.402. A positive linear relationship between the SMBE and silt content was observed for the Jensen method. The findings of this study have practical significance for simulating the soil infiltration in the unsaturated zone. [ABSTRACT FROM AUTHOR]

Background and aims: The effects of climate factors and rock fragment content (RFC) on soil nitrous oxide (N2O) emission and mineral N leaching can be important in reducing N losses in different agroecosystems. This study aimed to analyze and quantify the intertwined effects of precipitation intensity, temperature, RFC, and land use on these two kinds of soil N losses. Methods: The DayCent models were calibrated and validated using observed data for the representative tea garden (TG), bamboo forest (BF), and mixed forest (MF) hillslopes in the southeastern hilly region of China. The N2O emissions and mineral N leaching were then simulated under a total of 882 scenarios with the combinations of three land‐use types (TG, BF, and MF), seven precipitation intensities (PI, from 8 to 99.2 mm d−1 but the total precipitation amount maintained), seven temperature conditions (from baseline –1°C to baseline +2°C with 0.5°C increment), and six RFCs (from 0 to 50% in volume with 10% increment). Results: The TG had the greatest soil N2O emission (1.8 and 3.3 times of BF and MF, respectively) and mineral N leaching (5.5 and 36.8 times of BF and MF, respectively). On three land‐use hillslopes, the greatest soil N2O emissions were observed in summer, while great mineral N leaching were generally observed in spring and winter. Peaks of N2O emissions were observed as the PI reached 59.8 mm d−1, while peaks of mineral N leaching were always observed when the PI reached 8.0 and 59.8 mm d−1. With temperature increasing and RFC decreasing, both types of soil N losses increased. The land use and RFC had the primary influences on both types of soil N losses compared to climate factors. Under the same RFCs, the PI had higher influence on soil N2O emission compared to temperature, while those on mineral N leaching were just opposite. Conclusion: Findings of this study would provide scientific bases for designing strategies to reduce soil N losses on the stony‐soil hillslopes. [ABSTRACT FROM AUTHOR]

Spatio‐temporal variations of nitrate‐nitrogen (NO3-‐N) leaching is driven by both soil hydrology and biogeochemistry. However, the widely used soil hydrology and biogeochemistry models have their weaknesses in simulating soil N cycling and soil water movement processes, respectively. In this study, we proposed an alternative approach by simply combining the HYDRUS‐3D and DNDC models to investigate the spatio‐temporal variations of NO3-‐N leaching on a representative tea garden hillslope in Taihu Lake Basin, China. Results showed that the soil hydrology and N cycle were well simulated by HYDRUS‐3D and DNDC models, respectively. Based on the leaching equation, the soil water flux simulated by HYDRUS‐3D and soil NO3-‐N content simulated by DNDC were combined to calculate the leachate NO3-‐N concentrations with good accuracy. The accumulative NO3-‐N leaching flux during the simulation year was 71.7 kg N ha−1, with remarkable spatio‐temporal variations on this hillslope. Hot spots of NO3-‐N leaching were observed in blocks 24, 27, 31, 34, 37, and 40 with accumulative leaching fluxes > 82.0 kg N ha−1 y−1. The spatial variation of NO3-‐N leaching was mainly controlled by soil texture and soil hydraulic properties. Hot moments of NO3-‐N leaching were observed after the applications of spring fertilizer (16 March) and basal fertilizer (30 October). The temporal variation of NO3-‐N leaching was mainly controlled by precipitation and the spring fertilization. Methods and findings of this study will be benefit for the risk assessment of non‐point source N loss and the precise agricultural management. [ABSTRACT FROM AUTHOR]

This study was conducted on a stony‐soil hillslope that severely eroded due to land use change from bamboo to tea. First, three approaches were used to extract soil hydraulic properties and then inputted into the Denitrification–Decomposition model for simulations. The Denitrification–Decomposition model defaults and ROSETTA pedotransfer function were traditional approaches without considering the rock fragment impacts on soil hydraulic properties, while the dual‐porosity function considered rock fragment impacts since it was calibrated with observed soil water retention data. Results showed that dual‐porosity functions gave the most accurate or physically sound results in simulating the nitrous oxide (N2O) emissions and leachate nitrate nitrogen (NO3−‐N) concentrations, with the root‐mean‐square errors as 0.0043–0.0107 kg N/ha and 4.39–9.09 mg/L, respectively. This suggested that rock fragments could impact the soil N cycle by affecting soil hydraulic properties. Second, the model simulations in the first step were further corrected by the volumetric rock fragment contents. This correction significantly reduced the N2O emissions and NO3−‐N leaching by 25–43% and improved the simulations. This suggested that high rock fragment contents could reduce the N emission and leaching by decreasing the volume of soils participated in N cycle. Last, under the climate change, the NO3−‐N leaching were overestimated without considering the rock fragments, while simulation using regular sets of Denitrification–Decomposition was coincide to the real N2O emissions without physical meanings. Our findings suggested that rock fragments should be considered in soil N cycling studies in the stony‐soil areas. Key Points: The DNDC model was applied to test the impacts of rock fragments on the modeling of soil nitrogen cycleN2O emission was underestimated and NO3−‐N leaching was overestimated without considering the impacts of rock fragmentsWhen the simulation results were corrected by rock fragment contents, the accuracies improved obviously [ABSTRACT FROM AUTHOR]

Cation exchange capacity (CEC) have diverse applications from soil classification and management to agricultural/environmental simulations. Direct measurement of CEC is difficult. The objective of this study is to investigate the effect of calibration dataset size on accuracy and validity of soil CEC pedotransfer functions (PTFs) derived for selected arid–semi arid soils. Furthermore, the effect of the type of inputs on performance of the PTFs is evaluated. The soil organic carbon content along with either soil sand, silt, and clay fractions or dg are used as input variables. A large dataset (n = 1141) and another independent dataset (n = 205) are applied to develop and evaluate the validity of PTFs, respectively. Multiple linear regression analysis is used to correlate soil CEC with the inputs. To investigate the database‐size effect, random subsets with different sizes (n = 50, 100, 200, 300, 500, 700, 900) from the whole dataset are used for the development of PTFs. The accuracy of the PTFs decreased to some extent and then remained relatively constant, but their reliability increased with increasing the dataset size. Clay has the strongest correlation with CEC (r = 0.41 to 0.59, depending on the dataset size), so that using clay content resulted in more accurate PTFs than dg. The critical value of CEC is found to be about 15–16 cmolc kg−1, below which the PTFs overestimate CEC. The results support the hypothesis that the number of samples have to be large enough (≥500) to develop both accurate and reliable PTFs. [ABSTRACT FROM AUTHOR]

Core Ideas: Soil hydraulic properties from saturation to oven dryness are needed to simulate soil water movement.A new soil hydraulic model was compared with the classic van Genuchten–Mualem models.The new model with different fitted parameters was more efficient than the van Genuchten–Mualem models. Soil water retention characteristics and hydraulic conductivity from saturation to oven dryness are needed for simulating soil water movement and solute transport, especially in arid and semiarid regions. The aim of this study was to derive a new soil hydraulic model and compare this model with the classic van Genuchten–Mualem (VGM) models (M1). A total of 12 soils with varying basic properties were selected from the Unsaturated Soil Hydraulic Database (UNSODA) for the evaluation of the new model. Five pairs of soil hydraulic models were considered, including M1 and the new model with different parameters fitted (M2–M5). Correlation analysis was conducted to analyze the relationships between the new model parameters and basic soil properties. In addition, the Morris method was applied to quantify the parameter sensitivity of the new model. Results showed that M2 to M5 gave slightly better performance than M1 in predicting soil water retention characteristics, with a decrease of ∼15% in the root mean squared error (RMSE). However, they produced substantially better performance than M1 in estimating soil hydraulic conductivities, with a decrease of ∼40% in the RMSE. The new model with all of the model parameters being fitted (M2) was more efficient than those with one or two model parameters not fitted. However, an over‐parameterization problem was detected with M2. Physically unrealistic parameters (e.g., the saturated soil water content and effective grain diameter) exist in M2. These parameters were found to have important influences on the results of the soil hydraulic properties prediction based on the Morris‐based sensitivity analysis. Overall, the new model with two parameters not fitted (M5) can best describe the soil hydraulic properties from saturation to oven dryness and had the potential to simulate water and solute transport in the vadose zone. [ABSTRACT FROM AUTHOR]

Topsoil (0-20 cm) data of 25 997 sites were analyzed across five land uses in the Jiangsu Province of China. The results showed that the residential/industrial land stored the highest amount of soil organic carbon (SOC) among all the land uses. The polynomial model can best describe the relationship between SOC density (SOCD) and pH, showing that SOCD increases over the pH range of 4.2-6.5, decreasing sharply in the range of 6.5-9.2. The reason may be that microbial activity tends to be maximal at intermediate soil pH levels (pH 6-7). The relationship between SOCD and pH was found to vary substantially with the land use. Soil pH can explain 4.3-63.8% of the variation in SOCD for all land uses, suggesting that this variable should be introduced in SOC models to better predict SOC stock changes in the future. [ABSTRACT FROM AUTHOR]

Agricultural, environmental and ecological modeling requires soil cation exchange capacity (CEC) that is difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict CEC from easily measured physicochemical properties ( e.g., texture, soil organic matter, pH). This study developed the support vector machines (SVM)-based PTFs to predict soil CEC based on 208 soil samples collected from A and B horizons in Qingdao City, Shandong Province, China. The database was randomly split into calibration and validation datasets in proportions of 3:1 using the bootstrap method. The optimal SVM parameters were searched by applying the genetic algorithm (GA). The performance of SVM models was compared to those of multiple stepwise regression (MSR) and artificial neural network (ANN) models. Results show that the accuracy of CEC predicted by SVM improves considerably over those predicted by MSR and ANN. The performance of SVM for B horizon ( R2 = 0.85) is slightly better than that for A horizon ( R2 = 0.81). The SVM is a powerful approach in the simulation of nonlinear relationship between CEC and physicochemical properties of widely distributed samples from different soil horizons. Sensitivity analysis was also conducted to explore the influence of each input parameter on the CEC predictions by SVM. The clay content is the most sensitive parameter, followed by soil organic matter and pH, while sand content has the weakest influence. This suggests that clay is the most important predictor for predicting CEC of both soil horizons. [ABSTRACT FROM AUTHOR]