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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]

Abstract Characterizing soil moisture variation has critical implications for various ecosystem processes and the hydrological cycle. In this study, we identified the hot moments (times with high temporal variation rates) and hot spots (areas with high temporal variation rates) of the soil moisture variation and investigated their controlling factors on a tea garden hillslope in Taihu Lake Basin, China. Daily soil moisture data were calculated for 39 sites, and then, daily soil moisture maps were generated from March 1, 2014 to February 28, 2015. The soil moisture temporal variation rates (VR) at different locations and during different time periods were calculated based on these maps. We found that because soil water content at the subsurface was more spatially varied than that at the surface on this hillslope, spatial heterogeneity of VR was also greater at 0.3-m depth than that at 0.1-m depth. Elevation, sand content and rock fragment content were positively correlated (p < 0.05) with this spatial heterogeneity, while slope and clay content were negatively correlated (p < 0.05) with it. June and July 2014 were the hot moments with high VR at both depths of 0.1- and 0.3-m, while December 2014 was the cold moment with the lowest VR at both depths on this study hillslope. Meteorological factors (precipitation and temperature) explained 59.9% and 56.9% of the total variance in the time series of spatial mean VR at the depths of 0.1- and 0.3-m, respectively. Stronger variations in spatial mean VR were found during representative medium and large rainfall events than during representative small rainfall events. In addition, the response of spatial mean VR to rainfall at 0.3-m depth was delayed compared to that at 0.1-m depth. The findings and methodologies of this study can be useful in determining the hot spots and hot moments of soil moisture variations, which could be potentially useful for investigating various hydrological, ecological, environmental, and agronomic processes. Highlights • The hot moments and hot spots of the soil moisture variation are identified. • Soil moisture temporal variation rates at 0.3-m depth were greater than those at 0.1-m depth. • June and July were the hot months with high soil moisture temporal variation rates. • December was the cold month with the lowest soil moisture temporal variation rates. • The elevation, slope, soil texture and rock fragments jointly controlled the soil moisture temporal variation rates. [ABSTRACT FROM AUTHOR]

The spatio-temporal dynamics of soil water are the key critical zone processes that control hydrological, biogeochemical and environmental processes at various spatial scales. Soil water content (SWC), which has been widely adopted in traditional studies, does not consider the energy state of soil water and thus cannot directly reflect the active status of subsurface fast flow (flux when SWC is above field capacity). By subtracting water content at field capacity (− 33 kPa) from SWC, free water content (FWC) were calculated and used to indicate status of subsurface fast flow. In this study, the spatio-temporal variations and controlling factors of SWC and FWC were compared on a typical bamboo forest hillslope in Taihu Lake Basin, China. An improved temporal stability (TS) analysis replacing the spatial means of SWC in the equation by the field capacity was also proposed to better identify the active locations of subsurface fast flow. Results showed that the SWC and FWC had similar temporal trends and spatial patterns. Thresholds of spatial mean SWCs were found at 10- and 30-cm depths (0.17- and 0.18-m 3 m − 3 , respectively). Above these thresholds, the spatial means and variances of FWC started to increase with the spatial mean SWCs. This indicated that the subsurface fast flow starts to occur. Below these thresholds, nearly no free water existed and the subsurface fast flow ceased. The active locations of subsurface fast flow determined from the improved TS analysis were not always consistent with the high SWC locations. This indicated that traditional TS analysis was not adequate to interpret the active status of subsurface fast flow. Controlling factors of SWC and FWC spatial variations were generally similar. However, the spatial distribution of FWC was less affected by soil properties and topography. In addition, the influences of controlling factors on FWC were more temporally varied. These findings will be beneficial for identifying the “hot spots” of soil water movement and biogeochemical processes. [ABSTRACT FROM AUTHOR]

In this study, seven random combination sampling strategies were applied to investigate the uncertainties in estimating the hillslope mean soil water content (SWC) and correlation coefficients between the SWC and soil/terrain properties on a tea + bamboo hillslope. One of the sampling strategies is the global random sampling and the other six are the stratified random sampling on the top, middle, toe, top + mid, top + toe and mid + toe slope positions. When each sampling strategy was applied, sample sizes were gradually reduced and each sampling size contained 3000 replicates. Under each sampling size of each sampling strategy, the relative errors (REs) and coefficients of variation (CVs) of the estimated hillslope mean SWC and correlation coefficients between the SWC and soil/terrain properties were calculated to quantify the accuracy and uncertainty. The results showed that the uncertainty of the estimations decreased as the sampling size increasing. However, larger sample sizes were required to reduce the uncertainty in correlation coefficient estimation than in hillslope mean SWC estimation. Under global random sampling, 12 randomly sampled sites on this hillslope were adequate to estimate the hillslope mean SWC with RE and CV ≤10%. However, at least 72 randomly sampled sites were needed to ensure the estimated correlation coefficients with REs and CVs ≤10%. Comparing with all sampling strategies, reducing sampling sites on the middle slope had the least influence on the estimation of hillslope mean SWC and correlation coefficients. Under this strategy, 60 sites (10 on the middle slope and 50 on the top and toe slopes) were enough to ensure the estimated correlation coefficients with REs and CVs ≤10%. This suggested that when designing the SWC sampling, the proportion of sites on the middle slope can be reduced to 16.7% of the total number of sites. Findings of this study will be useful for the optimal SWC sampling design. [ABSTRACT FROM AUTHOR]

Soil moisture variations in space and time are critical in ecological, hydrological, pedological and environmental studies. This study used fractal analysis to detect the spatio-temporal variability of soil moisture on two contrasting land use hillslopes in the hilly area of Taihu Lake Basin of China. Surface (0–20 cm) soil moisture data from January 2013 to September 2015 (a total of 37 sampling days) were analyzed at 39 and 38 sites on the tea garden and forest hillslopes, respectively, with a spatial resolution of about 8 m. Results showed that the forest hillslope was significantly ( P < 0.05) wetter than the tea garden hillslope. The spatial mean soil moisture on both hillslopes had a significant negative linear correlation (R 2 = 0.753 for tea garden ( P < 0.05) and R 2 = 0.459 for forest ( P < 0.05)) with corresponding CV for 37 sampling dates. The fractal dimension (D) was found to be better than the nugget/sill ratio in describing the spatial dependence of soil moisture. The advantage of the D is that it does not require the modelling of the semivariogram since it can be calculated on the basis of the experimental semivariogram. Soil moisture on the forest hillslope showed stronger spatial dependence than that on the tea garden hillslope according to D. However, the soil moisture on both hillslopes showed similar temporal dependence and a low-to-moderate autocorrelation structure. In addition, the temporal variability of soil moisture content was spatially correlated on tea garden hillslope. If a location is temporally autocorrelated, the locations nearby tend also to be temporally autocorrelated. It would be possible to make more accurate moisture trend predictions for temporal autocorrelated locations with small D. These findings had important applications related to sampling design, simulation of soil water flow and agricultural water resources management. [ABSTRACT FROM AUTHOR]

The ROSETTA model has routinely been applied to predict the soil hydraulic properties for simulating the water flow at the hillslope scale. However, the uncertainties in water flow simulations are substantial due to the soil heterogeneity and ROSETTA model structure. In order to reduce these uncertainties, this study used the HYDRUS-2D and ensemble mean to simulate soil moisture based on the outputs of all candidate models. In addition, the bias correction techniques (including linear bias correction (LBC) and cumulative distribution function (CDF) matching) were also applied to improve the prediction of soil moisture. A total of 320 days of observed soil moisture data at two depths (10 and 30 cm) in the upper and lower slope positions were adopted to evaluate the performances of different bias correction methods results showed that the uncertainty in hillslope-scale soil moisture simulation due to the ROSETTA model structure was more important than that due to the soil heterogeneity. The CDF matching-based nonlinear bias correction approach was generally better than the LBC in reducing the uncertainty in soil moisture simulation. Combining the ensemble mean and CDF matching was a viable approach to improve the accuracy of the numerical model for simulating the hillslope-scale soil moisture variations. [ABSTRACT FROM AUTHOR]

The identification of representative soil moisture sampling sites is important for the validation of remotely sensed mean soil moisture in a certain area and ground-based soil moisture measurements in catchment or hillslope hydrological studies. Numerous approaches have been developed to identify optimal sites for predicting mean soil moisture. Each method has certain advantages and disadvantages, but they have rarely been evaluated and compared. In our study, surface (0–20 cm) soil moisture data from January 2013 to March 2016 (a total of 43 sampling days) were collected at 77 sampling sites on a mixed land-use (tea and bamboo) hillslope in the hilly area of Taihu Lake Basin, China. A total of 10 methods (temporal stability (TS) analyses based on 2 indices, K-means clustering based on 6 kinds of inputs and 2 random sampling strategies) were evaluated for determining optimal sampling sites for mean soil moisture estimation. They were TS analyses based on the smallest index of temporal stability (ITS, a combination of the mean relative difference and standard deviation of relative difference (SDRD)) and based on the smallest SDRD, K-means clustering based on soil properties and terrain indices (EFs), repeated soil moisture measurements (Theta), EFs plus one-time soil moisture data (EFsTheta), and the principal components derived from EFs (EFs-PCA), Theta (Theta-PCA), and EFsTheta (EFsTheta-PCA), and global and stratified random sampling strategies. Results showed that the TS based on the smallest ITS was better (RMSE = 0.023 m 3 m −3 ) than that based on the smallest SDRD (RMSE = 0.034 m 3 m −3 ). The K-means clustering based on EFsTheta (-PCA) was better (RMSE <0.020 m 3 m −3 ) than these based on EFs (-PCA) and Theta (-PCA). The sampling design stratified by the land use was more efficient than the global random method. Forty and 60 sampling sites are needed for stratified sampling and global sampling respectively to make their performances comparable to the best K-means method (EFsTheta-PCA). Overall, TS required only one site, but its accuracy was limited. The best K-means method required <8 sites and yielded high accuracy, but extra soil and terrain information is necessary when using this method. The stratified sampling strategy can only be used if no pre-knowledge about soil moisture variation is available. This information will help in selecting the optimal methods for estimation the area mean soil moisture. [ABSTRACT FROM AUTHOR]

Knowledge of soil water percolation below the rooting zone and its responses to the dynamic interactions of different factors are important for the control of non-point source pollution. Based on 3600 scenarios in Hydrus-1D simulation, this study revealed the integrated effects of rainfall characteristics (rainfall amount, maximum rainfall intensity or MRI, time distribution characteristics of rainfall or TDC), antecedent moisture and the season on deep percolation (DP) at a forest site in Taihu Lake Basin, China. Results showed that Hydrus-1D model can well simulate the soil water dynamics at this site. Antecedent moisture had the greatest relative contribution to DP (85.7%), followed by rainfall amount (10.9%) and MRI (3.4%). As the antecedent moisture increased, the relative contribution of the season on DP increased from 0.0% to 16.4%. In comparison, that of MRI decreased from 58.7% to 38.5% and that of rainfall amount followed a bell shape pattern (greatest when the antecedent moisture was 0.26 m 3 m −3 ). The relative contribution of antecedent moisture to DP in summer was the greatest (87.8%), while that of the rainfall was the least. The TDC influenced DP by affecting the responses of DP to other factors. When the rainfall amount was ⩾80 mm and the antecedent moisture content was ⩾0.34 m 3 m −3 , effect of TDC on DP could be observed. The DP of TDC_B (rainfall intensity linearly increased with time) was the lowest, while that of TDC_E (rainfall intensity kept constant with time) was the greatest. Findings of this study have practical significance for investigating the water and pollutant transport in vadose zone. [ABSTRACT FROM AUTHOR]

• The hot spots of N 2 O emissions and N leaching were distributed in the tea garden. • Temperature, precipitation and fertilization triggered the hot moments of N losses. • Land-use type was the primary factor for the hot spots formation of N losses. • Topography and soil properties also affected the hot spots formation of N losses. Identification of hot spots and hot moments (HSHMs) in regard to soil nitrogen (N) losses has received public attention. Soil nitrous oxide (N 2 O) emissions and mineral N leaching under varied annual precipitation were simulated with the DayCent model in a mixed landscape (tea garden, bamboo forest and coniferous and broad-leaved mixed forests). Their HSHMs were further quantified using the 3rd quartile value of all data as the baseline. Results showed that the hot moments of soil N 2 O emissions were the dry year, summer, and months with high air temperature (July to September) and months after fertilization (April or May) at inter-annual, seasonal and monthly time scales, respectively. The hot moments of mineral N leaching at these time scales were the normal year or the wet year, spring or rainy seasons after drought, and rainy months after fertilization or drought, respectively. The main factors controlling the formation of hot moments on N losses were temperature, precipitation and fertilization. In addition, the hot spots of N 2 O emissions and mineral N leaching in the mixed landscape were both distributed in the tea garden (TG). When the entire study area was assumed to be under the same land-use type (i.e., TG), the hot spots of these two kinds of N losses were mainly distributed in the areas with rock fragment content < 0.16 cm3 cm−3, field capacity > 0.24 cm3 cm−3, bulk density > 1.29 g cm−3, soil carbon (C)/N ratio < 13.26, and slope < 12°. These results indicated that fertilizer management, climate factors, soil properties and topography need to be comprehensively considered to alleviate the formation of HSHMs of N losses in mixed landscape mountainous areas. [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]

Abstract: Pedotransfer functions (PTFs) have routinely been used to estimate the soil hydraulic properties (SHPs) from easily measurable soil properties, such as particle-size distribution, organic matter content and bulk density. However, different PTFs often yielded different prediction results. In order to deal with the PTF selection problem, this study used multimodel ensemble approaches to simulate forest soil moisture based on the modelling results of different PTFs. A total of 300 days of observed soil moisture data at four depths (10-, 20-, 40- and 60-cm) were adopted to calibrate the Richards equation and obtain the SHPs by using the inverse option in HYDRUS-1D. Six published PTFs were selected to predict the SHPs, which were used to predict soil moisture temporal variations at these four different depths. Two multimodel ensemble methods, including the simple model average (SMA) and the multiple linear regression (MLR)-based superensemble, were used in this study. Under different selections of training periods (i.e. 50, 100 and 150 days), performances of these multimodel ensemble approaches were compared with those of the best single PTF model. The SMA always had worse performance than the best single model. However, the performances of the superensemble approach were better than those of the best single model, and even comparable to those of the calibrated soil water flow model. Results show that given the relatively long training period (>50 days), it is worthwhile to consider the superensemble method to simulate soil moisture contents in forestland. [Copyright &y& Elsevier]

• Soil nitrogen (N) biogeochemical models are often associated with multiple sources of uncertainty. • The multimodel ensemble prediction (MEP) of soil N biogeochemical processes was proposed. • The mechanisms of soil N cycle would be better understood if MEP was derived from members with better performances. Soil nitrogen (N) loss is a part of N biogeochemical processes, which plays an important role in the agricultural, ecological and environmental management. Because it is difficult to assess the temporal and spatial changes of different N forms in leachates by field measurement methods, conceptual and physical models are usually used to describe soil N loss. However, soil N models are often associated with multiple sources of uncertainty (e.g., model parameter and structure), which may largely influence the reliability and accuracy of the models. The multimodel ensemble prediction (MEP) is specifically designed to reduce the parameter and structural uncertainty in N biogeochemical modelling by representing a set of candidate models. However, the existing MEP methods still need to be improved by integrating various kinds of prior knowledge and quantifying each part of predictive uncertainty. In addition, published studies mainly focused on the regional scale MEP of the land carbon balance. However, the regional scale MEP of soil N losses is lacking. This paper firstly proposed the MEP methods of soil N losses at different spatial scales: 1) using the Monte-Carlo sampling to randomly alter the soil and crop parameters governing the N cycle and driving multiple soil N models at plot scale; and 2) generating an ensemble of TIGGE (THORPEX Interactive Grand Global Ensemble) weather forecasts and an ensemble of random soil and crop parameters and driving multiple soil N models at regional scale. This study also discussed different methods used for realizing MEP. It is found that the ensemble mean produced a large bias when simulating soil N losses. By using the bias correction technique, the RMSEs of the ensemble mean decreased by 57.5%~86.0%. Overall, the MEP can enhance our understanding of soil N cycle. In addition, this study is also helpful to accurately estimate the response of soil N loss to global change and provide support for agricultural production and environmental protection. [ABSTRACT FROM AUTHOR]

• Both the PTF prediction and model structural uncertainty were equally important in soil N modelling. • The ensemble method produced a very large bias in the prediction of the leachate NO 3 −-N concentrations. • The ensemble bias-correction method can improve the prediction of NO 3 −-N leaching in soil. Pedotransfer functions were often applied to predict the soil water contents at field capacity (FC) and permanent wilting point (PWP), which are the key parameters used in the soil nitrogen (N) biogeochemical models for simulating the nitrate (N O 3 - - N) leaching. However, the PTF prediction uncertainty was often ignored. In addition, the uncertainty of the N model structure (soil N cycling is described with a set of equations) can also be substantial. Based on the 12 classic pedotransfer functions (PTFs) (namely Baumer, Brakensiek/Rawls, British Soil Survey Topsoil, British Soil Survey Subsoil, EPIC, Hutson, Manrique, Rawls, Campbell, Mayr/Jarvis, Rawls/Brakensiek, and Vereecken) and 2 biogeochemical models (DayCent and DeNitrification-DeComposition (DNDC)), this study evaluated the PTF prediction and model structural uncertainty in soil NO 3 −-N leaching modelling on a tea garden hillslope in Taihu Lake Basin, China. The ensemble mean was then applied to combine the 12 outputs of each model and the 24 outputs of both models. Finally, the linear bias-correction combined with the ensemble mean, i.e., the ensemble bias-correction (EBC), was also applied for the prediction of the leachate NO 3 −-N concentrations. Data on basic soil properties were used to derive the FC and PWP by using the 12 PTFs. Results showed that both the PTF prediction and model structural uncertainty were equally important in soil N O 3 - - N leaching modelling at four slope positions. The coefficients of variation of the N O 3 - - N concentration forecasts obtained by different PTFs, representing the PTF prediction uncertainty, were positively related to the climate factors, especially when PTFs were used in DayCent. Ensemble mean was found to produce a very large bias in the prediction of the leachate N O 3 - - N concentrations, which is due to the prediction bias of PTFs. The EBC can substantially improve the prediction of the soil N O 3 - - N leaching, especially when the 24 outputs of both models were combined. [ABSTRACT FROM AUTHOR]

Although the effects of fertilization practices and environmental factors on tea yield and NO 3 −–N leaching have been acknowledged, the interactive effects of these influencing factors remain unknown. These effects were investigated in this study using the denitrification–decomposition (DNDC) model, which was validated using field data collected from a typical tea garden hillslope in the Tianmu Lake Basin, China. Environmental factors such as temperature, precipitation intensity, slope, saturated hydraulic conductivity (K sat), and fertilization practices (fertilizer amount and type) were combined to construct 1080 scenarios. The results revealed that DNDC achieved acceptable performance in simulating NO 3 −–N leaching under the application of a compound fertilizer (CF) and a slow-release fertilizer (SF). During the calibration period, the Nash–Sutcliffe efficiency (NSE) values were 0.48 and 0.24. During the validation period, the NSE values were 0.71 and 0.67. Scenario analysis indicated that precipitation intensity (p < 0.001), saturated soil hydraulic conductivity (K sat) (p < 0.001), fertilizer amount (p < 0.001), and fertilizer type (p < 0.001) significantly influenced the leaching flux of NO 3 −–N and tea yield. The interactive effects of different factors also varied across the different types of fertilizers. The amount of fertilizer had the most significant impact on NO 3 −–N leaching, with relative contribution rates of 70% and 53% for compound and SFs, respectively. As CF application increased, there was a significant increase (93%) in the average flux, whereas SF application resulted in an increase of only 76%. The highest contribution rate in terms of tea yield was observed for the amount of fertilizer under CF application at 44%. However, the highest contribution rate under SF application was attributed to K sat at 51%. The application of SFs resulted in a higher yield improvement rate than that of CFs as the fertilizer amount increased, with respective rates of 15% and 8%. Additionally, environmental factors, excluding temperature, exerted opposite effects on tea production and NO 3 −–N leaching flux. These findings contribute to optimizing fertilization management in tea plantations by integrating multiple environmental factors to control nonpoint nitrogen loss. Future research should conduct long-term monitoring of tea garden yield and nitrogen loss to accurately assess the impact of SF application on the ecological and economic benefits of tea gardens. • The fertilizer amount served as the dominant factor for the NO 3 --N leaching flux. • The yield was predominantly influenced by the saturated soil hydraulic conductivity (K sat) when slow-release fertilizer was applied. • Tea garden using slow-release fertilizer exhibited lower leaching flux/yield values. [ABSTRACT FROM AUTHOR]

Delirium is a common complication in intensive care unit (ICU) patients, and it can significantly increase the length of hospital stay and cost. Dexamethasone is widely used in various inflammatory diseases and must be used with caution in critically ill patients. Previous studies have shown that the effect of corticosteroid use on the development of delirium in critically ill patients is still controversial, and there is inconclusive conclusion about the effect of dexamethasone on delirium in such patients. Therefore, this study aimed to confirm the effect of dexamethasone use and the dose on the incidence of delirium and patient prognosis in critically ill patients through a large cohort study. A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care III database, which is a large and freely available database of all 46,476 patients who visited Beth Israel Deaconess Medical Center in Boston, Massachusetts, USA and were admitted to the ICU between 2001 and 2012. The primary outcome was the development of delirium, using multivariate logistic regression analysis to reveal the relationship between dexamethasone and delirium. Secondary endpoints were in-hospital mortality, ICU mortality, total length of stay, and length of ICU stay, and the relationship between dexamethasone and prognosis was assessed with Cox proportional hazards models. Propensity score matching with 1:1 grouping was used to eliminate the effect of confounders on both cohorts. The locally weighted scatter plot smoothing technique was used to investigate the dose correlation between dexamethasone and outcomes, subgroup analysis was used to account for heterogeneity, and different correction models and propensity matching analysis were used to eliminate potential confounders. Finally, 38,509 patients were included, and 2204 (5.7%) used dexamethasone. No significant statistical difference was observed in basic demographic information after propensity score matching between the two study groups. A significantly higher incidence of delirium (5.0% versus 3.4%, P < 0.001), increased in-hospital mortality (14.9% versus 10.3%, P < 0.001), ICU mortality (9.0% versus 7.5%, P = 0.008), and longer length of stay and ICU stay were observed in patients taking dexamethasone compared with those not taking dexamethasone. Multivariate logistic and Cox regression analyses confirmed that dexamethasone was significantly associated with delirium (adjusted odds ratio = 1.48, 95% confidence interval [CI] = 1.09-2.00, P = 0.012), in-hospital mortality (adjusted hazard ratio = 1.19, 95% CI = 1.02-1.40, P = 0.032), and ICU mortality (adjusted hazard ratio = 1.62, 95% CI = 1.22-2.15, P = 0.001). Compared with critically ill patients using high-dose dexamethasone, the risk of delirium was lower in the dose less than the 10 mg group, and patients using 10-14 mg may be associated with a lower risk of in-hospital death and the least ICU mortality, length of hospital stay, and ICU stay. This study demonstrated that the use of dexamethasone in critically ill patients exacerbated the occurrence of delirium while increasing the risk of in-hospital death, ICU death, and length of hospital stay, with a lower risk of delirium and a shorter total length of hospital stay with low-dose dexamethasone than with larger doses. [ABSTRACT FROM AUTHOR]

• Combing soil survey and literature soil sample data enhanced SOC stock prediction. • ANN and MGWR had their own merits and drawbacks in predicting SOC stock. • Fusing different predicting models improved prediction accuracy of SOC stock. • Fusion model with the spatial autocorrelation considered had stronger adaptability. Soil organic carbon (SOC) is a crucial soil property governing various agricultural, ecological, and environmental processes in the terrestrial ecosystem. However, acquiring accurate and high resolution SOC density (SOCD) map remained a challenge in the current researches. Based on soil samples collected from soil survey and published literatures (952 samples), this study tested different approaches for generating high accuracy dataset of 0–20 cm SOCD with the spatial resolution of 90-m in the Yangtze River Delta. Two spatial prediction methods, including artificial neural network (ANN, represented non-linear), multiscale geographically weighted regression (MGWR, represented linear) were applied to generated the SOCD maps. Then three fusion models, including multiple linear regression fusion (MLR_F), ANN fusion (ANN_F) and a self-developed model (multiscale geographically normalize weighted fusion, MGNW_F), were applied to fuse the prediction results of spatial interpolation methods. Among spatial interpolation methods, the ANN performed better in capturing the SOCD spatial pattern, while the MGWR performed better in accuracy measures of mean square error, mean absolute error and coefficient of determination. After data fusing by the three models, improved accuracy measures than the ANN and MGWR were achieved. The predictions of fusion models also showed better reliability than the soil dataset of high resolution National Soil Information Grids of China, mainly due to that more soil samples were included in the prediction. Among these fusion models, the MGNW_F performed slightly better than the other two and had the strongest adaptability to the overall environment of the study area. Findings of this study highlighted the potentials of acquiring additional soil samples from existing literatures and fusing different methods for enhancing the accuracy in mapping SOCD. [ABSTRACT FROM AUTHOR]

The rock fragment (RF) has been widely observed in the soil solum or on the soil surface. It regulates soil hydrological processes (SHPs) and thus has great impacts on soil and water conservation. However, responses of SHPs to RF characteristics (position, content, coverage area, mulching thickness, and size) remain unclear. Based on the dataset extracted from 168 published studies, effects of RF characteristics on SHPs (soil loss rate, evaporation rate, surface runoff rate, infiltration rate, soil water content, and soil water storage) and saturated hydraulic conductivity (Ks) were discussed in this study. Results showed that RFs completely inserted into the soil solum (CO_INS) improved the Ks by 6.9%, runoff rate by 12.8%, and soil loss rate by 22.3%, while it reduced the evaporation rate by 30.7%, infiltration rate by 15.4%, and soil water content by 9.9%. With the RF content (kg kg−1) increasing, its effects on these SHPs were strengthened. The RFs resting on the surface and partially covering soil surface (PA_COV) improved the infiltration rate but reduced the evaporation rate by 36.9%, surface runoff rate by 25.4%, and soil loss rate by 59.3%. This in turn enhanced soil water content and storage. However, as the RF coverage (% in area) increasing, the cross-sectional area of water flow decreased, and thus the infiltration rate reduced. The RFs completely mulch on the soil surface (CO_MUL) reduced the evaporation rate by 59.5% and infiltration rate by 76.5% but improved the soil water content and storage. Except the infiltration rate, the effects of CO_MUL on SHPs were strengthened as mulch thickness (cm) increased. The RFs partially embedded into soil surface (PA_EMB) promoted surface runoff rate by 15.3%, soil loss rate by 34.7%, and soil water content by 24.3%, but reduce the infiltration rate. However, the surface runoff rate was reduced at high embedded coverage due to increased tortuosity of water flow. In addition, the RF size exerted weaker effects on SHPs than RF positions, content, coverage, and mulch thickness. Our findings enhanced the understanding of SHP responses to RFs characteristics, which would be important for relate soil and water modeling and management. [ABSTRACT FROM AUTHOR]

Conservation tillage, defined as no-till with/without straw stubble covering, reduced tillage, or minimum tillage in this study, is widely used to mitigate the negative effects of intensive tillage. Achieving sustainable agricultural development and eco-environmental protection requires a deeper understanding of the effect of conservation tillage on soil chemical properties (e.g., organic matter (OM), nutrient elements, metal cations, and pH); we investigated these properties in this study using a global meta-analysis based on the data collected from 203 studies published worldwide since 1983. The results showed that compared with conventional tillage without straw stubble covering (CT), conventional tillage with straw stubble covering (CTS), no-till with straw stubble covering (NTS), no-till without straw stubble covering (NT), reduced tillage without straw stubble covering (RT), and minimum tillage without straw stubble covering (MT) significantly increased the topsoil (0–15 cm) organic matter (OM), organic carbon (OC), total carbon (TC), total nutrients (N, P, and K), available nutrients (N, P, and K), calcium ions (Ca2+), magnesium ions (Mg2+), and cation exchange capacity (CEC), respectively. However, the effects of conservation tillage on sodium ion (Na+) and pH are insignificant or inconsistent. In particular, the existing research data could not conclude whether conservation tillage can significantly increase Na+, or if pH was affected by conservation tillage practices, and both significant increases and decreases were observed. This meta-analysis also suggested that CTS, NTS, and NT significantly (p < 0.05) increased the subsoil (15–40 cm) OC, total nutrients (N, P, K), AN, and AP, respectively, compared to CT. Finally, compared with NT, NTS significantly increased the topsoil OM, OC, TC, TN, TP, TK, AN, and AP by 14%, 5%, 37%, 8%, 8%, 3%, 11%, and 16%, respectively. These findings indicate that conservation tillage can effectively improve the soil chemical properties, and straw stubble covering can greatly enhance the content of OM, OC, nutrient elements, and other soil chemical properties. • The nutrient stratification of no tillage is more obvious than that of conventional tillage. • No tillage significantly decreased surface soil pH, but significantly increased concentrations of topsoil Ca2+ and Mg2+. • Conservation tillage with different time periods had a great influence on the contents of soil nutrients. [ABSTRACT FROM AUTHOR]

• Rock fragment affects C and N cycles through changing soil water and C and N stocks. • Parabolic relationships were found between rock fragment and soil C and N outputs. • Effects of rock fragment on C and N stocks dominated the soil CO 2 emission. • Effects of rock fragment on soil water had obvious contributions to soil N output. Rock fragments (RFs, mineral particles with diameter > 2 mm) can substantially influence soil carbon (C) and nitrogen (N) cycles through different physical mechanisms. These physical mechanisms include changing soil hydraulic parameters (vSH) by reducing fine earth bulk density (vSHBD) and volume (vSHVo), and affecting soil C and N stocks (vCN) by reducing fine earth bulk density (vCNBD) and volume (vCNVo), and increasing fine earth C and N concentrations (vCNCo). In this study, based on soil and climate data in a typical hilly area of China, we construct scenarios by considering these physical mechanisms to investigate the responses of key soil C and N outputs (carbon dioxide or CO 2 , and nitrous oxide or N 2 O emissions, and N leaching) to RF content (RFC) in DNDC and DayCent models. Results showed that when considering vSH and vCN, parabolic relationships were observed between these soil C and N outputs and RFC, with maximum in RFC = 0.3–0.6 g g−1. The vCN dominated the responses of soil CO 2 emission to RFC, while vSH exerted comparative contributions to the responses of soil N 2 O emission and N leaching. When only considering vSH, opposite contributions of vSHBD and vSHVo were observed in DNDC model, and contributions of vSHBD overwhelmed those of vSHVo. However, vSHVo dominated these responses in DayCent model. When only considering vCN, the vCNCo dominated these responses, though considerable superimposed influences of vCNBD and vCNVo appeared under high RFC. The discrepancies between these responses to RFC in DNDC and DayCent models might be due to the different model complexities in simulating soil hydrology, biogeochemistry, and the role of bulk density in these models. Relatively, more distinct responses were achieved in DNDC model. These findings can extend our understandings of RFs and soil C and N cycles. [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]

Epidermal keratinocytes play a vital role in restoration of the intact skin barrier during wound healing. The negative effect of hyperglycemia may prolong the wound healing process. Epidermal keratinocytes have been demonstrated to modulate and directly initiate nociceptive responses in rat models of fractures and chemotherapy-induced neuropathic pain. However, it is unclear whether epidermal keratinocytes are involved in the development and maintenance of incisional pain in nondiabetic or diabetic animals. In the current study, using behavioral tests and immunohistochemistry, we investigated the differential keratinocytes proliferation and expression of pronociceptive inflammatory mediators in keratinocytes in C57BL/6J mice and diabetic KK mice. Our data showed that plantar incision induced postoperative pain hypersensitivity in both C57BL/6J mice and KK mice, while the duration of postoperative pain hypersensitivity in KK mice was longer than that in C57BL/6J mice. Moreover, plantar incision induced the keratinocytes proliferation and expression of IL-1β and TNF-α in keratinocytes in both C57BL/6J mice and KK mice. Interestingly, compared to C57BL/6J mice, the slower and more persistent proliferation of keratinocytes and expression of IL-1β and TNF-α in keratinocytes were observed in KK mice. Together, our study suggested that plantar incision may induce the differential keratinocytes proliferation and expression of IL-1β and TNF-α in kertinocytes in diabetic and nondiabetic animals, which might be associated with the development and maintenance differences in diabetic and nondiabetic postoperative pain. [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]

Ecosystem services (ESs) are the foundation of human well-being. Previous studies have paid limited attention to the interactions among several factors that influence the balance of ESs supply and demand at the grid scale. This study focused on four ESs–water yield (WY), grain yield (GY), carbon storage (CS), and recreational services (RS)–and examined their supply and demand in the Nanjing Metropolitan Area (NMA) in 2000, 2010, and 2020 at the pixel level. Our primary objective was to pinpoint regions characterised by imbalances in ESs supply and demand. In addition, we aimed to explore the interactions among the key elements that contribute to these imbalances. The Geodetector model (GD) was employed to elucidate the 14 distinct primary driving factors influencing the ESs supply-demand balance and their interactions. Our results showed that imbalances in WY and CS supply-demand were predominantly identified in the northern and southern regions of the Yangtze River Basin and certain urban centres. Imbalances in GY were occasionally found in the peripheral areas of cities. The spatial patterns of RS supply-demand imbalances resembled population patterns. The interaction between annual runoff depth and the proportion of built-up land (PB) had the highest explanatory power for changes in the WY supply-demand balance at 54%. The interaction between population density (POP) and the proportion of cultivated land (PC) had the highest explanatory power for the GY supply-demand balance at 29%. The interaction between the proportion of forest land (PF) and average annual ground temperature had the highest explanatory power for the CS supply-demand balance at 67%. The combined effect of PB and Net primary productivity had the highest explanatory power for the RS supply-demand balance at 41%. Overall, this study demonstrated the spatial heterogeneity of ESs at a finer scale than at the county level. This is important for the effective conservation and sound management of ESs. These findings provide decision-makers with insights into regulating regional ecosystem factors and improving local ecological environments based on local conditions. • We studied water yield, grain yield, carbon storage, recreational services. • We examined ecosystem service supply/demand in Nanjing Metropolitan Area in pixels. • A Geodetector model (GD) elucidated the driving forces of the supply-demand. • We demonstrated the spatial heterogeneity of ESs at the pixel level. [ABSTRACT FROM AUTHOR]

• DISPATCH performed better than RF approach in downscaling microwave-based SSM. • DISPATCH was inapplicable in pixels with weak link between SSM and evapotranspiration. • Combining DISPATCH and RF with seasonality considered achieved improved accuracy. Microwave remote sensing has great potential for capturing surface soil moisture (SSM) but suffers from its coarse spatial resolution. In this study, we combined the disaggregation based on physical and theoretical change (DISPATCH) and random forest (RF) approaches to downscale microwave-based SSM in the Yangtze River Delta region, China. Results showed that downscaled SSM by DISPATCH approach was superior to that by RF approach with Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), and the mean bias (Bias) of 0.778, 0.022 m3 m-3 and 0.061 m3 m-3, respectively. However, the DISPATCH approach was inapplicable in pixels with weak link between SSM and evapotranspiration, while the RF approach did not have such restriction. Therefore, the DISPATCH approach was supplemented with the RF approach to downscale the SSM for the entire study area. In addition, this downscaling process was conducted for the four different seasons and for the entire study period to test whether considering seasonality was necessary in the downscaling. The downscaled SSM was the best in autumn with R = 0.843, ubRMSE = 0.022 m3 m-3, and Bias = 0.048 m3 m-3, followed by summer and spring, and poorest in winter. However, the seasonally downscaled SSM performed better than that downscaled for the entire period. Moreover, over the entire study period, land surface temperature, land cover heterogeneity, soil texture, and water area percentage exerted significant influences on errors. However, contribution order of these environmental factors on errors varied with seasons. This study would be helpful to widen DISPATCH applicability in the semi-humid/humid region and maximize the potential of various data in generating high accurate and spatiotemporally continuous SSM estimates. [ABSTRACT FROM AUTHOR]

• RFR with climate classification is an effective method to predict global soil δ15N. • Key ecological limits on soil δ15N in 5 climate zones were respectively identified. • The global map of soil δ15N at 0.1° resolution was reliably generated. Spatial information of the natural abundance of soil nitrogen stable isotope (δ15N) is beneficial for deeply understanding the terrestrial nitrogen (N) cycle. However, to date, the precise global map of soil δ15N still lacks. In this study, based on the measured soil δ15N data by Craine et al. (2015) (https://doi.org/10.1038/srep08280) and related environmental variables including soil, topography, vegetation, and climate, we constructed the optimal relationship model between soil δ15N and environmental variables, and mapped the global pattern of soil δ15N at 0.1° × 0.1° resolution (in natural terrestrial ecosystem). Results indicated that satisfied performance was achieved (R2 = 0.68 and RMSE = 1.26‰) by separately building the optimal relationship models for soil δ15N in each of five climate zones (Tropical, Arid, Temperate, Cold and Polar) using the random forest regression algorithm. In addition, critical controls of the soil δ15N in different climate zones were thus identified based on the variable importance calculated by each random forest regression model. In the Tropical zone, soil δ15N might be primarily regulated by microbial N loss, and soil pH and organic matter were identified as two most important factors. In the Arid zone, abiotically gaseous N loss regulated by solar radiation would be the critical controls of soil δ15N. In the Temperate zone, temperature-related variables were identified as the critical controlling factors, and in the Cold zone, soil water and heat conditions had the equally greater importance, and bulk density was the dominated factor in the Polar zone. Furthermore, the predicted global soil δ15N ranged from −0.44‰ to 12.59‰, with the mean value of 5.06‰, and the standard deviation of 1.74‰. Significantly higher soil δ15N (P < 0.05) were observed in the Tropical and Arid zones with mean values of 6.52‰ and 6.11‰, respectively. This indicated that the soil N cycles were more open than those in the Temperate, Cold and Polar zones (mean soil δ15N of 4.37‰, 3.67‰ and 2.76‰, respectively). This study provides clues for potential environmental regulations on terrestrial N cycle in different climates, and the global soil δ15N map can be a reliable data support for future research. [ABSTRACT FROM AUTHOR]

Highlights • Acceptable accuracies were achieved for MultiRFHet and RosRFCHet. • RosRFCHet was more proper as calibrated by observed data was not required. • RFC and spatial variation of SHPs should be both considered for SWS simulation. Abstract Whether the spatial variation and influence of rock fragment contents (RFCs) on soil hydraulic parameters (SHPs) should be jointly considered in the soil water simulation have been less investigated in previous studies. In this study, we tested whether considering these factors were necessary in the soil water simulation on a representative stony-soil hillslope located in Taihu Lake Basin, China. Five schemes of SHPs in HYDRUS-3D model were established. They were (i) MultiRFHet: spatially varied SHPs extracted from Durner's multimodal retention function based on the observed soil water retention data; (ii) RosHom: spatially uniformed SHPs derived from ROSETTA; (iii) RosHet: spatially varied SHPs derived from ROSETTA; (iv) RosRFCHom: spatially uniformed SHPs derived from ROSETTA and adjusted by RFCs; (v) RosRFCHet: spatially varied SHPs derived from ROSETTA and adjusted by RFCs. Results indicated when the spatial variation and influence of RFCs on SHPs were both considered (MultiRFHet and RosRFCHet), acceptable accuracies (Nash-Sutcliffe efficiency or NSE ≥ 0.58 for MultiRFHet and > 0.17 for RosRFCHet) were achieved in simulating the soil water storage (SWS) variation. Since the MultiRFHet required the calibration by the observed soil water retention data, RosRFCHet was a good alternative in the simulation. On the contrary, the SHPs acquired without considering neither the RFCs nor the spatial variation yielded unacceptable simulation results (NSE general < 0). This demonstrated that spatial variation and influence of RFCs on SHPs should be included in the SWS simulation on this stony-soil hillslope, although it had limited effect in subsurface flow simulation. [ABSTRACT FROM AUTHOR]

Many approaches have been proposed to identify the representative sampling sites for estimating the spatial mean soil water contents. However, comparisons on these approaches have seldom been conducted to simultaneously predict the surface and subsurface mean soil water contents. In this study, five approaches were evaluated in identifying representative sites to estimate the surface and subsurface mean soil water contents on a typical hillslope in Taihu Lake Basin, China. They were temporal stability analysis (TSA), k -means clustering with environmental factors as inputs (EFs), combinations of TSA and EFs (EFs + TSA), k -means clustering with surface soil water contents as inputs (Theta), and combinations of TSA and Theta (Theta+TSA). The correlation coefficient ( r ) and root mean squared error (RMSE) between estimated and measured mean soil water contents were used to evaluate the accuracies during the calibration period (the first 25 dates) and validation period (the last 18 dates). Results showed the optimal number of representative sites on this hillslope was six. When >6 representative sites were selected, the TSA had the lowest accuracies for estimating both surface and subsurface mean soil water contents during validation period (mean RMSE ≥ 0.011 m 3  m −3 ). The Theta and Theta + TSA had better accuracies in estimating surface mean soil water contents during both calibration and validation periods (mean RMSE < 0.007 m 3  m −3 ). However, to estimate surface and subsurface mean soil water contents simultaneously, the EFs and EFs + TSA were more promising (mean RMSE < 0.011 m 3  m −3 during validation period), especially the EFs which only required one-time collection of environmental factors. These findings will be beneficial for choosing proper approach to calibrate and validate the remote sensed soil water contents. [ABSTRACT FROM AUTHOR]

Spatio-temporal variability of soil moisture (θ) is a challenge that remains to be better understood. A trade-off exists between spatial coverage and temporal resolution when using the manual and real-time θ monitoring methods. This restricted the comprehensive and intensive examination of θ dynamics. In this study, we integrated the manual and real-time monitored data to depict the hillslope θ dynamics with good spatial coverage and temporal resolution. Linear (stepwise multiple linear regression-SMLR) and non-linear (support vector machines-SVM) models were used to predict θ at 39 manual sites (collected 1–2 times per month) with θ collected at three real-time monitoring sites (collected every 5 mins). By comparing the accuracies of SMLR and SVM for each depth and manual site, an optimal prediction model was then determined at this depth of this site. Results showed that θ at the 39 manual sites can be reliably predicted (root mean square errors <0.028 m 3 m −3 ) using both SMLR and SVM. The linear or non-linear relationship between θ at each manual site and at the three real-time monitoring sites was the main reason for choosing SMLR or SVM as the optimal prediction model. The subsurface flow dynamics was an important factor that determined whether the relationship was linear or non-linear. Depth to bedrock, elevation, topographic wetness index, profile curvature, and θ temporal stability influenced the selection of prediction model since they were related to the subsurface soil water distribution and movement. Using this approach, hillslope θ spatial distributions at un-sampled times and dates can be predicted. Missing information of hillslope θ dynamics can be acquired successfully. [ABSTRACT FROM AUTHOR]

The electromagnetic induction (EMI) technique has been widely used to survey soil properties at intermediate spatial scales. However, EMI data interpretation remains a challenge for more accurate and robust mapping. Residual analysis is an alternative approach that can be used to improve the EMI data mining. On a tea garden (TG) hillslope and a bamboo forest (BF) hillslope, terrain indices were used to regress the apparent electrical conductivity (ECa) in ten repeated EMI surveys using stepwise multiple linear regressions (SMLR). Residuals of ECa in these regressions, which had terrain influence removed, were then calculated. The classification and regression tree (CART) model was adopted to quantify the relative contributions of terrain indices (elevation, slope, plane curvature–PLC, profile curvature–PRC, and topographic wetness index–TWI), static soil properties (rock fragment content–RFC, depth to bedrock–DB, contents of clay, silt and sand), and dynamic soil property (volumetric soil moisture–θ) to ECa and their residuals. Results show that contributions of terrain indices to ECa are around 50%. However, contributions of terrain indices to ECa residuals are < 20%, while great contributions of different soil properties to ECa residuals can be observed in some cases. On both hillslopes, better predicting accuracies were achieved when using ECa residuals as independent variables in SMLRs to predict soil properties than using only terrain indices or ECa as independent variables. Similarly, on both hillslopes, using terrain indices plus ECa residuals as independent variables also yield better prediction of θ than using only terrain indices or using terrain indices plus ECa as independent variables. Findings of this study indicate that residual analysis can be a useful technique in improving EMI data interpretation for estimating the spatial variations of soil properties. In cases that relationship between target soil properties and ECa readings are weak, this approach can probably be used to improve the mapping accuracy of the target soil properties. [ABSTRACT FROM AUTHOR]

• Soil δ15N and soil water content show an upward-concave relationship in the globe. • This is constituted by inconsistent relationships in different climate zone. • Positive relationship in Tropical zone due to hydrologically regulated N losses. • Negative relationship in Arid zone due to abiotic N loss and N input from bedrock. • Climate-limited low N loss lead to low soil δ15N in Temperate, Cold and Polar zones. Investigating the relationship between the natural abundance of soil nitrogen stable isotope (δ15N) and soil water content (SWC) in the globe can help to explain the different controls of soil δ15N in different regions, yet this was few concerned on. Based on the Craine et al. (2015) summarized soil δ15N data (https://doi.org/10.1038/srep08280), and the satellite retrieved SWC in 910 grids (size of 0.1° × 0.1°) of the globe, an upward-concave relationship between soil δ15N and SWC was observed. Higher soil δ15N existed at both the dry and wet ends (i.e., respectively below and above the intermediate range of SWC, which was about 0.20–0.30 m3/m−3(-|-)). Inconsistent relationships in five Köppen-Geiger climate zones constituted this upward-concave relationship. Positive relationship between soil δ15N and SWC in the Tropical zone determined the increasing trend of soil δ15N at the wet end in the globe. This can be explained by the increased hydrologically regulated soil N losses with the increase of SWC under the warm and moist condition. Negative relationship in the Arid zone determined the decreasing trend at the dry end in the globe. Increased abiotic N losses like ammonia volatilization and proportion of N supplement through bedrock weathering (higher δ15N) with the decrease of SWC under the dry, hot and infertile soil condition may be the main reasons. In addition, poor relationships between soil δ15N and SWC in the Temperate, Cold and Polar zones constituted the transition range of the upward-concave relationship in the globe. This reflected that not hydrologically but climatically determined low soil N losses was responsible for the low soil δ15N in these zones. These findings can help to deepen our understandings on the spatial pattern of soil δ15N and the indicated N cycle at the global scale. [ABSTRACT FROM AUTHOR]

Greenhouse gases dissolved in the soil water and transported through subsurface flow were poorly understood in comparison to their emissions at the soil-atmosphere interface. In this study, leachate CO 2 and N 2 O concentrations were monitored on a tea garden (TG) hillslope (cultivated) and a bamboo forest (BF) hillslope (naturally vegetated) from September 2019 to February 2022 in the south-eastern hilly area of China. Leachate CO 2 and N 2 O concentrations ranged from 1.07 to 9.83 mg C L−1 and from 0.32 to 11.74 μg N L−1, respectively, on the TG hillslope, while they ranged from 0.64 to 25.95 mg C L−1 and from 0.23 to 5.68 μg N L−1, respectively, on the BF hillslope. On both hillslopes, leachate CO 2 concentrations were the greatest in summer, while they were the lowest in winter. On the TG hillslope, the highest and the lowest leachate N 2 O concentrations were observed in spring and summer, respectively, while on the BF hillslope, they were observed in autumn and winter, respectively. On both hillslopes, soil temperature, precipitation during the previous 15 days, and matrix potential were positively related to the leachate CO 2 concentrations but not related to the leachate N 2 O concentrations. Soil water content was positively related to both leachate CO 2 and N 2 O concentrations, while such relationships with groundwater table depth were negative. Leachate total inorganic nitrogen and soil total inorganic nitrogen concentrations were positively related to the leachate N 2 O concentration on the TG hillslope, while such relationships were not observed on the BF hillslope. Spatial variations of leachate CO 2 and N 2 O concentrations were mainly influenced by land covers, terrain attributes (slope, elevation), soil particle size distribution, bulk density and soil organic matter content. The findings of this study supplemented the knowledge of soil water dissolved CO 2 and N 2 O in the cultivated and naturally vegetated land covers. Further researches are needed to quantify the CO 2 and N 2 O losses through subsurface flow and their contributions to the total soil CO 2 and N 2 O losses. • Tea had lower leachate CO 2 concentration but higher N 2 O concentration than bamboo. • Leachate CO 2 concentration was high in summer but low in winter on both hillslopes. • Seasonal patterns of leachate N 2 O concentration varied on different hillslopes. • Meteorology and soil water affected temporal variations of dissolved CO 2 and N 2 O. • Land cover, terrain and soil affected spatial variations of dissolved CO 2 and N 2 O. [ABSTRACT FROM AUTHOR]

• A high accurate SM product was generated by fusing the ESA CCI and SMAP products. • Accuracy of the fused SM product was evaluated. • The proposed approach provided a new insight for fusing SM products. Accurate surface soil moisture (SSM) information is essential for various investigations and implications in agronomy, hydrology, meteorology and ecology. In this study, an improved SSM product from 2015 to 2018 was generated by combining the triple collocation (TC) method and entropy value (EV) method (TC&EV) to fuse the European Space Agency Climate Change Initiative (version 04.7, ESA CCI v04.7) and Soil Moisture Active Passive (SMAP, version 5) products in the Yangtze River Delta (YRD) region. The ESA CCI v04.7 and SMAP products were corrected against a reference model-based SSM product using the cumulative distribution function matching method. These two corrected SSM products were fused for pixels in which they were significantly correlated (p < 0.05) with the model-based SSM product. Their fusing weights were determined by three methods including the equal-weight (EW) method, TC method, and TC&EV. In TC&EV, for pixels in which both corrected SSM products were significantly correlated (p < 0.05) with the five auxiliary factors, the EV method was applied to determine their fusing weights. Otherwise, the TC method was used. The SSM fused by TC&EV was compared with those fused by EW and by TC. The SSM fused by TC&EV outperformed the other two with the highest correlation coefficient (R) of 0.766 and lowest unbiased root mean square error (ubRMSE) of 0.022 m3 m−3. It also had higher R, lower ubRMSE, and lower absolute mean bias compared to the ECA CCI and SMAP products. Moreover, for pixels in which the EV method was used, the relative importance of the five auxiliary factors was ranked as land surface temperature > normalized difference vegetation index > precipitation > water area percentage > Gini-Simpson index. This study proposed an approach for accquiring accurate SSM information for the YRD region with complex land surface characteristics, which would be useful for various modeling and implication purposes. [ABSTRACT FROM AUTHOR]

Soil moisture and its spatial pattern are important for understanding various hydrological, pedological, ecological and agricultural processes. In this study, data of rainfall and soil moisture contents at different depths (10 cm, 20 cm, 40 cm and 60 cm) in forestland and vegetable plot in the Taihu Lake Basin, China were monitored and analyzed for characteristics of soil moisture variation and its response to several typical rainfall events. The following results were observed. First, great temporal variation of soil moisture was observed in the surface layer than in deeper layer in vegetable plot. In contrast, in forestland, soil moisture had similar variation pattern at different depths. Second, initial soil moisture was an important factor influencing the vertical movement of soil water during rainfall events. In vegetable plot, simultaneous response of soil moisture to rainfall was observed at 10- and 20-cm depths due to fast infiltration when initial soil was relatively dry. However, traditional downward response order occurred when initial soil was relatively wet. Third, critical soil horizon interface was an active zone of soil water accumulation and lateral movement. A less permeable W-B soil horizon interface (40-cm depth) in vegetable plot can create perched water table above it and elevate the soil water content at the corresponding depth. Fourth, the land cover was an effective control factor of soil moisture during small and moderate rainfall events. In the forestland, moderate and small rainfall events had tiny influences on soil moisture due to canopy and surface O horizon interception. Fifth, preferential flow and lateral subsurface interflow were important paths of soil water movement. During large and long duration rainfall events, lateral subsurface flow and preferential flow through surface crack or soil pipe occurred, which recharged the deep soil. However, in more concentrated large storm, surface crack or soil pipe connected by soil macropores was the main contributor to the occurrence of preferential flow. Findings of this study provide a theoretical foundation for sustainable water and fertilizer management and land use planning in the Taihu Lake Basin. [ABSTRACT FROM AUTHOR]

Abstract: Soil moisture is one of the most important parameters controlling various critical zone processes including energy balance and nutrient cycling. However, hillslope soil moisture variation and its response to rainfall are not fully understood yet. Through real-time monitoring systems, mechanisms of soil moisture response to rainfall were investigated at top, upper, middle, lower and toe slope positions along a typical mixed land-use hillslope in Taihu Lake Basin, China. The corresponding land use types for these five hillslope position are woodland, tea (Camellia sinensis), tea, meadow and woodland, respectively. This hillslope has annual precipitation around 1100mm. Soil moisture varied from <0.05m3 m−3 at the top slope during the dry period to >0.40m3 m−3 at the toe slope during wet period. Despite different land-use types, similar characteristics of soil moisture response to rainfall were observed at the top (woodland), upper (tea) and middle (tea) slope positions. At these three sites, degrees of soil moisture change (difference between maximum soil moisture during the rainfall and antecedent soil moisture) were significantly (P <0.05) influenced by precipitation amount and intensity, as well as antecedent soil moisture in some cases. However, at the lower slope position (meadow), soil moisture variation during the rainfall was mainly influenced by lateral subsurface flow; the cumulative precipitation was less than the increased soil water storage, indicating that water must come from the upslope areas to recharge this site. At the toe slope position (woodland), precipitation interception by tree canopy and surface organic matter reduced the degree of soil moisture change during rainfall events. Lateral subsurface flow can recharge deep soils at the toe slope position in rainfalls >35mm with relatively wet initial condition (e.g., soil moisture at 0.1-m depth of this site >0.30m3 m−3). Results suggest that lower and toe slope positions are hot spots receiving lateral surface and subsurface recharge. Perennial vegetation at toe slope position can remove nitrogen in lateral subsurface flow by promoting denitrification. Therefore, maintaining toe slope area in perennial vegetation may help to mitigate dissolved nitrogen losses from upslope fertilized tea plantations. [Copyright &y& Elsevier]

[Display omitted] • We proposed a framework to track phosphorus (P) sources for two sites in a lake. • Sediment and central Lake Taihu were critical (>95%) P sources for these two sites. • P control of these two sites required both internal and external P loading control. Excess phosphorus (P) in freshwater lakes is a global challenge due to its potential cause of harmful algal blooms threating drinking water safety. However, quantifying the P sources for a specific site in a large lake is extremely challenging due to the complex interaction between internal and external P loading to surface water. To address this challenge, this study developed a modelling framework to track P sources of two drinking water intakes in a large shallow lake (Lake Taihu) in China. The framework proposed a new-developed index (PSCI, P source contribution index) to quantify the contributions of various P sources for the drinking water intakes. PSCI was derived by a three-dimensional hydrodynamic and water quality model that describes the complex processes of P sources, sinks and transportation in both horizontal and vertical directions within the lake. Application of the framework to these two drinking water intakes (Jinshu and Shangshan) achieved a surprising finding that internal P loading from sediment was a significant P source with a contribution as high as 47.1% and 30.4%, respectively. Central Lake Taihu had a large contribution of 49.5% and 68.3% for Jinshu and Shangshan, and inflow river (Wangyu River) had a contribution of < 5%. The high contribution of internal P loading was mainly due to the sediment P accumulation for years and large external P loading. Our study highlighted the important role of internal P loading affecting the P concentration of drinking water intake in a large eutrophic lake, and demonstrated the high value of the modelling framework in quantifying the P sources for a specific site (e.g., drinking water intake) in a lake. [ABSTRACT FROM AUTHOR]

Although the frequency and intensity of seasonal drought and wetness are increasing under climate change background, their effects on soil nitrate nitrogen (NO 3 --N) leaching have remained unclear. In this study, validated by the field data on a typical tea garden hillslope in Taihu basin, China, the Decomposition-Denitrification (DNDC) model was used to investigate these effects. The decennial drought, decennial wetness, and normal conditions of different seasons were combined to construct 31 scenarios. Results showed that seasonal drought decreased annual NO 3 --N leaching, with a reduction of 6.52%−18.70% (one-season drought), 18.62%−29.68% (two-season drought), 36.64%−43.99% (three-season drought) and 51.44% (all-season drought) relative to the normal scenario. Except the spring drought, drought in other seasons had legacy effects that increased NO 3 --N leaching in their succession seasons. The legacy effect of summer drought even continued till the summer of next year. Seasonal wetness increased annual NO 3 --N leaching, with an increase of 2.58%−11.39% (one-season wetness), 10.04%−22.31% (two-season wetness), 19.50%−29.39% (three-season wetness), and 29.66% (all-season wetness) relative to the normal scenario. Autumn and winter wetness decreased the NO 3 --N leaching in their succession seasons, while spring and summer wetness had no such effect. Soil NO 3 --N leaching had positive correlations with precipitation (drought scenarios: r = 0.74; wetness scenarios: r = 0.54) and soil water seepage (drought scenarios: r = 0.62; wetness scenarios: r = 0.56). Weak correlation coefficients between soil NO 3 --N content and NO 3 --N leaching were also observed especially under spring drought (r = 0.17) and summer drought (r = 0.14). However, NO 3 --N leaching was not limited by soil NO 3 --N content after the application of basal fertilizer. Fertilization plus drought or wetness increased the risk of soil NO 3 --N leaching. These findings will be benefit for controlling the non-point N loss of tea plantation under the background of climate change. ● Seasonal drought had NO 3 --N leaching 6.5–51.4% lower than normal scenario. ● Seasonal wetness had NO 3 --N leaching 2.6–29.7% higher than normal scenario. ● Drought or wetness in autumn and winter had greater impacts on NO 3 --N leaching. ● Fertilization plus drought or wetness increased the NO 3 --N leaching risk. [ABSTRACT FROM AUTHOR]

• Rock fragments affect soil C and N cycles via physical and biogeochemical pathways. • Influences of rock fragments on soil C and N cycles should be specifically considered. • Methodologies are proposed to monitor, map and model C and N cycles in stony-soils. Soils with a significant proportion of rock fragments (RFs, with diameter >2 mm) are widely distributed in terrestrial ecosystems. However, a lack of quantitative information about RF characteristics restricts our ability to explain and predict the related soil carbon (C) and nitrogen (N) processes, and induces biases of C and N investigations in areas where soils contain significant RFs. In this paper, we reviewed the direct and indirect effects of RFs on soil C and N cycles, including via affecting soil C and N stocks, hydrology, temperature, and via chemical weathering that releases C, N, cations and anions, consumes CO 2 and regulates pH. Based on this, we discussed current challenges and potential solutions in investigating C and N cycles of high RF content soils. First, we proposed a series of approaches to quantifying the effects of RFs on C and N stocks and soil hydrology across scales. Second, we emphasized the evolutions of soil RFs' properties over time, and recommended including RF physicochemical properties in the routine soil inventory. Third, we suggested that both the effects of RFs and the evolutions of soil RFs over time should be considered in model simulation. Finally, we provided a framework by coupling monitoring, mapping and modelling to investigate the roles of RFs on soil C and N cycles from earth's critical zone to global scale. This review will improve our understandings of RFs and terrestrial C and N cycles, and their responses to global change. [ABSTRACT FROM AUTHOR]

Rock fragments (RFs, material particles with diameter > 2 mm) are widely distributed in soils over globe. However, spatial pattern of RF content (RFC) and its roles on hydrological and ecological patterns have remained unclear at the global scale. In this study, based on the collected global datasets, we investigated the relationships between mean annual temperature (MAT), mean annual precipitation (MAP), elevation and RFC at 0–5 cm soil depth. In addition, we also examined the links of RFC to representative hydrological and ecological indicators (soil water content or SWC, and normalized difference vegetation index or NDVI, respectively). Results showed that through affecting the pedogenic and morphogenic processes, temperature, precipitation and elevation presented significant relationships with RFC over the globe. When MAT < 20 ℃, RFC decreased with MAT increasing, due to that high temperature could promote RF weathering and thus soil production; while when MAT > 20 ℃, RFC slightly increased with MAT increasing, due to that extreme high temperature induced arid condition would surpass soil production. RFC decreased with MAP increasing, because precipitation increased SWC and hydrologic flow and thus enhanced soil production. In addition, higher elevation zones usually had colder condition and greater loss of fine-textured soils due to erosion, and thus RFC increased with elevation increasing. Furthermore, a RFC threshold of about 0.2 m3 m−3 was identified. When RFC < 0.2 m3 m−3, existence of RFs may improve the soil pore structure and thus water and nutrient retention capacity. Therefore, higher SWC and NDVI were observed. However, when RFC > 0.2 m3 m−3, due to poor soil water and nutrient retention capacity, as well as lower MAT with higher elevation, or lower MAP, lower SWC and NDVI were observed. These findings can supplement the lack of knowledge on RFs and its effects from the global perspective. [ABSTRACT FROM AUTHOR]

• For soil nitrogen losses, tea garden had above 3.28 times than bamboo forest. • Soil N 2 O fluxes were the greatest in spring but the lowest in winter. • Leachate NO 3 –-N concentrations were maximum in winter but minimum in summer. • Threshold of soil temperature controlled soil N 2 O fluxes. • Threshold of precipitation during the previous seven days controlled NO 3 –-N leaching. Due to the economic benefits, land use change (e.g. deforest to tea or fruit plantation) has been widely occurred in the south-eastern hilly area of China. This may stimulate serious soil nitrogen (N) losses due to large fertilizer inputs (about 1–2 times of that in regular rice-wheat rotation). Therefore, we investigated the soil N 2 O fluxes and leachate NO 3 –-N concentrations and their responses to multiple factors on a tea garden (TG) hillslope and an adjacent bamboo forest (BF) hillslope. Soil N 2 O fluxes and leachate NO 3 –-N concentrations on the TG hillslope were 3.28 and 4.24 times of those on the BF hillslope, respectively. Soil N 2 O fluxes measured in spring were the greatest while those measured in winter were the lowest. However, the measured leachate NO 3 –-N concentrations were the greatest in winter but the lowest in summer. On both hillslopes, soil temperature (ST) and precipitation during the previous seven days (API7) were positively related to soil N 2 O fluxes but negatively related to leachate NO 3 –-N concentrations, while the ground water table depth was opposite. Soil water content (SWC) and the ratio of SWC/field capacity (SWC/FC) negatively influenced leachate NO 3 –-N concentrations on both hillslopes. Positive influences of SWC and SWC/FC on soil N 2 O fluxes were observed on the TG hillslope, while quadratic relationships were observed on the BF hillslope. Thresholds of ST and API7 were existed in the controlling the spatial variations of soil N 2 O fluxes and leachate NO 3 –-N concentrations on both hillslopes. When ST was > 9.5 °C, spatial variations in soil N 2 O fluxes were controlled by topography, soil properties and soil hydrological parameters on both hillslopes. Similarly, when API7 were < 58.0 mm, the spatial variations in leachate NO 3 –-N concentrations were also influenced by these factors on both hillslopes. Finding of this study will supplement the knowledge of soil N 2 O emissions and NO 3 –-N leaching from the tea planation and bamboo forest. [ABSTRACT FROM AUTHOR]

• DNDC model was able to simulate N cycles on the representative hillslopes. • N loss of tea garden were about 2.50 times of that on the entire study area. • After land use pattern optimization, nitrate leaching can be reduced by 7.6%. • N 2 O flux can be reduced by 6.0% after land use pattern optimization. Spatial patterns of land use types (mixed forest: MF, tea garden: TG, bamboo forest: BF) in a mountainous area with rapid agricultural land development were optimized to reduce NO 3 −-N leaching and N 2 O emission in this study. Firstly, a process-oriented biogeochemical model (Denitrification Decomposition, DNDC) was calibrated and validated on representative MF, TG and BF hillslopes. To upscale the simulations, hydropedological function (HPF) units were generated by overlapping maps of land use, soil organic carbon, rock fragment content and slope, which were recognized as critical factors affecting soil N cycle. The calibrated DNDC models were then adopted to simulate the soil N cycles in different HPF units and assess temporal and spatial variations of NO 3 −-N leaching and N 2 O emission risks. Lastly, spatial allocations of TG were determined respectively for minimizing NO 3 −-N leaching (MLN_LU) and N 2 O emission (MEN_LU), and balancing the reductions of both (BRN_LU). Results showed that the DNDC model had acceptable accuracies on these representative hillslopes (R2 > 0.50, NSE > 0.30). Land use of TG had the greatest N loss risks, the mean annual NO 3 −-N leaching and N 2 O flux in TG HPF units (72.11- and 3.63- kg N ha−1, respectively) were respectively 2.61 and 2.50 times of those of the entire study area. Temporal variations of NO 3 −-N leaching and N 2 O flux were both controlled by the timing of precipitation, and their spatial patterns were both primarily controlled by land use and then respectively by soil hydraulic properties (NO 3 −-N leaching) and soil carbon and N concentrations (N 2 O flux). Due to the relatively small spatial variations of soil and terrain properties and rational land use spatial patterns in this study area, the MLN_LU only reduced 7.6% of NO 3 −-N leaching, the MEN_LU only reduced 6.0% of N 2 O flux, and the BRN_LU reduced both by 3.3% and 4.1%, respectively. This study emphasized the role of land use pattern optimization in reducing the non-point N losses in mountainous area, and could provide scientific guidelines for future agricultural land developments. [ABSTRACT FROM AUTHOR]