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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]

Purpose: Inconsistent controlling factors of soil water content (SWC) spatial variation across regions and spatial scales have been acknowledged in previous studies. However, universal principles explaining these inconsistencies are still needed to be explored. The main aim of this study was to conclude the universal principles across regions and scales. Materials and methods: In this study, based on the surface (< 10-cm soil depth) SWC dataset from Meng et al. (2021) (https://doi.org/10.5194/essd-13-3239-2021) and the relevant environmental variables (climate including temperature and precipitation, topography including elevation and slope, soil including sand and clay contents, and vegetation including normalized difference vegetation index or NDVI) in a 1500 km × 1500 km study region in China, we calculated the correlations between them, and identified the controlling factors of surface SWC in different subregions and at different spatial scales. Results and discussion: Local factors like temperature, precipitation, and clay content that controling the vertical soil water replenishment and removal dominated the surface SWC spatial variation in this dry study region (mean SWC ≤ 0.10 m3 m−3). Nonlocal factors like topography (e.g., elevation and slope) and soil porosity (sand content) determining the lateral soil water redistribution had outweighed effects in the representative wet subregion. In addition, topography also exerted considerable effects on SWC in this study region due to its great spatial variability, especially when spatial scale < 800 km. As NDVI was complicatedly controlled by other environmental variables, its effect on SWC performed dramatic regional differentiation. In contrast, effect of slope on SWC was more associated with its spatial variability as it was independent from other environmental variables. Conclusions: This study emphasized that the inconsistencies of controlling factors of SWC spatial variation can be comprehensively explained by different regimes in the dry and wet conditions, spatial variability of environmental variables and the interactions among different environmental variables. These findings can deepen our understanding of the controls on SWC spatial variation across regions and scales. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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: 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]

• 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]

• 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]

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]

• 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]

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]

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]