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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]

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]

• 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]

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]

• 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]

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]

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]

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]

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]

• 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]