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Abstract Soil erosion occurs extensively across China, leading to severe degradation of the land and ecosystem services. However, the spatial and temporal variations in soil erodibility ( k ) and the distribution of soil erosion across land use types and slopes remain unclear. We synthesized the results from 325 sites published in 152 literatures to analyze the factors affecting the k , such as land use type, climate, topography, soil, and vegetation restoration age. The results showed that areas with slopes >25° had a larger k factor ( k =  0.1047) than did those with slope <6° ( k =  0.0637) or 6–25° ( k =  0.0832). The k from 2006 to 2011 ( k =  0.0725) was higher than that from 1999 to 2005 ( k =  0.058) and that from 2012 to 2016 ( k =  0.0631). The k value initially increased with vegetation restoration age and then gradually decreased. Land use also had an impact on the k factor, with the k factor of cropland ( k =  0.0697) being higher than that of grassland ( k =  0.0663) but lower than that of forest ( k =  0.0967). Across China, North Shaanxi, Heilongjiang, and South Guizhou, which are located in the Loess Plateau in Northwest China, the Black Soil region of Northeast China, and the Karst areas in Southwest China, respectively, were the three most severely eroded regions due to hydraulic erosion, frost‐thaw erosion, and high‐intensity erosion, respectively. Overall, the most important factors affecting the k were soil characteristics, followed by topography and climate. Among them, soil nitrogen and precipitation were the two most critical factors influencing the k . , Key Points Grassland had lower soil erodibility than had cropland and forestland North Shaanxi, Heilongjiang, and South Guizhou were the three most severely eroded regions Precipitation and soil N play critical roles in controlling soil erosion

Abstract Background In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage (FCS) should first be clarified as much as possible, especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above- and below-ground biomass (AB biomass), so as to eliminate the significant bias in national scale estimations. Methods We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density (WD), and converts the AB biomass from stem biomass by using allometric relationships. Results Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges (relative errors: + 4.46% and − 4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD (0.7 t∙m − 3 ) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates. Conclusions Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.

The method of forest biomass estimation based on a relationship between the volume and biomass has been applied conventionally for estimating stand above- and below-ground biomass (SABB, t ha−1) from mean growing stock volume (m3 ha−1). However, few studies have reported on the diagnosis of the volume-SABB equations fitted using field data. This paper addresses how to (i) check parameters of the volume-SABB equations, and (ii) reduce the bias while building these equations. In our analysis, all equations were applied based on the measurements of plots (biomass or volume per hectare) rather than individual trees. The volume-SABB equation is re-expressed by two Parametric Equations (PEs) for separating regressions. Stem biomass is an intermediate variable (parametric variable) in the PEs, of which one is established by regressing the relationship between stem biomass and volume, and the other is created by regressing the allometric relationship of stem biomass and SABB. A graphical analysis of the PEs proposes a concept of “restricted zone,” which helps to diagnose parameters of the volume-SABB equations in regression analyses of field data. The sampling simulations were performed using pseudo data (artificially generated in order to test a model) for the model test. Both analyses of the regression and simulation demonstrate that the wood density impacts the parameters more than the allometric relationship does. This paper presents an applicable method for testing the field data using reasonable wood densities, restricting the error in field data processing based on limited field plots, and achieving a better understanding of the uncertainty in building those equations.

Abstract Climate change has a profound impact on the global carbon cycle, including effects on riverine carbon pools, which connect terrestrial, oceanic, and atmospheric carbon pools. Until now, terrestrial ecosystem models have rarely incorporated riverine carbon components into global carbon budgets. Here we developed a new process‐based model, TRIPLEX‐HYDRA (TRIPLEX‐hydrological routing algorithm), that considers the production, consumption, and transport processes of nonanthropogenic dissolved organic carbon (DOC) from soil to river ecosystems. After the parameter calibration, model results explained more than 50% of temporal variations in all but three rivers. Validation results suggested that DOC yield simulated by TRIPLEX‐HYDRA has a good fit ( R 2  = 0.61, n  = 71, p  < 0.001) with global river observations. And then, we applied this model for global rivers. We found that mean DOC yield of global river approximately 1.08 g C/m 2  year, where most high DOC yield appeared in the rivers from high northern or tropic regions. Furthermore, our results suggested that global riverine DOC flux appeared a significant decrease trend (average rate: 0.38 Pg C/year) from 1951 to 2015, although the variation patterns of DOC fluxes in global rivers are diverse. A decreasing trend in riverine DOC flux appeared in the middle and high northern latitude regions (30–90°N), which could be attributable to an increased flow path and DOC degradation during the transport process. Furthermore, increasing trend of DOC fluxes is found in rivers from tropical regions (30°S–30°N), which might be related to an increase in terrestrial organic carbon input. Many other rivers (e.g., Mississippi, Yangtze, and Lena rivers) experienced no significant changes under a changing environment. , Key Points Terrestrial ecosystem models rarely incorporate riverine DOC components into the global carbon cycle The TRIPLEX‐HYDRA model simulates the spatiotemporal variation in the DOC fluxes in global rivers The global riverine DOC flux simulated by the TRIPLEX‐HYDRA model has significantly decreased from 1951 to 2015

Summary Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis ( PCA ) was used to characterize trait variation, redundancy analysis ( RDA ) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life‐form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area ( LA ), internal‐to‐ambient CO 2 ratio (χ), leaf economics spectrum traits (specific leaf area ( SLA ) versus leaf dry matter content ( LDMC ) and nitrogen per area ( N area )), and photosynthetic capacities ( V cmax , J max at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life‐form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.