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Ecosystem water-use efficiency (WUE) is a crucial indicator for evaluating carbon and water cycles. Although greening and climate change have significantly altered the WUE in Chinese terrestrial ecosystems, the roles of physiological and ecological processes are not fully understood. To address this, WUE is broken down into two key ratios: gross primary productivity to transpiration (GPP/T), which mainly reflects the effect of plant physiological processes, and transpiration to evapotranspiration (T/ET), which primarily indicates the impact of vegetation changes. Both ratios are influenced by climate change. This study employed a newly developed satellite-based ecosystem service process model Carbon and Exchange between Vegetation, Soil, and Atmosphere-ecosystem service (CEVSA-ES) to examine the impact of GPP/T and T/ET on WUE in China's terrestrial ecosystems from 2000 to 2022, alongside an analysis of the environmental variables affecting these ratios. This study revealed a general increase in WUE during the study period with significant interdecadal differences. Between 2000 and 2010, WUE was relatively stable (slope = 0.0023 g C kg−1 H2O a−1, p > 0.05), primarily because the decrease in GPP/T (p 0.05). Factors affecting GPP/T and T/ET showed considerable variability. Precipitation had the main influence on GPP/T, accounting for 70 % of its variation. The initial decade of the 21st century experienced an overall precipitation deficiency, followed by a sustained surplus in the subsequent years, resulting in interdecadal fluctuations in GPP/T. In contrast, T/ET was affected by a combination of factors, including the leaf area index, temperature, and precipitation, contributing 39 %, 29 %, and 32 %, respectively. The present study advances our understanding of the interaction of terrestrial ecosystem with the atmosphere amid global changes, offering crucial insights for forecasting the future dynamics of carbon and water cycles.

Characterizing dynamics of evapotranspiration (ET) and energy exchange is of particular importance for the water resource use and management in the water-limited agricultural areas under global climate change. Based on eddy covariance method, this study analyzed the seasonal and interannual variations of ET and energy fluxes over a rain-fed spring maize cropland in the Chinese Loess Plateau during 2 years which presented contrasting precipitation (P) totals and patterns in 2019 (drought year) and 2020 (normal year). Results showed obvious intra- and inter-annual variabilities of ET and energy fluxes related with crop phenology and water availability. Annual ET were 339.3 and 386.5 mm yr–1 in 2019 and 2020, respectively, with separate annual ET/P ratios of 0.84 and 0.70. Annual ET was dominated by ET during growing season (May−September), with close proportions of 75.8% and 72.9% for the two years. The partitioning of net radiation (Rn) was dominated by latent heat flux (LE) during growing season, but was primarily driven by sensible heat flux (H) during non-growing season. Annual ratios of LE/Rn and H/Rn were comparable in the drought year (0.46 −0.47), while LE/Rn (0.52) dominated over H/Rn (0.46) in the normal year. Similar seasonal courses were observed in surface conductance (gs), decoupling factor (Ω), and Priestley-Taylor coefficient (α), consistently peaking during the rapid growth period of maize (July−August). The low annual means of Ω (0.20 −0.25) and α (0.44 −0.45) implied the strongly aerodynamical coupling and biophysical control on ET for the study area. Moreover, daily α in this study increased significantly with increasing gs until a threshold around 10 mm s−1. Structural equation modelling revealed that the environmental constraints on ET were mainly affected by Rn, soil water content and vapor pressure deficit (VPD) in the drought year, and prone to be dominated by Rn, leaf area index (LAI) and VPD in the normal year. These results indicated that drought may increase the sensitivity of maize ET to water supply condition, and decrease the sensitivity to changes in available energy, aerodynamic condition and vegetation coverage. This study will provide insights into the effects of drought on energy partitioning and the environmental and biophysical controls on ET in the region.

The resistance and recovery rate of forest ecosystems to droughts vary with the severity of the drought. Studies on the impacts of severe droughts on forest ecosystems have suggested that evergreen forests have higher resistance than deciduous forests. However, whether the resistance and recovery rate of forest ecosystems vary under moderate droughts remained largely unknown. Here, we used the Standardized Precipitation Index to identify drought characteristics in China’s forest ecosystems from 2000 to 2018. We quantified the resistance and recovery rate to moderate droughts under different drought timings based on the Enhanced Vegetation Index. We then adopted random forest regression to evaluate the relative importance of climatic variables and species richness as drivers of resistance and recovery rate to moderate droughts. We found that China’s forest ecosystems mainly experienced moderate drought events, resulting in different resistance and recovery rate compared to severe droughts. Deciduous forests had high resistance (30.39 ± 21.32) and evergreen forests had high recovery rate (1.38 ± 1.10) due to their different response strategies under moderate droughts. The resistance and recovery rate of China’s forest ecosystems varied under different drought timings. The differences between deciduous and evergreen forests’ resistance (43.88 – 78.24 %) and recovery rate (-31.04 – −52.20 %) were remarkable in spring, autumn, and winter. In evergreen and deciduous forests, climatic variables were the main influencing factors of resistance and recovery rate to moderate droughts, similar to the leading role of climatic variables under severe droughts. Global radiation (41.06 %) and temperature (30.97 %) were the dominant factors contributing to resistance, whereas the recovery rate was primarily explained by global radiation (40.30 %) and precipitation (27.33 %). Thus, the high resistance in deciduous forests and the high recovery rate in evergreen forests under moderate droughts suggest that evergreen and deciduous forests can be mixed to improve the stability of forest ecosystems under climate change.

An increasing trend of hydroclimatic disturbances, such as droughts, which are projected to become more frequent and intense with global warming and climate change. Droughts adversely affect the structure and function of terrestrial ecosystems, damage vegetation growth, and even increase mortality. In this study, we assess the drought resistance (Rd) of ecosystems in the Tibetan Plateau. Ecosystem water use efficiency (WUE) was used as an indicator of change in carbon and water cycles, and then ecosystem resistance in terms of change in WUE in drought years was analyzed. Our results suggested that the WUE in the Tibetan Plateau exhibited significant spatial heterogeneity and drastically reduced under dry and cool conditions. The evaluation of ecosystem drought resistance indicated that the grassland in most areas of the Tibetan Plateau is resistant to hydroclimatic fluctuations. In particular, the grassland ecosystem in areas with lower temperatures or higher precipitation exhibited greater drought resistance. Our results facilitate the identification of drought-vulnerable areas in the Tibetan Plateau to inform grassland ecosystem management and climate policy-making and sustain ecosystem stability under future climate conditions.

Previous studies of vegetation dynamics in the Yellow River Delta (YRD) predominantly relied on sparse time series or coarse-resolution images, which not only overlooked the rapid and spatially heterogeneous changes, but also limited our understanding of driving mechanisms. Here, employing spatiotemporal data fusion methods, we constructed a novel fused enhanced vegetation index (EVI) dataset with a high spatiotemporal resolution (30-meter and 8-day resolution) for the YRD from 2000 to 2020, and we analyzed the vegetation variations and their driving factors within and outside the YRD Nation Natural Reserve (YRDNRR). The fused EVI effectively captured spatiotemporal vegetation dynamics. Notably, within the YRDNRR core area, the fused EVI showed no significant trend before 2010, while a significant increase emerged post-2010, with an annual growth of 7%, the invasion of Spartina alterniflora explained 78% of this EVI increment. In the YRDNRR experimental area, the fused EVI exhibited a distinct interannual trend, which was characterized by an initial increase (2000–2006, p < 0.01), followed by a subsequent decrease (2006–2011, p < 0.01) and, ultimately, a renewed increase (2011–2020, p > 0.05); the dynamics of the fused EVI were mainly affected by the spring runoff (R2 = 0.71), while in years with lower runoff, it was also affected by the spring precipitation (R2 = 0.70). Outside of the protected area, the fused EVI demonstrated a substantial increase from 2000 to 2010 due to agricultural land expansion and human management practices, followed by stabilization post-2010. These findings enhance our comprehension of intricate vegetation dynamics in the YRD, holding significant relevance in terms of wetland preservation and management.

Continued climate warming and precipitation fluctuations are expected to further affect aboveground net primary productivity (ANPP) across alpine grasslands, with associated implications for ecosystem functions. The spatial and temporal variability of ANPP in Tibetan Plateau (TP) grasslands and its response to temperature and precipitation were investigated in this study, based on the ANPP estimated by means of ensemble analysis of multiple machine learning models. First, the response of the spatial distribution of ANPP to variations in the precipitation gradient was nonlinear and showed an S-shaped growth pattern, which could be divided into four stages (stationary, rapid growth, slower growth, and saturation) corresponding to arid (mean annual precipitation (MAP) < 250 mm), semiarid (250 mm < MAP < 450 mm), semihumid (450 mm < MAP < 650 mm) and humid (MAP > 650 mm) precipitation regimes, respectively. Second, regional precipitation regimes affected the sensitivity of ANPP’s interannual variability to temperature and precipitation; ANPP is more sensitive to temperature in wetter areas than in dryer areas of the TP region. The results of our study suggest that in the region of Tibetan Plateau, the response of grassland ANPP variation to climate was constrained by the background precipitation regimes.

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint.

To address severe grassland degradation problems, China has been implementing several national restoration programs, which have been reported to mitigate the deterioration of grassland ecosystem function. However, the regional differences in the effectiveness of the programs are not yet known. In this study, gross primary productivity (GPP) was selected as an indicator to analyze grassland degradation dynamics, and the residual trend method was used to assess the driving force of grassland degradation based on GPP. The results showed the GPP of Inner Mongolia grassland significantly increased by 3.94 g C m−2 yr−1 during 2000–2015, climate change was the dominant driving factors, while human activities slightly reduced grassland productivity (−0.17 g C m−2 yr−1). But there are obviously spatial heterogeneous on the impact of human activities. Specifically, grassland management significantly promoted the GPP increase in semidesert steppe, while grazing activity decreased the GPP in meadow and typical steppes. These findings suggest that ecological restoration programs could obviously improve vegetation functioning in ecologically fragile grassland which has relatively very low productivity. However, the grasslands with relative higher productivity or initially non-degraded are still facing with great degradation risk due to the continuous growing livestock scale. Therefore, policy-makers should pay more attention to the originally non-degraded or high productivity grasslands, especially at those years the negative impacts of intensive grazing could be concealed by the well-growth grass under abundant precipitation.

Ecological degradation has occurred in global grasslands and has impaired their ecosystem services severely, so ecological conservation of grasslands should be focused more on the effectiveness of management measures. The green-up process decides the year-round forage yield and ecological conditions of grasslands. Adopting rest-grazing during the green-up process can guarantee a successful green-up, thus realizing more economic benefits without grassland degradation. Therefore, studies should pay more attention to whether the green-up process is really covered by the rest-grazing period or not. We analyze the spatiotemporal variations and the stability of the annual green-up date in Xilin Gol Grassland from 2000 to 2018 based on MODIS time series images and compare the green-up date with the rest-grazing period to assess the effectiveness of the rest-grazing policy. The results show that the green-up date of Xilin Gol Grassland had advanced 15 days on average because of the increasing trend of both temperature and precipitation during 2000~2018. The green-up date is mostly 120~130 d in the east, about 10 days earlier than the west (130~140 d) and 20 days earlier than in the central areas (140~150 d), also because of the spatial variations of temperature and precipitation. The coefficient of variation (CV) of the green-up date showed a significant negative correlation with precipitation, so the green-up date is more unstable in the arid areas. The rest-grazing period started more than 45 days earlier than the green-up date and failed to cover it in several years, which occurred more frequently in southern counties. The average green-up date appeared after rest-grazing started in over 98% of areas, and the time gap is 15~45 days in 88% of areas, which not only could not avoid grassland degradation effectively but also increased herdsmen’s life burden. This study aims to accurately grasp the temporal and spatial variations of the green-up date in order to provide references for adjusting a more proper rest-grazing period, thus promoting ecological conservation and sustainable development of animal husbandry.

Abstract Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process‐based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process‐based ES model (CEVSA‐ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA‐ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon‐water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA‐ES model with nine flux sites comprising 39 site‐years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site‐years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA‐ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial‐temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins.

Spatially and temporally explicit information on the biomass in terrestrial ecosystems is essential to better understand the carbon cycle and achieve vegetation resource conservation. As a climate-sensitive critical ecological function area, accurate monitoring of the spatiotemporal variation in the grassland aboveground biomass (AGB) is important in the Three-River Headwater Region (TRHR) of China. In this study, based on field observation, remote sensing, meteorological and topographical data, we estimated the grassland AGB in the TRHR and analyzed its spatiotemporal change and response to climatic factors. Four machine learning (ML) models (random forest (RF), cubist, artificial neural network and support vector machine models) were constructed and compared for AGB simulation purposes. The AGB results estimated with the four ML models were then applied in integrated analysis via Bayesian model averaging (BMA) to obtain more accurate and stable estimates. Our results demonstrated that the RF model performed better among the four ML models (testing dataset: correlation coefficient ( r ) = 0.84; root mean squared error = 76.99 g m ^−2 ), and BMA improved grassland AGB prediction based on the multimodel results. The spatial distribution of the grassland AGB in the TRHR was heterogeneous, with higher values in the southeast and lower values in the northwest. The interannual variation in the grassland AGB in most areas of the TRHR exhibited nonsignificant increasing trends from 2000 to 2018, and the sensitivity of the AGB to the annual precipitation was obviously modulated by regional water conditions. This study provides a more precise method for grassland AGB estimation, and these findings are expected to enable improved assessments to obtain a greater grassland AGB understanding.

China’s terrestrial ecosystems play an important role in the global carbon cycle. Regional contributions to the interannual variation (IAV) of China’s terrestrial carbon sink and the attributions to climate variations are not well understood. Here we have investigated how terrestrial ecosystems in the four climate zones with various climate variabilities contribute to the IAV in China’s terrestrial net ecosystem productivity (NEP) using modeled carbon fluxes data from six ecosystems models. Model results show that the monsoonal region of China dominates national NEP IAV with a contribution of 86% (69%–96%) on average. Yearly national NEP changes are mostly driven by gross primary productivity IAV and half of the annual variation results from NEP changes in summer. Regional contributions to NEP IAV in China are consistent with their contributions to the magnitude of national NEP. Rainfall variability dominates the NEP annual variability in China. Precipitation in the temperate monsoon climate zone makes the largest contribution (23%) to the IAV of NEP in China because of both the high sensitivity of terrestrial ecosystem carbon uptake to rainfall and the large fluctuation in the precipitation caused by the East Asian summer monsoon anomalies. Our results suggest that NEP IAV can be mainly attributed to ecosystems with larger productivity and response to precipitation, and highlight the importance of monsoon climate systems with high seasonal and interannual variability in driving internannual variation in the land carbon sink.