Search

Ecological restoration projects implemented over the past 20 years have substantially increased forest coverage in China, but the high tree mortality of new afforestation forest remains a challenging but unsolved problem. It is still not clear how much vegetation can be sustained by the forest lands with given water, energy and soil conditions, i.e., the carrying capacity for vegetation (CCV) of forest lands, which is the prerequisite for planning and implementing forest restoration projects. Here, we used a simplified method to evaluate the CCV across forest lands nationwide. Specifically, based on leaf area index (LAI) dataset, we use boosted regression tree and multiple linear regression model to analyze the CCV during 2001–2020 and 2021–2030 and explore the contribution of environmental factors. We find that there are three typical regions with lower CCV located in the Loess Plateau and the southern region of the Inner Mongolia Plateau, the Hengduan Mountain region, and the Tianshan Mountains. More importantly, the vegetation in the regions near the dry-wet climate transition zone show excess local carrying capacity for vegetation over the past two decades and they are more susceptible to potential climatic stress. In comparison, in the Greater Khingan Mountains and Hengduan Mountains, there is high potential to improve the forest growth. Temperature, precipitation and soil affects the CCV by shaping the vegetation in the optimal range. This indicates that more consideration should be given to restrictions of regional environmental constraints when planning afforestation and forest management. This study has important implications for guiding future forest scheme in China.

Study region: Chesapeake Bay Watershed Study focus: Climate plays a critical role in regulating N loading from terrestrial ecosystems to coastal waters and further affecting the health and functioning of coastal ecosystems. However, the sensitivities of riverine exports of different N species to climate change have rarely been investigated. This study examines the response of riverine exports of ammonia (NH4+), nitrate (NO3–), dissolved organic nitrogen (DON), and particulate organic nitrogen (PON) to future changes in precipitation and temperature. A suite of climate forcings was used to drive a process-based terrestrial–aquatic model, DLEM (Dynamic Land Ecosystem Model), to project changes in N loading to the Chesapeake Bay in the mid-21st century, relative to the 1990s. New hydrological insights for the region: Our simulations show that, despite a relatively small average change in freshwater discharge driven by future climate change, annual average NH4+, NO3–, DON, PON, and total nitrogen exports are likely to change by –12 %, +13 %, +2 %, –9 %, and +9 %, respectively. Driven by rising temperature, NH4+ decreases as a result of enhanced volatilization and nitrification, but NO3– export may increase due to high mineralization and nitrification. The change in DON export is mainly regulated by discharge, and the PON change is highly uncertain due to its high sensitivity to extreme precipitation events. This study highlights the importance of considering different responses of N species to climate change when designing nutrient reduction strategies to mitigate estuarine hypoxia.

Abstract Lentic systems (lakes and reservoirs) are emission hotpots of nitrous oxide (N2O), a potent greenhouse gas; however, this has not been well quantified yet. Here we examine how multiple environmental forcings have affected N2O emissions from global lentic systems since the pre-industrial period. Our results show that global lentic systems emitted 64.6 ± 12.1 Gg N2O-N yr−1 in the 2010s, increased by 126% since the 1850s. The significance of small lentic systems on mitigating N2O emissions is highlighted due to their substantial emission rates and response to terrestrial environmental changes. Incorporated with riverine emissions, this study indicates that N2O emissions from global inland waters in the 2010s was 319.6 ± 58.2 Gg N yr−1. This suggests a global emission factor of 0.051% for inland water N2O emissions relative to agricultural nitrogen applications and provides the country-level emission factors (ranging from 0 to 0.341%) for improving the methodology for national greenhouse gas emission inventories.

Abstract U.S. rice paddies, critical for food security, are increasingly contributing to non‐CO2 greenhouse gas (GHG) emissions like methane (CH4) and nitrous oxide (N2O). Yet, the full assessment of GHG balance, considering trade‐offs between soil organic carbon (SOC) change and non‐CO2 GHG emissions, is lacking. Integrating an improved agroecosystem model with a meta‐analysis of multiple field studies, we found that U.S. rice paddies were the rapidly growing net GHG emission sources, increased 138% from 3.7 ± 1.2 Tg CO2eq yr−1 in the 1960s to 8.9 ± 2.7 Tg CO2eq yr−1 in the 2010s. CH4, as the primary contributor, accounted for 10.1 ± 2.3 Tg CO2eq yr−1 in the 2010s, alongside a notable rise in N2O emissions by 0.21 ± 0.03 Tg CO2eq yr−1. SOC change could offset 14.0% (1.45 ± 0.46 Tg CO2eq yr−1) of the climate‐warming effects of soil non‐CO2 GHG emissions in the 2010s. This escalation in net GHG emissions is linked to intensified land use, increased atmospheric CO2, higher synthetic nitrogen fertilizer and manure application, and climate change. However, no/reduced tillage and non‐continuous irrigation could reduce net soil GHG emissions by approximately 10% and non‐CO2 GHG emissions by about 39%, respectively. Despite the rise in net GHG emissions, the cost of achieving higher rice yields has decreased over time, with an average of 0.84 ± 0.18 kg CO2eq ha−1 emitted per kilogram of rice produced in the 2010s. The study suggests the potential for significant GHG emission reductions to achieve climate‐friendly rice production in the U.S. through optimizing the ratio of synthetic N to manure fertilizer, reducing tillage, and implementing intermittent irrigation.

Abstract Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world.

Tropical forests store more than half of the world's terrestrial carbon (C) pool and account for one-third of global net primary productivity (NPP). Many terrestrial biosphere models (TBMs) estimate increased productivity in tropical forests throughout the 21st century due to CO2 fertilization. However, phosphorus (P) limitations on vegetation photosynthesis and productivity could significantly reduce the CO2 fertilization effect. Here, we used a carbon-nitrogen-phosphorus coupled model (Dynamic Land Ecosystem Model; DLEM-CNP) with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests during 1860–2018. Our model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem productivity. We further compared the simulations from DLEM-CNP, DLEM-CN, and DLEM-C and the results showed that the inclusion of P processes reduced the CO2 fertilization effect on gross primary production (GPP) by 25% and 45%, and net ecosystem production (NEP) by 28% and 41%, respectively, relative to CN-only and C-only models. From the 1860s to the 2010s, the DLEM-CNP estimated that in tropical forests GPP increased by 17%, plant respiration (Ra) increased by 18%, ecosystem respiration (Rh) increased by 13%, NEP increased by 121% per unit area, respectively. Additionally, factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO2 fertilization effect had been offset by 135% due to deforestation from the 1860s to the 2010s. Our study highlights the importance of P limitation on the C cycle and the weakened CO2 fertilization effect resulting from P limitation in tropical forests.

In this study, the degradation system of Shewanella oneidensis MR-1 and goethite was constructed with chlorpyrifos as the target contaminant. The effects of initial pH, contaminant concentration, and temperature on the removal rate of chlorpyrifos during the degradation process were investigated. The experimental conditions were optimized by response surface methodology with a Box–Behnken design (BBD). The results show that the removal rate of chlorpyrifos is 75.71% at pH = 6.86, an initial concentration of 19.18 mg·L−1, and a temperature of 30.71 °C. LC-MS/MS analyses showed that the degradation products were C4H11O3PS, C7H7Cl3NO4P, C9H11Cl2NO3PS, C7H7Cl3NO3PS, C9H11Cl3NO4P, C4H11O2PS, and C5H2Cl3NO. Presumably, the degradation pathways involved are: enzymatic degradation, hydrolysis, dealkylation, desulfur hydrolysis, and dechlorination. The findings of this study demonstrate the efficacy of the goethite/S. oneidensis MR-1 complex system in the removal of chlorpyrifos from water. Consequently, this research contributes to the establishment of a theoretical framework for the microbial remediation of organophosphorus pesticides in aqueous environments.

Increasing food and biofuel demands have led to the cascading effects from cropland expansions, raised fertilizer use, to increased riverine nitrogen (N) loads. However, little is known about the current trade-off between riverine N pollution and crop production due to the lack of predictive understanding of ecological processes across the land-aquatic continuum. Here, we propose a riverine N footprint (RNF) concept to quantify how N loads change along with per unit crop production gain. Using data synthesis and a well-calibrated hydro-ecological model, we find that the RNF within the Mississippi–Atchafalaya River Basin peaked at 1.95 g N kg ^−1 grain during the 1990s, and then shifted from an increasing to a decreasing trend, reaching 0.65 g N kg ^−1 grain in the 2010s. This implies decoupled responses of crop production and N loads to key agricultural activities approximately after 2000, but this pattern varies considerably among sub-basins. Our study highlights the importance of developing a food–energy–water nexus indicator to examine the region-specific trade-offs between crop production and land-to-aquatic N loads for achieving nutrient mitigation goals while sustaining economic gains.

East Asia is a crucial region in the global methane (CH4) budget, with significant contributions from the livestock sector. However, the long-term trend and spatial pattern of CH4 emissions from livestock in this region have not been fully assessed. Here, we estimate CH4 emissions from 10 categories of livestock in East Asia during 1961−2019 following the Tier 2 approaches suggested by IPCC (2019). Our results show that livestock-sourced CH4 emission in 2019 was 13.22 [11.42−15.01] (mean [minimum−maximum of 95% confidence interval] Tg CH4 yr-1, accounting for an increase of 231% since 1961. From 1961 to 2019, the emissions increased first and then stabilized after 2000. The contribution of slaughtered livestock to total emissions increased from 3% in 1961 to 24% in 2019 as a result of a significant increase in the slaughtered population. Spatially, the emission hotspots were mostly distributed in eastern China, South Korea, and parts of Japan, but they tend to shift northward after 2000. This latest long-term inventory can help to understand CH4 budget and to assess CH4 mitigation potential at national and regional levels.

Abstract The biogeochemical processes of carbon (C), nitrogen (N), and phosphorous (P) are fully coupled in the Earth system, which shape the structure, functioning, and dynamics of terrestrial ecosystems. However, the representation of P cycle in terrestrial biosphere models (TBMs) is still in an early stage. Here we incorporated P processes and C‐N‐P interactions into the C‐N coupled Dynamic Land Ecosystem Model (DLEM‐CNP), which had a major feature of the ability in simulating the N and P colimitation on vegetation C assimilation. DLEM‐CNP was intensively calibrated and validated against daily or annual observations from four eddy covariance flux sites, two Hawaiian sites along a chronosequence of soils, and other 13 tropical forest sites. The results indicate that DLEM‐CNP significantly improved simulations of forest gross and net primary production (R2: 0.36–0.97, RMSE:1.1–1.49 g C m−2 year−1 for daily GPP at eddy covariance flux sites; R2 = 0.92, RMSE = 176.7 g C m−2 year−1 for annual NPP across 13 tropical forest sites). The simulations were also consistent with field observations in terms of biomass, leaf N:P ratio and plant response to fertilizer addition. A sensitivity analysis suggests that simulated results are reasonably robust against uncertainties in model parameter estimates and the model was very sensitive to parameters of P uptake. These results suggest that incorporating P processes and N‐P interaction into terrestrial biosphere models is of critical importance for accurately estimating C dynamics in tropical forests, particularly those P‐limited ones.

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 104 km2 in 1850 to 6.64 × 104 km2 in 1980 and then decreased to 6.16 × 104 km2 in 2018. Cropland expansion rate was the largest in the 1960s (749 km2 yr−1) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km2 yr−1 in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.

Herein, a relatively rare case is reported in which a knowledge, attitude, and practice (KAP) health education model was applied in a young female patient with metabolic syndrome (MS) and acute pancreatitis (AP), with a satisfactory effect. The purpose of this report is to provide a reference for a viable health education program in clinical practice for intervention of MS with concurrent AP in the absence of clinical trials. The patient’s unhealthy lifestyle led to obesity, diabetes mellitus, severe fatty liver, hyperlipidemia, and AP. We used a KAP health education model in a nursing intervention and evidence-based multidisciplinary cooperation to develop a personalized diet, exercise plan, education plan, and continuous care of the patient after discharge from the hospital. Within 2 months, the patient achieved weight loss, stable blood lipids, controlled blood sugar levels, and decreased glycated hemoglobin level from 9.0% to 5.4%. This KAP-based health education model has clinical importance as an intervention for lifestyle modification in patients with MS and AP. This approach can be adopted to help other patients to effectively control and prevent the recurrence of diseases.

Abstract To accurately assess the roles of agriculture in securing food security and maintaining environmental sustainability, it is essential to improve the representation of crop growth, development, and yield formation in global land models that traditionally focus on energy, water, carbon, and nitrogen exchanges between land and the atmosphere. In this study, a process‐based agricultural module has been coupled with the Dynamic Land Ecosystem Model (DLEM‐AG2.0) for assessing how multiple environmental factors (climate change, atmospheric CO2 concentration, tropospheric O3, and nitrogen deposition) and human activities (land use/cover change, nitrogen fertilizer use, and irrigation) have affected the crop growth, development, yield, carbon (C), nitrogen (N), and water cycles in agroecosystems. Here we describe the model structure for simulating crop growth, development, and yield formation in the DLEM‐AG2.0, and then we validate the model using field observations and a national yield survey for three major crops (wheat, maize, and rice) in China during 1980–2012. Results show that the DLEM‐AG2.0 is capable of simulating the dynamic processes of phenological development, leaf growth expansion, biomass accumulation, biomass allocation, and yield formation for wheat, maize, and rice with normalized root mean square errors of the simulations of less than 20%. Our model‐based yield estimation for the three major crops at the national scale for the period 1980–2012 is generally consistent with the national yield survey in China. The crop representation in the DLEM‐AG2.0 is flexible for extrapolating to a global scale after rigorous testing with both site‐specific and regional observations. Further advancement of agricultural modeling within the global land modeling framework will require consideration of human perception and behavior for adapting and mitigating global change.

Terrestrial ecosystems play a significant role in global carbon and water cycles because of the substantial amount of carbon assimilated through net primary production and large amount of water loss through evapotranspiration (ET). Using a process-based ecosystem model, we investigate the potential effects of climate change and rising atmospheric CO2 concentration on global terrestrial ecosystem water use efficiency (WUE) during the twenty-first century. Future climate change would reduce global WUE by 16.3% under high-emission climate change scenario (A2) and 2.2% under low-emission climate scenario (B1) during 2010–2099. However, the combination of rising atmospheric CO2 concentration and climate change would increase global WUE by 7.9% and 9.4% under A2 and B1 climate scenarios, respectively. This suggests that rising atmospheric CO2 concentration could ameliorate climate change-induced WUE decline. Future WUE would increase significantly at the high-latitude regions but decrease at the low-latitude regions under combined changes in climate and atmospheric CO2. The largest increase of WUE would occur in tundra and boreal needleleaf deciduous forest under the combined A2 climate and atmospheric CO2 scenario. More accurate prediction of WUE requires deeper understanding on the responses of ET to rising atmospheric CO2 concentrations and its interactions with climate.

As the fastest growing food production sector in the world, aquaculture may become an important source of nitrous oxide (N _2 O)—a potent greenhouse gas and the dominant source of ozone-depleting substances in the stratosphere. China is the largest aquaculture producer globally; however, the magnitude of N _2 O emission from Chinese aquaculture systems (CASs) has not yet been extensively investigated. Here, we quantified N _2 O emission from the CASs since the Reform and Opening-up (1979–2019) at the species-, provincial-, and national-levels using annual aquaculture production data, based on nitrogen (N) levels in feed type, feed amount, feed conversion ratio, and emission factor (EF). Our estimate indicates that over the past 41 years, N _2 O emission from CASs has increased approximately 25 times from 0.67 ± 0.04 GgN in 1979 to 16.69 ± 0.31 GgN in 2019. Freshwater fish farming, primarily in two provinces, namely, Guangdong and Hubei, where intensive freshwater fish farming has been adopted in the past decades, accounted for approximately 89% of this emission increase. We also calculated the EF for each species, ranging from 0.79 ± 0.23 g N _2 O kg ^−1 animal to 2.41 ± 0.14 g N _2 O kg ^−1 animal. The results of this study suggest that selecting low-EF species and improving feed use efficiency can help reduce aquaculture N _2 O emission for building a climate-resilient sustainable aquaculture.

Grassland ecosystems play an essential role in climate regulation through carbon (C) storage in plant and soil. But, anthropogenic practices such as livestock grazing, grazing related excreta nitrogen (N) deposition, and manure/fertilizer N application have the potential to reduce the effectiveness of grassland C sink through increased nitrous oxide (N _2 O) and methane (CH _4 ) emissions. Although the effect of anthropogenic activities on net greenhouse gas (GHG) fluxes in grassland ecosystems have been investigated at local to regional scales, estimates of net GHG balance at the global scale remains uncertain. With the data-model framework integrating empirical estimates of livestock CH _4 emissions with process-based modeling estimates of land CO _2 , N _2 O and CH _4 fluxes, we examined the overall global warming potential (GWP) of grassland ecosystems during 1961–2010. We then quantified the grassland-specific and regional variations to identify hotspots of GHG fluxes. Our results show that, over a 100-year time horizon, grassland ecosystems sequestered a cumulative total of 113.9 Pg CO _2 -eq in plant and soil, but then released 91.9 Pg CO _2 -eq to the atmosphere, offsetting 81% of the net CO _2 sink. We also found large grassland-specific variations in net GHG fluxes, with pasturelands acting as a small GHG source of 1.52 ± 143 Tg CO _2 -eq yr ^−1 (mean ± 1.0 s.d.) and rangelands a strong GHG sink (−442 ± 266 Tg CO _2 -eq yr ^−1 ) during 1961–2010. Regionally, Europe acted as a GHG source of 23 ± 10 Tg CO _2 -eq yr ^−1 , while other regions (i.e. Africa, Southern Asia) were strong GHG sinks during 2001–2010. Our study highlights the importance of considering regional and grassland-specific differences in GHG fluxes for guiding future management and climate mitigation strategies in global grasslands.

Abstract In arid and semi‐arid regions, grassland degradation has become a major environmental and economic problem, but little information is available on the response of grassland productivity to both climate change and grazing intensity. By developing a grazing module in a process‐based ecosystem model, the dynamic land ecosystem model (DLEM), we explore the roles of climate change, elevated CO2, and varying grazing intensities in affecting aboveground net primary productivity (ANPP) across different grassland sites in Mongolia. Our results show that both growing season precipitation totals and average temperature exert important controls on annual ANPP across six sites over a precipitation gradient, explaining 65% and 45% of the interannual variations, respectively. Interannual variation in ANPP, measured as the ratio of standard deviation among years to long‐term mean, increased from 9.5 to 18.9% to 23.9–32.5% along a gradient of high to low precipitation. Historical grazing resulted in a net reduction in ANPP across all sites ranging from 2% to 15.4%. Our results further show that grassland ANPP can be maintained at a grazing intensity of 1.0 and 0.5 sheep/ha at wet and dry sites, respectively, indicating that dry sites are more vulnerable to grazing compared to wet sites. In addition, precipitation use efficiency (PUE) decreased while nitrogen use efficiency (NUE) increased across a gradient of low to high precipitation. However, grazing resulted in a net reduction in both PUE and NUE by 47% and 67% across all sites. Our results indicate that seasonal precipitation totals, average temperatures and grazing are important regulators of grassland ANPP in Mongolia. These results have important implications for grassland productivity in semi‐arid regions in Central Asia and beyond.

This paper evaluates the ability of eight global vegetation models to reproduce recent trends and inter-annual variability of biomass in natural terrestrial ecosystems. For the purpose of this evaluation, the simulated trajectories of biomass are expressed in terms of the relative rate of change in biomass (RRB), defined as the deviation of the actual rate of biomass turnover from its equilibrium counterpart. Cumulative changes in RRB explain long-term changes in biomass pools. RRB simulated by the global vegetation models is compared with its observational equivalent, derived from vegetation optical depth reconstructions of above-ground biomass (AGB) over the period 1993–2010. According to the RRB analysis, the rate of global biomass growth described by the ensemble of simulations substantially exceeds the observation. The observed fluctuations of global RRB are significantly correlated with El Niño Southern Oscillation events (ENSO), but only some of the simulations reproduce this correlation. However, the ENSO sensitivity of RRB in the tropics is not significant in the observation, while it is in some of the simulations. This mismatch points to an important limitation of the observed AGB reconstruction to capture biomass variations in tropical forests. Important discrepancies in RRB were also identified at the regional scale, in the tropical forests of Amazonia and Central Africa, as well as in the boreal forests of north-western America, western and central Siberia. In each of these regions, the RRBs derived from the simulations were analyzed in connection with underlying differences in net primary productivity and biomass turnover rate ̶as a basis for exploring in how far differences in simulated changes in biomass are attributed to the response of the carbon uptake to CO _2 increments, as well as to the model representation of factors affecting the rates of mortality and turnover of foliage and roots. Overall, our findings stress the usefulness of using RRB to evaluate complex vegetation models and highlight the importance of conducting further evaluations of both the actual rate of biomass turnover and its equilibrium counterpart, with special focus on their background values and sources of variation. In turn, this task would require the availability of more accurate multi-year observational data of biomass and net primary productivity for natural ecosystems, as well as detailed and updated information on land-cover classification.

It has long been concerned how crop water use efficiency (WUE) responds to climate change. Most of existing researches have emphasized the impact of single climate factor but have paid less attention to the effect of developed agronomic measures on crop WUE. Based on the long-term field observations/experiments data, we investigated the changing responses of crop WUE to climate variables (temperature and precipitation) and agronomic practices (fertilization and cropping patterns) in the semi-arid area of northern China (SAC) during two periods, 1983-1999 and 2000-2010 (drier and warmer). Our results suggest that crop WUE was an intrinsical system sensitive to climate change and agronomic measures. Crops tend to reach the maximum WUE (WUEmax) in warm-dry environment while reach the stable minimum WUE (WUEmin) in warm-wet environment, with a difference between WUEmax and WUEmin ranging from 29.0%-55.5%. Changes in temperature and precipitation in the past three decades jointly enhanced crop WUE by 8.1%-30.6%. Elevated fertilizer and rotation cropping would increase crop WUE by 5.6-11.0% and 19.5-92.9%, respectively. These results indicate crop has the resilience by adjusting WUE, which is not only able to respond to subsequent periods of favorable water balance but also to tolerate the drought stress, and reasonable agronomic practices could enhance this resilience. However, this capacity would break down under impact of climate changes and unconscionable agronomic practices (e.g. excessive N/P/K fertilizer or traditional continuous cropping). Based on the findings in this study, a conceptual crop WUE model is constructed to indicate the threshold of crop resilience, which could help the farmer develop appropriate strategies in adapting the adverse impacts of climate warming.

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions ( E _LUC ) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al ( 2015 ), and 2) the land CO _2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F _Jena and F _CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models ( p < 0.05), which is consistent with the R + L estimate ( p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F _Jena and F _CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000–2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr ^−1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr ^−1 ), and close to the MTE (120 Pg C yr ^−1 ). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models’ ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.

Simulating vegetation photosynthetic productivity (or gross primary production, GPP) is a critical feature of the biome models used for impact assessments of climate change. We conducted a benchmarking of global GPP simulated by eight biome models participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a) with four meteorological forcing datasets (30 simulations), using independent GPP estimates and recent satellite data of solar-induced chlorophyll fluorescence as a proxy of GPP. The simulated global terrestrial GPP ranged from 98 to 141 Pg C yr ^−1 (1981–2000 mean); considerable inter-model and inter-data differences were found. Major features of spatial distribution and seasonal change of GPP were captured by each model, showing good agreement with the benchmarking data. All simulations showed incremental trends of annual GPP, seasonal-cycle amplitude, radiation-use efficiency, and water-use efficiency, mainly caused by the CO _2 fertilization effect. The incremental slopes were higher than those obtained by remote sensing studies, but comparable with those by recent atmospheric observation. Apparent differences were found in the relationship between GPP and incoming solar radiation, for which forcing data differed considerably. The simulated GPP trends co-varied with a vegetation structural parameter, leaf area index, at model-dependent strengths, implying the importance of constraining canopy properties. In terms of extreme events, GPP anomalies associated with a historical El Niño event and large volcanic eruption were not consistently simulated in the model experiments due to deficiencies in both forcing data and parameterized environmental responsiveness. Although the benchmarking demonstrated the overall advancement of contemporary biome models, further refinements are required, for example, for solar radiation data and vegetation canopy schemes.

Quantitative information on the response of global terrestrial net primary production (NPP) to climate change and increasing atmospheric CO2 is essential for climate change adaptation and mitigation in the 21st century. Using a process-based ecosystem model (the Dynamic Land Ecosystem Model, DLEM), we quantified the magnitude and spatiotemporal variations of contemporary (2000s) global NPP, and projected its potential responses to climate and CO2 changes in the 21st century under the Special Report on Emission Scenarios (SRES) A2 and B1 of Intergovernmental Panel on Climate Change (IPCC). We estimated a global terrestrial NPP of 54.6 (52.8-56.4) PgC yr(-1) as a result of multiple factors during 2000-2009. Climate change would either reduce global NPP (4.6%) under the A2 scenario or slightly enhance NPP (2.2%) under the B1 scenario during 2010-2099. In response to climate change, global NPP would first increase until surface air temperature increases by 1.5 °C (until the 2030s) and then level-off or decline after it increases by more than 1.5 °C (after the 2030s). This result supports the Copenhagen Accord Acknowledgement, which states that staying below 2 °C may not be sufficient and the need to potentially aim for staying below 1.5 °C. The CO2 fertilization effect would result in a 12%-13.9% increase in global NPP during the 21st century. The relative CO2 fertilization effect, i.e. change in NPP on per CO2 (ppm) bases, is projected to first increase quickly then level off in the 2070s and even decline by the end of the 2080s, possibly due to CO2 saturation and nutrient limitation. Terrestrial NPP responses to climate change and elevated atmospheric CO2 largely varied among biomes, with the largest increases in the tundra and boreal needleleaf deciduous forest. Compared to the low emission scenario (B1), the high emission scenario (A2) would lead to larger spatiotemporal variations in NPP, and more dramatic and counteracting impacts from climate and increasing atmospheric CO2.

The Loess Plateau of China has the highest soil erosion rate in the world where billion tons of soil is annually washed into Yellow River. In recent decades this region has experienced significant climate change and policy-driven land conversion. However, it has not yet been well investigated how these changes in climate and land use have affected soil organic carbon (SOC) storage on the Loess Plateau. By using the Dynamic Land Ecosystem Model (DLEM), we quantified the effects of climate and land use on SOC storage on the Loess Plateau in the context of multiple environmental factors during the period of 1961-2005. Our results show that SOC storage increased by 0.27 Pg C on the Loess Plateau as a result of multiple environmental factors during the study period. About 55% (0.14 Pg C) of the SOC increase was caused by land conversion from cropland to grassland/forest owing to the government efforts to reduce soil erosion and improve the ecological conditions in the region. Historical climate change reduced SOC by 0.05 Pg C (approximately 19% of the total change) primarily due to a significant climate warming and a slight reduction in precipitation. Our results imply that the implementation of "Grain for Green" policy may effectively enhance regional soil carbon storage and hence starve off further soil erosion on the Loess Plateau.

There is a critical need to monitor and predict terrestrial primary production, the key indicator of ecosystem functioning, in a changing global environment. Here we provide a brief review of three major approaches to monitoring and predicting terrestrial primary production: (1) ground-based field measurements, (2) satellite-based observations, and (3) process-based ecosystem modelling. Much uncertainty exists in the multi-approach estimations of terrestrial gross primary production (GPP) and net primary production (NPP). To improve the capacity of model simulation and prediction, it is essential to evaluate ecosystem models against ground and satellite-based measurements and observations. As a case, we have shown the performance of the dynamic land ecosystem model (DLEM) at various scales from site to region to global. We also discuss how terrestrial primary production might respond to climate change and increasing atmospheric CO2 and uncertainties associated with model and data. Further progress in monitoring and predicting terrestrial primary production requires a multiscale synthesis of observations and model simulations. In the Anthropocene era in which human activity has indeed changed the Earth’s biosphere, therefore, it is essential to incorporate the socioeconomic component into terrestrial ecosystem models for accurately estimating and predicting terrestrial primary production in a changing global environment.

Chemical nitrogen (N) fertilizer has long been used to help meet the increasing food demands in China, the top N fertilizer consumer in the world. Growing concerns have been raised on the impacts of N fertilizer uses on food security and climate change, which is lack of quantification. Here we use a carbon–nitrogen (C–N) coupled ecosystem model, to quantify the food benefit and climate consequence of agronomic N addition in China over the six decades from 1949 to 2008. Results show that N fertilizer-induced crop yield and soil C sequestration had reached their peaks, while nitrous oxide (N _2 O) emission continued rising as N was added. Since the early 2000s, stimulation of excessive N fertilizer uses to global climate warming through N _2 O emission was estimated to outweigh their climate benefit in increasing CO _2 uptake. The net warming effect of N fertilizer uses, mainly centered in the North China Plain and the middle and lower reaches of Yangtze River Basin, with N _2 O emission completely counteracting or even exceeding, by more than a factor of 2, the CO _2 sink. If we reduced the current N fertilizer level by 60% in ‘over-fertilized’ areas, N _2 O emission would substantially decrease without significantly influencing crop yield and soil C sequestration.

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance, which has been accumulating in the atmosphere since the pre-industrial period. The mole fraction of atmospheric N2O has increased by nearly 25% from 270 parts per billion (ppb) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr-1 in both 2020 and 2021. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), we present a global N2O budget that incorporates both natural and anthropogenic sources and sinks, and accounts for the interactions between nitrogen additions and the biochemical processes that control N2O emissions. We use Bottom-Up (BU: inventory, statistical extrapolation of flux measurements, process-based land and ocean modelling) and Top-Down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions increased 40% (or 1.9 Tg N yr-1) in the past four decades (1980-2020). Direct agricultural emissions in 2020, 3.9 Tg N yr-1 (best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources (including 'Fossil fuel and industry', 'Waste and wastewater', and 'Biomass burning' 2.1 Tg N yr-1), and indirect anthropogenic sources (1.3 Tg N yr-1). For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.3 (lower-upper bounds: 10.5-27.0) Tg N yr-1, close to our TD estimate of 17.0 16.6-17.4) Tg N yr-1. For the period 2010-2019, the annual BU decadal-average emissions for natural plus anthropogenic sources were 18.1 (10.4-25.9) Tg N yr-1 and TD emissions were 17.4 (15.8-19.20 Tg N yr-1. The once top emitter Europe has reduced its emissions since the 1980s by 31% while those of emerging economies have grown, making China the top emitter since the 2010s. The observed atmospheric N2O concentrations in recent years have exceeded projected levels under all scenarios in the Coupled Model Intercomparison Project Phase 6 (CMIP6), underscoring the urgency to reduce anthropogenic N2O emissions. To evaluate mitigation efforts and contribute to the Global Stocktake of the United Nations Framework Convention on Climate Change, we propose establishing a global network for monitoring and modeling N2O from the surface through the stratosphere. The data presented in this work can be downloaded from https://doi.org/10.18160/RQ8P-2Z4R (Tian et al. 2023). [ABSTRACT FROM AUTHOR]