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Abstract Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE‐AFP), which utilizes short‐wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE‐AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE‐AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE‐AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests.

Abstract Accelerating efforts for the Sustainable Development Goals requires understanding their synergies and trade-offs at the national and sub-national levels, which will help identify the key hurdles and opportunities to prioritize them in an indivisible manner for a country. Here, we present the importance of the 17 goals through synergy and trade-off networks. Our results reveal that 19 provinces show the highest trade-offs in SDG13 (Combating Climate Change) or SDG5 (Gender Equality) consistent with the national level, with other 12 provinces varying. 24 provinces show the highest synergies in SDG1 (No Poverty) or SDG6 (Clean Water and Sanitation) consistent with the national level, with the remaining 7 provinces varying. These common but differentiated SDG priorities reflect that to ensure a coordinated national response, China should pay more attention to the provincial situation, so that provincial governments can formulate more targeted policies in line with their own priorities towards accelerating sustainable development.

ABSTRACTUrban vertical features are crucial for understanding urban morphology. However, long-term information on three-dimensional buildings, which are important fundamental data for studying on the historical urbanization processes, remains scarce in China. In this study, we proposed a Random Forest model to generate an annual 1-km resolution building volume dataset covering mainland China from 2001 to 2019, by integrating the nighttime light data, population demographics, electricity consumption records, carbon dioxide emissions data, and various optical and statistical datasets. This new building volume data are highly consistent with that derived from Baidu Maps on 1-km scale, with Pearson’s correlation coefficient (R) of 0.847, root mean square error (RMSE) of 9.17 × 105 m3/km2 and mean absolute error (MAE) of 5.86 × 105 m3/km2. Notably, cross-validation indicate that the blooming problem was greatly improved when compared with previous model-based building three-dimensional data. The proposed method holds significant advantages, benefiting form low-cost implementation based on free open-source data and providing extendable algorithm to estimate the 3D shape of cities in the future. The time-series historical building volume data offer comprehensive insights into the historical development of urban structures, and provide valuable fundmental data for future urban planning, urban climate models and land use projections.

Summary: In 2016, the Yangtze River Protection Strategy was proposed and a series of measures were applied to restore the health and function of the Yangtze River ecosystem. However, the impact of these measures on the carbon (C) sink capacity of the Yangtze River estuary wetlands has not been exhaustively studied. In this work, the effects of these measures on the C sink capacity of Yangtze River estuary wetlands were examined through the long-term monitoring of C fluxes, soil respiration, plant growth and water quality. The C flux of the Yangtze River estuary wetlands has become increasingly negative after the implementation of these measures, mainly owing to reduction in soil CO2 emission. The decrease in the chemical fertilizer release and returning farmland to wetland had led to the improvement of water quality in the estuary area, which further reduced soil heterotrophic microbial activity, and ultimately decreasing soil CO2 emissions of estuary wetlands.

Over the past 4 decades, Southwest China has the fast vegetation growth and aboveground biomass carbon (AGC) accumulation, largely attributed to the active implementation of ecological projects. However, Southwest China has been threatened by frequent extreme drought events recently, potentially countering the expected large AGC increase caused by the ecological projects. Here, we used the L-band vegetation optical depth to quantify the AGC dynamics over Southwest China during the period 2013-2021. Our results showed a net AGC sink of 0.064 [0.057, 0.077] Pg C year−1 (the range represents the maximum and minimum AGC values), suggesting that Southwest China acted as an AGC sink over the study period. Note that the AGC loss of 0.113 [0.101, 0.136] Pg C year−1 was found during 2013-2014, which could mainly be attributed to the negative influence of extreme droughts on AGC changes in Southwest China, particularly in the Yunnan province. For each land use type (i.e., dense forests, persistent forests, nonforests, afforestation, and forestry), the largest AGC stock increase of 0.032 [0.028, 0.036] Pg C year−1 was found in nonforests, owing to their widespread land cover rate over Southwest China. For AGC density (i.e., AGC per unit area), the afforestation areas showed the largest AGC density increase of 0.808 [0.724, 0.985] Mg C ha−1 year−1, reflecting the positive effect of afforestation on AGC increase. Moreover, the karst areas exhibited a higher increasing rate of AGC density than nonkarst areas, suggesting that the karst ecosystems have a high carbon sink capacity over Southwest China.

Abstract Global agricultural trade creates multiple telecoupled flows of nitrogen (N) and phosphorus (P). The flows of physical and virtual nutrients along with trade have discrepant effects on natural resources in different countries. However, existing literature has not quantified or analyzed such effects yet. Here we quantified the physical and virtual N and P flows embedded in the global agricultural trade networks from 1997 to 2016 and elaborated components of the telecoupling framework. The N and P flows both increased continuously and more than 25% of global consumption of nutrients in agricultural products were related to physical nutrient flows, while virtual nutrient flows were equivalent to one-third of the nutrients inputs into global agricultural system. These flows have positive telecoupling effects on saving N and P resources at the global scale. Reducing inefficient trade flows will enhance resource conservation, environmental sustainability in the hyper-globalized world.

Crop harvested carbon (HC) is one of the most important components of the carbon cycle in cropland ecosystems, with a significant impact on the carbon budget of croplands. China is one of the most important crop producers, however, it is still unknown on the spatial and temporal variations of HC. This study collected statistical data on crop production at the province and county levels in China for all ten crop types from 1981 to 2020 and analyzed the magnitude and long-term trend of harvested crop carbon. Our results found a substantial increase of HC in cropland from 0.185 Gt C yr ^−1 in 1981 to 0.423 Gt C yr ^−1 in 2020 at a rate of 0.006 Gt C yr ^−1 . The results also highlighted that the average annual carbon sink removal from crop harvesting in China from 1981 to 2020 was 0.32 Gt C yr ^−1 , which was comparable to the net carbon sink of the entire terrestrial ecosystems in China. This study further generated a gridded dataset of HC from 2001 to 2019 in China by using jointly the statistical crop production and distribution maps of cropland. In addition, a model-data comparison was carried out using the dataset and results from seven state-of-the-art terrestrial ecosystem models, revealing substantial disparities in HC simulations in China compared to the dataset generated in the study. This study emphasized the increased importance of HC for estimating cropland carbon budget, and the produced dataset is expected to contribute to carbon budget estimation for cropland ecosystems and the entire China.

Ecosystem maximum photosynthetic rate (Amax) is an important ecosystem functional property, as it is critical for ecosystem productivity modeling. However, little is known about the mechanisms that regulate the seasonal variation of Amax in grasslands, one of the dominant vegetation types worldwide. In this study, we analyzed the seasonal variability of Amax of grassland sites across the globe and its environmental drivers. We found that grassland Amax had strong seasonal variations, which were influenced by the climate and agricultural management, such as grass cutting and grazing. Second, the seasonal variation of Amax at all arid grasslands [mean annual vapor pressure deficit (VPD) > 10 hPa] was driven more by changes in canopy physiological property (i.e., maximum photosynthetic rate per leaf area Amaxa) than canopy structural property (i.e., leaf area, presented by LAI), because Amaxa had stronger temporal variability than LAI in these ecosystems. Third, temperature and VPD were the most influential factors for the seasonal variability of Amax and LAI, but environmental variables only explained a small proportion of the seasonal variation of Amaxa, which was probably because Amaxa was more related to plant traits. Our findings provide new ideas for better parameterizations of Amax in terrestrial ecosystem models.

Natural forest growth does not decrease water yield and forest responses to human disturbances and management interventions vary with ecosystem complexity, according to a global meta-analysis that includes 496 watersheds and spans 25 years.

Background: COVID-19 has spread worldwide and become a pandemic. We report the epidemiological and clinical characteristics of cluster infections. Methods: Data of clustered cases were retrieved from the public health emergency monitoring information system of China. We analyzed the incubation period, generation gap, secondary attack rate, and viral load in various grouped cases. Results: A total of 60 COVID-19 infection clusters including 226 patients and 19 asymptomatic cases involving four generations were analyzed. With the increase of transmission generations, secondary attack rate decreased (P＜0.001) and severity alleviated (P = 0.008). The median incubation period and intergenerational interval were 9 and 6 days, respectively. The secondary attack rate was 7.1% in the index cases, 5.0% in the first generation, 1.0% in the second generation, and 4.7% overall. Severe cases were seen more in the index (13, 65%) and first generation (7, 35%) ones, who had a significantly higher viral load than the mild and moderate ones. Conclusions: With the increase of transmission generation, secondary infection rate and severity decreased. Severe patients had a higher virus load. Patients in the incubation period and asymptomatic carriers were potential infection sources who might play an important role in transmission.

Estuarine wetlands with high organic carbon (OC) accumulation rates due to their high plant biomass and interception of tide-derived OC are generally considered as large CO2 sinks. However, our previous study found that tidal OC input seems to stimulate soil CO2 emissions, potentially weakening CO2 sequestration in estuarine wetlands. To further verify this phenomenon, we first established a structural equation model, which confirmed a positive correlation between tidal OC input and soil organic carbon (SOC) and soil respiration. We then performed trace analysis to determine the stability of SOC derived from different sources and its effect on soil CO2 emissions by analyzing the input and retention of OC derived from tides and plants in the Yangtze Estuary wetlands. From upstream to downstream, as tidal OC input decreased, the relative retention ratio of the tidal OC in wetland soil increased from 1.259 to 2.148, whereas the relative retention ratio of plant OC in the soil decreased from 61.5% to 14.8%. Our findings indicated that the degradability of tidal OC was higher upstream than that downstream, but both inhibited plant OC degradation, thus providing an important reason for the higher CO2 emissions upstream of wetlands (with higher tidal OC input). In addition, the primarily contributor to CO2 (δ13) emissions’ transforming from plant SOC (81.35%) to tidal SOC (91.18%) was an increase in organic matter input from the tide in a microcosm system. Consequently, a higher CO2 output than CO2 input (plant OC) due to the ready degradation of tidal OC consequently weakens the CO2 sequestration capacity of the estuarine wetlands. This phenomenon is cause for concern regarding the CO2 sink function of estuarine wetlands intercepting large amounts of organic matter.

Over the past 2 to 3 decades, Chinese forests are estimated to act as a large carbon sink, yet the magnitude and spatial patterns of this sink differ considerably among studies. Using 3 microwave (L- and X-band vegetation optical depth [VOD]) and 3 optical (normalized difference vegetation index, leaf area index, and tree cover) remote-sensing vegetation products, this study compared the estimated live woody aboveground biomass carbon (AGC) dynamics over China between 2013 and 2019. Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps (mean correlation value R = 0.84), followed by L-VOD (R = 0.83), which outperform the other VODs. An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019. The performance of the AGC estimation model was good (root mean square error = 0.05 Pg C and R2 = 0.90 with a mean relative uncertainty of 9.8% at pixel scale [0.25°]). Results of the AGC estimation model showed that carbon uptake by the forests in China was about +0.17 Pg C year−1 from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year−1), Sichuan (+14.49 Tg C year−1), and Hunan (+11.42 Tg C year−1) provinces had the highest carbon sink rates during 2013 to 2019. Most of the carbon-sink regions have been afforested recently, implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

Abstract Droughts cause extreme anomalies in tropical forest growth, but the direction and magnitude of tropical forests in response to droughts are still widely debated. Here, we used four satellite‐based canopy growth proxies (CGPs), including three optical and one passive microwave, and in situ fluxes observations from eddy covariance (EC) measurements for a retrospective investigation of the impacts of historical droughts on tropical forest growth from a statistical point of view. Results indicate two opposite directions in drought‐related canopy dynamics across pantropical forests. The canopy of tropical forests with higher CGPs is more vulnerable to drought stress and recovers faster in the post‐drought recovery period. In contrast, the canopy of tropical forests with lower CGPs increases during the drought period and declines in the subsequent recovery period, which is beyond general expectation. In situ measurements from eddy‐covariance flux towers showed that forests with higher gross primary production and latent heat flux decreased photosynthesis and evapotranspiration during the drought period but increased photosynthesis and evapotranspiration faster during the post‐drought recovery period, supporting the findings from satellite observations. Our statistical analysis against climatic factors predicts that higher‐CGPs tress with probably taller and bigger canopies are more responsive to shortage of water availability caused by drought; while lower‐CGPs tress with shorter and smaller canopies are more responsive to sunlight availability and tend to increase their canopy leaves and enhance photosynthesis in sunnier days during the drought period. Our results highlight the differences in tropical forests in responding to drought stress, which are worth incorporated in Earth system models for time‐series evaluations.

A surface soil moisture (SM) condition at high spatiotemportal resolutions is required by regional Earth system applications. Here, we mapped daily 1-km SM in the Babao River Basin in the northwest of China during the summers from 2013 to 2015 using a random forest (RF) method by merging SM information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. Relative importance analysis was used to determine the optimal predictors for estimating high-resolution SM. A specific RF model (RFVI+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia (ATI), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). The RFVI+sup also accounted for missing observations of the thermal index (e.g., ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m3/m3. Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.

As vegetation plays a critical role in terrestrial ecosystems, understanding its status and variation is vital for preserving the stability of an ecosystem. Central Asia serves as a representative example of an arid and semi-arid region characterized by sparse vegetation and poor soils, making its vegetation particularly fragile and sensitive. To investigate the vegetation condition in the region, this study examined the spatial and temporal characteristics of vegetation variation from 2001 to 2020, utilizing the normalized difference vegetation index (NDVI) as an indicator. Meanwhile, trend analysis, Mann–Kendall abrupt change point test, geodetector, and correlation analysis were used to quantitatively analyze the natural and anthropogenic drivers of these variations over the past two decades. The results suggest that vegetation coverage in Central Asia was relatively low, with an annual average NDVI of 0.16 over the past 20 years. Moreover, the spatial distribution of NDVI in Central Asia exhibited significant spatial heterogeneity, with vegetation coverage declining from north to south and from east to west. Furthermore, the NDVI exhibited a slightly increasing trend during the period of 2001 to 2020 with an increased rate of 0.00025/yr. However, we detected an abrupt change point in vegetation dynamics in Central Asia around 2010, which indicated a significant shift in vegetation variation in the region. Land-use type has a great influence on the spatial heterogeneity of NDVI in Central Asia, which can explain 46% of the vegetation distribution in this region. Moisture factors such as precipitation and soil water content followed with 35% and 32% contributions, respectively. Regarding the temporal variation of NDVI, it is mainly driven by the fluctuation in precipitation, with the degree of influence of precipitation on NDVI varying for different regions in various geographical conditions. This study offers a more comprehensive insight into the spatial and temporal dynamics of NDVI in Central Asia and indicates that precipitation plays a significant role in driving the spatial heterogeneity and temporal variation of NDVI. These findings are essential for predicting vegetation changes in arid regions under future environmental conditions and formulating effective strategies to prevent and alleviate vegetation degradation.

Abstract Autumn phenology plays an important role in regulating ecosystem carbon and water cycling, but it has received less attention than spring phenology. Satellite‐based methods have been widely applied in monitoring autumn phenology at large spatial scales. However, few studies have evaluated and compared the performance of different satellite‐based methods in autumn phenology identification. Here, we compared the spatiotemporal variations of end of vegetation growing season dates (EOS) as determined from eight prevailing satellite‐based methods against long‐term field observations at 31 sites in China. We found that field‐based observations in forest and grassland sites, respectively, had rates of EOS delay of 2.11 and 3.85 days per 1°C increase in mean annual temperature (MAT) during 2001–2014. However, nearly all the eight satellite‐based methods underestimated these delay rates compared with the ground observations over all sites. We also found that the eight methods weakly agreed with the field‐observed interannual variations of EOS. At the regional scale, the identified average EOS differed up to 38 and 40 days among the investigated satellite‐based methods in forest and grassland ecosystems respectively. The delayed rate of identified EOS with the increase of MAT ranged from 0.77 to 3.51 days °C−1 for forests and from 0.41 to 2.95 days °C−1 for grasslands. The identified EOS by most of the eight methods had delayed temporal trends in forests during 2001–2014 while we found advanced trends in grassland ecosystems. The large discrepancy in EOS identification among the prevailing satellite‐based methods highlight the need for more accurate satellite‐based methods in data gap‐filling and phenometrics detection, and more extensive, multi‐species based field observations that can be used to constrain and validate the satellite‐based methods.

The prediction of vegetation growth under climate change has become much more important in recent years, but is still a challenge. This study developed a machine learning method to predict vegetation growth, indicated by satellite-based normalized difference vegetation index, for the coming growing season based on the meteorological forecast dataset SEAS5. First, we evaluated the accuracy of the meteorological forecast data against site-based meteorological observations in China. Air temperature, surface net radiation, and relative humidity of the monthly forecast data from SEAS5 were found to have excellent accuracy and were selected as the forcing data for predicting the vegetation index. The results show that our method is stable and highly accurate for forecasting the temporal changes of a vegetation index through the entire growing season, even under extreme drought conditions. Our results highlight that machine learning is a powerful tool for predicting vegetation indices during the growing season at the start of growing season and has the potential for improving our ability to predict crop yields and vegetation growth.

Local human activities can lead to cross-border environmental impacts through the food–energy–water–CO2 nexus. Here, the authors report wide variations in environmental impacts of irrigated agriculture across counties within the North China Plain under different environmental and socioeconomic scenarios.

The climatic drivers of leaf phenology and water stress regulation strategies in tropical/subtropical forest biomes are poorly understood on the continental scale. Widespread field observations and remotely sensed plant phenology and physiology data across tropical forest ecosystems at various scales provide new insight into the response of tropical forests to climate change. In this study, we collected seasonal litterfall data from 65 sites across tropical/subtropical America for analysis against remotely sensed photosynthetic and climatic indicators. We proposed an integrated field and remote sensing approach for partitioning three rhythms of forest phenology across tropical and subtropical America in response to various wet- and dry-season variabilities in sunlight, liquid water supply and atmospheric dryness. The results showed that in a dry-season leaf shedding and rejuvenation phenology, trees encounter limited soil water stress, shift to a young canopy by replacing old leaves with new leaves, and regulate water use through the xylem to maximize light capture and productivity. In a wet-season leaf shedding & rejuvenation phenology, trees on the contrary shed and rejuvenate canopy leaves to maximize light use and increase photosynthesis during the sunny wet season. Trees show a dominant stomatal regulation of seasonal water use, resulting in considerable seasonal changes in canopy photosynthesis and transpiration. However, in a dry-season leaf shedding and wet-season rejuvenation phenology, trees shed leaves due to water stresses and grew new leaves to maximize photosynthesis with sufficient water availability. Trees shift from wet-season stomatal regulation to dry-season xylem regulation with fewer canopy leaves. Our findings provide new physiological insight into the mechanism of sunlight availability, liquid water supply and atmospheric dryness in driving leaf phenology and photosynthesis across tropical and subtropical forests.

Abstract Forests and grasslands play a significant role in global nitrous oxide (N2O) emission, while a sophisticated model is missing to interpret the microbial processes involved in N2O production and consumption in these ecosystems. A new N2O emission model, MicN model, was developed for natural forest and grassland ecosystems by integrating with Integrated BIosphere Simulator (IBIS). This new model includes four major N2O correlated processes: autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification and denitrifier denitrification. Thirty‐nine observation sites, including various forest and grassland ecosystem types, were selected to calibrate and validate IBIS‐MicN model. The IBIS‐MicN model explained 89% and 85% of observed daily variability in N2O emission for all calibration and validation sites, respectively. In addition, the model successfully predicted the concentrations of two important intermediates (i.e., ammonium and nitrate) for N2O production at nine validation sites. Compared with Forest‐DNDC model, IBIS‐MicN model performed better in simulating N2O emission, soil NH4+ and NO3−concentrations. At site scale, the simulated N2O emissions by IBIS‐MicN model positively correlated with annual mean soil temperature and annual precipitation, but negatively correlated with soil pH. This study provides an alternative model to estimate global N2O emission from natural forest and grassland ecosystems and to investigate corresponding environmental regulations.

Abstract Plant roots play a critical role in regulating the uptake of soil water and nutrients. Shifts in root growth and biomass distribution resulting from climate change can impact ecosystem water and carbon cycling. Such interactions between root growth and ecosystem functioning have not been integrated into current terrestrial ecosystem models. Here a three‐dimensional dynamic root model (DyRoot) was developed and implemented into the Integrated Biosphere Simulator (IBIS) ecosystem model for simulating root growth, architecture (i.e., fine and coarse roots), and distribution driven by soil water and nitrogen availabilities. Field root biomass observations were used for model calibration and validation. Results showed that DyRoot was able to simulate the root biomass distribution across five forest ecosystem types with good performance (R2 = 0.79, P < 0.01). The validations of root distribution in three forest chronosequences suggested that DyRoot captured the changes in rooting depth with the increase in stand age. The sensitivity of simulated root distribution to soil water availability was compared against root biomass observations from two precipitation reduction experiments. Results indicated that DyRoot reasonably represented the decrease of root biomass in topsoil layers in response to the deficits of soil moisture simulated by (IBIS). The DyRoot model algorithm in this study has great implications for representing an optimized root distribution in ecosystem models, which can improve model performance by simulating the feedbacks of root evolution in response to climate change. Furthermore, allowing explicit root architecture in the DyRoot model also sheds light on depicting the responses of root traits to environmental changes.

Summary: Relationships among productivity, leaf phenology, and seasonal variation in moisture and light availability are poorly understood for evergreen broadleaved tropical/subtropical forests, which contribute 25% of terrestrial productivity. On the one hand, as moisture availability declines, trees shed leaves to reduce transpiration and the risk of hydraulic failure. On the other hand, increases in light availability promote the replacement of senescent leaves to increase productivity. Here, we provide a comprehensive framework that relates the seasonality of climate, leaf abscission, and leaf productivity across the evergreen broadleaved tropical/subtropical forest biome. The seasonal correlation between rainfall and light availability varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 forests. Where rainfall and light covary positively, litterfall and productivity also covary positively and are always greater in the wetter sunnier season. Where rainfall and light covary negatively, litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate; otherwise productivity is smaller in the drier sunnier season. This framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests. Public summary: • Three climate-phenology regimes are identified across tropical and subtropical forest biomes • Where light and water limit plant in dry season, litterfall and productivity peak in sunny wet season • Where light or water alternately limits plant, productivity peaks in wet season with low litterfall • Where water does not limit plant, litterfall and productivity peak in sunny dry season

It has been long established that the terrestrial vegetation in spring has stronger photosynthetic capability than in autumn. However, this study challenges this consensus by comparing photosynthetic capability of terrestrial vegetation between the spring and autumn seasons based on measurements of 100 in situ eddy covariance towers over global extratropical ecosystems. At the majority of these sites, photosynthetic capability, indicated by light use efficiency (LUE) and apparent quantum efficiency, is significantly higher in autumn than in spring, due to lower atmosphere vapor pressure deficit (VPD) at the same air temperature. Seasonal VPD differences also substantially explain the interannual variability of the differences in photosynthetic capability between spring and autumn. We further reveal that VPD in autumn is significantly lower than in spring over 74.14% of extratropical areas, based on a global climate dataset. In contrast, LUE derived from a data-driven vegetation production dataset is significantly higher in autumn in over 61.02% of extratropical vegetated areas. Six Earth system models consistently projected continuous larger VPD values in spring compared with autumn, which implies that the impacts on vegetation growth will long exist and should be adequately considered when assessing the seasonal responses of terrestrial ecosystems to future climate conditions. Public summary: • Autumn VPD is lower than spring VPD at the same air temperature over majority of the extratropical vegetated land • Photosynthetic capability is significantly higher in autumn than in spring due to lower VPD • Earth System Models projected continuous larger VPD values in spring as against autumn

Abstract Climatic drivers for canopy leaf shedding and flush of evergreen broadleaved forest biome are still unclear at the continental scale across tropical and subtropical region. This imposes a challenge for modeling pantropical photosynthesis seasonality in Earth system models. Here, we examined three potential climatic triggers, vapor pressure deficit–a proxy of atmospheric water deficit, downward shortwave incoming solar radiation–a proxy of sunlight availability, and terrestrial water storage–a proxy of soil water availability observed by the Gravity Recovery and Climate Experiment, by comparing with two satellite phenological proxies–the Enhanced Vegetation Index and Continuous Solar‐induced chlorophyll fluorescence. Results show that tropical leaf phenology varies greatly from equatorial bimodal seasonality to higher‐latitude unimodal seasonality. Sunlight availability dominantly controls the whole seasonal leaf phenology across the pantropical region. Atmospheric dryness is one main type of water stress for leaf phenology during the first half year. However, soil water stress strongly inhibits the first‐half of leaf phenology in tropical Asia and the second‐half of leaf phenology in Congo, but shows rare constraint on the leaf phenology in Amazon. Ignoring these various roles of soil moisture availability and atmospheric dryness in influencing tropical leaf phenology might lead to unexpected uncertainty for predicting the water and carbon cycles of tropical forest ecosystem in Earth system models.

The evaluation and quantification of water consumption based on water footprint (WF) is important for sustainable utilization of water resources and is becoming one of the key bases for formulating water resources management policies. However, there are few systematic assessments of both temporal changes and spatial patterns of WF in China, and the driving of water footprint intensity (WFI) is rarely reported. Based on the research background, this paper takes China, the world’s largest developing country, as an example to analyze the spatiotemporal evolution of WF through the input–output model. The total WF in China increased by 11.76% from 2002 to 2017. National WFI decreased from 550 m³/104 yuan (2002) to 152 m³/104 yuan (2017). The regions found to have the highest WF in China include Shandong, Henan, and Hebei, and regions with higher WFI are distributed in East China. From 2002 to 2017, the total WF of each province in China changed significantly. Guangdong, Fujian, and Zhejiang provinces’ total WF decreased markedly during the study period. The results show that the grain output per capita and GDP per capita have a significant driving effect on WFI. By adjusting the agricultural structure and improving the comprehensive ability of scientific and technological innovation, it is possible to reduce the WFI in China.

Abstract Wetlands are one of the most important terrestrial ecosystems for land‐atmosphere CH4 exchange. A new process‐based, biophysical model to quantify CH4 emissions from natural wetlands was developed and integrated into a terrestrial ecosystem model (Integrated Biosphere Simulator). The new model represents a multisubstance system (CH4, O2, CO2, and H2) and describes CH4 production, oxidation, and three transport processes (diffusion, plant‐mediated transport, and ebullition). The new model uses several critical microbial mechanisms to represent the interaction of anaerobic fermenters and homoacetogens, hydrogenotrophic, and acetoclastic methanogens, and methanotrophs in CH4 production and oxidation. We applied the model to 24 different wetlands globally to compare the simulated CH4 emissions to observations and conducted a sensitivity analysis. The results indicated that (1) for most sites, the model was able to capture the magnitude and variation of observed CH4 emissions under varying environmental conditions; (2) the parameters that regulate dissolved organic carbon and acetate production, and acetoclastic methanogenesis had the significant impact on simulated CH4 emissions; (3) the representation of the process components of CH4 cycling showed that CH4 oxidation was about half or more of CH4 production, and plant‐mediated transport was the dominant pathway at most sites; and (4) the seasonality of simulated CH4 emissions can be controlled by soil temperature, water table position, or combinations thereof.

Abstract Leaf phenology in the humid tropics largely regulates the seasonality of forest carbon and water exchange. However, it is inadequately represented in most global land surface models due to limited understanding of its controls. Based on intensive field studies at four Amazonian evergreen forests, we propose a novel, quantitative representation of tropical forest leaf phenology, which links multiple environmental variables with the seasonality of new leaf production and old leaf litterfall. The new phenology simulates higher rates of leaf turnover (new leaves replacing old leaves) in dry seasons with more sunlight, which is then implemented in ORCHIDEE, together with recent findings of ontogeny‐associated photosynthetic capacity, and is evaluated against ground‐based measurements of leaf phenology (canopy leaf area index and litterfall), eddy covariance fluxes (photosynthesis and latent heat), and carbon allocations from field observations. Results show the periodical cycles of solar radiation and vapor pressure deficit are the two most important environmental variables that are empirically related to new leaf production and old leaf abscission in tropical evergreen forests. The model with new representation of leaf phenology captures the seasonality of canopy photosynthesis at three out of four sites, as well as the seasonality of litterfall, latent heat, and light use efficiency of photosynthesis at all tested sites, and improves the seasonality of carbon allocations to leaves, roots, and sapwoods. This study advances understanding of the environmental controls on tropical leaf phenology and offers an improved modeling tool for gridded simulations of interannual CO2 and water fluxes in the tropics.

The well-documented energy balance dynamics within forest ecosystems are poorly implemented in studies of the biophysical effects of forests. This results in limitations to the accurate quantification of forest cooling/warming on local air temperature. Taking into consideration the forest air space, this study proposes a three-layered (canopy, forest air space and soil [CAS]) land surface energy balance model to simulate air temperature within forest spaces (Taf) and subsequently to evaluate its biophysical effects on forest cooling/warming, i.e., the air temperature gradient (∆Ta) between the Taf and air temperature of open spaces (Tao) (∆Ta = Taf − Tao). We test the model using field data for 23 sites across 10 cities worldwide; the model shows satisfactory performance with the test data. High-latitude forests show greater seasonal dynamics of ∆Ta, generating considerable cooling of local air temperatures in warm seasons but minimal cooling or even warming effects during cool seasons, while low-latitude tropical forests always exert cooling effects with less interannual variability. The interannual dynamics of ∆Ta are significantly related to the seasonality of solar geometry and canopy leaf phenology. The differences between forest canopy temperature (Tc) and Tao, which are the two most important terms attributed by the CAS model in impacting Taf, explain a large part of forest cooling and warming (May–July: R2 = 0.35; November–January: R2 = 0.51). The novel CAS model provides a feasible way to represent the energy balance within forest ecosystems and to assess its impacts on local air temperatures globally. Keywords: Forest cooling/warming, Forest biophysical effects, Radiative transfer equation, Land-atmosphere model, Local environmental health, Climate warming mitigation

Food consumption is closely associated with resource consumption and environmental sustainability. An unreasonable dietary pattern would cause great pressure or damage to resources and the environment. It is particularly important to reduce the negative impact of household food consumption on resources and the environment while simultaneously ensuring people’s nutrient intake and health. This study applied the China Health and Nutrition Survey (CHNS) database to quantitatively study the spatial-temporal analysis of multiple footprints of household food consumption at multiple scales and explored the driving mechanism of the multiple footprints. The results showed that, except land footprint (LF), the other four types of footprints all decreased at varying degrees; the water footprint (WF), carbon footprint (CF), nitrogen footprint (NF) and energy footprint (EF) decreased by 18.24%, 17.82%, 12.03% and 20.36%, respectively, from 2000 to 2011; multiple footprints of food consumption of household in Guizhou was the highest among the 12 provinces involved in the study; this shows that resource consumption (water, energy and land resource) and environmental influences (CO2 emissions and nitrogen emissions) brought by food consumption of per household in Guizhou are much greater than in other provinces, which has a negative influence on sustainable development; by analyzing the driving factors of multiple footprints, it is shown that nutrient intake, household attributes, educational level and health conditions were significantly correlated to multiple footprints. Among them, nutrient intake has greater impact on the multiple footprints of Chinese household food consumption. By comparing multiple footprints of different dietary patterns, it was found that the current Chinese dietary pattern would cause excessive resource consumption, which would bring more pressure on resources and the environment. Adjusting household living habits would possibly reverse the unsustainable situation, such as reducing the consumption of animal-derived foods and adjusting the dietary pattern of households with a higher educational level and income status. Chinese Dietary Guidelines 2016 has better sustainability; the promotion of this dietary pattern across the country would help China to relieve the pressure on resources and environment from the consumer side, promoting the realization of sustainable development.

Digitizing the land surface temperature (Ts) and surface soil moisture (mv) is essential for developing the intelligent Digital Earth. Here, we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving Ts and mv using the dual-polarized Tb of Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band (6.9 GHz) based on the simplified radiative transfer equation. Validation using in situ Ts and mv in southern China showed the average root mean square errors (RMSE) of Ts and mv retrievals reach 2.42 K (R2 = 0.61, n = 351) and 0.025 g cm−3 (R2 = 0.68, n = 663), respectively. The results were also validated using global in situ Ts (n = 2362) and mv (n = 1657) of International Soil Moisture Network. The corresponding RMSE are 3.44 k (R2 = 0.86) and 0.039 g cm−3 (R2 = 0.83), respectively. The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer Ts (R2 = 0.57; RMSE = 2.91 k) and ECV_SM mv (R2 = 0.51; RMSE = 0.045 g cm−3), respectively. Overall, this paper indicates an effective way to jointly modeling Ts and mv using passive microwave remote sensing.

The CO _2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO _2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr ^−1 for SR, 50.3 ± 25.0 (SD) Pg C yr ^−1 for HR and 35.2 Pg C yr ^−1 for AR during 1982–2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr ^−1 and 39.8 to 61.7 Pg C yr ^−1 , respectively. The most discrepancy lays in the estimation of AR, the difference (12.0–42.3 Pg C yr ^−1 ) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.

Despite its perennial canopy, the Amazonian tropical evergreen forest shows significant canopy growth seasonality, which has been represented by optical satellite-based observations. In this paper, a new Microwave Temperature–Vegetation Drought Index (MTVDI) based on Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) sensors was used to capture the canopy seasonality from 2003 to 2010 in comparison with four climatic dryness indicators (Palmer Drought Severity Index (PDSI), Climatological Water Deficit (CWD), Terrestrial Water Storage (TWS), Vapor Pressure Deficit (VPD)) and two photosynthesis proxies (Enhanced Vegetation Index (EVI) and Solar-Induced chlorophyll Fluorescence (SIF)), respectively. Our results suggest that the MTVDI shows opposite seasonal variability with two photosynthesis proxies and performs better than the four climatic dryness indicators in reflecting the canopy photosynthesis seasonality of tropical forests in the Amazon. Besides, the MTVDI captures wet regions that show green-up during the dry season with mean annual precipitation higher than 2000 mm per year. The MTVDI provides a new way for monitoring the canopy seasonality of tropical forests from microwave signals.

Lack of either spatial or temporal coverage in city-level carbon emissions analysis might curb our understanding of historical drivers and make future forecasting uncertain. To fill these gaps, we analyzed time-series energy-related industrial carbon emissions (EICEs) from manufacturing in over 99 cities nationwide in China during the period 2000–2015. We estimated these cities’ EICEs reduction potential up until 2030 by improving scenario design, which imposed constraints separately on different city groups based on historical drivers. Results indicated distinct changes of EICEs around 2013 for the heavy manufacturing [HM], light manufacturing [LM] and high-tech development [HD] city groups and of emissions intensity for the energy production [EP] city group. The slowing economic growth would partly explain these transformations since 2013. Energy efficiency and industrial structure contributed most to these switches for the EP and HD city groups, respectively, while energy mix and energy efficiency were also major contributors for the HM and LM city groups. Given economic growth at a normal speed, EICEs will increase by 59%, 78%, 90% and 95% for the EP, HM, LM and HD city groups, respectively, from 2015–2030. Our scenarios show that energy efficiency improvement and industrial structure optimization will spur the EICEs to peak before 2030 and limit future EICEs increase by 6.4% and 33.4% in 2030 for the EP and HD city groups, respectively. This implies that energy efficiency improvement and industrial structure optimization are key emissions mitigation factors for the EP and HD cities. Equally important, our study found more unclean fuel structure with higher coal share in the HM and LM city groups than in the other groups. It is therefore imperative to improve their energy efficiency and optimize energy and industrial structures in the HM and LM cities. Results highlight the need to impose different constraints in scenario design and provide mitigation strategies at city level.

Winter-triticeae crops, such as winter wheat, winter barley, winter rye, and triticale, are important in human diets and planted worldwide, and thus accurate spatial distribution information of winter-triticeae crops is crucial for monitoring crop production and food security. However, there is still a lack of global high-resolution maps of winter-triticeae crops because of the reliance of existing crop mapping methods on training samples, which limits their application at the global scale. In this study, we propose a new method based on the Winter-Triticeae Crops Index (WTCI) for global winter-triticeae crops mapping. This is a new sample-free method for identifying winter-triticeae crops based on differences in their normalized difference vegetation index (NDVI) characteristics from the heading to the harvesting stages and those of other types of vegetation. Based on this new method, we produced the first global 30 m resolution distribution maps of winter-triticeae crops from 2017 to 2022. Validation in 65 countries worldwide indicated that the method exhibited satisfying performance and stable spatiotemporal transferability, with producer’s accuracy, user’s accuracy and overall accuracy of 81.12 %, 87.85 % and 87.7 %, respectively. The identified area of winter-triticeae crops was consistent with the agricultural statistical area in almost all investigated counties or regions, and the correlation coefficient (R²) between the identified area and the statistical area was over 0.6, while the relative mean absolute error (RMAE) was less than 30 % in all six years. Overall, this study provides a reliable and automatic identification method for winter-triticeae crops without any training samples. The high-resolution distribution maps of global winter-triticeae crops are expected to support multiple agricultural applications. The distribution maps can be obtained at. [ABSTRACT FROM AUTHOR]

Leaf Area Index (LAI) is a crucial parameter for characterizing vegetation canopy structure and energy absorption capacity. The Moderate Resolution Imaging Spectroradiometer (MODIS) LAI has played a significant role in landmark studies due to its clear theoretical basis, extensive historical time series, reliable validation results, and open accessibility. However, MODIS LAI retrievals are calculated independently for each pixel and a specific day, resulting in high noise levels in the time series and limiting its applications. Existing reprocessing studies for MODIS LAI predominantly rely on temporal information to achieve smoother LAI profiles, with little use of spatial information. This may not only easily ignore genuine LAI anomalies but also likely lead to certain overfitting issues. To address these problems, we designed the Spatio-Temporal Information Compositing Algorithm (STICA) for the reprocessing of MODIS LAI products. This method integrates information from multiple dimensions, including pixel quality information, spatio-temporal correlation, and original retrieval, and thus enables both "reprocessing" and "data value-added" of the existing MODIS LAI products, leading to the development of the High-Quality LAI (HiQ-LAI) dataset. Compared to ground measurements, HiQ-LAI shows better performance than the original MODIS product with Root-Mean-Square Error (RMSE) / Bias decreased from 0.87 / -0.17 to 0.78 / -0.06. This is due to the improvement of HiQ-LAI in capturing the seasonality of vegetation phenology and in reducing time-series abnormal fluctuations. The Time-series Stability (TSS) index which represents temporal stability, indicated that the area with smooth LAI time-series expanded from 31.8% (MODIS) to 78.8% (HiQ) globally, and this improvement is more obvious in equatorial regions where optical remote sensing usually cannot achieve good performance. We found that the HiQ-LAI demonstrates superior continuity and consistency compared to raw MODIS LAI from both spatial and temporal perspectives. We anticipate that the global HiQ-LAI time-series, generated by the STICA procedure on the Google Earth Engine (GEE) platform, will significantly enhance support for diverse global LAI time-series applications. The 500 m/5 km-8 days HiQ-LAI dataset from 2000 to 2022 is available at https://doi.org/10.5281/zenodo.8296768 (Yan et al., 2023). [ABSTRACT FROM AUTHOR]