Sub-regions Of China Research Articles

Cross-scale characteristics and emission reduction pathways of trade-embodied carbon flows in the global supply Chains

In the context of the 1.5 °C warming target, there is a growing awareness of the increasingly frequent carbon flows between subregions. However, understanding the characteristics and pathways of cross-scale carbon flows between subregions and regions remains elusive and impedes achieving emission reduction targets. This study employs a nested multi-regional input–output model and the structural path analysis method, establishing the characteristics of the carbon emission transfer network within global supply chains and discerning the crucial structural pathways and nodes for carbon emissions. The study found that (1) in 36 major countries, the embodied carbon flows are predominantly influenced by the developed countries around China in Asia and the Pacific. (2) In 30 subregions, coastal subregions with large-scale foreign trade and energy-intensive subregions in China's central and western parts contribute significantly to the larger embodied carbon outflow due to exports. (3) “Manufacture of non-metallic mineral products (S09) → construction (S19)” and “manufacture and processing of metals (S10) → construction (S19)” are the two key pathways of paramount significance of embodied carbon emissions. (4) “R25 South Korea” has the highest number of key pathways and the greatest embodied carbon emissions from critical paths, followed by “R27 India” and “R22 United States.” These findings offer valuable insights for the development of regional emission reduction policies.

Impacts of urbanization on urban-rural VPD disparities across different subregions in China: A quantitative analysis based on station-pairing and partial differential methods

The climatic disparities between urban cores and surrounding rural areas have become increasingly pronounced due to rapid urbanization. The vapor pressure deficit (VPD) serves as a critical meteorological indicator reflecting atmospheric aridity, and identifying the contributions of urban and rural climate factors on the urban-rural VPD disparities plays an important role in understanding urban climate and ecosystem development patterns. This study introduces a quantitative method, utilizing partial differential equations, to assess the relative contributions of two crucial meteorological factors—air temperature (T) and relative humidity (RH)—toward urban-rural differences in VPD. Analyzing daily scale data from 116 pairs of urban and rural stations across mainland China for the period 2000–2017, we derived several key findings: Urban areas generally exhibit higher VPD than surrounding rural areas, with a relative difference in annual VPD of about 21.6 %, and the disparity increases over time. Furthermore, the influence of urbanization on VPD responds differently across seasons and geographical conditions. The simulated trends in VPD urban-rural differences using the partial differential method align closely with the actual observed trends, validating the effectiveness of our quantitative analysis approach. The VPD urban-rural disparity shows greater sensitivity to shifts in RH, with sensitivity coefficients exceeding 1.5 times to changes in T in most cases. RH is the primary factor causing VPD urban-rural differences in mainland China, and the relative contribution of RH is about 2.5 times of T. The patterns of sensitivity and the contribution of factors exhibit notable variation among subregions. This study enhances the understanding of how urbanization affects regional climate and provides insights for assessing the environmental impacts of future urban expansion.

What Are the Variation Patterns of Vegetation and Its Influencing Factors in China from 2000–2020 from the Partition Perspective?

China’s vegetation ecosystem has undergone profound changes, and there is an urgent need to explore the mechanisms behind vegetation changes in different ecological sub-regions and historical periods across China. Based on NDVI (normalized difference vegetation index) data, this study analyzed the spatial and temporal evolution patterns and driving mechanisms of six ecological sub-regions in China. The results showed that: (1) over the past 20 years, the vegetation coverage in mainland China showed a decreasing trend from east to west. (2) Over the past 20 years, the vegetation coverage of the six ecological sub-regions showed an increasing trend, with the highest increase in Central Southern China (0.0039) and the lowest increase in East China (0.002). (3) The gravity center of Northeast China showed a trend of migration to the northwest. The gravity center of North China, East China, and Central South China showed a trend of migration to the southwest, while that of Northwest and Southwest China showed a trend of migration to the southeast. (4) During the period from 2000 to 2020, vegetation cover levels showed an upward trend. (5) The lag time of vegetation types in different regions was different. (6) Precipitation was the dominant influencing factor in the evolution of vegetation in Northeast China, North China, Northwest China, and Southwest China. The dominant influencing factors of vegetation evolution in East China were land use and GDP (gross domestic product), while the dominant influencing factors of vegetation evolution in Central South China were precipitation and land use.

Time-series China urban land use mapping (2016–2022): An approach for achieving spatial-consistency and semantic-transition rationality in temporal domain

The global urbanization trend is geographically manifested through city expansion and the renewal of internal urban structures and functions. Time-series urban land use (ULU) maps are vital for capturing dynamic land changes in the urbanization process, giving valuable insights into urban development and its environmental consequences. Recent studies have mapped ULU in some cities with a unified model, but ignored the regional differences among cities; and they generated ULU maps year by year, but ignored temporal correlations between years; thus, they could be weak in large-scale and long time-series ULU monitoring. Accordingly, we introduce an temporal-spatial-semantic collaborative (TSS) mapping framework to generating accurate ULU maps with considering regional differences and temporal correlations. Firstly, to support model training, a large-scale ULU sample dataset based on OpenStreetMap (OSM) and Sentinel-2 imagery is automatically constructed, providing a total number of 56,412 samples with a size of 512 × 512 which are divided into six sub-regions in China and used for training different classification models. Then, an urban land use mapping network (ULUNet) is proposed to recognize ULU. This model utilizes a primary and an auxiliary encoder to process noisy OSM samples and can enhance the model's robustness under noisy labels. Finally, taking the temporal correlations of ULU into consideration, the recognized ULU are optimized, whose boundaries are unified by a time-series co-segmentation, and whose categories are modified by a knowledge-data driven method. To verify the effectiveness of the proposed method, we consider all urban areas in China (254,566 km2), and produce a time-series China urban land use dataset (CULU) at a 10-m resolution, spanning from 2016 to 2022, with an overall accuracy of CULU is 82.42%. Through comparison, it can be found that CULU outperforms existing datasets such as EULUC-China and UFZ-31cities in data accuracies, spatial boundaries consistencies and land use transitions logicality. The results indicate that the proposed method and generated dataset can play important roles in land use change monitoring, ecological-environmental evolution analysis, and also sustainable city development.

Evaluating the spatiotemporal dynamics of driving factors for multiple drought types in different climate regions of China

Extreme weather events and natural hazards are increasingly prevalent with global warming, making it imperative to focus on the driving factors and dynamic changes associated with drought—a disaster with widespread impacts and significant losses. This study used a longtime-series of ERA5 reanalysis data to elucidate the dynamic change characteristics of direct and indirect driving factors across China. Ground observation data spanning an extended period were used to estimate the performance of the reanalysis data. The selected driving factors for drought included precipitation (PRE), potential evapotranspiration, soil moisture (SM), bare soil evapotranspiration (BSET), vegetation canopy evapotranspiration (VCET), subsurface runoff (SUBRO), surface runoff (SURRO), temperature (TEM), specific humidity, and near-surface wind speed (WS). Meteorological drought, hydrological drought, and agricultural drought were delineated using the Standardized Precipitation Evapotranspiration Index, Standardized Runoff Index, and Standardized Soil Moisture Index. The Random Forest method was used to ascertain the relative importance of driving factors and their main contributions to different types of droughts. The findings revealed that ERA5 exhibited great simulation performance compared to observational data. However, the correlation coefficient of SM might be relatively lower than that of other driving factors across China. Notably, BSET and PRE exhibited higher relative importance degrees (RID) for meteorological drought in subregions 1, 2, and 3. At the same time, SUBRO demonstrated a more statistically significant influence in subregions 3, 4, and 5. SURRO played a statistically significant role in subregion 1. Moreover, BSET and PRE were pivotal for meteorological drought in northern China, whereas VCET assumes importance for hydrological drought in southern China and the North China Plain. Across different types of droughts, PRE, SM, and TEM generally exhibited relatively large RID values in China. The dynamic changes in RID for both direct and indirect driving factors varied for different drought types across different subregions of China. Notably, the RID displayed contrasting dynamic changes between PRE and BSET for meteorological drought in most subregions. Additionally, the degree of relative importance of near-surface WS tended to be lower for hydrological drought across various subregions. This study contributed to a deeper understanding of drought evolution and enhanced monitoring efforts.

Summertime compound heat wave and drought events in China: interregional and subseasonal characteristics, and the associated driving factors

This study investigates the characteristics of compound heat wave and drought events (CHDEs) across various subregions of China from 1961 to 2022 by utilizing a monthly probability-based index. The results uncover significant interregional and subseasonal variations. The trend analysis of CHDEs reveals statistically significant increases in most regions of China; however, there is no significant trend in the JiangHuai region throughout the entire summer season. The trends across regions exhibited subseasonal differences, especially in the eastern regions (Northeast China, North China, and South China (SC)). Furthermore, the occurrence of severe CHDEs (SCHDEs) in China has significantly increased in both frequency and extent since the 1990s. Southwest China and eastern Northwest China have witnessed the highest frequency of SCHDEs, while SC has remained relatively unaffected compared to other regions. The occurrences of SCHDE demonstrate a higher frequency occurred in June than in July and August, especially in the southern regions. The local driving factors are further explored. The incidence of CHDEs in eastern China is significantly influenced by anticyclonic circulation anomalies, which span from the upper to the lower troposphere. These anomalies are crucial in shaping the dynamic and moisture conditions necessary for CHDE formation. Their specific locations dictate the unique atmospheric conditions that lead to the regional characteristics of CHDEs across eastern China.

Assessing the risk of E. coli contamination from manure application in Chinese farmland by integrating machine learning and Phydrus

This study aims to present a comprehensive study on the risks associated with the residual presence and transport of Escherichia coli (E. coli) in soil following the application of livestock manure in Chinese farmlands by integrating machine learning algorithms with mechanism-based models (Phydrus). We initially review 28 published papers to gather data on E. coli's die-off and attachment characteristics in soil. Machine learning models, including deep learning and gradient boosting machine, are employed to predict key parameters such as the die-off rate of E. coli and first-order attachment coefficient in soil. Then, Phydrus was used to simulate E. coli transport and survival in 23692 subregions in China. The model considered regional differences in E. coli residual risk and transport, influenced by soil properties, soil depths, precipitation, seasonal variations, and regional disparities. The findings indicate higher residual risks in regions such as the Northeast China, Eastern Qinghai-Tibet Plateau, and pronounced transport risks in the fringe of the Sichuan Basin fringe, the Loess Plateau, the North China Plain, the Northeast Plain, the Shigatse Basin, and the Shangri-La region. The study also demonstrates a significant reduction in both residual and transport risks one month after manure application, highlighting the importance of timing manure application and implementing region-specific standards. This research contributes to the broader understanding of pathogen behavior in agricultural soils and offers practical guidelines for managing the risks associated with manure use. This study's comprehensive method offers a potentially valuable tool for evaluating microbial contaminants in agricultural soils across the globe.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

Annual atrazine residue estimation in Chinese agricultural soils by integrated modeling of machine learning and mechanism-based models

This study presents a comprehensive approach to estimating annual atrazine residues in China's agricultural soils, integrating machine learning algorithms and mechanism-based models. First, machine learning was used to predict essential parameters influencing atrazine's adsorption, degradation, and dispersivity of solute transport. The results demonstrated that soil organic matter was the most important input variable for predicting adsorption and degradation; clay content was the primary variable for predicting dispersivity. The SHapley Additive exPlanations (SHAP) contribution of various soil properties on target variables were also analyzed to reveal whether each input variable has a positive, negative, or complex effect. Subsequently, these parameters inform the construction of a detailed model across 23,692 subregions of China, with a 20 km × 20 km resolution. The model considered regional variations and soil layer heterogeneity, including rainfall, soil depth-specific properties, and parameters for adsorption, degradation, and dispersivity. Utilizing the convection-dispersion equations and the Phydrus, the model simulated atrazine's transport and degradation patterns across diverse soil environments after applying 250 mL of atrazine (40%) per Chinese mu. The outcomes provided a spatially explicit distribution of atrazine residues, specifying that the arid areas have the highest residual risk, followed by the Northeast, Southwest, and Southeast. Atrazine levels may exceed national drinking water standards at 50 cm depth in Inner Mongolia, the Qinghai-Tibet Plateau, and the Jungar Basin. This study's integrative approach may also offer valuable insights and tools for evaluating residues of various pesticides and herbicides in agricultural soils.

Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach

Droughts cause significant economic losses in many regions around the world, highlighting a need to more accurately quantify implications of drought on production and water management. Remote sensing technologies and machine learning-based models offer promising solutions for timely and accurate regional drought monitoring, but the accuracy of such approaches is constrained by both the conceptual design and algorithms underpinning such approaches. We developed a machine learning stacking ensemble approach to overcome such limitations, drawing upon Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR), Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products, and climate zoning data to estimate 3-month scale SPEI (Standardized Precipitation Evapotranspiration Index, SPEI-3) in 9 sub-regions of China. We compared 19 individual machine learning models and used the stacking approach to select the most robust meta-model for SPEI-3 prediction. We found that CatBoost Regressor (CBR), Extra Trees Regressor (ETR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF) were the top individual models for predicting drought, while using CBR as the stacked meta-model achieved the best performance. The R2 values for the stacking model with CBR as the meta-model were 0.9065 and 0.8218 in the eastern and western regions, respectively. We then employed the stacking model with CBR as the meta-model to generate seasonal drought maps based on SPEI-3 across different years and seasons. Our predicted SPEI-3 was compared with drought maps generated by the GPCC and ERA5 reanalysis datasets. We found a strong correlation between them with R2 exceeding 0.8 across multiple years and seasons, indicating our machine learning stacking ensemble approach had a good performance in monitoring seasonal drought conditions. The findings of our study establish a standardized protocol for predicting droughts in the agricultural sector, enabling its widespread application irrespective of variations in terrain and climate.

Assessing the environmental sustainability of the transnational area of China, North Korea, and Russia using an improved environmental degradation index

Timely and effective environmental sustainability assessment is critical for promoting sustainable development in the transnational area of Changbai Mountain (TACM). However, due to the limitations of the existing environmental degradation index, the environmental sustainability in this transnational area was still not comprehensively evaluated. In this study, the main objectives were to construct a comprehensive environmental sustainability index (CESI) by integrating four environmental elements of ecological, soil, water as well as air, and evaluate the environmental sustainability of TACM from 2000 to 2020. The results indicated that the environmental sustainability of the TACM exhibited a significantly degraded trend. The area of degraded environmental sustainability (73147.16 km2) accounted for 44.35 % of the total area of TACM. In the three subregions, the environmental sustainability showed decreasing trends in the subregions of China and the Democratic People’s Republic of Korea (DPRK), whereas showed an upward trend in Russia. In particular, the DPRK’s side exhibited the most significant degradation, where the area of reduced environmental sustainability (40405.60 km2) was 1.25 times and 129.98 times larger than that on the Chinese and Russian sides, respectively. Forest loss and cropland expansion were the primary reasons for the degradation of environmental sustainability in the TACM. The major findings of this study could contribute to our understanding of environmental sustainability and will be helpful to promote the sustainable development in this transnational area as well as Northeast Asia.

Attribution of differences in observed warming among subregions of China and observationally constrained projection

AbstractUnderstanding the impact of different external forcings on regional temperature changes in China and constraining future changes is important for formulating targeted climate mitigation policies. We conducted a detection and attribution analysis to examine the effects of different external forcings on the differences in warming across China and seven subregions. This analysis spanned from 1965 to 2014 and used observations as well as data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The signals of anthropogenic (ANT) forcing can be detected and separated from natural (NAT) forcing in all regions, and the effect of greenhouse gases are found to account for a significant portion of the observed warming in these regions. In the Yangtze River Basin, southeast China and southwest China, the observed warming trends are lower than other subregions. This can be attributed to the relatively weak impact of greenhouse gases, resulting in a temperature increase of only 0.51°C in the Yangtze River Basin, which accounts for 48.57% of the observed warming. While both southeast China and southwest China experience a cooling effect of aerosols, leading to temperature decreases of 0.20 and 0.53°C, respectively, even though the signal of aerosol forcing is not detected. In terms of future projection, the use of attribution constraint approach effectively corrects the biases of the CMIP6 model simulation and reduces projection uncertainty in China and most subregions under the new shared socioeconomic pathway (SSP) SSP2‐4.5 and SSP5‐8.5. The Tibetan Plateau has the largest reduction of the uncertainty range among seven subregions, with the decrease of 39.02% under SSP2‐4.5 scenario in the long‐term (2081–2098). In addition, north China and southwest China will experience the highest and lowest warming in the long‐term (2081–2098) after attribution constraints, increasing by 3.41 and 2.14°C, respectively, under SSP2‐4.5 scenario.

Heavy Rainfall Forecast of Landfalling Tropical Cyclone Over China With an Upgraded DSAEF_LTP Model

AbstractSince the release of the Dynamical‐Statistical‐Analog Ensemble Forecast for Landfalling Typhoon Precipitation (DSAEF_LTP) model, a series of upgrades has been made to improve its heavy rainfall forecast performance for different cases and regions of China. However, little effort has been given to systematic evaluation of its multi‐upgraded version with a large sample size, especially for all the coastal regions of China. This study evaluates the performance of Version 1.0 (V1.0) and its improved Version 1.1 (V1.1) of the DSAEF_LTP model in simulating heavy rainfall amounts associated with 76 landfalling tropical cyclones (LTCs) over China during 2004–2016. The optimized schemes from these simulations are then applied to the heavy rainfall forecasts of 37 LTCs during 2017–2020. To further improve the forecast performance of V1.1, a subregional re‐ensemble scheme, referred to as V1.1R, is introduced after examining its performances over three coastal subregions of China. A comparison of the forecast performances of V1.0, V1.1, V1.1R, and four operational numerical weather prediction (NWP) models for the 37 LTCs shows that the performance of V1.1 is much better than that of V1.0, with a 65% higher summed threat score (TS) for over 250‐mm (TS250) and 100‐mm (TS100) rainfall; and that V1.1R's TS100 and TS250 values are further improved by 8% and 20%, respectively, as compared to V1.1. These values are superior to those of the four operational NWP models. Additionally, case studies reveal that the track distributions and intensities of LTCs may significantly impact the forecast performance of heavy rainfall.

Century‐long variations of growing‐season compound dry–hot extremes and their links with large‐scale climate patterns in China

AbstractIn the context of global warming, the increasing frequency of compound dry–hot extremes pose varying degrees of threats to the natural environment and human society. However, due to the lack of long‐term and high‐accuracy meteorological observations, the characteristics and drivers of compound dry–hot extremes over a century‐long period is still poorly investigated in China. Here we characterize the spatiotemporal variations of growing‐season (May–September) compound dry–hot events (CDHEs) over China using Standardized Dry and Hot Index (SDHI) calculated by monthly temperature and precipitation during 1901–2017. The influences of precipitation and temperature extremes, as well as large‐scale climate indices (i.e., El Niño–Southern Oscillation Index [ENSO], Atlantic Multidecadal Oscillation [AMO], North Atlantic Oscillation [NAO], Pacific Decadal Oscillation [PDO] and North Pacific [NP]) on CDHEs were then explored using the relative contribution analysis, Pearson correlation analysis and cross‐wavelet spectrum analysis. Our findings reveal a significant increase in the occurrence of different severity levels of CDHEs in China with their spatial extent gradually increase, practically since the end of the 20th century. Due to the exacerbation of drought conditions in China since the mid‐20th century, the regions where the Standardized Precipitation Index (SPI) has a more prominent contribution to the SDHI have gradually expanded, predominantly in the eastern, northern and northwestern parts of China. The significant influence of large‐scale climate indices, particularly ENSO and AMO, on the occurrence of compound dry–hot extremes in China is noteworthy, as these indices control the variability of precipitation and temperature, respectively. The study findings demonstrate that AMO exhibits a more significant correlation with SDHI in all subregions of China, while ENSO exhibits marginally stronger periodic coherence with SDHI than the other indices. This study offers valuable insights and references for unravelling long‐term development process of and the underlying influence mechanisms of significant climate patterns on the growing‐season CDHEs in China.

Performance of regional climate model RegCM4 with a hydrostatic or non‐hydrostatic dynamic core at simulating precipitation extremes in China

AbstractIn this study, we investigated the performance of RegCM4 with a hydrostatic or non‐hydrostatic dynamic core and different land surface models at simulating the mean and extreme precipitation during 1991–1999 in China and the Beijing–Tianjin–Hebei urban agglomeration (JJJ) in northern China. The resolutions were 12 km with the whole of China included and 3 km nested over the JJJ region. The observed daily precipitation data comprised the CN05.1 data set from China Meteorological Administration. First, RegCM4 with a hydrostatic dynamic core forced by the Max Planck Institute Earth System Model (MPI‐ESM1.2‐HR) that participated in CMIP6 was used to simulate the east Asia area at a resolution of 12 km. Next, the RegCM4 results with 12 km resolution forced RegCM4 with a non‐hydrostatic dynamic core (RegCM4‐NH) to run over the JJJ region with a 3 km convection‐permitting scale through one‐way nesting. The results showed that RegCM4 with a hydrostatic and non‐hydrostatic core could obtain more detailed seasonal and annual precipitation in China and the JJJ region relative to MPI‐ESM1.2‐HR although their performance varied over different regions. RegCM4 with the community land surface model 4.5 (CLM4.5) generally performed better at simulating the mean and extreme precipitation over the four subregions of China than that with the Biosphere–Atmosphere Transfer Scheme because of more detailed descriptions of land surface processes of CLM4.5. RegCM4‐NH with convection‐permitting resolution improved the simulations of heavy precipitation extremes over JJJ compared with RegCM4 with a hydrostatic core and resolution of 12 km. In addition, RegCM4‐NH with CLM4.5 performed better at simulating heavy precipitation extremes over JJJ compared with all other simulations, with higher correlation coefficients and larger relative root mean squared errors.

Investigating Whether the Ensemble Average of Multi-Global-Climate-Models Can Necessarily Better Project Seasonal Drought Conditions in China

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various “possible future” scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China’s climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China’s moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study’s findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation.

Greenhouse gas emissions and environmental drivers in different natural wetland regions of China

Wetlands sequestrate carbon at the highest rate than any other ecosystems on Earth. However, the spatial and temporal dynamics of GHGs emissions from the wetland ecosystems in China are still elusive. We synthesized 166 publications that contain 462 in situ measurements of GHGs emissions from the natural wetlands in China, and further analyzed the variability and the drivers of GHGs emissions in eight subdivisions of China's wetlands. The results show that the current studies are mainly concentrated in the estuaries, Sanjiang Plain, and Zoige wetlands. The average CO2 emissions, CH4 fluxes and N2O fluxes from Chinese wetlands were 218.84 mg·m−2·h−1, 1.95 mg·m−2·h−1 and 5.8 × 10−2 mg·m−2·h−1, respectively. The global warming potential (GWP) of China's wetlands was estimated to be 1881.36 TgCO2-eq·yr−1, with CO2 emissions contributing more than 65% to the GWP value. The combined GWP values of Qinghai-Tibet Plateau wetlands, coastal wetlands and northeastern wetlands account for 84.8% of GWP of China's wetlands. Correlation analysis showed that CO2 emissions increased with the increasing mean annual temperature, elevation, annual rainfall, and wetland water level, but decreased with soil pH. CH4 fluxes increased with the mean annual temperature and soil water content but decreased with the redox potential. This study analyzed the drivers of GHGs emissions from wetland ecosystems at the national scale, and GWP values of eight wetland subregions of China were comprehensively assessed. Our results are potentially useful for the global GHGs inventory, and can help assess the response of GHGs emissions of wetland ecosystem to environmental and climate change.

Soil moisture content-based analysis of terrestrial ecosystems in China: Water use efficiency of vegetation systems

Water is a prerequisite for the formation of earth-biochemical-ecological systems. Differences in the spatial and temporal distribution of water resources are important factors in the formation of differences in the distribution of vegetation in terrestrial ecosystems and are key to the differences in vegetation productivity. Vegetation water use efficiency is calculated by the total amount of fixed biogenic carbon per unit mass of water consumed in photosynthesis and can be used to assess the intensity and capacity of an ecosystem to use water biomass. Based on remote satellite sensing data, this study proposes a new water use efficiency assessment model. The model was validated using flux site data, and we analyzed the relative contribution of climate factors to soil moisture use efficiency using a first-difference method. The results revealed the following: (1) The soil moisture use efficiencies (SUE) of remote sensing data inversions that were evaluated using flux site data based on correlation coefficients and Nash coefficients showed high reliability, and only the NMG (Inner Mongolia) station showed low correlation among the nine sites. (2) Among the nine agricultural sub-regions in China, only the SUE of the Qinghai-Tibet Plateau region showed a decreasing trend (−1.08 g C/m−2 kg H2O yr), while all other regions showed an increasing trend. (3) The highest vegetation soil moisture use efficiency (1.83 g C/m−2 kg H2O) was found in ferralisols, while the lowest vegetation SUE (0.17 g C/m−2 kg H2O) was found in arid soils. The SUE of different vegetation types showed the characteristics of forest > scrub > cultivated vegetation > wetland > grassland. (4) The relative contribution of gross primary productivity (GPP) to the change in SUE was 37.53%, while the relative contribution of soil moisture content to the change in SUE was −26.71%. Among the five climatic factors, temperature was the most dominant factor affecting the change in SUE, followed by precipitation, net radiation, leaf area index, and potential evapotranspiration. Revealing the relationship between terrestrial ecosystem GPP, soil moisture content, and their responses to climate factors is a prerequisite for understanding the adaptation strategies of regional terrestrial ecosystems to global climate change, which can help to inform decision-making for the sustainable development of ecosystems.

Identification and Analysis of Heatwave Events Considering Temporal Continuity and Spatial Dynamics

In the context of global warming, the general increase in temperature has led to an increase in heatwave events, as well as a dramatic intensification of economic losses and social risks. This study employs the latest intensity–area–duration (IAD) framework that takes into account the temporal continuity and spatial dynamics of extreme events to identify regional heatwave events, and extracts key parameters of heatwave events to study the associated changes in frequency, intensity, influence area, and duration in seven geographic subregions of China in the 1979–2018 period. Heatwaves of all durations increased in frequency and intensity during the research period, with shorter heatwaves increasing in frequency and intensity at a faster rate than longer heatwaves. Among the seven geographic subregions, Xinjiang (XJ) and Southern China (SC) are the regions with the most frequent heatwave occurrence, while the Southwest (SW) and SC have the highest increase in heatwave frequency. In terms of regional distributions, XJ has the strongest heatwave event intensity and the largest affected area, while SC has the longest duration. However, in terms of spatial trends, SC, XJ, and the SW have the highest rates of intensity growth, influence area, and duration, respectively. In addition, heatwaves with extended durations and vast influence areas are more likely to occur in SC, and their frequency is on the rise. During the study period, the intensity, influence area, and length of heatwave occurrences in China exhibited an upward tendency, and it was shown that the longer the duration, the greater the intensity and the broader the influence area. In addition, the evolutionary characteristics of heatwave events with the longest duration indicate a certain consistency in their intensity and influence. These findings can contribute to the development of strategies to prepare for and mitigate the adverse effects of heatwave occurrences.

Quantifying the dynamic processes of soil erosion and lake sediment deposition in the Holocene in China

The Holocene lake sediment records are critical indicators of past global change studies. This study attempts to reconstruct soil erosion rates (SERs) quantitatively based on lake sediment records from 35 lakes in China. Environmental sequences, e.g., precipitation, vegetation and human activity, were reconstructed and used to identify the dynamic processes of soil erosion based on the General Additive Model (GAM). The findings were as follows. First, drivers of soil erosion in lake basins in different sub-regions of China differed markedly due to the evolution of different monsoon systems and regional environments. Second, influenced by the evolution of different monsoon systems and regional environments, soil erosion of the Holocene showed different evolutionary processes in China. For example, SERs gradually increased in the northwestern arid zone and remained relatively stable in the monsoon zone until 2.0 ka (1 ka = 1000 calibrated a B.P.). Third, human activity played a more important role in soil erosion in all sub-regions during the Holocene. Human activities through vegetation/land-use change have led to rapid enhancement of soil erosion in the north, south, and southwest China since 2.0 ka, especially in the last 1000 years. This study contributes to our understanding of the evolution of soil erosion under different climate systems in China.