Search Results (72)

Against China’s dual-carbon strategy and watershed ecological high-quality development initiatives, exploring land-use-driven carbon storage variations is crucial for watershed spatial governance, ecological restoration and carbon sink improvement. As the Yellow River’s largest tributary, the Weihe River Basin straddles the ecotone of arid–semiarid Northwest China and the eastern monsoon region, with fragile ecosystems, intense human activities and dramatic land-use evolution. Clarifying its spatiotemporal carbon dynamics and nonlinear factor response patterns can support low-carbon ecological regulation and coordinated watershed sustainability. This study integrates PLUS and InVEST models to quantify the spatiotemporal patterns of land use and carbon storage across 2000–2020 land use data. Four development scenarios are established to predict 2030 carbon storage spatial heterogeneity, and an interpretable XGBoost-SHAP framework was employed to disentangle the nonlinear responses of carbon storage to natural and socioeconomic drivers. The results show that basin carbon storage exhibited an overall increasing trend, with a net increase of 452.29 × 10⁴ t over the 2 decades. Spatially, high-carbon areas presented patchy aggregation in the northeast, sporadic distribution in the west and zonal expansion in the south–central basin, while low-carbon areas were concentrated in the downstream Guanzhong Plain urban agglomeration. Land-use transitions represented a major source of regional carbon variability, with forest expansion driven by the Grain-for-Green Program generating the largest carbon sequestration increments. The 2030 scenario simulations revealed elevated carbon storage under natural development, cropland protection, and ecological protection scenarios, with the ecological protection scenario yielding the most prominent carbon gain. Conversely, unconstrained economic expansion coincided with prominent carbon storage declines, which implies that targeted ecological conservation and restoration could help alleviate carbon depletion across the watershed. NDVI, slope, and population density are the primary determinants of carbon storage, exhibiting typical staged nonlinear influencing effects. This study provides reliable scientific references for refined ecological restoration, spatial optimization, and carbon sink enhancement in ecologically fragile river basins. Full article

Seasonal prediction of summer rainfall over the middle and lower reaches of the Yangtze River Basin (MLYRB) remains challenging because the linkage between preceding climate signals and regional precipitation anomalies involves complex dynamic and thermodynamic processes. This study examines March precipitation over the eastern Mediterranean–West Asia region, hereafter referred to as PE, as an upstream spring signal associated with July rainfall anomalies over the MLYRB. The central objective is to determine, through a relative humidity decomposition framework, whether the humidification accompanying PE-related July rainfall anomalies is dominated by moisture changes or by temperature-related saturation effects. The results indicate that high-PE years are associated with a significant increase in July rainfall over the MLYRB. This rainfall enhancement is accompanied by anomalous circulation patterns favourable for moisture transport and convergence over eastern China. Meanwhile, positive relative humidity anomalies extend from the lower to the upper troposphere, with the 400 and 300 hPa levels showing a particularly close spatial correspondence with the significant rainfall anomalies over the rainfall region. Although absolute water vapour content decreases with height, the coherent upper-tropospheric relative humidity response indicates the presence of a deep moist layer, which is favourable for sustained condensation, reduced dry-air entrainment, and persistent monsoon rainfall. A further moisture–temperature decomposition shows that the PE-related relative humidity response is jointly controlled by changes in atmospheric moisture content and saturation vapour pressure. Over the MLYRB, the increase in relative humidity is primarily associated with enhanced moisture content, whereas temperature-induced changes in saturation conditions are more evident in regions with more coherent temperature anomalies. These findings suggest that the PE-related July rainfall anomaly is supported by a combination of dynamic moisture supply and thermodynamic humidification of the atmospheric column. The study provides a physically consistent explanation for the potential precursor relevance of the PE signal and emphasizes the importance of vertical humidity structure in understanding and predicting summer rainfall anomalies over eastern China. Full article

Seasonal delineation represents a critical interface between the natural environment and human activities. However, the conventional climate-temperature (C–T) method, which relies solely on thermal thresholds, has limited applicability in regions with complex climatic regimes. In this study, we develop and apply a composite seasonal index (CSI) integrating temperature and precipitation, using meteorological observations from 298 stations across mainland China during 1980–2020, with CSI calculation based on 278 stations that had valid paired temperature and precipitation records, to investigate spatial patterns of seasonal variability. The results show that incorporating precipitation improves the identification of regional heterogeneity in seasonal dynamics. In northeastern and northwestern China, spring rainfall advances spring onset, while autumn rainfall delays autumn termination, producing a CSI–defined spring duration 1–2 months longer than that derived from the C–T method and an autumn duration about one month longer. In some arid regions, concentrated precipitation prolongs summer by approximately 1–2 months. An independent comparison with land surface phenology metrics during 1982–2018 further shows that CSI–derived seasonal transition dates are broadly consistent with the spatial patterns of SOS, maturity, senescence, and EOS, especially in monsoonal and hydrothermally complex regions. Differences between the CSI and C–T methods are generally small (approximately ±1 month) where precipitation and temperature vary synchronously, but increase to approximately ±2 months where precipitation exerts stronger control. Overall, the CSI preserves the structure of the traditional C–T classification while accounting for hydrological influences, thereby enhancing seasonal delineation in climatically Eastern Monsoon Region and improving the ecological interpretability of hydrothermal seasonality assessment. Full article

Despite the growing threat of Pythium helicoides to forest plantations in China, a nationwide assessment of climatic suitability remains unavailable, limiting the development of preventive strategies. This study applied the Maximum Entropy model combined with geographic information system analysis to predict the potential distribution and suitable habitats of the pathogen across China. The model was constructed using occurrence records from the Global Biodiversity Information Facility and published literature, together with bioclimatic, topographic, and soil variables. Simulations were performed under current and future climate conditions throughout the twenty-first century across low, medium, and high emission scenarios. The model performed reliably, with Area Under the Curve values indicating favorable predictive accuracy across all periods. Habitat suitability was governed primarily by precipitation of the driest month, temperature annual range, and elevation. Under current conditions, highly suitable areas are concentrated in tropical and subtropical monsoon regions, particularly eastern Hainan and Taiwan. Under future scenarios, suitable habitats are projected to shift toward warm temperate regions while contracting overall, with plains, basin floors, and valleys retaining high suitability due to favorable moisture retention. Windward mountain slopes are generally unsuitable, although scattered medium-suitable habitats may form in lower-lying depressions with gentler slopes. Full article

Extreme summer droughts increasingly threaten freshwater biodiversity in monsoonal regions, yet community responses within heterogeneous small water bodies (SWBs) remain poorly understood. This study evaluated how drying events influence macroinvertebrate taxonomic and functional composition across different SWB types and explored the mechanisms driving post-drought recovery. We sampled isolated ponds (IPs), stream-fed ponds (SFPs), pond-linked streams (PLSs), and non-pond-linked streams (NPLSs) in Eastern China during an extreme summer drought (2022) and a subsequent recovery year (2023). Ponds exhibited high resistance, maintaining stable taxonomic and functional richness. PLSs suffered substantial summer biodiversity declines but showed rapid post-drought recovery, possibly facilitated by spatial dispersal from nearby pond refuges. In contrast, NPLSs experienced severe, lasting biodiversity loss. Drought conditions drove overall community homogenization, with spatial dispersal playing a more important role in structuring assemblages than environmental filtering. Furthermore, functional trait analysis indicated that post-drought recovery was more closely associated with resilience-linked traits rather than resistance traits. These findings demonstrate that lateral connectivity within SWB networks buffers drought impacts by providing refugial support and enabling rapid recolonization. Preserving diverse, interconnected SWBs is a critical management strategy for maintaining metacommunity resilience under growing climatic volatility. Full article

In recent decades, intensified temperature variability has increased the likelihood of abrupt transitions between anomalously cold and warm conditions, exerting substantial ecological and societal impacts. This study identifies rapid temperature flip events (RTFEs), including cold-to-warm transition events (C2Ws) and warm-to-cold transition events (W2Cs), across China using the CN05.1 gridded daily mean temperature data for 1961–2022, and further reveals their regional heterogeneity and long-term changes. Eastern China represents a hotspot of RTFEs, exhibiting higher frequencies and stronger intensities compared with western China. RTFEs are most frequent in spring, followed by summer. Over the period 1961–2022, both C2W and W2C became more frequent and more intense, with W2C showing a larger increase in frequency of 0.54 events century⁻¹ and a larger increase in intensity of 0.29 s.d. century⁻¹, compared with increases of 0.01 events century⁻¹ and 0.11 s.d. century⁻¹, respectively, for C2W. In addition, significant decadal changes in both types of events were observed across large areas of China during the 1990s–2000s and 2010s, following a high–low–high pattern. Analysis across the seven natural sub-regions reveals distinct high-hazard areas where RTFE hotspots coincide with increasing frequency and intensity: the eastern monsoonal regions of China for W2Cs and Inner Mongolia for both event types. These findings contribute to addressing climate change and mitigating the risk of RTFEs. Full article

Existing research on the landscape patterns of urban parks and green spaces demonstrates a disproportionate focus across tiers within China’s urban hierarchy. Numerous studies have concentrated on economically developed first-tier cities, such as Beijing, Shanghai, and Guangzhou. In contrast, medium-to-large non-first-tier cities, especially provincial capitals and emerging cities within the first- and second tiers, have been relatively understudied, although they have received increasing attention in recent years. This bias extends regionally, with studies predominantly examining cities in the more developed central and eastern regions, while less-developed areas and lower-tier cities receive significantly less attention. This study tracks changes in park quantity, spatial concentration, patch structure and driver associations at three planning-related time points. Shenyang provides a distinct cold-region and old industrial city case, shaped by long winters, industrial renewal and outward urban growth. Furthermore, to inform park and green-space planning in Northeast China’s cold-climate cities, exemplified here by Shenyang, a major metropolis with a monsoon-influenced humid continental climate (Köppen Dwa), long cold winters, and relatively short warm summers, we document a shift in park distribution from the urban core to peripheral areas. Based on park vector layers reconstructed from planning documents, remote sensing interpretation and field verification, this study combined spatial analysis, landscape metric calculation and driver-association modeling. ArcGIS Pro was used to identify changes in distribution centers, directional extension and local clustering; FRAGSTATS 4.2 was used to calculate park landscape metrics; and SIMCA-P 14.1 was used to examine the statistical associations between selected landscape indicators and potential driving variables. The results show that the number and total area of parks in central Shenyang increased substantially from 2000 to 2024. Spatially, park distribution became less concentrated in the traditional inner city, while new clusters gradually appeared in peripheral districts and newly developed urban areas. The old urban core remained important, but its dominance weakened as park provision expanded outward. The landscape metric results further indicate that park expansion was accompanied by more irregular patch forms, stronger fragmentation and declining structural continuity. The driver association analysis suggests that climate conditions, population change, industrial restructuring, real estate investment, road construction and urban greening policies were related to different aspects of park landscape change. These associations should be interpreted as statistical relationships rather than direct causal effects. Overall, this study clarifies the spatial restructuring of park green spaces in a cold-region old industrial city and provides planning evidence for improving park connectivity, coordinating green space expansion with urban construction and supporting sustainable park system development in Northeast China. Full article

Under the background of global change, the threshold for the propagation of meteorological drought to hydrological drought is crucial for drought early warning and water resource management. However, traditional threshold studies often adopt subjective and fixed conditional probabilities and lack the revelation of the driving mechanisms under macroscopic natural geographical differentiation. This study integrates terrestrial water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) mission, the standardized precipitation evapotranspiration index (SPEI), and multi-source environmental data to construct an objective threshold identification method based on Copula joint distribution and “system resilience loss”, and combines explainable machine learning to systematically explore the critical threshold for meteorological drought, triggering a TWSA deficit and its driving mechanisms from the perspectives of three major natural regions, the Eastern Monsoon Region (EMR), the Northwestern Arid Region (NAR), and the Tibetan Plateau Region (TPR). The results show that: (1) from 2005 to 2024, the TWSA significantly decreased in nearly half of China’s regions, with significant regional differentiation; (2) the response of the TWSA to meteorological drought has a significant lag (an average of 9–12 months), and shows a spatial pattern of slower in the east and faster in the northwest; (3) the probability of a TWSA deficit and the triggering threshold both have obvious grade dependence and spatial heterogeneity, with the lowest threshold in the northwest arid region, which is the most sensitive; (4) the threshold is driven by the synergy of multiple factors, with “water dominance and energy modulation”, and the dominant factors show regional differentiation; and (5) irrigation agriculture significantly reduces the drought triggering threshold and exacerbates system vulnerability. This study provides a scientific basis for understanding the geographical differentiation laws of drought propagation and regional early warning management. Full article

Global warming is reshaping the global dry–wet pattern, yet its future trajectory remains uncertain, with important implications for sustainable water resources. China, influenced by both the monsoon system and the mid-latitude westerlies, requires an integrated assessment linking net water balance (precipitation minus evaporation, PME) to moisture transport. Here we use precipitation, evaporation, and air temperature records for 1981–2023, together with Lagrangian moisture tracking and precipitation recycling diagnostics, to quantify changes in PME across China and to identify the underlying mechanisms. We further assess future evolution under different warming levels (1.5 °C, 2 °C, and 3–4 °C) for 2024–2099 using a CMIP6 multi-model ensemble. China experienced a pronounced warming during the historical period, while precipitation declined overall and evaporation remained nearly stable. As a result, reduced moisture supply strengthened drought sensitivity. Spatially, warming-driven drying is concentrated in the eastern and southern monsoon regions. In contrast, the inland arid and semi-arid Northwest and parts of high-elevation transition zones show a relative shift toward warmer and wetter conditions. Moisture transport diagnostics indicate that China’s moisture supply is jointly sustained by the mid- to high-latitude westerlies and low-latitude oceanic monsoon pathways. These pathways form a continuous transition from the Northwest to the Southeast. Land–atmosphere recycling is stronger in the Southeast, whereas the Northwest depends more on imported moisture, with plateau topography further reshaping the main transport corridors. In the future, PME continues to decline under 1.5 °C warming. Under 2 °C warming, PME enters a transitional state with patchy regional patterns. Under 3–4 °C warming, PME shifts to an overall increase, but uncertainty becomes larger. These results identify a critical turning window at around 2–3 °C warming for China’s PME response, providing a physical basis for sustainable water-resource management and adaptation planning. Full article

Wind energy is an important form of clean energy, and its rational utilization represents a crucial solution for mitigating the energy crisis and global warming. In this study, wind energy potential and its long-term changes in the South China Sea (SCS) are evaluated using ERA5 100 m wind data from 1944 to 2023, validated against ASCAT observations. High wind speeds and high wind power density (WPD) are concentrated southwest of Taiwan and southeast of Vietnam. Annual wind availability exceeds 6457 h across most regions, reaching up to 8283 h in optimal locations. WPD and capacity factor peak in winter (up to 2.4 × 10⁸ Wh·m⁻² and >50% capacity factor), with the most stable conditions occurring in the southwestern Taiwan Strait, southeast of the Pearl River Delta, and the Beibu Gulf. Empirical orthogonal function analysis reveals that the first mode of winter WPD accounts for 65.7% of the total variance, with a statistically significant increasing trend since 1990. The interannual variation in wind energy resources in the SCS during winter is controlled by the combined effects of sea surface temperature (SST) anomalies in the tropical Pacific and the Arctic Barents Sea. Specifically, in the years with strong wind anomalies in the SCS, mega-La Niña-type SST patterns in the tropical Pacific trigger anomalous cyclonic circulation in the SCS and the eastern Philippine Sea, while warm anomalies in the Arctic Barents Sea surface drive a wave-like structure of “anticyclone–cyclone–anticyclone” from Siberia to South China. The coupling of the two systems jointly promotes the strengthening of the South China Sea monsoon, leading to increased wind speeds and elevated WPD in the northern SCS. These findings provide a scientific basis for wind farm siting and long-term operational planning in the region. Full article

The characteristics and causes of the long-term trends of diurnal variation of sea surface temperature (DSST) in the South China Sea (SCS) are investigated in this study based on the global hourly sea surface temperature data generated by the mixed layer model (MLSST) from the National Marine Environmental Forecasting Center (NMEFC) of China. Validation of the MLSST dataset demonstrates excellent agreement with in-situ buoy observations in the SCS with a correlation coefficient of 0.951, confirming its reliability in the SCS. Based on this dataset, the long-term trend of DSST in the SCS exhibits significant seasonal variations with the strongest magnitude in spring and the weakest in winter. Specifically, a significant decreasing trend of −0.0014 °C yr⁻¹ during 1982–2009 transitioned to a pronounced increasing trend of 0.0057 °C yr⁻¹ from 2010–2019. Both climatic factors and local atmospheric variables jointly modulate the DSST in the SCS. On the long-term timescale, the Pacific Decadal Oscillation (PDO) served as the dominant factor driving DSST changes in most areas of the SCS. After 2010, the PDO shifted to a persistent positive phase, providing a crucial climatic background for the basin-wide DSST increase. While the El Niño–Southern Oscillation (ENSO) showed enhanced correlation with DSST post-2010, the Indian Ocean Dipole (IOD) had negligible influence overall. In addition, the SCS summer monsoon played an important regulatory role in shaping the long-term trend of summer DSST by altering air–sea heat exchange processes. Among local atmospheric variables, sea surface wind speed was significantly negatively correlated with DSST, and net heat flux was significantly positively correlated with DSST, with their effects showing regional differentiation. The regulatory role of wind speed dominated in the western SCS, whereas the net heat flux exerted a more prominent impact in parts of the eastern SCS. This work clarifies the spatiotemporal patterns and multi-driver framework governing DSST variability in the SCS, providing a basis for understanding regional ocean–atmosphere interactions. Full article

Carposina sasakii Matsumura is a significant lepidopteran pest in the Carposinidae family, inflicting substantial damage on stone and pome fruit trees such as jujube, peach, and apple. Using MaxEnt, we assessed the worldwide climatic suitability for C. sasakii and its key environmental drivers, evaluating how climate change impacts dispersal risks. Integrating global occurrence records with 37 environmental variables, the model (AUC = 0.982) quantitatively identifies July precipitation (prec7), minimum average temperatures in April and August (tmin4 and tmin8, respectively), and maximum average temperature in May (tmax5) as critical distribution determinants. Among these, prec7 exhibits the highest contribution (threshold approximately 370 mm). The current suitable habitat spans 10.39 × 10² km², concentrated predominantly in East Asia’s temperate monsoon zone (eastern China, the Korean Peninsula, and Japan) and southern North America. Under future climate scenarios, the high-emission pathway (SSP585) will reduce highly suitable areas, while moderately suitable zones expand coastward. In contrast, SSP370 projects a significant, albeit phased, habitat increase with a 19.61% growth rate. Precipitation regimes and extreme temperatures jointly regulate niche differentiation in C. sasakii, whose range shifts toward Southeast Asia and suboptimal regions in Europe and America, underscoring cascading climate change effects. These findings provide a scientific basis for transnational monitoring, early warning systems, and regional ecological governance. Full article

Under concurrent global warming and multi-scale climate anomalies, regional precipitation has become more uneven and less stable, and extreme events occur more frequently, amplifying water scarcity and ecological risk. Focusing on mainland China, we analyze nearly 70 years of monthly station precipitation records together with eight climate drivers—the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Multivariate ENSO Index (MEI), Arctic Oscillation (AO), surface air pressure (AP), wind speed (WS), relative humidity (RH), and surface solar radiation (SR)—and precipitation outputs from eight CMIP6 models. Using wavelet analysis and partial redundancy analysis, we systematically evaluate the qualitative relationships between climate drivers and precipitation and quantify the contribution of each driver. The results show that seasonal precipitation decreases stepwise from the southeast toward the northwest, and that stability is markedly lower in the northern arid and semi-arid regions than in the humid south, with widespread declines near the boundary between the second and third topographic steps of China. During the cold season, and in the northern arid and semi-arid zones and along the margins of the Tibetan Plateau, precipitation varies mainly with interdecadal swings of North Atlantic sea surface temperature and with the strength of polar and midlatitude circulation, and it is further amplified by variability in near-surface winds; the combined contribution reaches about 32% across the Northeast Plain, the Junggar Basin, and areas north of the Loess Plateau. During the warm season, and in the eastern and southern monsoon regions, precipitation is modulated primarily by tropical Pacific sea surface temperature and convection anomalies and by related changes in the position and strength of the subtropical high, moisture transport pathways, and relative humidity; the combined contribution is about 22% south of the Yangtze River and in adjacent areas. Our findings reveal the spatiotemporal variability of precipitation in China and its responses to multiple climate drivers and their relative contributions, providing a quantitative basis for water allocation and disaster risk management under climate change. Full article

Due to climate warming, extreme precipitation events have intensified in frequency and intensity. This trend has raised significant concerns about its impact on natural reserves in eastern China’s monsoon region. A risk assessment is, therefore, needed to evaluate the vulnerability of these protected areas. Based on observed and simulated daily precipitation data, this study analyzed the spatiotemporal trends of heavy rainfall in the eastern monsoon region of China and assessed the exposure risk of the protected areas to rainstorm events both in the historical and future periods. Results indicate that the annual average number of heavy rainfall days gradually increases from northwest to southeast, displaying a distinct zonal distribution pattern. The proportion of heavy rainfall days to total precipitation days and the average intensity of heavy rainfall show peak centers in the southeastern coastal areas, western Sichuan region, and North China Plain, with minimum values observed in the northwestern direction. Protected areas in China’s Eastern Monsoon Region display a north–south gradient of precipitation exposure risk that intensifies from historical (1995–2014) to near future (2031–2050) to far future (2081–2100) under SSP245 scenario, with highest vulnerability in southeastern coastal areas. National reserves generally experience lower exposure than provincial and municipal ones, though all categories face increasing precipitation risks over time. Full article

Retrieving and predicting summer average near-surface air temperature (SANSAT) across China remain challenging due to the country’s complex topography and heterogeneous vegetation cover. This study proposes an innovative deep learning framework that incorporates vegetation regionalization to achieve high-precision spatiotemporal temperature retrieval and prediction. Using MODIS land surface temperature, vegetation indices, weather station data (2000–2019) and other relevant datasets, we first apply GeoDetector to identify key influencing factors (e.g., nighttime surface temperature, elevation, vegetation index, and population density) within each vegetation region. Based on these findings, we develop a deep neural network (DNN) model, which achieves high accuracy in SANSAT retrieval (with validation R² ranging from 0.90 to 0.97 and RMSE from 0.46 to 0.64 °C). Results indicate that temperature variations in the eastern monsoon region are primarily influenced by human activity and topography, whereas natural factors dominate in the western regions. Subsequently, using a Long Short-Term Memory (LSTM) network with an optimal seven-year time step, we predict SANSAT for 2020–2023, achieving R² values of 0.71 in training and 0.69 in testing, which confirms the model’s high reliability in SANSAT prediction. The core innovation of this work lies in its vegetation-regionalized deep learning approach, which explicitly addresses landscape heterogeneity by customizing models to specific eco-climatic zones, thereby quantifying human-nature interactions more effectively than traditional, spatially uniform methods. This framework enhances the understanding of summer temperature dynamics and provides valuable spatial data to support applications in agricultural disaster prevention, ecological conservation, and carbon neutrality. Future research will incorporate multi-seasonal data and enhance the spatiotemporal resolution to further improve NSAT modeling. Full article