Water use efficiency (WUE) characterizes the coupling between ecosystem carbon and water cycles. However, under the combined influences of climate change and large-scale vegetation restoration, the WUE dynamics drivers in water-limited basins remain uncertain. Taking the Haihe River Basin, a typical water-scarce region in China, this study used MODIS gross primary productivity (GPP) and evapotranspiration (ET) data during the growing season from 2001 to 2020. Lindeman–Merenda–Gold (LMG) decomposition, and an eXtreme Gradient Boosting–Shapley Additive Explanations (XGBoost–SHAP) model were integrated to examine the spatiotemporal characteristics of WUE, the relative contributions of GPP and ET and the nonlinear responses of environmental factors. Results showed that, growing-season WUE in the Haihe River Basin exhibited a pronounced spatial pattern, with higher values in mountainous areas and lower values in plains. Temporally, growing-season WUE declined slightly from 2001 to 2020 at a rate of 0.0013 gC·kg⁻¹ H₂O·yr⁻¹, despite continuous greening. In 97.51% of areas with decreasing WUE, ET increased faster than GPP, indicating that enhanced evapotranspirative water consumption outpaced gains and was associated with the WUE decline. Further, vegetation growth rates (NDVI_S and LAI_S) were the main factors explaining WUE trends, showing clear threshold-type responses, and suggested that excessively rapid vegetation growth may weaken WUE improvement.

Accurately characterizing river water level dynamics is essential for understanding watershed hydrological processes, assessing flood risk, and supporting refined water resource management. Launched in December 2022, the Surface Water and Ocean Topography (SWOT) satellite carries the Ka-band Radar Interferometer (KaRIn), enabling wide swath, high resolution observations of inland water surface elevation (WSE) globally, marking a new era of high precision hydrologic remote sensing. However, under complex observational geometries and heterogeneous surface conditions, the accuracy of SWOT derived WSE may degrade, manifested as a reduction in both geolocation and elevation accuracy. This degradation arises from both observation geometry and environmental heterogeneity, including cross track distance, layover effects, terrain slope, water area, and water surface brightness. Here we examine the main stem of the Yangtze River using available SWOT WSE observations from 2023 to 2024, with temporally matched daily in situ water levels from six hydrological stations as reference. We use cross track distance as a controlling variable and quantify how layover and environmental heterogeneity modulate both systematic bias and random dispersion, thereby characterizing the dominant controls governing spatial accuracy degradation. Results show that (1) SWOT captures seasonal and longitudinal water level variations along the Yangtze River, with R2 values of 0.86 to 0.97 and RMSE of 0.15 to 0.42 m; (2) WSE error increases approximately linearly with cross track distance at an average rate of (6 to 9) & times; 10_6 m/m, and the increase is amplified under strong layover conditions; and (3) environmental heterogeneity further shapes the error distribution, with steep terrain (greater than 15 degrees) and small water area (less than 20,000 m2) showing larger random fluctuations and more pronounced systematic deviations. Overall, this study provides basin scale quantitative evidence that the spatial degradation of SWOT WSE accuracy is jointly controlled by observation geometry and environmental heterogeneity, and it clarifies their relative roles in shaping error magnitude and spatial structure. These findings support the development of multi factor error correction approaches and improve the reliability of SWOT applications in large basin hydrological modeling, flood monitoring, and data assimilation.

Increasing flash drought frequency poses a serious threat to ecosystem stability. Traditional greenness-based remote sensing methods are slow to detect early suppression of photosynthetic physiology and its recovery. While the earlier response of Solar-Induced Chlorophyll Fluorescence (SIF) to drought stress is well documented, the asymmetry between functional and structural recovery after drought termination has not been systematically quantified. Here we address this gap by comparing SIF and the Normalized Difference Vegetation Index (NDVI) within a unified resistance-resilience framework during the 2015 flash drought on the North China Plain. SIF showed strong spatiotemporal consistency with gross primary productivity (GPP, R2 = 0.95). Quantitative assessment revealed, for the first time, that recovery times differed markedly among indicators (SIF 30.7 d, NDVI 20.3 d, GPP 40.0 d). This ordering, with NDVI fastest, SIF intermediate, and GPP slowest, reflects a hierarchical recovery cascade. Structural re-greening occurs first, followed by photochemical efficiency restoration as captured by SIF, and finally full re-establishment of carbon assimilation metabolism as represented by GPP. Both functional recoveries, SIF and GPP, thus lag behind structural recovery. Both also showed high spatial consistency, identifying ecologically fragile areas such as the sandy lands of the Yellow River floodplain. Air temperature was the primary environmental predictor associated with SIF variations. These findings suggest that SIF enables early detection of photosynthetic decline and, more importantly, provides the first quantitative evidence that functional recovery lags behind structural recovery, a critical insight for post-drought ecological assessment.

As climate change intensifies and the demand for food increases, food security has become a focal point of research. The Huang-Huai-Hai Plain (HHHP), a major production region for winter wheat in China, plays a crucial role in ensuring regional and national food security through its wheat yield. However, there was no systematic analysis of the driving factors of winter wheat yield change in the HHHP under climate change, in order to quantify the contributions of different factors to winter wheat yield change in HHHP. This study utilized the localized Agricultural Production System sIMulator (APSIM) model to analyze the drivers of winter wheat yield change during historical (1981-2010) and future climate scenarios (2021-2050 and 2051-2080, under SSP126, SSP370 and SSP585 scenarios). The results indicated that under future climate scenarios, drought during the wheat growth season in the HHHP was alleviated, but there was an intensifying trend during the flowering (F) and start of grain filling (SGF) stage, and heat also showed an increasing trend during the SGF stage. Nevertheless, drought led to a 26.09% reduction in winter wheat yield, which was significantly greater than the negative impact of heat. Without considering extreme weather events and assuming no changes in wheat varieties or field management practices, the impact of climate change on winter wheat yield showed a positive feedback, with an increase of 8.97%. Therefore, to ensure a steady increase in future winter wheat yield in the HHHP, more attention should be paid to preventing future drought occurrences, which could be managed through appropriate irrigation and the cultivation of drought-resistant varieties, thus safeguarding food security in the HHHP.

Under climate warming, crop responses to drought are increasingly shaped by the interactions among climate, soil, topography, and human regulation. However, widely used drought indices may not adequately represent crop-perceived water deficits, and the ways in which lag effects interact with multiple environmental drivers to regulate crop responses remain insufficiently understood. In this study, we evaluated lagged correlations between three drought indices-the standardized evapotranspiration deficit index (SEDI), the standardized precipitation evapotranspiration index (SPEI), and the standardized soil moisture index (SSMI)-and multiple vegetation-related indicators across irrigated and rainfed croplands in China. We further characterized crop drought-response lags and used partial least squares structural equation model (PLS-SEM) to disentangle the pathways through which environmental drivers regulate crop dynamics. The results showed that SEDI outperformed SPEI and SSMI in capturing crop-relevant drought stress, with consistently stronger and more stable relationships in both irrigated and rainfed croplands. Crops were most sensitive to drought at one-month timescale, with irrigated croplands exhibiting a clearer pattern and more immediate responses. The PLS-SEM further revealed distinct pathways: in rainfed croplands, crop growth was mainly driven by precipitation but constrained by soil properties and human activities, whereas in irrigated croplands, anthropogenic water inputs reshaped the linkages among energy, water, topography, soil, and crop processes, weakening direct climate constraints and amplifying the regulatory effects of human activities. These findings clarify how environmental drivers interact with drought lag effects to regulate crop growth, providing a process-based foundation for drought-resilient agricultural management and climate adaptation strategies.

Compound heat and drought events (CHDEs) are occurring more frequently due to climate change. However, the role of urbanization as a key driver of climate change in modulating these events remains poorly quantified. Hence, this study evaluates the impacts of urbanization on CHDE frequency, duration, and severity using a daily scale analytical framework, and further quantifies the key influencing factors contributing to these impacts using explainable machine learning. The results revealed that: (1) the urban expansion rates ranged from 0.06% to 22.92% per decade in the Beijing–Tianjin–Hebei (BTH) region. Concurrently, the frequency, duration, and severity of CHDEs exhibited overall upward trends, with rates of 0.11, 1.45, and 0.06 per decade, respectively. (2) Urban stations exhibited more apparent upward trends in the frequency, duration, and severity of CHDEs than rural stations. Urbanization contributed to these increases in CHDEs, with its greatest contribution to duration (44.0%), followed by severity (39.1%) and frequency (33.6%). (3) The explainable machine learning analysis revealed that the attributes of CHDEs are driven by multiple urban and climatic factors under urbanization. While enhanced Tmean and UHI consistently intensified CHDEs, changes in urban underlying surfaces manifest complex non-linear relationships with CHDE attributes. Notably, BV and UrbF amplified the duration and severity of CHDEs more strongly than frequency. These findings suggest that urbanization may primarily amplify the duration and severity of CHDEs through enhanced thermal conditions and surface modification, highlighting the need to prioritize heat mitigation and urban land-use regulation in climate adaptation planning.

Compound heat and drought events (CHDEs) increasingly threaten crop production, yet conventional assessments rarely account for phenological changes in crop heat sensitivity and water demand. Here, we developed a phenology-aware daily framework for summer maize in the Huang–Huai–Hai (HHH) Plain, China, integrating stage-specific heat thresholds with a crop-coefficient-adjusted standardized precipitation evapotranspiration index (SPEI_KC) and a fixed cultivation distribution. Using daily meteorological observations from 1980 to 2020, CHDEs were characterized across the sowing-to-jointing, jointing-to-tasseling, and tasseling-to-maturity stages. Across the growing season, CHDE frequency and mean duration increased significantly, whereas mean intensity declined. Stage-specific responses differed markedly: frequency increased across all stages, while the tasseling-to-maturity stage showed the fastest increase in frequency and a significant lengthening of duration. Potential exposure also became progressively concentrated over crop development and was highest during tasseling-to-maturity in major maize-producing areas. By incorporating phenological variation into both heat and drought characterization, this framework resolves within-season differences in compound stress that are obscured by uniform-threshold approaches and provides a crop-relevant basis for stage-targeted monitoring and adaptation.

Vegetation greening regulates evapotranspiration and atmospheric moisture transport, thereby modulating regional drought conditions. However, its impacts on the drought development and recovery phases remain insufficiently understood. In this study, we quantified the effects of forest and grassland greening on the drought lifecycle based on the daily-scale Standardized Water Availability Index (SWAI) by controlled scenario simulations. The results showed that (1) the leaf area index (LAI) increased by 0.14/10 year for forests and 0.02/10 year for grasslands, corresponding to increases in actual evapotranspiration of 3.91 mm/year and 1.42 mm/year, respectively. (2) Vegetation greening generally intensified drought characteristics throughout the drought lifecycle. The drought duration, severity, intensity, and peak increased by 0.27 days (0.72

The increasing frequency of flash droughts poses a serious threat to ecosystem stability. Traditional greenness-based remote sensing methods are slow to detect the early suppression of vegetation photosynthetic physiology and its subsequent recovery. To address this gap, we use the 2015 flash drought event on the North China Plain to compare Solar-Induced Chlorophyll Fluorescence (SIF) and the Normalized Difference Vegetation Index (NDVI) in assessing vegetation resistance and resilience, integrating SIF with reanalysis data. SIF-based vegetation functional responses preceded structural changes derived from NDVI and showed strong spatiotemporal consistency with gross primary productivity (GPP, R² = 0.95). The recovery period of photosynthetic function assessed by SIF was significantly longer than that of canopy greenness revealed by NDVI, confirming a functional recovery lag behind structural recovery. SIF and GPP are highly consistent in spatial patterns, which can effectively identify ecologically fragile areas such as sandy lands of the Yellow River floodplain; air temperature is the dominant environmental factor driving SIF variations. This study confirms that SIF can effectively monitor the decline process of vegetation photosynthetic physiology in the early stage of flash drought and more accurately quantify its functional resistance, thus providing an important basis for the development of flash drought early warning and ecological resilience assessment system based on physiological signals.

Accurately delineating inland water bodies and monitoring surface water dynamics are crucial for hydrological research and climate adaptation. Surface water and ocean topography (SWOT) satellites has significantly improved global surface water observation capabilities. However, in complex inland environments, SWOT data are often affected by stripe noise and quality control (QC) marker failures, which can easily lead to water body extraction errors or the omission of narrow water bodies. To address these issues, we developed SDNet, a transformer-based multi-scale framework. This design suppresses high-frequency stripe noise while preserving fine-scale hydrological boundaries, thus significantly improving the reliability of water body extraction. Experimental results for different water bodies show that: (1) SDNet achieved high accuracy across water bodies of different scales. Compared to the QC-based classification, the Water Body Intersection Rate (WIR) and Background Intersection Rate (BIR) of our method increased by 23.68% and 45.96%, respectively, for large water bodies. WIR further increased by 2.83% for medium-scale water bodies and by a factor of 3.57 for small water bodies. (2) Cross-validation using ICESat-2 altimetry data showed that the SWOT altimetry error was positively correlated with cross-track distance. The median errors ranged from 0.117 m to 0.181 m at 100 m resolution and 0.111 m to 0.170 m at 250 m resolution, with the mean absolute error remaining in the sub-meter range. (3) Seasonal hydrological analysis revealed distinct response patterns across different water body types to water level changes. Natural lakes are mainly driven by climate processes, while controlled reservoirs exhibit multi-peak dynamic characteristics dominated by human regulation. These findings provide a scalable solution for multi-scale water body monitoring, contributing valuable support to hydrological research, flood risk assessment, and climate adaptation strategies.

The degradation of the thermal environment, changes in green space, and imbalanced socioeconomic development have become key constraints on high-quality development and regional governance in the Beijing–Tianjin–Hebei urban agglomeration. Existing studies have mostly examined single systems or pairwise relationships, while the spatiotemporal coupling among the thermal environment, green space, and socioeconomic system remains insufficiently understood. Taking the Beijing–Tianjin–Hebei urban agglomeration as the study area, this study constructs a pixel-scale evaluation framework for these three subsystems from 2000 to 2020. The Criteria Importance Through Intercriteria Correlation weighting method, Coupling Coordination Degree model, and exploratory spatiotemporal data analysis based on Local Indicators of Spatial Association were used to examine subsystem evolution, coupling coordination patterns, and spatiotemporal transitions. The results show that the three subsystems followed divergent trajectories. The thermal environment slightly degraded overall, with 67.6% of the area showing a decreasing trend, whereas green space improved in 78.5% of the area and the socioeconomic system increased in 90.6% of the area. The Coupling Coordination Degree increased slowly from 3.660 to 4.053, but remained at a relatively low level. Spatially, Low–Low clusters were mainly distributed in the north, while High–High clusters occurred in the central core area and parts of the south. Stable and persistently insignificant transitions accounted for 87.4%, indicating strong spatial stability and path dependence. These findings provide scientific support for thermal environment governance, green space optimization, and coordinated regional development.

Extreme climate events such as droughts and heatwaves significantly impact the stability of ecosystem function and are expected to intensify in the future. The mid-high latitude regions of the Northern Hemisphere (23.5° to 90°N) exhibit pronounced seasonality and are highly sensitive to climate variations. However, further research is needed to understand the vegetation decline and its changing trends driven by extreme hydroclimatic and their compound events in this region. This study, based on multi-source data including NDVI, LAI, and GPP from 1982 to 2015 as vegetation growth indicators, amid to identify vegetation decline during the growing season and explore its temporal trends, and to further reveal the seasonal response. The research supported the importance of drought and high temperature compared to extreme wet and cold conditions. Due to the high frequency, wide impact and long duration of impact, independent low SM dominated the cumulative vegetation decline, followed by low SM and high VPD compound events. High VPD caused stronger negative impacts on vegetation growth than high T and that it was more strongly coupled to SM. We further found a turning point in vegetation decline. Because of the significant increase in VPD and its enhanced coupling with low SM, low SM and its compound events, especially SM- & VPD+ & T+ compound events, led to a significant enhancement of the vegetation decline after about the 21st century. Furthermore, the sensitivity of vegetation growth to extreme hydroclimatic has also significantly increased, with stronger intensity of vegetation decline. Seasonally, early growing season vegetation was more vulnerable (with the strongest continuous decline) due to experiencing the longest duration of negative impacts, while summer vegetation was more sensitive to extreme hydroclimatic, with the strongest intensity. Notably, compound events of high VPD and low SM primarily affected summer vegetation growth. Additionally, there was a significant lag time in vegetation response to extreme hydroclimatic, especially to high VPD and high T. In over half of the regions, the vegetation response to high T and high VPD had a lag time exceeding two months, which may be associated with seasonal legacy. In the context of global warming, further investigation is needed to explore the inter-seasonal connections. This research significantly contributes to a deeper understanding of ecosystem responses to extremes hydroclimatic and its future changes.

Evaluating the spatiotemporal characteristics of flood hazard risk is essential for stakeholders to undertake disaster preparedness and mitigation activities. Traditional hydrological modeling, hydrological station methods, and multi-indicator analysis techniques struggle to objectively and accurately capture the spatiotemporal characteristics of flood disaster risk due to challenges in spatial calibration and sparse spatial distribution, respectively. Historical remote sensing data can identify areas actually affected by floods, thereby improving flood risk assessment. In the study, Landsat and Sentinel remote sensing data from 1990 to 2023 were utilized to delineate flood inundations in the Poyang Lake region. Employing the Flood Exceedance Probability as an indicator, the study analyzes the spatiotemporal dynamics of flood disaster risk. Results indicate that: (1) Flood disaster risk is higher on the west, southwest, south, and southeast sides of Poyang Lake, along rivers, and in urban built-up areas. (2) Since 2000, the flood disaster risk in the Poyang Lake region has significantly decreased. (3) About 9.36

As a remote sensing indicator of vegetation physiology, solar-induced chlorophyll fluorescence (SIF) serves as a proxy for photosynthesis and has been widely used for monitoring stress conditions such as drought and for estimating crop yields. However, few studies systematically evaluated the capacity of SIF to indicate crop yield variations under drought and non-drought conditions across different temporal scales. This study focused on the Huang-Huai-Hai Plain (HHHP) and used the Standardized Soil Moisture Index (SSMI) to identify the spatial distribution of agricultural drought. We compared the sensitivity of SIF740, SIF683, NDVI, and NIRv to drought during different growth stages of winter wheat, and examined the associations between SIF740 and winter wheat yield under drought and non-drought conditions at various temporal scales. The results indicated that SIF740 was more sensitive to drought than SIF683, NDVI, and NIRv, particularly during the regreening stage. Under non-drought conditions, cumulative SIF740 during the jointing-anthesis stage exhibited the strongest correlation with winter wheat yield, outperforming instantaneous and growing-season scale estimates. Under drought conditions, the association between SIF740 and yield improved with increasing temporal scale, with seasonal cumulative SIF740 showing the best performance. This study further elucidated the underlying mechanisms of the association between SIF and crop yield, providing a representative example for SIF-based regional yield estimation.

The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS/CM-1) utilizes a combination of multi-beam lidar, multi-spectral cameras, and other passive and active sensors for synergistic observations, enabling high-resolution, comprehensive, and three-dimensional atmospheric monitoring of clouds and aerosols. In recent years, traditional algorithms have faced challenges in terms of vertical layer retrieval accuracy and robustness in complex environments with low signal-to-noise ratios, near-surface observations, and mixed multi-layer structures. To address these issues, this pa- per proposes TECIS-CASNet, a generalized framework for atmospheric layer recognition and application, designed for the novel multi-beam lidar on the TECIS, leveraging the characteristics of the lidar data and deep learning attention mechanisms. To validate the reliability of this framework, the research team conducted multiple ground-based synchro- nous observation experiments to systematically evaluate its recognition accuracy. Finally, as a demonstrative applica- tion, the study focuses on a typical long-distance dust transport event in the Beijing-Tianjin-Hebei region of China, showcasing the practical application value of the framework. The results indicate that the TECIS-CASNet framework achieves high cloud-aerosol recognition accuracy, reaching 98. 41%, and is capable of reducing misidentification and missed detection in complex environments, including low signal-to-noise ratios, near-surface layers, and multi-layer mixed structures. The absolute accuracy of aerosol optical depth retrieval is 0. 01, with an overall accuracy of 98%. This paper, centered around the TECIS-CASNet framework, provides significant insights for lidar satellite atmospheric remote sensing data processing and environmental monitoring applications.

Soil moisture (SM) is crucial for ecosystems and agriculture. Since the root systems of plants absorb water at different depths with different intensities, monitoring multi-layer SM can better respond to the water demand of plants and offer a crucial technical backing for drought monitoring and precision irrigation. Synthetic aperture radar (SAR) and multispectral (MS) have been widely used in SM estimation; however, their combined application for multi-layer SM profiling remains underexplored. Existing research based on these two data types has primarily focused on surface soil moisture (SSM), with limited investigation into estimating SM at deeper or varying depths. Therefore, the aims of this research are to integrate Sentinel-1 SAR and Sentinel-2 MS data and employ machine learning algorithms to estimate multi-layer SM in the Shandian River Basin. The results showed that (1) MS + SAR-based SM estimation significantly outperformed single-source data (MS or SAR alone). Specifically, MS data performed better in the root-zone estimation, while SAR data showed superior performance in SSM estimation. (2) The BKA-CNN estimation accuracy significantly outperformed RF and XGBoost. The results of its five-fold cross-validation are as follows: R2 = 0.768 ± 0.011 at 3 cm, R2 = 0.777 ± 0.013 at 5 cm, R2 = 0.799 ± 0.011 at 10 cm, R2 = 0.792 ± 0.01 at 20 cm, and R2 = 0.782 ± 0.011 at 50 cm. (3) The BKA-CNN model performed better in grassland than in farmland. These findings indicate that the BKA-CNN model proposed in this study effectively improves the estimation precision of multi-layer SM by fusing SAR and MS data, demonstrating considerable generalization ability and robustness. It holds potential application value in ecological protection and agricultural water resource management.

While global vegetation shows widespread greening under climate change, the corresponding impacts on Net Ecosystem Production (NEP)-particularly the divergent responses between drylands and non-drylands-remain poorly understood. In this study, based on the Normalized Difference Vegetation Index (NDVI), Solar-Induced Chlorophyll Fluorescence (SIF), Gross Primary Productivity (GPP), and NEP, we analyzed the spatiotemporal dynamics of vegetation photosynthesis and NEP across Eurasia from 1982 to 2018, and explored their differential responses to environmental factors, with particular focus on the contrasting mechanisms between drylands and non-drylands. The results showed that the rate of increase in NEP standardized anomalies (0.004 yr(-1), p < 0.05) lagged behind NDVI/SIF/GPP standardized anomalies (0.009 - 0.014 yr(-1), p < 0.01), particularly after 2000, and it was more pronounced in drylands. Spatially, vegetation photosynthesis and NEP changed asynchronously in nearly one-third of Eurasia, with a slightly higher proportion in non-dryland areas and the most obvious patterns in forests and croplands. Explainable machine learning analysis revealed that this asynchrony was primarily driven by interactions among temperature, vapor pressure deficit (VPD), and evapotranspiration. Vegetation photosynthesis was mainly influenced by evapotranspiration, while NEP was constrained by increased VPD. Moreover, in drylands, NEP was highly sensitive to atmospheric moisture, whereas in non-drylands, temperature fluctuations played a dominant role. These findings offer important insights for regional carbon sequestration management and climate change adaptation strategies.

With the advancement of economic development, urbanization, and ecological civilization initiatives, land use patterns are undergoing significant changes. The allocation of land resources among urban areas, croplands, and forest/grass ecosystems has substantial impacts on regional carbon balance. Vegetation carbon sequestration and anthropogenic carbon emissions are two critical components of carbon balance, and their distinct formation mechanisms suggest differing roles of land use in each process. However, few studies have systematically explored the impact of land use on carbon balance from both composite and component perspectives. In this study, a dual-layered indicator system is employed to quantify land use and carbon balance. Based on this, a case study is conducted in the Beijing-Tianjin-Hebei (BTH) region following a "characteristic-relationship-strategy" analytical framework. The results reveal that: (1) From 2000 to 2020, the intensity of land development driven by human activities increased across BTH, primarily due to the conversion of cropland to urban areas. In contrast, land use intensity declined in the northwestern and northern areas, where cropland was converted to natural vegetation such as forests and grasslands. (2) BTH exhibited a carbon deficit, with carbon emissions exceeding sequestration. However, since 2011, the increasing trend in this deficit has gradually slowed and eventually reversed. (3) Overall, land use changes had a greater impact on vegetation carbon sequestration than on anthropogenic emissions. Specifically, natural vegetation restoration contributed significantly to increased sequestration; croplands in the plains demonstrated considerable carbon sequestration potential; and urban expansion led to substantial carbon emissions. Urban carbon reduction is the key to improving regional carbon balance. (4) Due to differences in natural endowment, economic development levels, and strategic positioning among BTH cities, the spatial association and temporal response of land and carbon variables exhibit strong spatial heterogeneity. Tailored and region-specific strategies, supported by coordinated development and scientific land management, are essential to promote low-carbon development.

As anthropogenic forces amplify extreme droughts, understanding their global connections is essential for prediction and mitigation plans to fortify ecosystem and societal resilience. However, traditional methods struggle to effectively capture the complex, nonlinear, and asynchronous spatiotemporal associations among drought events across regions. To address this, we introduced a novel complexity-based approach that constructs a global extreme drought complex network using monthly-scale Standardized Precipitation Evapotranspiration Index (SPEI) data from 1901 to 2021. By applying the Event Synchronization (ES) method and analyzing key network metrics, we revealed the spatiotemporal associations and synchronous propagation pathways of drought events. Our approach identified major global drought source regions (out-degree >667), including northern and southern Africa, western Australia, central Europe, and central Asia, as well as key sink regions (in-degree >863), such as the Tibetan Plateau (TP), Indonesia, central South America, and the Amazon Basin. Using network metrics, we quantified the dominant directions and propagation distances of drought teleconnections across regions, revealing that the average global drought propagation distance exceeds 11,000 km. Regions such as the TP and the Amazon exhibited high betweenness centrality (BC), underscoring their critical roles as hubs in the global drought propagation network. Furthermore, we used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Lagrangian particle transport model to simulate moisture transport pathways from the European drought source region to the TP sink region. By integrating complex network analysis with the Lagrangian transport model, we conducted an in-depth investigation of drought propagation pathways. This dual approach reveals previously unrecognized yet highly consistent physical mechanisms underlying drought occurrence and propagation. These findings offer valuable insights for the development of effective drought mitigation strategies.