Compound drought and surface heat can alter forest gross primary production (GPP), but their joint response need not equal the sum of their separate responses. We analyzed detrended relative GPP anomalies for five forest biomes during 2001–2023. Drought was defined using the Standardized Precipitation Evapotranspiration Index at a 3-month scale (SPEI-3) <−1.0, and heat using Moderate Resolution Imaging Spectroradiometer (MODIS) daytime land-surface temperature above the pixel- and calendar-month-specific 90th percentile. In the factorial model, θD and θH are the fitted drought and heat main effects, and θDH is their non-additive interaction. A negative θDH means that the fitted joint response is more negative than the additive expectation. Each bracketed range below is a spatial cluster-robust 95% confidence interval (CI). Interaction estimates, in percentage points, were −3.54 [−3.92, −3.17] for tropical/subtropical broadleaf forest (TRF), −4.32 [−5.62, −3.03] for tropical/subtropical coniferous forest (TSF), 0.58 [−0.69, 1.85] for Mediterranean forest (MEF), −6.08 [−6.80, −5.35] for temperate forest (TDF), and 0.36 [−0.60, 1.32] for boreal forest (BOR). Thus, intervals were below zero in TRF, TSF, and TDF, while MEF and BOR remained uncertain. Generalized additive model (GAM) residuals and intensity-stratified bootstrap median additive contrasts gave mixed descriptive results. In an exploratory commonality analysis, atmospheric-demand individual-block R2 exceeded moisture-block R2 in all 20 combinations of four soil-moisture configurations and five biomes, although the component magnitudes varied. The statistical interaction does not identify stomatal behavior or another physiological mechanism. Because GPP processing was not a mass-conserving aggregation, the conclusions concern relative anomalies rather than a global absolute carbon budget.

Leveraging the complementary advantages of InSAR and GNSS, this study proposes a refined method for monitoring mining-induced surface subsidence by integrating both technologies. The method begins with calculating the time-series cumulative subsidence basin from InSAR. Subsequently, a constraint condition is established to identify large-gradient deformations, thereby distinguishing the subsidence edge from the subsidence center. For the subsidence edge with minor deformation, the InSAR results are retained. For the large-gradient subsidence center, the subsidence basin around the mining panel is reconstructed by integrating InSAR and GNSS models. Continuous surface deformation information in a geographic coordinate system is then obtained through spatial interpolation, ultimately yielding comprehensive surface subsidence results across the mining area. Taking a mining area in Shanxi Province as the study region, the feasibility and accuracy of the proposed method were validated using 35 SAR images acquired between April 2016 and September 2017, along with leveling measurement data from the mining panel. The maximum surface subsidence rate of the settlement basin obtained from the solution is -186.68 mm/year, and the maximum surface subsidence amount is 248 mm. Compared with the InSAR monitoring results, the root mean square error of the data collaborative monitoring is reduced by 96.8%, and it is reduced by 64.4% compared with the GNSS probability integral method. The results demonstrate that the proposed method can achieve subsidence results consistent with the actual situation. Its monitoring capability is significantly superior to that of using either InSAR or GNSS alone, effectively compensating for the limitations inherent in each individual technology when applied to mining subsidence monitoring. Consequently, this integrated approach provides more accurate and reliable information on surface subsidence in mining areas.

The Shendong mining area, located in the transition zone between the northern Loess Plateau and the Mu Us Sandy Land, is a representative ecologically fragile region and desert coal base in China. Using Google Earth Engine (GEE) and Landsat imagery from 1999 to 2024, this study constructed a long-term remote sensing ecological index (RSEI) dataset and integrated the Theil–Sen median slope estimator, Mann–Kendall test, and Hurst exponent to examine the spatiotemporal evolution, future trajectories, and multi-stage driving mechanisms of eco-environmental quality (EEQ) at the mining-area and individual-mine scales. At the mining-area scale, RSEI showed pronounced interannual fluctuations and a weak downward trend, characterized by a two-stage, wave-like trajectory with a narrowing amplitude. The mean RSEI and standard deviation decreased from 0.5669 and 0.0855 during 1999–2010 to 0.5182 and 0.0501 during 2011–2024. Moderate and good grades predominated, with lower EEQ in the mining core and higher EEQ in peripheral buffer zones. Across 13 representative mines, ecological quality generally remained moderate but exhibited spatial and stage-dependent heterogeneity, with Liuta Mine showing the greatest variability. Slight degradation was the dominant trend, although local recovery occurred, and Hurst analysis revealed marked spatial differences in future trajectories. RSEI variations in the Shendong mining area exhibited pronounced spatial and stage-dependent associations with high-intensity mining, climatic water–heat conditions, topographic background, and ecological governance and restoration processes. No continuous and irreversible overall decline was observed; however, without an independent non-mining control, the findings represent integrated ecological responses within the mining area rather than causal estimates of mining’s net ecological effect.

Cultivated land underpins food security,and coal is fundamental to energy security,national development,and social stability.However,in China's plains,extensive coal-cropland overlapping areas face a key sustainability challenge:mining-induced subsidence damages farmland and disrupts ecosystems,making the coordination of extraction and protec-tion an urgent priority.To address the prevalent disconnect between subsurface mining design and surface protection in such areas,this study examines a coal mine in Shandong Province.To achieve 100％post-mining reclamation,regional surface deformation is linked to coordinated underground and surface measures via an active adaptive mining subsidence-control framework.This approach analyses the coupling mechanism between active adaptive mining and cultivated land protection,proposing a novel integrated strategy for plains coal-cropland zones.Building on this,a digital coupling model balancing underground mining benefits with surface reclamation returns was developed,enabling a comprehensive cost-benefit analysis of the active mining protection mechanism.The model was optimized using the Beluga Whale Op-timization algorithm.Engineering case results show that,compared to the conventional"mining first,reclamation later"governance model,the new strategy not only achieves 100％farmland reclamation with full resource recovery but also de-livers greater long-term economic and social benefits.The findings provide technical support for protecting cultivated land and ensuring stable coal supply in China's coal-cropland overlapping regions.

Landslides are among the most prevalent geological hazards worldwide, posing severe threats to public safety due to their sudden onset and destructive potential. The rapid and accurate automated segmentation of landslide areas is a critical task for enhancing capabilities in disaster risk assessment, emergency response, and post-disaster management. However, existing deep learning models for landslide segmentation predominantly rely on unimodal remote sensing imagery. In complex Karst landscapes characterized by dense vegetation and severe shadow interference, the optical features of landslides are difficult to extract effectively, thereby significantly limiting recognition accuracy. Therefore, synergistically utilizing multimodal data while mitigating information redundancy and noise interference has emerged as a core challenge in this field. To address this challenge, this paper proposes a Triple-Stream Guided Enhancement and Fusion Network (TriGEFNet), designed to efficiently fuse three data sources: RGB imagery, Vegetation Indices (VI), and Slope. The model incorporates an adaptive guidance mechanism within the encoder. This mechanism leverages the terrain constraints provided by slope to compensate for the information loss within optical imagery under shadowing conditions. Simultaneously, it integrates the sensitivity of VIs to surface destruction to collectively calibrate and enhance RGB features, thereby extracting fused features that are highly responsive to landslides. Subsequently, gated skip connections in the decoder refine these features, ensuring the optimal combination of deep semantic information with critical boundary details, thus achieving deep synergy among multimodal features. A systematic performance evaluation of the proposed model was conducted on the self-constructed Zunyi dataset and two publicly available datasets. Experimental results demonstrate that TriGEFNet achieved mean Intersection over Union (mIoU) scores of 86.27% on the Zunyi dataset, 80.26% on the L4S dataset, and 89.53% on the Bijie dataset, respectively. Compared to the multimodal baseline model, TriGEFNet achieved significant improvements, with maximum gains of 7.68% in Recall and 4.37% in F1-score across the three datasets. This study not only presents a novel and effective paradigm for multimodal remote sensing data fusion but also provides a forward-looking solution for constructing more robust and precise intelligent systems for landslide monitoring and assessment.

Accurate prediction of precipitable water vapor (PWV) is of great significance in meteorological applications. Global Navigation Satellite System (GNSS) is a widely used technique to obtain PWV data. However, many regions still suffer from sparse station coverage, with only a single station available in some cases. This study proposes a multichannel convolutional neural network (CNN)-long short-term memory (LSTM) model designed for a single-station scenario by integrating key meteorological variables, including surface pressure, weighted mean temperature, and zenith wet delay, using a 5-day historical input sequence. Compared to the widely used Global Forecast System (GFS) and classical LSTM model, the CNN-LSTM model achieved superior performance, with an mean absolute error (MAE) of 0.694 mm, a root mean square (RMS) of 0.789 mm, and a correlation coefficient of 0.997. This indicates 69.8% reductions in MAE and 68.6% reductions in RMS relative to GFS, and a slight reduction in MAE along with a 20.8% reduction in RMS compared to LSTM. The subsequent seasonal and weather-specific analyses reveal the high accuracy in winter with 0.443-mm MAE and 0.580-mm RMS, and under clear skies with 0.708-mm MAE and 0.769-mm RMS. Performance declined during summer and rainfall periods due to heightened atmospheric instability and rapid PWV variations. Nevertheless, the developed CNN-LSTM model maintains robust prediction capability across all conditions, demonstrating the benefit of integrating meteorological features for PWV prediction in effectively data-scarce or data-sparse regions.

Against the backdrop of promoting green buildings and a circular economy, the development of efficient, sustainable, and low-carbon cementitious materials is of great significance for reducing resource consumption and carbon emissions. In this study, plant ash (PA) was used as a partial cement replacement, and a series of alkali-activated composite cementitious materials (APAG) were prepared by regulating the dosages of PA and alkali activator (AA). The evolution of their workability, hydration behavior, and mechanical properties was systematically investigated. The results show that the incorporation of PA effectively delayed the setting process of the system; compared with P0, the initial and final setting times of P20 increased by approximately 302% and 100%, respectively, thereby mitigating the excessively rapid early-age reaction of the alkali-activated system while causing only a slight reduction in flowability. In contrast, the addition of AA shortened the setting time of APAG and led to a gradual decrease in fluidity. When the PA dosage was 20% and the AA dosage was 4%, APAG achieved a 28 d compressive strength of 57.8 MPa while maintaining good workability. Further analysis revealed a strong linear correlation between compressive strength and chemically bound water content under different PA and AA dosages, indicating that the reaction degree is a key factor governing macroscopic mechanical performance. Microstructural characterization confirmed that the incorporation of PA and AA significantly altered the reaction pathways and the morphology of hydration products, providing a reasonable microstructural explanation for the evolution of macroscopic properties. These findings provide valuable insights into the high-value utilization of biomass waste and the broader application of green cementitious materials.

Between 1 July and 30 November 2022, four spatially adjacent shallow MW ≥ 5.7 earthquakes successively struck the Hormozgan province in southern Iran. This earthquake sequence offers a vital opportunity to clarify the subsurface seismogenic structure and rupture evolution in the eastern segment of the Zagros Fold-and-Thrust Belt (ZFTB). In the paper, we apply multi-temporal archived SAR images from the Sentinel-1 satellite to extract the high-precision coseismic surface deformation covering the July and November earthquake events, respectively, and further investigate the related seismogenic fault structure and slip distribution. Geodetic inversion results reveal that the cumulative coseismic slip of the three MW ≥ 5.7 earthquakes in July is distributed at a downdip depth of 5.5 to 8 km on a SW-dipping thrust seismogenic fault plane, while the coseismic slip of the November MW 5.7 earthquake is concentrated in the shallow downdip range of 1.5 to 6 km on the same fault, finely characterizing a partially overlapping depth-segmented rupture. According to a joint analysis of the regional topography and geomorphology, active fault distribution, and coseismic inversions, we conclude that this earthquake sequence nucleated on a secondary blind back-thrust fault of the Zagros Frontal Fault (ZFF). Coseismic Coulomb stress changes reveal that the July earthquake sequence triggered the occurrence of the November earthquake and that the shallow eastern segment of the Mountain Frontal Fault (MFF) and the eastern segment of the ZFF exhibit significant stress loading, indicating a high risk of future rupture.

From 23 January 2024 to 4 December 2025, two moderate-to-strong earthquakes, 2024 MW 7.0 Wushi and 2025 MW 5.8 Akqi events, occurred sequentially around the major Maidan fault in the southern Tianshan region, providing a rare opportunity to explore the regional subsurface seismogenic structure and rupture behavior. We use Sentinel-1 and ALOS-2 Synthetic Aperture Radar (SAR) images to derive multi-view coseismic deformation for both earthquakes and further invert for their fault geometries. Coseismic inversion reveals that the 2024 earthquake nucleated on a NW-dipping, strike-variable moderate-angle oblique-thrust fault, corresponding to the mapped MDF and its previously unidentified western extension, whereas the 2025 earthquake activated a buried SE-dipping secondary back-thrust fault. The significant coseismic slips of up to 3.1 m and 0.48 m for the 2024 Wushi and 2025 Akqi earthquakes, respectively, combined with the regional fault kinematics and distribution, reveal a complex subsurface conjugate thrust system. Furthermore, coseismic Coulomb stress modeling shows that the 2024 earthquake promoted the 2025 earthquake and that both the deep western segment of northern branch fault of the MDF system and the eastern segment of its southern branch fault lie in stress-loading zones, implying potential future rupture hazard. The 2024-2025 earthquake sequence demonstrates the role of conjugate thrust faults in accommodating the thrust component of strain accumulation from the India-Eurasia collision.

Agricultural management influences progress towards carbon neutrality through its effects on water consumption, energy use, and greenhouse gas (GHG) emissions. However, few studies have translated policies across sectors into management scenarios and evaluated their combined consequences for the agricultural carbon neutrality. We quantified the water, energy use, and carbon nexus for wheat, rice, and corn production across the North China Plain using 2018 as a baseline scenario. We then evaluated conditional management scenarios informed by China’s 14th Five-Year Plan. The three crop production generated net emissions of 1.8 × 1010 kg C yr−1 in 2018, while cropland net ecosystem productivity offset 16.9% of GHG emissions related to crop production. Energy use was positively correlated with GHG emissions (r = 0.74, p < 0.01). The integrated scenario combining a 30% reduction in nitrogen fertilizer, more efficient nitrogen fertilizer production, sprinkler irrigation, and a 50% crop straw return rate reduced the water footprint, energy use, and GHG emissions by 4.9%, 27.6%, and 39.2%, respectively. By contrast, drip irrigation alone reduced the water footprint but increased energy use by 6.8% and GHG emissions by 12.9%. The results show that water saving measures do not necessarily improve the carbon neutrality when their energy requirements are overlooked. These findings also provide more enlightenment for local policy-makers.

Study region: The Upper Yellow River Basin (UYRB) Study Focus: This study integrates Global Navigation Satellite System (GNSS) and Gravity Recovery and Climate Experiment (GRACE) observations from 2011 to 2023 to jointly invert terrestrial water storage (TWS) changes. By combining these results with multi-source meteorological and hydrological datasets, we characterize the spatiotemporal evolution of hydrological droughts in the UYRB and, through correlation analysis and a robust machine-learning framework supported by SHAP interpretation, identify their dominant drivers. New Hydrological Insights for the Region: In the UYRB, TWS seasonal variability decreases from southwest to northeast, with overall storage increases in the southern basin and gradual declines in the north. Due to the basin's elongated geometry and uneven GNSS station distribution, neither GNSS nor GRACE alone can reliably capture basin-wide variations; however, the joint inversion framework effectively overcomes these limitations. The analysis reveals distinct spatial heterogeneity in drought behaviors, identifying nine events (2-12 months) in the northern sub-regions and six events (2-24 months) in the south. Interpretation of these patterns suggests that northern droughts are primarily driven by groundwater extraction and evapotranspiration, whereas southern droughts are predominantly regulated by multi-year precipitation variability associated with large-scale atmospheric circulation. Notably, we identify a long-term drought propagation timescale (e.g., 37 months in the south) that governs TWS-based drought evolution, distinguishing it from rapid surface-water propagation. These findings highlight the pronounced spatial differences in TWS dynamics and drought drivers across the UYRB, demonstrating the potential of GNSS-GRACE integration for fine-scale drought monitoring and sustainable water resource management in climate-sensitive basins.

During 22 May 2016 to 7 January 2025, three significant earthquakes (2016 Mw 5.4, 2020 Mw 5.7, and 2025 Mw 7.1 Dingri earthquakes) progressively ruptured the Shenza-Dingjie fault system, southern Tibetan plateau, effectively closing a remarkable seismic gap. We Radar images to extract coseismic deformation of the three events and the first-month postseismic deformation of 2025 event. Coseismic modeling demonstrates that the 2025 earthquake ruptured one west-dipping strike-variable moderate-angle main normal fault while the 2016 and 2020 earthquakes activated two shallow-dipping secondary normal faults, highlighting a complex synthetic-antithetic seismogenic structure. Further time-dependent postseismic analysis not only documents prominent postseismic signal of up to similar to 5 cm closely following 2025 earthquake, but also resolves discrete coseismic signals of similar to 3-4 cm related to three Ms >= 4:6 aftershocks. By the comprehensive analysis of geodetic inversion, fault distribution, and topography, we deduce that 2025 earthquake nucleation is likely driven by elevated gravitational potential energy within the southern Tibetan plateau extensional regime.

This study addresses the challenge of intelligent detection and spatial localization of massive cracks in deep-cut canal sections of the Middle Route of China’s South-to-North Water Diversion Project. An integrated framework for crack detection, localization, and three-dimensional visualization of concrete canal linings is proposed. UAV-based imitation ground photogrammetry was first employed to acquire 114,220 high-resolution images, from which a photogrammetric textured mesh model with a surface resolution of 0.47 cm/pixel was constructed. Based on 2886 representative images, a dedicated training dataset for intelligent crack detection was established. By integrating photogrammetric collinearity equations with real-time position and orientation system (POS) data from Unmanned aerial vehicle (UAV) imitation ground flights, a single-image crack coordinate calculation model was developed and embedded into the YOLOv7 object detection framework. This integration enables direct computation of crack spatial coordinates from a single image without reliance on stereo image pairs, allowing crack identification and spatial localization to be synchronously achieved within a unified deep learning framework. Experimental results show that YOLOv7 achieves an mAP@0.5 of 84.3% at an IoU threshold of 0.5, and the planar localization accuracy is better than 0.1 m. Finally, the detected and localized cracks are mapped onto the millimeter-level photogrammetric textured mesh model, enabling intuitive visualization of crack spatial distribution and providing technical support for structural condition assessment and intelligent operation and maintenance of long-distance water conveyance channels.

Landslide susceptibility maps (LSMs) are crucial for risk mitigation, but integrating Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data is often hampered by a lack of physical interpretation. To address this issue, this study proposes an enhanced modeling framework that integrates multi-source monitoring data by coupling dynamic deformation features. Ground deformation velocity is obtained using MT-InSAR and embedded as dynamic physical constraints into the loss function of a Multi-Layer Perceptron (MLP) model. This approach enables the joint optimization of static geological factors and dynamic deformation characteristics in landslide susceptibility prediction. The proposed framework was applied to Zunyi City, Guizhou Province, China, utilizing an inventory of landslide hazard sites and a dataset of 16 susceptibility factors for model training and evaluation. The results demonstrated that the dynamically constrained model significantly improved predictive performance (AUC = 0.976, an increase of 0.032 compared to the baseline model), and enhanced spatial consistency, reflected by an average increase of 0.0184 in predicted susceptibility for inventoried landslide hazard sites. The framework also outperformed other conventional machine learning models across multiple evaluation metrics. Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that slope (18.68%), DEM (13.26%), rainfall (11.57%), and mining activities (8.79%) were the primary contributing factors in high-susceptibility areas. This study offers a physically interpretable and robust methodology that advances landslide risk assessment and contributes to disaster prevention strategies.

Against the backdrop of the ongoing advancement of China's dual-carbon goals and the coordinated strategy for ecological protection and high-quality development in the Yellow River Basin (YRB), it is important to clarify the spatiotemporal dynamics of air pollution in the densely populated urban agglomerations of the mid-lower YRB. Using station-based daily observations from 2015 to 2024, this study examines six major air pollutants (PM2.5, PM10, CO, NO2, O3 and SO2) across the Shandong Peninsula, Central Plains, and Guanzhong Plain urban agglomerations. Sen's slope estimator and the Mann-Kendall test are applied to quantify long-term trends, while partial correlation analysis and the GeoDetector model are used to diagnose pollutant co-variations and the drivers of spatial heterogeneity. Results indicate that while PM2.5, PM10, NO2, SO2, and CO concentrations significantly decreased, O3 exhibited a statistically significant upward trend (Z = 2.32, p = 0.02), particularly with pronounced summer maxima. PM2.5 shows clear seasonal variation, with elevated levels during winter and reduced levels during summer. Marked spatial contrasts are also observed: elevated particulate matter and CO are concentrated in the northern part of the Central Plains, while higher O3 levels are more evident in coastal areas, particularly within the Shandong Peninsula urban agglomeration. In terms of inter-pollutant relationships, particulate matter and CO are positively associated with SO2, whereas O3 is negatively correlated with NO2. GeoDetector results further suggest that air temperature, wind speed, and topography are the key factors associated with the spatial differentiation of pollutant levels; notably, the interaction between wind speed and temperature provides the greatest explanatory power, with effects that vary seasonally. These findings provide a scientific basis for region-specific air-pollution control and for advancing the co-benefits of carbon reduction and pollution mitigation in the YRB.

Compound flash drought-heatwave extremes (FDHW) expose vegetation to rapid water and heat stress, but regional assessments often conflate event detection with vegetation response and rarely resolve delayed canopy trajectories. We quantified FDHW across China's Northeast Black Soil Region during the 1995-2024 growing seasons using ERA5-Land soil-moisture and temperature thresholds, applied a spatiotemporal graph neural network to regularize threshold-derived event masks, and reserved AVHRR NDVI for independent post-event impact assessment. Flash drought and FDHW frequencies exhibited strong interannual variability rather than a significant monotonic trend. FDHW occurrence increased from 3.8 to 4.8 d per growing season between 1995-2005 and 2016-2024, but the Theil-Sen trend was near zero (0.05 d per decade). Land-atmosphere composites indicate progressive soil-moisture depletion before FDHW occurrence and a transition from latent to sensible heat release roughly three days before maximum temperature anomalies. NDVI composites revealed a delayed greenness response: anomalies were negative through the first two post-event weeks, reached their minimum approximately one week after the reference FDHW grid-day, and then partially recovered during days 16-30. Mean NDVI suppression was modest (short-term -0.009; long-term -0.006), but persistent negative anomalies remained in 12.1% of southern cropland-dominant trajectories and 10.7% of northern forest-crop ecotone trajectories. These results show that FDHW impacts in the NBSR are expressed less as a steady rise in event frequency than as delayed and spatially heterogeneous vegetation stress, highlighting the need for post-event monitoring windows and cross-sensor validation to support agricultural risk assessment and adaptation planning.

Understanding plant water use strategies is critical for managing and restoring ecosystems affected by coal mining subsidence. However, how subsidence impacts water use strategies, especially across different soil types, remains inadequately explored. To address this gap, we employed a continuous isotopic mixing model (based on delta H-2 and delta O-18), coupled with delta C-13, soil water content and root distribution, to investigate the differences in water use strategies of Mongolian pine plantations in coal mining subsidence and non-subsidence areas with sandy and loess soils. Our results show that subsidence induces preferential flow, increases deep soil water (>80 cm), and enhances root growth and soil water-root coupling, especially in the loess areas. Isotopic mixing modeling revealed that in sandy areas, deep soil water uptake was similar between non-subsidence (79.43 +/- 3.83%) and subsidence (82.69 +/- 1.52%) plots. In loess areas, subsidence plots (26.36 +/- 1.98%) had significantly higher deep water uptake than non-subsidence plots (16.23 +/- 1.91%, P < .01). Leaf delta C-13 values decreased significantly in both soil types under subsidence, indicating reduced water stress via deep water utilization, particularly in loess areas. Soil-type dependent response highlights the necessity for distinct vegetation maintenance or restoration strategies in subsided areas across different soil matrices. These findings advance understanding of plant survival strategies and water resource relationships in subsidence zones, providing valuable references for sustainable land and water management in mining-impacted areas.

Compound environmental hazards – including drought, land degradation, and heatwaves - are intensifying under climate change, posing escalating risks to ecosystems, agriculture, and human well-being. However, current assessments often focus on individual hazards and lack integrated global perspectives. Here we present a scalable and interpretable framework that combines Earth observation data with machine learning to map compound hazards globally. We derive drought, heatwave, and degradation indicators from satellite records, compress them using deep learning, and model their drivers using ensemble algorithms. Model interpretation with SHAP (Shapley Additive exPlanations) and principal component analysis (PCA) enables the generation of a unified hazard index. To address the temporal mismatch between long-term drivers and short-term extremes, we explicitly separated the spatial baseline from seasonal dynamics. We incorporated a temporal diagnostic framework using a short-term drought index (SPEI-3) and annual heatwave extremes to evaluate interannual variability and capture El Niño-Southern Oscillation (ENSO) teleconnections. The resulting maps reveal distinct hotspots across semi-arid and transitional regions, where multiple hazards converge. Case studies in six regions validate the approach across diverse climates. This framework offers a reproducible and physically grounded method for compound hazard assessment and supports early-warning systems, adaptation planning, and environmental governance in the face of accelerating climate risks.