Earth and Environmental Sciences

The urban heat island (UHI) effect represents a critical urban climate phenomenon arising from the combined pressures of rapid urbanization and climate warming. Although its association with carbon emissions has received increasing scholarly attention, the underlying behavior-mediated pathways and cross-regional spillover patterns remain insufficiently understood. Using multi-source geospatial data for the Yangtze River Delta urban agglomeration from 2014 to 2023, this study develops a multi-scale analytical framework integrating 1 km urban agglomeration exploratory analysis and 5 km spatial econometric modeling. Anthropogenic Energy Activity Intensity (AEAI) is constructed as a proxy for energy-related human activities, and a spatial Durbin model, combined with a spatial mediation approach, is employed to examine the spatial associations and statistically mediated pathways within the “heat-energy-carbon” nexus. The results indicate that: (1) carbon emissions exhibit significant positive spatial spillover effects, consistent with thermal diffusion processes and socioeconomic network interactions; (2) AEAI represents a substantial partial statistical mediation pathway in the association between UHI and carbon emissions, accounting for 44.63% of the total association. This suggests that the UHI–carbon emission linkage is partly embedded in spatial patterns of energy-intensive human activities rather than reflecting a purely direct thermal effect. These findings suggest that regional climate governance may need to move beyond single-city interventions and purely physical cooling strategies toward integrated approaches that combine cross-regional coordination with behavioral regulation. Promoting passive cooling-oriented urban planning and demand-side energy transitions may help reduce carbon lock-in risks and support the development of climate-resilient urban agglomerations.

Introduction Against the background of global climate change and the increasing frequency of extreme weather events, the Guangdong–Hong Kong–Macao Greater Bay Area—as a typical coastal highdensity urban agglomeration—has long been exposed to compound natural disaster risks (typhoons, heavy rainfall, floods, storm surges, and geological disasters) under the combined influence of land–sea interactions, rapid urbanization, and multiple overlapping hazards. Regional disaster governance has therefore become a critical interdisciplinary issue bridging geoscience and public governance. To systematically evaluate the structural characteristics and design quality of disaster governance policies in this region, this study analyses 115 policy texts and constructs an integrated analytical framework. Methods We combine text mining, semantic network analysis, and the Policy Modeling Consistency (PMC) index. First, word segmentation statistics and keyword cooccurrence analysis are employed to identify policy themes and core semantic structures. Second, a PMC evaluation index system is built to quantitatively assess the policy texts. Finally, grade classification and PMC surface plots are used to compare structural differences among various policies. Results The results show that disaster governance policies in the Guangdong–Hong Kong–Macao Greater Bay Area have generally formed a relatively systematic institutional framework, with most sample policies rated as “excellent” or “good.” Excellent policies perform more evenly in terms of policy instruments, policy content, supporting measures, and target coverage, whereas average policies show deficiencies in the configuration of issuing bodies, temporal arrangements, crosslevel coordination, and evaluation and feedback mechanisms. Discussion Accordingly, this study suggests strengthening multiactor collaboration, improving closedloop policy management, and enhancing the balance and synergy of policy structures, so as to improve the governance capacity and regional resilience of the Greater Bay Area in response to compound natural disaster risks. These findings also provide a reference for disaster risk governance and regional disaster prevention and reduction policy optimization in the field of geoscience.

Abstract Environmental monitoring increasingly relies on wall-to-wall maps derived from remote sensing and machine learning for decisions, yet high map classification accuracy does not necessarily imply reliable knowledge across space. Here, we utilize a relatively simple classification scenario to examine how probabilistic ensemble models represent spatial uncertainty and whether these signals correspond to independent human interpretation. Using high-resolution imagery from the Bangladesh Sundarbans, we generated continuous mangrove probability maps by combining multiple base learners through stacked generalization. Rather than focusing solely on performance metrics, we quantified disagreement among models and compared stacked probabilities with scores assigned by three interpreters to pixels sampled from regions of agreement and ambiguity. Individual and stacking models achieved similarly high accuracy. Stacked probabilities served as a proxy for the epistemic uncertainty in the map, with disagreement among heterogeneous base learners reflecting where predictions are stable or contested. Extreme values corresponded to strong model consensus and higher interpreter confidence, whereas intermediate values coincided with greater variability in both model predictions and human judgment; the two signals proved complementary rather than interchangeable. These intermediate, most-contested probabilities covered only about 3.5% of the map, yet clustered along ecological transitions where conventional maps show false confidence. Propagated into a design-based area estimate, this uncertainty spans a 126 million to 1.26 billion US dollar range in REDD+ value across carbon-price tiers, so quantifying it lets projects target field verification where it most reduces financial risk.

ABSTRACT Compound climate extremes, such as concurrent precipitation and temperature extremes, cause significant impacts on socioeconomics and ecosystems. Recent studies have made substantial progress in the specific type of compound extremes; however, characteristics of different types of compound precipitation and temperature extremes across the globe and their driving mechanisms remain limited understood. This study investigated characteristics of compound extremes including dry‐warm (DW), wet‐warm (WW), dry‐cold (DC), and wet‐cold (WC) combinations in annual, JJA (June, July, and August), and DJF (December, January, and February) occurrences during 1901–2024 across global land areas and their relationships with climate variability modes. Results indicated that the spatial distribution of the frequency of DW (WW) showed a similar pattern to that of WC (DC). The overall frequency of compound warm‐related (cold‐related) extremes successively increased (increased and then decreased) from the period 1901–1941 to the period 1983–2024. The spatial extent of compound warm‐related (cold‐related) extremes presented an increasing (decreasing) trend in all the continents over the past 124 years. The areas with positive (negative) precipitation‐temperature correlation showed high frequency of WW and DC (DW and WC). The areas with positive (negative) dependence between precipitation and temperature extremes of DW and WC showed negative (positive) dependence between the two extremes of WW and DC, except for central Asia where the two extremes showed positive dependence in the four compound extremes. During 1901–2024, El Niño (La Niña) tended to induce annual high (low) DW and low (high) WC occurrences in northern South America, southern and central Africa, southern Asia, eastern Australia, and northwestern North America and high (low) WW and low (high) DC occurrences in South America, Africa, western and southern Asia, southern Europe, western North America, and western Australia. The positive (negative) Dipole Mode Index tended to induce high occurrences of compound warm‐related (cold‐related) extremes in most global land areas. The positive North Atlantic Oscillation (NAO) tended to induce high WW occurrences and low DC occurrences in central and northern Europe, northern and central‐eastern Asia, and eastern North America, especially during DJF. This study provides scientific insights into the spatiotemporal characteristics and driving mechanisms of different compound extremes across the globe under a changing climate.

Abstract. Marine Cloud Brightening (MCB) is a suggested solar radiation management approach to mitigate global warming by increasing the reflectance of clouds through the emission of additional aerosols. While stratocumulus are considered the preferred target for MCB, the present study investigates trade-wind cumulus clouds, which may be the dominant cloud type for certain regional MCB deployments. In this study, high-resolution large-eddy simulations with detailed Lagrangian cloud microphysics are used to assess the role of different aerosol sprayer heights on the efficacy of MCB. The study indicates that surface sprayers are the optimal placement, as they facilitate the most efficient dispersion of aerosol within the boundary layer, which increases the fraction of clouds affected by the sprayed aerosols, as well as the transport of the sprayed aerosols into the developing clouds, which increases the number of cloud droplets developing from the sprayed aerosols.

To make better use of solar energy, many crops and orchards are planted in rows around the world. Accurate monitoring of the heat radiation status of row-planted crops and orchards is important for water management and yield forecasting. However, the radiation observed by a sensor is generally angle-dependent due to the complex architecture of the canopy, which must be normalized with respect to a reference direction (e.g., the nadir direction). Compared to physical models with numerous input parameters about the canopy structure and thermal property (e.g. canopy height, leaf area index, leaf emissivity and leaf temperature), semi-empirical kernel-driven models are much easier to be calibrated using only multi-angle sensor observations. However, all existing kernel-driven models have been developed for discrete and continuous canopies, without considering the specific architecture of row structure. To extend the fitting ability to row-planted canopies, a unified kernel-driven modeling framework in the thermal infrared domain has been developed in this study. It includes an isotropic kernel, a base shape kernel, a hotspot kernel and a hot belt kernel. Both base shape and hot belt kernels can represent the angular effect of row structure. Two instantiated models (i.e., Kimes-RL-Ganis and Kimes-Chen-Ganis) are proposed within this unified framework. Then, three DART (Discrete Anisotropic Radiative Transfer)-simulated datasets and three airborne-measured multi-angle datasets have been adopted to evaluate our two new models against six existing models (i.e., Vinnikov, RL, Vinnikov-RL, Vinnikov-Chen, LSF-RL and LSF-Chen). Results show RMSE reduction percentages as large as 71.0–84.9% for the three simulated scenes (1.322–1.445 K to 0.300–0.349 K, 1.105–1.263 K to 0.265–0.321 K, and 1.926–2.047 K to 0.269–0.291 K) and 42.5–61.2% for the three measured scenes (2.043–2.227 K to 0.78–0.792 K, 1.316–1.426 K to 0.614–0.649 K, and 1.580–1.654 K to 0.882–0.909 K). Given the demonstrated adaptability of these new models to both continuous and discrete canopies, they offer significant potential for advancing angular normalization of land surface temperature across diverse vegetation types in applications. This framework is well suited for the bi-directional reflectance distribution function simulation over row-planted scenes in the optical domain.

Abstract. The teleconnections of the Quasi-Biennial Oscillation are revisited using ∼65 000 years of model output contributed by four modeling centers to the Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP). The large ensemble size (at least 10, and in many cases 50) allows isolation of weak signals that are usually hidden by internal variability, as well as better quantification of the role of internal variability in possible model–observation discrepancies in the magnitude of the signals. All four models simulate a Holton–Tan effect, and two of the models also simulate a subtropical downward arching wind horseshoe teleconnection that is most prominent in the Pacific sector. The magnitudes of these teleconnections are statistically indistinguishable from those observed in two of the models but not in the other two; this is a notable improvement from previous work that analyzed small ensembles. These large-scale teleconnections lead to surface temperature and precipitation anomalies over the mid-latitude continents, including an impact on western North America surface temperature which appears to have not been noted before. Furthermore, all models show impacts of the QBO on tropical surface temperature and precipitation, however the nature of these responses differs across the models due, in part, to qualitatively different interactions with El Niño. Remarkably, one of the models simulates a connection between the QBO and the Madden Julian Oscillation that mimics observations, although it remains too weak. Finally, the LESFMIP simulations allow an exploration of external forcings impacting the magnitude of teleconnections. Among these experiments, greenhouse gas forcing is seen to significantly strengthen the subtropical wind horseshoe of the QBO.

Abstract. This study presents the first systematic field evaluation of dock-based UAV (Uncrewed Aerial Vehicle) systems for geohazard monitoring in mountainous terrain. We assess their potential to provide reliable, high-frequency, and automated monitoring of surface changes across three different hazard scenarios: (1) a fast-moving glacier icefall (Supphellebreen, Norway), (2) an unstable rock slope (Skjøld, Norway), and (3) a post-failure landscape resulting from a catastrophic rock-ice avalanche (Blatten, Switzerland). Effective hazard management requires timely detection of displacement patterns and terrain change. To address these issues, we introduce an automated workflow integrating multitemporal UAV dock data acquisition with an end-to-end processing pipeline for displacement field generation and change detection. The results show that this workflow has the potential to provide data at centimetre-level accuracy before, during, and after hazard events, supporting both precautionary risk assessments and timely decision-making in critical phases of potential hazard evolution. Wider adoption will depend on supportive regulatory frameworks, reliable power and communication infrastructure, and sufficient expertise to ensure effective operation, maintenance, data interpretation and risk management. Overall, dock-based UAV systems represent a significant technological advancement in efficient geohazard monitoring, facilitating rapid response in critical situations, thereby contributing to increased resilience of communities living in vulnerable mountain environments.

Abstract Sundarbans, the world’s largest contiguous mangrove ecosystem and representing shallow coastal Bay of Bengal of the Northern Ocean, faces dynamic climate variations, including ocean acidification. To delineate ocean acidification from natural pH variations, it is crucial to perform long-term measurements of multiple carbonate chemistry parameters such as pH, total alkalinity (TA), and dissolved nutrients, among others. In the present study, surface water carbonate chemistry parameters, including TA, pH, and dissolved nutrients (o-phosphate and silicate), were analysed monthly between 2014 and 2022 in three pre-defined stations, namely Stn1, Stn2, and Stn3, part of Sundarbans Biological Observatory Time Series (SBOTS) located in Sagar Island, the largest island of the Indian Sundarbans. The observed deviation from the linear TA-Salinity curve in the studied sites of SBOTS showed the influence of freshwater in modulating TA. Generalized Additive Model (GAM) revealed substantial seasonal variability in the controls on TA. During monsoon, salinity was a dominant driver of carbonate chemistry, consistent with enhanced freshwater discharge. In contrast, during the post-monsoon season, primary productivity as indicated by the relationship with Chla, dissolved silicate, was found to exert a stronger influence on TA variability. Multilinear regression (MLR) analysis of calculated pCO2 further supported these seasonal trends. Overall, the findings highlight the importance of season-specific assessments, highlighting the critical role of freshwater discharge in shaping estuarine carbonate dynamics. These insights are vital for predicting the vulnerability and response of mangrove estuaries under future climate change scenarios.

The Piacenzian Age (3.60–2.58 Ma) of the Pliocene Epoch was characterized by globally warmer climates, higher sea levels, and atmospheric CO 2 similar to present. Utilizing a robust dataset of 2,101 samples and over 637,000 foraminifer specimens from 77 deep-sea core sites worldwide, we document planktic foraminifer biogeography and biodiversity during the mid-Piacenzian Warm Period (mPWP). Cluster analysis and multidimensional scaling reveal five major bioregions: tropical, warm subtropical, transitional, polar, and a distinct North Atlantic polar bioregion. Each bioregion was dominated by characteristic species with well-established temperature preferences. Analyses demonstrate higher species richness and evenness in low and mid-latitudes, with increased diversity associated with periods of climatic warming and poleward expansion of warm water assemblages. The long-term stability of biogeographic patterns underscores ecological conservatism but also highlights potential vulnerability to rapid anthropogenic climate change. Our findings emphasize the critical role of planktic foraminifers in reconstructing past ocean conditions and offer valuable insights into links between planktic biogeography, climate, and socioeconomic impacts on marine ecosystems. This work advances our understanding of marine ecosystem responses to climate extremes and provides a foundation for future regional and temporal analyses of planktic foraminifer biogeography under global change scenarios.

Abstract. As critical inputs for global climate studies, watershed hydrologic modeling, and satellite soil moisture product validation, in situ soil moisture measurements are frequently compromised by sensor-derived data gaps that disrupt hydrological continuity. To overcome this challenge, we develop ST-GapFill, a novel spatiotemporal reconstruction framework integrating multi-source contextual information through two key innovations: (1) Spatial correlation-guided neighbor selection that identifies optimal auxiliary stations; (2) A long short-term memory (LSTM) network is employed to capture the complex temporal dependencies within the soil moisture time series. Validation on in-situ networks demonstrates that ST-GapFill successfully reconstructs soil moisture dynamics with preserved diurnal-phase fluctuations, achieving 0.91 correlation coefficients with ground truth under low missing-rate conditions (<50 %). Comparative analysis reveals the ST-GapFill 's statistically superior performance (RMSE reduction: 27.0 % vs IDW, 67.8 % vs ARIMA). This method establishes a robust spatiotemporal imputation paradigm for environmental sensor networks, effectively bridging observation gaps to support precision agriculture and climate change impact assessments.

Introduction Cetaceans are key indicators of ecosystem health, yet their spatial ecology in the Mediterranean Sea remains incompletely resolved, limiting the effectiveness of conservation measures and marine spatial planning. Here, we provide a basin-scale assessment of habitat suitability for eight cetacean species and identify the environmental determinants shaping their distribution using survey-based occurrence data collected from 2006 to 2016 across the Mediterranean Sea. Methods We conducted a principal component analysis and a K-means cluster analysis on oceanographic variables to identify homogeneous areas that could explain cetacean distribution. A species distribution modelling tool, MaxEnt, was used to predict and assess habitat suitability for each species using environmental and topographic variables, with a bias file to account for unequal sampling effort. Results Principal component analysis identified the gradients along which the oceanographic variables change. The K-means clustering identified nineteen clusters which were associated with oceanographic features. MaxEnt modelling indicated that chlorophyll-a, sea surface temperature (SST), and distance to coast were the main environmental predictors shaping the potential distribution of Risso’s dolphin, whereas fin whale distribution was primarily influenced by SST. Discussion Overall, the models indicated that SST, productivity gradients, and seabed topography are the main drivers of cetacean habitat preferences. Habitat suitability maps were generated for each species to illustrate potential distribution. By integrating oceanographic zoning with species distribution modeling, this study provides reproducible, policy-relevant spatial products to support the design of conservation areas, reduce risks from human activities (e.g., shipping and fisheries), and safeguard ecosystem services that underpin human well-being in the Mediterranean.

Abstract The Heat Index (HI) developed by NOAA combines air temperature ( T ) and relative humidity to characterize human heat stress for real-time public warnings. While widely used, the HI framework exhibits two limitations: (1) under dry conditions, HI values can fall below T , which may reduce the diagnosed severity of hazardous heat in operational classification; and (2) the threshold for the Extreme Danger category is so high that it is rarely reached in surface observations or ERA5 reanalysis, limiting its practical usefulness. This study revisits the HI framework for sub-daily heat assessment by: (1) evaluating an HI ≥ T convention as a pragmatic classification choice for sensitivity testing in very hot, dry environments; (2) showing that percentile-based HI thresholds yield spatially uniform exceedance frequencies that obscure regional contrasts and underrepresent intense conditions in hotter climates; and (3) subdividing the upper HI range beginning at 90 °F into 5 °F increments as a simple descriptive framework to improve resolution at the upper end of heat severity. Using hourly ERA5 data from 1961–2025, we find that applying HI ≥ T avoids reductions in assigned hazard category relative to temperature-based classification in dry environments. The proposed 5 °F subcategories preserve geographic differences, enhance resolution at higher severity, and provide a compact descriptive summary of the frequency and temporal evolution of extreme heat. These features are illustrated through three examples: the 1995 Chicago heat wave, the 2024 Death Valley extreme heat episodes, and recent heat-related emergency department visits in New York City.

Generating satellite-based deforestation alerts with actionable latency requires frequent imaging, creating an imperative to use different sensors together. We introduce a simple and open-source framework called the Disturbance Index Alert System (DIAS), which is based upon transformation of imagery from different sources into an interoperable stream of Disturbance Index (DI) values. Whereas most alert systems target divergence of forested pixels from historical states, DIAS targets movement of a pixel’s Z-score position relative to the image-wide population of forest pixels along a forest-sensitive axis. This strategy provides the following practical benefits: (1) it reduces the need to process the historical archive; (2) it reduces dependence upon stable sensor calibration; (3) it allows Z-score-based DI values to be combined across sensors; and (4) it accommodates changes to the group of sensors providing measurements. We demonstrated in Madagascar that sensor integration through DIAS can provide more timely alerts than both conventional individual-sensor systems and additive combination of such systems. Across our study sites, using a commercial source of daily imaging (PlanetScope) in conjunction with imagery from public sources (Landsat, Sentinels-1 and -2) allowed high-confidence detection (false alert rate of approximately 20%) of two-thirds of deforestation occurring at 10 m reference pixels within one month; 40% were detected in that timeframe with public data alone. As commercial options for Earth observation proliferate, flexible and computationally lightweight approaches such as DIAS are needed to accommodate diverse and sometimes only loosely calibrated instruments in support of timely forest monitoring.

Understanding how groundwater storage responds to extreme precipitation is essential for assessing aquifer resilience under climate variability. In this study, we developed a 1 km groundwater storage anomaly (GWSA) dataset for the Baiyangdian Watershed from 2002 to 2024 by downscaling GRACE observations with a Light Gradient Boosting Machine (LightGBM) model. The downscaled GWSA showed good consistency with independent hydrological datasets, including GLDAS and groundwater-level anomalies. Based on the downscaled product, we characterized long-term groundwater changes and quantified GWSA responses to extreme precipitation events (EPEs). Groundwater storage exhibited three distinct phases: rapid depletion before 2014 (−1.35 cm/yr), a slower decline during 2014–2019 (−1.04 cm/yr), and marked recovery after 2020 (+3.45 cm/yr). Spatially, GWSA generally increased from the southwest to the northeast of the watershed. Composite analysis of 11 EPEs revealed a delayed groundwater response, with the strongest signal occurring approximately two months after precipitation. Monthly effective precipitation was more closely associated with GWSA recovery than short-duration daily precipitation extremes, and the response was stronger in plains than in mountainous areas. These findings indicate that EPEs provide episodic recharge pulses, while sustained groundwater recovery depends on cumulative climatic inputs and human water-management influences.

Study region The Yellow River Basin(YRB), a region in China characterized by significant water scarcity and intense human activity. Study focus Water resource value(WRV) serves as a critical signal and instrument for supporting governance decision-making. This study developed an integrated framework bridging historical insights, future trends, and practical applications, employed the fuzzy comprehensive evaluation method to quantify WRV at the city scale in the YRB and reveal its spatiotemporal characteristics, applied Geographically and Temporally Weighted Regression (GTWR) to examine the influencing factors of WRV, and further used time-series forecasting and machine learning models to predict both WRV and its determinants, ultimately constructed a zonal governance framework based on the Water Supply Index (WSI), Water Demand Index (WDI), and WRV to provide prospective governance recommendations for cities in different zones. New hydrological insights for the region The results indicated pronounced spatial heterogeneity in WRV across the YRB along both east-west and north-south directions. In the early study period, cities in the western YRB exhibited higher WRV than those in the east, whereas this pattern reversed in the later period. Meanwhile, cities in the northern YRB consistently showed higher WRV than those in the south. GTWR results revealed that supply-side factors exerted a stronger overall influence on WRV than demand-side factors, with annual precipitation, technological innovation capacity, and mean elevation ranking as the top three supply-side determinants, and industrial upgrading level, industrialization level, and social consumption level ranking as the top three demand-side determinants. Predictions indicated that WRV in approximately 60% of cities is likely to decline slightly in the future, suggesting that supply-demand tensions in some regions may be alleviated under strengthened water-saving constraints and improved utilization efficiency. Finally, a four-quadrant zonal governance framework was constructed based on the predicted WSI, WDI, and WRV. By treating WRV as both a governance signal and a governance instrument, zone-specific governance recommendations were proposed to support differentiated water resource management and high-quality development in the YRB.

Abstract Low‐level jets (LLJs)—wind speed maxima typically occurring a few hundred meters above the surface—are common off the U.S. East Coast and influence many atmospheric processes with societal importance, including cloud formation, aviation safety, and search‐and‐rescue. However, their vertical structure and frequency remain poorly quantified due to limited offshore observations. This study presents new scanning Doppler LiDAR and infrared spectroradiometer data from the 2024 summer deployment of an offshore barge during the Wind Forecast Improvement Project 3. These coupled wind and temperature profiles provide unprecedented resolution to assess LLJ behavior and model performance. LLJs occurred in over 21% of observed profiles, with a weak diurnal preference for nighttime and early morning hours and maximum winds typically near 300 m. Both ERA5 and High‐Resolution Rapid Refresh analysis underestimate jet wind speeds and misrepresent the boundary layer thermal structure. These results highlight persistent model biases and the critical need for high‐resolution offshore observations.

ABSTRACT Understanding how terrestrial primary productivity responds to climate change is essential for regional carbon budgeting and climate adaptation planning. This study investigated historical (1985–2014) and future (2015–2100) changes in Gross Primary Productivity (GPP) over India using biogeochemically coupled Earth system models (ESMs) from CMIP6, together with a comparative assessment of CMIP5 and CMIP6 simulations under high‐emission scenarios. Model projections are evaluated in the context of climatic variability, land‐cover changes and biosphere–climate interactions. Results indicate a robust increase in annual GPP over India during the historical period, with continued enhancement under the SSP5–8.5 scenario. CMIP6 projects substantially stronger future GPP increases than CMIP5, with future GPP trends up to ~2.5 times the historical trend magnitude over the country. Spatially, the largest increases occur over the Indo‐Gangetic Plain, Northeastern India and the Western Ghats region. These modelled trends are broadly consistent with observed increases in forest and crop cover. However, CMIP models generally underestimate the magnitude of GPP relative to flux tower and satellite‐based estimates. A comparison between CMIP5 and CMIP6 indicates that CMIP6 multimodel average (MMA) projections show stronger GPP trends than CMIP5, particularly after mid‐century. However, improvements in the strength of the climate–GPP correlation are not statistically significant. The enhanced GPP trends projected by CMIP6 appear to be linked to stronger precipitation increases together with coupled CO 2 fertilisation effects. Spatial attribution analyses suggest a moderate coupling between increasing rainfall and increasing GPP, particularly across semi‐arid and water‐limited ecosystems. Detrended analyses further suggest that higher rainfall years generally enhance productivity, whereas higher temperature years suppress GPP through heat and moisture stress. However, partial correlation and regression analyses reveal substantially weaker climate–GPP relationships after detrending, indicating a strong influence of shared long‐term forcing. Overall, the study demonstrates that long‐term greening over India appears to be associated with combined CO 2 fertilisation and enhanced precipitation trends. In contrast, regional rainfall anomalies and temperature‐related stress responses modulate interannual variability in productivity. These findings highlight the importance of accurately representing hydroclimatic processes and biosphere–atmosphere interactions in projecting future terrestrial productivity over monsoon‐dominated regions.

ABSTRACT This study used the observed daily minimum temperature from 1995 to 2014 in China, combined with simulations from 21 models from CMIP6, to define and extract characteristic indicators of cold spells from a process‐oriented perspective. A method using probability distribution (Cold Spell Model, CSM) is constructed based on extreme value theory, which can effectively characterise the intensity, frequency and duration of cold spells. Results demonstrate that the intensity of extreme cold spells decreases from north to south across China. Northern China typically experiences low‐frequency, longer‐duration and extreme cold spells, whereas southern China is characterised by shorter‐duration events. A cumulative probability distribution transformation method is applied to correct biases in model simulations, showing that bias correction simulation has better performance over the raw simulations. Based on the corrected projections, the extreme cold spells in China are expected to become shorter, less frequent and generally less intense under future warming, with more pronounced changes under the 2.0°C warming compared to 1.5°C warming. However, notable spatial variations exist: the intensity of cold spells decreases in most eastern regions but increases in western regions. The average annual frequency of cold spells generally declines, with more significant changes observed on the Qinghai‐Tibet Plateau. Furthermore, the duration of cold spells shortens significantly across most regions. Future cold spell risks in China will generally decrease across most return periods. However, risks are projected to increase for low‐frequency (once per year) and short‐duration (1‐day) extreme events in certain regions, while risks for high‐frequency and long‐duration events significantly decline. An additional 0.5°C of warming further reduces the probability of cold spells.

ABSTRACT A comprehensive understanding of drought evolution in the Yellow River Basin (YRB) is essential for effective drought mitigation and water resource management. Based on the daily‐scale Modified Comprehensive Meteorological Drought Index (MCI), this study analyzes the spatiotemporal characteristics of drought in the YRB over the past 60 years. The main findings are: The average annual drought days exceed 60, with high‐frequency areas (> 120 days) concentrated in the upper‐middle reaches junction, the eastern middle reaches, and the western lower reaches. Most areas show a decreasing trend in drought days, with the most significant reduction in the source region (20 days/decade). Drought regimes vary regionally: the source region experiences infrequent, short, and mild droughts; the middle‐lower section of the upper reaches has infrequent but prolonged and intense droughts; while most middle‐lower reaches face frequent, short‐duration, yet relatively high‐intensity droughts. Spring drought dominates (25%–40%), followed by summer drought (15%–30%), with spring–summer consecutive drought being the most common cross‐seasonal pattern. During drought events, precipitation deficits are more pronounced in the middle‐lower section of the upper reaches and the lower reaches. Evapotranspiration anomalies are generally higher in the middle‐lower reaches. Precipitation‐dominated droughts (20%–35%) mainly occur in the middle‐lower section of the upper reaches. Evapotranspiration‐aggravated droughts (25%–35%) are concentrated in the source region. Synergistic droughts (25%–30%) are mainly found in the eastern middle reaches, the lower reaches, and localized areas. Since 1961, precipitation‐dominated droughts have decreased, while evapotranspiration‐aggravated and synergistic droughts have increased in most parts of the YRB. Daily‐scale monitoring enables a more accurate characterization of drought processes, providing critical insights for regional drought management and supporting ecological conservation and high‐quality development in the basin.

Abstract. Flushing and dilution are major phenomena of solute export dynamics during precipitation events in headwater catchments but are hard to predict, even if catchment properties are well known. Normalized cumulative load (NCL) functions have been used to visualize and classify event-based discharge–load relationships, distinguishing between dilution, flushing, and constant export behavior. This study presents an enhanced version of the classical NCL function approach by combining it with hydrograph separation. Over an 18 month period, discharge and solute concentrations were monitored in an agriculturally influenced headwater catchment in the German low mountain ranges, with a focus on nitrate (NO3-) and total phosphorus, and a complementary dataset of major ions. Discharge was separated using stable water isotope signals into event water and total discharge. Both discharge components were then analyzed for solute loads (NO3-, total phosphorus, and major ions). The results reveal significant differences in solute export dynamics between event water and total discharge, including unexpected similarities in the export patterns of nitrate and total phosphorus. The proposed method also highlights a shift from predominantly constant export behavior in the total discharge (coefficient of variation: 0.13) to more pronounced flushing or dilution patterns in the event water (coefficient of variation: 0.36). These findings indicate a fundamental difference between the hydrological processes governing the solute export dynamics of the catchment. While the signal of total event discharge indicates constant behavior, the separated event water exhibits strong flushing or dilution tendencies. The observed shifts in the export patterns, which are likely linked to the activation of drainage systems and depletion of NO3- legacy storages, raise the question if the event water fraction should be monitored more closely in terms of its potential for dynamic pollutant transport. The proposed method is straightforward to implement, yields statistically robust results for the dataset and provides new insights into solute input pathways in headwater catchments.

High Resolution Image Download MS PowerPoint Slide Regulators and voluntary corporate sustainability efforts are increasingly adopting time-matching requirements (TMRs) for clean electricity procurement for large loads, such as data centers, and electricity-intensive fuel production, such as hydrogen. We use a stochastic capacity expansion model (CEM) framework to assess how interannual weather variability affects the cost and emissions impact of procurement-driven infrastructure to meet annual and hourly TMRs using the case study of a grid-connected hydrogen producer in Texas. Our approach, which relies on co-optimizing investments and hourly operations over nine weather scenarios, reveals that hourly TMR comes at a higher cost premium compared to annual TMR than previously estimated by single-scenario deterministic modeling, while emissions outcomes remain directionally consistent. Demand flexibility and partial hourly TMR (80–90%) lowers the cost premium while preserving emissions benefits. We further examine how binding renewable portfolio standards (RPS) interact with TMR costs and emissions outcomes. When an RPS is applied to non-H 2 electricity demand, annual TMR reduces emissions comparably to hourly TMR at a lower cost. Incorporating H 2 -related electricity demand directly into the RPS constraint, rather than imposing a separate TMR, achieves similar emissions outcomes at still lower cost, suggesting that TMR-based clean electricity procurement─particularly hourly matching─offers limited additional value in regions with stringent grid decarbonization policies.

Within the domain of Earth observation, tracking terrestrial transitions via high-resolution optical data plays a fundamental role. Nevertheless, current methods face critical challenges, including the difficulty in collaborative modeling of local details and global features and the singularity of bi-temporal difference representation, along with insufficient cross-scale feature communication, thereby constraining both the precision and resilience of models when applied to complicated environments. To solve these problems, we propose LADENet (Locally Adaptive Mamba and Multi-scale Feature Enhancement Network), an innovative framework that synergizes CNN, Transformer, and Mamba paradigms. By leveraging customized local contextual refinement alongside sophisticated hierarchical fusion, this integration delivers highly precise and resilient detection performance. LADENet adopts a weight-sharing multi-level Transformer encoder combined with a sequence reduction mechanism to generate multi-scale global features, achieving precise alignment of bi-temporal features and global context modeling while reducing computational complexity. To realize accurate localization and local enhancement of changed regions, we design a dual spatiotemporal adaptive local feature marking module based on State-Space Scanning (SSS). This module screens high-saliency changed regions through an adaptive scanning strategy, realizes pixel-aligned spatiotemporal feature fusion via cross-temporal state-space scanning, and introduces a sliding window boundary calibration mechanism to alleviate boundary information loss caused by window segmentation. To strengthen the feature representation of changed regions, a dual-branch difference enhancement module is constructed, which collaboratively captures global change trends and fine-grained local features through an attention-enhanced difference branch and a multi-scale convolution concatenation branch, effectively suppressing background interference. To address the semantic gap between cross-scale features, a global cross-scale spatial feature fusion decoder is proposed, which balances local detail preservation and global context perception through the synergy of spatial attention and two-dimensional selective scanning, completing refined multi-scale feature fusion and spatial resolution recovery. To rigorously validate the proposed LADENet, comprehensive experiments were conducted across four widely adopted bi-temporal benchmarks: LEVIR-CD, WHU-CD, CLCD-CD, and GVLM-CD. The presented architecture establishes substantial superiority over existing cutting-edge methodologies across primary evaluation criteria. Specifically, it yields an F1-measure of 91.06% alongside an IoU of 85.28% in the LEVIR-CD tests, while registering 90.51% (F1) and 82.45% (IoU) for WHU-CD. Similarly, robust outcomes are delivered on CLCD-CD (82.15% F1, 72.83% IoU) as well as GVLM-CD (89.12% F1, 77.78% IoU). These results demonstrate that LADENet possesses excellent detection accuracy, boundary delineation capability and generalization performance in diverse and intricate bi-temporal observation environments.