Search Results (157)

The mechanisms by which the regional hydroclimate responds to global climate forcing are complex, particularly in geographically heterogeneous countries like China. Focusing on Hunan Province, this study employs the Standardized Precipitation Index (SPI) derived from long-term precipitation records at 87 meteorological stations to delineate climatic sub-regions with coherent dry–wet variability. Using rotated empirical orthogonal function analysis, we systematically characterize the spatiotemporal patterns of SPI components and quantify their teleconnections with global ocean–atmosphere circulation modes. The analysis of multi-timescale SPI reveals four distinct sub-regions and a pronounced northwest–southeast dipole in long-term trends. Despite an overall reduction in annual drought, the northwestern sub-region experienced intensification. Seasonally, a pattern of spring/autumn drying versus summer/winter wetting emerged. Wavelet analysis identified dominant interannual (2–7 years) and interdecadal (13–71 months) oscillations. These periodicities are significantly teleconnected to large-scale circulation indices (e.g., Southern Oscillation and Pacific Decadal Oscillation), with influences peaking at 16–64-month and 2–5-year scales. Importantly, the primary circulating driver differs by sub-region, revealing a complex teleconnection landscape. The findings delineate region-specific atmospheric pathways, offering insights to bolster drought preparedness and optimize water allocation, thereby enhancing climate resilience in vulnerable monsoon transition zones. Full article

The 2025 fire season in Portugal was marked by large fires, underscoring the vulnerability of the forested areas to fire. The study analyzes the main meteorological conditions during a critical period of fire activity and addresses the following question: Why can the northeast (NE) weather pattern be so critical for fire danger in Portugal? Fire severity in the Arouca wildfire, the largest fire of the period, was estimated using a methodology that integrates foundation vision models with computer vision algorithms. ECMWF analyses and convection-permitting Meso-NH simulations are used to examine large-scale circulation and the mesoscale environment, respectively. Synoptic-scale analysis revealed the Azores anticyclone centered slightly northwest of the Iberian Peninsula (IP), with its eastern sector directly affecting the northern IP under north/northeast winds. The hectometric-scale simulation demonstrated that orographically enhanced wind gusts over the northern Portuguese mountains substantially intensified near-surface fire-weather conditions when the winds were nearly easterly. Furthermore, strong low-level winds and atmospheric stability constrained vertical plume growth, favoring horizontal smoke transport. In addition, the study highlights that Arouca’s fire had 88% of its area affected with moderate to high severity. Overall, the results demonstrate that the interaction between large-scale NE circulation and local orography plays a decisive role in amplifying fire danger in northern Portugal, emphasizing the need for high-resolution atmospheric modeling to identify fire-prone regions under specific synoptic patterns. Full article

Accurate assessment of the cooling effect from urban green space (UGS) is largely hindered by insufficient field samples or consideration of the internal and surrounding three-dimensional (3D) structure. This study developed a transferable modeling-optimization framework that integrated a multi-temporal sampling strategy, multimodal 3D environmental reconstruction, and Bayesian-based optimization. First, the potential influencing factors of the cooling effect were quantified from three aspects of inner 2D/3D structure, surrounding building ventilation, and background meteorology through fusing field measurements, multi-spectral UAV images, and Sentinel-2 images. Then, a generalized additive mixed-effects model was used to explore cooling-related patterns of UGS, and a Bayesian network was further applied to identify potential optimized configurations. The results suggest the following: (1) The adopted multi-temporal sampling strategy enhances the stability of detected cooling signals and minimizes spatial interference among neighboring UGS patches and water bodies. (2) Temporal changes in the cooling effect are mainly driven by average air temperature and maximum wind speed, while the spatial variation by the UGS inner characteristics of area and shape index and surrounding ventilation. (3) The “win–win” situation of cooling intensity and range occurred in UGSs with larger areas, higher shape regularity, and medium ventilation. This approach is useful for model-based planning of climate-responsive green infrastructure and city-scale ventilation systems in heat-vulnerable environments. Full article

Agriculture is particularly vulnerable to climate change, as shifting seasonal patterns disrupt farming cycles and changing rainfall patterns, along with extreme weather events, present significant challenges. From the perspectives of risk perception and risk attitudes, this study elucidates the decision-making mechanisms underlying climate adaptation behaviors among maize growers in China, providing insights to inform climate adaptation policies, land management strategies, and food security protection. This study surveyed 752 maize growers in Jilin province, China, and employed factor analysis to quantify climate risk perception and risk attitudes. Using the Probit model and moderation analysis, this study examines the impact of climate risk perception on adaptive behavior and investigates the moderating effect of risk attitude on the relationship between risk perception and climate adaptation behavior. It then explores heterogeneity across production scales and generations. (1) We categorize adaptation behaviors into three types—capital-based, labor-based, and technology-based—according to the input factors involved. Climate risk perception promotes all three types of adaptation behaviors, whereas risk aversion primarily exerts a significant inhibitory effect on technology-based adaptations. (2) Risk attitudes exert a negative moderating effect on the relationship between climate risk perception and the adaptation behaviors of maize growers. Specifically, a higher propensity for risk aversion attenuates the positive influence of risk perception on labor-based and technology-based adaptation behaviors. (3) Heterogeneity analysis reveals that the moderating effect of risk attitude is more pronounced among small-scale farmers and younger generations. In contrast, it remains statistically insignificant for large-scale operators and older-generation cohorts. Therefore, it is important to enhance farmers’ awareness of climate risks by strengthening the dissemination of meteorological information and early warnings. Technical guidance should be intensified to improve maize growers’ understanding and mastery of relevant technologies. Develop targeted land-use strategies for climate change adaptation based on maize growers’ age, farm size, and geographic location. Full article

The increasing occurrence of unfavorable meteorological conditions under global warming has significantly impacted urban atmospheric environments, particularly ozone (O₃) and fine particulate matter (PM_2.5) pollution in densely populated cities. Using nationwide air quality observations and reanalysis data from 2013 to 2022, we assessed the variations in three typical unfavorable meteorological conditions—heatwave (HW), atmospheric stagnation (AS), and temperature inversion (TI)—in Eastern China and their influences on air pollution, as well as the large-scale synoptic drivers behind them. Results indicate that HW and AS events have increased substantially by 9.61 and 1.72 days/decade, leading to remarkable rises in O₃ and PM_2.5 concentrations. Compound events (e.g., HW + AS and HW + TI) exhibit even stronger synergistic impacts, raising O₃ and PM_2.5 concentrations by more than 57.34% and 46.76%, respectively, compared to individual events. In addition, by applying the T-mode Principal Component Analysis (T-PCA), this study identified typical synoptic patterns favorable for such conditions and air pollution events. Synoptic patterns such as the northward displacement of Western Pacific Subtropical High (WPSH) were identified as critical large-scale drivers. These findings highlight linkages between unfavorable meteorological conditions and air quality, providing scientific support for air-quality management and pollution control in Eastern China. Full article

Aridity represents a fundamental climatic constraint governing water resources, ecosystem functioning, and agricultural systems in transitional climate zones. This study examines the spatial organization and temporal variability of aridity and thermal continentality in North Macedonia using observational records from 13 meteorological stations distributed across contrasting altitudinal and physiographic settings. The analysis is based on homogenized monthly and annual air temperature and precipitation series covering the period 1991–2020. Aridity and continentality were quantified using the Johansson Continentality Index (JCI), the De Martonne Aridity Index (I_DM), and the Pinna Combinative Index (I_P). Temporal consistency and trend behavior were evaluated using Pettitt’s nonparametric change-point test, linear regression, the Mann–Kendall test, and Sen’s slope estimator. Links between aridity variability and large-scale atmospheric circulation were examined using correlations with the North Atlantic Oscillation (NAO) and the Southern Oscillation Index (SOI). The results show a spatially consistent and statistically significant increase in mean annual air temperature, with a common change point around 2006, while precipitation displays strong spatial variability and limited temporal coherence. Aridity patterns display a strong altitudinal control, with extremely humid to very humid conditions prevailing in mountainous western regions and semi-humid to semi-dry conditions dominating lowland and southeastern areas, particularly during summer. Trend analyses do not reveal statistically significant long-term changes in aridity or continentality over the study period, although low-elevation stations exhibit weak drying tendencies. A moderate positive association between I_DM and I_P (r = 0.66) confirms internal consistency among aridity indices, while summer aridity shows a statistically significant relationship with the NAO. These results provide a robust climatic reference for North Macedonia, establishing a first climatological baseline of aridity conditions based on multiple indices applied to homogenized observations, and contributing to regional assessments of hydroclimatic variability relevant to climate adaptation planning. Full article

In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets—Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data—with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983–2020). Statistical metrics including the Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r < 0.5) at daily timestep and low RMSE, bias, and ME. Monthly performance improved substantially (r > 0.8 at most stations) particularly during the wet season (r = 0.52–0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, bias, and ME declined at higher timescales, particularly during the wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. Spatially, CHIRPS and MSWEP reproduced coherent basin-scale patterns of drought persistence, with longer dry-spells concentrated in the northern, central, and western parts of the basin and shorter dry-spells in the eastern and southern regions. Trend analysis further revealed that most products captured consistent large-scale changes in dry-spell characteristics, although localized drought events were more variably detected. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with the exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use, and strengthen resilience to extreme weather events. Full article

Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH₃-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (COD_Mn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R² values exceeding 0.45 for all parameters (DO: 0.70, NH₃-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, COD_Mn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions. Full article

This study takes the source region of the Yellow River from 2000 to 2024 as the research area, and integrates multi-source remote sensing, long-term meteorological observation, and land use data from 2000 to 2024. Using GIS spatial analysis, the standard ellipse model, and a geographic detector, this study systematically depicts the spatio-temporal heterogeneity and multi-scale evolution trend of soil and water conservation services, and then quantifies the spatial differentiation of the contribution rate of climate fluctuation, land use transformation, and human activity intensity to service change. The results showed the following: (1) The land use pattern in the source region of the Yellow River showed a one-way transformation of “grassland dominated, forest land increased alone, and the rest decreased”. The net increase in forest land 204.3 km² was all from the transformation of grassland. The vegetation coverage increased by 9.9%, and the low-value area of soil and water conservation services in the northwest continued to expand. (2) The overall moving distance of the center of gravity of soil and water conservation service capacity is not significant compared with the spatial scale of the source area of the Yellow River. The standard deviation ellipse of each year also did not show systematic and large changes in area, shape, or direction. (3) Annual mean temperature (Q = 0.590) and vegetation coverage (Q = 0.527) are the most influential single factors, while the interaction between annual mean temperature and precipitation (bidirectional enhancement) is the most stable synergistic driving combination. The single-factor Q values of topography and human activities were <0.10. (4) Climate and economic factors are the key factors driving the spatial differentiation of soil and water conservation service capacity, and the role of each driving factor has an optimal range to reduce the risk of soil erosion. The optimal range of population density is 7~9 person/km², the optimal range of average GDP is 11,900~14,100 yuan/km², the optimal range of annual average temperature is 1.71~3.47 °C, the optimal range of annual precipitation is 682~730 mm, the optimal range of vegetation coverage is 81.7~100%, and the optimal range of altitude is 3390~3740 m. The optimal range of slope is 18.3~24.3°. The optimal range of soil moisture is 26.7~29.4%. The optimal range of grazing intensity is 0.352~0.652. The study proposes countermeasures such as strict control of development in high-value areas of soil and water conservation services and key ecological restoration in low-value areas, the establishment of breeding bases and catchment areas in low-precipitation areas to cope with climate change, the optimization of grazing strategies, so as to provide scientific support for the stability of alpine grassland ecosystem services, and the high-quality development of the Yellow River Basin. Full article

This study presents a comparative analysis of two rare thundersnow events accompanied by snowfall that occurred on 11 November 2023 and 10 December 2023 in Shaanxi province, China. Multiple data sources were integrated, including MICAPS surface and upper-air conventional detection observations, hourly meteorological records from Yanliang Airport, lightning location data, and ERA5 reanalysis, to examine and contrast the synoptic conditions, moisture transport mechanisms, and convective characteristics underlying these two events. The results indicate that the large-scale circulation patterns were characterized by a “high in the west and low in the east” configuration and a “two troughs-one ridge” pattern for the November and December cases, respectively. In both episodes, Shaanxi Province was located on the rear side of a high-pressure ridge, where a strong pressure gradient induced pronounced northerly winds that advected cold air southward, forming a distinct near-surface cold pool. During the November event, the convective cloud system developed east of the Tibetan plateau, guided by a westerly flow, and propagated eastward while gradually weakening, with a minimum brightness temperature of −42 °C. Conversely, in December, the convective activity initiated over southwestern Shaanxi and moved northeastward under a southwesterly flow, reaching a lower minimum brightness temperature of −55 °C, indicative of stronger vertical development. In both events, the principal water vapor transport occurred near the 700 hPa height level and was primarily sourced from the Bay of Bengal via a southwesterly flow. The November event featured a stronger northwesterly cold-air intrusion, whereas the December case exhibited a broader moisture channel. The CAPE values peaked during the afternoon and nighttime periods in both cases. The cold-pool and inversion-layer thickness were approximately 2 km/45 hPa in November and 0.8 km/150 hPa in December. Full article

Precise rainfall-runoff modeling (RRM) is vital for disaster management, resource conservation, and mitigation. Recent deep learning-based methods, such as long short-term memory (LSTM) networks, often struggle with major challenges, including temporal sensitivity, feature selection, generalizability, and hyperparameter tuning. The objective of this study is to develop an accurate and generalizable rainfall–runoff modeling framework that addresses the four aforementioned challenges. We propose a novel RRM framework that integrates transductive LSTM (TLSTM) to capture fine-grained temporal changes, off-policy proximal policy optimization (PPO) combined with Shapley Additive exPlanations (SHAP)-based reward functions for feature selection, an enhanced generative adversarial network (GAN) for online data augmentation, and Bayesian optimization hyperband (BOHB) for efficient hyperparameter tuning. TLSTM uses transductive learning, where samples near the test point are given extra weight, to capture fine-grained temporal shifts. Off-policy PPO contributes to this process by selecting features sensitive to temporal patterns in RRM. Our improved GAN conducts online data augmentation by excluding some gradients, increasing diversity and relevance in synthetic data. Finally, BOHB accelerates hyperparameter tuning by merging Bayesian optimization with the scaling efficiency of Hyperband. We evaluate our model using the Comprehensive Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset under individual and regional scenarios. It achieves Nash–Sutcliffe efficiency (NSE) scores of 0.588 and 0.873, surpassing the baseline scores of 0.548 and 0.830, respectively. The generalizability of our approach was assessed on the hydro-climatic datasets for North America (HYSETS), also yielding improved performance. These improvements indicate more accurate capture of flow dynamics and peak events, supporting a robust and interpretable framework for RRM. Full article

Wildfires pose a growing threat to ecosystems, infrastructure, and public safety, particularly in the province of British Columbia (BC), Canada. In recent years, the frequency, severity, and scale of wildfires in BC have increased significantly, largely due to climate change, human activity, and changing land use patterns. This study presents a comprehensive, data-driven approach to wildfire prediction, leveraging advanced machine learning (ML) and deep learning (DL) techniques. A high-resolution dataset was constructed by integrating five years of wildfire incident records from the Canadian Wildland Fire Information System (CWFIS) with ERA5 reanalysis climate data. The final dataset comprises more than 3.6 million spatiotemporal records and 148 environmental, meteorological, and geospatial features. Six feature selection techniques were evaluated, and five predictive models—Random Forest, XGBoost, LightGBM, CatBoost, and an RNN + LSTM—were trained and compared. The CatBoost model achieved the highest predictive performance with an accuracy of 93.4%, F1-score of 92.1%, and ROC-AUC of 0.94, while Random Forest achieved an accuracy of 92.6%. The study identifies key environmental variables, including surface temperature, humidity, wind speed, and soil moisture, as the most influential predictors of wildfire occurrence. These findings highlight the potential of data-driven AI frameworks to support early warning systems and enhance operational wildfire management in British Columbia. Full article

The Tibetan Plateau is a globally critical climate-sensitive and ecologically fragile region. Vegetation phenology serves as a key indicator of ecosystem responses to climate change and simultaneously influences regional carbon cycling, water regulation, and ecological security. However, systematic quantitative assessments of phenological responses under the combined effects of multiple climate factors remain limited. This study integrates multi-source remote sensing data (MODIS MCD12Q2) and ERA5-Land meteorological data from 2001 to 2023, leveraging the Google Earth Engine (GEE) cloud platform to extract key phenological metrics, including the start (SOS) and end (EOS) of the growing season, and growing season length (GSL). Sen’s slope estimation, Mann–Kendall trend tests, and partial correlation analyses were applied to quantify the independent effects and spatial heterogeneity of temperature, precipitation, solar radiation, and evapotranspiration (ET) on GSL. Results indicate that: (1) GSL on the Tibetan Plateau has significantly increased, averaging 0.24 days per year (Sen’s slope +0.183 days/yr, Z = 3.21, p < 0.001; linear regression +0.253 days/yr, decadal trend 2.53 days, p = 0.0007), primarily driven by earlier spring onset (SOS: Sen’s slope −0.183 days/yr, Z = −3.85, p < 0.001), while autumn dormancy (EOS) showed limited delay (Sen’s slope +0.051 days/yr, Z = 0.78, p = 0.435). (2) GSL changes exhibit pronounced spatial heterogeneity and ecosystem-specific responses: southeastern warm–wet regions display the strongest responses, with temperature as the dominant driver (mean partial correlation coefficient 0.62); in high–cold arid regions, warming substantially extends GSL (Z = 3.8, p < 0.001), whereas in warm–wet regions, growth may be constrained by water stress (Z = −2.3, p < 0.05). Grasslands (Z = 3.6, p < 0.001) and urban areas (Z = 3.2, p < 0.01) show the largest GSL extension, while evergreen forests and wetlands remain relatively stable, reflecting both the “climate sentinel” role of sensitive ecosystems and the carbon sequestration value of stable ecosystems. (3) Multi-factor interactions are complex and nonlinear; temperature, precipitation, radiation, and ET interact significantly, and extreme climate events may induce lagged effects, with clear thresholds and spatial dependence. (4) The use of GEE enables large-scale, multi-year, pixel-level GSL analysis, providing high-precision evidence for phenological quantification and critical parameters for carbon cycle modeling, ecosystem service assessment, and adaptive management. Overall, this study systematically reveals the lengthening and asymmetric patterns of GSL on the Tibetan Plateau, elucidates diverse land cover and climate responses, advances understanding of high-altitude ecosystem adaptability and climate resilience, and provides scientific guidance for regional ecological protection, sustainable management, and future phenology prediction. Full article

Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship between CG lightning occurrences and altitude, topography, and various meteorological elements. Our findings reveal a stark east–west divide: high lightning density in the Sichuan Basin and the central Yungui Plateau contrasts sharply with lower densities over the eastern Tibetan Plateau and Hengduan Mountains. This geographical dichotomy extends to the diurnal cycle, where positive cloud-to-ground (PCG) lightning activities are more prevalent in the western part of the study area, while significant nocturnal activity defines the eastern basin and plateau. The study also finds that the relationship between CG lightning activities in the four sub-regions and 2 m temperature, precipitation, convective available potential energy, and Bowen ratio (the ratio of sensible heat flux to latent heat flux) exhibits similarities. Furthermore, we show that the relationship between lightning frequency and altitude is highly region-specific, with each area displaying a unique signature reflecting its underlying topography: a normal distribution over the eastern Tibetan Plateau, a bimodal pattern in the Hengduan Mountains, a sharp low-altitude peak in the Sichuan Basin, and a complex trimodal structure on the Yungui Plateau. These distinct regional patterns highlight the intricate interplay between large-scale circulation, complex terrain, and local meteorology in modulating lightning activity. Full article