Search Results (4,340)

Cotton is a vital cash crop that underpins regional agricultural systems and the global textile supply chain. However, climate change and increasing human activity are reshaping the spatial distribution of areas suitable for cotton cultivation, with the potential impacts being particularly pronounced in arid and semi-arid regions. This study integrated high-resolution cotton distribution data, environmental variables and human activities and employed ensemble model and niche analysis methods to systematically assess cotton suitability in Xinjiang under current and future climate scenarios. The results indicate that the ensemble models demonstrate high predictive performance, with both model types (Model 1: Environmental; Model 2: Environmental and human activity) achieving AUC values exceeding 0.97 and TSS values exceeding 0.84. Under current climatic conditions, suitable cotton-growing areas are primarily distributed on both sides of the Tianshan Mountains, and the inclusion of human activity factors results in a 13.71% reduction in suitable area. Moreover, Future climate change is projected to result in an increase in its suitable range of between 28.25% and 94.10%, with the most significant expansion occurring under the high-emissions scenario. MESS analysis indicates that the newly identified suitable areas in the future bear a high degree of similarity to current environmental conditions, whilst MOD analysis further highlights that temperature and precipitation are the key drivers of environmental variation. Additionally, Xinjiang cotton will retain a high degree of ecological niche under future climatic conditions. These findings provide important scientific evidence for optimizing the spatial distribution of cotton cultivation in Xinjiang and for climate-adaptive agricultural management. Full article

Floods are among the most catastrophic natural disasters globally, causing significant damage to both life and infrastructure. Consequently, immediate and accurate assessment of inundated areas is critical for effective emergency response. While optical remote sensing is typically used for flood assessment, it is often ineffective during active flood events due to persistent cloud cover and precipitation. To address this, this research develops a deep learning method utilizing Synthetic Aperture Radar (SAR), which offers all-weather, 24 h imaging capabilities. Specifically, an attention-based differential Siamese U-Net was developed to detect temporal changes in bi-temporal SAR imagery (e.g., Sentinel-1) acquired before and after flood events. The method was evaluated on the S1GFloods dataset, comprising 5360 bi-temporal Sentinel-1 SAR image pairs across 46 flood incidents on six continents. Experimental results demonstrate a flood Intersection over Union (IoU) of 92.43%, an F1 score of 96.07%, and a recall of 97.64%. These metrics rank the proposed approach third overall among top-performing methods on this dataset. Notably, the high recall rate indicates the model is particularly beneficial for emergency response, as it minimizes the number of undetected flooded areas. Despite utilizing a CNN-based architecture that is less complex than Vision Transformer models, this method achieves results comparable to the state-of-the-art DAM-Net, with a performance difference of only 0.77%. Full article

Flash droughts, characterized by rapid onset and intensification, are occurring more frequently under global warming. Accurately identifying the frequency and hazard severity of flash droughts remains challenging, as they are influenced by multiple hydroclimatic drivers, including precipitation deficits, temperature increases, and soil moisture depletion. We developed a daily-scale Flash Drought Hazard Index (FDHI) by integrating the interactive effects of multiple driving factors, aiming to assess the spatiotemporal patterns of flash drought hazard in the Black Soil Region of Northeast China during the period 2000–2020. The FDHI employs the daily Standardized Precipitation Evapotranspiration Index, Standardized Soil Moisture Index, Standardized Soil Temperature Index, and Standardized Runoff Index to characterize short-term anomalies in multiple hydrometeorological variables. Results showed that flash droughts occurred most frequently in the southern part of the Black Soil Region of Northeast China, particularly in the Songnen Plain and the Liaohe Plain, with annual frequencies of 5.98 and 5.80 events, respectively. Flash drought severity in the Liaohe Plain exhibited a significant increasing trend during the past decade. Moreover, the dominant driving factors varied substantially among regions. Flash droughts in the Liaohe Plain were mainly associated with precipitation deficits and enhanced evapotranspiration, whereas soil moisture depletion and temperature anomalies played a more important role in the Songnen Plain. These results reveal pronounced regional heterogeneity in flash drought mechanisms across the Black Soil Region of Northeast China and demonstrate the effectiveness of the proposed FDHI for daily-scale agricultural flash drought monitoring. The study provides scientific support for agricultural drought risk management and disaster mitigation under climate change. Full article

Amid the ongoing acceleration of climate change over recent decades, extreme precipitation events have become more frequent and intense on a global scale, triggering severe natural hazards and considerable socioeconomic damage. Nevertheless, how extreme precipitation has evolved at the national level over long time spans, and what role atmosphere–ocean teleconnections play in driving regional differences, remains insufficiently explored. This study addresses that knowledge gap by conducting a comprehensive assessment of eight ETCCDI-based extreme precipitation indices (PRCPTOT, CWD, R20, R95p, R99p, RX1day, RX5day, and SDII) across six climatic sub-regions of China (Northeast, North, East, Central South, Northwest, and Southwest) over 1960–2020, drawing on daily records from 695 quality-controlled meteorological stations. Key atmospheric and oceanic circulation drivers were further diagnosed and their joint influence was quantified via multiple wavelet coherence (MWC). The analysis shows that five of the eight indices (CWD, R95p, R99p, RX1day, and RX5day) underwent statistically significant fluctuating changes (p < 0.05) throughout the 61-year record. Seven indices, all except CWD, demonstrated upward tendencies, with mutation points clustering after 2010, most notably between 2011 and 2016. Wavelet power spectra indicates elevated energy concentrations at multiple time scales, although only CWD exhibited a statistically significant periodicity of approximately 8–10 a (p < 0.05 against red noise). In terms of spatial patterns, index magnitudes generally increased along a northwest-to-southeast gradient. Stations registering significant upward shifts were concentrated in East and Central South China, whereas significant downward shifts appeared mainly in North China and the northern portion of East China. An altitude-dependent pattern was also detected: CWD rose with elevation, while the remaining indices declined sharply below 1288 m, fluctuated in the 1288–2090 m band, and dropped again above 2090 m. Wavelet coherence analysis uncovered significant resonance between extreme precipitation and four circulation indices—SCSMMI, WPSHI, PNA, and NAO. MWC further identified three driver combinations—ENSO-PNA, SCSMMI-WPSHI, and ENSO-NAO-EASMI—as the most influential, acting both individually and synergistically. These results furnish an empirical basis for forecasting, preventing, and managing precipitation-related disasters across China under future climate scenarios. Full article

Ongoing global climate change and intensified human activities have increased the frequency and intensity of droughts, posing a serious threat to global ecosystems and agricultural sustainability. However, the seasonally differentiated effects of droughts on cropland phenology and productivity, especially in Northeast China, remain insufficiently understood, limiting the assessment of agro-ecosystem vulnerability and the development of effective adaptation strategies. In this study, the standardized precipitation evapotranspiration index (SPEI) was used to assess the frequency and severity of extreme drought in Northeast China based on run theory. Cropland phenology parameters and productivity were derived from time-series MODIS normalized difference vegetation index (NDVI), and gross primary productivity (GPP) products, which were smoothed using a Savitzky–Golay (S–G) filter. Correlation analyses were conducted to examine regional associations between SPEI-defined drought conditions and cropland phenology and productivity. Results show that: (1) Drought events occurred frequently in the central and southern parts of Northeast China, particularly in the Songnen Plain (5.22 events per decade) and the Liaohe Plain (4.89 events per decade); (2) the Songnen Plain showed significant increases (Sen’s slope > 0, p < 0.05) across all drought metrics over 2001–2020, which coincided with LOS shortening (−0.18 d a⁻¹) and GPP decline (−9.12 g C m⁻² a⁻¹); in contrast, the Sanjiang Plain exhibited slight declines (Sen’s slope, p > 0.05) in drought metrics, resulting in LOS lengthening (0.06 d a⁻¹) and GPP increases (7.84 g C m⁻² a⁻¹); and (3) drought impacts were strongly season-dependent, with autumn droughts showing a stronger association with reductions in crop productivity in local areas of Northeast China. These findings highlight the need to account for crop responses to drought events, which is essential for developing measures to cope with drought and protecting regional food security. Full article

This article presents the results of a field study conducted in 2022 on groundwater outflows located at the edge of the Kashubian Lake District and the Reda-Łeba Proglacial Stream Valley in northern Poland. The recharge of numerous springs was found to occur from the first aquifer, locally supported by a deeper aquifer connected to the first one near the bowl of Lubowidzkie Lake. Groundwater drainage occurs by gravity. It is relatively abundant for young glacial areas and averages 82 dm³·s⁻¹, making the springs capable of acting as a drinking water reservoir. This assessment is based on major ions and nutrients only; microbiological and trace-organic/metal analyses are required before any drinking-water designation. Spring water is important in the lake’s supply, accounting for 18.0% of the total inflow to the basin. The hydrochemical characteristics of these waters keep the lake in ecological balance. The waters from the springs are characterized by little variation in chemical composition, with the Ca-HCO₃ hydrochemical type. They represent young infiltration waters associated with direct recharge from precipitation (the average age of the water is 60 years). Currently, low nitrate and chloride suggest limited agricultural and urban influence, but phosphate levels and observed human activities warrant caution. Forest management is gradually developing in its catchment, which may result in a reduction of the spring yield and a deterioration of their quality in the future. This may result in a disturbance of the hydrological balance of structures hydraulically connected to spring recharge and to groundwater inflow (river, lake). Although the springs studied are local hydrological phenomena, their functioning and the need for protection are closely linked to global challenges in the field of sustainable development. This primarily concerns the protection of groundwater-dependent ecosystems and, more broadly, water security and increased resilience to climate change. Full article

Under the continuous impact of global warming, the water cycle has undergone significant changes, causing a series of problems such as water shortage, frequent climate disasters and ecological environment deterioration. Therefore, understanding the evolution of regional historical and future drought and wet conditions is crucial for adapting and mitigating disasters. This paper discusses the evolution of drought and pluvial events in the Henan section of the Yellow River from 1970 to 2014, projects the future evolution of drought and wet conditions, and assesses the performance of various climate models from Coupled Model Intercomparison Project Phase 6 in simulating precipitation and temperature. Subsequently, future drought and wet conditions in the Henan section were projected for the 2015–2100 period across four SSP-RCP scenarios using Standardized Precipitation and Evapotranspiration Index (SPEI) and run theory. The results indicate that the Henan section of the Yellow River exhibited a significant drying trend during the historical period, with a rate of 0.15 per decade. Looking ahead, a wetting tendency is projected under the SSP1-2.6 scenario, with an increasing rate of 0.02 per decade, whereas the other three scenarios consistently show drying trends, with rates of −0.11, −0.15, and −0.23 per decade, respectively. Across all scenarios, drought and wetness variations exhibit pronounced periodicity, particularly at timescales of approximately 20–30 years, suggesting the persistence of multi-decadal hydroclimatic oscillations. Furthermore, drought and wetness events are projected to become more persistent and severe during the mid-to-late 21st century. Compared with the historical baseline, increasing radiative forcing is associated with an expansion in drought-affected areas, accompanied by reduced event frequency but longer duration and greater severity. In terms of risk, the SSP3-7.0 scenario presents the highest overall drought and wetness risk with the widest spatial extent, whereas the SSP2-4.5 scenario shows relatively lower risk levels and a more balanced spatial distribution. Full article

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

The normalized difference vegetation index (NDVI) is widely used to track vegetation cover and ecological change. However, in arid watersheds where irrigated farmland and natural vegetation coexist, it remains unclear how irrigation changes the relative effects of climate, terrain, and soil on vegetation growth. Using the Hetao irrigation district watershed in Inner Mongolia, this study analyzed NDVI dynamics and their environmental controls from 2001 to 2024 through trend analysis, spatial autocorrelation, XGBoost-SHAP, GeoDetector, and geographically weighted regression. NDVI increased significantly across the watershed at 0.0035 yr⁻¹, but the increase was much stronger inside the irrigation district (mean NDVI = 0.58; slope = 0.0061 yr⁻¹) than outside it (mean NDVI = 0.26; slope = 0.0015 yr⁻¹). Global Moran’s I values remained above 0.86, showing persistent spatial clustering. The main drivers also differed by zone. DEM, SOC, and precipitation were most important for the whole watershed; SOC, TP, pH, and TN were more important inside the irrigation district; and precipitation and DEM were more important outside it. GeoDetector confirmed that paired drivers strengthened each other, including SOC ∩ DEM at the watershed scale and DEM ∩ TP outside the irrigation district. GWR further showed that rainfall effects were stronger outside the irrigation boundary, while soil-related effects were stronger in the irrigated agricultural belt. These results show that irrigation not only increases NDVI but also changes how vegetation responds to environmental conditions by weakening direct rainfall limitation and strengthening soil-related controls in managed landscapes. The findings provide evidence for zone-specific vegetation restoration and land-water management in dryland irrigation watersheds. Full article

Stratospheric Aerosol Injection (SAI) has been investigated as a climate intervention strategy to offset global warming, and regional impacts studies rely on simulations from the Geoengineering Large Ensemble (GLENS). The probabilistic behavior of the GLENS ensemble has not been systematically characterized for Southeast Asia. Because GLENS is a counterfactual experiment combining the Representative Concentration Pathway 8.5 (RCP8.5) forcing with active SAI, comparison with observations cannot validate the SAI response itself. In the early protocol years, the SAI forcing is small, so the early window provides a diagnostic of statistical consistency between the ensemble and the observed climate and of ensemble spread reliability. We compare the 21-member GLENS ensemble for 2020–2025 with ERA5 for daily precipitation and mean and maximum temperature using empirical coverage of the 95% prediction interval, rank histograms with the Jolliffe–Primo decomposition, the Continuous Ranked Probability Score, and the Brier Score for rainfall occurrence. Coverage is well below nominal for all variables, and rank histograms show pronounced U-shapes dominated by the dispersion error component, indicating systematic underdispersion. Because the underlying mechanisms are properties of the ensemble system rather than of the SAI forcing, this underdispersion is expected to persist in the future record, motivating statistical post-processing of GLENS before its use in SAI impact assessments. Full article

Assessing the ecological and environmental quality of old industrial cities is crucial for understanding the spatial heterogeneity of ecological quality and its associated factors during regional transformation. Taking Tangshan, a typical old industrial city in China, as a case study, this study employed Landsat 8/9 remote sensing imagery and multi-source auxiliary data from 2015 to 2024 to calculate annual Remote Sensing Ecological Index (RSEI) values using a unified multi-year standardization and principal component analysis framework. Global and local Moran’s I analyses were conducted to examine spatial clustering patterns, and the Optimal-Parameter Geographical Detector (OPGD) was used to quantify the spatial correspondence between RSEI and selected natural and anthropogenic explanatory factors. The results indicate the following. (1) The mean RSEI in Tangshan fluctuated between 0.34 and 0.54 from 2015 to 2024, exhibiting significant interannual variability. (2) Higher RSEI values were primarily distributed in the northern mountainous and southern coastal ecological zones, while lower values were concentrated in the central and eastern industrial-mining zones. (3) The global Moran’s I was significantly positive in all years (0.702–0.778, p = 0.001), indicating the persistence of spatial clustering; the proportion of non-significant local spatial units decreased from 72.00% in 2015 to 69.46% in 2024. (4) Land use/land cover (LULC) exhibited the most consistently high explanatory power. Elevation (ELE), nighttime light (NTL), and built-up intensity (BUILT) also formed a leading group of spatially associated factors, although their relative ranking varied between the optimal-parameter results and the robustness analysis. Slope (SLOPE), annual precipitation (Pre), and annual mean temperature (Tmean) generally showed relatively lower explanatory power. Interaction detection showed that pairwise factor combinations generally had higher q values than individual factors, with LULC × ELE showing consistently high explanatory power in representative years. This study provides a scientific reference for ecological and environmental monitoring and differentiated management in old industrial cities. Full article

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals. Full article

The steno-endemic Verbascum rupicola faces a precarious future due to its extreme habitat specialization on tectonically active hydrothermal quartz veins. This study presents a long-term assessment based on periodic population censuses spanning 18 years (2007, 2016, and 2025) to assess the demographic and spatial trends of its global population in the Tahtalı Dam basin, Türkiye. Field surveys, GIS-based habitat mapping, and controlled pollination experiments were integrated with seed germination kinetics and ex situ cultivation trials. Results reveal a precipitous 69.12% global population decline, primarily driven by a 33.41% habitat loss from agricultural expansion in 2011 and the total extirpation of three sub-populations by a major wildfire in 2017. Furthermore, a “reproductive squeeze” was identified, where climate-induced reductions in flower production (18.87%) are compounded by intensifying floral predation by Pieris rapae. Reproductive analysis revealed random monomorphic enantiostyly—reported for the first time in the genus—which functions as a flexible mating system prioritizing outcrossing while providing reproductive assurance. Despite high intrinsic seed viability (69.12%), ex situ cultivation largely failed (3.5% survival; 1 out of 28 transplanted individuals), underscoring the species’ obligate chasmophytic nature. Consequently, V. rupicola meets the criteria for Critically Endangered (CR) status, necessitating urgent “micro-reserve” protection of its remaining habitat and in situ restoration efforts. Full article

Background: Climate change has contributed to the global resurgence of dengue, with a spike of more than 14.4 million dengue cases. This study aimed to analyze the association between dengue frequency with climatic factors, circulating serotypes, and disease severity in northwestern Mexico. Methods: A retrospective time-series study was conducted using dengue molecular diagnostic data reported between September 2017 and January 2025 by the Laboratorio de Apoyo a la Vigilancia e Investigación Epidemiológica del Centro de Investigación Biomédica de Occidente, Mexico. Data included dengue frequency, serotype distribution, and clinical severity across seven states in northwestern Mexico (Colima, Guanajuato, Jalisco, Michoacán, Nayarit, Sinaloa, and Sonora). Meteorological data were obtained from the Automatic Meteorological Stations of the National Water Commission. Associations between dengue frequency and climatic variables were evaluated using linear regression models. Statistical analyses were performed using SPSS v24 and R v3.5. Results: In Jalisco, minimum, mean and maximum temperatures, as well as precipitation, were significant predictors of dengue cases, explaining approximately 21.7% of the variance (adjusted R² = 0.217, p < 0.001). In Colima and Michoacán, precipitation showed no predictive value. In Guanajuato, the maximum temperature was excluded from the model (adjusted R² = 0.226). Models for Nayarit, Sinaloa, and Sonora excluded two or more climatic variables, with adjusted R² values of 0.111, 0.151, and 0.049, respectively. Conclusions: Climatic conditions and epidemiological time trends explain a modest proportion of dengue cases in northwestern Mexico, with the strongest association observed in Jalisco. Additional determinants, including vector ecology, host immunity, circulating serotypes, population mobility, and public health interventions, should be considered to better understand dengue dynamics. Full article

South China is characterized by abundant and complex precipitation, with frequent typhoons, heavy rainfall, and pronounced extreme events, making it an ideal region for precipitation microphysics research. This study uses rainfall observations from an OTT Parsivel² (Parsivel) laser disdrometer and a Micro Rain Radar–2 (MRR–2) collected in Zhuhai during 2022–2023 to analyze the characteristics of stratiform rainfall (SR) and convective rainfall (CR). The results show that, although SR lasts longer, CR contributes much more to the total accumulated rainfall. In SR, samples with rain rate (RR) < 5 mm h⁻¹ account for about 27% of occurrences and contribute less than 10% of total rainfall, whereas in CR, samples with RR > 8 mm h⁻¹ represent only 7% of occurrences but contribute more than 45% of the accumulated rainfall. CR is characterized by a larger mass-weighted mean diameter (Dm), while SR shows a higher normalized intercept parameter (Nw). In SR, Dm increases with RR, whereas Nw changes little; in CR, both Dm and Nw increase with RR. Finally, by analyzing temporal/spatial collocated vertical rain profiles from MRR and Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM DPR), the results show that CR exhibits larger RR, radar reflectivity and stronger vertical variability than SR, along with greater variations in Dm and log₁₀(Nw). Ground-based MRR also provides an independent vertical reference for evaluating DPR-derived precipitation structure and interpreting the consistency and discrepancies between satellite and ground-based observations. Although the results are not conclusive due to a limited number of events, both instruments capture distinct microphysical characteristics in the analyzed SR and CR cases, despite differences in their retrieved vertical DSD structures. Full article