Search Results (451)

Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. Full article

Vegetation dynamics are complexly influenced by multiple factors such as climate, human activities, and topography. In recent years, the frequency, intensity, and diversity of human activities have increased, placing substantial pressure on the growth of vegetation. Arid and semi-arid regions are particularly sensitive to climate change, and climate change and large-scale ecological restoration have led to significant changes in the dynamic of dryland vegetation. However, few studies have explored the nonlinear relationships between these factors and vegetation dynamic. In this study, we integrated trend analysis (using the Mann–Kendall test and Theil–Sen estimation) and machine learning algorithms (XGBoost-SHAP model) based on long time-series remote sensing data from 2001 to 2020 to quantify the nonlinear response patterns and threshold effects of bioclimatic variables, topographic features, soil attributes, and anthropogenic factors on vegetation dynamic. The results revealed the following key findings: (1) The kNDVI in the study area showed an overall significant increasing trend (p < 0.01) during the observation period, of which 26.7% of the area showed a significant increase. (2) The water content index (Bio 23, 19.6%), the change in land use (15.2%), multi-year average precipitation (pre, 15.0%), population density (13.2%), and rainfall seasonality (Bio 15, 10.9%) were the key factors driving the dynamic change of vegetation, with the combined contribution of natural factors amounting to 64.3%. (3) Among the topographic factors, altitude had a more significant effect on vegetation dynamics, with higher altitude regions less likely to experience vegetation greening. Both natural and anthropogenic factors exhibited nonlinear responses and interactive effects, contributing to the observed dynamic trends. This study provides valuable insights into the driving mechanisms behind the condition of vegetation in arid and semi-arid regions of China and, by extension, in other arid regions globally. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

The Caucasus region, characterized by its complex topography and diverse climatic regimes, exhibits pronounced spatial variability in temperature and precipitation patterns. This study investigates the seasonal behavior of air temperature, precipitation, vertical temperature gradients, and inversion phenomena across distinct landscape types using observational data from 63 meteorological stations for 1950–2022. Temperature trends were analyzed using linear regression, while vertical lapse rates and inversion layers were assessed based on seasonal temperature–elevation relationships. Precipitation regimes were evaluated through Mann-Kendall trend tests and Sen’s slope estimators. Results reveal that temperature regimes are strongly modulated by landscape type and elevation, with higher thermal variability in montane and subalpine zones. Seasonal temperature inversions are most frequent in spring and winter, especially in western lowlands and enclosed valleys. Precipitation patterns vary markedly across landscapes: humid lowlands show autumn–winter maxima, while arid and semi-arid zones peak in spring or late autumn. Some landscapes exhibit secondary maxima and minima, influenced by Mediterranean cyclones and regional atmospheric stability. Statistically significant trends include increasing cool-season precipitation in humid regions and decreasing spring rainfall in arid areas. These findings highlight the critical role of topography and landscape structure in shaping regional climate patterns and provide a foundation for improved climate modeling, ecological planning, and adaptation strategies in the Caucasus. Full article

A comprehensive analysis of the evolution of rainfall characteristics in Catalonia, NE Spain, was conducted using monthly data from 72 rain gauges over the period 1950–2022. A moving-window approach was applied at annual, seasonal, and monthly scales, calculating mean values, coefficients of variation (CV), and trends across 43 overlapping 31-year periods. To assess trends in these moving statistics, a modified Mann–Kendall test was applied to both the 31-year means and CVs. Results revealed a significant 10% decrease in annual rainfall, with summer showing the most pronounced decline, as nearly 90% of stations exhibited negative trends, while the CV showed negative trends in coastal areas and mostly positive trends inland. At the monthly scale, February, March, June, August, and December exhibited negative trends at more than 50% of stations, with rainfall reductions ranging from 20% to 30%. Additionally, the temporal evolution of Mann–Kendall trend coefficients within each 31-year moving window displayed a fourth-degree polynomial pattern, with a periodicity of 30–35 years at annual and seasonal scales, and for some months. Finally, at the annual scale and in two centennial series, the 80-year oscillations found were inversely correlated with the large-scale climate indices North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO). Full article

Research on forest carbon sinks is crucial for mitigating global climate change and achieving carbon peaking and neutrality. However, studies at the county level remain relatively limited. This study utilized multi-source remote sensing data and the Carnegie–Ames-Stanford Approach (CASA) and soil respiration models to estimate the forest net ecosystem productivity (NEP) in Taoyuan County from 2000 to 2023. The spatiotemporal differentiation was analyzed using seasonal Mann–Kendall tests, Theil–Sen slope estimation, and standard deviation ellipses. The forest NEP for 2035 was predicted under multiple climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) by applying a discrete coupling of the Patch-generating Land Use Simulation (PLUS) model, incorporating territorial spatial planning policy, and using the CASA model. The results indicated that the Taoyuan County forest NEP exhibited a fluctuating upward trend from 2000 to 2023, with higher (lower) values in the west/south (east/north). Under future warming and humidification, the overall forest NEP in Taoyuan County was projected to decrease by 2035, with predicted NEP values across scenarios ranking as SSP5-8.5 > SSP1-2.6 > SSP2-4.5. The findings offer practical insights for improving local forest management, optimizing forest configuration, and guiding county-level “dual-carbon” policies under future climate and land use change, thereby contributing to ecological sustainability. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

Snow water equivalent (SWE), an essential parameter of snow, is largely studied to understand the impact of climate regime effects on snowmelt patterns. This study developed a Siamese Attention U-Net (Si-Att-UNet) model to detect daily change events in the winter season. The daily SWE change event detection task is treated as an image content comparison problem in which the Si-Att-UNet compares a pair of SWE maps sampled at two temporal windows. The model detected SWE similarity and dissimilarity with an F1 score of 99.3% at a 50% confidence threshold. The change events were derived from the model’s prediction of SWE similarity using the 50% threshold. Daily SWE change events increased between 1979 and 2018. However, the SWE change events were significant in March and April, with a positive Mann–Kendall test statistic (tau = 0.25 and 0.38, respectively). The highest frequency of zero-change events occurred in February. A comparison of the SWE change events and mean change segments with those of the northern hemisphere’s climate anomalies revealed that low temperature and low precipitation anomalies reduced the frequency of SWE change events. The findings highlight the influence of climate variables on daily changes in snow-related water storage in March and April. Full article

Monitoring land cover/use dynamics and wildfire occurrences is very important for land management planning and risk mitigation practices. In this research, moderate-resolution imaging spectroradiometer (MODIS) annual land cover images for the period 2001–2023 are utilized for the twenty administrative regions of Italy. Monthly MODIS burned area images are utilized for the period 2001–2020 to study wildfire occurrences across these regions. In addition, monthly Global Precipitation Measurement images for the period 2001–2020 are employed to estimate correlations between precipitation and burned areas annually and seasonally. Boxplots are produced to show the distributions of each land cover/use type within the regions. The non-parametric Mann–Kendall trend test and Sen’s slope are applied to estimate a linear trend, with statistical significance being evaluated for each land cover/use time series of size 23. Pearson’s correlation method is applied for correlation analysis. It is found that grasslands and woodlands have been declining and increasing in most regions, respectively, most significantly in Abruzzo (−0.88%/year for grasslands and 0.71%/year for grassy woodlands). The most significant and frequent wildfires have been observed in southern Italy, particularly in Basilicata, Apulia, and Sicily, mainly in grasslands. The years 2007 and 2017 experienced severe wildfires in the southern regions, mainly during July and August, due to very hot and dry conditions. Negative Pearson’s correlations are estimated between precipitation and burnt areas, with the most significant one being for Basilicata during the fire season (r = −0.43). Most of the burned areas were mainly within the elevation range of 0–500 m and the lowlands of Apulia. In addition, for the 2001–2020 period, a high positive correlation (r > 0.7) is observed between vegetation and land surface temperature, while significant negative correlations between these variables are observed for Apulia (r ≈ −0.59), Sicily (r ≈ −0.69), and Sardinia (r ≈ −0.74), and positive correlations (r > 0.25) are observed between vegetation and precipitation in these three regions. This study’s findings can guide land managers and policymakers in developing or maintaining a sustainable environment. Full article

This study analyzes precipitation patterns and future trends in the Kuye River Basin in the context of climate change, providing a scientific foundation for water resource management and ecological protection. Using methods such as the Mann–Kendall test, Pettitt test, and complex Morlet wavelet analysis, this study examines both interannual and intra-annual variability in historical precipitation data, identifying abrupt changes and periodic patterns. Future projections are based on CMIP5 models under RCP4.5 and RCP8.5 scenarios, forecasting changes over the next 30 years (2023–2052). The results reveal significant spatiotemporal variability in precipitation, with 88.16% concentrated in the summer and flood seasons, while only 1.07% falls in winter. The basin’s multi-year average precipitation is 445 mm, exhibiting stable interannual variability, but with a significant increase starting in 2006. Projections indicate that the average annual precipitation will rise to 524.69 mm from 2023 to 2052, with a notable change point in 2043. Precipitation is expected to increase spatially from northwest to southeast. This research underscores the importance of understanding precipitation dynamics in managing drought and flood risks. It highlights the role of soil and water conservation and vegetation restoration in improving water resource efficiency, supporting sustainable development, and guiding climate adaptation strategies. Full article

In a warming climate, rising temperature are expected to influence atmospheric humidity. This study examined the spatio-temporal dynamics of temperature (TEMP) and relative humidity (RH) across Equatorial Africa from 1980 to 2020. The analysis used RH data from European Centre of Medium-range Weather Forecasts Reanalysis v.5 (ERA5) reanalysis, TEMP and precipitation (PRE) from Climate Research Unit (CRU), and soil moisture (SM) and evapotranspiration (ET) from the Global Land Evaporation Amsterdam Model (GLEAM). In addition, four teleconnection indices were considered: El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). This study used the Mann–Kendall test and Sen’s slope estimator to analyze trends, alongside multiple linear regression to investigate the relationships between TEMP, RH, and key climatic variables—namely evapotranspiration (ET), soil moisture (SM), and precipitation (PRE)—as well as large-scale teleconnection indices (e.g., IOD, ENSO, PDO, and NAO) on annual and seasonal scales. The key findings are as follows: (1) mean annual TEMP exceeding 30 °C and RH less than 30% were concentrated in arid regions of the Sahelian–Sudano belt in West Africa (WAF), Central Africa (CAF) and North East Africa (NEAF). Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). Winter (0.27 °C decade⁻¹) and spring (0.20 °C decade⁻¹) exhibited the strongest warming, followed by autumn (0.18 °C decade⁻¹) and summer (0.10 °C decade⁻¹). (4) RH trends showed stronger seasonal decline compared to annual changes, with reduction ranging from 5 to 10% per decade in certain seasons, and about 2% per decade annually. (5) Pearson correlation analysis demonstrated a strong negative relationship between TEMP and RH with a correlation coefficient of r = − 0.60. (6) Significant associations were also observed between TEMP/RH and both climatic variables (ET, SM, PRE) and large scale-teleconnection indices (ENSO, IOD, PDO, NAO), indicating that surface conditions may reflect a combination of local response and remote climate influences. However, further analysis is needed to distinguish the extent to which local variability is independently driven versus being a response to large-scale forcing. Overall, this research highlights the physical mechanism linking TEMP and RH trends and their climatic drivers, offering insights into how these changes may impact different ecological and socio-economic sectors. Full article

This study compares long-term air pollution trends and seasonal patterns in Beijing and Seoul from 2014 to 2024, focusing on PM2.5, PM10, CO, NO₂, SO₂, and O₃. Using statistical analyses including Mann–Kendall tests and generalized additive models, we found that Beijing achieved notable reductions in particulate matter, largely due to stricter industrial controls and reduced coal use, though winter pollution peaks remain. In contrast, Seoul’s improvements were slower, mainly due to persistent vehicular emissions and recurring spring dust storms from northern China. Seasonal analysis showed winter peaks in Beijing linked to coal heating, and spring peaks in Seoul driven by transboundary dust, with higher summer ozone in Seoul reflecting photochemical activity. These findings highlight the need for city-specific air quality management and regional cooperation, recommending further reductions in vehicular emissions for Seoul and continued transition from coal in Beijing to mitigate health impacts. This study identifies specific seasonal trends and pollution sources that require targeted policy interventions to improve air quality. Full article

Puerto Rico has experienced recent increases in annual, seasonal and daily temperatures that have been associated with climate change. More recently, the island has been experiencing an increase in the frequency of extremely warm days that are causing significant environmental and socio-economic impacts. This study focuses on examining how annual, seasonal and daily temperatures have changed over recent decades in 12 historical sites spread across the island for the 1970–2024 period and how it relates to climate change. The Mann–Kendall tests for trends were employed for the annual and seasonal series to identify areas of the island where warming has been found to be statistically significant. The 90th, 95th, and 99th percentiles of daily temperature series were also analyzed. This study found that Puerto Rico has experienced significant warming from 1970 to 2024, with the most consistent increases in minimum temperatures, especially during the summer and nighttime hours. The frequency of extreme heat events has increased across nearly all stations in different areas of the island. Stepwise regression models identified surface air temperature (SAT), sea surface temperature (SST), and total precipitable water (TPW) as the most influential regional climate predictors driving mean temperature trends and the occurrence of extreme heat events. Full article

One of the emerging services provided by roadside green infrastructure is its contribution to the quality of landscape phenology, which is measured through the succession of colours and forms throughout the seasons. In the seasonal dynamics of space, flowering phenological patterns play a key role, particularly in early blooming species such as Cornus mas L. Therefore, this paper aims to highlight the significance of the Cornelian cherry as a component of roadside green infrastructure in the southwestern suburban zone of Belgrade. Through an integrative approach to phenological and climatic elements, and by means of a specific case study covering the period from 2007 to 2025, under climate change conditions, the influence of air temperature and precipitation on local flowering patterns of the Cornelian cherry has been assessed. Based on 1140 phenological observations conducted over 19 consecutive years, from January to April, key flowering elements were identified—those that influence pollination, fruiting, and the species’ practical potential. The Mann–Kendall, Sen’s slope, Rayleigh, and Watson–Williams tests were used to examine spatio-temporal changes in flowering patterns, while the Spearman Rank test and circular statistics were applied to quantify correlations among the analysed parameters. The results confirm that Cornelian cherry is an adaptive and sustainable species that continuously provides visual identity during its flowering period, while simultaneously reflecting climate change through phenological responses. These phenological responses are closely linked to local climatic conditions. In addition to enriching landscape phenology with vibrant visual features during the colder months, Cornelian cherry also enhances biodiversity by providing ecosystem services as a nectar-producing species, with its pollen serving as an early and valuable food source for bees. The study also confirms that the seasonal dynamics of landscape phenology can be used as a scientifically valid criterion for assessing the ecological quality of roadside green infrastructure. Full article

The Precipitation Concentration Index (PCI) is considered a powerful tool that can be used to analyse the spatial and temporal distribution and variability of precipitation over a region. It plays a significant role in planning and managing water resources, including monitoring and forecasting drought and flood risks. As such, the present study used the PCI to investigate the spatio-temporal distribution of precipitation in summer rainfall regions covering six selected South African provinces. Specifically, this study analysed monthly precipitation data from 49 rainfall districts spanning from 1979 to 2023 and assessed the spatio-temporal variability patterns of annual, seasonal and supra-seasonal PCI values and their trends based on the Mann–Kendall trend test. Pearson’s correlation was used to evaluate the relationship between the PCI values and precipitation across the provinces. Moderate annual PCI values were observed mainly in KwaZulu-Natal and the eastern regions of the Free State and Mpumalanga provinces. A large portion of the study site exhibited irregular annual precipitation concentrations. The PCI decreased by −1.5 and −1.2 magnitudes of change during 1979–1989 and 2000–2011 and increased by 2.1 and 2.8 magnitudes between 1990–2000 and 2012–2023, respectively. Uniform precipitation concentration was mostly recorded during the December–January–February (DJF) season. The entire study area recorded moderate precipitation concentration during the March–April–May (MAM) and September–October–November (SON) seasons (with exceptions for KwaZulu-Natal (KZN)). In addition, irregular precipitation concentration dominated during the June–July–August (JJA) rainy season. All provinces except KZN recorded positive trends in annual PCI. Also, positive trends in PCI were observed during the supra-wet season across the provinces, except KZN and in parts of the Free State. Furthermore, negative trends in seasonal PCI were mostly dominant during DJF and MAM, while positive trends were mostly observed during SON and JJA rainy seasons. The annual PCI values were positively correlated with annual precipitation in KZN, Free State and Limpopo, while negative correlations were observed in Mpumalanga and North West provinces. The results presented in this study contribute to drought and flood monitoring in support of water resource management and planning. Full article