Search Results (1,357)

Amid accelerating global warming, surface albedo is a key indicator and regulator of how Earth’s surface reflects solar radiation, directly affecting the planetary radiation balance and climate. In this paper, we combined MODIS shortwave albedo (MCD43A3, 500 m), MODIS NDVI (MOD13A3, 1 km; NDVI = normalized difference vegetation index) and 1-km gridded meteorological data to analyze the spatiotemporal variations of surface albedo across China during 2001–2020 at a gridded scale. Temporal trends were quantified with the Theil–Sen slope and the Mann–Kendall test, and the seasonal contributions of NDVI, air temperature, and precipitation were assessed with a geographically and temporally weighted regression (GTWR) model. China’s mean annual shortwave albedo was 0.186 and showed a significant decline. Attribution indicates NDVI is the dominant driver (~48% of total change), followed by temperature (~27%) and precipitation (~25%). Seasonally, NDVI explains ~43.94–52.02% of the variation, ~26.81–28.07% of the temperature, and ~21.17–28.57% of the precipitation. Clear spatial patterns emerge. In high-latitude and high-elevation snow-dominated regions, albedo tends to decrease with warmer conditions and increase with greater precipitation. In much of eastern China, albedo is generally positively associated with temperature and negatively with precipitation. NDVI—reflecting vegetation greenness and canopy structure—captures the effects of vegetation greening, canopy densification, and land-cover change that reduce surface reflectivity by enhancing shortwave absorption. Temperature and precipitation affect albedo primarily by regulating vegetation growth. This study goes beyond correlation mapping by combining robust trend detection (Theil–Sen + MK) with GTWR to resolve seasonally varying, non-stationary controls on albedo at 1-km over 20 years. By explicitly separating snow-covered and snow-free conditions, we quantify how NDVI, temperature, and precipitation contributions shift across climate zones and seasons, providing a reproducible, national-scale attribution that can inform ecosystem restoration and land-surface radiative management. 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

This study investigates the long-term trends and variability of heatwaves in Greece, analyzing their frequency, duration, and intensity from 1960 to 2022 using high-quality meteorological data from the Hellenic National Meteorological Service. The research utilizes robust statistical methods, including Theil–Sen regression and the Mann–Kendall trend test, to assess long-term trends across different timescales. The findings reveal a significant increase in heatwave frequency and intensity, particularly in recent decades, with notable seasonal differences. While summer remains the most affected period, an upward trend in spring and autumn heatwaves suggests an extension of the heatwave season. The intensity of heatwaves has also increased, indicating a growing risk to vulnerable populations and critical infrastructure. Full article

Climate change and its impacts on precipitation patterns have intensified the occurrence of prolonged dry periods in agricultural regions of Brazil, particularly in the MATOPIBA region (comprising the states of Maranhão, Tocantins, Piauí, and Bahia). This study analyzes the seasonal variability and trends of the Consecutive Dry Days (CDDs) index in the MATOPIBA region from 1981 to 2023. Daily precipitation data from the Brazilian Daily Weather Gridded Data (BR-DWGD) dataset were used for the analysis. The novelty of this work lies in its focus on the seasonal characterization of CDD across the entire MATOPIBA field of agriculture, addressing the following main research question: how have the frequency and persistence of dry spells evolved during the rainy and dry seasons over the past four decades? The methodology involved trend detection using the Mann–Kendall test and Sen’s Slope estimator. The results indicated that during the rainy season, the average CDD ranged from 20 to 60 days, with higher values concentrated in the states of Piauí and Bahia. In contrast, during the dry period, averages exceeded 100 days across most of the region. Trend analysis revealed a significant increase in CDD over extensive areas, particularly in Tocantins and Southern Bahia. The increasing trends were estimated at 1 to 4 days per decade during the rainy season and 4 to 14 days per decade in the dry period. Although a decreasing CDD trend was observed in small areas of Northern Maranhão, possibly associated with the influence of the Intertropical Convergence Zone, the overall scenario indicates a greater persistence of long dry spells. This pattern suggests an increase in vulnerability to water scarcity and agricultural losses. These findings highlight the need for implementing adaptation strategies, such as the use of drought-tolerant cultivars, conservation management practices, irrigation expansion, and public policies aimed at promoting climate resilience in the MATOPIBA region. Full article

Drought is a major climatic hazard affecting water resources, agriculture, and livelihoods in semi-arid regions, with increasing severity under climate change. This study assessed long-term drought in Dura City, Palestine, from 2000 to 2023 using the Standardized Precipitation Index (SPI) at 3-, 6-, and 12-month timescales. Monthly precipitation and temperature data were obtained from local meteorological stations, with mean annual precipitation of 408 mm and average summer and winter temperatures of 28 °C and 12 °C, respectively. Trends were analyzed using the Mann–Kendall test and Sen’s slope estimator. SPI-3 values ranged from −3.13 to 3.87, including 67 moderates to severe drought months and 12 extreme wet months. SPI-6 ranged from −2.97 to 2.53, showing 34 drought months and 40 wet months, while SPI-12 ranged from −1.94 to 2.32, reflecting generally stable long-term precipitation. Annual rainfall exhibited no significant trend (Sen’s slope = −1.34 mm/year, p = 0.785), whereas yearly average temperature increased significantly by 0.054 °C/year (p = 0.02), raising evapotranspiration and drought risk. Results indicate high short- and medium-term drought variability despite stable annual precipitation, underscoring the need for integrated water management strategies, including rainwater harvesting, groundwater protection, and efficient irrigation, to improve resilience under evolving climate conditions. Full article

This study investigates the influence of different effective precipitation (P_e) estimation methods on drought index performance in a humid region of Türkiye. The standard precipitation index (SPI) and the reconnaissance drought index (RDI) were compared with their effective precipitation-based counterparts, Agricultural Standardized Precipitation Index (aSPI) and Effective Reconnaissance Drought Index (eRDI), using four P_e estimation methods: USBR (U.S. Bureau of Reclamation), USDA-(Simplified and CROPWAT) (U.S. Department of Agriculture), and FAO (Food and Agriculture Organization). Data from three closely located meteorological stations (Çanakkale, Bozcaada, and Gökçeada) were analyzed across multiple time scales (1-, 3-, 6-, 12-month, and annual). Statistical metrics—coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE)—were used to assess the indices, and trend analyses were conducted using the Mann–Kendall and Sen’s Slope tests. The USDA-Simplified method consistently showed the highest accuracy across all stations and time scales (R² ≈ 0.99; lowest RMSE ≈ 0.09; NSE > 0.95), while the FAO method performed poorly, particularly at the 1-month scale. Drought frequency and severity were found to increase with time scale, contrary to trends observed in arid regions. Trend analysis revealed no significant changes at short time scales, but statistically significantly increasing drought severity was detected in longer scales, especially in Çanakkale, with slopes reaching up to –0.018 per year. The findings highlight the importance of selecting appropriate P_e estimation methods for accurate drought assessment, even in humid climates, and support the use of aSPI and eRDI with the USDA-Simplified method. Full article

This study assesses the long-term impact of mine dewatering on groundwater resources in the fractured–porous Upper Cretaceous chalk aquifer of the Chełm region, SE Poland. Using precipitation records (1994–2024) and groundwater levels from 50 sites (2009–2025), temporal trends were tested with Mann–Kendall, Sen’s slope, Kendall’s tau, and regression, while spatial patterns were evaluated with Local Moran’s I and Getis–Ord Gi*. Results show no significant changes in total annual precipitation but declining snow days (–1 to –1.5 days/year) and rising temperatures (0.02–0.05 °C/year), indicating reduced snowmelt recharge. In contrast, groundwater levels declined consistently, with a median Sen’s slope of –0.14 m/year and drawdowns > 22 m near the chalk mine. Spatial clustering confirmed coherent zones of decline in mining and watershed areas. These findings indicate that climate variability alone cannot explain the observed drawdown; mine dewatering is the dominant driver, reinforced by reduced winter recharge. The results highlight the urgent need for integrated monitoring and adaptive management to protect groundwater-dependent ecosystems. Sustainable water management in mining-affected aquifers must address both anthropogenic pressures and climate-induced reductions in recharge. Full article

Climate variability and extreme events disproportionately affect rural regions with limited adaptive capacity. In Minas Gerais, Brazil, mesoregions with semi-arid characteristics face severe vulnerabilities, underscoring the importance of detailed regional climate trend analyses. This study analyzed historical air temperature (maximum, minimum, and average) and precipitation from 1990 to 2019 in four mesoregions of Minas Gerais. The goal was to support climate planning and the development of local responses. Daily data from the National Institute of Meteorology (INMET) and a gridded meteorological database were analyzed using Mann–Kendall and Sen’s non-parametric tests, with a 95% confidence level (p-value ≤ 0.05) to identify significant trends. Annual results showed significant increases in maximum temperature in 15 of 24 evaluated areas, with rates from −0.03 to +0.15 °C year⁻¹. For minimum and average temperatures, significant increases were observed in 17 locations. Annual precipitation showed a downward trend in 21 areas. Monthly and seasonal analyses confirmed this pattern of warming and reduced rainfall. These findings indicate an intensification of climate stress in over 80% of the studied locations, potentially impacting agriculture, public health, and ecosystems, requiring specific regional adaptive responses. Full article

This study analyses the dynamics of snow cover in the Neelum Watershed of Pakistan and the expected changes in temperature and precipitation. Google Earth Engine was used to analyze the variability of winter snow cover with the help of MODIS 8-day data from 2000 to 2020. Two model combinations totaling five CMIP6 General Circulation Models were used to interpret future climate projections based on three Shared Socioeconomic Pathways (SSP2-4.5, SSP3-7.0, and SSP5-8.5) for 2021–2050. The modified Mann–Kendall test was used to identify trends, and the Theil–Sen estimator was used to analyze the impact. The results demonstrate that the extent of snow-covered area increased significantly between 2000 and 2020, and approximately 6448.83 km² (approximately 87% of the watershed) was covered by snow in winter. All SSP scenarios indicated positive trends in winter precipitation with average rates of 1.87, 0.44, and 0.80 mm/yr under SSP2-4.5, SSP3-7.0, and SSP5-8.5. In all the scenarios, the minimum temperature (0.0405 °C yr⁻¹) and maximum temperature (0.0305 °C yr⁻¹) are consistently growing, as per temperature predictions. These projected changes indicate the danger of more frequent extreme weather events that will put a strain on the region’s ecosystems, agriculture, and hydropower operations. The findings offer the necessary information to inform strategies regarding climate adaptation and mitigation in the Neelum River basin. Full article

Forests play a critical role in regulating hydrological processes and reducing soil erosion and sediment load. However, climate change has increased the frequency and severity of wildfires, which can significantly impact these ecosystem services. A historical megafire burned in January of 2017 in Central Chile, affecting the Purapel in Sauzal experimental watershed (an area dominated by Pinus radiata plantations), providing a unique opportunity to study post-fire sediment load dynamics. We hypothesized that sediment load would significantly increase following the wildfire, especially in areas with exotic commercial plantations. To test this, we analyzed daily sediment load and streamflow data collected the Purapel River during the 1991–2018 period, as well as other variables. Descriptive statistics and a sediment rating curve model were used to assess temporal variations in sediment load. Contrary to expectations, results showed no significant increase in sediment concentration following the devastating 2017 wildfire event. In fact, the Mann–Kendall test revealed a significant decreasing trend in winter sediment production over the study period. These findings may be explained by a reduction in precipitation during the mega-drought of the 2010s and, importantly, a rapid and dense post-fire pine seedling regeneration. This study highlights the complex interactions between climate, vegetation, and geomorphic processes, as well as the need for further research on post-fire sediment dynamics in Mediterranean plantation forests. Full article

This study evaluated the decadal trends and spatial distribution of four irrigation suitability indices—Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), Magnesium Hazard (MH), and Kelley’s Ratio (KR)—using agricultural groundwater data collected from 157 monitoring sites across Korea between 2015 and 2024. Internationally recognized classification criteria were applied, long-term trends were analyzed using the Mann–Kendall test and Sen’s slope estimator, and spatial distributions for 2015, 2020, and 2024 were visualized using Inverse Distance Weighting (IDW). The results showed that EC and SAR remained at generally low absolute levels but exhibited statistically significant increasing trends with Sen’s slopes of +0.0038 and +0.0053/year, respectively, indicating the necessity of long-term salinization management. KR remained largely stable throughout the study period. In contrast, MH displayed a distinct pattern, with unsuitable levels concentrated in Jeju Island—approximately 15% of monitoring sites were classified as unsuitable for irrigation. This was interpreted as the combined effect of the basaltic aquifer’s geological and hydrological characteristics, seawater intrusion, and the relatively high mobility of Mg compared with Ca. This study uniquely integrates temporal trend tests with spatial mapping at a national scale and offers a mechanistic interpretation of MH vulnerability in Jeju’s volcanic aquifers. These findings emphasize the need for tailored regional management centered on groundwater abstraction control and continuous monitoring to ensure the sustainable use of agricultural groundwater. Full article

Within the context of global climate change, the hydrological and sediment load dynamics in the Huai River Basin are expected to continue evolving due to intensified human activities and environmental changes. Effective river management requires a clear understanding of the magnitude, causes, and characteristics of these changes, coupled with insight into the dynamic response processes of the river channel. This study applied a suite of statistical methods, including the Mann–Kendall test, Sen’s slope estimator, Pettitt’s test, double mass curve, and morphological analysis, to examine trends in streamflow and sediment load at two hydrological stations in the mid-Huai River from 1982 to 2016, and to assess channel evolution between Wujiadu and Xiaoliuxiang. The results indicate that: (1) both hydrological stations exhibited no significant decrease in annual streamflow, but a significant reduction in sediment load, with a change point detected in 1991 at Wujiadu Station; (2) compared to 1982–1990, the mean streamflow and sediment load decreased by 23% and 50% during 1991–2016, with a significant shift in the streamflow-sediment relationship; (3) while temperature and evapotranspiration increased significantly, precipitation remained relatively stable, indicating that climate change had a minor effect on hydrological elements, and sediment load reduction was primarily driven by large-scale ecological restoration and engineering activities; and (4) differential channel adjustments were observed in response to reduced sediment supply and human activities, modulated by local boundary conditions. Erosion occurred in the WJD section, resulting in a transformation from a U-shape to a V-shape cross-section, whereas the XLX section remained stable with a local adverse gradient. This study reveals the complex mechanisms of hydro-sedimentary and channel evolution under human dominance, offering scientific support for the sustainable management of the Huai River basin and similar regulated rivers. Full article

PM_2.5 is the primary source of urban atmospheric pollution, as it not only damages the ecological environment but also poses a threat to human health. Taking the Yangtze River Economic Belt as the research object, this study analyzes the spatiotemporal variation characteristics of PM_2.5 concentrations in the region from 2005 to 2020. Furthermore, by combining the Geodetector model with Geographically and Temporally Weighted Regression (GTWR) model, the spatiotemporal heterogeneity of its influencing factors is revealed at three scales: municipal, watershed, and grid. The results show that, from 2005 to 2020, the annual average PM_2.5 concentration in the Yangtze River Economic Belt exhibited an inverted U-shaped trend with 2013 as the inflection point, showing distinct spatial clustering characteristics. Overall, the spatiotemporal variation in annual average PM_2.5 concentration demonstrated a significant downward trend during this period, with slower decline rates in the western region and faster rates in the central and eastern regions. Spatial differentiation of annual average PM_2.5 concentrations within the region was primarily influenced by three factors: PFA, PISA, and PD. NDVI and PWA exerted their effects mainly at large scales, while MAT and SDE primarily acted at small scales. Within the region, NDVI and CVO predominantly suppressed PM_2.5 concentrations, whereas MAT, PFA, PD, and SDE primarily promoted PM_2.5 pollution. The spatial distribution of effects for factors within the same category is broadly consistent across the three scales, though details vary. This study overcomes previous limitations of administrative-scale research, yielding more refined results. It provides new methodologies and insights for future research while offering more precise scientific support for regional PM_2.5 governance. Full article

Urban green spaces provide measurable cooling that can mitigate urban heat islands, yet few studies have quantified these effects over multiple decades. This study analyzed Landsat imagery from four epochs (1990, 2000, 2013, 2018) to derive land surface temperature (LST) and vegetation indices—NDVI for greenness and NDMI for moisture content—for four large urban parks in Krakow. Late spring/summer LST in parks was compared with that of urban areas within 0–150 m and 150–300 m of park boundaries. Statistical significance was evaluated using bootstrapped confidence intervals, long-term trends were assessed via the Mann–Kendall test, and correlation analysis was used to examine relationships between LST and each vegetation index. Results show a persistent park cooling effect, with park interiors ~2–3 °C cooler than adjacent urban areas in all years. Despite an overall city-wide LST rise of ~5–6 °C from 1990 to 2018, the park cool island intensity (temperature difference between park and city) remained stable (no significant long-term trend, p > 0.7). Bootstrapped 95% confidence intervals confirmed that each park’s cooling effect was statistically significant in each year analyzed. NDMI (vegetation moisture content) correlated more strongly with LST (r ~ −0.90) than NDVI (r ~ −0.7 to −0.9), highlighting the importance of vegetation moisture in park cooling. These findings demonstrate that well-watered urban parks can sustain substantial cooling benefits over decades of urban development. The persistent ~2–3 °C daytime cooling observed underscores the value of water-sensitive green space planning as a long-term urban heat mitigation strategy. Full article

The study aims to analyze the long-term trends of climate variables in Erzurum, Türkiye. Trend analyses were conducted on the maximum, minimum, and average temperature and total precipitation series for a 96-year period covering the period 1929–2024. Annual, seasonal, and monthly time series of the variables were illustrated along with their linear trends. Statistical analysis was conducted using the Mann–Kendall test and Sen’s innovative trend analysis methods. The Mann–Kendall test Z-statistic results were evaluated at the 90%, 95%, and 99% significance levels. Maximum temperature series show increasing trends in all months except November, December, January, and February and on the annual scale at the α = 0.01 significance level. Minimum temperature series show decreasing trends for all time periods except March and April. The average temperature variable shows no trend in the annual, summer, and winter series, increasing in spring, March, and April (α = 0.05) and decreasing in November (α = 0.1). Trend analysis of the precipitation series indicates a decreasing trend in winter snowfall, as well as in March and June precipitation. Sen’s methodology, in addition to trend indicators, offers a layered assessment opportunity for any time series, with subcategorization based on the magnitude of the data values. According to annual average values, the diurnal temperature range was determined as 11.3 °C in 1929 and 13.5 °C in 2024. Important findings have been obtained for determining sustainable resource management strategies through the monitoring of climate variables. Full article