Search Results (354)

This study investigates the spatio-temporal dynamics of Climate Potential Productivity (CPP) in Central Africa during 1901–2019 using the Thornthwaite Memorial model coupled with Mann–Kendall tests based on high spatial and temporal resolution data. The results demonstrate the climate–vegetation interactions under global warming: (1) Central Africa exhibited a statistically significant warming trend (r² = 0.33, p < 0.01) coupled with non-significant rainfall reduction, suggesting an emerging warm–dry climate regime that parallels meteorological trends observed in North Africa. (2) Central Africa exhibited an overall increasing trend in CPP, with temporal fluctuations closely aligned with precipitation variability. Specifically, the CPP in Central Africa has undergone three distinct phases: an increasing phase (1901–1960), a decreasing phase (1960–1980), and a slow recovery phase (1980–2019). The multiple intersection points between the UF and UB curves indicate that Central Africa’s CPP has been significantly affected by climate change under global warming. (3) The correlation of CPP–Temperature was mainly positive, mainly distributed in the Lower Guinea Plateau and the northern part of the Congo Basin (r² = 0.26, p < 0.1). The relationship of CPP–Precipitation showed predominantly a very strong positive correlation (r² = 0.91, p < 0.01). 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

Snow cover, as a critical component of the cryosphere, serves as a vital water resource for arid regions in Northwest China. The Qilian Mountains (QLM), situated on the northeastern margin of the Tibetan Plateau, function as an important ecological barrier and water conservation area in western China. This study presents the first high-resolution historical snow cover product developed specifically for the QLM, utilizing a multi-level snow classification algorithm tailored to the complex topography of the region. By employing Landsat satellite data from 1986–2024, we constructed a comprehensive 39-year snow cover dataset at a resolution of 30 m. A dual adaptive cloud masking strategy and spatial interpolation techniques were employed to effectively address cloud contamination and data gaps prevalent in mountainous regions. The spatiotemporal characteristics and driving mechanisms of snow cover changes in the QLM were systematically analyzed using Sen–Theil trend analysis and Mann–Kendall tests. The results reveal the following: (1) The mean annual snow cover extent in the QLM was 15.73% during 1986–2024, exhibiting a slight declining trend (−0.046% yr⁻¹), though statistically insignificant (p = 0.215); (2) The snowline showed significant upward migration, with mean elevation and minimum elevation rising at rates of 3.98 m yr⁻¹ and 2.81 m yr⁻¹, respectively; (3) Elevation-dependent variations were observed, with significant snow cover decline in high-altitude (>5000 m) and low-altitude (2000–3500 m) regions, while mid-altitude areas remained relatively stable; (4) Comparison with MODIS data demonstrated good correlation (r = 0.828) but revealed systematic differences (RMSE = 12.88%), with MODIS showing underestimation in mountainous environments (Bias: −8.06%). This study elucidates the complex response mechanisms of the QLM snow system under global warming, providing scientific evidence for regional water resource management and climate change adaptation strategies. Full article

Increasing drought conditions at the global level have created concerns about the decrease in water resources. This situation has made the correct planning of irrigation applications the most important situation. Irrigation management in future periods is possible with the correct determination of the reference evapotranspiration (ET₀) trend. In the current situation, the trend is usually determined using one or two methods. Failure to conduct a detailed trend analysis results in incorrect irrigation management. With the new approach presented in the research, all of the Mann–Kendall (MK), innovative trend analysis (ITA), Sen’s slope (SS) and Spearman’s rho (SR) tests were used, and the common results of the four tests, namely increase, decrease, and no trend, were taken into account. The ET₀ values calculated in different approaches were focused on temporal and spatial analysis for the future irrigation management of Türkiye with the Blaney–Criddle (BC), Turc (TR), and Coutagne (CT) methods. The future period forecast was made using four different trend analyses with geographical information system (GIS) based spatial applications using 12-month ET₀ data calculated from 59 years of data between 1965 and 2023. Statistical analysis was performed to reveal the relationship between ET₀ calculation methods. The findings showed that although there is a general increasing trend in ET₀ values in the region, this situation is more pronounced, especially in the provinces in the western and central regions. The research results improve the determination of plant water needs for future periods in terms of irrigation management. This new approach, which determines ET₀ trend analysis in the Black Sea region, can be used in regional, national, and international studies by supporting different calculations to be made in order to plan future water management correctly, to reduce the concern of decreasing water resources in drought conditions, and to obtain comprehensive data in order to provide appropriate irrigation. Full article

Cropland is a critical resource for safeguarding food security. Ensuring both the quantity and quality of cropland is essential for achieving zero hunger and promoting sustainable agriculture. However, whether urbanization-induced cropland loss poses a substantial threat to regional food security remains a key concern. This study examines the central region of the Yangtze River Delta (YRD) in China, integrating CLCD (China Land Cover Dataset) land use/cover data (2001–2023), MOD17A2H net primary productivity (NPP) data, and statistical records to evaluate the impacts of urban expansion on grain yield. The analysis focuses on three components: (1) grain yield loss due to cropland conversion, (2) compensatory yield from newly added cropland under the requisition–compensation policy, (3) yield increases from stable cropland driven by agricultural enhancement strategies. Using Sen’s slope analysis, the Mann–Kendall trend test, and hot/coldspot analysis, we revealed that urban expansion converted approximately 14,598 km² of cropland, leading to a grain production loss of around 3.49 million tons, primarily in the economically developed cities of Yancheng, Nantong, Suzhou, and Shanghai. Meanwhile, 8278 km² of new cropland was added through land reclamation, contributing only 1.43 million tons of grain—offsetting just 41% of the loss. In contrast, stable cropland (102,188 km²) contributed an increase of approximately 9.84 million tons, largely attributed to policy-driven productivity gains in areas such as Chuzhou, Hefei, and Ma’anshan. These findings suggest that while compensatory cropland alone is insufficient to mitigate the food security risks from urbanization, the combined strategy of “Safeguarding Grain in the Land and in Technology” can more than compensate for production losses. This study underscores the importance of optimizing land use policy, strengthening technological interventions, and promoting high-efficiency land management. It provides both theoretical insight and policy guidance for balancing urban development with regional food security and sustainable land use governance. 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

Against the backdrop of global climate change and rapid urbanization, understanding the spatiotemporal dynamics and driving mechanisms of vegetation net primary productivity (NPP) is critical for ensuring regional ecological security and achieving carbon neutrality goals. This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA) and integrates multi-source remote sensing data with socioeconomic statistics. By combining interpretable machine learning (XGBoost-SHAP) with multiscale geographically weighted regression (MGWR), and incorporating Theil–Sen trend analysis and Mann–Kendall significance testing, we systematically analyze the spatiotemporal variations in NPP and its multiscale driving mechanisms from 2001 to 2020. The results reveal the following: (1) Total NPP in the YRDUA shows an increasing trend, with approximately 24.83% of the region experiencing a significant rise and only 2.75% showing a significant decline, indicating continuous improvement in regional ecological conditions. (2) Land use change resulted in a net NPP loss of 2.67 TgC, yet ecological restoration and advances in agricultural technology effectively mitigated negative impacts and became the main contributors to NPP growth. (3) The results from XGBoost and MGWR are complementary, highlighting the scale-dependent effects of driving factors—at the regional scale, natural factors such as elevation (DEM), precipitation (PRE), and vegetation cover (VFC) have positive impacts on NPP, while the human footprint (HF) generally exerts a negative effect. However, in certain areas, a dose–response effect is observed, in which moderate human intervention can enhance ecological functions. (4) The spatial heterogeneity of NPP is mainly driven by nonlinear interactions between natural and anthropogenic factors. Notably, the interaction between DEM and climatic variables exhibits threshold responses and a “spatial gradient–factor interaction” mechanism, where the same driver may have opposite effects under different geomorphic conditions. Therefore, a well-balanced combination of land use transformation and ecological conservation policies is crucial for enhancing regional ecological functions and NPP. These findings provide scientific support for ecological management and the formulation of sustainable development strategies in urban agglomerations. Full article

Detection and analysis of hydroclimatic trends are crucial for quantifying climate change, global warming, and their potential impacts. This study investigates hydroclimatic trends over the South African Sugar Belt (SASB) under a changing climate using nonparametric and innovative trend detection techniques for the periods 1980–2022, 2025–2050, and 2050–2080. Statistical tests, including the original and modified Mann–Kendall test, sequential Mann–Kendall test, and Innovative Trend Analysis were performed to detect trends and changes in hydroclimatic variables over the SASB’s dryland and irrigated regions. An 18-month low-pass filter was applied to 19 GCMs of the CMIP6, which were downscaled to a local setting. The results indicate contrasting rainfall trends: a positive trend in the dryland region and a negative trend in the irrigated region from 1980 to 2022. Under low- (SSP2–4.5) and high-emission (SSP5–8.5) scenarios, both regions exhibited a significant drying trend from 1980 to 2080, with the irrigated region drying and warming faster than the dryland region. Mann–Kendall tests and Innovative Trend Analysis revealed robust positive trends in surface air temperatures across the SASB, with even stronger trends projected for the future, potentially promoting water loss in the area. Compound dry–hot events were also projected to cause significant socioeconomic impacts in the near and distant future. Future studies can explore nonparametric and monotonic trend detection and analysis for water quality parameters in the SASB under a changing climate. Full article

The Eastern Mediterranean region is experiencing accelerated climate warming, yet localized patterns remain poorly understood, particularly in areas with complex topography. This study examines long-term air temperature trends from 1992 to 2024 at two sites in Lebanon: Beirut Airport (urban–coastal) and Houch Al Oumaraa station in Zahlé (inland–valley). Using homogeneity testing, linear regression, and the Mann–Kendall trend test, we assess trends in minimum, maximum, and mean temperatures. The results show a strong and statistically significant warming trend in Beirut, with mean temperatures rising by +0.536 °C per decade and minimum temperatures showing the steepest increase (+0.575 °C/decade). In Zahlé, the warming trend is less pronounced, particularly for maximum temperatures (+0.369 °C/decade), while minimum temperatures increased by +0.528 °C/decade. Data from fixed stations and drone-based vertical profiling in Zahlé confirmed the presence of cold-air pooling and thermal inversions, which moderate air temperatures and may contribute to a subdued warming trend. The strongest inversion recorded in 2022 reached 6.7 °C between ground level and an altitude of 500 m. In contrast, the urban heat island (UHI) effect in Beirut and Zahlé appear to drive nighttime warming, particularly in summer and early autumn months. These findings highlight the roles of topography and urbanization in shaping local climate trends. Full article

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

This study investigated the influence of long-term climatic phenomena on the hydroclimatic dynamics of the Grey River Basin in Chilean Patagonia. By analyzing hydroclimatological datasets from the last four decades (1980 to 2020), including precipitation, temperature, wind speed, potential evapotranspiration, and streamflow, we identified key trends and correlations with three large-scale climate indices: the Antarctic Oscillation (AAO), El Niño—Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO). Statistical methods such as the Mann–Kendall test, Sen’s slope, PCA, and wavelet coherence were applied. The results indicate a significant upward trend in streamflow, with Sen’s slope of 0.710 m³/s/year (p-value = 0.020), particularly since 2002, while other variables showed limited or no significant trends. AAO exhibited the strongest correlations with streamflow and wind speed, while ENSO 3.4 was the most influential ENSO index, especially during the two extreme El Niño events in 1982, 1997, and 2014. PDO showed weaker relationships overall. Wavelet analysis revealed coherent periodicities at 1- and 2-year frequencies between AAO and flow (wavelet coherence = 0.44), and at 2- to 4-year intervals between ENSO and precipitation (wavelet coherence = 0.63). These findings highlight the sensitivity of the Grey River basin to climatic variability and reinforce the need for integrated water resource management in the face of ongoing climate change. 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