Search Results (1,499)

We modelled, for the first time, the seasonal dynamics and long-term trends of Abies marocana forests (Rif Mountains, northern Morocco) using remote-sensing-derived vegetation indices. Using the MODIS Terra Vegetation Indices product MOD13Q1 (enhanced vegetation index, EVI; 16-day frequency; 250 m spatial resolution) from 2000 to 2024 (575 images over 25 years), we applied a robust seasonal trend analysis (RSTA) workflow, representing an inferential extension of classical seasonal trend analysis (STA) through the explicit control of Type I error under serial and spatial correlation. This approach combined: (i) harmonic regression to capture the annual and semi-annual cycles of A. marocana forests, estimating seasonal amplitudes and phases while filtering out low-frequency noise; (ii) an iterative trend-free prewhitening (TFPW) procedure following Wang and Swail, applied only to time series with significant serial autocorrelation according to the Durbin–Watson test; (iii) the Theil–Sen slope (TS) estimator, a robust non-parametric method, to quantify the magnitude and direction of seasonality trends; (iv) the contextual Mann–Kendall (CMK) test to assess the statistical significance of seasonality trends, while correcting for spatial autocorrelation and accounting for cross-correlation among neighbouring pixels; (v) the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), ensuring that only statistically robust seasonality trends were retained; and (vi) reconstruction of seasonal curves representing the beginning and end of the study period and derivation of phenological metrics from the statistically significant seasonal trends retained after inferential filtering. After applying the complete analytical workflow, statistically significant trends were detected in 79.2% of pixels within A. marocana forests, compared with 86.4% when prewhitening and false discovery rate control were not applied. All Theil–Sen slopes retained by the RSTA workflow were positive, with a mean slope of approximately 0.00175 EVI year⁻¹, corresponding to an average annual increase of roughly 0.7% and an overall increase of approximately 15% over the 2000–2024 study period relative to the initial mean EVI conditions. Browning trends identified by classical STA were not supported after inferential filtering and FDR control, indicating that all these patterns were spurious or only marginal, and confined to limited areas and edge zones. The reconstructed seasonal trend curves were consistent with a longer growing season, although this inference is based on land-surface vegetation dynamics rather than direct phenological observations. The long-term ecological consequences of these changes in seasonal vegetation activity will hinge on the interactions among warming, rising water demand, and potential disturbance regimes under future climatic conditions. Full article

Cryptomeria japonica (Japanese cedar) is extensively planted as windbreaks in Jeju, Korea, producing highly allergenic pollen that significantly affects local populations. This study analyzed 10-year trends of airborne C. japonica pollen concentrations and their relationship with meteorological factors in Jeju to provide essential data for allergy management and climate adaptation strategies. Daily airborne pollen sampling was conducted using Burkard traps from 2015 to 2024 at a monitoring site in Jeju. Meteorological data, including temperature, wind speed, relative humidity, precipitation, solar radiation, and cloud amount, were obtained from the Korea Meteorological Administration. Temporal trends were analyzed using linear regression and the Mann–Kendall test, while correlations between pollen parameters and meteorological variables were calculated using Spearman’s correlation coefficients. Over the 10-year period, annual pollen integral (APIn) and peak concentrations showed statistically significant increasing trends. Pollen season start dates demonstrated a tendency toward earlier occurrence. Season onset was strongly negatively correlated with pre-season temperatures in January and February. January solar radiation showed positive correlations with both season end and period duration. C. japonica pollen concentrations in Jeju demonstrate significant increasing trends with earlier seasonal onset, primarily driven by pre-season warming in January and February. These changes may lead to prolonged allergen exposure periods, necessitating enhanced public health preparedness and adaptation of clinical management strategies for allergic populations. Full article

This article presents the results of a study assessing changes in climatic resources in various natural zones of the Zhetysu region, Republic of Kazakhstan, conducted based on long-term climate data for the period 1966 to 2024 (from 12 meteorological stations). The study examines current trends in climatic indicators in spatial and temporal aspects that influence agricultural land use within the region. The first part of this study examines current trends in climate indicators from both spatial and temporal perspectives within the Zhetysu Region of the Republic of Kazakhstan; the second part focuses on studying trends in climate indicators using the non-parametric Mann–Kendall test and the Sen’s slope test, as well as Fisher’s t-test. The authors identified divergent trends in relative air humidity and precipitation and detected a steady trend toward an increase in the average annual air temperature across the region. Based on the analysis of time series of climate-forming and climate–environment-forming indicators, a persistent increasing trend in mean annual air temperature was identified, while relative humidity, precipitation, and evaporation exhibited divergent (both positive and negative) trends across the territory of the region. The developed climate–resource-forming models and a series of estimated applied maps of climate indicators for 1966–1975 and 2016–2024 serve as the scientific basis for climate change forecasting and can be used by administrative bodies to improve agricultural land use strategies in the region. Full article

The Arctic is warming nearly four times faster than the global mean, driving unprecedented sea ice loss and threatening permafrost coasts and human settlements. Existing pan-Arctic vulnerability indices typically rest on satellite-era baselines and on expert-driven weighting schemes whose robustness is rarely tested. Here, we present an integrated, multi-centennial framework that jointly ingests SIBT1850 sea ice concentration (1850–2017), extended to 2024 with the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration v6 (G02202 v6), together with ESA CCI Permafrost products (1997–2019), the Arctic Coastal Dynamics database, and pan-Arctic settlement inventories. Using non-parametric Mann–Kendall trend tests, Sen’s slope, and the Pettitt change point test across nine Seas (S1–S9), five permafrost-adjacent core seas exhibit summer Sen’s slopes of −0.105 to −0.185% yr⁻¹ with Pettitt change points clustered in 1929–1953 (mean 1936), whereas three of four support seas cluster around 1978, suggesting an approximately bimodal regime shift timing that we interpret cautiously given the limited sample. A Composite Vulnerability Index integrating six normalised indicators identifies the Chukchi (CVI = 0.630) and East Siberian (0.624) seas as the highest-priority hotspots at the SIBT1850 baseline. A satellite-era robustness check using NSIDC G02202 v6 confirms that the Chukchi–East Siberian–Laptev corridor remains in the top three highest-vulnerability basins under the 1850–2024 extension, with the Beaufort Sea retaining rank 5, validating the basin mean conclusions of the SIBT1850-based analysis. Robustness checks—PCA re-weighting, one-at-a-time and global (Sobol, PAWN) sensitivity analyses, and Monte Carlo Dirichlet perturbation—confirm that the top-two ranking is stable across weighting schemes (baseline–PCA Spearman ρ = 0.80). We explicitly avoid claiming forecasting validation, operational testing, or benchmarking against existing pan-Arctic vulnerability indices, all of which we identify as priority directions for future work. The framework provides a transparent, reproducible basis for prioritising adaptation across the Chukchi–East Siberian–Laptev corridor. Full article

The Liujiaxia–Heishanxia reach is critical for water and sediment regulation in the upper Yellow River, where changes in runoff–sediment relationships greatly affect downstream channel stability and flood safety. Climate change and intensive human activities have substantially altered local hydrological regimes in recent decades. Using long-term hydrological records from five stations during 1956–2020, this study applied the Mann–Kendall test, moving t-test, wavelet analysis and XGBoost algorithms to analyze the trends, abrupt changes and periodic features of runoff and sediment load, and quantify the contributions of natural and human drivers. The results show that both runoff and sediment load decreased significantly, with a sharper decline in sediment load. Major abrupt changes occurred in 1969, 1986, 1996 and 2008, and both variables presented a dominant 40-year interdecadal cycle. Human-induced landscape changes became the leading factor driving hydrological variations after 1996. Our findings suggest that future watershed management should combine landscape optimization and climate adaptation to maintain stable runoff-sediment conditions. This work provides scientific references for water resource management and the construction of the Heishanxia Water Conservancy Project. Full article

This study investigates the spatiotemporal evolution of ecological environment quality and its driving mechanisms in the Sichuan section of the Yellow River Basin using Landsat imagery from 2000 to 2023. The Remote Sensing Ecological Index (RSEI) was constructed on the Google Earth Engine platform, and a comprehensive evaluation model was developed using principal component analysis. Sen’s slope, the Mann–Kendall test, and the Hurst exponent were applied to assess temporal trends and future persistence, while the optimal parameter-based Geodetector model was used to identify the driving factors of spatial differentiation. Results show that: (1) ecological environment quality exhibits a fluctuating but overall increasing trend, with a multi-year mean RSEI of 0.58, indicating a transition from “moderate” to “good–excellent” conditions; (2) spatially, ecological quality demonstrates significant heterogeneity and clear altitudinal gradients, with better conditions in the northwest than in the southeast, where low- and mid-altitude areas show higher ecological quality and stronger improvement, whereas high-altitude areas remain relatively poor due to strong natural constraints; (3) the spatial differentiation is jointly driven by multiple factors, among which precipitation and temperature are dominant, elevation exerts a fundamental constraint, and human activity plays a relatively minor role, while the interaction between climate and topographic factors shows the strongest explanatory power. These findings provide insights into the evolution and drivers of ecological environment quality in high-altitude regions and support ecological protection and regional management in the upper Yellow River Basin. Full article

This study presents an integrated framework for agricultural drought monitoring in data-scarce regions, utilizing the Google Earth Engine (GEE) platform to analyze multisource Earth observation data over the South Wollo highlands, Ethiopia, from 2001 to 2024. The analysis was complemented by Mann–Kendall trend testing, Sen’s slope estimation, and Pettitt change-point detection to identify and quantify long-term trends and abrupt shifts in drought dynamics. The methodology integrates climatic and satellite-derived indicators within a hybrid analytical framework. It incorporates the standardized precipitation evapotranspiration index (SPEI), vegetation condition index (VCI), vegetation health index (VHI), temperature condition index (TCI), and land surface temperature (LST), which are derived from MODIS (NDVI, LST, PET) and CHIRPS precipitation datasets. The analysis focused on the main growing season (June–September) to capture critical crop growth and moisture-sensitive periods for agricultural production in the study area. The findings reveal pronounced interannual variability in drought occurrence and intensity across the study period. Severe agricultural drought conditions were most extensive in 2009 and 2014, with VHIs indicating 15% and 4% of the area under severe and extreme drought in 2009, respectively, and 2.6% and 2% in 2014, respectively. In contrast, 2001, 2005, 2020, and particularly 2024 were characterized by predominantly no-drought to mild-drought conditions, with no-drought coverage increasing from 86.7% (2009) to 98.0% (2024). Vegetation-based indices demonstrate that drought impacts are episodic rather than persistent and strongly controlled by rainfall timing and early-season moisture availability. The LST exhibited marked year-to-year variability (28.8 °C to 33.8 °C), with elevated temperatures coinciding with drought periods and suppressed evaporative cooling. Correlation analysis confirmed a strong positive relationship between the SPEI and VHI (r = 0.77), with moderate correlations for the VCI (r = 0.40) and TCI (r = 0.36), underscoring the sensitivity of integrated vegetation health to the climatic water balance. The study concludes that combining the SPEI with satellite-derived vegetation and thermal indices provides a robust, scalable approach for agricultural drought assessment in regions with limited ground-based observations. The integrated framework effectively captures both moisture deficits and thermal stress components, offering a scientific basis for improving drought early warning systems and climate-resilient agricultural planning in Ethiopia and similar environments. Full article

Drought is a widespread natural hazard in China that can sequentially trigger meteorological, hydrological, agricultural, and socio-economic drought types, yet traditional drought indices typically focus on a single hydrologic component and cannot capture integrated water deficits across multiple compartments. This study aims to systematically characterize the spatiotemporal evolution of comprehensive drought across China’s ten major river basins and to identify and quantify the main natural and anthropogenic factors associated with drought dynamics. We utilized the Gravity Recovery and Climate Experiment (GRACE) Mascon dataset spanning the entire mission period (April 2002–June 2017), which provides a continuous 15-year observation window suitable for detecting decadal-scale trends and inter-annual variability. Given the documented asynchrony between precipitation and terrestrial water storage changes, a zoned index framework was applied: the Combined Climatologic Deviation Index (CCDI) for arid basins and the Drought Severity Index (DSI) for humid basins. The Theil–Sen estimator and Mann–Kendall test, both non-parametric and robust to outliers, were employed for trend detection, and Pearson correlation analysis was used to evaluate statistical associations between drought indices and potential influencing factors. The results reveal a clear “dry gets drier, wet gets wetter” pattern during 2002–2017: severe drought episodes in humid basins (e.g., the Yangtze) were concentrated in 2002–2006, whereas those in arid basins (e.g., the Haihe) occurred mainly in 2013–2017. Groundwater storage anomaly (GWSA) constituted the primary component of total water storage changes in most basins, with the most rapid depletion rate of −45 mm yr⁻¹ in the northern arid basins. Land use/cover change, especially urban expansion, showed a significant statistical association with drought intensification in arid regions, with its standardized contribution being comparable to that of natural factors such as runoff. This study provides a systematic cross-basin assessment and offers scientific insights for differentiated drought mitigation strategies and water resources management. Full article

Global warming has become a core challenge for human society. This study adopted the global surface temperature anomaly dataset from 1891 to 2023 released by the Japan Meteorological Agency (JMA). Multiple quantitative methods, including Sen’s slope estimation, Modified Mann–Kendall (MMK) test with pre-whitening, Pettitt test and GIS spatial analysis, were comprehensively applied to investigate the long-term climate change trends and regional response characteristics across the globe and China. The results indicated that the global warming rate reached 0.0802 °C per decade, while the warming rate of China was 0.1139 °C per decade, which is 42.0% higher than the global average level. Both global and Chinese temperature changes experienced three evolutionary stages, namely slow growth period, stagnation period and accelerated warming period, with an abrupt turning point occurring during 1979–1980, which was closely linked to the phase transition of Pacific Decadal Oscillation and atmospheric circulation adjustment. Obvious spatial differentiation characteristics of climate warming were identified in China, with a more rapid warming trend in northern and inland regions and a relatively slow warming rate in southern and coastal areas. Since 1980, regional accelerated warming has been driven by both anthropogenic activities and natural climate variability. The research findings can provide solid scientific support for formulating regional climate adaptation strategies and promoting collaborative global climate governance. Full article

This study assesses the future availability of water resources in Lake Guiers by 2050, considering the combined impacts of climate change and human activities, using the Water Evaluation and Planning System. As Senegal’s main freshwater source, the lake faces growing pressure from agricultural expansion, aquatic plant overgrowth, competing stakeholder demands, and increasing water use. The study combines field data on hydrological flows and agricultural water use with climate projections under the Shared Socioeconomic Pathways 4.5 and 8.5 scenarios. Climate data were downscaled and bias-corrected using CMhyd, multiple linear regression, and the Mann–Kendall test. Model calibration showed strong performance (NSE = 0.95; R² = 0.96). Results reveal decreasing precipitation and rising temperatures under both scenarios. Agricultural withdrawals (79,331,457.14 m³/year) already exceed crop water needs (69,115,088.03 m³/year), resulting in significant water losses estimated at over 10 million m³ per year. Scenario analysis indicates that high water demand under Shared Socioeconomic Pathways SSP8.5 could lead to critical declines in lake volume as early as 2026 (550 million m³), while moderate demand growth under SSP4.5 could maintain water availability until 2050. The proposed PREFERLO-Grand Transfer project would add further stress to the lake’s capacity. These findings emphasize the urgent need for sustainable water management and policy actions. Full article

This study investigates the hydroclimatic impacts of climate change on the Göynük Stream Basin, a snow-fed tributary within the Euphrates River Basin, utilizing flow, precipitation, and temperature data from 1975 to 2022. The Göynük Stream Basin is characterized by high-altitude, harsh continental conditions, with its flow regime heavily influenced by snowmelt, rendering it particularly sensitive to climate change. Employing a suite of trend analysis methods, including Mann–Kendall, Spearman Rho, Theil–Sen, Şen-Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA), the research evaluated annual and seasonal data from one stream and four meteorological stations across multiple significance levels (90%, 95%, 99%). Unlike conventional hydroclimatic studies based solely on monotonic trend detection, this study integrates classical trend tests, innovative trend approaches, temporal regime-based analysis (RAPS), and machine learning techniques within a unified assessment framework to evaluate both hydroclimatic variability and runoff predictability under climate change conditions. Key findings indicate a significant decline in annual flow rates by approximately 9.37%, with a notable decrease in maximum flow rates evidenced by a negative trend slope of −0.2726 m³/s/year. While precipitation trends were generally decreasing, temperature data exhibited significant increases, especially during winter and spring. Seasonal analysis revealed substantial flow reductions in summer and autumn, coupled with an earlier timing of the annual maximum flow, shifting from mid-May to late March/early April, suggesting earlier snowmelt. The study concludes that the Göynük Stream Basin is experiencing increasing hydroclimatic pressures attributable to climate change. These insights are crucial for water resource management and serve as a guideline for similar snow-fed sub-basins within the broader Euphrates River Basin. Furthermore, the integration of a machine learning approach, utilizing meteorological and seasonal data, demonstrated strong monthly runoff prediction capabilities with NRMSE of 4.11% and R² equal to 0.951. Feature importance analysis highlighted seasonality and temperature as primary predictive factors. However, a marked decline in model accuracy after 2011 was observed, indicating a non-stationarity in the hydroclimatic system, likely driven by climate change impacts and underscoring the need for adaptive management strategies. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article

Industrial settlements of the East Kazakhstan Region face a persistent technogenic burden driven by the dense concentration of non-ferrous metallurgy and heat-and-power enterprises, further compounded by unfavorable pollutant dispersion conditions inherent to the region’s mountain–basin topography. This study evaluated spatiotemporal associations between annual mean concentrations of NO₂, SO₂, H₂S, and CO, the integrated air pollution index (API₅), and primary neoplasm morbidity across five settlements over the period 2014–2024. A retrospective ecological analysis was carried out for Ust-Kamenogorsk, Ridder, Altai, Shemonaikha, and the settlement of Glubokoe, incorporating Spearman’s rank correlation, lag analysis (1–3 years), and the Mann–Kendall trend test. Throughout the study period, neoplasm morbidity in the region consistently exceeded the national average by a factor of 1.3 to 2.0. In Ust-Kamenogorsk—where metallurgical SO₂ and NO₂ emissions are most heavily concentrated—strong positive associations were found in children for SO₂ (ρ = 0.791, p < 0.05) and in adolescents for NO₂ and CO, reflecting elevated inhalation exposure under conditions of chronic pollution. The negative associations with API₅ observed in Ridder and Altai, where the index showed a statistically significant downward trend, are interpreted as evidence of the inertial character of oncological processes and the lasting influence of cumulative past exposure. Across all studied settlements, SO₂ emerged as the most consistent predictor of morbidity variation. These findings support prioritizing stricter emission controls for SO₂ and NO₂ from metallurgical and energy facilities, establishing oncological screening programs for children and adolescents in chronically polluted areas, and strengthening ambient air monitoring—measures whose effective implementation will require coordinated action between public health authorities and environmental regulators. Full article

Evapotranspiration (ET) is a key component of the terrestrial water and energy cycle, and its long-term dynamics are essential for regional hydrological assessment in subtropical China. In this study, two widely used satellite-based ET products, MOD16 and PML-V2, were selected for intercomparison because they provide consistent spatial (500 m) and temporal (8-day) resolutions. Validation against flux observations showed that PML-V2 performed better than MOD16 and was therefore used for subsequent analysis. Based on the 500 m, 8-day PML-V2 dataset, the spatiotemporal variation in ET in subtropical China during 2001–2020 was examined using the Theil–Sen slope estimator, Mann–Kendall test, and Hurst exponent. To identify the most relevant controls on ET variation, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to screen environmental factors and rank their relative importance. Multiple linear regression (MLR) was then applied only to the selected dominant factors to quantify their contributions. Residual analysis was used to distinguish climate–vegetation effects from residual influences, which could arise from human activities and unmodeled natural processes. The results showed that annual ET averaged 669 mm and increased significantly at a rate of 2.03 mm yr⁻¹ from 2001 to 2020, with an accelerated increase after 2010. Spatially, ET exhibited clear gradients from south to north and from coastal to inland regions. Downward shortwave radiation (SWDown) and leaf area index (LAI) were the dominant drivers over most of the study area, although their controls varied geographically, with northern subregions being more energy-limited and southern subregions being jointly influenced by vegetation and temperature. Residual ET trends largely coincide with cropland and urbanising areas, indicating a partial influence of human activities, while in subregions such as XM, complex terrain and hydrological heterogeneity suggest that unmodeled natural processes may dominate. These findings enhance understanding of ET dynamics in subtropical China and demonstrate the value of high-resolution remote sensing products for regional hydrological monitoring and driver attribution. Full article

Understanding spatio-temporal rainfall variability is critical for water resource management, especially for climate-sensitive river basins. This study examines rainfall trends and variability in the Baitarani River Basin (eastern India) using high-resolution gridded data for 1979–2020. Rainfall trends were investigated using non-parametric Mann–Kendall test (MK test) and Sen’s slope estimator (SSE). The shift point was detected using multiple homogeneity tests [Pettitt test, Standard Normal Homogeneity Test (SNHT), and Buishand test], while rainfall variability was quantified using an entropy-based Marginal Disorder Index (MDI). The analyses were performed at annual and seasonal scales. MK Z-statistic indicates the increasing or decreasing nature of a series, whereas Sen’s β slope provides the rate of change in that particular series. The MK test and SSE were applied again to examine trends before and after the identified change point. Finally, maps illustrating spatial trends and percentage changes were produced using ArcGIS 10.6. Over the 42-year period, the MK test revealed significant increasing annual trends in both districts, Keonjhar (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +21.8%, and Mayurbhunj (Z = +2.4, β = 0.7 mm/year), with a percentage change of around +19.2%. During 1979–2020 post-monsoon rainfall showed the highest increase (62–70%) while, post 2001, monsoon rainfall declined substantially (1.7–3.3 mm/year) across all districts, with Balasore showing the largest decrease (−3.3 mm/year). The earlier period (1979–2001) had stable monsoon rainfall but greater variability in retreating monsoon, especially in northern regions. Entropy-based variability analysis indicated the Bhadrak and Balasore districts as having maximum variability with an MDI value of 1.44 and 1.35, respectively, for monsoon and annual rainfall series. These findings underscore the importance of incorporating changing seasonal dynamics into water-resource planning and flood-risk management for the Baitarani River Basin in the context of climate change. Full article