Search Results (4,343)

Long-term monitoring of tree crown condition is essential for assessing forest resilience under increasing climatic variability. This study presents a comprehensive evaluation of oak (Quercus spp.) defoliation trends in Poland from 2015 to 2024, based on national forest health monitoring data. Mean defoliation remained relatively stable until 2018, followed by a significant increase in 2019 (+5.1 percentage points; p < 0.001), coinciding with a major drought event across Central Europe. In subsequent years, defoliation gradually decreased and stabilised, indicating partial canopy recovery. Segmented regression and spline models revealed a consistent breakpoint in 2019 across all age classes, with the most severe crown damage recorded in stands older than 100 years. Younger stands showed lower defoliation levels and higher regenerative capacity. A nonlinear relationship between defoliation and growing-season precipitation was also identified, showing that when rainfall fell below 40 mm, canopy loss exceeded 30%. The results confirm that oak defoliation reflects both short-term climatic stress and long-term structural changes. Integrating monitoring data with climatic analyses and statistical modelling improves the detection of stress-related drivers and the assessment of recovery processes. The combined use of these approaches supports adaptive forest management strategies, including the promotion of mixed-species and multi-aged stands, improvement of soil nutrient conditions, and targeted monitoring of drought-sensitive age classes, thereby enhancing the resilience of oak ecosystems to climate change. Full article

Background: Estimating the postmortem interval (PMI) is complicated by extrinsic environmental and intrinsic individual factors. Methods: Improved accuracy may be achieved through a better understanding of desiccation. This study examines moisture loss and desiccation in human remains in western North Carolina, validating previous research in central Texas. Ten donated individuals were placed across three seasonal trials at Western Carolina University’s Forensic Osteology Research Station (FOREST). Soft tissue moisture measurements were recorded from 20 locations on the body using a Delmhorst RDM-3^TM meter, and environmental data were recorded on-site. Results: Moisture content declined rapidly until ~500 accumulated degree days (ADD), after which patterns became highly variable. Linear mixed-effects models identified temperature as the strongest predictor of moisture loss, particularly in spring and fall, while precipitation was the most influential in summer, coinciding with rapid skeletonization. Compared to central Texas, western North Carolina exhibited less consistent moisture loss patterns and greater environmental variability. Fixed effects explained 36–63% of moisture variation across body regions, with conditional R² values modestly higher when accounting for individual differences. Conclusions: These findings highlight the importance of region-specific research for PMI estimation. Full article

Forest productivity is a critical indicator of forest ecosystem vitality and carbon budget status. Understanding its historical trends and driving mechanisms is essential for assessing forest responses to climate change. Currently, widely used methods for productivity reconstruction, including forest inventories, eddy covariance observations, and remote sensing models, have temporal limitations and cannot adequately meet the demands of long-term ecological research. Tree-ring data, with their advantages of annual resolution and extended time series, have become an important tool for reconstructing historical forest productivity. Research has demonstrated that tree-ring width, stable isotopes, wood density, and anatomical properties are closely related to forest productivity. Mechanistic studies indicate that the climate–canopy–stem coupling relationship exhibits three key nonlinear characteristics: the bidirectional threshold effect of precipitation, the inverted U-shaped temperature response, and the carbon allocation “legacy effect”. Correlation analyses show that the optimal response period between tree rings and productivity is concentrated primarily in the growing season or summer, reflecting the critical regulatory role of temperature and moisture on tree growth. Based on this understanding, existing research has focused predominantly on mid- to high-latitude temperate forests in the Northern Hemisphere that are sensitive to climate, with tree-ring chronologies from arid regions showing stronger correlations with forest productivity. Given current progress and existing limitations, future research should address the impact of stand dynamics on reconstruction accuracy, strengthen linkages between vegetation indices and tree-ring data, integrate belowground productivity, and deepen understanding of the physiological mechanisms underlying forest productivity. Full article

We assess precipitation and key atmospheric water-cycle terms over South America (SA) in three modern reanalyses—MERRA-2, ERA5, and CFSR/CFSv2—during 1980–2021. Two observation-based datasets (CPC Unified Gauge and MSWEP-V2) serve as references to bracket observational uncertainty. Diagnostics include regional means for the Tropical and Subtropical South Atlantic Convergence Zone (TSACZ, SSACZ) and southeastern South America (SESA), Taylor-diagram skill metrics, and a vertically integrated moisture-budget residual as a proxy for closure. All products reproduce the large-scale spatial and seasonal patterns, but disagreements persist over the Andes and parts of the central/northern Amazon. Relative to CPC/MSWEP-V2, MERRA-2 exhibits the smallest precipitation biases and the highest correlations, followed by ERA5; CFSR/CFSv2 shows a warm-season wet bias. Moisture-budget residuals are smallest in MERRA-2, moderate in ERA5, and largest in CFSR/CFSv2, with clear regional and seasonal dependence. These results document improvements in the new generation of reanalyses while highlighting persistent challenges in gauge-sparse and complex-orography regions. For hydroclimate applications that depend on internally consistent P, E, moisture-flux convergence, and runoff, MERRA-2 provides the most coherent depiction among the three, whereas ERA5 is a strong alternative when higher spatial/temporal resolution or dynamical fields are needed and CFSR/CFSv2 should be applied with caution for warm-season precipitation and closure-sensitive analyses. Full article

Lacustrine fine-grained sediments commonly exhibit well-developed laminations, with significant variations in structural characteristics such as thickness and continuity, which are closely related to depositional environments and genetic processes. This paper focuses on the characteristics and formation mechanisms of the upper Es4 to lower Es3 members of the Shahejie Formation in the Dongying Sag. Through polarized light microscopy, field-emission environmental scanning electron microscopy (FE-SEM), electron probe microanalysis (EPMA), and X-ray diffraction (XRD), we systematically analyzed the types, characteristics, and genetic mechanisms of laminations in fine-grained sedimentary rocks. Results indicate that the mineral composition of these rocks is dominated by carbonates and clay minerals, allowing classification into calcareous and argillaceous mudstones. The types of laminae include calcareous laminae, argillaceous laminae, and silty laminae, which are formed by chemical precipitation, suspension settling, and low-density turbidity currents, respectively. The primary lamination associations are argillaceous–calcareous interbeds and argillaceous–silty interbeds, exhibiting rhythmic cyclicity. In the upper Es4 member, variations in climate, sediment supply, and seasonal factors caused fine-grained sediments to transition from flocculent suspension settling to chemical precipitation, forming periodic intercalations of argillaceous and calcareous laminae. In the lower Es3 member, seasonal turbidity currents triggered the deposition of normally graded silty layers and fine-silty laminae, followed by a return to suspension deposition, resulting in argillaceous–silty interbeds. This study reveals diverse transport and depositional mechanisms of fine-grained sediments under varying hydrodynamic conditions. It provides a new case for understanding the genesis of fine-grained sedimentary rocks and offers geological insights for shale oil exploration and development in the Dongying Sag. Full article

Alpine grassland is extremely sensitive to climate change and external interference, and it does not easily recover once damaged. Grasshopper outbreaks pose a serious threat to grassland ecosystem health and to the sustainable development of animal husbandry. However, most of the current grasshopper hazard studies focus on tropical, temperate, and desert areas, and there is still a lack of understanding of the spatial and temporal variation in the Qinghai–Tibet Plateau. Hence, the alpine grassland in the Qilian National Park (located in the eastern Qinghai–Tibet Plateau) was selected as the study area to investigate the primary environmental drivers, construct optimal ecological niche models, and evaluate the impact of climate change on the inhabitable areas of the dominant species Chorthippus dubius (Zubovski, 1898), abbreviated as C. dubius. The results indicate that (1) temperature seasonality, slope, and precipitation were the main influence factors for the distribution of C. dubius; (2) among the 10 ecological niche models, the random forest (RF) model exhibited the highest performance, achieving kappa, TSS, and ROC values of 0.86, 0.90, and 0.98, respectively; and (3) in the future climate scenario (SSP126–SSP585), most of the lower presence probability area (less than 20%) will be transformed into other types, affecting 93.17% of its current area. In addition, the presence probability increased from northwest to southeast gradually. This study clarified the spatial and temporal variation in C. dubius presence probability and its response to climate change, providing a scientific basis for grasshopper control and grassland management in Qilian Mountain National Park. Full article

Chile, the leading exporter of cherries (Prunus avium L.) in the southern hemisphere, faces sustained variations in precipitation patterns and high evaporative demand in its productive areas. The low availability of water during the period of highest environmental demand makes it essential to reduce or suspend irrigation applications. In this scenario, regulated deficit irrigation (RDI) after harvest is an efficient strategy for optimizing water use without compromising orchard yields. This study was conducted over three consecutive seasons in a traditional commercial orchard of ‘Santina’ cherry trees grafted onto Colt rootstock, evaluating the effect of two levels of RDI, moderate (MDI) and severe (SDI), on productive and ecophysiological parameters. Both treatments resulted in water savings of between 10% and 28%, without negatively affecting yield or fruit quality. The SDI treatment, despite reaching higher levels of cumulative water stress, improved intrinsic water use efficiency while maintaining stable photosynthetic efficiency. In addition, an increase in the abundance of fine roots and beneficial rhizosphere bacteria populations, such as Azospirillum and Bacillus, was observed, suggesting the activation of water resilience mechanisms mediated by plant–microbiota interaction, possibly associated with stress-induced ecological memory and microbial legacy effects. These results position after-harvest RDI as a sustainable tool for coping with climate variability and water scarcity in commercial cherry orchards. Full article

Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and machine learning. Youyi Farm in the Sanjiang Plain, Heilongjiang Province, was selected as the study area, covering three representative years: 2019 (flood), 2020 (normal), and 2021 (drought). Based on multi-temporal Sentinel-2 imagery and environmental covariates, Random Forest models were used to evaluate single- and dual-period combinations. Results showed that combining bare-soil and crop-season images consistently improved accuracy, with optimal combinations varying by year (R² = 0.544–0.609). Incorporating temperature, precipitation, and elevation enhanced model performance, particularly temperature, which contributed most to prediction accuracy. Feature selection further improved model stability and generalization. Spatially, SOM showed a pattern of higher values in the northeast and lower in the central region, shaped by topography and cultivation. This study innovatively integrates interannual climatic variability with remote sensing temporal combination and feature selection, constructing a climate-adaptive SOM mapping framework and providing new insights for accurate inversion of cropland SOM under extreme climates, highlights the importance of multi-temporal imagery, environmental factors, and feature selection for robust SOM mapping under different climatic conditions, providing technical support for long-term cropland quality monitoring. Full article

Terrestrial water storage includes soil water storage, groundwater storage, surface water storage, snow water equivalent, plant canopy water storage, biological water storage, etc., which can comprehensively reflect the total change in water volume during processes such as precipitation, evapotranspiration, runoff, and human water use in the basin hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a powerful tool and a new approach for observing changes in terrestrial water storage and groundwater storage. The North China Plain (NCP) is a major agricultural region in the northern arid area of China, and long-term overexploitation of groundwater has led to increasingly prominent ecological vulnerability issues. This study uses GRACE and Global Land Data Assimilation System (GLDAS) hydrological model data to assess the spatiotemporal patterns of groundwater drought in the NCP and its various sub-regions from 2003 to 2022, identify the locations, occurrence probabilities, and confidence intervals of seasonal and trend mutation points, quantify the complex interactive effects of multiple climate factors on groundwater drought, and reveal the propagation time from groundwater drought to agricultural drought. The results show that: (1) from 2003 to 2022, the linear tendency rate of groundwater drought index (GDI) was −0.035 per 10 years, indicating that groundwater drought showed a gradually worsening trend during the study period; (2) on an annual scale, the most severe groundwater drought occurred in 2021 (GDI = −1.59). In that year, the monthly average GDI in the NCP ranged from −0.58 to −2.78, and the groundwater drought was most severe in July (GDI = −2.02); (3) based on partial wavelet coherence, the best univariate, bivariate for groundwater drought were soil moisture (PASC = 19.13%); and (4) in Beijing, Tianjin and Hebei, the propagation time was mainly concentrated in 1–5 months, with average lag times of 2.87, 3.20, and 2.92 months, respectively. This study can not only reduce and mitigate the harm of groundwater drought to agricultural production, social life, and ecosystems by monitoring changes in groundwater storage, but also provide a reference for the quantitative identification of the dominant factors of groundwater drought. Full article

Sweet cherry (Prunus avium L.), a temperate fruit species highly sensitive to thermal and hydric stress, faces increasing cultivation challenges in semi-arid regions such as Fez-Meknes (Morocco) due to climate change. This study aims to identify ecologically suitable zones for sweet cherry cultivation by modeling its current potential distribution using the MaxEnt (Maximum Entropy) approach. A total of 1151 georeferenced occurrence records were collected through field surveys and validated with satellite imagery. Nineteen bioclimatic variables from the WorldClim database were initially considered, and a subset with low multicollinearity (|r| < 0.7) was retained for analysis. Model performance, evaluated using the area under the ROC curve (AUC), yielded a high mean value of 0.960 ± 0.014, indicating excellent predictive accuracy. Elevation, annual precipitation (BIO12), and precipitation seasonality (BIO15) emerged as key drivers of the species’ distribution, as confirmed by both Jackknife and SPCPI analyses. Spatial prediction maps highlighted high-suitability zones in the provinces of Ifrane, El Hajeb, Azrou, and Sefrou, aligning with known agro-climatic production areas. In contrast, lower suitability was observed in more arid or heat-prone provinces such as Boulemane and Midelt. These findings provide a robust bioclimatic framework for agroecological planning, supporting adaptive varietal zoning and long-term planning for climate-resilient horticulture. Full article

The Miocene epoch witnessed the emergence of modern biomes and biodiversity hotspots. Understanding its history in South China is crucial for informing conservation under modern climate change, yet quantitative constraints on the evolution of climate and vegetation from the tropical–subtropical transition zone remain scarce. Here, we present the first quantitative Miocene paleoclimatic and paleoecological reconstructions based on an integrated analysis of leaf-based proxies applied to exceptionally preserved and highly diverse dicotyledonous leaf megafossils from the Erzitang Formation, Guiping Basin, Guangxi. Results indicate a mean annual temperature of 22.3 °C and mean annual precipitation of 1991 mm, with a monsoon intensity index higher than present, indicating a humid monsoonal climate regime. Vegetation analysis identifies the Miocene Guiping flora as tropical forest. Rather than a simple forest replacement, South China maintained dynamic tropical forest patches that expanded northward to 23° N under Asian monsoons, forming a mosaic with evergreen broad-leaved forests. Overall, the Miocene Guiping vegetation represents a tropical forest situated in a tropical rainforest to seasonal forest ecotone under a humid monsoonal climate, rather than a per-humid rainforest, underscoring the pivotal role of monsoon evolution in shaping low-latitude forest patterns and providing a deep-time benchmark for predicting vegetation responses to future climate change. Full article

The Pearl River Basin (PRB) is a humid subtropical system where frequent floods and recurrent droughts challenge water management. GRACE and GRACE Follow-On provide basin-scale constraints on terrestrial water storage anomalies (TWSA), yet their coarse native resolution limits applications at regional scales. We employ a downscaled TWSA product derived via a joint inversion that integrates GRACE/GFO observations with the high-resolution spatial patterns of WaterGap Global Hydrological Model (WGHM). Validation against GRACE/GFO shows that the downscaled product outperforms WGHM at basin and pixel scales, with consistently lower errors and higher skill, and with improved terrestrial water flux (TWF) estimates that agree more closely with water balance calculations in both magnitude and phase. The TWSA in the PRB exhibits strong seasonality, with precipitation (P) exceeding evapotranspiration (E) and runoff (R) from April to July and storage peaking in July. From 2002 to 2022, the basin alternates between multi-year declines and recoveries. On the annual scale, TWSA covaries with precipitation and runoff, and large-scale climate modes modulate these relationships, with El Niño and a warm Pacific Decadal Oscillation (PDO) favoring wetter conditions and La Niña and a cold PDO favoring drier conditions. extreme gradient boosting (XGBoost) with shapley additive explanations (SHAP) attribution identifies P as the primary driver of storage variability, followed by R and E, while vegetation and radiation variables play secondary roles. Drought and flood diagnostics based on drought severity index (DSI) and a standardized flood potential index (FPI) capture the severe 2021 drought and major wet-season floods. The results demonstrate that joint inversion downscaling enhances the spatiotemporal fidelity of satellite-informed storage estimates and provides actionable information for risk assessment and water resources management. Full article

This study examines long-term trends in maximum (Tmax) and minimum (Tmin) near-surface air temperatures and precipitation across South America, focusing on Brazilian biomes and national capitals, using ERA5 reanalysis data for 1979–2024. To isolate the underlying climate signal, seasonal cycles were removed using Seasonal-Trend decomposition based on Loess (STL), which effectively separates short-term variability from long-term trends. Temperature trends were quantified using ordinary least squares (OLS) regression, allowing consistent estimation of linear changes over time, while precipitation trends were assessed using the non-parametric Mann–Kendall test combined with Theil–Sen slope estimation, a robust approach that minimizes the influence of outliers and serial correlation in hydroclimatic data. Results indicate widespread but spatially heterogeneous warming, with Tmax increasing faster than Tmin, consistent with reduced cloudiness and evaporative cooling. A meridional precipitation dipole is evident, with drying across the Cerrado, Pantanal, Caatinga, and Pampa, contrasted by rainfall increases in northern South America linked to ITCZ shifts. The Pantanal emerges as the most vulnerable biome, showing strong warming (+0.51 °C decade⁻¹) and the steepest rainfall decline (−10.45 mm decade⁻¹). Satellite-based fire detections (2013–2024) reveal rising wildfire activity in the Amazon, Pantanal, and Cerrado, aligning with the “hotter and drier” climate regime. In the capitals, persistent Tmax increases suggest enhanced urban heat island effects, with implications for public health and energy demand. Although ERA5 provides coherent spatial coverage, regional biases and sparse in situ observations introduce uncertainties, particularly in the Amazon and Andes, these do not alter the principal finding that the magnitude and persistence of the 1979–2024 warming lie well above the range of interdecadal variability typically associated with the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO). This provides strong evidence that the recent warming is not cyclical but reflects the externally forced secular warming signal. These findings underscore growing fire risk, ecosystem stress, and urban vulnerability, highlighting the urgency of targeted adaptation and resilience strategies under accelerating climate change. Full article

Water scarcity, driven by climate change, spatial and temporal variations in precipitation, and seasonal water demands, is becoming an increasingly pressing issue for Mediterranean islands such as Crete. Strategically placed dams could offer a sustainable solution to these challenges. To identify optimal sites, we employed a multi-criteria decision-making (MCDM) framework, integrating Analytic Hierarchy Process (AHP) and fuzzy AHP methodologies with remote sensing data using Geographical Information Systems (GIS). This process generated two different suitability maps for dam construction across the island of Crete. The following analyses were also performed on the results: (1) validation; (2) sensitivity; and (3) 3D analysis of three highly suitable locations. The findings are promising, showing a widespread distribution of numerous highly suitable locations. Validation revealed satisfactory predictive performance, while the sensitivity analysis indicates stability of the top locations. Subsequent 3D analysis revealed favorable morphological characteristics for two locations but severe limitations for the third. This study can serve as a starting point for further investigation into dam construction as a viable mitigation strategy for Crete’s water crisis. Full article

The Dongting Lake Basin (DLB), a region of key importance in the national project of the Yangtze River Protection and Economic Belt Construction, experienced dramatic land use changes caused by anthropogenic disturbances and climate change. Understanding vegetation dynamics is crucial for improving ecosystem structure and function and environmental sustainability. Here, a long-term (2000–2020) Normalized Difference Vegetation Index (NDVI) dataset, integrated with multiple statistical methods, was applied to investigate the spatiotemporal characteristics of vegetation coverage in the DLB. The Geodetector model and partial correlation analysis were then applied to determine the main factors affecting spatial and temporal vegetation coverage change. The results showed the following: (1) The DLB showed an overall increasing NDVI at a rate of 0.37% per year from 2000 to 2020; NDVI dynamics shifted in 2010, changing from a slow to a significant increase. The seasonal average NDVI increased differently among the four seasons, in the following descending order: winter (0.56%) > spring (0.22%) > summer (0.17%) > autumn (0.05%). (2) The area with an upward NDVI trend was primarily distributed in the forest zones in the eastern and western parts, accounting for 87.55% of the total area, whereas the area with a decreasing trend was mainly clustered in the northern plains of the DLB, accounting for 6.27% of the total area. (3) The annual variation rate of the NDVI during 2010–2020 was faster than that from 2000 to 2010; the gains and losses of the transmission area were varied among different vegetation levels. (4) The DEM and slope comprised a stronger influence on the NDVI spatial variation, while the annual average temperature was the controlling climate factor, with a q-value of 26.09%. The interaction of each independent factor showed a strengthening effect for explaining the spatial variability of the NDVI. (5) Climatic factors exerted a positive correlation with the NDVI, and the temperature had a stronger influence on vegetation coverage change than that of precipitation. These results can guide the development of ecosystem models to enhance their predictive accuracy, which can provide a scientific basis for the sustainable management of vegetation resources. Full article