Search Results (127)

Beach nourishment is a widely used strategy to mitigate coastal erosion, yet its long-term geological impacts remain poorly understood. This study provides a multi-decadal synthesis of shoreline change and sedimentological evolution on the nourished beaches of Cape May and Wildwood, New Jersey, USA. Using shoreline positions from 1991 to 2024, we identify contrasting trajectories: Wildwood exhibits ‘persistent transition’ with severe northern erosion (EPR: −10.0 m/yr) feeding southwards accretion, while Cape May demonstrates a ‘managed equilibrium’ with widespread accretion (mean EPR: +1.15 m/yr). Wave energy correlations account for less than 15% of shoreline variability, indicating natural drivers have been superseded by human sediment inputs. Direct sediment comparison shows substantial textural transformation, with median grain sizes increasing from 153 to 435 μm to 467–982 μm and sorting degrading from very well to moderately well sorted, reflecting sustained disequilibrium. These findings are synthesized into a conceptual model where nourishment initiates feedback cycles that create human-dependent morphodynamic trajectories. This study concludes that the long-term resilience of developed coasts will depend on a strategic evolution from managing ‘sand as volume’ toward stewarding ‘sediment as a system,’ where textural compatibility is a primary determinant of success. Full article

Persistence of seagrass meadows varies depending on community composition, substrate stability, environmental forcing, and water quality/clarity. Spatial trends in decadal scale persistence are difficult to assess at the meadow scale using in situ approaches and assessments using Earth Observation often lack temporal consistency. This study utilises a multi-decadal field monitoring dataset and high-resolution multispectral satellite imagery in a cloud-processing environment to assess species distribution, seagrass cover, and meadow persistence. In this work, we investigate long-term trends in overall meadow and species-specific persistence in the Eastern Banks, Moreton Bay, Australia, a shallow, semi-enclosed, subtropical embayment (∼200 km²). Here, we have identified an overall decline in seagrass cover (−15% of the total study area), between 2011 and 2025, through contraction of meadow extent, with most losses in colonising species (Halophila spinulosa and Halophila ovalis) across the deeper sections of the study area. We have also quantified the spatial extent of a previously identified broad-scale ecosystem shift from meadows dominated by Zostera muelleri to a prevalence of Oceana serrulata, and reduction in the sparse cover species H. spinulosa and H. ovalis. We have presented a semi-automated cloud-processing based pipeline to combine in situ seagrass observations, derived from an expertly trained machine learning model, with high resolution multispectral data to assess seagrass cover and persistence. The variability in decadal-scale persistence between the six key species found in this region has been assessed, with dense cover species (e.g., O. serrulata and Z. muelleri) exhibiting moderate persistence (>0.32) and sparse cover species (H. ovalis and H. spinulosa) with low persistence (∼0.15). Colonising/opportunistic growth patterns characterise the species examined in this study, indicating quick response to disturbance but a lack temporal consistency in meadow form, which has critical implications for resilience. Full article

The South China Sea (SCS) sea surface temperature (SST) plays a crucial modulating effect on the climate of East Asia. While the interannual variability of South China Sea SST has been extensively examined, the decadal-scale linkages and underlying physical mechanisms between South China Sea SST and the three major ocean basins (the Atlantic, Pacific, and Indian Oceans) remain inadequately comprehended. To fill the gap, the study investigates the decadal variability of winter SST in the SCS during 1940–2023, utilizing long-term observational datasets and methods such as empirical orthogonal function decomposition, regression analysis, and teleconnections analysis. The first dominant mode of this decadal variability is characterized by basin-warming across the SCS, which is mainly driven by the Atlantic Multidecadal Oscillation (AMO, r = 0.62, p < 0.05). Specifically, the AMO imposes its remote influence on the SCS through three distinct pathways: the tropical Pacific pathway, the North Pacific pathway, and the tropical Indian Ocean pathway. These pathways collectively trigger an anomalous cyclone in the western North Pacific and SCS, and further induce basin-wide SST warming via a positive feedback that includes SST, sea level pressure, cloud cover, and longwave radiation. The second leading mode of SCS winter SST decadal variability displays a north–south dipole pattern, which is positively correlated with the Interdecadal Pacific Oscillation (IPO, r1 = 0.85, p1 < 0.05). Notably, this South China Sea SST dipole–IPO relationship weakened significantly after 1985 (r2 = 0.23, p2 < 0.05), related to the strengthening of the anomalous anticyclone over the SCS and the weakening of the anomalous cyclone over the tropical Indian Ocean. Furthermore, both the AMO and IPO influence the SST in the northern SCS by regulating wind field anomalies in the bifurcation region of the North Equatorial Current. This wind-driven modulation subsequently affects the intensity of Kuroshio intrusion into the SCS. These findings provide a novel mechanistic pathway for interpreting decadal-scale climate variability over East Asia, with implications for improving long-term climate prediction in the region. Full article

In the context of global warming, hydroclimatic conditions in the monsoon marginal zone are governed by two primary drivers: the East Asian monsoon and the westerly winds. As a sensitive indicator of climatic change, this region experiences disproportionately amplified adverse effects of climate change are markedly amplified, positioning it as a focal area for climatological research. However, the limited temporal coverage of instrumental records poses significant challenges for understanding historical hydroclimatic variability and its underlying mechanisms. To address this limitation, tree-ring width indices derived from 73 cores of Styphnolobium japonicum ((L.) Schott (1830)) are hereby employed to reconstruct summer maximum temperatures over a 433-year period in the central monsoon fringe zone—specifically, the northwestern Yan Mountains. Results confirm a strong correlation between the tree-ring width index of Styphnolobium japonicum and local summer maximum temperatures (r = 0.770, p < 0.01). Compared to the 19th century, the frequency of temperature fluctuations has increased substantially, with four abrupt regime shifts identified in the reconstructed series (1707, 1817, 1878, and 1994). Spectral analysis reveals cyclical patterns at interannual (2–7 years), decadal (10–30 years), and multidecadal (50 years) timescales. These oscillations align closely with known climate modes, including the EI Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO). Among them, the AMO presents particularly strong coherence with the reconstructed temperature variability. These outcomes improve insights into long-term temperature dynamics in the region and highlight the value of dendroclimatic proxies in reconstructing past climate conditions. Full article

This study models the spatiotemporal heterogeneity of extreme rainfall over the Korean Peninsula using the generalised additive extreme value models (EVGAM) to address the limitations of traditional stationary approaches under climate change. Analyzing 30 years of daily precipitation data (1995–2024), we conducted a comparative analysis between typhoon-inclusive and non-typhoon scenarios to isolate the meteorological drivers of extremes. The results revealed distinct covariate dependencies: while spatial location (latitude and longitude) governs rainfall variability in non-typhoon conditions, elevation emerged as the critical determinant for the scale parameter during typhoon events, highlighting the role of orographic effects. Furthermore, the shape parameter exhibited multi-decadal oscillations corresponding to climate variability indices. To ensure local accuracy, a dual fitting strategy was implemented, supplementing EVGAM with standalone generalized extreme value (GEV) estimation for stations exhibiting poor goodness-of-fit. The resulting 50-year and 100-year return level maps quantify regional risks, identifying the southern coast as a high-vulnerability zone driven by typhoons, while inland basins benefited from orographic shielding. This comprehensive framework provides a robust scientific basis for adaptive water resource management and infrastructure design. Full article

The North Atlantic Oscillation (NAO) represents the dominant atmospheric mode governing climate variability across the Northern Hemisphere, particularly influencing precipitation regimes in regions such as northern Algeria. This study investigates the nonlinear linkage between monthly NAO indices and rainfall over northern Algeria for the period 1970–2009 using a cross-multiresolution analysis framework based on seven wavelet families—Daubechies, Biorthogonal, Reverse Biorthogonal, Discrete Meyer, Symlets, Coiflets, and Fejer–Korovkin—comprising a total of 106 individual mother wavelets. More than 700 cross-correlations were computed per NAO–rainfall pair to identify wavelet families that yield stable and physically coherent teleconnection structures across seven decomposition scales (D1–A7). The maximum decomposition level (2⁷ = 128 months, ≈10.6 years) captures intra-annual to decadal variability without extending into multidecadal regimes, ensuring temporal representativeness relative to the 40-year record length. The results reveal that short-term scales (D1–D3) are dominated by noise, masking weak correlations (≤±0.20), while stronger and more coherent relationships emerge at longer scales, reaching ±0.4 at the annual and ±0.75 at the decadal bands. These findings confirm the pronounced influence of low-frequency NAO variability on regional precipitation. Unlike previous studies limited to a few Daubechies wavelets, this work systematically compares 106 wavelet forms and evaluates robustness through reproducibility across scales, consistency among wavelet families, and physical coherence with known NAO periodicities (2–4 and 8–12 years). By emphasizing stability and physical plausibility over statistical significance alone, this approach minimizes the risk of spurious correlations due to multiple testing and highlights genuine scale-dependent teleconnection patterns. The application of discrete wavelet transforms thus enhances signal clarity, isolates meaningful oscillations, and provides a robust diagnostic framework for understanding NAO–rainfall dynamics in northern Algeria. Full article

High-mountain regions such as the Ile-Alatau range of the Northern Tien Shan are highly sensitive to climatic fluctuations, where even minor variations in temperature and precipitation significantly influence glacier mass balance and hydrology. Despite this sensitivity, few long-term studies have examined the links between hydro-climatic trends, glacier retreat, and moraine lake development. This study investigates multi-decadal glacier and lake dynamics (1955–2024) in relation to observed climate variability, using an integrated hydro-climatic and remote-sensing approach. Temperature and precipitation records from four high-altitude meteorological stations were assessed using linear regression and the Mann–Kendall test, while glacier and lake extents were derived from aerial photographs and Landsat, Sentinel-2, and PlanetScope imagery across ten river basins. Results show statistically significant warming at all stations, with mean annual temperatures increasing by 0.14–0.28 °C per decade and summer temperatures by 0.15–0.30 °C, while precipitation remained stable or slightly decreased. Glacierized area decreased from approximately 269.6 km² in 1955 to 141.7 km² in 2021, representing a 47.4% reduction (≈−0.72% yr⁻¹) over six decades and underscoring the rapid regional cryospheric response to sustained climatic warming. Simultaneously, moraine-dammed lakes increased by 16–18% between 2017 and 2024. These trends highlight the dominant climatic control on glacier loss and lake evolution, emphasizing growing glacial lake outburst floods (GLOFs) and the need for adaptive water-resource management in Central Asia. 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

This study presents a spatially explicit, multidecadal analysis of how land use and land cover (LULC) change and socio-demographic dynamics have influenced land surface temperature (LST) patterns in the Phoenix metropolitan area between 2001 and 2021. Using Landsat-derived summer LST, socio-demographic indicators, and land cover data, we quantify urban land transformation and socio-demographic changes over two decades. To account for spatial heterogeneity, we apply Multiscale Geographically Weighted Regression (MGWR), which improves upon conventional regression models by allowing for variable-specific spatial scales. Results show that the 2001–2011 period was characterized by rapid suburban expansion and widespread conversion of croplands and open space to higher-intensity development, while 2011–2021 experienced more limited infill development. Correlation analysis reveals that agricultural and open space conversions were linked to population and housing growth, whereas redevelopment of existing urban areas was often associated with socio-demographic decline. MGWR results highlight that agricultural land conversion drives localized warming, while shrub/scrub-to-developed transitions are linked to broader-scale cooling. By combining spatial sampling, area-weighted interpolation, and MGWR, this study offers a fi-ne-grained assessment of urban thermal dynamics in a fast-growing desert region. The findings provide actionable insights for planners and policymakers working toward sustainable and climate-resilient urban development in arid environments. Full article

This study presents a comprehensive analysis of key meteorological parameters at the Lenghu site, a premier astronomical observing location, with particular emphasis on understanding their variability patterns and long-term trends. The research systematically investigates regional distribution characteristics, periodic variations, seasonal changes, and the temporal evolution of critical atmospheric parameters that influence astronomical observations. Furthermore, this study explores the potential connections between these parameters and major climate oscillation patterns, including ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), and AMO (Atlantic Multidecadal Oscillation). Utilizing ERA5 (the fifth-generation atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts) reanalysis data, we examine the regional atmospheric conditions (82°–102° E and 31°–46° N) surrounding the Lenghu site from 2000 to 2023 (24 years). The analysis focuses on fundamental meteorological parameters: precipitable water vapor (PWV), temperature, wind speed at 200 hPa ($W_{200}$), and total cloud cover (TCC). For the Lenghu site specifically, we extend the temporal coverage to 1990–2023 (34 years) to include additional parameters such as high cloud cover (HCC) and total column ozone (TCO). The analysis reveals that the ENSO and PDO indices are negatively correlated with $W_{200}$. The AMO index has a positive correlation with PWV and a slight positive correlation with $W_{200}$, temperature, and TCO. Moreover, a comparative analysis of Lenghu, Mauna Kea, and Paranal reveals distinct variation trends across sites due to regional climate differences. Notably, while all observatory sites are affected by global climate change, their response patterns and temporal characteristics exhibit subtle variations. Full article

Pakistan’s coastline encompasses the Indus Delta, a critical ecosystem that sustains biodiversity, fisheries, and local livelihoods, yet it is increasingly threatened by both natural and anthropogenic pressures. This study quantifies multi-decadal shoreline changes in the Indus Delta and examines how changes in climatic factors (precipitation and wind) affect these changes, using the Digital Shoreline Analysis System (DSAS v5.1) and multi-temporal Landsat imagery (TM, ETM+, OLI) to quantify long-term shoreline dynamics from 1990 to 2020 (30-year period). Key metrics, including End Point Rate (EPR), Net Shoreline Movement (NSM), and Linear Regression Rate (LRR), indicated an overall retreat, with a mean NSM of −1810 m and a mean LRR of −173 m·year across the 30-year period. Shoreline change rates exhibited a significant relationship with climatic variables, particularly wind speed and precipitation, with dynamics shifting from erosion-dominated to localized accretion in areas where mangrove rehabilitation programs were implemented after 2005. Seasonal variability further influenced shoreline behavior: low-rainfall years intensified erosion due to reduced sediment availability, while high-rainfall years enhanced accretion through increased sediment input. These findings underscore the urgent need for integrated coastal management strategies, including mangrove conservation, sustainable sediment management, and climate-adaptive planning, to strengthen the resilience of the Indus Delta. Full article

Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha⁻¹. Red and red-edge bands saturated at around 80 Mg·ha⁻¹, while certain spectral indices delayed the threshold to 100–150 Mg·ha⁻¹. Topographic features maintained stable contributions below 300 Mg·ha⁻¹, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring. Full article

The ocean heat content (“OHC”)—the heat energy within the ocean integrated to a reference depth—has physical drivers spanning spatial and temporal scales, including seasonality, the El Niño/Southern Oscillation (ENSO), and others. The present article investigates changes in the OHC100 during the period 1994–2020 using GLORYS12 monthly averaged ocean reanalysis. OHC100–ENSO correlation patterns are explored to glean insights about the oceanic mechanisms that facilitate the ENSO’s global teleconnections. After extracting known seasonality and ENSO signals using the Oceanic Niño Index (ONI), the OHC100 residual is analyzed to investigate multidecadal drivers of the OHC100. Lagged ENSO–OHC100 correlations (±12 months) reveal basin-scale oscillations in the sign of ENSO influence likely attributable to Rossby waves. The OHC100 is increasing globally (in total, 2.4 × 10²² J decade⁻¹), with the greatest increases near western boundary currents (WBCs). Some regions are decreasing, notably the Atlantic main development region (MDR) for tropical cyclones (TCs). Correlations and multidecadal variability in the OHC100 tendency (OHCT) and zonal and meridional advections of the OHC100 (ZAO and MAO) support the hypothesis that upper-ocean dynamics mediate ENSO teleconnections as well as exert independent control on OHC100 variability. Local increases in the OHC100 would support the observed TC rapid intensification irrespective of the ENSO phase as the TC-supporting region expands. Full article

The hydrology of the transboundary region encompassing the western Red River Basin headwaters, such as Devils Lake Basin (DLB) in North America, is complex and highly sensitive to climate variability, impacting water resources, agriculture, and flood risk. Understanding hydrological shifts in this region is critical, particularly given recent hydroclimatic changes. This study aimed to simulate and analyze key hydrological processes and their evolution from 1981 to 2020 using an integrated modeling approach. We employed the NASA Land Information System (LIS) framework configured with the Noah-MP land surface model and the HyMAP routing model, driven by a combination of reanalysis and observational datasets. Simulations revealed a significant increase in precipitation inputs and consequential positive net water storage trends post-1990, indicating increased water retention within the system. Snow dynamics showed high interannual variability and decadal shifts in average Snow Water Equivalent (SWE). Simulated streamflow exhibited corresponding multi-decadal trends, including increasing flows within a major DLB headwater basin (Mauvais Coulee Basin) during the period of Devils Lake expansion (mid-1990s to ~2011). Furthermore, analysis of decadal average seasonal hydrographs indicated significant shifts post-2000, characterized by earlier and often higher spring peaks and increased baseflows compared to previous decades. While the model captured these trends, validation against observed streamflow highlighted significant challenges in accurately simulating peak flow magnitudes (Nash–Sutcliffe Efficiency = 0.33 at Mauvais Coulee River near Cando). Overall, the results depict a non-stationary hydrological system responding dynamically to hydroclimatic forcing over the past four decades. While the integrated modeling approach provided valuable insights into these changes and their potential drivers, the findings also underscore the need for targeted model improvements, particularly concerning the representation of peak runoff generation processes, to enhance predictive capabilities for water resource management in this vital region. Full article

This study presents a comprehensive analysis of climate evolution in the Porto region (Northern Portugal) using 148 years (1863–2010) of continuous meteorological data from the Serra do Pilar weather station (WMO station 08546). The research employs both traditional linear statistical methods and advanced non-linear analysis techniques, including polynomial trend fitting and multidecadal oscillation analysis, to accurately characterize long-term climate patterns. Results reveal that linear trend analysis is misleading for this dataset, as both temperature and precipitation follow parabolic (U-shaped) distributions with minima around 1910–1970. The early period (1863–1900) exhibited higher values than the recent period, contradicting linear trend interpretations. Advanced analysis shows that the mean temperature follows a parabolic pattern (R² = 0.353) with the minimum around 1935, while precipitation exhibits similar behavior (R² = 0.053) with the minimum around 1936. Multidecadal oscillations are detected with dominant periods of 46.7, 15.6, and 10.0 years for temperature, and 35.0, 17.5, and 4.5 years for precipitation. Maximum temperatures show complex oscillatory behavior with a severe drop around 1890. Seasonal analysis reveals distinct patterns across all seasons: winter (+0.065 °C/decade) and autumn (+0.059 °C/decade) show warming trends in maximum temperatures, while spring (−0.080 °C/decade) and summer (−0.079 °C/decade) demonstrate cooling trends in minimum temperatures, with no significant trends in spring (+0.012 °C/decade) and summer (+0.003 °C/decade) maximum temperatures or winter (−0.021 °C/decade) and autumn (−0.035 °C/decade) minimum temperatures. The study identifies a significant change point in mean temperature around 1980, which occurs approximately one decade earlier than the global warming acceleration typically observed in the 1990s, suggesting regional Atlantic influences may precede global patterns. Extreme event analysis indicates stable frequencies of hot days (averaging 3.6 days/year above 25.0 °C) and heavy precipitation events (averaging 1.2 days/year above 234.6 mm) throughout the study period. These findings demonstrate that the Porto region’s climate is characterized by natural multidecadal variability rather than monotonic trends, with the climate system showing oscillatory behavior typical of Atlantic-influenced coastal regions. The results contribute to understanding regional climate variability and provide essential baseline data for climate change adaptation strategies in Northern Portugal. The results align with broader patterns of natural climate variability in the Iberian Peninsula while highlighting the importance of non-linear analysis for comprehensive climate assessment. Full article