Search Results (73)

Ferdinandea Island, part of a shallow-water submarine volcanic field, emerged in the Sicily Channel between Italy and Tunisia in July 1831 and was eroded below sea level within months; its submerged remnant forms the shallowest water depth region of Graham Bank. Here, we review nearly two centuries of historical accounts, geological interpretations and geomorphological data analysis and reassess them against high-resolution multibeam bathymetry, sub-bottom profiles (CHIRP) and published multichannel seismic data. The field comprises six volcanic edifices (V1–V6), 100–170 m high, located along structural trends characteristic of the Sicily Channel Rift. V3, the shallowest edifice, is the remnant of Ferdinandea Island formed during the 1831 Surtseyan eruption. Its flat summit, wave-reworked terrace and steep flanks record rapid post-eruptive modification. Historical observations and hydrographic surveys document the destruction of the emergent island and a further ~6 m lowering of its shallowest point between 1883 and 2012–2015; the separate contributions of wave erosion, subsidence and gravitational adjustment cannot be resolved from the available data. The same regional structural framework appears to have governed the distribution of the other volcanic centres, pockmarks, erosional escarpments and mass-transport deposits of the study area. Seventeen pockmarks, up to ~540 m wide and 22 m deep, occur as isolated, clustered and locally aligned depressions; they are associated with subsurface concave-upward reflectors and local water-column acoustic anomalies, consistent with focused fluid escape. Failures of volcanic and sedimentary slopes are widespread, with the largest debris-avalanche deposit covering ~2.2 km². Taken together, these observations indicate that tectonics, volcanism, fluid migration, wave- and bottom-current reworking, and gravitational instability have operated over different timescales to shape Graham Bank. Ferdinandea thus offers a rare historical and geological reference for investigating the rapid construction, degradation and long-term evolution of shallow-water volcanic edifices and highlights the still-open questions regarding the evolution and fate of ephemeral volcanic islands. Full article

Gridded precipitation products are widely used in hydroclimatic studies, yet their suitability for anomaly-sensitive applications cannot be determined from magnitude-based statistics alone. This study introduces Bidirectional Extreme Response Analysis (BERA), a class-based evaluation framework designed to assess the ability of precipitation datasets to reproduce anomalously dry, near-normal, and anomalously wet monthly conditions. BERA decomposes gauge and product-based precipitation series into calendar-month-specific anomaly classes and quantifies agreement, no-response, false-extreme, and opposite-direction outcomes through a directional agreement matrix. The framework was demonstrated in the Upper Tigris River Catchment in southeastern Türkiye using monthly observations from 11 TSMS gauge stations and six precipitation products: CHIRPS v3.0, GPCP v3.3, ERA5, MSWEP v2.80, CHELSA, and CMIP6 EC-Earth3 over the common 1983–2011 period. Results show that conventional metrics alone provide contradictory product rankings, whereas BERA reveals distinct directional performance differences that are directly relevant to anomaly-sensitive applications. CHELSA achieved the highest overall BERA agreement rate (0.757), followed by GPCP v3.3 (0.705), whereas CMIP6 showed the weakest class reproduction (0.340) and the largest share of opposite-direction responses. Across most products, wet anomalies were reproduced more successfully than dry anomalies, indicating that precipitation deficits remain more difficult to identify reliably. Elevation-based comparisons further showed that high-elevation gauges were associated with weaker magnitude performance, while class-based agreement did not decline monotonically with altitude. Station-level differences further indicate that anomaly-class agreement is influenced by local hydroclimatic variability and gauge–grid representativeness, rather than by elevation alone. These findings show that BERA captures a distinct dimension of precipitation product behavior by separating magnitude errors from directional class misclassification. Because of its flexible classification structure, the framework can also be refined for different levels of anomaly severity or application-specific thresholds, allowing product performance to be interpreted according to the criticality of the intended use. Full article

Extreme precipitation and waves both drive coastal hazards along the American Mediterranean Sea, yet whether the two have changed in coordinated ways over recent decades remains unclear. We analyzed 43 years (1981–2023) of coastal precipitation and wave climate across five sub-regions, using CHIRPS v2.0 daily rainfall at 2656 coastal land pixels and ERA5 wave reanalysis at 1159 nearshore ocean pixels. Five ETCCDI precipitation indices and five wave metrics were computed; their trends were estimated with the Mann–Kendall test and Sen’s slope, and their uncertainty was quantified by bootstrap resampling. Coastal precipitation shows a “fewer but more intense” pattern in parts of the basin: along the Mexican Gulf and Central American coasts, heavy-precipitation frequency (R10mm) declines by about 1.4 to 2.6 days/decade while the most extreme events intensify, with one-day maxima rising by about 6 mm/decade and five-day maxima by 7 to 10 mm/decade; trends in annual total precipitation are comparatively uncertain. Wave trends are generally positive, with significant wave height rising most rapidly off Central America (about 0.021 to 0.027 m/decade) and peak wave period lengthening most along the South American Caribbean coast. To test whether these changes are spatially linked, we paired neighboring land and ocean pixels and applied pairwise trend correlations, sub-regional time-series analysis, and joint clustering. Across the basin, coastal segments with stronger increases in extreme rainfall tend to coincide with segments of rising wave height. Among the examined index pairs, the largest positive correlation was observed between the trends in short-duration precipitation extremes (Rx1day and Rx5day) and the 90th-percentile wave height, although the association was modest ($\rho \approx 0.28$). Joint clustering suggests that concurrent positive trends are more frequently represented along the southwestern Gulf of Mexico and the Caribbean coast of Central America. These results indicate that the principal drivers of compound coastal flooding may not be evolving independently across this basin, and they support considering precipitation and wave climate change jointly in regional coastal hazard assessment. Full article

Reliable rainfall information is fundamental for climate-risk analysis and operational monitoring in Mediterranean regions such as Apulia (Southern Italy), one of the areas most affected by climate change-driven shifts in rainfall patterns. Recent evaluations across Italy and comparable Mediterranean settings consistently show that gridded precipitation performance is highly dependent on orography and dataset typology: reanalyses often provide the best overall agreement with gauges, while satellite and blended products can exhibit larger biases, with persistent challenges in complex terrain and for high-intensity events. In this context—and given the documented spatial heterogeneity of rainfall extremes within Apulia—validation of such gridded datasets with respect to ground observations remains essential for early warning and climatological applications. In the present work, we evaluate four widely used precipitation products—CHIRPS-v2, the newly released CHIRPS-v3, IMERG, and ERA5—benchmarking them against the Apulia region Civil Protection rain-gauge network. We provide diagnostics aligned with early warning and climate monitoring: bias and error statistics, rainfall intensity distributions, and dry spell duration. A key contribution is, to our knowledge, the first dedicated validation of CHIRPS-v3 in Apulia, which is timely given that CHIRPS-v3 was explicitly developed to address shortcomings such as underestimated temporal variance and to leverage expanded station inputs. The results indicate that CHIRPS-v3 yields systematic improvements over CHIRPS-v2 across multiple metrics, while ERA5 generally shows the strongest overall agreement with gauges—consistent with broader Italian evidence. Full article

Forecasting monthly precipitation in mountainous terrain poses challenges that push conventional deep learning approaches to their limits: convective processes operate locally while orographic effects span entire drainage basins. We compare three architecture families on precipitation prediction across the Colombian Andes: ConvLSTM (convolutional recurrent), FNO-ConvLSTM (spectral–temporal), and GNN-TAT (graph attention LSTM). Using CHIRPS v2.0 and SRTM topography for Boyacá department (61 × 65 grid, 3965 nodes), we evaluate 39 configurations across feature bundles (BASIC, KCE elevation clusters, and PAFC autocorrelation lags) and horizons from 1 to 12 months. GNN-TAT matches ConvLSTM accuracy ($R^2$: 0.628 vs. 0.642; RMSE: 82.29 vs. 79.40 mm) with 95% fewer parameters (∼98K vs. 2.1M). Across configurations, GNN-TAT produces a lower mean RMSE (92.12 vs. 112.02 mm; $p=0.015$) and a 74.7% lower variance. The explicit graph structure, with edges weighted by elevation similarity, appears to reduce sensitivity to hyperparameter choices. Pure FNO struggles with precipitation’s spatial discontinuities ($R^2=0.206$), though adding a ConvLSTM decoder recovers much of the lost skill ($R^2=0.582$). Elevation clustering improves GNN-TAT significantly ($p=0.036$) but not ConvLSTM, suggesting that feature design should match the spatial encoding paradigm. ConvLSTM achieves peak accuracy on local patterns; GNN-TAT provides robust predictions with interpretable spatial reasoning. These complementary strengths motivate stacking ensembles that combine grid-based and graph-based representations. Full article

Background: Auditory brainstem responses (ABRs) are widely used for objective hearing threshold estimation in both adults and children. Click and CE-Chirp stimuli differ substantially in cochlear activation and neural synchrony, yet their relative performance in patients with ski-sloping hearing loss remains insufficiently characterized, particularly with regard to pediatric diagnostic implications. Methods: This study compared ABRs elicited by click and CE-Chirp stimuli in adults with ski-sloping sensorineural hearing loss. The same comparison was also performed in a pediatric cohort including hearing-impaired and normal-hearing children. Adult subjects were further stratified according to audiometric configuration (DROP 1 kHz vs. DROP 2 kHz). ABR thresholds, wave V latency, amplitude, and detectability were analyzed across stimulus types and intensity levels. Associations between ABR thresholds and behavioral audiometric measures were also examined. Results: In adults with ski-sloping hearing loss, CE-Chirp stimulation yielded significantly lower ABR threshold estimates than click stimulation, particularly in the DROP 2 kHz subgroup, and showed stronger correlations with behavioral pure-tone averages across low-, mid-, and high-frequency ranges. Wave V latencies were consistently shorter with CE-Chirp stimulation, while wave V amplitudes did not differ significantly between stimuli at suprathreshold levels. In children, ABR thresholds obtained with CE-Chirp were generally equal to or lower than those obtained with clicks, although statistical significance was limited by sample size. CE-Chirp stimulation was associated with shorter wave V latencies in both hearing-impaired and normal-hearing children and produced larger wave V amplitudes at selected suprathreshold intensities in hearing-impaired children. Conclusions: Click and CE-Chirp stimuli provide complementary information in ABR assessment. While click stimulation remains essential for robust waveform identification, CE-Chirp stimulation appears to offer advantages in threshold estimation and neural synchrony, particularly in ski-sloping hearing loss and pediatric evaluations. Discrepancies between click- and CE-Chirp-derived ABR thresholds should not be attributed solely to maturational or synchrony-related factors but may warrant further frequency-specific audiological assessment to optimize diagnosis and rehabilitation strategies. Full article

Droughts are one of the most significant hazards that affect human life due to the imbalanced distribution of water across the world. Some parts of the world are usually dry, and meteorological conditions affect these regions rapidly. In water-scarce regions, droughts significantly put at risk socio-economic stability and food security, which may cause a major challenge to sustainable development. Therefore, a precise definition of drought and the identification of early warning signals can help to minimize the negative effects of droughts, especially in terms of agriculture. In this study, drought signals of three major agricultural provinces of Turkey, namely Antalya, Şanlıurfa, and Konya, were investigated. For this purpose, the Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Evaporative Demand Drought Index (EDDI), and Vegetation Condition Index (VCI) were computed for each province. A composite score index was proposed for the evaluation of multiple indices together. All datasets were obtained from remote-sensing products to ensure reproducibility. A dataset for the 2003–2023 period was used. The monthly precipitation derived from CHIRPS data and potential evaporation (PEV) data were obtained from the ERA5-Land. Therefore, the SPEI and EDDI values were calculated by using ERA5-Land PEV values but not the evapotranspiration. The Normalized Difference Vegetation Index (NDVI) values for each province were obtained from the MODIS/Terra MOD13A3 v061. The Mann–Kendall test and Sen’s slope were applied to the computed time series to detect the trends. As a result, the dry and wet periods were identified for each province individually. The VCI was found to have an increasing trend for all tested provinces. Overall, from a future perspective, the most vulnerable province in terms of meteorological drought was indicated to be Antalya. Full article

High-resolution satellite precipitation products are key inputs for basin-scale rainfall estimation, but they still exhibit substantial biases in complex terrain and during heavy rainfall. Recent multi-source fusion studies have shown that simply stacking multiple same-type microwave satellite products yields only limited additional gains for high-quality precipitation estimates and may even introduce local degradation, suggesting that targeted correction of a single, widely validated high-quality microwave product (such as IMERG) is a more rational strategy. Focusing on the mountainous, gauge-sparse Lüshui River basin with pronounced relief and frequent heavy rainfall, we use GPM IMERG V07 as the primary microwave product and incorporate CHIRPS, ERA5 evaporation, and a digital elevation model as auxiliary inputs to build a daily attention-enhanced CNN–LSTM (A-CNN–LSTM) bias-correction framework. Under a unified IMERG-based setting, we compare three network architectures—LSTM, CNN–LSTM, and A-CNN–LSTM—and test three input configurations (single-source IMERG, single-source CHIRPS, and combined IMERG + CHIRPS) to jointly evaluate impacts on corrected precipitation and SWAT runoff simulations. The IMERG-driven A-CNN–LSTM markedly reduces daily root-mean-square error and improves the intensity and timing of 10–50 mm·d⁻¹ rainfall events; the single-source IMERG configuration also outperforms CHIRPS-including multi-source setups in terms of correlation, RMSE, and performance across rainfall-intensity classes. When the corrected IMERG product is used to force SWAT, daily Nash-Sutcliffe Efficiency increases from about 0.71/0.70 to 0.85/0.79 in the calibration/validation periods, and RMSE decreases from 87.92 to 60.98 m³ s⁻¹, while flood peaks and timing closely match simulations driven by gauge-interpolated precipitation. Overall, the results demonstrate that, in gauge-sparse mountainous basins, correcting a single high-quality, widely validated microwave product with a small set of heterogeneous covariates is more effective for improving precipitation inputs and their hydrological utility than simply aggregating multiple same-type satellite products. Full article

Satellite-based rainfall products (SRPs) are indispensable for hydro-climatological research, particularly in data-limited environments such as Ethiopia. This study systematically evaluates the performance of three widely used SRPs: Climate Hazards Group InfraRed Precipitation with Station data version 2 (CHIRPS), Tropical Applications of Meteorology using Satellite and ground-based observations version 3.1 (TAMSAT), and Multi-Source Weighted Ensemble Precipitation version 2.8 (MSWEP) across the North and South Gojjam sub-basins of the Abbay Basin. Using ground observations as benchmarks, spatial and temporal accuracy was assessed under varying elevation and rainfall intensity conditions, employing bias decomposition, error analysis, and detection metrics. Results show that rainfall variability in the region is shaped more by the local climate and topography than elevation, with elevation alone proving a weak predictor (R² < 0.5). Among the products, MSWEP v2.8 demonstrated the highest daily rainfall detection skill (≈ 87–88%), followed by TAMSAT (≈78%), while CHIRPS detected only about half of rainfall events (≈54%) and tended to overestimate no-rain days. MSWEP’s error composition is dominated by low random error (~52%), though it slightly overestimates rainfall and rainy days. TAMSAT provides finer-resolution data that capture localized variability and dry conditions well, with the lowest false alarm rate and moderate random error (~59%). CHIRPS exhibits weaker daily performance, dominated by high random error (~66%) and missed bias, though it improves at monthly scales and better captures heavy and violent rainfall. Seasonally, SRPs reproduce MAM rainfall reasonably well across both sub-basins, but their performance deteriorates markedly in JJAS, particularly in the south. These findings highlight the importance of sub-basin scale analysis and demonstrate that random versus systematic error composition is critical for understanding product reliability. The results provide practical guidance for selecting and calibrating SRPs in mountainous regions, supporting improved water resource management, climate impact assessment, and hydrological modeling in data-scarce environments. Full article

Satellite and reanalysis rainfall estimates (SREs) are valuable alternatives to gauge data in data-scarce regions; however, their reliability in areas with complex terrain and variable precipitation remains uncertain. This study evaluated six SREs (CHIRPS v2, ERA5, ERA5-Land, IMERG v07, MSWEP v2.8, and TRMM 3B42) against gauge observations over the period 2005 to 2019. The evaluation was conducted using multiple statistical, categorical, and distributional metrics at daily to seasonal timescales. Terrain-based classification and rainfall intensity categories were used to explore the influence of topography and event magnitude on product performance. The accuracy of SREs improves with temporal aggregation, the monthly scale offering the highest reliability for water resource management. However, their tendency to overestimate light and underestimate heavy daily rainfall requires careful bias adjustment in flood and extreme event analysis. MSWEP, CHIRPS, and IMERG provided balanced and consistent performance across all metrics, rainfall intensities, and terrain zones. Notably, ERA5 and ERA5-Land consistently overestimated average rainfall. All SREs identified dry days well, and their performance declined with increasing intensity. No significant performance variation was observed across different altitudes. This study provides valuable insights into the selection of rainfall products, supporting climate and hydrological studies in data-scarce areas of the Ethiopian highlands. Full article

The (3 + 1)-dimensional KdV–Calogero–Bogoyavlenskii–Schiff equation, a model that describes long-wave interactions and has numerous applications in mathematics, engineering, and physics, is examined in this work. First, a wave transformation is used to reduce the equation to lower dimensions. The modified Khater method is then used to derive different types of solitary wave solutions, such as chirped, kink, periodic, and kink-bright types. By allocating suitable constant parameters, 3D, 2D, and contour plots are created to demonstrate the physical behavior of these solutions. Phase portraits are used to qualitatively analyze the undisturbed planar system using bifurcation theory. The system is then perturbed by an external force, resulting in chaotic dynamics. Chaos in the system is confirmed using multiple diagnostic tools, including time series plots, Poincaré sections, chaotic attractors, return maps, bifurcation diagrams, power spectra, and Lyapunov exponents. The stability of the model is further investigated with varying initial conditions. A bidirectional scatter plot technique, which efficiently reveals overlapping regions using data point distributions, is presented for comparing solution behaviors. Overall, this work offers useful tools for advancing applied mathematics research as well as a deeper understanding of nonlinear wave dynamics. Full article

Reliable precipitation estimates are critical for climate analysis and ecosystem management in regions with complex topography and limited ground-based observations. Bolivia, where the Andes, inter-Andean valleys, and Amazonian lowlands converge, presents sharp climatic heterogeneity that challenges both satellite retrievals and reanalysis products. This study evaluated three widely used datasets, MSWEP V2.2, CHIRPS V2, and ERA5-Land, against monthly station records from 1980 to 2022 to identify the most reliable precipitation estimations for hydrological and climate applications in five distinct regions. We applied a robust validation framework that integrates continuous and categorical performance metrics into a Combined Accuracy Index (CAI), providing a balanced measure of magnitude and event detection skill. Additionally, we implemented a conservative trend analysis with explicit correction for serial autocorrelation to ensure reliable identification of long-term changes. The results showed that MSWEP V2.2 consistently outperforms CHIRPS V2 and ERA5-Land across most regions, achieving the highest combined skill. In the Altiplano, MSWEP reached a CAI of 0.91, compared to CHIRPS (0.80) AND ERA5-Land (0.68). In the Valles region, MSWEP also led with 0.85, outperforming CHIRPS (0.79) and ERA5-Land (0.51). By contrast, CHIRPS V2 performed better in the Llanos (0.85) relative to MSWEP (0.82) and ERA5-Land (0.79). In the Chaco, MSWEP and CHIRPS performed similarly (0.80 and 0.81, respectively), while ERA5-Land scored 0.70. In the Amazonian lowlands, all three products performed well, with MSWEP ranking first (0.93), followed by ERA5-Land (0.88) and CHIRPS (0.86). ERA5-Land systematically overestimated precipitation across Bolivia, with annual biases above 36 mm month⁻¹. Trend analysis revealed significant precipitation declines, particularly in the Llanos (MSWEP: −0.88 mm year⁻¹; CHIRPS: −1.19 mm year⁻¹; ERA5-Land: −0.90 mm year⁻¹), while changes in the Altiplano, Valles and Amazonia were weaker or nonsignificant. These findings highlight MSWEP V2.2 as the most reliable dataset for Bolivia. The methodological framework proposed here offers a transferable approach to validate gridded products in other data-scarce and environmentally diverse regions. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. Full article

Floods and droughts are natural disasters that disrupt livelihoods and destroy the environment, with floods constituting up to 40% of all natural disasters globally. South Sudan has experienced severe, recurrent flooding for decades, with two-thirds of the country affected. An integrated flood management system is urgently needed to mitigate impacts and improve community resilience. This requires understanding the inundation process and analyzing flood causes and characteristics. This research leverages data from the Climate Hazards Center InfraRed Precipitation with Station (CHIRPS v2.0) to examine rainfall patterns and analyze trends in annual total precipitation (PRCPTOT), days with precipitation ≥ 20 mm (R20 mm), and simple precipitation intensity (SDII) at the basin scale. It also incorporates Nile River flow data from the Mangala station and Lake Victoria water levels from satellite altimetry. Findings indicate decreasing trends in PRCPTOT, R20 mm, and SDII in Jonglei and Unity States, but increasing trends in river flows and Lake Victoria levels. The Global Surface Water dataset reveals increased water surface areas in these states. These findings suggest that river flow trends oppose rainfall patterns, indicating that local rainfall is not the primary contributor to the recurrent flooding in the area. Full article

With the increasing communication frequencies in 6G networks, high-speed terahertz (THz) modulation signal generation has become a critical research area. This study first proposes an on-chip high-speed THz modulation signal generation system based on lithium niobate (LN), which integrates a pair of racetrack resonator-integrated Mach–Zehnder modulators (RMZMs) with a chirped periodically poled lithium niobate (CPPLN) waveguide. The on-chip system combines near-infrared electro-optic modulation and cascaded difference-frequency generation (CDFG) for high-speed THz modulation signal generation. At 300 K, utilizing two input optical waves at frequencies of 193.55 THz and 193.14 THz, this on-chip system enables high-speed THz modulation signal generation at 0.41 THz, with a 1 Gbit/s modulation rate and a 0.25 V modulation voltage. During the simulation, when the intensity of the input optical waves is 1000 MW/cm², the generated 0.41 THz signal reaches a peak intensity of 21.24 MW/cm². Furthermore, based on theoretical analysis and subsequent simulation, the on-chip system is shown to support a maximum modulation signal generation rate of 7.75 Gbit/s. These results demonstrate the potential of the proposed on-chip system as a compact and efficient solution for high-speed THz modulation signal generation. Full article