Search Results (2,087)

The Philippines experiences intense rainfall but has limited ground-based monitoring infrastructure for flood prediction. Satellite rainfall products provide broad coverage but contain systematic biases that reduce operational usefulness. This study evaluated whether three correction methods—Quantile Mapping (QM), Random Forest (RF), and Hybrid Ensemble—maintain accuracy when applied to future periods with substantially different rainfall characteristics. Using the Cagayan de Oro River Basin in Northern Mindanao as a case study, models were trained on 2019–2020 data and tested on an independent 2021 period exhibiting 120% higher mean rainfall and 33% increased rainy-day frequency. During training, Random Forest and Hybrid Ensemble substantially outperformed Quantile Mapping (R² = 0.71 and 0.76 versus R² = 0.25 for QM). However, when tested under realistic operational constraints using seasonally incomplete calibration data (January–April only), performance rankings reversed completely. Quantile Mapping maintained operational reliability (R² = 0.53, RMSE = 5.23 mm), while Random Forest and Hybrid Ensemble failed dramatically (R² dropping to 0.46 and 0.41, respectively). This demonstrates that training accuracy poorly predicts operational reliability under changing rainfall regimes. Quantile Mapping’s percentile-based correction naturally adapts when rainfall patterns shift without requiring recalibration, while machine learning methods learned magnitude-specific patterns that failed when conditions changed. For flood early warning in data-limited basins with equipment failures and variable rainfall, only Quantile Mapping proved operationally reliable. This has practical implications for disaster risk reduction across the Philippines and similar tropical regions where standard validation approaches may systematically mislead model selection by measuring calibration performance rather than operational transferability. Full article

Due to the lack of in situ observations in mountainous locations, the use of remote sensing data is an alternative to analyze rainfall distribution patterns during the passage of major hurricanes. In this work, gridded precipitation data from the CHIRPS database are evaluated by comparing with observations from weather stations during the passage of category 3–5 hurricanes for the period 1980–2024. The comparison between estimated and observed values is performed by regression analysis and the use of K and K₀ coefficients. An advantage of using K-ratio and K₀-ratio is the identification of overestimated or underestimated precipitation in the pixel records. The distribution of daily precipitation helped in a more concise way to better understand how well CHIRPS reproduced the observed rainfall patterns. Results show that correlations between observations and database estimates are in the range of 0.40–0.76, for eastern Pacific hurricanes, and 0.49–0.78 for Atlantic hurricanes, all of which are statistically significant; however, these results do not imply congruence between observations and estimates since CHIRPS fails to adequately reproduce the position of the highest precipitation core. In the initial stages of a tropical cyclone, near-zero correlations between observations and estimates indicate that CHIRPS is not able to reproduce the observed rainfall. It is recommended to use CHIRPS with caution when the focus is on analyzing rainfall patterns during the development of intense tropical cyclones. Full article

Geostationary atmospheric motion vectors (e.g., FY4A AMVs) are routine mid-upper atmospheric observations used in numerical weather prediction (NWP) models, yet their complex spatiotemporal errors and assimilation limitations, i.e., high-temporal/coarse-spatial data and large-scale-adjustment/direct-assimilation scheme, leave unclear impacts of AMVs assimilation on nowcasting forecasts. To this end, a Nudging-Forced–3DVar scheme (NFV) is designed within a multi-scale (i.e., 12, 4, and 1 km) regional NWP framework to exploit AMVs characteristics; ablation experiments for the Zhengzhou “7.20” rainstorm isolate Nudging and 3DVar impacts on assimilation and nowcasting. Results show the following: (1) large-scale Nudging and high-resolution 3DVar both improve mid-upper analyses, with the former ingesting more observations; (2) Nudging retains large-scale background updates but yields significant misses, whereas 3DVar intensifies rainfall extremes yet blurs fine structures; (3) NFV merges its strengths, modulating deep convection through upper-level systems and markedly improving rainfall spatiotemporal patterns. Therefore, NFV is recommended for the FY4A AMVs’ future numerical nowcasting, which provides useful guidance for the regional application of geostationary 3D winds. Full article

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes. Full article

Flood disasters are increasing worldwide due to climate change, posing growing risks to infrastructure and human life. Korea, where nearly 70% of annual rainfall occurs during the summer monsoon, is particularly vulnerable to extreme precipitation events intensified by El Niño and La Niña. This study investigates how terrain resolution influences flood simulation accuracy by comparing a 1 m LiDAR digital elevation model (DEM) with a DEM generated from a 1:5000 topographic map. Flood depth and velocity fields produced by the two DEMs show notable quantitative differences: for final flood depth, the 1:5000 DEM yields a mean absolute error of approximately 56.9 cm and an RMSE of 76.4 cm relative to LiDAR results, with substantial local over- and underestimations. Flow velocity and maximum velocity also show significant deviations, with RMSE values of 58.0 cm/s and 68.4 cm/s, respectively. Although the 1:5000 DEM captures the general inundation pattern, these discrepancies—particularly in narrow channels and urbanized floodplains—demonstrate that coarse-resolution terrain data cannot reliably reproduce hydrodynamic behavior. We conclude that while 1:5000 DEMs may be acceptable for reconnaissance-level hazard screening, high-resolution LiDAR DEMs are essential for accurate flood depth and velocity simulation, supporting their integration into engineering design, urban flood risk assessment, and disaster management frameworks. Full article

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R² values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article

The advance and delay of the rainy season is among the most frequently cited effects of climate change in the central Ecuadorian Andes. However, its assessment is not feasible using the indicators recommended by the standardized indices of the Expert Team on Climate Change Detection and Indices (ETCCDI), designed to detect changes in intensity, frequency, or duration of intense events. This study aims to analyze such advances and delays through harmonic analysis in Tungurahua, a predominantly agricultural province in the Tropical Central Andes, where in situ data are scarce. Daily in situ data from five meteorological stations were used, including precipitation, maximum, and minimum temperature records spanning 39 to 68 years. The study involved an analysis of the region’s climatology, climate change indices, and harmonic analysis using Cross-Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) to identify seasonal patterns and their variability (advance or delay) by comparing historical and recent time series, and Krigging for regionalization. The year 2000 was used as a study point for comparing past and present trends. Results show a generalized increase in both minimum and maximum temperatures. In the case of extreme rainfall events, no significant changes were detected. Harmonic analysis was found to be fruitful despite of the missing data. Furthermore, the observed advances and delays in seasonality were not statistically significant and appeared to be more closely related to the geographic location of the stations than to temporal shifts. Full article

Sustainable groundwater management is critical in semi-arid coastal regions, where municipal landfills pose a severe threat to aquifer integrity and long-term water security. However, there is still a lack of seasonally resolved hydrogeochemical monitoring around newly established landfills, particularly in rapidly urbanizing Mediterranean settings. This study assesses the hydrogeochemical impact of the newly operational Tangier Landfill and Recovery Center on local groundwater resources to inform sustainable remediation strategies. A combined approach was applied to samples collected in dry and wet seasons, using Piper and Stiff diagrams to trace facies evolution together with a dual-index assessment based on the Canadian (CCME-WQI) and Weighted Arithmetic (WAWQI) Water Quality Indices. Results show that upgradient waters remain of Good–Excellent quality and are dominated by Ca–HCO₃ facies, whereas downgradient wells display extreme mineralization, with EC up to 15,480 µS/cm and Cl⁻ and SO₄²⁻ exceeding 1834 and 2114 mg/L, respectively. At hotspot sites P4 and P8, As reaches 0.065 mg/L and Cd 0.006 mg/L, far above the WHO drinking-water guidelines. While the CCME-WQI captures the general salinity-driven degradation pattern, the WAWQI pinpoints these acute toxicity zones as Very poor–Unsuitable. The study demonstrates that rainfall intensifies toxicity through a seasonal “Piston Effect” that mobilizes stored contaminants rather than diluting them, underscoring the need for seasonally adaptive monitoring to ensure the environmental sustainability of landfill-adjacent aquifers. Full article

Rainfall-induced failures in red clay slopes are common, yet the coupled influence of soil structure degradation and rainfall temporal patterns on slope hydromechanical behavior remains poorly understood. This study advances the understanding by investigating a cut slope failure in Yunnan through integrated field monitoring, laboratory testing, and numerical modeling. Key advancements include: (1) elucidating the coupled effect of structure degradation on both shear strength reduction and hydraulic conductivity alteration; (2) systematically quantifying the impact of rainfall temporal patterns beyond total rainfall; and (3) providing a mechanistic explanation for the critical role of early-peak rainfall. Mechanical and hydrological parameters were obtained from intact and remolded samples, with soil-water retention estimated via pedotransfer functions. A hydro-mechanical finite element model of the slope was constructed and calibrated using recorded rainfall, displacement data and failure surface. Six simulation scenarios were designed by combining three strength conditions (intact at natural water content, intact at saturation, remolded at natural water content) with two hydraulic conductivity values (intact vs. remolded). Additionally, four synthetic rainfall patterns, including uniform, peak-increasing, peak-decaying and bell-shaped rainfall, were simulated to evaluate their influence on pore water pressure development and slope stability. Results show remolding reduced hydraulic conductivity 4.7-fold, slowing wetting front advance and increasing shallow pore water pressure. Intact soil facilitated deeper drainage, elevating pressure near the soil-rock interface. Strength reduction induced by structure degradation (water saturating and remolding) enlarged the slope deformation zone by 1.5 times under same hydraulic conductivity. Simulations using saturated intact strength best matched field observations. The results from this specific slope indicate that strength parameters primarily control stability, while permeability affects deformation depth. Simulations considering different rainfall patterns indicate that slope stability depends more critically on the temporal distribution of rainfall intensity than on the total amount. Overall, peak-decaying rainfall led to the most rapid rise in pore water pressure, earliest instability and lowest failure rainfall threshold, whereas peak-increasing rainfall showed the opposite trends. Our findings outline a practical framework for assessing red clay slope stability during rainfall. This framework recommends using saturated intact strength parameters in stability analysis. It highlights the important influence of rainfall temporal patterns, especially those with an early peak, on failure timing and rainfall threshold. Full article

Precipitation in high mountain areas is of critical importance as these regions are major sources of freshwater, supporting river basins, ecosystems, and downstream communities. Changes in precipitation regimes in these regions can have cascading impacts on water availability, agriculture, hydropower, and biodiversity. The present study aims to give new information about precipitation variability in high mountain regions of Bulgaria (Musala, Botev Peak, and Cherni Vrah) and to assess the role of large-scale atmospheric circulation patterns for the occurrence of extreme precipitation months. The study period is 1937–2024, and the classification of extreme precipitation months is based on the 10th and 90th percentiles of precipitation distribution. The temporal distribution of extreme precipitation months was analyzed by comparison of two periods (1937–1980 and 1981–2024). The impact of atmospheric circulation was evaluated by correlation between the number of extreme precipitation months and indices for the North Atlantic Oscillation (NAO) and Western Mediterranean Oscillation (WeMO). Results show a statistically significant decrease in winter and spring precipitation at Musala and Cherni Vrah, and a persistent drying tendency at Cherni Vrah across all seasons. The frequency of extremely wet months in winter and autumn has sharply declined since 1981, whereas extremely dry months have become more common, particularly during the cold season. Precipitation erosivity also exhibits station-specific responses, with Musala and Cherni Vrah showing reduced monthly concentration, while Botev Peak retains pronounced warm-season erosive rainfall. Circulation analysis indicates that positive NAOI phases favor dry extremes, while positive WeMOI phases enhance wet extremes. These findings reveal a shift toward drier and more seasonally uneven conditions in Bulgaria’s alpine zone, increasing hydrological risks related to drought, water scarcity, and soil erosion. The identified shifts in precipitation seasonality and intensity offer essential guidance for forecasting hydrological risks and mitigating soil erosion in vulnerable mountain ecosystems. The study underscores the need for adaptive water-resource strategies and enhanced monitoring in high-mountain areas. Full article

This study aims to investigate the effects of Chan-Hom (2015) typhoon-induced variations in enthalpy flux (EF) and moisture flux (MF) on intensity variations and rainfall. Chan-Hom (2015) made landfall at Zhoushan, then changed its direction and moved towards Korea. This analysis used ERA5 reanalyzed data, encompassing daily surface latent and sensible heat flux, along with wind measurements at a height of 10 m. Furthermore, wind components and specific humidity data from the 1000–200 hPa level in ERA5 were utilized to compute the MF and MF convergence, in accordance with the equations outlined in the methodology. This study examines the correlation among typhoon intensity, precipitation, MF, and EF. The mechanism by which Typhoon Chan-Hom has caused a decline in sea surface temperature (SST) was analyzed. Typhoons need a higher EF that can affect them before landfall to maintain their intensity. The highest LHF was observed (340 W/m²) prior to typhoon landfall, indicating that LHF responds to intensity-induced wind during Chan-Hom. Typhoon-induced rainfall is mainly controlled by the MF convergence, rather than the typhoon intensity. The spatial and temporal distributions of MF and MF convergence (MFC) during typhoon formation to landfall reveal that the symmetric MFC is dominated by typhoon intensity; a symmetrical structure is observed when the intensity is high. MFC includes wind convergence and moisture advection. Wind convergence dominates the MFC during typhoons, but moisture advection forms at the eyewall. MF during the typhoon’s landfall can relate to the amount of rainfall that occurred over the land. However, the rainfall pattern changed after landfall, and the typhoon changed its direction. SST cooling observed in the study area is mainly due to the upwelling process with strong cyclonic winds. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

This study develops a statistical and technological framework to analyze extreme rainfall in Colima, Mexico, by integrating historical precipitation records, probabilistic modeling, and spatial visualization. Using data from CONAGUA meteorological stations, we identify high-intensity rainfall events and model their recurrence using the Generalized Extreme Value (GEV) distribution to estimate key return periods. The results support flood-risk assessment and territorial planning in Colima. Spatial interpolation was performed in Python (version 3.13), and QGIS (version 3.38) produces exceedance maps that illustrate geographic variations in rainfall intensity across the state. These exceedance maps reveal a consistent spatial pattern, with the northern and western areas of Colima experiencing the highest frequencies of extreme events. Based on these results, the integration of real-time sensor technologies and satellite observations may improve flood monitoring and risk management frameworks. Full article

In sub-Saharan Africa, land degradation and climate change continue to undermine agricultural productivity by reducing soil productivity and water availability. This review identifies soil and water conservation technologies successfully applied in climatically analogous regions of sub-Saharan Africa with the aim of informing effective technology transfer to Senegal, particularly Sédhiou and Tambacounda. Using K-means clustering on WorldClim bioclimatic variables, 35 comparable countries were identified, of which 17 met inclusion criteria based on data availability and ≥60% climatic similarity. Eighty-five technologies were documented and assessed for their compatibility across rainfall patterns, land gradients, and uses, with 12 emerging as consistently effective. Quantitative evidence shows that zai/tassa pits, stone bunds, and half-moons increase crop yields by 50–200%, while stone bunds and mulching reduce runoff by up to 80% and improve soil moisture retention. Terracing and tied-ridging were also linked to higher water-use efficiency, with tied-ridging increasing soil moisture by 13%. Burkina Faso, Kenya, and Malawi lead in adoption and diversity, whereas Senegal lags due to institutional gaps, limited funding, and weak extension systems. These technologies offer a readily available, evidence-based toolkit for building agricultural resilience in Senegal. However, their successful adoption requires stronger policy integration, stakeholder empowerment, cross-border learning, and private-sector engagement. Full article

Microplastics (MPs) and nanoplastics (NPs) have emerged as pervasive pollutants across different aquatic systems on a global basis, yet integrated assessments linking wastewater, riverine, and marine environments remain scarce. The present study provides the first comprehensive evaluation of MPs in three interconnected aquatic matrices of Northern Greece, namely surface seawater from the Thermaic Gulf, surface freshwater from the Axios River, and influent and effluent wastewaters from the Thessaloniki WWTP (Sindos). During two sampling periods spanning late 2023 and spring 2024, suspected MPs were isolated, morphologically classified by stereomicroscopy, and chemically characterized through pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS). MPs were ubiquitously detected in all substrates, exhibiting distinct spatial and compositional patterns. Seawater samples displayed moderate concentrations (1.5–4.8 items m⁻³) dominated by fibers and fragments, while riverine samples contained slightly higher levels (0.5–2.5 items m⁻³), enriched in fibrous forms and polyolefins (PE, PP). Wastewater influents showed the highest MP abundance (78–200 items L⁻¹; 155.6–392.3 µg L⁻¹), decreasing significantly in effluents (11–44 items L⁻¹; 27.8–74.3 µg L⁻¹), corresponding to a removal efficiency of 81–87.5%, being the first indicative removal efficiencies in a Greek WWTP. Among the different polymers detected, polyethylene, polypropylene, and poly(ethylene terephthalate) were identified as the most prevalent polymers across all matrices. Interestingly, a shift toward smaller size classes (125–500 µm) in effluents indicated in-plant fragmentation processes, while increased concentrations during December coincided with increased rainfall, highlighting the influence of hydrological conditions on MP fluxes. The combined morphological and polymer-specific approach provides a holistic zunderstanding of MP transport from inland to marine systems, establishing essential baseline data for Mediterranean environments and reinforcing the need for integrated monitoring and mitigation strategies. Full article