Search Results (919)

The tropospheric zenith wet delay (ZWD) serves as a pivotal parameter for atmospheric water vapour inversion. By converting it into precipitable water vapour, high-temporal-resolution atmospheric humidity monitoring becomes feasible, providing crucial support for enhancing short-term rainfall forecast accuracy. However, ZWD exhibits significant non-stationarity due to complex influencing factors, and traditional models struggle to achieve precise predictions across all scenarios owing to limitations in local feature extraction. This article employs a ZWD prediction method based on the dynamic temporal decomposition module of TimesNet, re-constructing one-dimensional high-frequency ZWD time series into two-dimensional tensors to overcome the technical limitations of conventional models. Comprehensively considering topographical characteristics, climatic features, and seasonal factors, experiments were conducted using 30 s ZWD data from 20 IGS stations. This dataset comprised four consecutive days of PPP solutions for each season in 2023. Through comparative experiments with CNN-ATT and Informer models, the global prediction accuracy, seasonal adaptability, and topographical robustness of TimesNet were systematically evaluated. Results demonstrate that under the input-prediction window configuration where each can achieve the optimal accuracy, TimesNet achieves an average seasonal Root Mean Square Error (RMSE) of 5.73 mm across all seasonal station samples, outperforming Informer (7.89 mm) and CNN-ATT (10.02 mm) by 27.4% and 42.8%, respectively. It maintains robust performance under the most challenging conditions—including summer severe convection, high-altitude terrain, and climatically variable maritime zones—while achieving sub-5 mm precision in stable environments. This provides a reliable algorithmic foundation for short-term precipitation forecasting in Global Navigation Satellite System (GNSS) real-time meteorology. Full article

Until recently, the Karpathos water frog (Pelophylax cerigensis) was considered endemic to Karpathos Island (Greece) and has recently been reclassified by the IUCN as Endangered (EN), having been previously assessed as Critically Endangered (CR). The species faces severe threats primarily associated with the scarcity of freshwater bodies in the southern Aegean Sea. Over the past decade, demographic assessments have revealed a marked population decline, driven by the intensifying effects of climate change, including reduced rainfall, and increasing summer temperatures. In addition, the few natural ponds that persist during the dry summer months are often shared with the Levantine freshwater crab (Potamon potamios), resulting in increased frog mortality due to predation. Despite these challenges, recent developments provide cautious optimism. These include the construction of a dam in southern Karpathos and the taxonomic reassessment of the water frog population on the neighboring island of Rhodes as conspecific with P. cerigensis. In response to the species’ precarious status, the Hellenic Herpetological Society designed and implemented a National Action Plan aimed at the protection and conservation of the Karpathos water frog. The Action Plan includes a series of targeted mitigation measures, such as the construction of artificial ponds to retain water during the summer, as well as a hydrological study addressing the seasonal drying of the ecologically important Eleimonitria spring. A key component of the Action Plan involves education and outreach initiatives targeting primary school students, local residents, and visitors, highlighting the frog’s ecological importance and conservation needs. Informational brochures will be distributed across the island to raise awareness of the species’ conservation status and the importance of safeguarding its habitat. The implementation of this Action Plan aims to secure the long-term survival of the Karpathos water frog and to strengthen integrated conservation efforts across its extremely limited range. Full article

Farmland N₂O emissions exhibit significant fluctuations in subtropical regions due to notable seasonal rainfall and temperature variations. The dominant factors influencing N₂O emissions in red-soil sloping farmland, which is widely distributed and actively cultivated in the region, remain uncertain. To investigate N₂O emission characteristics of red-soil sloping farmland and responses to meteorological and soil environmental variables and tillage practices, a typical planting system (summer peanut-winter rapeseed rotation system) in southern China was selected. Two common soil micro-environments (conventional tillage, CT, n = 6; and conventional tillage with straw mulching, MT, n = 4) were established within this system, and in situ N₂O emissions were monitored over two consecutive years using the static chamber–gas chromatography method. The N₂O emission peaks across various growing seasons occurred primarily within 1 to 16 days after fertilization. The N₂O emission hotspot periods were observed during the first month following fertilization, accounting for 74.13–91.01% of the total emissions during each growing season. Significant interannual variations in seasonal N₂O cumulative emissions were observed, whereas no significant difference in cumulative N₂O emissions was observed between MT and CT. Changes in weather and soil environment jointly drive the dynamics of N₂O emissions from red soil sloping farmland. Rapeseed-season N₂O emissions were driven mainly by rainfall and air temperature, whereas peanut-season N₂O emissions were also influenced by soil temperature and NO₃⁻-N content at 0–10 cm depths. These findings provide a sound basis for developing eco-agricultural mitigation pathways in subtropical red-soil hilly regions. Full article

Although heavy rainfall occurs infrequently during summer (June–August, JJA) in the Mu Us Sandy Land (MUSL), it has almost the same contribution to summer precipitation as light rainfall. However, it remains unclear on forcing mechanism of heavy rain events and their differences with moderate and light rainfall events from the perspective of moisture sources. In this paper, based on the Dynamical Recycling Model (DRM), we analyze moisture source and atmospheric circulation differences for summer rainfall in different intensity classes over MUSL. The results show that the moisture of summer precipitation in MUSL comes primarily from external terrestrial moisture supplies from the west and southwest directions. As the precipitation intensity increases, moisture contributions from the southwest direction increase significantly, especially for the northeastern part of the Tibet Plateau (defined as Key Region), which accounts for about 39.3% of all moisture sources for heavy rainfall events. Further analysis reveals that anomalous atmospheric circulations, such as the cyclonic circulation anomaly at lower troposphere and anomaly wave train at middle level, also favor the occurrences of different precipitation intensities. Based on these findings, our paper possibly contributes to the conservation of this fragile ecosystem and the prevention of damage caused by precipitation extremes. 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

Rainfall erosivity is a key driver of soil erosion and sediment delivery in the Lancang River Basin, but its spatiotemporal variations and relationship with sediment delivery changes remain unquantified. Based on the daily precipitation data from meteorological stations and the annual sediment delivery data from the Yunjinghong hydrologic station, the spatial and temporal variations in rainfall erosivity and how rainfall erosivity changes contribute to the sediment delivery changes were examined in this study. The results showed that the annual average rainfall erosivity varied from 202.6 to 15,946.6 MJ mm ha⁻¹ h⁻¹ a⁻¹ among stations. The rainfall erosivity increased from the upstream to the downstream as elevation decreased. Basin-wide average rainfall erosivity declined by about ten percent from 1958 to 2019, with a decreasing rate of −6.3 MJ mm ha⁻¹ h⁻¹ a⁻¹ per year. Summer rainfall erosivity accounted for the largest portion of the rainfall erosivity throughout the whole year. The sediment delivery increased from 1963 to 2000 but has sharply decreased since 2001. Double mass curve analysis revealed that rainfall erosivity reduction accounted for 32% of the sediment delivery decrease after 2001, with human activities (vegetation restoration and dam operations) contributing the remaining 68%. Full article

This paper presents a new method for a regional-scale rainfall-induced landslide early warning system (LEWS) based on the outputs of the “Fast Shallow Landslide Assessment Model” (FSLAM), a physically based model used to compute slope stability at a regional scale. The LEWS combines landslide susceptibility and rainfall thresholds to depict the areas that are prone to slope failures and issues qualitative warnings over the study area. Both the susceptibility map and the rainfall thresholds were obtained based on the outputs from running FSLAM with 25 different rainfall scenarios. The final output of the LEWS is a slope-unit-based map. The LEWS was implemented for Cili County, Hunan Province, China, and tested for the year 2020. The warning level stayed “Low” during most of the year. High warnings were issued during the summer and were either due to intense rainfall events or abundant long-duration precipitation. The LEWS was able to issue appropriate warnings corresponding to the time and location of three known landslides that occurred in the study area in 2020. Although long-term validation with more landslide data and improved geotechnical data is needed to reduce the LEWS uncertainties, this approach is promising and could support authorities managing landslide risk. Full article

Polystigma amygdalinum the causal agent of Red Leaf Blotch (RLB), is responsible for one of the most important foliar diseases affecting almond [Prunus dulcis (Miller) D.A. Webb] in the Mediterranean Basin and the Middle East. The study is aimed at improving knowledge on RLB epidemiology and the role of environmental conditions in disease development. Field monitoring was conducted from 2022 to 2025 in three almond orchards located in Apulia (southern Italy) and characterized by different microclimatic conditions. A total of 39 cultivars, including Apulian local germplasm and international cultivars (‘Belona’, ‘Genco’, ‘Guara’, ‘Ferragnès’, ‘Filippo Ceo’, ‘Lauranne^® Avijor’, ‘Soleta’, and ‘Supernova’), were evaluated. Symptoms occurred from late spring to summer, resulting particularly severe on ‘Guara’ and ‘Lauranne^® Avijor’, whereas ‘Belona’, ‘Ferragnès’, ‘Genco’, and ‘Supernova’ exhibited the highest tolerance. To our knowledge, this is also the first report of RLB tolerance by ‘Filippo Ceo’, ‘Ficarazza’, ‘Centopezze’, and ‘Rachele piccola’ representing potential genetic resources for breeding programs. Moreover, these findings reinforced previous observations proving that RLB was less severe on medium-late and late cultivars. Disease incidence varied significantly among sites and years and was strongly associated with increased rainfall, higher relative humidity, and mild temperatures recorded in November, influencing disease occurrence in the following growing season. P. amygdalinum was consistently detected by qPCR in all RLB-affected tissues and, in some cases, from mixed early RLB + Pseudomonas-like symptoms. From some leaves with early RLB symptoms, P. amygdalinum was also successfully isolated in pure culture. Overall, our results provide clear evidence that P. amygdalinum is the sole fungal pathogen consistently associated with typical RLB symptoms in Apulia (southern Italy) and highlight important cultivar-dependent differences. Its frequent molecular detection in leaves showing atypical or mixed symptoms suggests unresolved epidemiological aspects requiring further investigation. Full article

This study investigated the site-specific ionic composition and wet deposition loads of rainwater collected from eight actively cultivated agricultural regions across South Korea, with the aim of quantifying spatial and seasonal variability and interpreting how regional agricultural characteristics and surrounding site conditions influence major ion concentrations and deposition patterns. Rainfall samples were obtained using automated samplers and analyzed via high-performance ion chromatography for major cations (Na⁺, $\mathrm{N}{\mathrm{H}}_4^{+}$, K⁺, Ca²⁺, Mg²⁺) and anions (Cl⁻, $\mathrm{N}{\mathrm{O}}_3^{-}$, $\mathrm{S}{\mathrm{O}}_4^{2-}$, $\mathrm{N}{\mathrm{O}}_2^{-}$). The results revealed significant seasonal fluctuations in ion loads, with $\mathrm{N}{\mathrm{H}}_4^{+}$ (peak 1.13 kg/ha) and K⁺ (peak 0.25 kg/ha) reaching their highest levels during summer due to increased fertilizer use and crop activity. Conversely, Cl⁻ peaked in winter (2.11 kg/ha in December), particularly in coastal regions, likely influenced by de-icing salts and sea-salt aerosols. Correlation analysis showed a strong positive association among $\mathrm{N}{\mathrm{H}}_4^{+}$, $\mathrm{N}{\mathrm{O}}_3^{-}$, and $\mathrm{S}{\mathrm{O}}_4^{2-}$ (r = 0.89 and r = 0.84, respectively), indicating shared atmospheric transformation pathways from agricultural emissions. Ternary diagram analysis further revealed regional distinctions: coastal regions such as Gimhae and Muan exhibited Na⁺ and Cl⁻ dominance, while inland areas like Danyang and Hongcheon showed higher proportions of Ca²⁺ and Mg²⁺, reflecting differences in aerosol sources, land use, and local meteorological conditions. These findings underscore the complex interactions between agricultural practices, atmospheric processes, and local geography in shaping rainwater chemistry. The study provides quantitative baseline data for evaluating non-point source pollution and developing region-specific nutrient and soil management strategies in agricultural ecosystems. Full article

Accurate precipitation data is vital for hydrological modeling, climate research, and water resource management, especially in arid regions like the United Arab Emirates (UAE), where rainfall is sparse and highly variable. This study assesses the performance of the Multi-Source Weighted-Ensemble Precipitation v2.8 (MSWEP) dataset against ground-based gauge data and three satellite precipitation products—CMORPH, IMERG, and GSMaP—across the UAE from 2004 to 2020. Evaluation metrics include statistical, categorical, and extreme precipitation indices. MSWEP shows a moderate correlation with gauge data (mean CC = 0.62), performing better than CMORPH (0.54) but below IMERG (0.68). It also yields lower RMSE and MAE than CMORPH and GSMaP, indicating improved error metrics. However, MSWEP overestimates light rainfall and underestimates extreme events, reflected in a lower KGE (0.42) and weak performance in the 95th percentile rainfall, especially in coastal and mountainous areas. Seasonal analysis reveals overestimation in winter and underestimation during summer convective storms. While MSWEP offers strong global coverage and temporal consistency, its application in arid environments like the UAE requires bias correction. These findings highlight the need for integrating multiple datasets and regional adjustments to enhance rainfall estimation accuracy for hydrological and climate-related applications. Full article

Rainfall and streamflow analyses have long been central to hydrological research, yet traditional approaches often overlook the complexity introduced by changing climate signals, land-use dynamics, and human infrastructure. This study applies an integrated, data-driven framework to explore emerging hydroclimatic shifts in the Navasota River Watershed of east-central Texas. By combining autocorrelation analysis, Mann–Kendall and modified Mann–Kendall trend tests, and Pettitt’s change-point detection, we examine more than a century of precipitation and streamflow records alongside post-1978 reservoir operations. Results reveal an accelerating wetting tendency, particularly evident in decadal rolling averages and early-summer precipitation, accompanied by a statistically significant increase in 10-year moving averages of annual peak streamflow. While abrupt regime shifts were not detected, subtle but persistent changes point to evolving watershed memory and heightened flood risk in the post-dam era. This study reframes rainfall and streamflow trend analysis as a dynamic tool for anticipating hydrologic regime shifts, highlighting the urgent need for adaptive water infrastructure and flood management strategies in rapidly urbanizing and climate-sensitive watersheds. Full article

In arid regions, rainfall is scarce, summer-concentrated, and prone to extreme events, while evaporation exceeds precipitation, creating fragile ecosystems that need scientific stormwater management for flood resilience. Sponge cities, through the implementation of green infrastructure, can alleviate urban flooding, improve rainwater utilization, and enhance the urban ecological environment. Under the “dual carbon” target, sponge city construction has gained new developmental significance. It must not only ensure core functions and minimize construction costs but also fully leverage its carbon reduction potential, thereby serving as a crucial pathway for promoting urban green and low-carbon development. Therefore, this study focused on Xining, a typical arid city in Northwest China, and couples the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) with the Storm Water Management Model (SWMM) to construct a multi-objective optimization model for Low Impact Development (LID) facilities. The layout optimization design of LID facilities is conducted from three dimensions: life cycle cost (LCC), rainwater utilization rate (K), and carbon emission intensity (CI). Hydrological simulations and scheme optimizations were performed under different design rainfall events. Subsequently, the entropy-weighted TOPSIS method was utilized to evaluate and compare these optimized schemes. It is shown by the results that: (1) The optimized LID schemes achieved a K of 76.2–80.43%, an LCC of 2.413–3.019 billion yuan, and a CI of −2.8 to 0.19 kg/m²; (2) Compared with the no-LID scenario, the optimized scheme significantly enhanced hydrological regulation, flood mitigation, and pollutant removal. Under different rainfall return periods, the annual runoff control rate increased from 64.97% to 80.66–82.23%, with total runoff reduction rates reaching 46.41–49.26% and peak flow reductions of 45–47.62%. Under the rainfall event with a 10-year return period, the total number of waterlogging nodes decreased from 108 to 82, and the number of nodes with a ponding duration exceeding 1 h was reduced by 62.5%. The removal efficiency of total suspended solids (TSS) under the optimized scheme remained stable above 60%. The optimized scheme is highly adaptable to the rainwater management needs of arid areas by prioritizing “infiltration and retention”. Vegetative swales emerge as the primary facility due to their low cost and high carbon sink capacity. This study provides a feasible pathway and decision-making support for the low-carbon layout of LID facilities in arid regions. Full article

Rainfall characteristics proven to trigger general erosive events (EE) and rill erosion events (RE) under reference experimental conditions of soil type, slope, and land use—previously established at a test site in central Italy—are applied as likely thresholds to characterize their spatiotemporal variability across Umbria using 24 years of semi-hourly data from 53 stations. Marked spatial patterns emerge, with mean EE frequencies per station ranging from 1.14 to 2.36 per month, while mean RE frequencies per station vary between 0.04 and 0.45 per season. No significant temporal trends are observed over the study period. Monthly and seasonal comparisons between EE and RE frequencies often deviate from the corresponding USLE R-factor dynamics, highlighting limitations of relying solely on this parameter. These findings are contextualized within common soil conservation practices—such as cover crops—to identify critical periods during which maintaining soil cover. For example, winter—when cover crops are typically present in Central Italian agroecosystems—is among the seasons with the highest EE frequency (4.45 yr⁻¹), second only to autumn (6.47 yr⁻¹). However, when focusing on REs, winter shows the lowest mean frequency (0.08 yr⁻¹). In contrast, the mean RE frequency increases in summer (0.24 yr⁻¹) and reaches its maximum in autumn (0.26 yr⁻¹), when bare soil or poorly developed cover crops are common. Overall, results provide actionable insights for aligning protective measures with high-impact erosive event probabilities. Full article

The upper Xin’an River basin serves as a critical ecological barrier and water-conservation area for the Yangtze River Delta. However, with rapid economic development, nitrogen pollution in the surface waters of this region has become increasingly pronounced. This study analyzed river water samples collected on four occasions from the upper Xin’an River basin for ammonium (NH₄⁺–N), nitrate-nitrogen (NO₃⁻–N), total nitrogen (TN), and nitrate isotopic (δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻). The sources of nitrate (NO₃⁻) were apportioned using the MixSIAR stable-isotope mixing model, and the spatial distribution of these sources across the basin was characterized. Across the four sampling rounds, the mean TN concentration exceeded 1.3 mg/L, with NO₃⁻–N accounting for over 45% of TN, indicating that nitrate was the dominant inorganic nitrogen species. The δ¹⁵N–NO₃⁻ values ranged from 2.17‰ to 13.0‰, with mean values following the order summer > winter > autumn > spring. The δ¹⁸O–NO₃⁻ values varied from −5.20‰ to −3.48‰, and the average value showed a completely opposite seasonal variation pattern to that of δ¹⁵N–NO₃⁻. Process-based analysis of nitrogen transformations revealed that nitrification predominates during nitrate transport and transformation, whereas denitrification is comparatively weak. MixSIAR-based estimates indicate marked seasonal differences in the source composition of nitrate pollution in the upper Xin’an River basin; NO₃⁻ derives primarily from soil nitrogen (SN) and livestock/sewage manure nitrogen (LSN). LSN was the dominant contributor in spring and summer (49.2% and 59.9%, respectively). SN dominated in autumn (49.2%) and winter (54.1%). Fertilizer nitrogen (FN) contributed more during summer and autumn, when fertilization is concentrated and rainfall is higher. Atmospheric deposition (AN) contributed approximately 1% across all seasons and was thus considered negligible. These findings provide a scientific basis for source control of nitrogen pollution and water-quality management in the upper Xin’an River. Full article

This study evaluated the seasonal greenhouse gas (GHG) emissions and carbon assimilation of Bambusa edulis under four soil amendment treatments—control (C), biochar (B), fertilizer using vermicompost (F), and biochar plus fertilizer (B + F)—in a coastal shelterbelt system in south-western Taiwan. Over a 12-month period, CO₂ and N₂O fluxes and photosynthetic carbon uptake were measured. The control (C) treatment served as the baseline, exhibiting the lowest greenhouse gas (GHG) emissions and carbon assimilation. Its summer N₂O emissions were 39.54 ± 20.79 g CO₂ e m⁻², and its spring carbon assimilation was 13.2 ± 0.84 kg CO₂ clump⁻¹. In comparison, the amendment treatments significantly enhanced both emissions and carbon uptake. The fertilizer-only (F) treatment resulted in the highest levels, with peak summer N₂O emissions increasing by 306.5% (to 160.73 ± 96.22 g CO₂ e m⁻²) and spring carbon assimilation increasing by 40.2% (to 18.5 ± 0.62 kg CO₂ clump⁻¹). An increase in these values was also observed in the combined biochar and fertilizer (B + F) treatment, although the magnitude was less than that of the F treatment alone. In the B + F treatment, summer N₂O emissions increased by 130.3% (to 91.1 ± 62.51 g CO₂ e m⁻²), while spring carbon assimilation increased by 17.4% (to 15.5 ± 0.36 kg CO₂ clump⁻¹). Soil CO₂ flux was significantly correlated with atmosphere temperature (r = 0.63, p < 0.01) and rainfall (r = 0.45, p < 0.05), while N₂O flux had a strong positive correlation with rainfall (r = 0.71, p < 0.001). The findings highlight a trade-off between nutrient-driven productivity and GHG intensity and demonstrate that optimized organic and biochar applications can enhance photosynthetic carbon gain while mitigating emissions. The results support bamboo’s role in climate mitigation and carbon offset strategies within nature-based solution frameworks. Full article