Search Results (2,544)

Observed rainfall erosivity risk (ORE) index is defined as the erosivity risk in the event of extreme rainfall events. ORE measures the kinetic energy of raindrops generated during a period of maximum precipitation intensity with the formula ORE = (ED · TEI)/10, where ED = erosivity density, TEI = total erosivity index, and ORE is measured in MJ mm ha^‒1 h^‒1 yr^‒1. The goal of this study is to model ORE, estimate its spatiotemporal variability, and predict (PRE) and simulate ORE for the state of Sinaloa (1969–2018). Five indices of rainfall erosivity were calculated: the modified Fournier index, precipitation concentration index, ED, TEI, and rainfall erosivity factor. The nonparametric trend in ORE was calculated. Using multiple nonlinear regressions (MNR), PRE (dependent variable) was calculated as a function of cumulative annual, annual average, seasonal average, and seasonal cumulative rainfall (independent variables). To simulate PRE, cumulative distribution functions, adjusted return periods (ARPs), and the 99th percentile were used. ORE ranged from 51.39 MJ mm ha⁻¹ h⁻¹ yr⁻¹ in 1970 (Culiacán) to 92679.40 MJ mm ha⁻¹ h⁻¹ yr⁻¹ in 1998 (Sta. C. de Alaya). The only year that had very high ORE at all nine stations was 1998. The only significant trend was ORE = 34.64 MJ mm ha⁻¹ h⁻¹ yr⁻¹ (Culiacán). The nine PRE models were significantly predictive (Spearman correlation > 0.280). Guatenipa, Rosario, and Siqueros registered very high PRE, since one to eight extreme erosivity events per century are predicted on average. A new methodology is proposed for calculating ORE and PRE, which can be used to develop alternatives for managing and protecting agricultural land in the state considered “the breadbasket of Mexico”. 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

Geothermal fluids are the main carrier of hydrothermal geothermal resources. Identifying the differences in geothermal fluids in different types of reservoirs is a prerequisite and fundamental for the efficient development of geothermal resources and is of great significance for scientific research on geothermal resources. The North China Plain contains a typical carbonate thermal reservoir, and in this paper, the hydrochemical, isotopic, and redox characteristics of the geothermal fluids in sandstone and carbonate reservoirs are studied to obtain the differences in the geothermal fluids in the Rongcheng geothermal field in Xiong’an New Area. The results indicate that the geothermal fluids in the sandstone and carbonate reservoirs are mainly supplied by atmospheric rainfall, and the hydrochemical type is mainly Cl-Na type. By comparing and analyzing the stable isotope (O, H, C, S, and Sr) characteristics of the two types of geothermal fluids, it is found that the variation range of δ13C values for two types of sandstone thermal storage geothermal fluids was found to be −10.6‰~−12.8‰, while the variation range of δ¹³C values for carbonate thermal storage geothermal fluids was −3.3‰~−7.5‰. The ⁸⁷Sr/⁸⁶Sr ratio of sandstone thermal storage geothermal fluids was distributed between 0.708–0.718, and the ⁸⁷Sr/⁸⁶Sr ratio of carbonate thermal storage geothermal fluids was distributed between 0.708–0.713. The range of δ³⁴S values for sandstone thermal storage geothermal fluids was +9.46‰~+10.5‰, and the range of δ³⁴S values for carbonate thermal storage geothermal fluids was +24.84‰~+34.49‰. The two types of geothermal fluids have been subjected to varying degrees of oxidation-reduction, and their cycling and mixing characteristics are different. This has resulted in the formation of relatively oxidized geothermal fluids in the sandstone geothermal reservoir and relatively reduced geothermal fluids in the carbonate geothermal reservoir. In future development and utilization of geothermal resources, paying attention to the basic characteristics of the geothermal fluids in different reservoirs and identifying the differences in different geothermal fluids can further improve the efficiency of geothermal resource development and utilization. Full article

Rainfall infiltration is one of the main factors inducing slope instability, while the spatial heterogeneity and uncertainty of soil parameters have profound impacts on slope response characteristics and stability evolution. Traditional deterministic analysis methods struggle to reveal the dynamic risk evolution process of the system under heavy rainfall. Therefore, this paper proposes an uncertainty analysis framework combining Karhunen–Loève Expansion (KLE) random field theory, Polynomial Chaos Kriging (PCK) surrogate modeling, and Monte Carlo simulation to efficiently quantify the probabilistic characteristics and spatial risks of rainfall-induced slope instability. First, for key strength parameters such as cohesion and internal friction angle, a two-dimensional random field with spatial correlation is constructed to realistically depict the regional variability of soil mechanical properties. Second, a PCK surrogate model optimized by the LARS algorithm is developed to achieve high-precision replacement of finite element calculation results. Then, large-scale Monte Carlo simulations are conducted based on the surrogate model to obtain the probability distribution characteristics of slope safety factors and potential instability areas at different times. The research results show that the slope enters the most unstable stage during the middle of rainfall (36–54 h), with severe system response fluctuations and highly concentrated instability risks. Deterministic analysis generally overestimates slope safety and ignores extreme responses in tail samples. The proposed method can effectively identify the multi-source uncertainty effects of slope systems, providing theoretical support and technical pathways for risk early warning, zoning design, and protection optimization of slope engineering during rainfall periods. Full article

Japanese modern irrigation management is considered a successful model of water governance worldwide. However, debates continue over whether this success is due to natural water abundance or to water management practices. This study evaluates pre-modern water scarcity risk for six irrigation schemes, developed during that period in the Kinu River Basin (1603–1868); a period without large reservoirs, canal systems, or modern regulatory technologies. As the methodology, pre-modern river flows were reconstructed by removing the effects of four modern dams from the present-day river discharge, adjusting the conveyance efficiency, changes in paddy field area, rainfall input, and return flows. Water demand was assessed using Japanese irrigation standards of 5 mm/d (minimum water demand corresponding to evapotranspiration) and 20 mm/d (easy management), and risk was evaluated under both the prior appropriation and Equal Water Distribution rules. Results show that modern flow in the dry season is approximately 25 m³/s, whereas reconstructed natural flow during drought years declines to 10–18 m³/s, and about 15 m³/s after rainfall adjustment. Under the 20 mm/d demand scenario, scarcity occurred in four schemes (2 of 17 years in the third scheme and 7 of 17 years for the sixth scheme), while no scarcity occurred under the minimum-demand scenario (5 mm/d), even during low-flow conditions. This indicates that the available water in these schemes was at a level where drought damage could occur under extensive irrigation management, but could be avoided by intensive irrigation management to supply the minimum necessary water to all paddy fields. Full article

Background: Rainfall-induced landslides are a widespread and destructive geological hazard that resist precise prediction. They pose serious threats to human lives and property, ecological stability, and socioeconomic development. Methods: To address the challenges in mitigating rainfall-induced landslides in high-altitude mountainous regions, this study proposes a digital twin framework that couples multiple physical fields and is based on the spherical discrete element method. Results: Two-dimensional simulations identify a trapezoidal stress distribution with inward-increasing stress. The stress increases uniformly from 0 kPa at the surface to 210 kPa in the interior. The crest stress remains constant at 1.8 kPa under gravity, whereas the toe stress rises from 6.5 to 14.8 kPa with the slope gradient. While the stress pattern persists post-failure, specific magnitudes alter significantly. This study pioneers a three-dimensional close-packed spherical discrete element method, achieving enhanced computational efficiency and stability through streamlined contact mechanics. Conclusions: The proposed framework utilizes point-contact mechanics to simplify friction modeling, enhancing computational efficiency and numerical stability. By integrating stress, rainfall, and seepage fields, we establish a coupled hydro-mechanical model that enables real-time digital twin mapping of landslide evolution through dynamic parameter adjustments. Full article

The intensification of extreme rainfall events under changing climate regimes has heightened concerns over nutrient losses from paddy agriculture, particularly nitrogen (N), a primary contributor to non-point source pollution. Despite advances in drainage management, limited studies have integrated probabilistic rainfall modeling with N transport simulation to evaluate mitigation strategies in rice-based systems. This study addresses this critical gap by coupling the Log-Pearson Type III (LP-III) distribution with the DRAINMOD-N II model to simulate N dynamics under varying rainfall exceedance probabilities and drainage design configurations in the Kunshan region of eastern China. The DRAINMOD-N II showed good performance, with R² values of 0.70 and 0.69, AAD of 0.05 and 0.39 mg L⁻¹, and RMSE of 0.14 and 0.91 mg L⁻¹ for NO₃⁻-N and NH₄⁺-N during calibration, and R² values of 0.88 and 0.72, AAD of 0.06 and 0.21 mg L⁻¹, and RMSE of 0.10 and 0.34 mg L⁻¹ during validation. Using around 50 years of historical precipitation data, we developed intensity–duration–frequency (IDF) curves via LP-III to derive return-period rainfall scenarios (2%, 5%, 10%, and 20%). These scenarios were then input into a validated DRAINMOD-N II model to assess nitrate-nitrogen (NO₃⁻-N) and ammonium-nitrogen (NH₄⁺-N) losses across multiple drain spacing (1000–2000 cm) and depth (80–120 cm) treatments. Results demonstrated that NO₃⁻-N and NH₄⁺-N losses increase with rainfall intensity, with up to 57.9% and 45.1% greater leaching, respectively, under 2% exceedance events compared to 20%. However, wider drain spacing substantially mitigated N losses, reducing NO₃⁻-N and NH₄⁺-N loads by up to 18% and 12%, respectively, across extreme rainfall scenarios. The integrated framework developed in this study highlights the efficacy of drainage design optimization in reducing nutrient losses while maintaining hydrological resilience under extreme weather conditions. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

Arthropod-borne viruses (arboviruses) such as dengue, chikungunya, Zika, and yellow fever pose significant global health risks, with mosquitoes from the Aedes genus as the primary vectors responsible for human transmission. The Republic of the Marshall Islands (RMI), particularly the urbanized areas of Kwajalein and Majuro atolls, has experienced multiple outbreaks of dengue, Zika, and chikungunya with substantial health and economic impacts. Vector control remains the most effective method for reducing disease risk, but comprehensive data on local mosquito vector composition, distribution, and abundance are needed to guide new, effective control efforts. From 2022 to 2024, we conducted a longitudinal baseline assessment of mosquito abundance and species composition on Ebeye and nearby islets in Kwajalein Atoll, RMI, using BG-Sentinel traps and ovitraps. Aedes aegypti was the most prevalent species, accounting for 58% of all adult females collected across study locations, with higher relative abundances on Ebeye than on northern islets (4.7 vs. 2.3 per trap/night). Aedes albopictus was more abundant on northern islets (0.7 vs. 3.2 per trap/night), and Culex quinquefasciatus showed similar abundances (1.2 vs. 1.7 per trap/night). Rainfall and anthropogenic factors, including water storage practices and housing density, influenced mosquito abundance. These findings provide multi-seasonal baseline data to support targeted vector control strategies in RMI. Full article

Accurate rainfall monitoring is essential for mitigating hydrometeorological disasters and understanding hydrological changes under climate change. This study presents a deep learning-based rainfall estimation framework using multispectral GEO-KOMPSAT-2A (GK2A) satellite imagery. The analysis primarily focuses on daytime observations to take advantage of visible channel information, which provides richer representations of cloud characteristics during daylight conditions. The core model, Model-HSP, is built on the Pix2PixCC architecture and trained with Hybrid Surface Precipitation (HSP) data from weather radar. To further enhance accuracy, an ensemble model (Model-ENS) integrates the outputs of Model-HSP and a radar based Model-CMX, leveraging their complementary strengths for improved generalization, robustness, and stability across rainfall regimes. Performance was evaluated over two periods—a one year period from May 2023 to April 2024 and the August 2023 monsoon season—at 2 km and 4 km spatial resolutions, using RMSE and CC as quantitative metrics. Case analyses confirmed the superior capability of Model-ENS in capturing rainfall distribution, intensity, and temporal evolution across diverse weather conditions. These findings show that deep learning greatly enhances GEO satellite rainfall estimation, enabling real-time, high-resolution monitoring even in radar sparse or limited coverage regions, and offering strong potential for global and regional hydrometeorological and climate research applications. Full article

To mitigate the frequent occurrence of rear-end collisions on urban expressways under rainy weather conditions, firstly, accident risk levels were classified using traffic conflict indicators. Secondly, three machine learning models were employed to predict the accident severity across different scenarios. Furthermore, key influencing factors of rear-end collisions were identified and analyzed based on SHAP values. Case studies were conducted by simulating vehicle trajectory data under light, moderate, and heavy rain scenarios, using an open urban expressway dataset and car-following parameters for rainy conditions. Next, the Modified Time-to-Collision (MTTC) metric was calculated. Risk thresholds for low-, medium-, and high-risk levels were established for each rainfall category using percentile-based cumulative distribution analysis. Finally, real-time risk prediction under the three rainfall scenarios was conducted using XGBoost, LightGBM, and Random Forest models. The model performances were evaluated in terms of accuracy, recall, precision, and AUC. Overall, the study finds that the LightGBM model achieves the highest predictive capability, with AUC values exceeding 0.78 under all weather conditions. Moreover, the study concludes that factors ranked by SHAP values reveal that the minimum distance has the greatest influence in light rain scenarios. As rainfall intensity increases, the influences of minimum headway time and average vehicle speed are found to grow, highlighting an interaction pattern characterized by “speed-distance-flow” coupling. Full article

An integrated early warning system (EWS) for compound coastal and fluvial flooding is developed for Pyrgos, Western Greece, where low-lying geomorphology and past storm events highlight the need for rapid, impact-based forecasting. The methodology couples historical and climate-informed metocean and river discharge datasets within a numerical modeling framework consisting of a mild-slope wave model, the CSHORE coastal profile model, and HEC-RAS 2D inundation simulations. A weighted K-Means clustering approach is used to generate representative extreme scenarios, yielding more than 4000 coupled simulations that train and validate Artificial Neural Networks (ANNs). The optimal feed-forward ANN accurately predicts spatially distributed flood depths across the HEC-RAS grid using only offshore wave characteristics, water level, and river discharge as inputs, reducing computation time from hours to seconds. Blind tests demonstrate close agreement with full numerical simulations, with average differences typically below 5% and minor deviations confined to negligible water depths. These results confirm the ANN’s capability to emulate complex compound flooding dynamics with high computational efficiency. Deployed as a web application (EWS_CoCoFlood), the system provides actionable, near-real-time inundation forecasts to support local civil protection authorities. The framework is modular and scalable, enabling future integration of urban and rainfall-induced flooding processes and coastal morphological change. Full article

This study reviews advanced extreme value theory techniques and applies them to extreme rainfall events recorded at two meteorological stations, Port Edward and Virginia, in the KwaZulu-Natal province of South Africa. The study aims to provide a comparative analysis of the performance of three extreme value theory models—the generalised extreme value distribution (GEVD), the generalised extreme value distribution for r-largest order statistics (GEVDr), and the blended generalised extreme value distribution (bGEVD)—in modelling extreme rainfall events. The monthly maximum rainfall data used in the study was obtained from the South African Weather Service. The Shapiro–Wilk test demonstrated the non-normality of the rainfall datasets. Parameter estimation was performed using maximum likelihood estimation and Bayesian estimation methods, both yielding positive shape parameters consistent with the Fréchet class of distributions. The goodness-of-fit tests confirmed the suitability of the GEVD model for the data. The results of both the standard GEVD and GEVDr models provided consistent return level estimates, suggesting strong model performance. The bGEVD model produced lower return level estimates compared to the GEVD and GEVDr models. Overall, the findings of the study offer valuable insights into the behaviour of extreme rainfall in KwaZulu-Natal province, with significant implications for risk management, infrastructure planning, and disaster preparedness. This study will add value to the literature and knowledge of statistics. Full article

Wet deposition is a major sink for atmospheric aerosols, but its representation in conventional global climate models (GCMs) remains highly uncertain, partly as a result of the partitioning between convective and stratiform precipitation. Using the Super-parameterized Community Atmosphere Model (SPCAM) as a benchmark, we evaluate the performance of the conventional CAM5 model in simulating precipitation and aerosol wet deposition. SPCAM explicitly resolves convection and provides a more physical representation of cloud and precipitation processes. Compared to SPCAM, CAM5 overestimates the frequency of light convective rainfall by up to 50% at rain rates from 1 to 20 mm day⁻¹ and underestimates heavy convective precipitation, leading to a more than 90% contribution from convective precipitation to total rainfall in the tropics, far exceeding that in satellite observations. Accordingly, this bias results in an overestimation of aerosol wet removal by convective precipitation (74.2% in CAM5 versus 47.6% in SPCAM) and an underestimation by large-scale precipitation, as well as an overestimation of aerosol wet removal by light rain (84.0% in CAM5 versus 65.5% in SPCAM). As a result, CAM5 shows systematic biased wet deposition fluxes simulations across aerosol types and sizes compared to SPCAM, particularly in tropical regions. The misrepresentation of convective-stratiform rainfall partitioning in conventional GCMs like CAM5 significantly distorts aerosol lifetime and distribution. Improving convective parameterizations to better capture precipitation frequency distribution and partitioning is essential for credible aerosol-climate projections. Full article

Rapid urbanization has intensified nitrate pollution in megacity rivers, posing severe challenges to urban water governance and sustainable nitrate management. This study presents nitrate dual-isotope signatures (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻) from surface water samples collected during the wet season from the Yongding River (YDR) and Chaobai River (CBR) in the Beijing–Tianjin–Hebei megacity region of North China. Average concentrations of nitrate (as NO₃⁻) were 8.5 mg/L in YDR and 12.7 mg/L in CBR. The δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻ values varied from 6.1‰ to 19.1‰ and −1.1‰ to 10.6‰, respectively. The spatial distribution of NO₃⁻/Cl⁻ ratios and isotopic data indicated mixed sources, primarily sewage and manure in downstream sections and agricultural inputs in upstream areas. Isotopic evidence revealed widespread nitrification processes and could have potentially localized denitrification under low-oxygen conditions in the lower YDR. Bayesian mixing model (MixSIAR) results indicated that sewage and manure constituted the main nitrate sources (49.4%), followed by soil nitrogen (23.7%), chemical fertilizers (19.2%), and atmospheric deposition from rainfall (7.7%). The self-organizing map (SOM) further revealed three nitrate regimes, including natural and agricultural, mixed, and sewage dominated conditions, indicating a clear downstream gradient of increasing anthropogenic influence. The results suggest that efficient nitrogen management in megacity rivers requires improving biological nutrient removal in wastewater treatment, regulating fertilizer application in upstream areas, and maintaining ecological base flow for natural denitrification. This integrated framework provides a quantitative basis for nitrate control and supports sustainable water governance in highly urbanized watersheds. Full article