Search Results (77)

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

During the global transition of energy structures toward renewable sources, offshore wind power has experienced rapid advancement, coinciding with increasingly complex wave environments. This study focuses on the wave conditions of an offshore wind farm project in Vietnam. A dual-nested numerical framework (WAVEWATCH III + SWAN) is established, integrated with 32-year (1988–2019) high-resolution WRF wind fields and fused bathymetry data (GEBCO + in situ measurements). This framework overcomes the limitations of short-term datasets (10–22 years) in prior studies and achieves 1′ × 1′ (≈1.8 km) intra-farm resolution—critical for capturing topographic modulation of waves. A systematic analysis of the regional wave climate characteristics is performed, encompassing wave roses, joint distributions of significant wave height and spectral peak period, wave–wind direction correlations, and significant wave height–wind speed relationships. Extreme value theory, specifically the Pearson Type-III distribution, is applied to estimate extreme wave heights and corresponding periods for return periods ranging from 1 to 100 years, yielding critical design wave parameters for wind turbine foundations and support structures. Key findings reveal that the wave climate is dominated by E–SE (90°–120°) monsoon-driven waves (60% of $H_s$ = 0.5–1.5 m), while extreme waves are uniquely concentrated at 120°—attributed to westward Pacific typhoon track alignment and long fetch. For the outmost site (A55, 7.18 m water depth), the 100-year return period significant wave height ($H_{s100}$ = 4.66 m, $T_{p100}$ = 13.05 s) is 38% higher than sheltered shallow-water sites (A28, $H_{s100}$ = 2.7 m), reflecting strong bathymetric control on wave energy. This study makes twofold contributions: (1) Methodologically, it validates a robust framework for long-term wave simulation in tropical monsoon–typhoon regions, combining 32-year high-resolution data with dual-nested models. (2) Scientifically, it reveals the directional dominance and spatial variability of waves in the Mekong estuary, advancing understanding of typhoon–wave–topography interactions. Practically, it provides standardized design parameters (compliant with DNV-OS-J101/IEC 61400-3) for offshore wind projects in Southeast Asia. Full article

The increasingly severe phenomenon of groundwater drought poses a dual threat to the development and construction of a region, as well as its ecological environment. Traditional groundwater drought monitoring methods rely on observation wells, which makes it difficult to obtain dynamic drought information in areas with limited measurement data. Based on Gravity Recovery and Climate Experiment (GRACE) satellite technology and data, the suitability of the standardized groundwater index (GRACE_SGI) was explored for drought characterization in the Mu Us Sandy Land. Multiscale and seasonal trend changes in groundwater drought in the study area from 2002 to 2021 were comprehensively identified. Subsequently, the characteristics of hysteresis time between the GRACE_SGI and the standardized precipitation index (SPI) were clarified. The results show that (1) different fitting functions impact the parameterized GRACE_SGI fitting results. The Anderson–Darling method was used to find the best-fitting function for groundwater data in the study area: the Pearson III distribution. (2) The gain and loss characteristics of the GRACE_SGI are similar, showing downward trends at different time scales, including seasonal scales. (3) The absolute values based on the maximum correlation coefficients between the SPI and the GRACE_SGI at different time scales were 0.1296, 0.2483, 0.2427, and 0.5224, with time lags of 0, 0, 12, and 11 months, respectively. The vulnerability of semiarid ecosystems to hydroclimatic changes is highlighted by these findings, and a satellite-based framework for monitoring groundwater drought in data-scarce regions is provided. Full article

This paper addresses the challenge of systemic extreme risk in long-service gravity dams under human-controlled operation. It is the first study to construct a Generalized Extreme Value (GEV) distribution model using long-term operational monitoring data. The model, validated by multiple statistical tests and engineering boundary conditions, is then applied within a Response Surface Method-Monte Carlo (RSM-MC) reliability framework. Results indicate that the historical GEV model accurately captures the high-water-level tail characteristics and significantly overcomes the risk underestimation inherent in the uniform distribution model. Compared to the Log-Pearson Type III (Log-P3) design condition model, the GEV model yields a significantly lower probability of failure, e.g., the probability of cracking at the dam heel, the most sensitive failure mode, is reduced by nearly six times. This quantitative difference fully demonstrates GEV’s ability to precisely quantify the effective risk reduction achieved by human control, establishing a more scientific and realistic foundation for risk assessment of long-service gravity dams. Full article

This paper proposes the lower-bounded Fréchet–log-logistic distribution (LFLD), a probability model designed for robust flood frequency analysis (FFA). The LFLD addresses key limitations of traditional distributions (e.g., generalized extreme value (GEV) and log-Pearson Type III (LP3)) by combining bounded support ($\alpha <x<\infty$) to reflect physical flood thresholds, flexible tail behavior via Fréchet–log-logistic fusion for extreme-value accuracy, and maximum entropy characterization, ensuring optimal parameter estimation. Thus, we obtain the LFLD’s main statistical properties (PDF, CDF, and hazard rate), prove its asymptotic convergence to Fréchet distributions, and validate its superiority through simulation studies showing MLE consistency (bias < 0.02 and mean squared error < 0.0004 for $\alpha$) and empirical flood data tests (52- and 98-year AMS series), where the LFLD outperforms 10 competitors (AIC reductions of 15–40%; Vuong test p < 0.01). The LFLD’s closed-form quantile function enables efficient return period estimation, critical for infrastructure planning. Results demonstrate its applicability to heavy-tailed, bounded hydrological data, offering a 20–30% improvement in flood magnitude prediction over LP3/GEV models. Full article

This study presents a comprehensive flood frequency analysis for Australia’s Brisbane River basin using annual maximum flood (AMF) data from 26 stream gauging stations. This evaluates five different probability distributions in fitting the AMF data of the selected stations, which are the Lognormal, Log Pearson Type III (LP3), Gumbel, Generalized Extreme Value (GEV), and Generalized Pareto (GP) distributions (the recommended distributions in FLIKE software (School of Civil Engineering, University of Newcastle Australia, Australia, Release_x86_5.0.306.0). Three different goodness-of-fit tests (Chi-Squared, Anderson–Darling, and Kolmogorov–Smirnov) are adopted. This study also examines trends in the observed AMF data using several trend tests. It is found that the LP3 is the best-fit probability distribution at majority of the selected stations, followed by the GP distribution. Although the AMF data at most of the stations show an increasing linear trend, these trends are generally statistically non-significant. Full article

This manuscript investigates the applicability and behavior of the Generalized Extreme Value (GEV) distribution in flood frequency analysis, comparing it with the Pearson III and Wakeby distributions. Traditional approaches often rely on a limited set of statistical distributions and estimation techniques, which may not adequately capture the behavior of extreme events. The study focuses on four hydrometric stations in Romania, analyzing maximum discharges associated with rare and very rare events. The research employs seven parameter estimation methods: the method of ordinary moments (MOM), the maximum likelihood estimation (MLE), the L-moments, the LH-moments, the probability-weighted moments (PWMs), the least squares method (LSM), and the weighted least squares method (WLSM). Results indicate that the GEV distribution, particularly when using L-moments, consistently provides more reliable predictions for extreme events, reducing biases compared to MOM. Compared to the Wakeby distribution for an extreme event (T = 10,000 years), the GEV distribution produced smaller deviations than the Pearson III distribution, namely +7.7% (for the Danube River, Giurgiu station), +4.9% (for the Danube River, Drobeta station), and +35.3% (for the Ialomita River). In the case of the Siret River, the Pearson III distribution generated values closer to those obtained by the Wakeby distribution, being 36.7% lower than those produced by the GEV distribution. These results support the use of L-moments in national hydrological guidelines for critical infrastructure design and highlight the need for further investigation into non-stationary models and regionalization techniques. Full article

Accurately estimating storm hydrographs for various return periods is crucial for planning and designing hydrological infrastructure, such as dams and drainage systems. A key aspect of this estimation is the separation of baseflow from storm runoff. This study proposes a method for deriving storm hydrographs for different return periods based on hydrological station records. The proposed approach uses three baseflow separation methods: constant, linear, and master recession curve. A significant advantage of the proposed method over traditional rainfall–runoff approaches is its minimal parameter requirements during calibration. The methodology is tested on records from the Lengupá River watershed in Colombia, using data from the Páez hydrological station, which has a drainage area of 1090 km². The results indicate that the linear method yields the most accurate hydrograph estimates, as demonstrated by its lower root mean square error (RMSE) of 0.35%, compared to the other baseflow separation techniques, the values of which range from 2.92 to 3.02%. A frequency analysis of hydrological data was conducted using Pearson Type III and Generalized Extreme Value distributions to identify the most suitable statistical models for estimating extreme events regarding peak flow and maximum storm hydrograph volume. The findings demonstrate that the proposed methods effectively reproduce storm hydrographs for return periods ranging from 5 to 200 years, providing valuable insights for hydrological design, which can be employed using the data from stream gauging stations in rivers. Full article

Statistical distributions play a crucial role in water resources management and civil engineering, particularly for analyzing data variability and predicting rare events with extremely long return periods (e.g., T = 1000 years, T = 10,000 years). Among these, the Pearson III (PE3) distribution is widely used in hydrology and flood frequency analysis (FFA). This study aims to provide a comprehensive guide to the practical application of the PE3 distribution in FFA. It explores five parameter estimation methods, presenting both exact and newly developed approximate relationships for calculating distribution parameters and frequency factors. The analysis relies on data from four rivers with varying morphometric characteristics and record lengths. The results highlight that the Pearson III distribution, when used with the L-moments method, offers the most reliable quantile estimates, characterized by the smallest biases compared to other methods (e.g., 31% for the Nicolina River and, respectively, 5% for the Siret and Ialomita Rivers) and the highest confidence in predicting rare events. Based on these findings, the L-moments approach is recommended for flood frequency analysis to improve the accuracy of extreme flow forecasts. Full article

This paper examines the essential role of intervehicle spacing on two-lane rural roads, highlighting its significance for traffic safety and management. Recent technological advancements have enabled the precise positioning of vehicles on highways through video recordings and image processing techniques. However, these systems are less applicable to rural roads due to the absence of extensive sensor networks. This study bridges this gap by proposing a simulation-based model to evaluate the probability density of intervehicle spacing under varying traffic conditions. The simulation model integrates macroscopic traffic flow theories with microscopic car following models, simulating intervehicle spacings over a considerable highway segment. Calibration and validation were conducted using data from a two-lane road in Northern Italy. The simulation results identify key characteristics of spacing distribution, including positive skewness (i.e., a longer tail toward higher values), high kurtosis (a peaked distribution with frequent extreme values), non-zero minimum values, and autocorrelation at high traffic densities (indicative of platooning behavior). The Pearson type III distribution was determined to be the most suitable fit for the experimental data. Thus, future research should focus on parameter estimation for the Pearson type III distribution to further understand intervehicle spacing under varying traffic conditions and to expand applications to various road types and traffic scenarios. Full article

Recent progress in Artificial Intelligence and Machine Learning (AIML) has accelerated improvements in the prediction performance of many hydrological processes. Yet, flood prediction remains a challenging task due to its complex nature. Two common challenges afflicting the task are flood volatility and the sensitivity and complexity of flood generation attributes. This study explores the application of Recurrent Neural Networks (RNNs)—specifically Vanilla Recurrent Neural Networks (VRNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in flood prediction and assessment. By integrating catchment-specific hydrological and meteorological variables, the RNN models leverage sequential data processing to capture the temporal dynamics and seasonal patterns characteristic of flooding. These models were employed across diverse terrains, including mountainous watersheds in the state of South Carolina, USA, to examine their robustness and adaptability. To identify significant hydrological events for flash flood analysis, a discharge frequency analysis was conducted using the Pearson Type III distribution. The 1-year and 2-year return period flows were estimated based on this analysis, and the 1-year return flow was selected as a conservative threshold for flash flood event identification to ensure a sufficient number of training instances. Comparative benchmarking with the National Water Model (NWM v3.0) revealed that the RNN-based approaches offer notable enhancements in capturing the intensity and timing of flood events, particularly for short-duration and high-magnitude floods (flash floods). Comparison of predicted disharges with the discharge recorded at the gauges revealed that GRU had the best performance as it achieved the highest mean NSE values and exhibited low variability across diverse watersheds. LSTM results were slightly less consistent compared to the GRU albeit achieving satisfactory performance, proving its value in hydrological forecasting. In contrast, VRNN had the highest variability and the lowest NSE values among the three. The NWM model trailed the machine learning-based models. The study highlights the efficacy of the RNN models in advancing hydrological predictions. Full article

The control of rainfall extremes is essential in the design of hydro-agricultural works, as their performance depends on it. This study aims to determine the best-fit probability model suited to current climatic conditions in the Mono-Couffo basin in Benin. To this end, daily rainfall data from six rainfall stations from 1981 to 2021 were used. The application of the Decision Support System (DSS) with graphical and numerical performance criteria (such as RMSE, SD, and CC represented by the Taylor diagram; AIC and BIC) made it possible to identify the best distribution class and then to select the most suitable distribution for this basin. The results indicate that class C distributions, characterized by regular variations, are the most appropriate for the modeling maximum annual daily precipitation at all stations (78% of cases). Of these, the Inverse Gamma distribution proved to be the most suitable, although its estimation errors ranged from 16.47 mm/d at Aplahoué to 39.80 mm/d at Grand-Popo. The second most appropriate distribution is the Log-Pearson Type III. The use of the Inverse Gamma distribution is, therefore, recommended for hydro-agricultural development studies in the Mono-Couffo basin. Full article

It is essential to understand the characteristics of surface runoff generating processes under different heavy rainfall events in mountainous areas. The intensity and duration of precipitation play an important role in surface runoff processes. In this study, annual rainfall characteristics from 1987 to 2023 in the Taihang Mountains were analyzed using the Pearson-III frequency curve, homogeneity tests, and the Mann–Kendall (MK) test. Four surface runoff generation events between 2014 and 2023 were monitored. The contribution of rainfall to runoff variations was quantified through the double mass curve method. Results indicate a significant increase in the frequency of moderate and heavy rainfall events over the last decade. Spatial variability of rainfall and elevation effects in the Taihang Mountains becomes less pronounced when 24 h rainfall is below 50 mm. The two surface runoff processes in 2016 and 2023 were typical runoff resulting from excess rain, which belonged to the storm runoff. The two surface runoff processes in 2021 were runoff generation under saturated conditions. For runoff generation under saturated conditions, the contribution of rainfall was only 58.17%. When the runoff coefficient exceeded 0.5, the surface runoff generating processes were entirely determined by rainfall. This study suggested that for semi-arid regions, where rainfall is unevenly distributed over the seasons, more soil water is needed to maintain local and downstream water demand during the non-rainy season. The limitations of the study are the lack of research on factors other than rainfall that intrinsically affect the surface runoff generating process. Full article

The geological and environmental conditions of the northern Shaanxi Loess Plateau are highly fragile, with frequent landslides and collapse disasters triggered by rainfall and human engineering activities. This research addresses the limitations of current landslide hazard assessment models, considers Zhuanyaowan Town in northern Shaanxi Province as a case study, and proposes an integrated model combining the information value model (IVM) with ensemble learning models (RF, XGBoost, and LightGBM) employed to derive the spatial probability of landslide occurrences. Adopting Pearson’s type-III distribution with the Bayesian theorem, we calculated rainfall-induced landslide hazard probabilities across multiple temporal scales and established a comprehensive regional landslide hazard assessment framework. The results indicated that the IVM coupled with the extreme gradient boosting (XGBoost) model achieved the highest prediction performance. The rainfall-induced hazard probabilities for the study area under 5-, 10-, 20-, and 50-year rainfall return periods are 0.31081, 0.34146, 0.4, and 0.53846, respectively. The quantitative calculation of regional landslide hazards revealed the variation trends in hazard values across different areas of the study region under varying rainfall conditions. The high-hazard zones were primarily distributed in a belt-like pattern along the Xichuan River and major transportation routes, progressively expanding outward as the rainfall return periods increased. This study presents a novel and robust methodology for regional landslide hazard assessment, demonstrating significant improvements in both the computational efficiency and predictive accuracy. These findings provide critical insights into regional landslide risk mitigation strategies and contribute substantially to the establishment of sustainable development practices in geologically vulnerable regions. Full article

Accurately selecting an appropriate probability distribution model is a critical challenge when predicting extreme rainfall in arid and semi-arid regions, especially in countries with diverse climatic conditions. This study presents a comprehensive methodology for evaluating rainfall probability distributions across Pakistan, and aims to create a probabilistic zoning map that could serve as a valuable resource to inform the development of strategies for efficient water resource management and improved flood resilience in diverse climatic and geographic conditions. Precipitation data from the Pakistan Meteorological Department (PMD) over 42 years were compared with CHIRPS, confirming their accuracy. Nine probability distributions were assessed, with five models—log Pearson type-III (LP3), Weibull (W2), log normal (LN2), Generalized Extreme Value (GEV), and gamma (GAM)—deemed most suitable for the region’s climatic variability. The spatial applicability of these distributions was identified as follows: LP3 (30%), LN2 (30%), W2 (15%), GEV (10%), and GAM (15%). The central and southern regions of Punjab were predominantly characterized by LN2, while GAM was prevalent in the coastal areas of Sindh. Balochistan exhibited a heterogeneous distribution of W2, LP3, and LN2, while the mountainous Gilgit-Baltistan region was exclusively associated with GEV. Khyber Pakhtunkhwa demonstrated a mix of GEV and LP3 distributions. Beyond provincial variations, distinct patterns emerged: GEV dominated high-altitude, cold-temperate areas; LP3 was common in mountainous regions with variable temperature profiles; and W2 was prevalent along the flood-prone Indus River. This study provides a robust framework for region-specific disaster preparedness and contributes to sustainable development initiatives by offering tailored strategies for managing extreme rainfall events across Pakistan’s diverse climatic zones. Full article