Search Results (2,296)

The general unit hydrograph (GUH), recently established by Guo, represents the most advanced hydrograph model today, but how to implement it with hydrologic data is another story. In this work, an effective initial value-based method for estimating the parameters in the GUH model is proposed and applied to the analysis of flood processes. In contrast to the flood-rainfall united fitting method, which heavily depends on the flood records and has a broad range of parameter variations, which makes it practically intractable, the initial value-based method enables the calculation of model parameters directly from the measured rainstorm data and greatly enriches the discharge dataset so that more accurate prediction of flood processes becomes achievable. From the data collected from several watersheds, we find that smaller-shape parameters usually indicate a multi-peak flood process, and the rainfall patterns have a significant impact on flood peaks. These results provide a reliable approach for the prediction of floods in streams with scarce discharge data. Additionally, it is observed that the peak time lags have a notable increase from the southwest to the northeast of Zhejiang. Full article

Extreme rainfall analysis is essential for accurate flood hazard assessment. Traditional approaches, such as the use of annual maxima, may overlook seasonal variations and lead to underestimated precipitation extremes, compromising effective flood risk management strategies. This study applies a point process model to uninterrupted daily rainfall records (1901–2023) from the National Observatory of Athens meteorological station in Thiseion. This method analyzes both the frequency of exceedances above a given threshold and the values of those exceedances, incorporating seasonality into the modeling process. Preliminary analysis using annual maxima revealed no statistically significant trend but indicated clear monthly seasonality in precipitation extremes. By incorporating seasonality, the point process method yielded estimates up to 22% higher than those obtained using traditional annual maxima approaches, such as those employed in Greece’s National Flood Risk Management Plans. These findings highlight the need for a revision of current methodologies, which could significantly impact flood risk assessments and management strategies. Full article

This study evaluates the sensitivity of the Weather Research and Forecasting (WRF-ARW) model in its version 4.3.3 during different experiments on a torrential precipitation event associated with the 2017 El Niño Costero in Peru. The results are compared with two reference datasets: precipitation estimations from CHIRPS satellite data and SENAMHI meteorological station values. The event, which had significant economic and social impacts, is simulated using two nested domains with resolutions of 9 km (d01) and 3 km (d02). A total of 22 experiments are conducted, resulting from the combination of two planetary boundary layer (PBL) schemes: Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ), with five cumulus parameterization schemes: Betts–Miller–Janjic (BMJ), Grell–Devenyi (GD), Grell–Freitas (GF), Kain–Fritsch (KF), and New Tiedtke (NT). Additionally, the effect of turning off cumulus parameterization in the inner domain (d02) or in both (d01 and d02) is explored. The results show that the YSU scheme generally provides better results than the MYJ scheme in detecting the precipitation patterns observed during the event. Furthermore, it is concluded that turning off cumulus parameterization in both domains produces satisfactory results for certain regions when it is combined with the YSU PBL scheme. However, the KF cumulus parameterization is considered the most effective for intense precipitation events in this region, although it tends to overestimate precipitation in high mountain areas. In contrast, for lighter rains, combinations of the YSU PBL scheme with the GD or NT parameterization show a superior performance. It is worth nothing that for all experiments here used, there is a clear underestimation in terms of precipitation, except in high mountain regions, where the model tends to overestimate rainfall. Full article

Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981–2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The Revised Universal Soil Loss Equation, remote sensing, and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall data, sourced from meteorological stations and reanalysis datasets, which capture the temporal variability of erosive forces. Soil properties, including texture and organic matter content, were derived from FAO global soil databases to assess soil erodibility. High-resolution digital elevation models (30 m) provided detailed topographic information, crucial for calculating slope length and steepness factors. Land use and land cover data were extracted from satellite imagery, enabling the analysis of vegetation cover and anthropogenic impacts over four decades. By integrating and treating these data, this study reveals that the estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981–2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold. This research is the first study of soil erosion quantification with the RUSLE method and GIS in the Mékrou watershed, and fills a critical knowledge gap of the water erosion in this watershed, providing insights into erosion dynamics and supporting future sustainable land management strategies in vulnerable Sahelian landscapes. Full article

We propose a semi-empirical method—based on a filtered Markov process—to convert 10 min rain rate time series into 1 min time series, i.e., quasi-instantaneous rainfall—the latter to be used as input to the synthetic storm technique, which is a very reliable tool for calculating rain attenuation time series in satellite communication systems or for estimating runoff, erosion, pollutant transport, and other applications in hydrology. To develop the method, we used a very large data bank of 1 min rain rate time series collected in several sites with different climatic conditions. The experimental and simulated 1 min rain rate time series agree very well. Afterward, we used them to simulate rain attenuation time series at 20.7 GHz, in 35.5° slant paths to geostationary satellites. The two simulated annual rain attenuation probability distributions show very small differences. We conclude that the rain rate conversion method is very reliable. Full article

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement. Full article

The nonstationarity of extreme precipitation is now well established in the presence of climate change and low-frequency variability. Consequently, the implications for urban flooding, for which there are not long flooding records, need to be understood better. The vulnerability is especially high in coastal cities, where the flat terrain and impervious cover present an additional challenge. In this paper, we estimate the time-varying probability distributions for hourly and daily extreme precipitation using the Generalized Additive Model for Location Scale and Shape (GAMLSS), employing different climate indices, such as Atlantic Multi-Decadal Oscillation (AMO), the El Niño 3.4 SST Index (ENSO), Pacific Decadal Oscillation (PDO), the Western Hemisphere Warm Pool (WHWP) and other covariates. Applications to selected coastal cities in the USA are considered. Overall, the AMO, PDO and WHWP are the dominant factors influencing the extreme rainfall. The nonstationary model outperforms the stationary model in 92% of cases during the fitting period. However, in terms of its predictive performance over the next 5 years, the ST model achieves a higher log-likelihood in 86% of cases. The implications for the time-varying design rainfall in coastal areas are considered, whether this corresponds to a structural design or the duration of a contract for a financial instrument for risk securitization. The opportunity to use these time-varying probabilistic models for adaptive flood risk management in a coastal city context is discussed. Full article

The use of organic mulch as a natural practice to enhance water retention and absorption is underexplored, highlighting the need for a deeper understanding of its effectiveness under varying conditions. The aim of this study was to investigate the process of interception, retention, and absorption of rainwater by different types, sizes, and densities of some organic mulch covers. Six organic mulches of various sizes were used, all largely available in the Brazilian semiarid: coconut leaf (cc), cashew leaf (ca), elephant grass (el), corn leaf (co), Brachiaria grass (br), and sugar cane leaf (su), under simulated rainfall conditions. The experimental scheme consisted of a factorial of six types of mulches, three sizes (50, 100, and 200 mm), and four densities (1, 2, 4, and 8 t ha⁻¹). Water adsorption and retention curves were constructed, and the interception capacity of different vegetation materials was estimated. Analysis of variance, Tukey Test, Regression polynomial, and Principal Components Analysis were applied. It was observed that increasing density systematically led to an increase in water retention and absorption. For 8 t ha⁻¹ the values were 11 to 23% for water retention and 7 to 16% for water absorption of the gross rainfall depth. When comparing 8 t ha⁻¹ and 2 t ha⁻¹ densities, rainfall retention and absorption increased more than 100%. Higher values were obtained for cashew and Brachiaria grass, improving water retention and cashew leaves for absorption. Coconut leaves promoted only 83% retention and 67% water absorption, when compared to the cashew leaf and Brachiaria grass. Full article

In a climate change scenario where extreme precipitation events occur more frequently and intensely, risk assessment plays a critical role in ensuring the safety and operational efficiency of facilities. This case study uses a combination of the multi-criteria analysis approach and hydrological studies that use machine learning algorithms to simulate new rainfall events in order to estimate the risk of flooding on railroads. Risk variables, including terrain, drainage capability, accumulated flow, and land use and land cover, will be weighed using the multicriteria approach. A methodical evaluation of the most vulnerable locations on the railroad network will be possible thanks to the analysis of these parameters based on the geographic information system (GIS) approach. In the meantime, historical precipitation, flow, and hydrological balance data will be used to calibrate and validate hydrological models. The database required for the machine learning model can be created with these hydrological data. The research regions are situated in the densely rail-networked state of Minas Gerais. The geographical and climatic diversity of Minas Gerais makes it the perfect place to test and validate the suggested approaches. The models evaluated included linear regression, random forest, decision tree, and support vector machines. Among the evaluated models, Linear Regression emerged as the best-performing model with an R² value of 0.999998, a mean squared error (MSE) of 0.018672, and a low tendency to overfitting (0.000011). Full article

Both extreme-excess modeling and extreme-value analysis of precipitation events frequently utilize the Generalized Pareto (GP) distribution to model peaks above a selected threshold. However, selecting an appropriate threshold remains a complex and challenging task, which has discouraged many practitioners from employing Pareto or Pareto–Poisson distributions for extreme-value analysis. Recent analyses of threshold selection methods proposed in the technical literature, particularly when applied to rainfall records with high quantization levels, have shown that nonparametric methods are often unreliable. Additionally, methods relying on the asymptotic properties of the GP distribution tend to produce unrealistically high threshold estimates. In contrast, graphical methods and goodness-of-fit (GoF) metrics that account for the pre-asymptotic behavior of the GP distribution have demonstrated better performance. Despite these improvements, there remains no automatic and statistically robust methodology for threshold selection. This study develops an automatic, statistically sound procedure for optimal threshold selection, leveraging weighted mean square errors and internally studentized residuals. The proposed method outperforms existing approaches in terms of accuracy, as demonstrated through numerical experiments and its application to real-world data from the NOAA NCDC Daily Rainfall Database. Results indicate that the method not only improves threshold estimation precision but also enhances the reliability of extreme-value analysis for precipitation records, making it a valuable tool for hydrological applications. The findings emphasize the practical implications of the method for analyzing extreme rainfall events and its potential for broader climatological studies. Full article

Climate change has the potential to significantly impact various aspects of Earth’s climate systems, including precipitation patterns, necessitating region-specific action plans. This study examines the Wadi Al Rummah region in Qassim province, Saudi Arabia, by analyzing Intensity-Duration-Frequency (IDF) curves across six locations, utilizing observed daily precipitation data from 1986 to 2014. The nonparametric quantile mapping method was employed to adjust the outputs of eight Regional Climate Models (RCMs) within the CMIP6 ensemble. These models were evaluated under four Shared Socioeconomic Pathways (SSPs), ranging from a stringent mitigation scenario to one with very high greenhouse gas emissions. Also, two statistical tests, namely the Kolmogorov-Smirnov and Chi-Square tests, were used to assess the best-fitting distribution to estimate the maximum rainfall values. Temporal disaggregation of daily precipitation data was performed using the K-nearest neighbors (KNN) method. The IDF curves were generated for both historical and three projected future periods using Gumbel distribution, which proved to be the best-fitting statistical model, using six return periods: 2, 5, 10, 25, 50, and 100 years. Results indicate that high-emission scenarios and longer timeframes exhibit greater uncertainty in IDF projections. Additionally, rainfall intensity is expected to increase over shorter durations, with significant increases observed in Buriydah and Nabhaniyah under SSP 8.5. In contrast, Al Rass, Badayea, and Al Mithnab show mixed trends, while Unaizah shows little to no significant change. These findings emphasize the need for sustainable development and adaptive strategies to mitigate risks in Qassim province, as climate impacts are projected to intensify, particularly in the short to long term. Full article

Soil hydrological response is crucial for controlling water flow and biogeochemical processes on hillslopes. Understanding soil water dynamics in response to rainfall is essential for accurate hydrological modeling but remains challenging in humid mountainous regions characterized by high antecedent moisture and substantial heterogeneity. We sought to elucidate soil water response patterns to rainfall by estimating lag time, wetting front velocity, rainfall threshold, and preferential flow (PF) frequency in 166 rainfall events across 36 sites on two hillslopes within the Hailuogou catchment, located on the eastern Qinghai–Tibet Plateau. Results indicated that over 90% of the events triggered rapid soil water potential (SWP) responses to depths of 100 cm, with faster responses observed at steeper upslope positions with thinner O horizons. Even light rainfall (2–3 mm) was sufficient to trigger SWP responses. PF was prevalent across the hillslopes, with higher occurrence frequencies at upslope and downslope positions due to steep terrain and consistently moist conditions, respectively. Using the Multivariate Adaptive Regression Splines (MARS) model, we found that site factors (e.g., soil properties and topography) had a greater influence on SWP responses than rainfall characteristics or antecedent soil wetness conditions. These findings highlighted the value of SWP in capturing soil water dynamics and enhancing the understanding and modeling of complex hillslope hydrological processes. Full article

Irrigation schemes represent the backbone of Sudan’s food security and economy. The Gezira, Rahad, and El-Gunied irrigation schemes depend mainly on the Blue Nile as their primary water source. However, the construction of the Grand Ethiopian Renaissance Dam (GERD) in the Blue Nile at the Sudan border has changed water flow regulations along the Blue Nile. Therefore, the Sudanese irrigation schemes that depend on the Blue Nile are affected by the operation and management of the GERD. This study used datasets derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), specifically the enhanced vegetation index (EVI) and crop water use efficiency (CWUE), alongside climate time-series data obtained from the Climate Research Unit, to evaluate the performance of irrigation schemes in Sudan affected by climate variability and the construction and filling of the GERD. The analysis was carried out using R version 4.4.1 and spreadsheets. A dummy variable approach was employed to examine the effects of the GERD on the EVI, given the limited timeframe of the study, whilst Grey Relational Analysis was applied to investigate the influence of selected climate variables on the EVI. The results revealed that in the Gezira scheme, the impact of the GERD on the EVI was minimal, with rainfall and temperature identified as the predominant factors. In contrast, the construction of the GERD had significant negative repercussions on the EVI in the Rahad scheme, while it positively affected the El-Gunied scheme. The advantageous effects observed in the El-Gunied scheme were linked to the mitigation measures employed by the heightening of the Roseires Dam in Sudan since 2013. The Rahad and El-Gunied schemes exhibited heightened sensitivity to GERD-induced changes, primarily due to their reliance on irrigation water sourced from pumping stations dependent on Blue Nile water levels. Additionally, this study forecasts a decrease in cropping intensity attributed to the GERD, estimating reductions of 3.9% in Rahad, 1.5% in Gezira, and 0.8% in El-Gunied. Ultimately, this study highlights the detrimental impact of the GERD on Blue Nile water levels as a significant adverse factor associated with its construction and filling, which has led to a marked decline in CWUE across the irrigation schemes. The research underscores the intricate inter-relationship among environmental, political, institutional, and infrastructural elements that shapes irrigation efficiency and water management practices. This study concludes that enhancing irrigation efficiency and assessing the performance of irrigation schemes require significant consideration of institutional, economic, and political factors, especially in Sub-Saharan Africa. Full article

Nitrate leaching from soil presents a significant threat to soil health, as it can result in nutrient loss, soil acidification, and structural damage. It is crucial to quantify the spatial heterogeneity of nitrate leaching and its drivers. A total of 509 observational data points regarding nitrate leaching in northern China were collected, capturing the spatial and temporal variations across crops such as winter wheat, maize, and greenhouse vegetables. A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. However, the performance improved significantly after integrating the four models with Bayesian optimization (all models had R² > 0.56), which realized quantitative prediction capabilities for nitrate leaching loss concentrations. Moreover, the XGBoost model exhibited the highest fitting accuracy and the smallest error in estimating nitrate leaching losses, with an R² value of 0.79 and an average absolute error (MAE) of 3.87 kg/ha. Analyses of the feature importance and SHAP values in the optimal XGBoost model identified soil organic matter, chemical nitrogen fertilizer input, and water input (including rainfall and irrigation) as the main indicators of nitrate leaching loss. The ML-based modeling method developed overcomes the difficulty of the determination of the functional relationship between nitrate loss intensity and its influencing factors, providing a data-driven solution for estimating nitrate–nitrogen loss in farmlands in North China and strengthening sustainable agricultural practices. Full article

Changes in land use and land cover (LULC) and climate increasingly influence flood occurrences in the Gumara watershed, located in the Upper Blue Nile (UBN) basin of Ethiopia. This study assesses how these factors impact return period-based peak floods, flood source areas, and future high-flow extremes. Merged rainfall data (1981–2019) and ensemble means of four CMIP5 and four CMIP6 models were used for historical (1981–2005), near-future (2031–2055), and far-future (2056–2080) periods under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Historical LULC data for the years 1985, 2000, 2010, and 2019 and projected LULC data under business-as-usual (BAU) and governance (GOV) scenarios for the years 2035 and 2065 were used along with rainfall data to analyze flood peaks. Flood simulation was performed using a calibrated Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS) model. The unit flood response (UFR) approach ranked eight subwatersheds (W1–W8) by their contribution to peak flood magnitude at the main outlet, while flow duration curves (FDCs) of annual maximum (AM) flow series were used to analyze changes in high-flow extremes. For the observation period, maximum peak flood values of 211.7, 278.5, 359.5, 416.7, and 452.7 m³/s were estimated for 5-, 10-, 25-, 50-, and 100-year return periods, respectively, under the 2019 LULC condition. During this period, subwatersheds W4 and W6 were identified as major flood contributors with high flood index values. These findings highlight the need to prioritize these subwatersheds for targeted interventions to mitigate downstream flooding. In the future period, the highest flow is expected under the SSP5-8.5 (2056–2080) climate scenario combined with the BAU-2065 land use scenario. These findings underscore the importance of strategic land management and climate adaptation measures to reduce future flood risks. The methodology developed in this study, particularly the application of RF-MERGE data in flood studies, offers valuable insights into the existing knowledge base on flood modeling. Full article