Search Results (55)

Frequency analysis is essential for designing hydraulic structures and managing water resources, as it helps assess hydrological extremes. However, changes in river basins can impact their accuracy, complicating the link between discharge and return periods. This study aims to comprehensively assess the probability characteristics of long-term M-day maximum/minimum discharges in the Carpathian region of Slovakia. We analyze the long-term data from 26 gauging stations covering 90 years of observation. Slovak rivers show considerable intra-annual variability, especially between the summer–autumn (SA) and winter–spring (WS) seasons. To allow consistent comparisons, we apply a uniform methodology to estimate T-year daily maximum and minimum specific discharges over durations of 1 and 7 days for both seasons. Our findings indicate that 1-day maximum specific discharges are generally higher during the SA season compared to the WS season. The 7-day minimum specific discharges are lower during the WS season compared to the SA season. Slovakia’s diverse orographic and climatic conditions cause significant spatial variability in extreme discharges. However, the estimated T-year 7-day minimum and 1-day maximum specific discharges, based on the mean specific discharge and the altitude of the water gauge, exhibit certain nonlinear dependences. These relationships could support the indirect estimation of T-year M-day discharges in regions with similar runoff characteristics. Full article

The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability. Full article

This article attempts to estimate the potential of supplying a residential building in Europe with energy exclusively from RESs during a whole year, including the heating period. The aim of the tests carried out was to minimize the purchase of energy required to achieve the thermal comfort (HVACR + DHW) of a residential building powered solely by electricity. During the tests carried out, the EVs were used by the residents as their daily means of transport, topped up during working hours, and the excess energy remaining in their batteries was discharged into the building when they returned home. Energy for the EVs/PHEVs was sourced from RESs (mostly for free) while they were parked at the workplace, and also on the way home. Two one-month tests in the spring and autumn resulted in a state where, instead of purchasing a significant volume of black energy from the grid, the building was mostly powered by green energy from roof-top PVs and RES energy brought in by the PHEVs/EVs. This study identified days when the building became a real nZEB, which was not possible in previous years. The results of economic gains and carbon footprint reduction were calculated. After a period of testing, the degree of degradation of traction batteries used to carry the energy of EVs/PHEVs was checked. A high potential for such an operation was identified, especially in areas where there are periodic shutdowns (due to a call from the grid operator) of local RESs situated near the residential areas. The proposed solution may be of interest to all countries where the use of grid energy is associated not only with a doubling of costs (grid charges), but also with significant emissions, particularly in the heating period (e.g., Poland). 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

Hydrology and meteorological data from relatively undisturbed watersheds aid in identifying effects on ecosystem services, tracking hydroclimatic trends, and reducing model uncertainties. Sustainable forest, water, and infrastructure management depends on assessing the impacts of extreme events and land use change on flooding, droughts, and biogeochemical processes. For example, global climate models predict more frequent high-intensity storms and longer dry periods for the southeastern USA. We summarized 17 years (2005–2021) of hydrometeorological data recorded in the 52 km², third-order Turkey Creek watershed at the Santee Experimental Forest (SEF), Southeastern Coastal Plain, USA. This is a non-tidal headwater system of the Charleston Harbor estuary. The study period included a wide range of weather conditions; annual precipitation (P) and potential evapotranspiration (PET) ranged from 994 mm and 1212 mm in 2007 to 2243 mm and 1063 in 2015, respectively. The annual runoff coefficient (ROC) varied from 0.09 in 2007 (with water table (WT) as deep as 2.4 m below surface) to 0.52 in 2015 (with frequently ponded WT conditions), with an average of 0.22. Although the average P (1470 mm) was 11% higher than the historic 1964–1976 average (1320 mm), no significant (α= 0.05) trend was found in the annual P (p = 0.11), ROC (p = 0.17) or runoff (p = 0.27). Runoff occurred on 76.4% of all days in the study period, exceeding 20 mm/day for 1.25% of all days, mostly due to intense storms in the summer and lower ET demand in the winter. No-flow conditions were common during most of the summer growing season. WT recharge occurred during water-surplus conditions, and storm-event base flow contributed 23–47% of the total runoff as estimated using a hydrograph separation method. Storm-event peak discharge in the Turkey Creek was dominated by shallow subsurface runoff and was correlated with 48 h precipitation totals. Estimated precipitation intensity–duration–frequency and flood frequency relationships were found to be larger than those found by NOAA for the 1893–2002 period (for durations ≥ 3 h), and by USGS regional frequencies (for ≥10-year return intervals), respectively, for the same location. We recommend an integrated analysis of these data together with available water quality data to (1) assess the impacts of rising tides on the hydroperiod and biogeochemical processes in riparian forests of the estuary headwaters, (2) validate rainfall–runoff models including watershed scale models to assess land use and climate change on hydrology and water quality, and (3) inform watershed restoration goals, strategies, and infrastructure design in coastal watersheds. Full article

Design flood estimation is an essential step in many water engineering design tasks such as the planning and design of infrastructure to reduce flood damage. Flood frequency analysis (FFA) is widely used in estimating design floods when the at-site flood data length is adequate. One of the problems in FFA with an annual maxima (AM) modeling approach is deciding how to handle smaller discharge values (outliers) in the selected AM flood series at a given station. The objective of this paper is to explore how the practice of censoring (which involves adjusting for smaller discharge values in FFA) affects flood quantile estimates in FFA. In this regard, two commonly used probability distributions, log-Pearson type 3 (LP3) and generalized extreme value distribution (GEV), are used. The multiple Grubbs and Beck (MGB) test is used to identify low-flow outliers in the selected AM flood series at 582 Australian stream gauging stations. It is found that censoring is required for 71% of the selected stations in using the MGB test with the LP3 distribution. The differences in flood quantile estimates between LP3 (with MGB test and censoring) and GEV distribution (without censoring) increase as the return period reduces. A modest correlation is found (for South Australian catchments) between censoring and the selected catchment characteristics (correlation coefficient: 0.43), with statistically significant associations for the mean annual rainfall and catchment shape factor. The findings of this study will be useful to practicing hydrologists in Australia and other countries to estimate design floods using AM flood data by FFA. Moreover, it may assist in updating Australian Rainfall and Runoff (national guide). Full article

Flood analyses when using a GIS-based integrated approach have been successfully applied around the world in large-sized watersheds. This study employed hydrological-hydraulic modeling to analyze flash floods by integrating HEC-HMS, HEC-RAS, and ArcGIS software for flood evaluation and control in a micro-watershed in the Samaru River, Nigeria. The watershed boundaries, its characteristics (soil and land use), the topographical survey, and the intensity duration frequency curve (IDF) of the study area were produced using data-driven techniques. The HEC-HMS model was used to derive the peak discharges for 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods with the frequency storm method. Afterward, the water surface profiles for the respective return periods were estimated using the HEC-RAS hydrodynamic model. The simulated design flood for the 2-, 5-, 10-, 25-, 50-, 100-, and 200-year return periods at the reference location (the NUGA gate culvert) were 3.5, 6.8, 9.1, 12.1, 14.3, 16.6, and 19.0 m³/s, respectively, while those at the watershed outlet for the respective return periods were 7.5, 14.9, 20.3, 27.3, 32.6, 38.0, and 43.5 m³/s, respectively (with a water height of 0.9 m, 1.1 m, 1.3 m, 1.33 m, 1.38 m, 1.5 3m, and 1.8 m, respectively), at the NUGA gate culvert cross-section. The maximum water depths of about 0.9 m and 1.0 m were recorded in the right and left overbanks, which were similar to the simulated water depth for the 2- and 5-year return periods. Hence, for the smart control of floods passing through the river and major hydraulic structures, a minimum design height of 1.50 m is recommended. For the most economic trapezoidal channel section, a normal depth of 1.50 m, a bottom width of 1.73 m, a top width of 3.50 m, and a free board of 0.30 m is proposed to curb the overtopping of floods along the channel sub-sections. The findings of this study could help hydraulic engineers minimize flooding in streams and rivers overbanks in a micro-watershed. Full article

This study assesses coastal flooding extension mapping based on two innovative approaches. The first is based on the coupling of two robust numerical models (SWASH and LISFLOOD); in this case, discharges were extracted from the wave overtopping results from SWASH 1D and set as boundary conditions for LISFLOOD on the crest of an existing seawall where overtopping typically occurs. The second, hereby called the ‘Tilted Bathtub Approach’ (TBTA), is based on wave run-up levels and buffering the affected area of a prior flooding event, adjusting it for expected sea states according to different return periods. The proposed approaches are applied to a case study on the Northern Portuguese coast, at Furadouro beach, in the municipality of Ovar, which has been facing multiple flooding episodes throughout recent years, including a dramatic storm in February 2014. This event was used as validation for the proposed methods. A 30-year-long hourly local wave climate time series was used both to perform an extreme value analysis in order to obtain expected sea states according to different return periods and also for performing a sensitivity test for established empirical formulas to estimate wave run-up in this particular case. Results indicate both approaches are valuable: they yield coherent flood extension predictions that align well with the real inundated area from the 2014 storm. The convergence of these findings underscores the potential for these methods in future coastal flood risk assessment, planning, and understanding of coastal responses under extreme weather conditions. Full article

Climate change has increased forest fire risk across Europe, leading to negative impact on air and water quality, biodiversity, soil and landscape aesthetics. Fire events in Mediterranean areas are linked to adverse effects on watersheds’ hydrological regimes and increased surface runoff. The objective of this work is to investigate the hydrological response under pre- and post-fire conditions at a catchment scale. The study area is the mountainous area affected by the recent fire events of July 2021 in Cyprus. The methodological approach that was developed and applied involves the GIS-based implementation of the time-area (TA) diagram method for the unit hydrograph (UH) determination, under the two scenarios (pre- and post-fire), as well as the estimation of hydrological losses using the SCS-CN method for both scenarios. Two typical rainfall-runoff events for return periods of 20 and 100 years are compared for both scenarios regarding total runoff volume, peak discharge and time to peak. This first investigation of hydrological changes before and after a fire event in the area leads to the conclusion that, mainly due to the reduction of vegetation and soil permeability, there is a significant increase in the peak flow discharge and the total runoff volume. Full article

In recent years, extreme rainfall events with short delays and heavy rainfall have often occurred due to severe climate change. In 2015, Typhoon Soudelor caused a short-delayed heavy rainfall event in Nanshih River, which caused damage to a section of the Lansheng Bridge discharge station. The section was relocated upstream to rebuild the discharge station in 2019. However, the new discharge station cannot measure high flow due to the bridge structure. The flow observation range of Lansheng Bridge is therefore limited to normal flow, making it impossible to accurately estimate the flow during high-water stages. The purpose of this study is to use the past flow data of Nanshih River to estimate the flow rate under different return periods using frequency analysis. We used a Digital Elevation Model (DEM) to map the river’s topography, and used the 3D hydraulic calculations of the FLOW-3D model to estimate the water stage and discharge of the Lansheng Bridge. We then verified the accuracy of the model with the measured flow and water stage, and finally used the water stage and discharge data obtained from numerical simulation to construct the stage–discharge rating curve of the Lansheng Bridge. In addition to preventing flood disasters, this study approach can provide reliable data for use in water conservation. It may also be utilized to overcome the problem of measuring and estimating high flow during typhoon floods. Full article

This study’s primary goal was to conduct an analysis regarding the flood susceptibility of the main watercourse of the São João (Funchal) drainage basin. In addition, if proven necessary, we also aimed to suggest mitigation measures, such as sizing a detention basin and promoting adjustments of the riverbed’s roughness coefficient. This study also resorted to geomorphological data—obtained during the watershed characterization process—that were then utilized in the SIG ArcGIS software, in order to estimate the expected peak flow rate, considering a return period of 100 years using the Gumbel distribution. Finally, the Manning–Strickler equation was utilized to determine the river discharge point’s drainage capacity; the reason for that was to verify whether its drainage capacity was sufficient to drain the entire volume of rainwater associated with an extreme flood event. In summary, the results obtained by this study indicate that the drainage capacity of the river discharge point of the São João watershed (Funchal) is insufficient when considering an extreme flood event, for a return period of 100 years. Hence, it became necessary to explore the two aforementioned mitigation measures: first, regarding the detention basin, its sizing was calculated through both the Dutch method and the simplified triangular hydrograph method; second, aiming to increase the drainage capacity of the river discharge point, it is suggested that the roughness coefficient should also be modified. Full article

In hydrological practice, individual elements of the hydrological cycle are most often estimated and evaluated separately. Uncertainty in the size estimation of extrema discharges and their return period can affect the statistical assessment of the significance of floods. One example is the simultaneous occurrence and joining of extremes at the confluence of rivers. The paper dealt with the statistical evaluation of the occurrence of two independent variables and their joint probabilities of occurrence. Bivariate joint analysis is a statistical approach for the assessment of flood threats at the confluence of rivers. In our study, the annual maximum discharges monitored on four selected Slovak rivers and their tributaries represent the analyzed variables. The Archimedean class of copula functions was used as a set of mathematical tools for the determination and evaluation of the joint probability of annual maximal discharges at river confluences. The results of such analysis can contribute to a more reliable assessment of flood threats, especially in cases where extreme discharges occur simultaneously, increasing the risk of devastating effects. Finally, the designed discharges of the different return periods calculated by using the univariate approach and the bivariate approach for the gauging station below the confluence of the rivers was evaluated and compared. Full article

An accurate estimation of the mean annual sediment yield from basins contributes to optimizing water resources planning and management. More specifically, both reservoir sedimentation and the damage caused to infrastructures fall within its field of application. Through a simple probabilistic combination function implemented in hydrometeorological models, this sediment yield can be estimated on a planning and management scale for ungauged basins. This probabilistic combination methodology requires the use of probability distribution functions to model design storms. Within these functions, SQRT-ET max and log-Pearson type III are currently highlighted in applied hydrology. Although the Gumbel distribution is also relevant, its use has progressively declined, as it has been considered to underestimate precipitation depth and flow discharge for high return periods, compared to the SQRT-ET max and log-Pearson III functions. The quantification of sediment yield through hydrometeorological models will ultimately be affected by the choice of the probability distribution function. The following four different functions were studied: Gumbel type I with a small sample size, Gumbel type I with a large sample size, log-Pearson type III and SQRT-ET max. To illustrate this, the model with these four functions has been applied in the Alto Palmones basin (South Iberian Peninsula). In this paper, it is shown that the application of Gumbel function type I with a small sample size, for the estimation of the mean annual sediment yield, provides values on the conservative side, with respect to the SQRT-ET max and log-Pearson type III functions. Full article

Flood frequency analysis is accepted as one of the most important applications of water resource engineering. Measurements with higher and lower values, such as outliers, can be seen in hydrological data sets based on longer observation periods that extend the overall range. This study used 50 and 25 years of annual maximum flow data from 1962 to 2011 and from 1987 to 2011 from the Stream Gauging Stations (SGS) numbered 1712, 1717, and 1721 located within the borders of the Eastern Mediterranean Basin. The flood discharges were estimated using Normal, Gumbel, and Pearson Type III probability distributions. The study adopted Kolmogorov–Smirnov (K-S) and Chi-squared goodness-of-fit tests to investigate the suitability of probability distribution functions. The maximum flow rates were obtained by utilizing Normal distribution in the 2-year and 5-year return periods for the flood values calculated with the raw data; however, after the modification of the outliers, maximum flood discharges were estimated by adopting the Pearson Type III function. While the maximum discharges for the 1717 SGS were determined using the Gumbel distribution, the Pearson Type III distribution function was utilized for the 1712 and 1721 SGSs. As a result of the K-S and Chi-squared tests, it was determined that adjustment of the outliers resulted in positive goodness-of-fit results with the Pearson Type III function. Full article

Low-lying coastal communities are often threatened by compound flooding (CF), which can be determined through the joint occurrence of storm surges, rainfall and river discharge, either successively or in close succession. The trivariate distribution can demonstrate the risk of the compound phenomenon more realistically, rather than considering each contributing factor independently or in pairwise dependency relations. Recently, the vine copula has been recognized as a highly flexible approach to constructing a higher-dimensional joint density framework. In these, the parametric class copula with parametric univariate marginals is often involved. Its incorporation can lead to a lack of flexibility due to parametric functions that have prior distribution assumptions about their univariate marginal and/or copula joint density. This study introduces the vine copula approach in a nonparametric setting by introducing Bernstein and Beta kernel copula density in establishing trivariate flood dependence. The proposed model was applied to 46 years of flood characteristics collected on the west coast of Canada. The univariate flood marginal distribution was modelled using nonparametric kernel density estimation (KDE). The 2D Bernstein estimator and beta kernel copula estimator were tested independently in capturing pairwise dependencies to establish D-vine structure in a stage-wise nesting approach in three alternative ways, each by permutating the location of the conditioning variable. The best-fitted vine structure was selected using goodness-of-fit (GOF) test statistics. The performance of the nonparametric vine approach was also compared with those of vines constructed with a parametric and semiparametric fitting procedure. Investigation revealed that the D-vine copula constructed using a Bernstein copula with normal KDE marginals performed well nonparametrically in capturing the dependence of the compound events. Finally, the derived nonparametric model was used in the estimation of trivariate joint return periods, and further employed in estimating failure probability statistics. Full article