Hydrologic Design Research Articles

Estimation of catchment response time using a new automated event-based approach

• The proposed method greatly speeds up response-time estimation on the event scale. • The response time values are in good agreement with those of the expert-knowledge-based approach. • Parameter values result from sensitivity analysis. The estimation of catchment response time ( T r ) plays an important role in several hydrological and civil engineering design problems. The non-linear relationship between T r and rainfall intensity necessitates the estimation of an event-based set of T r values instead of a characteristic constant value. However, there is no generally accepted method to define individual rainfall-runoff events from time-series. Here we propose a new, automated method which results in the selection of rainfall-runoff events and the corresponding T r values. The proposed method yields an event-based set of T r values more efficiently than other existing methods and has only two parameters. The results of the new method were compared to those of a statistical and a semi-manual event selection approach. The latter calculates eight different T r values, including the time of concentration, lag time, time to peak, and time to equilibrium. The median T r value of the proposed method yields the strongest agreement with the median of the time elapsed between the maxima of the total rainfall and runoff with a root-mean-square error of 4.94 h. It is also demonstrated that a median time of concentration value can be estimated as the maximum of the event based T r values by the current method. A sensitivity analysis explores the robustness of the proposed method, and also yields the optima of its two parameters. Once calibrated, the present automated methodology dispenses with any event selection procedure.

A probability distribution for precipitation data analysis

Hydrologic design is often based on assessments of large return interval measures; it is vital to be able to conclude them as precisely as possible. Henceforth, the selection of a probability distribution is very crucial for such cases. In view of this scenario, we propose and study a pliant probability distribution for precipitation data analysis. Some mathematical and statistical properties are analyzed. In order to make stronger predictions and judge the realistic return period, we have also characterized the model via Laplace transformation. We have estimated its parameters via the maximum likelihood estimation and constructed its information matrix for developing the confidence belt of population parameters. Moreover, a real‐life setup is also considered by applying the model over precipitation data of diverse regions, including Jacksonville, Florida (USA), Barkhan (Pakistan), British Columbia (Canada), and Alexandria (Egypt). This investigated study is based on various statistical parametric and nonparametric tests, which indicates that the proposed model is one of the better strategies for precipitation data analysis when compared with the famous three‐parameter Kappa model.

Research on Hydrologic Planning and Design Methods and Sustainable Development

Research on Hydrologic Planning and Design Methods and Sustainable Development

Climate change impact on blue and green water resources distributions in the Beijiang River basin based on CORDEX projections

Abstract This study assessed the potential impact of climate change on spatiotemporal distribution of blue and green water resources in the Beijiang River basin, southern China, using the SWAT hydrological model and CORDEX-EAS regional climate models (RCMs). The outputs of three RCMs (namely RMOH, RMPI and RNCC) under two emission scenarios (RCP 2.6 and RCP 8.5) were bias-corrected by using the quantile delta mapping method for the control (1975–2004), near future (2021–2050) and far future (2061–2090) periods. Driven by the corrected climate variables, future blue and green water were assessed by using the SWAT model calibrated with streamflow data. The results indicated that the green water flow in the northwest basin predicted by RMOH under RCP 2.6 would increase up to about 2.7% in 2061–2090; RNCC suggests that most of the basin would experience the most significant reduction in green water storage (over 30%) under RCP 8.5 in the far future. For blue water, the ensemble mean of three RCMs indicates a slight decreasing trend in the northern part of the basin and an increasing one in the southern part under RCP 8.5 in the far future. Our findings could help watershed managers evaluate future hydrological risks and design appropriate adaptation strategies.

A Procedure for Estimating Drought Duration and Magnitude at the Uniform Cutoff Level of Streamflow: A Case of the Weekly Flows of Canadian Rivers

At times, hydrological drought is defined using Q90 or Q95 (90% or 95% flows equaling or exceeding) or even at higher levels, such as Q75 as the cutoff level regardless of their seasonal variation (i.e., truncation at the uniform flow level). In the past, the estimation of drought length and magnitude at the aforesaid uniform cutoff levels of flow has been a challenging issue. A procedure is presented to first estimate the drought magnitude (M), which then forms the basis for estimating the drought duration or length (L). The drought magnitude (M) and the length of the critical period (Lcr) are estimated using the concept of behavior analysis prevalent in the hydrologic design of reservoirs. This information is used for estimating the drought length (LT-e′, the estimated value of drought length for the return period of T weeks) involving a Markov chain model on the standardized weekly flow sequences. A weighted average of Lcr and LT-e′ (=0.60 Lcr + 0.40 LT-e′) results in the estimate of drought length, which is compatible to the observed counterpart. The performance of the procedure to estimate drought length was found to be satisfactory up to the truncation level of Q75, whereas the estimation of drought magnitude was rated as good.

Consideration of high-quality development strategies for soil and water conservation on the loess plateau

The construction of check dams is an important measure to prevent soil erosion on the Loess Plateau and reduce the amount of sediment entering the Yellow River. Based on an analysis of the current situation of soil and water conservation on the Loess Plateau and the three major problems faced by the traditional homogeneous soil check dam construction, the study of anti-scouring materials, hydrological calculation methods, dam design and construction technology and soil and water conservation monitoring are carried out in this paper. The results showed that the current soil and water conservation measures on the Loess Plateau have achieved remarkable outcomes. The new design and application concept of check dams with anti-burst and multi-sand interceptions is innovatively proposed in this paper. The new materials of solidified loess have good durability and anti-scouring characteristics and could meet the overflow and anti-scouring requirements of the new check dam. The small watershed high sand content of hydrological calculation can establish the upper limit of the flood sediment boundary for the anti-scouring protection layer of the check dam. The new technology of dam design and construction can achieve no collapse or slow collapse when encountering floods exceeding the standard. Intelligent monitoring systems can realize real-time dynamic monitoring for soil and water conservation on the Loess Plateau. The results will eventually contribute to the national strategy of the Ecological Protection and High Quality Development in the Yellow River basin.

Development and Application of a Rainfall Temporal Disaggregation Method to Project Design Rainfalls

A climate model is essential for hydrological designs considering climate change, but there are still limitations in employing raw temporal and spatial resolutions for small urban areas. To solve the temporal scale gap, a temporal disaggregation method of rainfall data was developed based on the Neyman–Scott Rectangular Pulse Model, a stochastic rainfall model, and future design rainfall was projected. The developed method showed better performance than the benchmark models. It produced promising results in estimating the rainfall quantiles for recurrence intervals of less than 20 years. Overall, the analysis results imply that extreme rainfall events may increase. Structural/nonstructural measures are urgently needed for irrigation and the embankment of new water resources.

Datasets for characterizing extreme events relevant to hydrologic design over the conterminous United States

Despite the close linkage between extreme floods and snowmelt, particularly through rain-on-snow (ROS), hydrologic infrastructure is mostly designed based on standard precipitation Intensity-Duration-Frequency curves (PREC-IDF) that neglect snow processes in runoff generation. For snow-dominated regions, such simplification could result in substantial errors in estimating extreme events and infrastructure design risk. To address this long-standing problem, we applied the Next Generation IDF (NG-IDF) technique to estimate design basis extreme events for different durations and return periods in the conterminous United States (CONUS) to distinctly represent the contribution of rain, snowmelt, and ROS events to the amount of water reaching the land surface. A suite of datasets were developed to characterize the magnitude, trend, seasonality, and dominant mechanism of extreme events for over 200,000 locations. Infrastructure design risk associated with the use of PREC-IDF was estimated. Accuracy of the model simulations used in the analyses was confirmed by long-term snow data at over 200 Snowpack Telemetry stations. The presented spatially continuous datasets are readily usable and instrumental for supporting site-specific infrastructure design.

Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia

The study shows that the current network of hydrometeorological observation in the permafrost zone of Russia is insufficient to provide data for the statistical approaches adopted at the state level for engineering surveys and calculations. The alternative to the financially costly and practically impossible expansion of the monitoring network is the development of hydrological research stations and the implementation of new methods for calculating streamflow characteristics based on mathematical modeling. The data of the Kolyma Water-Balance Station, the first research basin in the world in a permafrost environment (1948–1997), and the process-based hydrological model Hydrograph are applied to simulate streamflow hydrographs in remote mountainous permafrost basins. The satisfactory results confirm that mathematical modeling may substitute or replace statistical approaches in the conditions of extreme data insufficiency. The improvement of the models in a changing climate requires the renewal of historical observations at currently abandoned research stations in Russian permafrost regions. The study is important for forming the state policy in climate change adaptation and mitigation measures.

A satellite-based Standardized Antecedent Precipitation Index (SAPI) for mapping extreme rainfall risk in Myanmar

In recent decades, substantial efforts have been devoted in flood monitoring, prediction, and risk analysis for aiding flood event preparedness plans and mitigation measures. Introducing an initial framework of spatially probabilistic analysis of flood research, this study highlights an integrated statistical copula and satellite data-based approach to modelling the complex dependence structures between flood event characteristics, i.e., duration (D), volume (V) and peak (Q). The study uses Global daily satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) (spatial resolution of ∼5 km) during 1981–2019 to derive a Standardized Antecedence Precipitation Index (SAPI) and its characteristics through a time-dependent reduction function for Myanmar. An advanced vine copula model was applied to model joint distributions between flood characteristics for each grid cell. The southwest (Rakhine, Bago, Yangon, and Ayeyarwady) and south (Kayin, Mon, and Tanintharyi) regions are found to be at high risk, with a probability of up to 40% of flood occurrence in August and September in the south (Kayin, Mon, and Tanintharyi) and southwest regions (Rakhine, Bago, Yangon, and Ayeyarwady). The results indicate a strong correlation among flood characteristics; however, their mean and standard deviation are spatially different. The findings reveal significant differences in the spatial patterns of the joint exceedance probability of flood event characteristics in different combined scenarios. The probability that duration, volume, and peak concurrently exceed 50th-quantile (median) values are about 60–70% in the regions along the administrative borders of Chin, Sagaing, Mandalay, Shan, Nay Pyi Taw, and Keyan. In the worst case and highest risk areas, the probability that duration, volume, and peak exceed the extreme values, i.e., the 90th-quantile, about 10–15% in the southwest of Sagaing, southeast of Chin, Nay Pyi Taw, Mon and areas around these states and up to 30% in the southeast of Dekkhinathiri township (Nay Pyi Taw). The proposed approach could improve the evaluation of exceedance probabilities used for flood early warning and risk assessment and management. The proposed framework is also applicable at larger scales (e.g., regions, continents and globally) and in different hydrological design events and for risk assessments (e.g., insurance).

Estimating Optimal Design Frequency and Future Hydrological Risk in Local River Basins According to RCP Scenarios

In South Korea, flood damage mainly occurs around rivers; thus, it is necessary to determine the optimal design frequency for river basins to prevent flood damage. However, there are not enough studies showing the effect of climate change on hydrologic design frequency. Therefore, to estimate the optimal design frequency according to future climate change scenarios, this study examined urban flooding area and extreme rainfall frequency that can change in the future. After estimating the optimal design frequency, hydrological risks of 413 local river basins were evaluated according to Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 after regenerating daily rainfalls from the HadGEM2-ES model into hourly rainfalls using the Poisson cluster. For the RCP 4.5, hydrological risks increased relative to the established design frequency by 3.13% on average. For the RCP 8.5, hydrological risks increased by 2.80% on average. The hydrological risks increased by 4.58% in the P2(2040–2069) period for the RCP 4.5, and by 4.39% in the P1 (2021–2039) period for the RCP 8.5. These results suggest that the hydrologic design frequency in the future will likely decrease, and the safety of river basins will also decrease.

Bivariate Flood Frequency Analysis: A case study of Rib River, Upper Blue Nile Basin, Ethiopia

Hydrological design, planning and design of flood mitigation structures require detailed knowledge of the characteristics of the flood event, i.e. peaks, volumes, occurrence times and duration. In addition to the uncertainty associated with the occurrence in both space and time, these events may often have a correlation of varying strengths. The literature study interestingly reveals that the majority of studies are based on a univariate approach rather than a more realistic approach that recognizes the multivariate nature of the underlying phenomenology. Therefore, the main objective of this study is to revisit the topic of sizing of flood events in terms of their frequency of occurrence using the ‘Copula’ based bivariate approach to analyze the joint distributions of correlated flood variables with a special focus on Ribb sub basin, Upper Blue Nile (Ethiopia) as the case study. The methodology was applied to flood attributes, i.e. flood peaks and volume generation from partial duration series (PDS) by applying run theory. The Joint Cumulative Distribution Function, the Conditional Cumulative Distribution Function and the associated Return Periods can be easily achieved based on the bivariate distribution of the copula and compared to the univariate analysis.

Intensity-Duration-Frequency Curves at Ungauged Sites in a Changing Climate for Sustainable Stormwater Networks

Intensity-duration-frequency (IDF) curves representing the variation of the magnitude of extreme rainfall events with a return period and storm duration are widely used in hydrologic infrastructure design, flood risk management projects, and climate change impact studies. However, in many locations worldwide, short-duration rainfall-observing sites with long records do not exist. This paper introduces a new methodological framework for extracting IDF curves at ungauged sites transferring information from gauged ones with a relatively homogeneous extreme rainfall climate. This methodology is grounded on a simple scaling concept based on the multifractal behaviour of rainfall. A nonstationary Generalized Extreme Value (GEV) distribution fitted to annual rainfall monthly maxima at the ungauged site using a moving-time window approach is also applied to consider effects of a changing climate on IDF curve construction. An application is presented at the study site of Fourni, Crete, to derive IDF curves under changing climate conditions and present implications of the proposed methodology in the design of a sustainable stormwater network. The methodology introduced in this work results in increased rainfall extremes up to 20.5%, while the newly designed stormwater network is characterised by increased diameters of its primary conduits, compared to the ones resulting under fully stationary conditions.

Multivariate Dam-Site Flood Frequency Analysis of the Three Gorges Reservoir Considering Future Reservoir Regulation and Precipitation

Under a changing environment, the current hydrological design values derived from historical flood data for the Three Gorges Reservoir (TGR) might be no longer applicable due to the newly-built reservoirs upstream from the TGR and the changes in climatic conditions. In this study, we perform a multivariate dam-site flood frequency analysis for the TGR considering future reservoir regulation and summer precipitation. The Xinanjiang model and Muskingum routing method are used to reconstruct the dam-site flood variables during the operation period of the TGR. Then the distributions of the dam-site flood peak and flood volumes with durations of 3, 7, 15, and 30 days are built by Pearson type III (PIII) distribution with time-varying parameters, which are expressed as functions of both reservoir index and summer precipitation anomaly (SPA). The multivariate joint distribution of the dam-site flood variables is constructed by a 5-D C-vine copula. Finally, by using the criteria of annual average reliability (AAR) associated with the exceedance probabilities of OR, AND and Kendall, we derive the multivariate dam-site design floods for the TGR from the predicted flood distributions during the future operation period of the reservoir. The results indicate that the mean values of all flood variables are positively linked to SPA and negatively linked to RI. In the future, the flood mean values are predicted to present a dramatic decrease due to the regulation of the reservoirs upstream from the TGR. As the result, the design dam-site floods in the future will be smaller than those derived from historical flood distributions. This finding indicates that the TGR would have smaller flood risk in the future.

Flood Frequency Analysis and Hydraulic Design of Bridge at Mashan on River Kunhar

Abstract Kunhar River hydrology and hydraulic design of a bridge on this river are being studied using HEC-Geo-RAS and Hydrologic Engineering Centers River Analysis System (HEC-RAS). The river flows in the northern part of Pakistan and is 170 km long. On both sides of the river, there are residential settlements. The river hydraulics is studied by using 30-metre remotely sensed shuttle radar topographic mission - digital elevation model (SRTM DEM) and Arc Map. 32 cross-sections are imported from Geographic Information System (GIS) to HEC-RAS. On historical peak flow results, the extreme value frequency distribution is applied, and a flood is determined for a 100-year return period, with a discharge estimated as 2223 cubic metres. Three steady flow profiles are adopted for HEC-RAS, the first is for the maximum historical peak data, the second is for the 100-year return period, and the third profile is for the latter 100-year period with a safety factor of 1.28. With remote sensing-based assessments, the proposed location for a bridge is determined and then verified with a field survey which was physically conducted. The maximum water height estimated in the river is about 4.26 m. This bridge will facilitate about 50 thousand population of Masahan and its surroundings. It will create a shortest link between Khyber Pakhtunkhwa and Azad Kashmir and thus will enhance tourism and trade activities.

Conversão de taxa de infiltração de água em solo com e sem cobertura vegetal usando o modelo de Kostiakov-Lewis

ABSTRACT Soil water infiltration rates are essential for hydrological studies, planning and design of irrigation and drainage systems, among other applications. Various studies have been carried out in plots with and without vegetation cover, aiming to identify the influence of the cover on the water infiltration process in soil. However, a few works have addressed the relationship between infiltration rates of a plot with and without vegetation cover. Here we investigated the ability to iterate between infiltration rates with and without vegetation cover, seeking to identify potential correlations. We propose an innovative and easy-to-use empirical model that allows the conversion of infiltration rates in systems with vegetation cover into infiltration rates without coverage and vice versa. Altogether, we used a dataset comprising 142 rainfall simulation experiments under plots with and without cover, including 6 different types of soil and 18 types of land cover and management. The proposed model was based on the Kostiakov-Lewis model, presenting performance similar to other infiltration models, which is effective in a variety of planting and vegetation cover systems.

AltBlock - Software para determinação de Chuvas de Projeto

A design storm can be defined as a precipitation pattern used to determine indirectly the design flood, which is an important data in many hydrologic design projects. Due to difficult to obtain historical data on precipitation, it has become common to construct design storms using general characteristics of precipitation in the surrounding region, represented by intensity-duration-frequency (IDF) relationships. In addition, it’s also necessary specify design return period, storm duration and the temporal distribution. For this purpose, is used the Alternating Block Method. In the view of software development applied to hydrology and to make more practical using this Alternating Block Method, this article intended to develop the AltBlock, a software able to calculate design storms automatically. Using Visual Studio .NET and the programming language Visual Studio .NET it was sought to build a graphical user-friendly interface. The AltBlock is applicable to any location and it’s useful for educational uses, engineering projects and hydrological studies.

Quantifying uncertainty in multivariate quantile estimation of hydrometeorological extremes via copula: A comparison between bootstrapping and Markov chain Monte Carlo

AbstractThe performance of uncertainty estimation methods, namely bootstrapping and Markov chain Monte Carlo (MCMC), in univariate frequency analysis of hydrometeorological extremes has been well tested in the literature. However, the two methods have not been thoroughly compared for multivariate frequency analysis of such events. In this study, we compare the performance of bootstrapping and MCMC in estimating the uncertainty of bivariate quantiles of extremes as defined by the return period quantiles of hydrologic drought duration and severity, and concurrent meteorological drought and heat wave. Using a copula framework, we analyse the accuracy and size of confidence intervals of the bivariate quantiles, and bias in point estimates of them. We also investigate the performance of the two methods in estimating the uncertainty of univariate quantiles of the marginal distributions of the resulting bivariate copulas. This is to evaluate if any advantage of one method over the other is consistent, whether in estimating the univariate or bivariate quantiles. We conduct this study with synthetic datasets of various sample sizes and predefined distributions derived from a set of empirical data. The results show MCMC to be superior when estimating the uncertainty of bivariate quantiles where the sample size is small (~50). Where the sample size is large (~100 and ~200), the results show bootstrapping to be the better option for estimating uncertainties of bivariate quantiles. For estimating uncertainties of univariate quantiles, bootstrapping is performing better under all investigated sample sizes. Results and conclusions in this study will be beneficial for hydrometeorological risk assessment, hydrologic infrastructure design, and water resources assessment.

A Review on Constructed Treatment Wetlands for Removal of Pollutants in the Agricultural Runoff

Constructed wetland (CW) is a popular sustainable best management practice for treating different wastewaters. While there are many articles on the removal of pollutants from different wastewaters, a comprehensive and critical review on the removal of pollutants other than nutrients that occur in agricultural field runoff and wastewater from animal facilities, including pesticides, insecticides, veterinary medicine, and antimicrobial-resistant genes are currently unavailable. Consequently, this paper summarized recent findings on the occurrence of such pollutants in the agricultural runoff water, their removal by different wetlands (surface flow, subsurface horizontal flow, subsurface vertical flow, and hybrid), and removal mechanisms, and analyzed the factors that affect the removal. The information is then used to highlight the current research gaps and needs for resilient and sustainable treatment systems. Factors, including contaminant property, aeration, type, and design of CWs, hydraulic parameters, substrate medium, and vegetation, impact the removal performance of the CWs. Hydraulic loading of 10–30 cm/d and hydraulic retention of 6–8 days were found to be optimal for the removal of agricultural pollutants from wetlands. The pollutants in agricultural wastewater, excluding nutrients and sediment, and their treatment utilizing different nature-based solutions, such as wetlands, are understudied, implying the need for more of such studies. This study reinforced the notion that wetlands are effective for treating agricultural wastewater (removal > 90%) but several research questions remain unanswered. More long-term research in the actual field utilizing environmentally relevant concentrations to seek actual impacts of weather, plants, substrates, hydrology, and other design parameters, such as aeration and layout of wetland cells on the removal of pollutants, are needed.

Revisiting the Application of Halphen Distributions in Flood Frequency Analysis

Flood frequency analysis is the foundation for hydrological and hydraulic design, and fundamental to frequency analysis is the selection of a suitable probability distribution. This study therefore revisits the Halphen frequency distribution family, with parameters estimated by the maximum likelihood estimation (MLE) method and goodness-of-fit evaluated by the Kolmogorov-Smirnov (KS) test. Besides revising the Halphen family, this study (1) proposes the study of kurtosis to assist the selection of a probability density function; (2) applies the kernel density function as the parent distribution function to evaluate flood risk (using 100-year event as an example); and (3) evaluates the mixed Halphen distribution for the heavy-tailed peak flow falling into the Halphen-B region. Using 198 peak flow datasets selected from 18 hydrologic regions in 48 states in the continental US, the study showed that: (1) Halphen-A/Halphen-B distributions were the preferred distributions for 190 datasets; (2) the moment-ratio diagram was found to be a reliable indicator for selecting the appropriate distribution; (3) kurtosis may be a tool to assist with the distribution selection; (4) comparison and risk assessment indicated that the identified Halphen distributions may properly model the flood frequency distribution; and (5) overall, the study validated the applicability of Halphen distributions for flood frequency analysis.