Journal of Hydrologic Engineering

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Pressure flushing of deposited sediments behind large dam walls causes development of a funnel-shaped crater due to the induced vortex and considerable shear flow around the outlets. Laboratory experiments were conducted to investigate interaction of flow and sediment parameters on the development and final size of the scour cone. The volume of the flushed sediment increases with increase of discharge, and decreases with increase of sediment bulk density and the water level above the sediment. While the water depth over the sediments is the most significant parameter in the collapse of sediments above the intake and the initial development of the cone, sensitivity analysis indicated bulk density of cohesive sediment to be the most effective parameter to determine the final size of the scour cone. Using experimental results, two equations were developed to estimate scour cone volume and length with readily defined and measurable parameters of flow and sediment. The field measurements confirm the capability of the equations for large-scale application and flushing practice.

This paper summarizes the methodology and procedure used in an optimization and statistics computer model developed for determining the water quality capture volume (WQCV) for storm water best management practices (BMP) and low-impact development (LID) facility designs. The WQCV is directly related to the local rainfall pattern, watershed imperviousness, and drain time applied to BMP/LID storage devices. Aided by a computer model, the performance of a LID/BMP basin can be predicted using the local rainfall-runoff continuous simulation that computes the long-term runoff volume-based and event-based capture ratios using the principle of water volume balance among rainfall amount, hydrologic losses, and runoff volume captured in and bypass flow overtopping the storage basin. For a regional study, this procedure can be applied to a range of basin sizes to produce the optimized design value for WQCV. The numerical algorithm used in the computer model offers both runoff volume capture and event capture ratios. Typically, but not always, the optimal runoff volume and event capture ratios lie between the 80 and 90th percentile of the local runoff volume population. The computer model used was developed as freeware for evaluating the performance of a BMP facility or producing regional design charts. The model accepts the standard hourly or 15-min rainfall data format provided by the National Climatic Data Center. Hourly data are typically available for major metro areas in the United States for a period of 20 to 60 years.

Changepoint (CP) detection methods can be applied to a broad range of real-world problems and are recently gaining popularity in hydrologic studies. The hydrology of river basins is impacted by several factors such as changes in land use, operation of water storage and distribution systems, and climate change; and as a result, a hydrologic series may exhibit distributional shifts and nonstationarity. In this paper, a fuzzy Bayesian model is studied for CP detection in hydrologic time series of annual rainfall and streamflow in the Mahanadi River Basin in India. The model is based on a two-step, fuzzy Bayesian formulation, which gives a probability distribution for the location of the CP. The methodology has broad applicability since it does not require making any distributional assumptions for the data. The first step consists of a fuzzy clustering of raw time series which transforms the initial data with arbitrary distribution into data that can be approximated with a beta distribution. The second step uses the Bayesian approach with the Markov Chain Monte Carlo (MCMC) method for CP detection in the transformed time series. The method is applied to annual maximum and annual average streamflow and subbasin rainfall for Basantpur and Hirakud stations on the Mahanadi river in India. The results obtained are compared with those obtained using standard CP detection procedures, such as a modified standard normal homogeneity (SNH) test and Wilcoxon’s nonparametric rank sum test. Both classical and Bayesian CP detection methods used show that the annual streamflow and the annual rainfall have decreased significantly in the Mahanadi Basin, with a possible CP for the Basantpur station between 1975 and 1980 and for the Hirakud station around 1964. The abrupt decrease was most marked in the annual maximum streamflow series for the Basantpur and Hirakud stations and was attributed to the effects of climate change in the upstream regions of the Mahanadi River.

This study evaluates the performance of three Budyko-type equations (Fu’s equation, Turc-Pike’s equation, and Milly’s equation) in modeling annual evapotranspiration in 32 watersheds covering both humid and arid regions in Northern China. Daily meteorological data and monthly runoff data are used to calculate potential and actual evapotranspirations in the 32 watersheds. The results show that the Budyko-type equations are adaptive in predicting annual evapotranspiration over most of the watersheds, and Fu’s and Turc-Pike’s equations perform better than Milly’s. In addition, the validity of the framework by Koster and Suarez in predicting the evapotranspiration deviation ratio (EDR) (i.e., the ratio of the standard deviation of evapotranspiration to the standard deviation of rainfall) based on Fu’s and Ture-Pike’s equations is also examined. Given the unexpected Nash–Sutcliffe efficiency values ($-0.915$ and $-1.026$ in Fu’s and Ture-Pike’s, respectively), a linear one-variable model is employed to improve the accuracy of the EDR estimation. Two revised EDR estimation equations are developed in two cases: one includes and the other excludes the three humid watersheds on the basis of the original framework. The results show that the first revised equation may be applied to both humid and arid watersheds, whereas the second revised equation is more appropriate in calculating the EDR for arid watersheds.

The Rainfall Frequency Atlas (TP40) was last updated for Kansas in 1961, using weather data from 1911 to 1958. Rainfall information contained in the atlas is the basis for important engineering and hydrologic design decisions in the state. With growing concern about the effects of global climate change and predictions of more extreme weather events, it is necessary to explore rainfall distribution patterns using the most current and complete data available. In this study, extreme rainfall frequency was analyzed using daily precipitation data (1920–2009) from 24 stations in Kansas and 15 stations from adjacent states. The Weibull distribution was used to calculate the precipitation probability distribution frequency at each station. Weather station point data were spatially interpolated using kriging. The overall analysis showed an increase in extreme precipitation events in Kansas with extreme event values tending to increase in magnitude from the northwest to southeast part of the state. Comparing results of the original TP40 analysis to the last of three study periods (1980–2009) showed that approximately 84% of the state had an increase in short-term rainfall event magnitudes. Long-term event magnitudes were predicted to be less than those reported earlier, but have increased over time, most likely because of the short data period used to calculate the TP40 precipitation probability distribution frequency. Results show a shift in rainfall distribution patterns in Kansas across both time and space. This shift changes the design criteria for water management systems, both in runoff control and storage structures.

In recent years, flood-related risk has been increasing worldwide, being inundations among the natural disasters which induce the maximum damage in terms of economic losses. In the research reported in this paper, a methodology to map the flooding residual hazard due to levee failure events induced by piping in embankments protecting flood-prone areas is proposed. Ensemble simulations are used to account for uncertainties in location, geometry, and time-evolution of the levee breaches. Probabilistic flooding-hazard maps are generated combining the results of 192 inundation scenarios, simulated by using one-dimensional (1D) and two-dimensional (2D) hydrodynamic models. The methodology is applied considering 96 different locations and sizes of breaches occurred along a 23-km reach protected by the right levee of the Po River, the right levee of the Taro River, and the left levee of the Parma River, which delimit a $100\text{-}{\mathrm{km}}^2$ study area. The influence of obstacles to the flood propagation and consequent hazard-mapping was investigated, taking into account several standard criteria to map the flooding hazard in different European countries. The results obtained from the research reported in this paper provide an example of a rational method to map the residual hazard in areas protected against flooding due to embankment-overtopping, taking into account uncertainties due to breach location, levee fragility, and the presence of topographic obstacles. All these details should be taken into account in land-planning and flood-risk management.

Highly uncertain precipitation input greatly reduces the overall reliability of rainfall-runoff modeling for estimating stream flows, which in turn forms the basis for decisions leading towards sustainable management of water resources. Probabilistic weather forecasting using multiple numerical weather prediction (NWP) models known as ensemble prediction system (EPS), has gained popularity amongst meteorologists in recent years. However, keeping in mind the computational burden and complexities of hydrologic modeling, scientists are constrained to use a limited number of ensemble members to drive the operational river flow forecasting systems. In this paper, 12 weather ensembles of different size are evaluated for their performance in forecasting occurrence of precipitation over a wet month in a large New Zealand catchment by comparing them against point observations of the corresponding events at rain gauge stations. The authors investigate various configurations of 12-h accumulated precipitation forecasts including members from the grand global ensemble consisting of 179 member forecasts issued at eight data centers connected through the observing research and predictability experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) network. The ensembles are developed using four different techniques including a one-dimensional (1D) clustering approach to group the members into clusters of different sizes in order to study the impact of ensemble size on precipitation forecasting with lead time of 10 days. This approach groups the members into different clusters by minimizing the sum of square distances between the members and the cluster centre. The results indicate that there is generally little improvement in the forecast skill by increasing the ensemble size, and some smaller ensembles can reasonably replace the grand global ensemble without significantly compromising the overall quality of the probabilistic forecast for precipitation occurrence. The results further infer that the grand control ensemble (GCE) combining the control forecasts from all the centers is as good a candidate as the grand global ensemble despite being much smaller in size.

Taking Suxichang and Hangjiahu urban agglomerations in Taihu Lake Basin as study areas, the authors explore differences in annual precipitation, flood season precipitation, annual maximum daily precipitation, and rainfall days of different types between cities and suburbs from 1961 to 2006. The authors also investigate the trends of precipitation change by means of Mann-Kendall test and rescaled range analysis. The results show that the amount of precipitation increased with the level of urbanization at spatial and temporal scales. The maximum daily precipitation and the number of rainstorms in urban areas also increased. With further development of urbanization, the increase would continue in the future. The two urban agglomerations with similar level of urbanization and climatic conditions are consistent in terms of all research indicators applied in this paper. The results are basically consistent with many previous studies, while the study area has its own characteristics in that the maximum rainfall generally happened after the plum rains, and that rainstorms often occurred later than they did in other regions. The results above suggest that urbanization has an effect on precipitation, and this is a matter of concern for the cities located in the East Asian Monsoon Region with its heavy rainfall.

In order to improve the accuracy of real-time flood forecasting, a new accurate and efficient real-time flood forecasting error correction method based on a dynamic system response curve (DSRC) is developed. The dynamic system response curve was introduced into the flood forecasting error correction to establish the dynamic error feedback updating model tracing the source of the error. In this study, the flow concentration of the Xinanjiang (XAJ) model is generalized into a system. The physical basis of the system response curve is the flow concentration of the hydrological model. The theoretical basis of the concept is the differential of the system response function of the runoff time series. Based on the observed and calculated discharge, the calculated runoff series was corrected using least-squares estimation, and then the flow was recalculated with the corrected runoff. The Xinanjiang model was selected to calculate runoff. The method was tested in both an ideal scenario and in a real case study. The proposed method was applied to 26 floods in the Wangjiaba basin. The ratio of qualified flood increased from 65.4 to 92.3% after correction by the DSRC. Comparison with the second-order autoregressive error forecast model [AR(2)] shows that the method can improve the forecasting results effectively. The method has a simple structure, the performance indices will not deteriorate as the forecasting period (i.e., lead time) increases, and the method does not increase the number of model parameters.

In this paper, genetic programming (GP) was used to simulate outflow hydrograph from earthen dam breach. The required data for the modeling were collected from literature, new experimental tests, and a physically based model. A number of homogeneous cohesive and noncohesive embankments were constructed in the flume and breached by overtopping flows. In the experiments, 40 embankments were constructed and tested in various sizes with different materials. The results demonstrate that the results of the GP method are in good agreement with the observed values. The model was tested for a case study (Teton Dam). In addition, an experimental-based broad-crest weir (EXBCW), a lumped model for calculating the peak outflow discharge due to an earth dam breach, was proposed on the basis of the governing equation of broad-crest weir outflow and experimental data. Finally, the results of the EXBCW model were compared with the observed data and results of empirical and GP-based methods.

Area reduction factors (ARFs), which are used to convert estimates of extreme point rainfall to estimates of extreme area-averaged rainfall, are central to conventional flood risk assessment. Errors in the estimation of ARFs can result in large errors in subsequent estimates of design rainfall and discharge. This paper presents a critical examination of commonly used ARFs, particularly those from the U.S. Weather Bureau TP-29, demonstrating that they do not adequately represent the true properties of extreme rainfall. This lack of representativeness is due mainly to formulations that mix rainfall observations from different storms and different storm types. Storm catalogs developed from a 10-year high-resolution radar rainfall data are used set to estimate storm-centered ARFs for Charlotte, North Carolina. Storms are classified as either tropical or nontropical to demonstrate that storm type strongly influences spatial rainfall structure. While there appears to be some relationship between ARF structure and areal rain rate, basin-specific ARFs for the five largest storms from 2001 to 2010 in Little Sugar Creek in Charlotte do not show any systematic deviation from the larger population of storms. Given the challenges presented in this paper as well as other difficulties associated with ARF estimation, the authors suggest that research and practice should shift toward more robust methods, such as stochastic storm transposition, that incorporate realistic representations of the spatial and temporal structure and variability of extreme rainfall and its interactions with watershed surface, subsurface, and drainage network properties into flood risk estimation.

Water resources globally face unprecedented challenges, but these are at their greatest in the world’s arid and semiarid regions. Groundwater plays an important role in the economic development and ecological balance of the arid area of southeast Tunisia. This paper presents the potential to use saline water in sustainable agricultural production in arid southeast Tunisia and the development of a numerical model of groundwater flows for the aquifer system of the El Hicha aquifer. It presents the quantity and quality of saline water resources data and the integration of poverty indicator information with saline water resources. The numerical model constitutes the main method used to solve the groundwater quantity management problems and to evaluate the groundwater flow mixing between different aquifer levels. A groundwater numerical model for El Hicha Basin, an arid area of southeast Tunisia, was developed using MODFLOW software, a Geographic Information System (GIS)-based package, to simulate regional groundwater changes under steady-state and transient conditions. The results from calibration of the model show reasonable agreement between observed and calculated water for the observation wells. The reliability of the model is tested by a long series of historical groundwater monitoring data, and the model is then used to predict the impact of groundwater exploitation until 2020 and 2050. It is shown a significant drawdown and an enhanced seawater intrusion in the El Hicha aquifer. Under current water resources management conditions, groundwater levels in the El Hicha aquifer are in a continuous drawdown trend. It is necessary to take measures to reduce groundwater exploitation to protect the ecological environment.

This paper deals with ground response to surface water infiltration from an aquifer storage and recovery facility in Arizona. It presents a coupled groundwater flow–soil deformation model to investigate the role of low-hydraulic conductive materials (LHCMs), such as clays and silts, on the spatial and temporal flow and ground response. Finite element analyses were conducted that simulated the flow from surface injection ponds into the ground with and without LHCMs. The results showed that the presence of LHCMs significantly influences the flow path and the ground surface deformation profile.

This paper describes a case study of event and continuous hydrologic modeling in the Kelani River basin in Sri Lanka using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC–HMS). An extremely high rainfall event in November 2005 was used to calibrate model parameters, and extremely high rainfall events in April–May 2008, May–June 2008, and May 2010 were used to validate the event model. The calibrated, direct runoff and base flow parameters were then used in the continuous hydrologic model. The Green and Ampt infiltration loss method was used to account for infiltration loss in event-based modeling and a five-layer soil moisture accounting loss method was employed in continuous modeling. The Clark unit hydrograph method and the recession base flow method were used to simulate direct runoff and base flow, respectively. The results depict the capability of HEC–HMS to reproduce streamflows in the basin to a high accuracy with averaged computed Nash–Sutcliffe efficiencies of 0.91 for event–based simulations and 0.88 for continuous simulations. Simulated river flows affirm that the event-based hydrologic modeling supported by intensive field data is useful to derive calibrated parameters for continuous hydrologic modeling. The study demonstrates potential HEC–HMS application in disaster mitigation, flood control, and water management in medium-size river basins in tropical countries.

The partial duration series (PDS) model is preferable for hydrological frequency analysis when dealing with values exceeding a certain threshold, and the model is capable of capturing more information about extreme events than the rival annual maximum series (AMS) model. However, the use of PDS in hydrological application encounters the difficulty of determining the exceedance threshold attributable to physical and statistical complexity. An inverse relationship exists between the threshold value and the number of peaks selected above a threshold, and a fixed number of average peaks per year is an option to define the threshold value. Additionally, a PDS is generated under the assumption that the series is best described by a generalized Pareto (GP) distribution. To date, the question arises as to how many peaks should be appropriate for modeling the PDS. This paper investigates the optimum number of peaks for integer values on the basis of the variability of the GP/PDS parameters with an increase in the number of peaks. This paper uses daily rainfall data from 36 stations across the North Island region of New Zealand, with record length varying between 31 and 105 years.

This study applied the Soil and Water Assessment Tool (SWAT) to the Neshanic River watershed, a suburban watershed with mixed land uses in Central New Jersey, United States. The urban lands were prominently in low-density residential development. The simulated streamflow and water quality parameters including total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) were compared to those of the measured values in the watershed. The Nash-Suttcliffe efficiency was 0.60 for daily streamflow and 0.68 for monthly streamflow during the calibration period 1997–2002, and 0.37 and 0.69 during the validation period 2003–2008, respectively. The model satisfactorily simulated the streamflow, but underestimated the streamflow variability (i.e., the flashiness of this suburban watershed). The measured water quality data at seven locations in the watershed were in general agreement with their corresponding simulated values, but the agreement varied by pollutant. The observed and simulated values show the most consistent agreement in TN, followed by TP and TSS. With the embedded algorithms and parameters used to simulate the hydrological and water quality responses in urban lands, the SWAT model reasonably simulated the hydrological and water-quality conditions in this suburban watershed. The modeling results confirmed that both TSS and TP were water-quality concerns under the current land-use and management conditions in the watershed. Management measures should be implemented to achieve 9% load reduction for TSS and 49% load reduction for TP to meet the required water-quality standards in the watershed.

Climate change scenarios generated by general circulation models (GCMs) have too coarse a spatial resolution to be useful in planning disaster risk reduction and climate change adaptation strategies at regional to river/lake basin scales. This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely, single conjunctive rule learner, decision table, M5P model tree, decision stump, and REPTree. Downscaling models are developed to obtain projections of mean monthly maximum and minimum temperatures (Tmax and Tmin) as well as pan evaporation to lake-basin scale in an arid region in India using these algorithms. The predictor variables, such as air temperature, zonal wind, meridional wind, and geo-potential height, are extracted from the National Centers for Environmental Prediction (NCEP) reanalysis data set for the period 1948–2000 and from the simulations using third-generation Canadian coupled global climate models for emission scenarios for the period 2001–2100. A simple multiplicative shift was used for correcting predictand values. The performances of various models have been evaluated on several statistical performance parameters such as correlation coefficient, mean absolute error, and root mean square error. The M5P model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. An increasing trend is observed for Tmax and Tmin for emission scenarios, whereas no trend has been observed for pan evaporation in the future.