Journal of Hydrologic Engineering

This paper presents a weather generator that produces new values of precipitation to generate realistic weather sequences. The model has been applied to a network of 14 meteorological stations around the Upper Thames River Basin (UTRB), Ontario, Canada. We developed a simple model that employs the k-nearest neighbor resampling approach with gamma kernel perturbation. This gamma kernel perturbation enables the production of new values rather than merely reshuffling the historical data to generate realistic weather sequences. Daily precipitation was simulated at all the locations in and around the considered basin. The comparison of simulated data to the observed data led to the conclusion that the proposed perturbation algorithm performs quite well at preserving the monthly and annual historical statistics. The improved model was shown to produce precipitation amounts different from those observed in the past record.

Sediment wash-off from impervious land is believed to be related to various factors including sediment buildup, rainfall intensity, rainfall energy, runoff rate, sediment sizes, and land surface characteristics. Most previous studies have used controlled rainfall in small areas to investigate mechanisms of sediment wash-off. This paper presents sediment wash-off results from an impervious urban surface under actual accumulation and rainfall events. Two temporal scales were considered when the sediment wash-off processes were investigated. On the event scale, sediment wash-off is strongly correlated with runoff volume. On the instantaneous scale, sediment wash-off from eight rainfall events was investigated using first-order and zero-order wash-off equations. Source-limited and transport-limited wash-off processes were discussed. The wash-off process on October 10, 2002, was linearly dependent on runoff and can be described using a first-order exponential equation. The wash-off processes on May 15, 2003, and June 7, 2003, can be described using a zero-order equation. For the remaining storms, both first-order and zero-order wash-off equations can represent the wash-off processes.

The probability distributions of Gumbel, three-parameter lognormal (LN3), general extreme values (GEV), three-parameter gamma (G3), and three-parameter log-gamma (LG3), whose parameters are computed by the methods of moments (MOM), maximum-likelihood (ML), probability-weighted moments (PWM), and self-determined probability-weighted moments (SDPWM) are compared from the aspect of predicting the right-tail quantiles of return periods in the range: $10\le T\le \mathrm{10,000}\mathrm{years}$ from finite-length sample series by a Monte Carlo analysis. The parameters of the LN3 distribution are also computed by the method of zero-sample-skewness. Synthetic series of 1 million elements having skewness coefficients: $+0.5$, $+1$, $+2$, $+3$, $+5$ are generated by LN3, GEV, and G3 distributions, separately, resulting in 15 different 1 million-element synthetic series ($=5\text{skewnesses}\times 3\text{distributions}$). The right-tail quantiles having exceedance probabilities (Pex) 0.1, 0.05, 0.02, 0.01, 0.005, 0.002, 0.001, 0.0005, 0.0002, 0.0001 are first computed by the parent distribution. The right-tail quantiles having those Pexs are also computed by these 21 probability models using all $1\mathrm{million}/n$ short series of lengths: $n=20$, 30, 50, 100, 200. Instead of biases and root mean square errors of $1\mathrm{million}/n$ differences of quantiles from those of the parent distribution separately for individual return periods ($T$), (e.g., 100 years, 1,000 years), which has been the usual procedure so far, mean relative differences (${\mathrm{MRD}}_j$s), mean absolute relative differences (${\mathrm{MARD}}_j$s), standard deviations of relative differences (${\mathrm{SDRD}}_j$s), and standard deviations of absolute relative differences (${\mathrm{SDARD}}_j$s) of the areas between the frequency curves of the short series and the frequency curve of the parent distribution over the entire range: $0.0001\le \mathrm{Pex}\le 0.1$ are proposed. Ranked tables of ${\mathrm{MRD}}_j$s, ${\mathrm{MARD}}_j$s, ${\mathrm{SDRD}}_j$s, and ${\mathrm{SDRD}}_j$s computed from $1\mathrm{million}/n$ $n$-element series are investigated as a more comprehensive criterion of goodness of a probability distribution to predict right-tail population quantiles from short-length sample series. The G3-PWM distribution is found to be better, followed by the LN3-MOM, LN3-PWM, G3-MOM, GEV-MOM, and LN3-ML distributions for the ranges covered.

Concepts of risk and uncertainty are commonly used for designing and evaluating flood-related hydraulic structures. A general framework for estimating the uncertainty of the return period and risk is presented based on first-order analysis of uncertainty for determining the variance of the return period and risk of failure and the methods of moments, probability weighted moments, and maximum likelihood. The general method is illustrated using the exponential and Gumbel distributions. The derived variance of the return period is a function of the sample size $N$ and the nonexceedance probability $q$, while that of the risk is function of $N$, $q$, and design life $n$. Simulation experiments were performed to analyze the behavior of the variance of risk for various values of $N$, $q$, and $n$. They showed that the derived variances of the risk can be applicable for a wide range of conditions, particularly for sample sizes bigger than 50 and design lives smaller or equal to 50. An example is included to illustrate the applicability of the proposed concepts and equations.

Rainfall results in the transfer of chemicals from soil to surface runoff. A physically-based solute transport model was developed for estimating the solute concentration in runoff originating from the soil surface. The model accounts for the effects of soil infiltration, raindrops, the water runoff rate, and the return flow, all of which influence the concentration of the solutes in the runoff. It was assumed that the depth of mixing zone changed with the varieties of the raindrop hits, return flow, and overland flow. It was also assumed that runoff and soil in the mixing zone mixed instantaneously and that the solute in the soil beneath the mixing zone was moved to the mixing zone by diffusion. The mixing zone was included in the model and was based on the deposited layer or shield concept. To test the model, laboratory experiments were carried out that used two soil types that were exposed to simulated rainfall. The results simulated by the model were highly correlated with the experimental data. In the first few minutes after rainfall began, the solute concentration in the runoff was mainly controlled by the rainfall rate and solute concentration in the mixing zone; higher solute levels in the mixing zone resulted in higher solute concentrations in runoff. When the solute concentration in the runoff stabilized, the solute concentration in the runoff was mainly controlled by the diffusion of solutes from the soil beneath the mixing zone. The simulated data showed a high level of correlation with the measured data for both runoff volume and solute concentration in the runoff. This demonstrates that the model captured the temporal behavior of the runoff and solute transport in the runoff.

Because of the benefits of solar energy, the number of solar farms is increasing; however, their hydrologic impacts have not been studied. The goal of this study was to determine the hydrologic effects of solar farms and examine whether or not storm-water management is needed to control runoff volumes and rates. A model of a solar farm was used to simulate runoff for two conditions: the pre- and postpaneled conditions. Using sensitivity analyses, modeling showed that the solar panels themselves did not have a significant effect on the runoff volumes, peaks, or times to peak. However, if the ground cover under the panels is gravel or bare ground, owing to design decisions or lack of maintenance, the peak discharge may increase significantly with storm-water management needed. In addition, the kinetic energy of the flow that drains from the panels was found to be greater than that of the rainfall, which could cause erosion at the base of the panels. Thus, it is recommended that the grass beneath the panels be well maintained or that a buffer strip be placed after the most downgradient row of panels. This study, along with design recommendations, can be used as a guide for the future design of solar farms.

A new linear programming method with an option for topographical factors is developed for estimating missing precipitation. It is simply assumed that missing precipitation depth at a base station is expressed as a linear combination of precipitation depths at neighboring index stations in the same period using weighting factors. Also, the topographical factor, which is proportional to the weighting factor, is introduced into the method. The topographical factor is associated with distance and difference in elevation between the base station and the index station. In this research two case studies show an introduction of the topographical factors into the existing linear programming method for estimating missing precipitation and makes weighting factors in the method change into those reflecting the topography of precipitation points. The developed method with an option is useful in estimating the missing precipitation values in the case of hilly regions only when the option is taken after applying four options.

Hydrological effects of land-use change are of great concern to ecohydrologists and watershed managers, especially in the Atlantic coastal plain of the southeastern United States. The concern is attributable to rapid population growth and the resulting pressure to develop forested lands. Many researchers have studied these effects in various scales, with varying results. An extended watershed-scale forest hydrologic model, calibrated with 1996–2000 data, was used to evaluate long-term hydrologic effects of conversion to agriculture (corn–wheat–soybean cropland) of a $29.5\text{-}{\mathrm{km}}^2$ intensively managed pine-forested watershed in Washington County in eastern North Carolina. Fifty years of weather data (1951–2000) from a nearby weather station were used for simulating hydrology to evaluate effects on outflows, evapotranspiration, and water table depth compared with the baseline scenario. Other simulation scenarios were created for each of five different percentages (10, 25, 50, 75, and 100%) of land-use conversion occurring at upstream and downstream locations in the pine-forest watershed. Simulations revealed that increased mean annual outflow was significant ($\alpha =0.05$) only for 100% conversion from forest (261 mm) to agricultural crop (326 mm), primarily attributed to a reduction in evapotranspiration. Although high flow rates $>5\mathrm{mm}{\mathrm{day}}^{-1}$ increased from 2.3 to 2.6% (downstream) and 2.6 to 4.2% (upstream) for 25 to 50% conversion, the frequency was higher for the upstream location than the downstream. These results were attributed to a substantial decrease in soil hydraulic conductivity of one of the dominant soils in the upstream location, which is expected after land-use conversion to agriculture. As a result, predicted subsurface drainage decreased, and surface runoff increased as soil hydraulic conductivity decreased for the soil upstream. These results indicate that soil hydraulic properties resulting from land-use conversion have a greater influence on hydrologic components than the location of land use conversion.

This paper aims to establish design criteria that simultaneously meet (1) the stochastic nature of the streamflow regime in Portuguese rivers and (2) the relationship between such a regime and the mean annual flow depth. The data set consists of 54 streamflow samples from unregulated Portuguese rivers. A large number of synthetic streamflow series were generated by using a probabilistic model at the annual level and a disaggregation model, namely the method of fragments, at the monthly level. Based on an operation with fixed yield and time-based reliability, behavioral analysis was applied to the synthetic series to estimate the storage capacities of hypothetical reservoirs. The Gumbel distribution was fitted to the previous estimates to obtain design storage capacities with specified values of reliability. After showing that the former design capacities were significantly correlated with the mean annual flow depth of the sample series, curves that expressed that relationship were defined. The results consistently showed that, for a given operation condition, as the mean annual flow depth increases, the required specific storage decreases.

Transmissivity and storage estimates are questioned in laterally heterogeneous aquifers as to their representativeness of underlying assumptions in the classical aquifer test methods. For this purpose, the aquifer parameter estimations are obtained by the slope-matching procedure, and their statistical properties are investigated. The change of lateral heterogeneity is obtained with radial distance from the well center. Frequency- and distance-based weighted, harmonic, and geometric averages of aquifer parameter estimations are suggested in addition to the most frequently occurring (mode) estimates. The application of slope method leads to a sequence of aquifer parameter estimations, the arithmetic averages of which correspond to classical type curve match or straight-line results. Because of the lateral heterogeneities in the geological set up, aquifer parameter estimates cannot be constant.

Assessing climate change effects on water resources is the first step in preparing climate change adaptation measures. However, this is often clouded by the large range of uncertainty resulting from a long chain of modeling activities. Despite progress made to improve climate models, downscaling methods, and hydrological models, uncertainties will remain. This paper proposes a framework to propagate and quantify the uncertainty from the different sources that can be applied at the full cascade but focuses on the climate-modeling component, i.e., different climate models and emissions scenarios. This framework is based on the generalized likelihood uncertainty estimation (GLUE) methodology, which is widely used in the hydrologic community but has not been applied as such to climate impact modeling. This paper presents a preliminary application of the proposed framework to the flow of the main Nile at Dongola.

The Mississippi River Flood of 2011 was a record-breaking flood that required the opening of the Morganza Spillway for only the second time since its completion in 1954. The opening brought a large quantity of nitrogen-rich Mississippi River water into the Atchafalaya River, causing widespread inundation in the river corridor wetlands and flood plains. A rapid sampling study was conducted from May 14 through July 20 at the Atchafalaya River’s inflow and outflow locations to determine nitrate transport and retention of the basin during a historic flood event. The goal was to test the hypothesis that riverine wetlands and floodplains in the Lower Mississippi River Basin (LMRB) function as a significant sink for nitrate during an extreme flood event. Based on intensive sampling data, a total nitrate mass of 97,100 Mg entering and 93,500 Mg leaving the Atchafalaya for the 10-week high flood period was estimated. This large quantity of nitrate export represents approximately 54% of the reported long-term average annual nitrate output from the Atchafalaya River Basin. The marginal 4% mass reduction during such a record flood suggests that riverine wetlands and floodplains of the LMRB require substantial engineering to become an effective sink for riverine nitrate.

Side weirs are flow-diversion devices that are widely used in irrigation and drainage systems and combined sewer systems. Side weirs are also used as an emergency structure. They are important parts of distribution channels of irrigation systems. Computation of a water surface profile over side weirs is essential to determine the flow rate of the side weir. No closed-form solution is available for a rectangular side weir located in a U-shaped main channel. The U-shaped channel possesses an important application in sewer systems. This structure is used as a transitional cross section in manholes and in changes from circular to rectangular cross sections. This research presents an elegant closed-form solution for establishing the water surface profile along a side weir located in a U-shaped channel. The solution should be a useful computational tool for evaluation and in the design of rectangular side weirs in sewer systems and U-shaped channels.

Bangladesh is a flood prone country where huge damages take place every year. Therefore, to minimize flood extremes, it is important to control the flood peaks at the upstream area through suitable watershed management practices. The flood control management at the watershed scale requires good quality flood data. However, in developing countries like Bangladesh, such hydrological information is rarely available at the watershed level. Under such circumstances, it is important to use a hydrological model representing the rainfall-runoff process to arrive at the extreme flows in the rivers, which require extreme rainfall data as a major inflow to the hydrologic system. Furthermore, the density of rain gauges in Bangladesh is low and the quality of available flood data is poor. Considering this, it is important to develop regional extreme rainfall maps for the reliable estimation of flood flows in the river by using a suitable modeling approach. Therefore, in the present paper, an attempt has been made to derive the regional best fit extreme rainfall pattern for Bangladesh for the estimation of extreme rainfall quantiles. This study uses the annual maximum daily rainfall of 68 rain gauge stations. An autocorrelation test is applied to test the independency of the data. Later, considering the heterogeneity in the hydroclimatic and topographic details, entire rain gauge stations have been clustered into six hydroclimatically homogeneous regions; namely, northeast (NE), northwest (NW), southeast (SE), southwest (SW), coastal, and central regions, by using the $k$-mean clustering technique. The stations that did not pass the discordant and heterogeneity test were discarded from the regional frequency analysis. For regional frequency analysis, the L-moment method was applied. Based on the $Z_{\mathrm{DIST}}$ goodness of fit test and the L-moment ratio diagram, the generalized extreme values distribution was identified as the best fit for the SE, NW, and coastal regions. However, for NE, central, SW regions, the best fit distributions were generalized logistic and generalized Pareto, respectively. Using the derived distributions, regional extreme rainfall quantiles were estimated, followed by geo-mapping in ArcGIS 9.2.