Journal of Hydrologic Engineering

Continuous-simulation water balance models may be used to study the soil moisture dynamics of small urban areas. These models require as input many soil-texture and land-use-related parameters. Difficulties encountered in determining the values of these input parameters warrant an investigation on their relative importance. In this study, a series of global sensitivity analyses were performed to evaluate the response of selected outputs from a continuous-simulation soil moisture model to variations of specified input parameters. Using randomly generated input parameter values representing various site conditions, the soil moisture model was run with meteorological data from Toronto, Ontario, Canada. Three output statistics, namely, average soil moisture, the standard deviation, and skewness of the output daily soil moisture distributions, were determined from each model run. Four types of sensitivity indices between the output statistics and the input parameters were calculated. Based on these sensitivity indices, it was concluded that the wilting and hygroscopic-point soil moisture levels and the soil moisture level below which plants start to endure water stress are the most important input parameters for all three output statistics. The relative importance of soil’s porosity, saturated conductivity, and the runoff curve number of the study area becomes greater and almost reaches the same level as the most important parameters when the skewness of the output daily soil moisture distributions is the output statistic of interest.

Stochastic rainfall generators are used in hydrologic analysis because they can provide precipitation input to models whenever data are not available, and their parameters are calculated so that the long-term statistics of the synthetic rainfall time series match those of the rainfall records. However, although mentioned in the literature, the relative importance of each rainfall statistic on the watershed response has not been addressed yet, and no guidance on how to account for it has been provided. In this paper, this relative importance is estimated and used to ponder each statistic differently in the calibration of rainfall generators so that it better reflects the watershed hydrology. Rainfall records of 1,249 rain gauges throughout the contiguous United States were used in the study. It was found that when synthetic rainfall time series are generated by weighting the precipitation statistics according to their relative importance, predicted runoff depths and peak flows are underestimated by 4 and 3%, respectively, whereas when they are generated by giving the same weight to all statistics, the underestimation is by 20 and 14%, respectively. These results, based on a significant number of rain gauges, confirm the benefit of weighing the statistics differently for watershed analysis.

Watershed-scale rainfall-runoff models are used for environmental management and regulatory modeling applications, but their effectiveness is limited by predictive uncertainties associated with model input data. This study evaluated the effect of temporal and spatial rainfall resolution on the predictive performance of Hydrological Simulation Program—Fortran (HSPF) using manual and automatic calibration procedures. Furthermore, the effect of automatic parameter estimation on the physical significance of calibrated parameter values was evaluated. Temporal resolutions examined included 15 min, 30 min, 1 h, and 2 h, and spatial resolution effects evaluated included the effect of a spatially averaged network of four rain gauges and Next-Generation Radar (NEXRAD) for selected rain events. Model efficiencies ranged from 0.31 to 0.86 when individual rain gauges (RG71, RG73, RG74, and RG77) were used one at a time. Model efficiency improved and ranged from 0.86 to 0.94 when a spatially averaged network of four rain gauges was used. The effect of temporal resolution on model performance varied with rain gauge location in the watershed and with use of a single gauge or spatially averaged rain gauges for model calibration. Rainfall resolution has a strong influence on parameter estimation because, to achieve high model performance, parameter values must shift whenever the resolution of the rainfall data is changed. Despite a shift in parameter values as a result of changes in rainfall resolution, the results showed that Parameter Estimation Software (PEST)-calibrated values remained within their parameter bounds. In summary, results obtained from a medium-sized Piedmont watershed in Georgia, USA, revealed that model performance was more sensitive to spatial resolution than temporal resolution.

The management of groundwater resources is complex, especially when decisions about the ecological environment and the social environment are considered. In addition to this, economic efficiency should be considered. The solution to these complex decision problems requires the use of mathematical techniques that are formulated to take into account conflicting objectives. With the development and application of coordinate game theory, many models based on the Nash solution exist for groundwater management systems, but there is still a gap in applying them to practical problems. In this paper, a model based on the marginal solution and the satisfaction function is adopted. In order to solve these multiperson and multi-objective decision-making problems, the arbitrating solution, which is the ideal state accepted by those parties, is first deduced by two-stage satisfying programming; and the final satisfying decision is then chosen by the close-degree principle, which is proposed using fuzzy pattern recognition. Additionally, this article uses groundwater system management in the Jinan Spring area as an example to show how the model can be applied. It is shown that this model is easy to solve and feasible to apply.

The prediction of the sediment loading generated within a watershed is an important input in the design and management of water resources projects. High variability of hydro-climatic factors with sediment generation makes the modelling of the sediment process cumbersome and tedious. The methods for the estimation of sediment concentration based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment concentration. The focus of this paper is to present the development of models using Artificial Neural Network (ANN) with back propagation and Levenberg-Maquardt algorithms, radial basis function (RBF), Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir, located in the Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering the statistical properties such as auto-correlation function, partial auto-correlation, and cross-correlation function of the time series. It was found that the M5 model performed well compared to other soft computing techniques such as ANN, fuzzy logic, radial basis function, and REPTree investigated in this study, and results of the M5 model indicate that all ranges of sediment concentration values were simulated fairly well. This study also suggests that M5 model trees, which are analogous to piecewise linear functions, have certain advantages over other soft computing techniques because they offer more insight into the generated model, are acceptable to decision makers, and always converge. Further, the M5 model tree offers explicit expressions for use by field engineers.

In engineering hydrology, a lumped rainfall-runoff model applied to a watershed often requires a calculation of the effective rainfall, which deducts the precipitation losses from the total rainfall contributing to the direct runoff. In the model, these loss elements vary individually with time during a rain storm on a watershed. An estimation of the loss elements or abstractions, such as infiltration, depression storage, evapotranspiration, and interception, for the calculation of the effective rainfall in a watershed presents a practical challenge. Previous research has estimated infiltration and other loss elements in a watershed individually in the case of a storm event. Moreover, to date, these other loss elements have been treated as minor. However, this study proposes a simple model that estimates precipitation losses not individually but by combining the loss components together. A composite loss rate model that combines four loss rates over time is derived as a lumped form of a continuous time function for a storm event. The loss rate model developed is an exponential model similar to Horton’s infiltration model, but its parameters have different meanings. In this model, the initial loss rate is related to antecedent precipitation amounts before a storm event, and the decay factor of the loss rate is a composite decay of four losses. As a case study, a method for estimating the parameters of this model is presented. In the case study, data sets from 10 rain storms observed at the Jeonjeokbigyo stage station in the experimental watershed of Selma-Cheon near Seoul in South Korea are used.

A stochastic kinematic wave model for open channel flow is developed under uncertain channel properties and uncertain lateral flow conditions. Applying a known methodology, the Fokker-Planck equation (FPE) of the kinematic open-channel flow process under uncertain channel properties and uncertain lateral flow conditions is derived using the method of characteristics. Because every stochastic partial differential equation has a one-to-one relationship with a nonlocal Lagrangian-Eulerian Fokker-Planck equation (LEFPE), the LEFPE for the governing equation of any hydrologic or hydraulic process can be developed as the physically based stochastic model of the particular process. To quantify the ensemble behavior of a process, LEFPE provides a quantitative description of the time-space evolution of the probability density function of the state variables of the process at one shot. The nonlocal LEFPE reduces to the classical local FPE, which is more convenient to solve, under certain assumptions. The developed methodology is applied to two test problems under varying channel and lateral flow conditions, and the results are validated by Monte Carlo simulations. The numerical applications show that the developed FPE can express the ensemble behavior of the kinematic wave process under uncertain channel properties and uncertain lateral flow conditions adequately.

This paper presents a characterization of the paradoxical hydrological stationarity problem using long-term precipitation data of geographically sparse gauging stations in California. The metrics of this characterization for a gauging station are given by (1) the average of recorded long-term precipitation; (2) the average precision with which the precipitation can be estimated for a climatological time period; (3) an identifier of the wettest climatological period in the long-term record; (4) an identifier of the driest climatological period in the long-term record; (5) the net-gain or net-loss rate of long-term precipitation; and (6) the interclimatological period variability of precipitation. The paper then presents a step taken to find deterministic outcomes for the paradoxical hydrological stationarity problem by showing that these outcomes may hinge on the transient positions of the Earth and Moon around the sun, as observed in Saros series and cycles of historical solar eclipse trajectories (SETs). Two solar eclipse events that are one Saros cycle apart have the same Saros series; they occur at nodes that share similar geometry with the Moon at nearly the same distance from the Earth and at the same time of year. On the basis of the observation of these cyclic events, this work demonstrates that two different water years with solar eclipse event trajectories separated by one Saros cycle of about 18 years have similar hydrological conditions on Earth, as measured by the precipitation data recorded in California. This finding may significantly improve decadal to multidecadal predictions of hydrological conditions for planners and decision makers. Although these results are likely to have implications for continent and global hydrological variability, they need further validation studies using available data for other locations globally.

Although many studies have evaluated the hydrologic effects of bioretention at the site-level, few have investigated the role bioretention plays when distributed throughout a watershed. This study aims to assess bioretention’s effects on an urbanized watershed in Blacksburg, Virginia by using two modeled scenarios: one where runoff from many land uses was routed through the practice, and another in which only runoff from large impervious areas was routed. Peak flows, volumes, and lag times from these models were compared to the watershed’s current and predeveloped conditions. Both scenarios provided reductions in peak flows with respect to existing conditions for modeled storm events, sometimes to levels below the predeveloped condition. Neither case was able to reduce volumes to predevelopment levels; the option to treat impervious areas had a negligible effect on runoff volume. Both cases were able to extend lag times from the existing development condition. On the basis of these results, bioretention appears to have the capability to improve watershed hydrologic characteristics. Furthermore, only treating impervious areas could be a viable alternative when funds or space are limiting factors.

The occurrence of rainfall events has been significantly linked to waterborne-disease outbreaks in the United States and other parts of the world, indicating that wet-weather events may have significant impacts on microbial risk. An important step in developing a quantitative microbial risk assessment for exposure to pathogens originating from land application of soil amendments is to characterize the risk of storm-induced infiltration and runoff, the hydrologic processes most likely to introduce soil amendment-associated pathogens to source water. This paper develops an approach to estimate the risks of producing sufficient infiltration to saturate the soil to the water table and runoff to surface water on the basis of widely available intensity-duration-frequency curves for rainfall events. An infiltration model is developed to determine the runoff volume and infiltration depth associated with specific storm events. For a given return period the maximum infiltration and maximum runoff (which will typically be produced by different storm events) are then associated with that return period. For a specified return period the maximum runoff is produced by intermediate duration events, as very short storms do not saturate the soil and very long duration storms are of lower intensity and generally do not exceed the infiltration capacity of the soil. Infiltration amounts tend initially to increase with duration and then plateau as longer duration events become less intense. The inverse of the return period of the critical rainfall events provide the probabilities of infiltration and runoff that may introduce fertilizer-associated pathogens to source waters. This approach provides a sound method for determining the storm events with the greatest potential for mobilization of pathogens in the environment and the greatest risk to human health.

The soil and water assessment tool (SWAT) has been calibrated and validated to predict stream flow, and to transport sediment and non-point source (NPS) pollutants to the downstream water resources from a small (1,695 ha) watershed in sub-humid subtropics that receives variable monsoon rains. Observed rainfall, temperature, stream flow, and sediment yield data for three years have been utilized to test the model's prediction capability for daily stream flow and sediment yield during the monsoon months from June to October. Because of the variability of monsoon rains, the model has been calibrated for a normal rainfall year ($\mathrm{M}\mathrm{\text{-}}\mathrm{SD}<\mathrm{RF}<\mathrm{M}+\mathrm{SD}$ and $\mathrm{RF}={1.2}^{*}\mathrm{M}$) and then validated for a relatively dry ($\mathrm{RF}<\mathrm{M}\mathrm{\text{-}}\mathrm{SD}$) and a medium rainfall year ($\mathrm{RF}>\mathrm{M}\mathrm{\text{-}}\mathrm{SD}$ and $\mathrm{RF}<0.8\mathrm{M}$). The results reveal that a calibrated model for a normal rainfall year can be used successfully for predicting hydrologic processes and NPS pollution for a relatively dry rainfall year. However, for the medium rainfall year the model prediction shows more deviations from the measured values. The Nash-Sutcliffe efficiencies in dry and medium rainfall years are 0.70 and 0.62 for daily stream flow and 0.73 and 0.69 for daily sediment yield. NPS pollutants simulation results indicate that a calibrated SWAT model is used in estimating hydrologic responses related to water quality problems of watersheds situated in monsoon regions in which the nature of rainfall shows varying characteristics every year. The results of the study have implications for watershed management to reduce the sediment and NPS pollutants load into downstream water bodies.

The direct integration method is used to compute free surface profiles in gradually varied flow (GVF) along the length of a prismatic open channel. Analytical solutions of the GVF equation, based on the Manning equation, are available in the technical literature only for the special case of triangular and wide rectangular channels. No closed-form (direct) solution is available for this equation for the case of trapezoidal channels. Open channels with trapezoidal cross sections are widely used as drainage, irrigation, urban stormwater, water transmission, and power channels. In the current study, by applying the Manning equation, a semianalytical solution to compute the length of the GVF profile for trapezoidal channels is derived. This solution, which allows an accurate computation of the flow profiles with minimal computational effort and time, should be a useful tool for direct quantitative analysis and evaluations of trapezoidal channels and thus should be of interest to practitioners in the water engineering community.