Journal of Hydrologic Engineering

This paper presents a two-step process to estimate the fraction of an urban watershed covered by a hydraulically effective impervious area. The first step applies maximum likelihood classification of fine-scale multispectral satellite imagery to derive urban land cover. The second step uses an automated macro in a geographic information system to trace the water flow path from pixels classified as impervious and subclassify them as noneffective or effective. The two steps were verified independently, with verification of the second step using idealized data. The two-step process was then tested with a small watershed study of model calibration and rooftop connectivity impact on runoff. At the watershed scale the land cover classification differences were approximately 6%, while at the pixel scale matches of 50, 60, and 83% were achieved for the rooftop, asphalt/concrete, and vegetation land covers, respectively. The effective impervious area was estimated to comprise 16% of the watershed surface, which was close to the actual value of 22%. At the pixel scale, the effective impervious area match was less accurate at 48%. Differences in both land cover and effective impervious area classification at all scales are attributed to high land surface heterogeneity, data limitations and errors, and tree canopy covering impervious surfaces. The verification tests and runoff simulations validate the method as a useful means to rapidly estimate with reasonable accuracy an essential urban hydrologic model parameter.

The accuracy of flood quantile estimates is constrained by the data available at a site. To improve the accuracy of quantile estimators, Bulletin 17B recommends combining the station skew with a regional skew using the inverse of their mean square errors (MSEs) as weights. While these weights can yield the minimum MSE skewness estimator, they do not provide the minimum MSE quantile estimators except when the true at-site skew is zero. In this paper, optimal weights which provide minimum MSE quantile estimators are derived. A Monte Carlo experiment illustrates the value of different weighting schemes and the value of using an informative regional skew. For reasonable values of the regional skew, the MSE of quantile estimators is reduced when the sample skew is combined with an informative regional skew. Modest improvements in the MSE of quantile estimates are obtained using optimal quantile weights rather than the MSE-skew weights. When the regional skew is actually very informative, there is a large loss of efficiency for positively skewed populations when either weight is incorrectly computed using a regional skew estimation error of 0.302 as recommended by the map in Bulletin 17B. Approximations for the MSE, variance, and bias of sample skewness estimators are provided with accuracies on the order of $\pm 0.1\%$ . In addition, a factor for unbiasing skewness estimators for $\mid \gamma \mid ⩽1.414$ is developed for use in regional skew studies.

The present study was carried out to develop generalized artificial neural network (GANN) based reference crop evapotranspiration models corresponding to FAO-56 PM, FAO-24 Radiation, Turc, and FAO-24 Blaney–Criddle methods. The generalized ANN models were developed using the data from four California Irrigation Management Information System (CIMIS) stations, namely, Davis, Castroville, Mulberry, and West Side Field Station. The average weighted standard error of estimate (WSEE) for the developed models, namely, GANN (4-5-1), GANN (3-4-1), GANN (5-6-1), and GANN (6-7-1) corresponding to the FAO-24 Blaney–Criddle, FAO-24 Radiation, Turc, and FAO-56PM was 0.72, 0.85, 0.63, and $0.48\mathrm{mm}{\text{day}}^{-1}$ , respectively. The developed ANN models were applied at 2 CIMIS stations namely, Lodhi and Fresno, without any local training. The average WSEE for models GANN (4-5-1), GANN (3-4-1), GANN (5-6-1), and GANN (6-7-1) was 0.68, 0.71, 0.65, and $0.46\mathrm{mm}{\text{day}}^{-1}$ , respectively In addition, the GANN (4-5-1) model corresponding to FAO-24 Blaney–Criddle was directly applied to four Indian locations, namely, Hoshangabad, Gwalior, Jabalapur, and Pendra. The model gave the average WSEE of $0.57\mathrm{mm}{\text{day}}^{-1}$ . Based on the results it was concluded that the GANN models can be used directly to predict evapotranspiration (ETo) under the arid conditions, since they performed better than the conventional evapotranspiration (ETo) estimation method.

A hydrological model (SWAT) is used to quantify some of the indicators that make up the analysis matrix of a multicriteria decision support system (DSS). The analysis concerns the possible construction of a second, new dam on the Caia catchment in southern Portugal, its connectivity to an existing dam, and the impact of a hypothesized climate change scenario. We describe the definition of the decision criteria for the Caia problem, the linking of a reservoir operation module to SWAT (needed to transform SWAT output into DSS indicator values), an improvement to SWAT’s weather generator for the future climate scenario, the data processing and model simulation effort, and the use of various DSS functionalities to analyze the three options (existing dam and reservoir, new dam with reservoir hydraulically connected to the existing one, two reservoirs not hydraulically connected) and the two scenarios (current and future climate). This example of the conjunctive use of a distributed simulation model and a multicriteria DSS illustrates how these tools can interact in providing decision support for a multifaceted water resources management problem.

Estimation of antecedent wetness conditions is one of the most important aspects of storm rainfall-runoff modeling. This study investigated the use of observations of near-surface soil moisture carried out in a small experimental plot to estimate wetness conditions of five nested catchments, from 13 to $137{\mathrm{km}}^2$ in area, in Central Italy, including the plot itself. In particular, the relationship between the observed degree of saturation, ${\theta }_e$ , and the potential maximum retention parameter, $S$ , of the soil conservation service-curve number (SCS-CN) method for abstraction was investigated using 15 rainfall-runoff events (ten for calibration and five for verification) that occurred in the period 2002–2005. Two antecedent precipitation indices (API) and one base flow index (BFI) were also considered for the estimation of wetness conditions. When interpreting $S$ as the mean soil water deficit of the catchment, an inverse linear relationship with ${\theta }_e$ was found with the coefficient of determination decreasing with catchment area, but still significant for the largest catchment. On the contrary, the reliability of regression increased with catchment area when BFI was employed. Both API indices led to poor results for all investigated catchments. The accuracy of the modified SCS-CN method, i.e., incorporating ${\theta }_e$ for the estimation of $S$ , coupled with a geomorphological unit hydrograph transfer function, was tested in simulating the catchment response. Assimilating the observed soil moisture in the rainfall-runoff model, both the runoff volume and the peak discharge were well predicted with average Nash–Sutcliffe efficiency greater than 90% in the verification phase.

Confluence zones in rivers are common occurrences in natural rivers, and their flow structures have a great impact on sediment transport and pollutant dispersion in confluence regions. Previous studies mainly focused on two-dimensional properties of the separation zone, and the studies of the variation of the separation zone from the water surface to the bottom are limited. Based on the confluence experiments carried out in a junction flume, the detailed characteristics of the separation zone were analyzed by using the commonly used streamline and the velocity isoline first proposed in this study, respectively. The velocity isoline method is conducted by the longitudinal velocity streamline considering its great impact on flow structure. Based on the analysis of the selected data from experiments, the geometry and the tendency of the separation zone present different forms with variations of distance to the flume bottom. Further, the discharge ratio also influences the separation zone from bed bottom to surface.

Quantile estimation in rainfall/flood frequency analysis is very important in engineering design of water infrastructure. Many existing methods are based on parametric modeling with the assumption that the underlying probability distribution is known a priori. The estimation performance hence relies largely on the assumed distribution of the observations in addition to the historical measurements. If the distribution is not appropriate to describe the observations, the estimated parameters are prone to large errors. In this paper, artificial neural network and fuzzy logic based methods are used to obtain quantile estimates which avoid the difficult problem of distribution determination while increasing the accuracy of the estimated quantiles. A complete comparison with the conventional parametric methods is given through realistic annual maximum daily rainfall data and Monte Carlo simulations for various sample sizes. The results demonstrate that the artificial neural network techniques yield higher accuracy in quantile estimation than the conventional parametric methods for all sample sizes, particularly in the upper tail region of the frequency curve.

The upper White Nile Basin above Malakal, Sudan, is considered to be one of the most complicated and diverse hydrologic settings on Earth. Accurately depicting and predicting the streamflow at Malakal is essential for water managers considering Nile Basin-wide initiatives and potential large-scale projects. Dynamical, statistical, and combination models are assessed for their ability to predict monthly streamflow at Malakal. The dynamical model represents a lumped parameter, average-monthly water balance, whereas the statistical model incorporates a nonparametric approach based on local polynomial regression, utilizing principal components of precipitation and temperature. The combination of dynamical and statistical models through linear regression produces model weights of 0.44 and 0.59, respectively, implying a relatively balanced influence. Evaluation of the combination model demonstrates significant overall skill (correlation coefficients equal to 0.83), outperforming either individual model for the validation periods selected. Peak streamflow analyses of timing and quantity also exhibit superior performance by the combination model. An ensemble approach, practical for planning and management from a probabilistic standpoint, is additionally demonstrated.

Floods spreading through reservoir spillways are usually generalized into a first-order nonlinear ordinary differential equation in engineering. With the classical Runge–Kutta–Fehlberg (RKF) method, ignoring the local stability of the numerical solution, a Runge–Kutta–Fehlberg–Reverse (RKFR) method is advanced and employed to route reservoir floods. In the improved method the step length will further be rectified following the RKF using a local stability parameter, which is based on the fact that as the local truncation error is small enough, the local stability might be approximately characterized by the relative error between the initial dependent variable associated with every step and the feedback that may be calculated through a Runge–Kutta (RK) method. The method presented is first verified with two numerical examples, and the results are more accurate and stable with comparable computed speed compared with the RKF method. Subsequently, the RKFR is coupled with the reservoir flood routing program, and tested with a flood control decision support system of the Huanren–Huilong reservoir group in Hunjiang river basin in Northeast China. The testing results demonstrate that it is more scientific and reliable than the RKF and RK methods.

In this technical note, a nonstandard explicit integration algorithm (EIA) developed by Ramos in 2007 is used to solve the Green and Ampt infiltration equation. EIA is a nonlinear one-step method to solve initial-value problems and yields second-order accuracy. The EIA solution is tested using literature data and the estimated cumulative infiltration is compared with measured, an implicit and two explicit solution techniques for dry and wet soil conditions. The EIA solution estimates cumulative infiltration closer to the measured than the other methods and the relative percentage errors ranged from $-5.5$ to 4% and from $-1.9$ to 6.5% for dry and wet soil conditions, respectively. The higher values of Nash–Sutcliffe coefficients (closed to 1) in the case of EIA reveal that the model is more efficient than the other methods for both wet and dry soil conditions. Further, the effect of time step size on the EIA solution is tested and found that Ramos’ EIA is a promising tool for solving Green Ampt infiltration equation using small (time) step sizes.