Journal of Hydrologic Engineering

Most storm water numerical models assume that the entire watershed area is covered by the design storm and shall be considered as the tributary area to the design point. Care must be taken when simulating storm runoff generation from a large watershed because the rain storm may only cover a portion of the watershed. Because the area-averaged rainfall depth decays with respect to the storm-cover area, the experience of the larger the watershed, the higher the flood flow is no longer true. In this study, a storm centering technique is developed to identify the conservative size of a storm cell so that the design runoff rate and volume can be maximized among various locations of a storm center. Without any storm water detention in the watershed, the product of a tributary area and area-weighted rainfall depth serves as the indicator of runoff accumulation through the waterway. When taking detention basins into consideration, the effect of flow attenuation is converted into an equivalent tributary area that is used to maximize the runoff volume at the design point. This maximization procedure has been tested by the Lower Detention Basin designed and built in Las Vegas. The method is simple, but sensitive enough to identify the critical storm size for conservative designs.

On the basis of eddy covariance measurements, the results of three years (2005–2007) of direct evapotranspiration (ET) measurements over a mixed temperate forest in Northeast China are presented. The diurnal and seasonal variations in ET and its main driving factors were analyzed. Annual ET values for the forest were 437, 506, and 632 mm in 2005, 2006, and 2007, respectively. The contribution of ET during the dormant season was not negligible, ranging from 17 to 23% of the annual ET for the study years. On an annual course, the increase in ET is associated with the increasing air temperature ($T_a$) and plant growth in late April and early May, peaking in July or August with monthly mean rates of $2.9\mathrm{mm}{\mathrm{day}}^{-1}$ (July 2005), $3.4\mathrm{mm}{\mathrm{day}}^{-1}$ (July 2006), and $3.8\mathrm{mm}{\mathrm{day}}^{-1}$ (August 2007). Priestley-Taylor parameter $\alpha$ also varied seasonally, with its minimum and maximum values occurring in the dormant and growth seasons, respectively. During the growth season, the values of $\alpha$ were generally between 0.6 and 0.9, indicating that the vapor pressure deficit was the main factor affecting ET. In addition, canopy conductance ($g_c$) also drove the ecosystem ET. Our results show no significant soil water stress in the growth season at the study site. On an annual basis, the ratio of ET to precipitation was 74.3%, indicating that ET was the main water-loss component of water balance in the temperate forests of Northern China.

This paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using gene expression programming (GEP) to perform symbolic regression. The GEP multimodel combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments. The four selected models for the multimodel combinations are the linear perturbation model (LPM), the linearly varying gain factor model (LVGFM), the soil moisture accounting and routing (SMAR) model, and the probability-distributed interacting storage capacity (PDISC) model. The first two of these models are black-box models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical locations and hydrological and climatic conditions are used to test the performance of the GEP combination method. The results of the model using the GEP method are compared with the original forecasts obtained from the individual models that contributed to the development of the combined model by means of a few global statistics. The findings show that a GEP approach can successfully be used as a multimodel combination method. In addition, the GEP combination method has the benefit over other hitherto tested approaches such as an artificial neural network combination method in that its formulation is transparent, can be expressed as a simple mathematical function, and therefore is useable by people who are unfamiliar with such advanced techniques. The GEP combination method is able to combine model outcomes from less accurate individual models and produce a superior river flow forecast.

Accurate estimation of sediment loads is important for the management and construction of water resources projects. In the first part of this study, the convenient gene expression programming (GEP), neuro-fuzzy (NF), and artificial neural network (ANN) techniques were applied to estimate suspended sediment loads by using recorded daily river discharge and sediment load data. These models were compared with one another in terms of the coefficient of determination, root mean square error, mean absolute error, variance accounted for, and Nash-Sutcliffe statistic criteria. It was found that the GEP model performed better than the NF and ANN models. In the second part of this study, the discrete wavelet conjunction models with convenient GEP, NF, and ANN techniques were constructed and compared with one another. Comparison results indicated that the wavelet conjunction models significantly increased the accuracy of single GEP, NF, and ANN models in suspended sediment estimation. The wavelet-GEP model performed better than the wavelet-NF and wavelet-ANN models.

This paper presents an investigation of the role of climatic variability on interannual groundwater and streamflow variability in the southeast United States. For this purpose, streamflow and associated groundwater levels are analyzed for 20 basins that are minimally affected by reservoirs and groundwater pumping. Using the spatially averaged monthly precipitation time series obtained from the Precipitation-elevation Regressions on Independent Slopes Model (PRISM), this paper identifies the recharge and discharge periods that influence the groundwater levels during the winter [January, February, March (JFM)] and summer [July, August, September (JAS)] seasons. Recharge-discharge dependency analyses indicate that precipitation during the previous 3 months influences the groundwater level in a given month. Streamflow in any given month depends on the groundwater level during the previous 3 months. Principal component analysis (PCA) on the precipitation, temperature, streamflow, and groundwater data indicates that groundwater levels and streamflow are the two dominant variables influencing the basin hydroclimatology. Furthermore, relating the percentage variance explained from the PCA to baseflow index (BFI) clearly shows that basins with high BFI have higher eigenvalues, indicating that groundwater is a spatial integrator of hydroclimatic processes. Relating the groundwater levels with El Niño/ Southern Oscillation (ENSO) index, NINO3.4, shows that interannual variability in JFM groundwater levels could be partially explained by the ENSO conditions, but the relationship between JAS groundwater levels and JAS NINO3.4 is not statistically significant. Precipitation forecasts from the ECHAM4.5 general circulation model indicate that it is possible to quantify groundwater availability during the winter season on the basis of the forecasted precipitation and ENSO conditions.

Federal guidelines specify the log-Pearson Type III (LP3) distribution as the basis for the estimation of the 100-year peak discharge that serves as the regulatory flood for floodplain management in the United States. The LP3 distribution has been shown to provide a reasonable and flexible model of flood risk. Use of the LP3 distribution involves a number of explicit assumptions about the annual maximum series that are often implicitly assumed but not shown directly to be satisfied in flood frequency analysis studies of individual stations. In this case study, the validity of these assumptions for the Red River of the North at Grand Forks gaging station is examined. None of the four examined assumptions are satisfied completely. The stationarity assumption is not met because the regional climate samples two separate climatic modes, the peak discharge time series is not a random and independent set of events, the precipitation—runoff relationships are not uniform over time, and the flood peaks result from two flood-causing mechanisms. It is recommended that the validity of the explicit assumptions of the LP3 distribution be demonstrated directly as a standard step in flood frequency analyses, both as a means of advancing estimation of the regulatory flood and as a way to better inform decisions by floodplain managers and decision makers.

In this study, a Bayesian, inference-based, Markov chain Monte Carlo (MCMC) method coupled with an autoregressive moving average (ARMA) error model framework was used to assess the uncertainty of the process-based, continuous, distributed hydrological model HYDROTEL when simulating daily streamflows. The uncertainty analysis was performed, as a case study, in two distinct watersheds (Montmorency, Quebec, Canada, and Sassandra, Ivory Coast, West Africa). The MCMC uncertainty analysis showed to be effective, primarily with respect to the fulfillment of the statistical assumptions of the error model. The results of the uncertainty analyses demonstrated that almost 95% of the observed daily outlet flows were bracketed by the 95% prediction uncertainty bands. This indicates that the parameter uncertainty associated with the ARMA error model could reach the prediction uncertainty. It was possible to mimic the prediction uncertainty using only the most sensitive model parameters for the Montmorency and Sassandra watersheds. The uncertainty framework, as presented herein, may be applied to any distributed, continuous, hydrological model such as HYDROTEL. Given sufficient computational resources, such framework could become quite useful in providing uncertainty bands within the scope of predicting inflows to reservoirs for subsequent planning and management purposes.

A hybrid rainfall-runoff model that integrates artificial neural networks (ANNs) with Xinanjiang (XAJ) model was proposed in this study. The writers extracted the digital drainage network and subcatchments from digital elevation model (DEM) data considering the spatial distribution of rain-gauge stations. Then the semidistributed XAJ model was established based on DEM. Considering the runoff routing cannot be calculated by the linear superposition of the route runoff from all subcatchments, artificial neural networks as effective tools in nonlinear mapping are employed to explore nonlinear transformations of the runoff generated from the individual subcatchments into the total runoff at the entire watershed outlet. The integrated approach has been demonstrated as feasible and was applied successfully in the Yanduhe watershed, the upper tributary of Yangtze River Basin. The results indicated that the approach of integrating back-propagation ANN with semidistributed XAJ model may achieve the promising results with acceptable accuracy for flood events simulation and forecast.

Hydrometeorological time series include recent trends, especially over the past 30 years, as a result of climate change impact according to the Intergovernmental Panel on Climate Change (IPCC). Although there are commonly used trend identification techniques, such as Mann-Kendall (MK) and Spearman’s rho (SR) tests, their validity is possible under a set of restrictive assumptions, such as independent structure of the time series, normality of the distribution, and length of data. It is also not possible to calculate trend magnitude (slope) except through regression approach, which brings additional assumptions for the theoretical validation in practical applications. This paper presents a new methodology on the basis of subsection time series plots derived from a given time series on a Cartesian coordinate system. In such a plot, trend free time series subsections appear along the 45° straightline. Increasing (decreasing) trends occupy upper (lower) triangular areas of the square area defined by the variation domain of the variable concerned. The validity of this new approach is documented through a set of Monte Carlo simulations by taking into consideration independent and dependent processes. In the new approach, all the aforementioned assumptions in the MK and SR tests are avoided, and additionally it is possible to calculate trend magnitude from square area plots. The application of this methodology is given for a set of precipitation and runoff time series from different parts of the world.

Analytical solutions of one-dimensional (1D) advection-diffusion equations (ADE) are obtained subject to an initially pollutant-free domain and varying pulse-type input conditions. The medium is considered heterogeneous and of semi-infinite extent. The heterogeneity is defined by considering the velocity as a spatially dependent, linear, non homogeneous increasing function. It is interpolated in a finite domain in which concentration values are to be evaluated. The unsteadiness of the exponential form of velocity and dispersivity is also considered. The expression for velocity is written in degenerate form. Analytical solutions are obtained when dispersivity depends upon the velocity. The Laplace integral transform technique (LITT) has been used.

Water balances, for two-dimensional (2D) laboratory experiments with long-duration rainfalls over planar aluminum surfaces, indicate that at time of concentration the detention storage comprises about 70% of the accumulating inflow. Under the applied experimental conditions, its spatially averaged depth is 1 to 6 mm. This depth increases with rainfall intensity and decreases with main watershed slope. The time of concentration is defined here from rainfall commencement until the outflow begins a gradual approach towards the equilibrium state. It was shown, in an earlier paper, that the ratio of peak runoff discharge to rate of supply attributable to rainfall of a shorter duration is linearly related to the ratio of rainfall duration to time of concentration. Such a relation led to the formulation of the 120-year-old Rational Formula, commonly used for hydrologic design of populous districts. The similarity between laboratory and field results allows considering the detention depths in the laboratory as fairly representing the depths over outdoor impervious surfaces.