Journal of Hydrologic Engineering

The quantitative analysis of the hypsometric integral (HI) and other statistical moments (e.g., kurtosis and skewness) of hypsometric curves conducted using a total of 195 main catchments and 223 subcatchments from South Australia reveal that most of the study catchments are at either a young or mature stage of landscape development, which indicates that they are at a high risk of future erosion if disturbed by anthropogenic activities. Results of this study indicated significant correlations among hypsometric attributes, confirming the relationships proposed by previous studies between landscape features and hypsometry. No significant correlation between hypsometric characteristics (e.g., HI and poly skew) and selected flow statistics [mean ($\mu$), coefficient of variation (cv), lag-one auto correlation coefficient ($r_1$), and Q50-Q10 range flow duration values] was found for South Australian catchments. Contrary to some of the previous study outcomes, no significant vertical or horizontal scale dependency of the HI was observed in this study. Further study recommendations are provided in the conclusion.

The influence of river sinuosity on the distribution characteristics of pollutants in a wide river is investigated three-dimensionally with a numerical model. It is found that, for a meandering river, the hydrodynamic force strongly controls the distribution of a conservative pollutant in the river. Because of secondary flow at the channel bend, high sinuosity leads to a lighter-than-water pollutant concentrating more on the inner bend apex region at the surface, whereas at the deep near-bed region, it concentrates more on the outer bend apex. For a heavier-than-water pollutant gravity flows in sinuous channels, there are two unique features resulting from the super-elevation and multicell cross-sectional secondary flows with alternate circulation directions: (1) the pollutant is distributed at the bottom more on the inner bend apex region; and (2) more on the outer bend apex region at a depth corresponding to the mixing region of the bottom two secondary flow cells of opposite circulation direction. These findings may assist field investigations in locating the best sampling points and in determining cost-effective measures for accidental or planned river pollution events.

The transport of reactive solutes through fractured porous formations has been analyzed. The transport through the porous block is represented by a general multiprocess nonequilibrium equation (MPNE), which, for the fracture, is represented by an advection-dispersion equation with linear equilibrium sorption and first-order transformation. An implicit finite-difference technique has been used to solve the two coupled equations. The transport characteristics have been analyzed in terms of zeroth, first, and second temporal moments of the solute in the fracture. The solute behavior for fractured impermeable and fractured permeable formations are first compared and the effects of various fracture and matrix transport parameters are analyzed. Subsequently, the transport through a fractured permeable formation is analyzed to ascertain the effect of equilibrium sorption, rate-limited sorption, and the multiprocess nonequilibrium transport process. It was found that the temporal moments were nearly identical for the fractured impermeable and permeable formations when both the diffusion coefficient and the first-order transformation coefficient were relatively large. The multiprocess nonequilibrium model resulted in a smaller mass recovery in the fracture and higher dispersion than the equilibrium and rate-limited sorption models.

The objective of this research is to develop and demonstrate a methodology to specifically assess the interrelationships among estimated precipitation, observed streamflow, and hydrologic model performance. To satisfy this objective, this work introduces a new concept called “precipitation fidelity,” which is the correspondence of stream outflow to the estimated precipitation used as input into a hydrologic model. Simple annual and daily precipitation fidelity indexes are defined. The use of the precipitation fidelity indexes is then demonstrated for the Rivanna watershed in central Virginia modeled using an existing hydrologic model, the Chesapeake Bay Program Phase 5 watershed model, and the associated precipitation input data set. The precipitation fidelity results are used in conjunction with model output to identify the effect of precipitation estimation accuracy on model performance at both long and short time scales. Based on the daily precipitation fidelity measure, fidelity between the precipitation input and the observed streamflows was not observed for approximately one quarter of the days in the headwater watersheds. Days for which the estimated input precipitation has runoff-generating rainfall, but the observed stream discharge does not increase, have the highest average relative daily modeling errors and high area-weighted daily modeling errors. These results indicate that precipitation needs to be better represented in the headwater subwatersheds. Regression analysis using the analysis of covariance method was used to determine the statistical similarity between annual estimated precipitation and observed and modeled streamflows. Regression results suggested that direct hydrology calibration of the subwatershed of interest leads to both a higher level of correspondence between the estimated precipitation and modeled flows and an acceptable goodness-of-fit between the modeled and observed data.

The indicators of hydrologic alteration (IHA) is a statistical flow methodology for characterizing ecologically important streamflows. Typically, IHA has been used to identify the extent of human effects on a stream’s hydrology and to set management goals to restore the stream ecology. In this work, the use of the seven “extreme low flow” statistics of the IHA is extended to the evaluation of the performance of a hydrologic simulation model under low flow conditions. Specifically, this work uses the IHA framework to evaluate the accuracy of the Chesapeake Bay Program Phase 5 (CBP5) watershed model during low flow events on a regional scale that is relevant to many water supply planners and managers. Because the CBP5 model’s primary focus is predicting the Bay’s water quality, the measures used to calibrate the CBP5 model focused primarily on the calibration of the entire hydrological record and had only a secondary emphasis on specific flow regimes, such as low flows and very low flows, although these flows are important for both stream ecologies and water supply planners. To provide a comparative performance benchmark, the performance of the simple drainage area ratio (DAR) method relative to the IHA low flow statistics is also determined. This paper demonstrates the use of IHA statistics for model evaluation in a case study, the Rivanna River watershed, a subcatchment within the Chesapeake Bay drainage. For rivers with a large proportion of unregulated flow contributions, the authors conclude that the computationally simple DAR model with appropriate surrogate watershed generally characterizes the extreme low flow conditions slightly more accurately than the CBP5 model. However, unlike the CBP5 model, the DAR model predicts future flows based solely on historical data; and thus, the DAR model cannot predict flow effects caused by hydrological alterations, thus limiting its use in water supply management. Nevertheless, this IHA analysis suggests that the incorporation of a low-flow—specific metric into the CBP5 calibration could improve its utility for water supply management and planning at a regional scale.

A semidistributed hydrologic model addressed to flood risk assessment is presented. The model has been applied and calibrated by means of a multistep calibration scheme to the Pejibaye River Basin in a series of flash flood events that occurred between 2006 and 2009. The model consists of a rainfall-runoff module, a surface runoff routing module (unit hydrograph method), a base flow module (linear reservoir method), and a channel-routing module. Two different unit hydrographs were contrasted and two different approaches (continuous and discrete) were used for average rainfall estimation. Model parameters were initially estimated by using classical formulation and documentation, and afterward were compared with calibrated parameters. The combination of the different unit hydrographs and average rainfall estimation procedures presented significantly different prediction errors. Rainfall-runoff and base flow methods showed a stable and reasonable behavior. Rainfall input was highlighted as a major source of error. However, the continuous method for average rainfall estimation was noted to slightly reduce timing errors as compared with the discrete method. Overall, the model reproduced reasonably the flash floods analyzed in this paper, both in calibration and validation ($R^2$ between 0.81 and 0.87; Nash-Sutcliffe between 0.70 and 0.80), and therefore could be also suitable for other watersheds with similar climatic and geomorphologic conditions.

In this paper, a bivariate-copula-based methodology is presented to assess the risk associated with hydroclimatic variability on groundwater levels in an unconfined aquifer at the Manjara basin in India. Rank correlation analysis is used to identify the association between the El Niño–Southern Oscillation (ENSO) index, precipitation, and groundwater levels. It is found that the dependencies among the hydroclimatic variable pairs are statistically significant and the dependence structure can be modeled by using bivariate Archimedean copulas. The groundwater level or depth-to-groundwater table (DGWT) in the study region is found to be responsive toward interannual precipitation variations that are influenced by the ENSO phenomenon. For probabilistic representation of hydroclimate variables, various probability distributions are evaluated and it is found that the precipitation and DGWT are best fitted using lognormal and Weibull distributions, respectively, whereas the ENSO index is best fitted using nonparametric-based normal kernel density function. For modeling joint dependence structure of hydroclimatic variable pairs (precipitation-DGWT, ENSO index-precipitation, and ENSO index-DGWT), appropriate Archimedean copulas (viz, Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank families) are evaluated. On performing standard statistical tests, it is found that the Frank copula is best representing the joint dependence structure for all three variable pairs. Then the Frank copula-based joint distributions are used to derive conditional distributions and to perform risk analysis of groundwater levels. The study suggest that the copula-based methodology can be used effectively for modeling dependence structure of hydroclimatic variables and for risk assessment of groundwater levels under changes in hydroclimatic conditions.

The paper gives a closed-form theoretical solution for the steady seepage below the flat horizontal impervious apron of a hydraulic structure with equal end cutoffs, with a step at the downstream end, founded on a pervious medium of finite depth. This is obtained by using Schwarz-Christoffel transformation in two stages. The numerical solution of the resulting implicit equations involving elliptic integrals gives the uplift pressures at key points, seepage discharge factor, and exit gradient factor in terms of nondimensional floor profile ratios. Design charts have been given for these seepage characteristics.

The sediment load transported in a river is the most complex hydrological phenomenon due to a large number of obscure parameters and the existence of both spatial variability of the basin characteristics and temporal climatic patterns. In this paper two artificial neural network (ANN) models were developed for semidistributed modeling of the suspended sediment load process of the Eel River watershed located in California. The first model was an integrated ANN model trained by the data of multiple stations inside the watershed. In the second model, a geomorphology-based ANN model, space-dependent geomorphologic parameters of the subbasins, extracted by geographic information system tools, accompanied by time-dependent meteorological data, were imposed on the network. In both models, three-layer perceptron neural networks were trained considering various combinations of input and hidden layers’ neurons, and the optimum architectures of the models were selected according to the computed evaluation criteria. Furthermore, the ability of the models for spatiotemporal modeling of the process was examined through the cross-validation technique for a station. The obtained results demonstrate that although the predicted sediment load time series by both models are in satisfactory agreement with the observed data, the geomorphological ANN model produces better performance than an integrated model because it employs spatially variable factors of the subbasins as the model’s inputs. Therefore, the model can operate as a nonlinear time-space regression tool rather than a fully lumped model.

This study used previously established unit critical discharges, annual allowable erosional hours, and annual allowable volume of eroded bed-load standards to evaluate two types of storm water control measures (SCMs): low-impact development (LID) practices and a large detention SCM (wet pond). Nine initial scenarios modeled in PCSWMM incorporated different combinations of wet ponds, green roofs, rainwater harvesting systems, permeable pavement, and rain gardens to determine the best scenario for reducing stream erosion potential within a highly urbanized watershed in Chapel Hill, NC. The best-case scenario to reduce annual erosional hours and eroded bed load within the stream consisted of an aggressive utilization of LID practices in combination with an undersized wet pond. Although this scenario did not meet the annual erosional hour standard for rural reference streams, $0.35\mathrm{h}/\mathrm{ha}/\mathrm{year}$, it did reduce erosional hours and eroded bed-load sediment by factors of 2.4 and 2.5, respectively, improving the existing condition. An alternative wet pond outlet structure that used two elevations of small drawdown orifices was explored to determine if current wet pond design practices could be improved to include stream stability. The new configuration provided a modest reduction in the number of erosional hours, a factor of 1.3, but increased the volume of eroded bed load by a factor of 1.2 when compared with the normal wet pond. However, adding widespread LID practices to the alternative outlet design reduced erosional hours and bed load transport by factors of 1.8 and 1.2, respectively, when compared with the normally configured wet pond. The failure to meet the erosional standards in all scenarios demonstrated the difficulty of requiring highly urbanized watersheds (60% impervious) to meet such strict stream protection measures.

In comparison with a kinematic wave–based equation for estimation of the time of concentration, three methods, Ben-Zvi’s method, the modified Ben-Zvi method, and Izzard’s method, were applied to determine the time of concentration from the experimental hydrographs reaching an equilibrium flow state. The experiments were performed for stationary rainstorms on a V-shaped aluminum watershed in the watershed experimentation system (WES) at the University of Illinois in Urbana-Champaign, Illinois. The times of concentration determined by these three methods were compared to mathematical equations in the existing literature. It is found that the time corresponding to 89% of the maximum discharge as per the modified Ben-Zvi method yielded a generally agreeable time of concentration for the WES experimental hydrographs, while the criterion of 97% of the peak discharge as per Izzard’s method overestimates the time of concentration. It is found that Woolhiser and Liggett’s and Wong’s equations can predict the time of concentration acceptably well for the simplified laboratory watershed with overland slopes in the range of 0.5–5% and channel slopes in the range of 0.5–3%.

This study proposes a reservoir model for evaluation of aquifer parameters from a long duration pumping test conducted with a radial collector pumping well and nine observation wells in an unconfined aquifer in the Tailan River basin of China. The proposed model, based on the concept of double continuum, was used to conceptualize the pumping test site into conduit and porous reservoirs coupled by a linear flow exchange for simulating flow during the pumping test. The set of model equations developed from the concept were solved by an iterative method. The model-simulated hydraulic heads agree reasonably well with the observation heads in both the pumping and observation wells at an average normalized root mean square error of 10.99 and 8.06%, respectively, during pumping but were weaker in the recovery period. This notwithstanding, the specific yield estimates compare well with the range obtained for a numerical modeling of the entire aquifer basin. Significantly, the model was applied successfully in simulating sustainable withdrawal rates from the aquifer and may be a useful tool for analyzing flows to radial collector wells for applications in water resources management.

Drought forecasting plays an important role in the planning and management of water resources systems, especially during dry climatic periods. In this study, a nonlinear aggregated drought index (NADI) was developed first to classify the drought condition of a catchment considering all significant hydrometeorological variables that have effects on droughts. An artificial neural network (ANN)—based drought forecasting approach was then developed by using the time series of the NADI to forecast NADI values. In forecasting future drought conditions, the NADI produces the overall dryness within the system as compared to the traditional forecasting of rainfall deficiency, which considers only the meteorological droughts. Two ANN forecasting models, namely a recursive multistep neural network (RMSNN) and a direct multistep neural network (DMSNN), were developed in this study. Overall, these models were capable of forecasting drought conditions well for up to 6 months of future forecasts, which were statistically significant at the 1% level. Moreover, it was found that both models showed the same performance for 1-month lead-time forecasts. The RMSNN model gave slightly better forecasts than the DMSNN model for lead times of 2–3 months, and the DMSNN model produced slightly better forecasts than the RMSNN model for forecast lead times of 4–6 months. Beyond the forecast lead time of 6 months, poor forecasts were observed. A comparative study was conducted to investigate the effectiveness of ANN-based drought forecasting models over an autoregressive integrated moving average (ARIMA) model (which is a traditional linear stochastic model), and the results showed that both RMSNN and DMSNN models performed better than the ARIMA model.