Journal of Hydrologic Engineering

A new semianalytical stream-depletion solution is obtained for pumping from a well beside a stream in a two-layer leaky aquifer system. The well abstracts water from an upper unconfined aquifer underlain by an aquitard and a second semiconfined aquifer. The governing equations reduce to the equations that describe flow in a Hantush-Jacob leaky aquifer, in which the bottom aquifer has zero drawdown for all time, when the storativity of the bottom aquifer becomes infinite. This allows a Hantush-Jacob leaky aquifer solution to be compared with the solution for a bottom aquifer with finite storativity, and this comparison shows that the Hantush-Jacob approximation significantly underestimates stream depletion at larger values of time. In addition, comparison of the two-aquifer solution with a stream-depletion solution for a single aquifer shows that a single-aquifer solution closely approximates the two-aquifer solution when the single-aquifer transmissivity is replaced with the sum of transmissivities for the two-layer system. This suggests that a similar approximation might be used in a previously obtained solution to obtain a very general stream depletion model for multiaquifer systems.

A total soil water evaporation model that includes both transient (event based) evaporation and water table evaporation was developed by modifying an existing water balance type model incorporated in the widely used FAO-56 method. Using results from a parametric numerical study, the method applies a predictive equation for soil water table evaporation as a function of distance to the water table, soil properties, and climatic conditions. The model, which can be easily implemented in a spreadsheet, can account for the effects of water management decisions relating to tree canopies and surface mulch. Applied to a location in the Middle Rio Grande riparian zone over the course of a year, the model provides insight into how soil water evaporation may be impacted by restoration and management activities. Soil hydraulic properties were shown to greatly affect the predicted amount of total soil water evaporation. With a relatively shallow water table and an exposed soil surface, the amount of soil water evaporation can be comparable to water consumption by riparian vegetation. Canopy shade reduces the amount of total soil water evaporation by more than 50%. As expected, the model indicates that surface mulch yields an even greater reduction in total soil water evaporation. Mulching bare soil surfaces can effectively reduce evaporative losses after exposing a soil surface by removing undesirable vegetation. This study shows that riparian tree removal in areas of shallow ground water may result in no water savings.

A numerical model is developed in this study for simulating soil moisture flow in layered soil profile with plant growth. A dynamic root compensation mechanism (RCM) is used for a nonuniform root distribution pattern to compute water uptake by plants in a moisture scarce environment. The governing soil moisture flow equation integrated with the roots water uptake function is solved numerically by the implicit finite difference method coupled with the Picard iteration technique. The model is first tested for a barren layered soil profile using numerical simulation data available in the literature. A nonlinear function for water uptake by roots is then incorporated in the flow equation and the rate of water removal is simulated with and without considering the RCM for a characteristic example under optimal and water scare conditions. The model is finally applied to a rain-fed wheat (Triticum aestivum) field using a dynamic root growth model. The simulation considering the RCM shows better agreement with the observed data compared to the model results obtained without considering the RCM. Results show that under favorable soil moisture conditions, plants extract water at the maximum rate according to the root distribution pattern and when the moisture stress is developed in the upper soil profile, the diminished water uptake rate in the water scarce region is compensated for by an enhanced water uptake from the lower wetter layers. The model can be used for sound irrigation management practices especially in the water scarce arid and semiarid regions and can also be integrated with a transport equation to predict the solute uptake by plant root biomass.

A distributed rainfall-runoff model, Grid-Xinanjiang model based on the topographical information of each grid cell extracted from the digital elevation model (DEM), was developed to simulate the hydrologic processes within watersheds. The water exchange among grids within the watershed and the runoff routing along the river drainage networks are taken into consideration in the model. The Grid-Xinanjiang model was applied to the two watersheds in China for flood simulations. The performance of the developed model was compared with that of the original Xinanjiang model. Initial tests show that the Grid-Xinanjiang model can perform well as the original model in terms of estimates of streamflow and of the model efficiency coefficient. All of the qualified ratios relative to peak flow, runoff, and peak time for the two models applied to the Misai watershed and the Xixian watershed are more than 88%. The Grid-Xinanjiang model outperformed the original model in predicting flood runoff and outlet hydrographs, especially in the larger watershed. The simulation results show the robust capacity of obtaining spatial distributions of hydrologic variables over the original Xinanjiang model, and also revealed that runoff yield has large temporal and spatial variability during storm period and the runoff response is sensitive to the antecedent soil moisture, topography, and spatial distribution of rainfall.

One of the first steps in removing a dam is the construction of a cofferdam so that demolition activities can proceed safely. The time necessary for removing a small low-head dam is often on the order of a few months. To minimize the cofferdam construction cost, these dams are generally removed during the low flow months of the year. Therefore, design of cofferdams should be based on river flow series during the dam removal period, rather than the entire year. In this study, available hourly and daily average records at the recently removed Marmot Dam on the Sandy River, Oregon are analyzed for four dam removal periods, varying from 1 to 4 months (July through October). $L$ -moment ratio diagrams and probability plot correlation coefficients are used to select log-Pearson Type 3 and two- and three-parameter lognormal probability distribution functions for representing at-site maximum flow series. From a hydrologic engineering consideration, the analysis suggests an optimal dam removal period of 3 months (July through September). An estimate of flow on the basis of annual maximum series is an order of magnitude higher, which would result in an uneconomical design of the cofferdam.

This study formulates an experimental strategy that couples rainfall observations from a locally deployed mobile X-band dual polarization radar with recording rain gauge measurements to create an error assessment of the spatio-temporal variability of high-resolution gridded rainfall fields. We discuss the trade-offs and values in the radar-rainfall uncertainty associated with different measurement resolutions. Overall, we found that radar measurement errors naturally decrease with averaging in space and time, but there is a generally negative value associated with the increase in spatial scale from 300 m to 5 km. Spatial averaging of radar rainfall increased the probability of detection slightly by 4%. However the decrease in relative root-mean-squared error (R-RMSE) was negligible as the false alarm rate increased by 10% and the Heidke skill score reduced by 16%. In terms of reducing measurement uncertainty, there was greater overall value by averaging temporally up to 60 min than averaging spatially to 5 km, where R-RMSE decreased up to 40%, probability of detection increased by 5%, false alarm ratio decreased by 14%, and the Heidke skill score increased by 7%. However the value added in spatio-temporal integration is dependent on the usage of the rainfall data as important information on the natural variability of rainfall gets lost with averaging in time and space.

Prediction of suspended sediment load (SSL) is important for water resources quantity and quality studies. The SSL of a stream is generally determined by direct measurement of the suspended sediment concentration or by employing sediment rating curve method. Although direct measurement is the most reliable method, it is very expensive, time consuming, and, in many instances, problematic for inaccessible sections, especially during floods. On the other hand, measuring precipitation and flow discharge is relatively easier and hence, there are more rain and flow gauging stations than SSL gauging stations in Turkey. Furthermore, due to its cost, measurements of SSL are carried out in longer periods compared to precipitation and flow measurements. Although daily precipitation and flow measurements are available for most of the Turkish river basins, at best semimonthly measurements are available for SSL. As such, it is essential to predict SSL from precipitation and flow data and to fill the gap for the missing data records. This study employed artificial intelligence methods of artificial neural networks (ANN) and neurofuzzy inference system, the sediment rating curve method, multilinear regression, and multinonlinear regression methods for this purpose. The comparative analysis of the results showed that the artificial intelligence methods have superiority over the other methods for predicting semimonthly suspended sediment loads. The ANN using conjugate gradient optimization method showed the best performance among the proposed models. It also satisfactorily generated daily SSL data for the missing period record of Gediz River, Turkey.

This study uses the mobile-immobile model (MIM) and the traditional convection-dispersion equation (CDE) to analyze the observed breakthrough curves (BTCs) at different distances in a 1,250-cm-long saturated and highly heterogeneous soil column. It provides a simple method to determine the mobile water fraction independently as the ratio of effective porosity over total porosity of the packed soil materials. The effective porosity is calculated a priori as the ratio of measured flow rate and estimated pore-water velocity. It is found that there is a significant amount of immobile water in the soil column, resulting in the anomalous early breakthrough and tailing behaviors of the measured BTCs. Comparing to the CDE, the measured asymmetric BTCs at various scales can be better described by the MIM, especially their early arrival and long tailing parts. The degree of anomalous transport behavior in this large heterogeneous soil column is reduced with transport scale due to the increased mobile water fraction and associated greater solute mixing, and the MIM can detect this evolution adequately. The solute mass transfer timescales at various distances are compared to the advection timescales and longitudinal interaction timescales, respectively. It is found that the latter is better correlated with the timescales of solute mass transfer, which implies that the mass transfer rate in the heterogeneous soil column is predominantly subjected to the local velocity variation.

Spatially distributed on-site devices such as detention tanks and bioretention are becoming more common as a means of controlling urban storm-water quantity and quality. One approach to modeling the cumulative catchment-scale effects of such devices is to resolve the catchment down to the scale of a land parcel or finer, and then to model each device separately. This involves computational and input data demands that may be impracticable, especially in planning or preliminary design stages of storm-water system design. To reduce these demands, the spatial resolution can be coarsened by aggregating land parcels and devices, but this may compromise model accuracy. The focus of this study was examination of the effects of aggregation on predictions of water quantity and quality (for a representative contaminant, total suspended solids) for detention, infiltration, and bioretention devices. A detailed model for urban storm water improvement conceptualization simulation was set up for a $0.83{\text{km}}^2$ catchment with 810 source areas and associated devices, and the model was then reduced to three aggregation levels (55 devices, seven devices, and one device). The influence of aggregation was assessed by comparing the predictions of the aggregated models against the predictions of the detailed model. Aggregation had little effect on the predictions of maximum concentration ( $<2\%$ difference compared with the detailed model), load $\left(<4\%\right)$ , and baseflow $\left(<5\%\right)$ when the devices were sized in proportion to the impervious area and when there was high soil permeability. Aggregation to a single device increased peak flow compared with the detailed model, by up to 38.1% for bioretention and less for other devices. The peak flow increase was a consequence of reducing the range of travel times in the aggregated drainage network. Aggregation to seven devices had considerably less effect on peak flow (up to 8.7% increase). Addition of variability to the size of the devices introduced further aggregation effects. Methods to extend the aggregation approach to cater for variability in device sizing are proposed in the paper. The results of the study suggest that aggregation can be used to reduce computational and input data demands, with little penalty in terms of prediction accuracy.

Due to rapid developments in computational mechanics, various advanced numerical and analytical methods for simulation of differential equations in applied science have been proposed. Variational iteration method (VIM) and its developments are a class of exact solutions which are mostly implemented in applied physics. The present paper focuses on three hydrological phenomena, namely surface infiltration equation (Green-Ampt equation), surface water simulation (fully dynamic equation), and fully infiltration equation (Richards’ equation) and their exact solution with VIM and its modified version presented by Padé approximation. The possibility of the application of this type of mathematical approach in applied hydrology has been studied and comparison demonstrates competent developments in physics and applied physics solution procedures.

Two models are herein presented to derive at-site hourly based rainfall depth-duration-frequency (DDF) relationships by incorporating daily rainfall information. The model development is motivated by the desire to utilize daily rainfall data that are in existence prior to the conversion from a manned gauge to an automatic one for improving the establishment of hourly based rainfall DDF relation at the site. The performance of the proposed models is assessed by comparing the rainfall DDF relationships derived from the conventional method using solely annual maximum rainfalls of various durations. Hourly rainfall data at Hong Kong Observatory over the period of 1884–1990 are used to demonstrate the application of the two proposed models and to examine their performance. Results from the numerical experiments show that the two proposed models, which incorporate daily rainfall data, are capable of producing more accurate and reliable rainfall DDF relationships than the conventional method solely on the basis of annual maximum hourly rainfalls.

$L$ -moment-based index-flood method (LMIF) is used in a regional flood frequency analysis of the Red River of the North basin. The results are appraised with respect to the traditional methods based on the 1982 Interagency Advisory Committee on Water Data (Bulletin 17B) procedure. The $L$ -moments approach detects the necessity of subdividing the basin into three main homogeneous hydrologic regions through hierarchical clustering techniques, regionalization, distribution fittings, and simulations. Each region has a unique index-flood growth curve. The dominant distributions include the Pearson Type III, the generalized Pareto, and the generalized extreme value. For a certain number of gauged sites, the quantiles obtained by the LMIF differ significantly from those obtained by pursuing the Bulletin 17B-regression procedures. Generally, the latter gives lower quantiles for most sites. Overall, the LMIF is effective in identifying homogeneous regions, coherent in statistical distribution fitting, and free from state boundary issues. The investigation also illustrates the flexibility of the method in allowing flood estimations for a special regulated region and a main stem region. Further studies on the use of the LMIF on a national or global scale are recommended.

Area-capacity curves are usually used for reservoir flood routing, reservoir operation, determination of water surface area, and capacity corresponding to each elevation, reservoir classification, and reservoir sediment distribution. In this study, the similarity between the natural logarithmic function curve and the reservoir capacity curve is used to obtain the mathematical equation for dimensionless capacity curve. The obtained equation has only one unknown dimensionless parameter, named the “reservoir coefficient” $N$ . If the obtained dimensionless capacity equation is plotted on a log-log paper, for any values of $N$ , it will be approximately a straight line and the reciprocal of the line slope will be equal to the reservoir shape factor. The reservoir area equation and equation of reservoir coefficient are obtained by differentiating the reservoir capacity equation. Also, more precise values of $N$ for the area and capacity equations can be obtained by using the least-squares method. The obtained equations are evaluated with the area-capacity data from 16 reservoirs. This study shows that the obtained equations agree well with the actual data, and for reservoirs with the smoothed area-depth curve, the certainty of the obtained equations are more.

The Muskingum model is the most widely used method for flood routing in hydrologic engineering. However, the application of the model still suffers from a lack of an efficient method for parameter estimation. Particle swarm optimization (PSO) is applied to the parameter estimation for the nonlinear Muskingum model. PSO does not need any initial guess of each parameter and thus avoids the subjective estimation usually found in traditional estimation methods and reduces the likelihood of finding a local optimum of the parameter values. Simulation results indicate that the proposed scheme can improve the accuracy of the Muskingum model for flood routing. A case study is presented to demonstrate that the proposed scheme is an alternative way to estimate the parameters of the Muskingum model.

The effect of nonuniformity of flow on hydraulic geometry relations is investigated. Using data from a historic channel in Isfahan, central Iran, which was constructed 400 years ago, it is found that the nonuniformity of flow significantly influences the prediction of depth and velocity. However, flow nonuniformity reveals less effect on the prediction of slope due to nonlinear shear stress distribution. It is considered that under the uniform flow assumption $\left(\tau =\gamma hS\right)$ , depth and slope acting as dependent and independent variables, respectively, result in spurious correlation. However, the depth equation reveals this is not a correct conclusion because $R^2$ is not large and the exponent of ${\tau }_{\ast }$ is quite small. Likewise, although the Shields parameter is among the controlling variables for the prediction slope, its application under the uniform flow assumption has a risk of spurious correlation.

Conventional methods and models available for estimation of reservoir sedimentation process differ greatly in terms of complexity, inputs, and other requirements. An artificial neural network (ANN) model was used to estimate the volume of sediment retained in a reservoir. Annual rainfall, annual inflow, and capacity of the reservoir were chosen as inputs. Thirty Two years of data pertaining to Gobindsagar Reservoir on the Satluj River in India, were used in this study (23 years for training and 9 years for testing). The pattern of the sediment volume retained in this reservoir was well captured by the Multi-Layer Perceptron (3–5-1) ANN model using the back propagation algorithm. Based on several performance indices, it was found that the ANN model estimated the volume of sediment retained in the reservoir with better accuracy and less effort as compared to conventional regression analysis.