Journal of Hydrologic Engineering

In the layered aquifers of a mountainous watershed, multiple finite aquifer extents open to an overlying surface soil layer. In such a hydro-geological setup, interflow characteristics have a scope for research, in view of the fact that the normal scheme of rainfall-runoff modeling does not cater to interdependent flow situations. To study the interflow characteristics in the above multiaquifer setup of a mountainous watershed, a study area in the Maharashtra state of India has been examined. The three-dimensional groundwater flow algorithm MODFLOW has been used to develop the base case model, incorporating observed subsurface flow conditions. The objective is to study the behavior of artificial recharge techniques, such as percolation tanks and recharge wells, in an unusual flow condition of a mountainous watershed. Results of the analysis indicate that the interflow followed an integrated and continuous hydraulic gradient across multiaquifers. Finite aquifer extents supplement the recharge and discharge processes. These findings could be useful in planning artificial recharge schemes.

A probabilistic method of modeling the floodwater level in a dam reservoir is described the purpose of this paper is to add a probability dimension to hydraulic loading computations. Unlike the standard engineering approach, the method presented here incorporates two innovative aspects. First, the method uses a probabilistic hydrological method (simulated hydrographs for flood probability estimation method) based on simulation techniques, for generating multiple hydrological scenarios. Second, it can address the variability in reservoir level at the time of the occurrence of the flood event. The method generates a random set of floodwater levels from a random selection of flood hydrographs and headwater levels. Statistical analysis of the sample of reservoir levels obtained over a given period allows one to assess a probability distribution for floodwater levels. The method was tested on three applications representing dams having different operating rules and hydromorphological properties. The lessons learned with the present method as compared with the standard engineering approach are discussed from the viewpoint of inclusion in the modeling of the hydraulic loading on the dam and the assessment of the hydraulic safety of the dams.

This study developed an indicator generalized parameterization (IGP) method to cope with the problem of selecting interpolation points in estimating hydraulic conductivity fields. The IGP method introduced data indicators and d-neighborhoods to describe the actual contribution of sample data to unsampled locations. Moreover, the IGP method was applied to nonkriging basis functions to characterize spatially correlated hydraulic conductivity. This study used probabilistic data indicators to take into consideration the randomness and heterogeneity of hydraulic conductivity. The groundwater inverse method, along with an adjoint state method, was adopted to estimate the indicator probabilities. Then a cutoff was applied to determine the values of the data indicators for the IGP to estimate hydraulic conductivity. The numerical example validated the IGP and illustrated the significance of selecting interpolation points for hydraulic conductivity distributions. Further, the study demonstrated the IGP applicability to estimating hydraulic conductivity in the Alamitos Gap area, Calif. It was concluded that increasing the amount of data selected for interpolation results in smaller conditional variances, but causes the ensuing distribution to be smoother. However, smooth distributions of hydraulic conductivity may not be preferred. Proper selection of interpolation points can result in better hydraulic conductivity distributions for groundwater modeling purposes.

This paper investigates the problem of seepage under the floor of hydraulic structures considering the compartment of flow that seeps through the surrounding banks of the canal. A computer program, utilizing a finite-element method and capable of handling three-dimensional (3D) saturated–unsaturated flow problems, was used. Different ratios of canal width/differential head applied on the structure were studied. The results produced from the two-dimensional (2D) analysis were observed to deviate largely from that obtained from 3D analysis of the same problem, despite the fact that the porous medium was isotropic and homogeneous. For example, the exit gradient obtained from 3D analysis was as high as 2.5 times its value obtained from 2D analysis. Uplift force acting upwards on the structure has also increased by about 46% compared with its value obtained from the 2D solution. When the canal width/ differential head ratio was 10 or higher, the 3D results were comparable to the 2D results. It is recommended to construct a core of low permeability soil in the banks of canal to reduce the seepage losses, uplift force, and exit gradient.

The South Florida Water Management District (SFWMD) relies on a network of nearly 300 rain gauges in order to provide rainfall data for use in operations, modeling, water supply planning, and environmental projects. However, the prevalence of convective and tropical disturbances in South Florida during the wet season presents a challenge in that the current rain gauge network may not fully capture rain events that demonstrate high spatial variability. Next Generation Radar (NEXRAD) technology offers the advantage of providing a spatial account of rainfall, although the quality of radar-rainfall measurements remains largely unknown. The comparison of rainfall estimates from a gauge-adjusted, NEXRAD-based product developed by the OneRain Company with precipitation measurements from SFWMD rain gauges was performed for the Upper and Lower Kissimmee River Basins over a four-year period from 2002 to 2005. Overall, NEXRAD was found to underestimate rainfall with respect to the rain gauges for the study period, demonstrating a radar to gauge ratio of 0.95. Further investigation of bias revealed the tendency for NEXRAD to overestimate small rainfall amounts and underestimate large rainfall amounts relative to the gauge network. The nature of bias present in the data led to the development of a radar-rain gauge relationship to predict radar precipitation estimates as a function of rain gauge measurements. The intent of this paper is to demonstrate the importance of identifying systematic offsets which may be present in radar-rainfall data before application in hydrologic analysis.

Water tightness is an essential factor for the operation of underground highly pressurized gas storage caverns. To secure this tightness, the groundwater pressure must be greater than the gas pressure, and the groundwater head at a piezometer should not be lowered below the marginal head. However, various factors may affect variations in the groundwater head around storage caverns. The most influential factors are rainfall and the operating pressure of caverns. Bacterial clogging may reduce permeability and geological compaction may cause an abnormal head drop. Therefore, it is very difficult to predict the groundwater head under given conditions and find the quantitative reasons for head changes. In this study, the response function model, which explains the relationship between the groundwater head and external stresses is developed using the weighted multiple linear regression method. This model is effectively used to understand the cause of head variation in the area of underground liquefied petroleum gas storage caverns. The head difference in the observed and computed value may be used to evaluate the stability of the groundwater system.

A reliable hydrograph separation method is necessary for surface runoff modeling and hydrological studies. This paper investigates and compares the separation characteristics of two single-parameter digital filters, which are herein referred to as the one-parameter algorithm and the conceptual method. The application of the one-parameter algorithm was found to be restricted to low and medium baseflow separations, with a maximum separation limit of 50% of the total runoff hydrograph. The one-parameter algorithm was also observed to produce unrealistic sharp peaks under the peaks of the measured hydrograph when recession constant is smaller than 0.96. On the other hand, the conceptual method is applicable even for catchments fed largely by groundwater discharge. However, a reliable estimation of recession constant is a prerequisite for applying the conceptual method for large baseflow separations. Based on the hydrograph separation results, useful empirical relationships were developed for a partially urbanized watershed to estimate total runoff and direct runoff from the measured rainfall depth. The relationships between rainfall depth and total runoff depth and rainfall depth and direct runoff depth were found to be well represented by linear equations. The empirical relationships were then applied to estimate the long-term contribution of baseflow and surface runoff to total runoff at the study site. Baseflow was found to contribute about 58–61% of the annual total runoff.

Annual trends in baseflow and groundwater storage over the past $40\text{years}$ are deduced from stream flow observations in four upland catchments in the northern parts of the Kanto region. The analysis is based on the assumption that baseflow is an exponential decay function of time, which yields a linear relationship between storage and baseflow. These catchments are part of the water supply system for the Greater Tokyo metropolitan area, and groundwater constitutes an integral component of their storage capacity. Although the data exhibit great variability from year to year, no evidence was found that any persistent or systematic changes in low streamflow regime and in groundwater storage have taken place in this region over the period of record.

This paper deals with the challenging problem of hydrological interpretation of the internal functioning of artificial neural networks (ANNs) by extracting knowledge from their solutions. The neural network used in this study is based on the structure of the radial-basis-function neural network (RBFNN), which is considered as an alternative to the multilayer perceptron for solving complex modeling problems. This network consists of input, hidden, and output layers. The network is trained using the daily data of two catchments having different characteristics and from two different regions in the world. The present day and antecedent observed discharges are used as inputs to the network to forecast the flow one day ahead. A range of quantitative and qualitative techniques are used for hydrological interpretation of the internal functioning by examining the responses of the hidden layer nodes. The results of the study show that a single hidden layered RBFNN is an effective tool to forecast the daily flows and that the activation of the hidden layer nodes are far from arbitrary, but appear to represent flow components of the predicted hydrograph. The results of the study confirm that the three nodes in the hidden layer of this model effectively divide the input data space in such a way that the contribution from each node dominates in one of the flow domains—low, medium, or high—and form, in a crude manner, the base flow, interflow and surface runoff components of the hydrograph.

This study explores the value of incoming solar radiation estimates derived from geostationary operational environmental satellites (GOES) as an alternative solar radiation data source to historical solar radiation estimates for use in rainfall–runoff simulations. At 26 sites in the United States, the GOES-based solar radiation $(R^2=0.79)$ and ${\mathrm{ET}}_p$ $(R^2=0.94)$ estimates agreed well with the ground-based estimates. There was a slight positive bias for the GOES-based solar radiation (5.4%) and potential evapotranspiration (8.7%) estimates compared to the ground-based estimates, with the strong and significant biases noted during the warm seasons. However, individual stations biases ranged from $-12$ to 27%. Daily streamflow predictions made using National Weather Service River Forecast System’s Sacramento Soil Moisture Accounting rainfall–runoff model for four NOAA Distributed Model Intercomparison Project watersheds show that although notable peak storm flow differences occurred occasionally during extended drying periods, on average, the GOES-based potential evapotranspiration estimates slightly outperformed the simulations using ground-based data on an annual basis and were relatively insensitive to biases. However, on seasonal time scales, differences in soil water storage are evident and can result in numerous moderate differences in streamflow predictions.