Showing papers in "Journal of Hydrologic Engineering in 2007"

Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask

Abstract: This paper presents an introduction to inference for copula models, based on rank methods. By working out in detail a small, fictitious numerical example, the writers exhibit the various steps involved in investigating the dependence between two random variables and in modeling it using copulas. Simple graphical tools and numerical techniques are presented for selecting an appropriate model, estimating its parameters, and checking its goodness-of-fit. A larger, realistic application of the methodology to hydrological data is then presented.

Streamflow Forecasting Using Different Artificial Neural Network Algorithms

Abstract: Forecasts of future events are required in many activities associated with planning and operation of the components of a water resources system. For the hydrologic component, there is a need for both short term and long term forecasts of streamflow events in order to optimize the system or to plan for future expansion or reduction. This paper presents a comparison of different artificial neural networks (ANNs) algorithms for short term daily streamflow forecasting. Four different ANN algorithms, namely, backpropagation, conjugate gradient, cascade correlation, and Levenberg–Marquardt are applied to continuous streamflow data of the North Platte River in the United States. The models are verified with untrained data. The results from the different algorithms are compared with each other. The correlation analysis was used in the study and found to be useful for determining appropriate input vectors to the ANNs.

Suitability of SWAT for the Conservation Effects Assessment Project: Comparison on USDA Agricultural Research Service Watersheds

Abstract: Recent interest in tracking environmental benefits of conservation practices on agricultural watersheds throughout the United States has led to the development of the U.S. Department of Agriculture's USDA Conservation Effects Assessment Project CEAP. The purpose of CEAP is to assess environmental benefits derived from implementing various USDA conservation programs for cultivated, range, and irrigated lands. Watershed scale, hydrologic simulation models such as the Soil and Water Assessment Tool SWAT will be used to relate principal source areas of contaminants to transport paths and processes under a range in climatic, soils, topographic, and land use conditions on agricultural watersheds. To better understand SWAT's strengths and weaknesses in simulating streamflow for anticipated applications related to CEAP, we conducted a study to evaluate the model's performance under a range of climatic, topo- graphic, soils, and land use conditions. Hydrologic responses were simulated on five USDAAgricultural Research Service watersheds that included Mahantango Creek Experimental Watershed in Pennsylvania and Reynolds Creek Experimental Watershed in Idaho in the northern part of the United States, and Little River Experimental Watershed in Georgia, Little Washita River Experimental Watershed in Oklahoma, and Walnut Gulch Experimental Watershed in Arizona in the south. Model simulations were performed on a total of 30 calibration and validation data sets that were obtained from a long record of multigauge climatic and streamflow data on each of the watersheds. A newly developed autocalibration tool for the SWAT model was employed to calibrate eleven parameters that govern surface and subsurface response for the three southern watersheds, and an additional five parameters that govern the accumulation of snow and snowmelt runoff processes for the two northern watersheds. Based on a comparison of measured versus simulated average annual streamflow, SWAT exhibits an element of robustness in estimating hydrologic responses across a range in topographic, soils, and land use conditions. Differences in model performance, however, are noticeable on a climatic basis in that SWAT will generally perform better on watersheds in more humid climates than in desert or semidesert climates. The model may therefore be better suited for CEAP investiga- tions in wetter regions of the eastern part of the United States that are predominantly cultivated than the dryer regions of the West that are more characteristically rangeland.

On the Use of Copulas in Hydrology: Theory and Practice

Abstract: The use of copulas in hydrology is quite recent, and many important results still are to be discovered and/or derived. In this work we present some recent advances in hydrological modeling that exploit copulas. These include: (1) the calculation of conditional probabilities and their use in bivariate simulation; (2) the calculation of the level curves of joint distributions; (3) the calculation of the return periods of bivariate events, both in the conditional and unconditional cases; (4) the definition and calculation of the secondary return period; (5) a trivariate model for the temporal structure of the sequence of storms; (6) the statistical calculation of the storm depth; and (7) the calculation of the convolution variance. Several applications to hydrological data are shown. The models presented here may have important implications in many areas of water resources and hydrologic systems.

Drought Forecasting Using a Hybrid Stochastic and Neural Network Model

Abstract: Treating the occurrence and severity of droughts as random, a hybrid model, combining a linear stochastic model and a nonlinear artificial neural network (ANN) model, is developed for drought forecasting. The hybrid model combines the advantages of both stochastic and ANN models. Using the Standardized Precipitation Index series, the hybrid model as well as the individual stochastic and ANN models were applied to forecast droughts in the Kansabati River basin in India, and their performances were compared. The hybrid model was found to forecast droughts with greater accuracy.

Importance of Tail Dependence in Bivariate Frequency Analysis

Abstract: This paper highlights the importance of taking into account the tail dependence in the context of bivariate frequency analysis based on copulas. Three nonparametric estimators of the tail-dependence coefficient are compared by simulations with seven families of copulas. We choose the two estimators most adapted to a bivariate frequency analysis of the annual maximum flows and the corresponding flow hydrograph volumes of the Loire River France. In this example, the bivariate return period and the conditional density of the volume given that the flow exceeds a given threshold are computed. The results show, as can be expected, that out of the seven copula families tested, five overestimate the return periods of correlated extreme events. These results bring to the forefront the importance of taking into account the tail dependence in order to estimate the risk adequately.

Gumbel–Hougaard Copula for Trivariate Rainfall Frequency Analysis

Abstract: Joint distributions of rainfall intensity, duration, and depth or those of rainfall intensity and duration, rainfall depth and duration, and rainfall intensity and depth are important in hydrologic design and floodplain management. Considering the dependence among rainfall intensity, depth, and duration, multivariate rainfall frequency distributions have been derived using one of three fundamental assumptions. Either the rainfall intensity, duration, and depth have been assumed independent, or they each have the same type of marginal probability distribution or they have been assumed to have the normal distribution or have been transformed to have the normal distribution. In reality, however, rainfall intensity, duration, and depth are dependent, do not follow, in general, the normal distribution, and do not have the same type of marginal distributions. This study aims at deriving trivariate rainfall frequency distributions using the Gumbel–Hougaard copula which does not assume the rainfall variables to b...

Sizing of Rainwater Storage Units for Green Building Applications

Abstract: Green building design principles advocate the use of rainwater storage units to collect roof runoff during nonwinter seasons for landscaping, hardscape cleaning, and/or maintenance purposes, either in the form of rain barrels for smaller scale applications or cisterns for larger scale applications. This not only saves water which would otherwise be supplied from municipal water distribution systems but also reduces storm-water runoff which would otherwise be handled through urban storm-water management systems. The size of the storage units needs to be commensurate with the area of the roof and the desired water use rate. The local climate has an influence on the required size and achievable use rate as well. In this paper, analytical formulas are derived to estimate the required rainwater storage volume as a function of desired water use rate, reliability and local climate. In deriving these formulas, local climate characteristics are represented by probabilistic models and incorporated into the stochastic description of storage unit operating procedures and requirements. The resulting formulas may be used by engineers, architects, municipal governments, and storage unit manufactures for the estimation or recommendation of suitable rainwater storage unit sizes.

Trivariate Flood Frequency Analysis Using the Gumbel–Hougaard Copula

Abstract: Using the Gumbel–Hougaard copula, trivariate distributions of flood peak, volume, and duration were derived, and then conditional return periods were obtained. The derived distributions were tested using flood data from the Amite River Basin in Louisiana. A major advantage of the copula method is that marginal distributions of individual variables can be of any form and the variables can be correlated.

Introduction to Copulas

Abstract: In 1959, in response to a question by M. Frechet, A. Sklar introduced the notion of a copula and proved the basic theorem that now bears his name. In the next 15 years, all results regarding copulas were obtained in connection with problems stemming from the theory of probabilistic metric spaces. Things changed in 1974, when, quite by accident, I reread a paper by A. Renyi entitled “On Measures of Dependence” and realized that I could easily construct such measures by using copulas. Subsequently, E. F. Wolff and I took up the study of these measures. This led to our 1976 note in the Comptes rendus de l’Academie des Sciences, Paris, followed in 1981 by our paper in the Annals of Statistics. After the publication of these papers and the appearance, in 1983, of the book Probabilistic Metric Spaces, written jointly with A. Sklar, the pace quickened as more of our students and colleagues became involved. Moreover, since interest in questions of statistical dependence was increasing, others came to the subject from different directions. In 1986, the enticingly entitled article “The Joy of Copulas,” by C. Genest and R. C. MacKay, attracted more attention. By the end of the 1980s, the volume of work on copulas and related matters had grown to such an extent that G. Dall’Aglio organized a symposium on distributions with given marginals, which was held in Rome in 1990. At that symposium, I presented a paper entitled “Thirty Years of Copulas” in which I was able to survey virtually the entire field. The conference and the published proceedings that followed G. Dall’Aglio et al. 1991 led to more interest in and involvement with the subject. There followed conferences, with published proceedings, in Seattle in 1993 Ruschendorf et al. 1996 , Prague in 1996 Benes and Stepan 1997 , and Barcelona in 2000 Cuadras et al. 2002 . The century was capped by the publication of An Introduction to Copulas, by R. B. Nelsen 1999 , which has become the standard overview of the basics of the subject. The first years of this new century have witnessed an explosion in copula-centered activity—not only among those who do statistics, but also among those who use statistics. The growth of this activity has been most pronounced in the areas of finance and

Log-Pearson Type 3 Distribution and Its Application in Flood Frequency Analysis. I: Distribution Characteristics

Abstract: Since the adoption of the log-Pearson Type 3 (LP3) distribution by U.S. federal agencies, it has been widely used in hydrology, but its properties are not well understood. This paper explores the characteristics of the LP3 distribution in both real space and log space, and their relationship. Comparisons with U.S. flood data summaries reveal that the LP3 distribution provides a reasonable model of the distribution of annual flood series from unregulated watersheds for log space skews ∣ γx ∣⩽1.414 (though ∣ γx ∣⩽1 is more realistic), and for γx =0 with standard deviations in the range 0.1 to 1.0 with base-e natural logarithms (0.04 to 0.43 with base-10 common logarithms). L-moment ratio relationships for the LP3 distribution are also developed so they can be compared to summary statistics for a region, and to several other distributions frequently recommended for modeling hydrometeorological extremes.

Fully Nested 3-Copula: Procedure and Application on Hydrological Data

Abstract: In multivariate frequency analysis, when the number of variables increases, different mutual structures of dependence among the analyzed quantities are usually observed. To correctly model this behavior, a very flexible joint distribution function is needed. A quite simple approach to build such distributions is based on the copula function. Precisely, using the so-called fully nested or asymmetric Archimedean copulas, it is possible not only to focus attention on the structures of dependence overlooking the margins—a property common to all copulas—but also to analyze more complex asymmetric structures of dependence. The aim of this paper is to describe the inference procedure to carry out a trivariate frequency analysis via asymmetric Archimedean copulas. The writers highlight the differences between the symmetric Archimedean copulas and asymmetric ones, show the inference procedure, and carefully describe some goodness- of-fit tests proposed in the literature to choose the best fitting model when one us...

Using Copulas in Hydrology: Benefits, Cautions, and Issues

Abstract: This paper discusses the bivariate modeling of extreme tails of correlated hydrological random variables. We take a copula approach and model the dependence structure independently of the marginal distributions. We apply results from the classical extreme value theory to choose marginal distributions for excesses of high thresholds and consider six copula families to capture the dependence structure of these excesses. While copulas can differ somewhat in the degree of association that they provide, differences in which part of the distribution this association is more pronounced can be substantial. We discuss certain pertinent properties of copulas and give some insight to assist the practitioner in their selection. We examine the effects of model misspecification and the impact of the chosen method of estimation, targeting the estimated quantities frequently used by hydrologists. A simulation study shows not only the dangers of improper copula selection, but also the possible benefits of using a bivariate approach to estimate univariate quantities. We apply the methodology to the study of low-flow events and analyze two Canadian hydrometric data sets.

IDF Curves Using the Frank Archimedean Copula

Abstract: This study aims at deriving intensity-duration-frequency (IDF) curves from bivariate rainfall frequency analysis using the copula method, which does not assume the rainfall variables to be independent or normal or have the same type of marginal distributions. The bivariate distributions are then employed to determine conditional return periods and IDF curves needed for urban drainage design. The IDF curves are tested using rainfall data from catchments in Louisiana and are also compared with those of Technical Paper 40 (TP-40) of the U.S. National Weather Service as well as those published in the literature.

Improved K-Nearest Neighbor Weather Generating Model

Abstract: A major limitation of K -nearest neighbor based weather generators is that they do not produce new values but merely reshuffle the historical data to generate realistic weather sequences. In this paper, a modified approach is developed that allows nearest neighbor resampling with perturbation of the historic data. A strategy is introduced that resamples the historical data with perturbations while preserving the prominent statistical characteristics, including the interstation correlations. The approach is similar in spirit to traditional autoregressive models except that the new values are obtained by adding a random component to the individual resampled data points. An advantage of the approach is that unprecedented precipitation amounts are generated that are important for the simulation of extreme events. The approach is demonstrated through application to the Upper Thames River Basin in Ontario. Daily weather variables (maximum temperature, minimum temperature, and precipitation) were simulated at mu...

Evolution of Flood Frequency Analysis with Bulletin 17

Abstract: The current methodology recommended for flood-frequency analyses by U.S. Federal agencies is presented in Bulletin 17B. Bulletin 17 was first published in 1976, minor corrections were made in 1977 resulting in Bulletin 17A, which was later succeeded by Bulletin 17B published in 1982. Bulletin 17B sought to resolve ambiguities in the recommended procedures, including the treatment of low outliers, and the use of both regional skew and historical flood information. As the 25th anniversary of the publication of Bulletin 17B approaches, this paper provides a discussion and evaluation of the procedures that document recommends. The fields of hydrology and flood frequency analysis have substantially evolved since Bulletin 17 was first published. New techniques are now available which should become part of these standard procedures. A comparison is provided which demonstrates how the standard and weighted Bulletin 17B quantile estimators perform relative to alternative LP3 quantile estimators that also make use ...

Storm Event and Continuous Hydrologic Modeling for Comprehensive and Efficient Watershed Simulations

Abstract: Based on recent reviews of 11 physically based watershed models, the long-term continuous model soil and water assessment tool (SWAT) and the storm event dynamic watershed simulation model (DWSM) were selected to examine their hydrologic formulations, calibrate, and validate them on the 620 km2 watershed of the upper Little Wabash River at Effingham, Ill., and examine their compatibility and benefits of combining them into a more comprehensive and efficient model. Calibration and validation of the SWAT by comparing monthly simulated and observed flows and adjusting the model-assigned resulted in coefficients of determination and Nash–Sutcliffe coefficients for individual years and cumulatively for the calibration period (1995–1999) and for the entire simulation period (1995–2002) mostly above or near 0.50 with an exception of 0.05 and −0.27 , respectively, in 2001, relatively a dry year. Visual comparisons of the hydrographs showed SWAT’s weakness in predicting monthly peak flows (mostly underpredictions....

Sensitivity of a Distributed Watershed Simulation Model to Spatial Scale

Abstract: The results of distributed watershed models could be sensitive to spatial and temporal scales at which inputs and model parameters are aggregated. This paper reports findings of a detailed sensitivity analysis conducted on the U.S. Department of Agriculture’s distributed watershed simulation model, known as the Soil and Water Assessment Tool (SWAT). The Big Creek Watershed, located in southern Illinois, is used for the study. The model is calibrated to improve accuracy of its streamflow and sediment concentration predictions using observed data at two locations in the study watershed. Streamflow and sediment concentrations that are simulated by the calibrated model at various spatial scales of discritization are extracted and compared, and inputs and model parameters responsible for sensitivity of model responses are identified. Several indices that could be used as indicators of model behavior are also derived. In addition, feasibility analysis of SWAT is conducted to see if the watershed simulation mode...

Linking Long-Term Water Balances and Statistical Scaling to Estimate River Flows along the Drainage Network of Colombia

Abstract: Long-term average river discharges as well as peak and low flows of different return periods are estimated along the entire river network of Colombia, through the conjoint use of the long-term water balance in the river basins and the framework of statistical scaling, taking the average flow field as the scaling variable. Estimation of the long-term water balance considers the spatial variability of hydrologic fields, in which drainage basins are considered the basic hydrological control volumes for estimation. A systematic effort has been made to estimate the long term average precipitation field combining rain gauge measurements with existing handmade expert maps as an input trend for a universal Kriging interpolation technique. Evaluation of estimates for actual and potential long-term evapotrans- piration was implemented using diverse methods. Results were tested using the long term water balance equation against 200 streamflow gauging stations. No method for actual evapotranspiration showed significant superiority. Overall, we conclude that the magnitude of errors arises fundamentally from deficiencies in the data and the sparsity of the observations.

Impacts of Climate Change on Regional Hydrological Regimes in the Spokane River Watershed

Abstract: This study develops and implements a methodology to estimate the impacts of global climate change on regional hydrological regimes using ArcGIS Geostatistical Analyst. The model is easily used and can be expanded to different watersheds. The ArcGIS Geostatistical Analyst interface provides a comprehensive set of tools for creating surfaces from measured sample points compared with the previous method of using adjustable tension continuous curvature surface gridding. As a result, users can rapidly compare different interpolation techniques in order to obtain the best solution. Model results can subsequently be used in GIS models for visualization and analyses. The methodology was applied to the Spokane River Watershed. Results indicate that a 30% precipitation increase causes a 50% increase of streamflow when the temperature is normal compared to only a 20–30% increase in streamflow if the average annual air temperature is 1.5°C higher than normal. Conversely, a 20% precipitation decrease results in approx...

Log-Pearson Type 3 Distribution and Its Application in Flood Frequency Analysis. II: Parameter Estimation Methods

Abstract: A number of parameter estimation methods for the log-Pearson type 3 distribution have been explored in the hydrologic literature, including the method of moments (MOM) in both log space and real space, maximum likelihood estimators (MLEs), and the method of mixed moments (MXM). Several studies have compared MLE and MXM estimators to a so-called Bulletin 17B MOM estimator, but only marginal gains in accuracy are reported and the conclusions are often conflicting. This paper resolves these discrepancies. The observed performance of MLEs can depend critically on the convergence criteria and parameter constraints. Reasonable constraints on parameters can also improve the performance of log space MOM estimators. The method of mixed moments does very well in comparison to MOM and MLE in the absence of regional information. However, a Monte Carlo analysis demonstrates that log space MOM estimators with regional skew information as recommended by Bulletin 17B are likely to be more attractive, and the more precise...

Long lead time forecasting of U.S. streamflow using partial least squares regression

Abstract: Pacific and Atlantic Ocean sea surface temperatures (SSTs) were used as predictors in a long lead-time streamflow forecast model in which the partial least squares regression (PLSR) technique was used with over 600 unimpaired streamflow stations in the continental United States. Initially, PLSR calibration (or test) models were developed for each station, using the previous spring-summer Pacific (or Atlantic) Ocean SSTs as predictors. Regions were identified in the Pacific Northwest, Upper Colorado River Basin, Midwest, and Atlantic states in which Pacific Ocean SSTs resulted in skillful forecasts. Atlantic Ocean SSTs resulted in significant regions being identified in the Pacific Northwest, Midwest, and Atlantic states. Next, streamflow stations were selected in the Columbia River Basin, Upper Colorado River Basin, and Mississippi River Basin and a PLSR cross-validation model (i.e., forecast) was developed. The results of the PLSR cross-validation model for each station varied with linear error in probab...

Artificial Neural Networks Approximation of Density Dependent Saltwater Intrusion Process in Coastal Aquifers

Abstract: The flow and transport processes in a coastal aquifer are highly nonlinear, where both the flow and transport processes become density dependent. Therefore, numerical simulation of the saltwater intrusion process in such an aquifer is complex and time consuming. An approximate simulation of those complex flow and transport processes may be very useful, if sufficiently accurate, especially where repetitive simulations of these processes are necessary. A simulation methodology using a trained artificial neural network model (ANN) is developed to approximate the three-dimensional density dependent flow and transport processes in a coastal aquifer. The data required for initially training the ANN model is generated by using a numerical simulation model (FEMWATER). The simulated data consisting of corresponding sets of input and output patterns are used to train a multilayer perceptron using the back-propagation algorithm. The trained ANN predicts the concentration at specified observation locations at differe...

Cluster-Based Hydrologic Prediction Using Genetic Algorithm-Trained Neural Networks

Abstract: Most hydrological processes are nonlinear in nature. Although there have been many successful applications of artificial neural networks (ANNs) to capture these nonlinear relationships, there are cases when ANNs have not been able to predict flow extremes (low and high flows) accurately. In a more general sense, ANNs have not performed well when data are clustered. The objective of this study is to demonstrate the influence of clustering on neural network performance by constructing a cluster-based conjunction model based on clustering, neural networks, and genetic algorithm (GA). The performance of the GA-trained cluster-based model is compared to that of the Bayesian regularization back-propagation algorithm, the Levenberg–Marquatrdt algorithm, and a regular GA-trained ANN model. The cluster-based neural network model was tested on (1) chaotic time series data (the Henon map); (2) cross-correlated monthly streamflow data. Results from the study indicate that the cluster-based neural network model offers...

Derivation of a Distributed Unit Hydrograph Integrating GIS and Remote Sensing

Abstract: The paper describes the results of a study based on the integration of remote sensing and geographical information system techniques to evaluate a distributed unit hydrograph model linked to an excess rainfall model for estimating the streamflow response at the outlet of a watershed. Travel time computation, based on the definition of a distributed unit hydrograph, has been performed, implementing a procedure using (1) a cell-to-cell flow path through the landscape determined from a digital elevation model (DEM); and (2) roughness parameters obtained from remote sensing data. This procedure allows the taking into account of the differences, in terms of velocity, between the hillslopes and the stream system. The proposed procedure has been applied to two watersheds in Sicily, in order to establish the level of agreement between the estimated and recorded hydrographs, using as a tool to calculate the excess rainfall a simplified version of the probability distributed model.

Response of Streamflow to Weather Variability under Climate Change in the Colorado Rockies

Abstract: We examine the response of streamflow to long-term rainfall variability under climate change by coupling downscaled global climate model precipitation to a distributed hydrologic model. We use daily output of the coupled global climate model (CGCM2) of the Canadian Centre for Climate Modelling and Analysis corresponding to the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios B2 scenario. The B2 scenario envisions slower population growth (10.4 billion by 2100) with a more rapidly evolving economy and more emphasis on environmental protection. We use the Hydrologic Modeling System of the Hydrologic Engineering Center for distributed hydrologic modeling. Because of the incongruence between the spatial scale of the CGCM2 output and that of the hydrologic model, a new space-time stochastic random cascade model was implemented in order to downscale the CGCM2 precipitation. The downscaling model accounts for the observed spatial intermittency of precipitation as well as for the sel...

Quantifying Long-Term Hydrologic Response in an Urbanizing Basin

Abstract: Long-term hydrologic response is described within a rapidly developing watershed west of Washington, D.C. Data consist of up to 24 years of observed rainfall, basin discharge, and land use/land cover from four headwater basins of the Occoquan River. Three of the four study basins, ranging in size from 67 to 400 km², are predominantly forest and mixed agriculture. The fourth basin, the 127 km² Cub Run watershed, which is the focus of this study, has urbanized rapidly over the past 20 years (current impervious surface approximately 18%). Results indicate that Cub Run basin has higher annual and seasonal storm discharge per surface area than nonurban basins after 1983, when impervious surface in Cub Run basin reached approximately 9%. Only during the summer and fall is long-term storm runoff in Cub Run basin higher than nonurban basins. Long-term results support expected biophysical reductions in interception, infiltration, and evapotranspiration due to higher imperviousness, indicating that these reductions persist throughout the growing season, unlike adjacent nonurban areas.

New runoff simulation model for small urban catchments

Abstract: This note presents a new runoff simulation model based on the improvement of the rational hydrograph method. This improved rational hydrograph method explicitly considers the contribution of pervious and impervious areas, the time variability of rainfall, the initial abstraction on impervious areas, and the infiltration on pervious areas. Moreover, the traditional rational method appears to be a special case of the proposed rational hydrograph method. A structured procedure is proposed to calibrate parameters of the improved rational hydrograph method on the basis of appropriate storm events. The proposed method was applied to ten rainfall events gauged in two different urban catchments. Runoff computed with the improved rational hydrograph method was compared to measured runoff, as well as to the runoff derived from the nonlinear reservoir model. A good agreement was achieved between simulated and observed runoff hydrographs. Moreover, runoffs derived with the proposed method are equivalent to those comp...

Flood Regionalization: A Hybrid Geographic and Predictor-Variable Region-of-Influence Regression Method

Abstract: To facilitate estimation of streamflow characteristics at an ungauged site, hydrologists often define a region of influence containing gauged sites hydrologically similar to the estimation site. This region can be defined either in geographic space or in the space of the variables that are used to predict streamflow (predictor variables). These approaches are complementary, and a combination of the two may be superior to either. Here we propose a hybrid region-of-influence (HRoI) regression method that combines the two approaches. The new method was applied with streamflow records from 1,091 gauges in the southeastern United States to estimate the 50-year peak flow ( Q50 ) . The HRoI approach yielded lower root-mean-square estimation errors and produced fewer extreme errors than either the predictor-variable or geographic region-of-influence approaches. It is concluded, for Q50 in the study region, that similarity with respect to the basin characteristics considered (area, slope, and annual precipitation)...

Flow to a Well in a Two-Aquifer System

Abstract: An approximate solution for flow to a well in an aquifer overlain by both an aquitard and a second aquifer containing a free surface is obtained from numerical inversions of exact analytical solutions for Laplace transforms. This study extends previous work on this topic in three ways: (1) it improves an eigenvalue decomposition solution method used earlier by Hemker and Maas in 1987 for unsteady flow in multiaquifer systems; (2) it obtains an approximate solution that is simpler to evaluate than previously known solutions; and (3) it utilizes a much more general solution from a MODFLOW finite-difference model to explore both limitations of the approximate solution and the physical behavior of the two-aquifer system. In addition, criteria are obtained that show when the two-aquifer solution can be replaced with the simpler Boulton solution when analyzing pumping test field data.