A Computer Method for Pumping‐Test Analysis

Abstract

The knowledge of aquifer parameters is invaluable for quantitative description of geohydrologic systems and for optimal utilization of ground‐water resources. The classical methods of pumping‐test analysis are mostly graphical in nature and there is room for error in individual judgment in the geographical analysis. The computer method for aquifer evaluation comes under the inverse analysis techniques and is based on the classical principle of least squares. The sum of the weighted squares of differences between the observed drawdowns and the drawdowns calculated using the theoretical drawdown equations for the flow system under consideration is minimized, treating the aquifer parameters as decision variables. The method can be applied to any flow system for which analytical expressions for the potential distribution are known. The method was successfully applied to four pumping tests, two in nonleaky confined flow 1 systems, one in a leaky confined flow system, and one in an anisotropic nonleaky confined flow system. The percentage differences between the values of storativity and of transmissivity arrived at by the two methods are found to be as much as 24‐and 1.6 percents, respectively.

A HYBRID OPTIMIZATION APPROACH TO THE ESTIMATION OF DISTRIBUTED PARAMETERS IN TWO‐DIMENSIONAL CONFINED AQUIFERS1

“…In recent years, hydrogeologists have investigated various methodologies for estimating these parameters using sparse observations of the hydraulic head behavior of an aquifer, results of previous aquifer tests and boundary identification, called "prior information," and some form of a performance function (Carrera, 1984), and have been applied to synthetic and field data for regional and local aquifers (Haimes et al, 1968;Saleem, 1970;Knowles et al, 1972;Car-rera and Neuman, 1986c;Heidari, 1992). Many of these studies use a weighted least squares function as the performance function.…”

Variability in the vertical hyporheic water exchange affected by hydraulic conductivity and river morphology at a natural confluent meander bend

“…Additionally, so that the minimum value of F(β) can be yielded, the Solver tool (a macro command) in Microsoft Excel was used to FIGURE 4 Temperature measurements with one pipe at multiple depths of riverbed sediment near Location 5 from January 13 to 15, 2016, indicating the approximate steady-state conditions of the temperature profiles during the field campaign period in winter determine the optimum value of β (Saleem, 1970). After F(β) was minimized to approximately 0 (satisfying the requirement of Equation 2), the hyporheic water exchange flux (q) was determined using the one-dimensional steady-state heat equation (Equation 8).…”

The use of sensitivity analysis in on-line aquifer parameter estimation

“…The aquifer parameters can also be obtained by parameter estimation model (PEM) which coupled an analytical solution or a numerical model in terms of aquifer drawdown along with a numerical approach such as nonlinear programming (e.g., Saleem, 1970), Marquardt algorithm (e.g., Chander et al, 1981), sensitivity matrix, (McElwee, 1980;Paschetto and McElwee, 1982), nonlinear least-squares and Newton's method (e.g., Yeh, 1987;Yeh and Han, 1989), nonlinear regression (e.g., Lebbe, 1999), and extended Kalman filter (e.g., Leng and Yeh, 2003;Yeh and Huang, 2005). Some commercial softwares, like AQTESOLV (Duffield, 2002), also use nonlinear weighted least-squares approach to match the time-displacement data obtained from an aquifer test with type curves or straight lines for parameter estimation.…”