Identification of aquifer parameters from residual drawdowns: an optimization approach

Abstract

An optimization method based on nonlinear least squares is proposed for the identification of aquifer transmissivity and two storage coefficients, one for the pumping phase and the other for the recovery phase, using only residual drawdowns at an observation well. The method is applicable irrespective of whether pumping and recovery storage coefficients are the same or are different and the argument of well function during recovery is less than or greater than 0.01. The method can check for the changed value of storage coefficient during the recovery phase and can quantify this change. Use of the method is illustrated through examples, which show that the parameters are estimated reliably.     

Estimation of unconfined aquifer parameters by genetic algorithms

Abstract

Unconfined aquifer parameters, viz. transmissivity, storage coefficient, specific yield and delay index from a pumping test are estimated using the genetic algorithm optimization (GA) technique. The parameter estimation problem is formulated as a least-squares optimization, in which the parameters are optimized by minimizing the deviations between the field-observed and the model-predicted time–drawdown data. Boulton's convolution integral for the determination of drawdown is coupled with the GA optimization technique. The bias induced by three different objective functions: (a) the sum of squares of absolute deviations between the observed and computed drawdown; (b) the sum of squares of normalized deviations with respect to the observed drawdown; and (c) the sum of squares of normalized deviations with respect to the computed drawdown, is statistically analysed. It is observed that, when the time–drawdown data contain no errors, the objective functions do not induce any bias in the parameter estimates and the true parameters are uniquely identified. However, in the presence of noise, these objective functions induce bias in the parameter estimates. For the case considered, defining the objective function as the sum of the squares of absolute deviations between the observed and simulated drawdowns resulted in the best possible estimates. A comparison of the GA technique with the curve-matching procedure and a conventional optimization technique, such as the sequential unconstrained minimization technique (SUMT), is made in estimating the aquifer parameters from a reported field pumping test in an unconfined aquifer. For the case considered, the GA technique performed better than the other two techniques in parameter estimation, with the sum-of-squares errors obtained from the GA about one fourth of those obtained by the curve matching procedure, and about half of those obtained by SUMT.

Citation Rajesh, M., Kashyap, D. & Hari Prasad, K. S. (2010) Estimation of unconfined aquifer parameters by genetic algorithms. Hydrol. Sci. J. 55(3), 403–413.     

Estimating anisotropic aquifer parameters by artificial neural networks

In recent years, many approaches have been developed using the artificial neural networks (ANN) model incorporated with the Theis analytical solution to estimate the effective hydrological parameters for homogeneous and isotropic porous media, such as the Lin and Chen approach (ANN approach) and the principal component analysis (PCA)‐ANN approach. The above methods assume a full superimposition of the type curve and the observed drawdown and try to use the first time‐drawdown data as a match point to make a fine approximation of the effective parameters. However, using first time‐drawdown data or early time‐drawdown data does not always allow for an accurate estimation of the hydrological parameters, especially for heterogeneous and anisotropic aquifers. Therefore, this article corrects the concept of the superimposed plot by modifying the ANN approach and the PCA‐ANN approach, as well as incorporating the Papadopoulos analytical solution, to estimate the transmissivities and storage coefficient for anisotropic, homogeneous aquifers. The ANN model is trained with 4000 training sets of the well function, and tested with 1000 sets and 300 sets of synthetic time‐drawdown generated from the homogeneous and heterogeneous parameters, respectively. In situ observation data from the time‐drawdown at station Shi‐Chou on the Choushui River alluvial fan, Taiwan, is further adopted to test the applicability and reliability of the proposed methods, as well as provide a basis for comparison with the Straight‐line method and the Type‐curve method. Results suggest that both of the modified methods perform better than the original ones, and using late time‐drawdown to optimize the effective parameters is shown to be better than using early time‐drawdown. Additionally, results indicate that the modified ANN approach is better than the modified PCA‐ANN approach in terms of precision, while the efficiency of the modified PCA‐ANN approach is approximately three times better than that of the modified ANN approach. Copyright © 2010 John Wiley & Sons, Ltd.     

Balancing cost-risk in management optimization of water resource systems under uncertainty

When a scarce water resource is distributed between different users by a Water Resource Management Authority (WRMA), the replenishment of this resource as well as the meeting of users’ demand is subject to considerable uncertainty. Cost optimization and risk management models can assist the WRMA in its decision about striking the balance between the level of target delivery to the users and the level of risk that this delivery will not be met. Addressing the problem as a multi-period dynamic network optimization, the proposed approach is also based on further developments in stochastic programming for scenario optimization. This approach tries to obtain a “robust” decision policy that minimizes the risk of wrong decisions when managing scarce water resources. In the paper we also illustrate two application examples for water resources management problems.     

New graphical methods for estimating aquifer hydraulic parameters using pumping tests with exponentially decreasing rates

Actual pumping tests may involve continuously decreasing rates over a certain period of time, and the hydraulic conductivity (K) and specific storage (S_s) of the tested confined aquifer cannot be interpreted from the classical constant‐rate test model. In this study, we revisit the aquifer drawdown characteristics of a pumping test with an exponentially decreasing rate using the dimensionless analytical solution for such a variable‐rate model. The drawdown may decrease with time for a short period of time at intermediate pumping times for such pumping tests. A larger ratio of initial to final pumping rate and a smaller radial distance of the observation well will enhance the decreasing feature. A larger decay constant results in an earlier decrease, but it weakens the extent of such a decrease. Based on the proposed dimensionless transformation, we have proposed two graphical methods for estimating K and S_s of the tested aquifer. The first is a new type curve method that does not employ the well function as commonly done in standard type curve analysis. Another is a new analytic method that takes advantage of the decreasing features of aquifer drawdown during the intermediate pumping stage. We have demonstrated the applicability and robustness of the two new graphical methods for aquifer characterization through a synthetic pumping test.     

近断层脉冲型地震动重要参数的识别方法

在拟合或合成近断层脉冲型地震动的研究中,速度等效脉冲中的参数需要依据实际地震动确定,因此,如何从地震动中识别这些参数就成为一个需要解决的问题.详细分析了已有脉冲周期和脉冲速度峰值的识别方法,分析结果表明这些方法均具有局限性,尤其是对于不规则地震动.提出了基于平滑后的地震动识别脉冲周期和脉冲速度峰值的方法,并验证了这种识别方法的有效性.结果表明: 提出的脉冲周期和脉冲速度峰值识别方法可以克服以往方法的局限性,可以实现脉冲周期和脉冲速度峰值识别的程序化.     

Investigating the uncertainty and transferability of parameters in SWAT model under climate change

ABSTRACT

There is an implicit assumption in most work that the parameters calibrated based on observations remain valid for future climatic conditions. However, this might not be true due to parameter instability. This paper investigates the uncertainty and transferability of parameters in a hydrological model under climate change. Parameter transferability is investigated with three parameter sets identified for different climatic conditions, which are: wet, intermediate and dry. A parameter set based on the baseline period (1961–1990) is also investigated for comparison. For uncertainty analysis, a k-simulation set approach is proposed instead of employing the traditional optimization method which uses a single best-fit parameter set. The results show that the parameter set from the wet sub-period performs the best when transferred into wet climate condition, while the parameter set from the baseline period is the most appropriate when transferred into dry climate condition. The largest uncertainty of simulated daily high flows for 2011–2040 is from the parameter set trained in the dry sub-period, while that of simulated daily medium and low flows lies in the parameter set from the intermediate calibration sub-period. For annual changes in the future period, the uncertainty with the parameter set from the intermediate sub-period is the largest, followed by the wet sub-period and dry sub-period. Compared with high and medium flows/runoffs, the uncertainty of low flows/runoffs is much smaller for both simulated daily flows and annual runoffs. For seasonal runoffs, the largest uncertainty is from the intermediate sub-period, while the smallest is from the dry sub-period. Apart from that, the largest uncertainty can be observed for spring runoffs and the lowest one for autumn runoffs. Compared with the traditional optimization method, the k-simulation set approach shows many more advantages, particularly being able to provide uncertainty information to decision support for watershed management under climate change.

EDITOR Z.W. Kundzewicz ASSOCIATE EDITOR not assigned     

地震作用下结构时变物理参数识别

利用小波分析函数多尺度逼近方法,将剪切型结构在地震作用下时变的阻尼和刚度用尺度函数的线性组合表示,将时变参数的辨识问题转化为由已知的正交尺度函数和系统的输入输出来估计线性组合中的时不变系数问题,用最小二乘法对剪切型框架结构在地震作用下时变的刚度和阻尼进行了有效的辨识。此方法无需事先假定系统参数的时变规律,在有噪声情况下可以用Tikhonov正则化方法减小识别方程的不适定性对识别结果的影响。数值算例表明了该方法的有效性。     

Probabilistic modeling of aquifer heterogeneity using reliability methods

A probabilistic model of groundwater contaminant transport is presented. The model is based on coupling first- and second-order reliability methods (FORM and SORM) with a two-dimensional finite element solution of groundwater transport equations. Uncertainty in aquifer media is considered by modeling hydraulic conductivity as a spatial random field with a prescribed correlation structure. FORM and SORM provide the probability that a contaminant exceeds a target level at a well, termed the probability of failure. Sensitivity of the probability of failure to basic variabilities in grid block conductivity is also obtained, at no additional computational effort. The effect of the choice of the predetermined target level at the observation well is provided, along with its effect on the relevant sensitivity information. Considerable saving in computational time was achieved by superimposing a coarse random variables mesh on a finer numerical mesh. The presence of regions of lower conductivity on the probabilistic event is analyzed, and the regions in which conductivity most affects the results are identified.     

Identification of heterogeneous aquifer transmissivity using an AE-based method

Determination of hydraulic head, H, as a function of spatial coordinates and time, in ground water flow is the basis for aquifer management and for prediction of contaminant transport. Several computer codes are available for this purpose. Spatial distribution of the transmissivity, T(x,y), is a required input to these codes. In most aquifers, T varies in an erratic manner, and it can be characterized statistically in terms of a few moments: the expected value, the variance, and the variogram. Knowledge of these moments, combined with a few measurements, permits one to estimate T at any point using geostatistical methods. In a review of transmissivity data from 19 unconsolidated aquifers, Hoeksema and Kitanidis (1985) identified two types of the logtransmissivity Y= ln(T) variations: correlated variations with variance sigma2Yc and correlation scale, I(Y), on the order of kilometers, and uncorrelated variations with variance sigma2Yn. Direct identification of the logtransmissivity variogram, Gamma(Y), from measurements is difficult because T data are generally scarce. However, many head measurements are commonly available. The aim of the paper is to introduce a methodology to identify the transmissivity variogram parameters (sigma2Yc, I(Y), and sigma2Yn) using head data in formations characterized by large logtransmissivity variance. The identification methodology uses a combination of precise numerical simulations (carried out using analytic element method) and a theoretical model. The main objective is to demonstrate the application of the methodology to a regional ground water flow in Eagle Valley basin in west-central Nevada for which abundant transmissivity and head measurements are available.     

Hydraulic parameters of a multilayered aquifer system in Kuwait City

The Kuwait Group consists mainly of clastic sediments overlying unconformably the Dammam Formation of Tertiary age. The Kuwait Group is generally divided into three main hydrostratigraphic units: the upper and lower aquifers separated by an aquitard. The upper aquifer is further divided into the water table aquifer, an aquitard and a semiconfined aquifer. This semiconfined unit was pumped and the drawdowns were observed in piezometers screened in various subunits of the Kuwait Group. Some pumping tests of short duration were carried out in the top water table aquifer as well. These tests showed that the subunits of the Kuwait Group are hydraulically interconnected to a varying degree.

The pumping test data were analysed using conventional analytical solutions. The semiconfined pumping test was also simulated by a quasi-three-dimensional model using a leaky multiaquifer modelling technique. The initial hydraulic parameters were improved manually in the model till best fit drawdowns were obtained.

The final parameters obtained by simulation of the pumping tests were used in designing a pilot drainage system for the control of a rising groundwater table in parts of Kuwait City.     

Estimation of spatial variability of aquifer parameters from geophysical methods: a case study of Sindhudurg district,Maharashtra, India

Accurate estimation of aquifer parameters, especially from crystalline hard rock area, assumes a special significance for management of groundwater resources. The aquifer parameters are usually estimated through pumping tests carried out on water wells. While it may be costly and time consuming for carrying out pumping tests at a number of sites, the application of geophysical methods in combination with hydro-geochemical information proves to be potential and cost effective to estimate aquifer parameters. Here a method to estimate aquifer parameters such as hydraulic conductivity, formation factor, porosity and transmissivity is presented by utilizing electrical conductivity values analysed via hydro-geochemical analysis of existing wells and the respective vertical electrical sounding (VES) points of Sindhudurg district, western Maharashtra, India. Further, prior to interpolating the distribution of aquifer parameters of the study area, variogram modelling was carried out using data driven techniques of kriging, automatic relevance determination based Bayesian neural networks (ARD-BNN) and adaptive neuro-fuzzy neural networks (ANFIS). In total, four variogram model fitting techniques such as spherical, exponential, ARD-BNN and ANFIS were compared. According to the obtained results, the spherical variogram model in interpolating transmissivity, ARD-BNN variogram model in interpolating porosity, exponential variogram model in interpolating aquifer thickness and ANFIS variogram model in interpolating hydraulic conductivity outperformed rest of the variogram models. Accordingly, the accurate aquifer parameters maps of the study area were produced by using the best variogram model. The present results suggest that there are relatively high value of hydraulic conductivity, porosity and transmissivity at Parule, Mogarne, Kudal, and Zarap, which would be useful to characterize the aquifer system over western Maharashtra.     

Determination of aquifer parameters based on measurements of tidal effects on a coastal aquifer near Beihai,China

Measurements of the tide and groundwater levels in coastal zones are of importance in determining the properties of coastal aquifers. The solution to a one‐dimensional unsteady groundwater flow model in a coastal confined aquifer with sinusoidal fluctuation of the tide shows that the tidal efficiency decreases exponentially with distance and the time lag increases linearly with distance from the coast. The aquifer property described by the ratio of storage coefficient to transmissivity is determined if the damping constant of the tidal efficiency or the slope of the time lag with distance are obtained on the basis of tidal measurements. Hourly observations of the tide and groundwater levels at 10 wells on the northern coast near Beihai, China show that with distance from the coast, tidal efficiency decreases roughly exponentially and the time lag increases roughly linearly. The estimated ratio of storage coefficient to transmissivity of the confined aquifer ranges from 1·169 × 10^?6 d m^?2 to 1·83 × 10^?7 d m^?2. For a given transmissivity of 750 m² d^?1, the storage coefficient of the aquifer is 8·7675 × 10^?4 with the tidal efficiency method and 1·3725 × 10^?4 with the time lag method. The damping constant of the tidal efficiency with distance can be defined as the tidal propagation coefficient. The value of the confined aquifer is determined as 0·0018892 m^?1. Copyright © 2007 John Wiley & Sons, Ltd.     

关于井炮激发参数优化设计的思考

利用正演模拟的手段进行分析,对影响井炮激发的不同影响因素展开分析,结合对炸药特征阻抗与围岩的阻抗匹配系数、炸药几何耦合系数、激发药量、井深等方面进行分析,对井炮激发参数进行优化设计.通过优化设计,寻求有效提高地震子波品质的方法.结合实际的地震地质条件和地震勘探目标,设计最佳的井炮激发参数,提高地震勘探的效益.     

A stochastic optimization framework for the restoration of an over-exploited aquifer

ABSTRACT

This study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.

Editor Z.W. Kundzewicz; Guest editor S. Weijs     

Field study of hydrogeologic characterization methods in a heterogeneous aquifer

Hydraulic conductivity (K) and specific storage (S(s)) are required parameters when designing transient groundwater flow models. The purpose of this study was to evaluate the ability of commonly used hydrogeologic characterization approaches to accurately delineate the distribution of hydraulic properties in a highly heterogeneous glaciofluvial deposit. The metric used to compare the various approaches was the prediction of drawdown responses from three separate pumping tests. The study was conducted at a field site, where a 15 m × 15 m area was instrumented with four 18-m deep Continuous Multichannel Tubing (CMT) wells. Each CMT well contained seven 17 cm × 1.9 cm monitoring ports equally spaced every 2 m down each CMT system. An 18-m deep pumping well with eight separate 1-m long screens spaced every 2 m was also placed in the center of the square pattern. In each of these boreholes, cores were collected and characterized using the Unified Soil Classification System, grain size analysis, and permeameter tests. To date, 471 K estimates have been obtained through permeameter analyses and 270 K estimates from empirical relationships. Geostatistical analysis of the small-scale K data yielded strongly heterogeneous K fields in three-dimensions. Additional K estimates were obtained through slug tests in 28 ports of the four CMT wells. Several pumping tests were conducted using the multiscreen and CMT wells to obtain larger scale estimates of both K and S(s). The various K and S(s) estimates were then quantitatively evaluated by simulating transient drawdown data from three pumping tests using a 3D forward numerical model constructed using HydroGeoSphere (Therrien et al. 2005). Results showed that, while drawdown predictions generally improved as more complexity was introduced into the model, the ability to make accurate drawdown predictions at all CMT ports was inconsistent.     

An improved neural network approach to the determination of aquifer parameters

In this paper, an artificial neural network (ANN) approach to the determination of aquifer parameters is developed. The approach is based on the combination of an ANN and the Theis solution. The proposed ANN approach has advantages over the existing ANN approach. It avoids inappropriate setting of a trained range. It also determines the aquifer parameters more accurately and needs less required training time. Testing the existing and the proposed ANN approaches by 1000 sets of synthetic data also demonstrates these advantages. As to the comparison between the proposed ANN approach and the type-curve graphical method, an application to actual time-drawdown data shows that the proposed ANN approach determines the aquifer parameters more precisely. The proposed ANN approach is recommended as an alternative to the type-curve graphical method and the existing ANN approach.     

3种远震P波到时拾取方法的比较及其参数优化

分析了自回归赤池信息准则（AR-AIC）、高阶统计量（HOS）和累积和（CUSUM）等3种到时拾取方法中参数对远震P波到时估计的影响,以450个远震P波信号为样本集,参考人工拾取到时,以网格搜索方式确定了每种方法的最优参数。之后重新选取信噪比处于[2,20]区间的100个远震P波信号,用确定最优参数后的3种到时拾取方法估计其P波初至时间,并比较了3种方法对低信噪比远震P波的拾取准确度。结果表明,AR-AIC方法和CUSUM方法对低信噪比远震P波的拾取准确度要优于HOS方法,CUSUM方法的计算速度最快,HOS方法由于其原理的限制更适用于信噪比较大、初动较尖锐的信号。      

Analysis of methods to estimate spring flows in a karst aquifer

Hydraulically and statistically based methods were analyzed to identify the most reliable method to predict spring flows in a karst aquifer. Measured water levels at nearby observation wells, measured spring pool altitudes, and the distance between observation wells and the spring pool were the parameters used to match measured spring flows. Measured spring flows at six Upper Floridan aquifer springs in central Florida were used to assess the reliability of these methods to predict spring flows. Hydraulically based methods involved the application of the Theis, Hantush-Jacob, and Darcy-Weisbach equations, whereas the statistically based methods were the multiple linear regressions and the technology of artificial neural networks (ANNs). Root mean square errors between measured and predicted spring flows using the Darcy-Weisbach method ranged between 5% and 15% of the measured flows, lower than the 7% to 27% range for the Theis or Hantush-Jacob methods. Flows at all springs were estimated to be turbulent based on the Reynolds number derived from the Darcy-Weisbach equation for conduit flow. The multiple linear regression and the Darcy-Weisbach methods had similar spring flow prediction capabilities. The ANNs provided the lowest residuals between measured and predicted spring flows, ranging from 1.6% to 5.3% of the measured flows. The model prediction efficiency criteria also indicated that the ANNs were the most accurate method predicting spring flows in a karst aquifer.