On solution of the inverse problem for confined aquifer flow via maximum likelihood

Joint estimation of transmissivity (T) and storativity (S) in a confined aquifer is done via maximum likelihood (ML). The differential equation of groundwater flow is discretized by the finite-element method, leading to equation _t+x _t=u _t. Elements of matrices and , as well as estimated covariance matrix of noise termu _t, are functions of T and S. By minimizing the negative loglikelihood function corresponding to discretized groundwater flow equation with respect to T and S, ML estimators are obtained. The ML approach is found to yield accurate estimates of T and S (within 9 and 10% of their actual values, respectively) and showed quadratic convergence in Newton's search technique. Prediction of aquifer response, using ML estimators, results in estimated piezometric heads accurate to ±0.5 m from their actual, exact values. Statistical properties of ML estimators are derived and some basic results for statistical inference are given.     

Groundwater for People and the Environment: A Globally Threatened Resource

The intensity of global groundwater use rose from 124 m³ per capita in 1950 to 152 m³ in 2021, for a 22.6% rise in the annual per capita use. This rise in global per capita water use reflects rising consumption patterns. The global use of groundwater, which provides between 21% and 30% of the total freshwater annual consumption, will continue to expand due to the sustained population growth projected through most of the 21st century and the important role that groundwater plays in the water-food-energy nexus. The rise in groundwater use, on the other hand, has inflicted adverse impacts in many aquifers, such as land subsidence, sea water intrusion, stream depletion, and deterioration of groundwater-dependent ecosystems, groundwater-quality degradation, and aridification. This paper projects global groundwater use between 2025 and 2050. The projected global annual groundwater withdrawal in 2050 is 1535 km³ (1 km³ = 10⁹ m³ = 810,713 acre-feet). The projected global groundwater depletion, that is, the excess of withdrawal over recharge, in 2050 equals 887 km³, which is about 61% larger than in 2021. This projection signals probable exacerbation of adverse groundwater-withdrawal impacts, which are worsened by climatic trends and the environmental requirement of groundwater flow unless concerted national and international efforts achieve groundwater sustainability.     

Planning Urban Growth in Ground Water Recharge Areas: Central Valley, Costa Rica

Nitrate levels in the ground water of the Central Valley of Costa Rica have increased in relation to the past. Previous studies determined that the unseweved sanitation systems in the recharge areas are the main source of nitrogen. Calculations are made in this study to estimate the maximum population density allowable without improved sewage systems in order to keep the nitrogen levels in ground water below the World Health Organization (WHO) criteria. Results were achieved employing a mass balance that involved the concentration and rate of domestic effluents and the flow rate in the aquifer, as well as an estimation of the effects caused by the agricultural activity. It was concluded that, in general terms, the population density must not exceed 45 inhabitants per hectare. Otherwise, sewage systems and treatment plants are necessary. These conclusions provide a basis for urban growth planning, which will protect ground water quality. The method used in this case should apply to similar situations.     

Linear spatial interpolation: Analysis with an application to San Joaquin Valley

The properties of linear spatial interpolators of single realizations and trend components of regionalized variables are examined in this work. In the case of the single realization estimator explicit and exact expressions for the weighting vector and the variances of estimator and estimation error were obtained from a closed-form expression for the inverse of the Lagrangian matrix. The properties of the trend estimator followed directly from the Gauss-Markoff theorem. It was shown that the single realization estimator can be decomposed into two mutually orthogonal random functions of the data, one of which is the trend estimator. The implementation of liear spatial estimation was illustrated with three different methods, i.e., full information maximum likelihood (FIML), restricted maximum likelihood (RML), and Rao's minimum norm invariant quadratic unbiased estimation (MINQUE) for the single realization case and via generalized least squares (GLS) for the trend. The case study involved large correlation length-scale in the covariance of specific yield producing a nested covariance structure that was nearly positive semidefinite. The sensitivity of model parameters, i.e., drift and variance components (local and structured) to the correlation length-scale, choice of covariance model (i.e., exponential and spherical), and estimation method was examined. the same type of sensitivity analysis was conducted for the spatial interpolators. It is interesting that for this case study, characterized by a large correlation length-scale of about 50 mi (80 km), both parameter estimates and linear spatial interpolators were rather insensitive to the choice of covariance model and estimation method within the range of credible values obtained for the correlation length-scale, i.e., 40–60 mi (64–96 km), with alternative estimates falling within ±5% of each other.     

1-, 2-, and 3-dimensional effective conductivity of aquifers

Starting with a stochastic differential equation with random coefficients describing steady-state flow, the effective hydraulic conductivity of 1-, 2-, and 3-dimensional aquifers is derived. The natural logarithm of hydraulic conductivity (lnK) is assumed to be heterogeneous, with a spatial trend, and isotropic. The effective conductivity relates the mean specific discharge in an aquifer to the mean hydraulic gradient, thus its importance in predicting Darcian discharge when field data represent mean or average values of conductivity or hydraulic head. Effective conductivity results are presented in exact form in terms of elementary functions after the introduction of special sets of coordinate transformations in two and three dimensions. It was determined that in one, two, and three dimensions, for the type of aquifer heterogeneity considered, the effective hydraulic conductivity depends on: (i) the angle between the gradient of the trend of lnK and the mean hydraulic gradient (which is zero in the one-dimensional situation); (2) (inversely) on the product of the magnitude of the trend gradient of lnK, b, and the correlation scale of lnK, and (3) (proportionally) on the variance of lnK, _f ² . The productb plays a central role in the stability of the results for effective hydraulic conductivity.     

Effective hydraulic conductivity of nonstationary aquifers

Assuming that the ln hydraulic conductivity in an aquifer is mathematically approximated by a spatial deterministic surface, or trend, plus a stationary random noise, we treat the problem of finding what the effective hydraulic conductivity of that aquifer is. This problem is tackled by spectral methods applied to a type of diffusion equation of groundwater flow, together with suitable coordinate transformations. Analytical (exact) solutions in terms of elementary functions are presented for one- and three-dimensional finite and infinite domains. Stability criteria are obtained for the solutions, in terms of a critical parameter, that turns out to involve the product of correlation scale and trend gradient. For the case of finite and symmetrical domains, additional provisions to insure the stability of numerical calculations of effective hydraulic conductivity are provided. Effective hydraulic conductivity is an important property, with potential applications in the calibrations of groundwater and transport numerical models.     

Effective hydraulic conductivity of nonstationary aquifers

Assuming that the ln hydraulic conductivity in an aquifer is mathematically approximated by a spatial deterministic surface, or trend, plus a stationary random noise, we treat the problem of finding what the effective hydraulic conductivity of that aquifer is. This problem is tackled by spectral methods applied to a type of diffusion equation of groundwater flow, together with suitable coordinate transformations. Analytical (exact) solutions in terms of elementary functions are presented for one- and three-dimensional finite and infinite domains. Stability criteria are obtained for the solutions, in terms of a critical parameter, that turns out to involve the product of correlation scale and trend gradient. For the case of finite and symmetrical domains, additional provisions to insure the stability of numerical calculations of effective hydraulic conductivity are provided. Effective hydraulic conductivity is an important property, with potential applications in the calibrations of groundwater and transport numerical models.     

Estimation of Submarine Groundwater Discharge

Leading methods for the evaluation of submarine groundwater discharge are presented, and their possible application under different hydrogeological conditions is discussed.     

Stochastic groundwater flow analysis in the presence of trends in heterogeneous hydraulic conductivity fields

Due to changes in lithostatic pressure, differential fracturing across bedding planes and irregularities in depositional environments, hydraulic conductivity exhibits heterogeneities and trends at various spatial scales. Using spectral theory, we have examined the effect of trends in hydraulic conductivity on (1) the solution of the mean equation for hydraulic head, (2) the covariance of hydraulic head, (3) the cross-covariances of hydraulic head and log-hydraulic conductivity perturbations and their gradients, and (4) the effective hydraulic conductivity. It is shown that the field of hydraulic head is sensitive to the presence of trends in ways that cannot be predicted by the classical analysis based on stationary hydraulic conductivity fields. The controlling variables for the second moments of hydraulic head are the mean hydraulic gradient, the correlation scale of log-hydraulic conductivity and its variance, and the slope of the trend in log-hydraulic conductivity. The mean hydraulic gradient introduces complications in the analysis since it is, in general, spatially variable. In this respect, our results are approximate, yet indicative of the true role of spatially variable patterns of log-hydraulic conductivity on groundwater flow systems.