[1] Groundwater can serve as an important resource for woody vegetation in semiarid landscapes, particularly when soil water is functionally depleted and unavailable to plants. This study examines the uptake of groundwater by deciduous blue oak trees (Quercus douglasii) in a California oak savanna. Here we present a suite of direct and indirect methods that demonstrate its occurrence and quantify its rates. The study site is underlain by a thin soil layer and fractured metavolcanic bedrock. Typical depth to groundwater is approximately 8 m. A variety of water storage and flux measurements were collected from 2005 to 2008, including groundwater levels, soil moisture contents, sap flows, and latent heat fluxes. During the dry season, groundwater uptake rates ranged from 4 to 25 mm month −1 and approximately 80% of total ET during June, July, and August came from groundwater. Leaf and soil water potentials supported these results, indicating that groundwater uptake was thermodynamically favorable over soil water uptake for key portions of the growing season. These findings strongly suggest that blue oaks should be considered obligate phreatophytes and that groundwater reserves provide a buffer to rapid changes in their hydroclimate, if these assets are not otherwise depleted by prolonged drought or human consumption. While groundwater uptake may provide for short-term protection, it should be viewed not as a mechanism for continued plant growth. It allows the woody vegetation to subsist during the summer but not to flourish.

A spatial correlation model is presented for the case of a spatially distributed, bimodal attribute. This model can be used for modeling the hydraulic conductivity in sand-shale or sand-clay formations or in fractured rocks, where the conductivities of the fractured and nonfractured rocks display dramatically different spatial structures. In the proposed model each of the modes is defined by a different multivariate probability density function and correlation scale. A length scale other than the one specified for each mode is used to characterize the relative distribution of the modes in space. Effective conductivity and transport parameters are then defined and analyzed. In developing the transport parameters our goal is to see the effects of the different scales and the different modes on transport. Unlike the case of a unimodal distribution, the macrodispersion is not a linear function of the total variance of the population, and the relative contributions of the variabilities of the different modes are determined by the ratios between the various length scales. We found that the effects of the large-scale variability on longitudinal spread become significant only after a large travel distance, but that its contribution to lateral spread occurs at a relatively early travel time.

Abstract. This paper describes the first major attempt to compare seven different inverse approaches for identifying aquifer transmissivity. The ultimate objective was to determine which of several geostatistical inverse techniques is better suited for making probabilistic forecasts of the potential transport of solutes in an aquifer where spatial variability and uncertainty in hydrogeologic properties are significant. Seven geostatistical methods (fast Fourier transform (FF), fractal simulation (FS), linearized cokriging (LC), linearized semianalytical (LS), maximum likelihood (ML), pilot point (PP), and sequential self-calibration (SS)) were compared on four synthetic data sets. Each data set had specific features meeting (or not) classical assumptions about stationarity, amenability to a geostatistical description, etc. The comparison of the outcome of the methods is based on the prediction of travel times and travel paths taken by conservative solutes migrating in the aquifer for a distance of 5 km. Four of the methods, LS, ML, PP, and SS, were identified as being approximately equivalent for the specific problems considered. The magnitude of the variance of the transmissivity fields, which went as high as 10 times the generally accepted range for linearized approaches, was not a problem for the linearized methods when applied to stationary fields; that is, their inverse solutions and travel time predictions were as accurate as those of the nonlinear methods. Nonstationarity of the "true" transmissivity field, or the presence of "anomalies" such as high-permeability fracture zones was, however, more of a problem for the linearized methods. The importance of the proper selection of the semivariogram of the 1og•0 (T) field (or the ability of the method to optimize this variogram iteratively) was found to have a significant impact on the accuracy and precision of the travel time predictions. Use of additional transient information from pumping tests did not result in major changes in the outcome. While the methods differ in their underlying theory, and the codes developed to implement the theories were limited to varying degrees, the most important factor for achieving a successful solution was the time and experience devoted by the user of the method. •2Stanford University, Stanford, California.•3Duke Engineering and Services, Inc., Austin, Texas.•4University of Arizona, Tucson.•Slnstitut Franqais du Pftrole, Rueil-Malmaison, France.•6University of California, Berkeley.Copyright 1998 by the American Geophysical Union. Paper number 98WR00003.0043-1397/98/98WR-00003509.00 tion, or performance assessment of planned waste disposal projects, it is no longer enough to determine the "best estimate" of the distribution in space of the aquifer parameters. A measure of the uncertainty associated with this estimation is also needed. Geostatistical techniques are ideally suited to filling this role. Basically, geostatistics fits a "structural model" to the data, reflecting their spatial variability. Then, both "best estim...

A method for the prediction of dispersion processes of inert solutes occurring in heterogeneous porous media, is presented. Its main features are as follows: (1) The velocity field is assumed to be a space random function. Its moments are expressed through the physical parameters of the log transmissivity and head random fields by linearizing the flow equation. The random velocity, being a linear function of the head and log transmissivity, which are assumed to be jointly multivariate normal (MVN), is thus also MVN. Its pdf is completely defined by its first two moments. (2) The statistics of the dispersion process are obtained by the particle-tracking method through Monte Carlo simulations which are based on Gaussian conditioning. The method is applied to the Borden natural gradient tracer test (Freyberg, 1986), showing favorable results.

Stochastic Subsurface Hydrogeology is the study of subsurface, geological heterogeneity, and its effects on flow and transport process, using probabilistic and geostatistical concepts. This book presents a rational, systematic approach for analyzing and modeling subsurface heterogeneity, and for modeling flow and transport in the subsurface, and for prediction and decision-making under uncertainty. The book covers the fundamentals and practical aspects of geostatistics and stochastic hydrogeology, coupling theoretical and practical aspects, with examples, case studies and guidelines for applications, and provides a summary and review of the major developments in these areas.