Geology topics
Publications and source records attributed to F. A. D’Agnese.
The development of a regional ground-water flow model of the Death Valley region in the southwestern United States is discussed in the context of the fourteen guidelines of Hill. This application of the guidelines demonstrates how they may be used for model calibration and evaluation, and to direct further model development and data collection.
Calibrated models of groundwater systems can provide substantial information for guiding data collection. This work considers using such models to guide hydrogeologic data collection for improving model predictions by identifying model parameters that are most important to the predictions. Identification of these important parameters can help guide collection of field data about parameter values and associated flow system features and can lead to improved predictions. Methods for identifying parameters important to predictions include prediction scaled sensitivities (PSS), which account for uncertainty on individual parameters as well as prediction sensitivity to parameters, and a new “value of improved information” (VOII) method presented here, which includes the effects of parameter correlation in addition to individual parameter uncertainty and prediction sensitivity. In this work, the PSS and VOII methods are demonstrated and evaluated using a model of the Death Valley regional groundwater flow system. The predictions of interest are advective transport paths originating at sites of past underground nuclear testing. Results show that for two paths evaluated the most important parameters include a subset of five or six of the 23 defined model parameters. Some of the parameters identified as most important are associated with flow system attributes that do not lie in the immediate vicinity of the paths. Results also indicate that the PSS and VOII methods can identify different important parameters. Because the methods emphasize somewhat different criteria for parameter importance, it is suggested that parameters identified by both methods be carefully considered in subsequent data collection efforts aimed at improving model predictions.
A regional-scale, steady-state, saturated-zone ground-water flow model was constructed to evaluate potential regional ground-water flow in the vicinity of Yucca Mountain, Nevada. The model was limited to three layers in an effort to evaluate the characteristics governing large-scale subsurface flow. Geoscientific information systems (GSIS) were used to characterize the complex surface and subsurface hydrogeologic conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. Subsurface properties in this system vary dramatically, producing high contrasts and abrupt contacts. This characteristic, combined with the large scale of the model, make zonation the logical choice for representing the hydraulic-conductivity distribution. Different conceptual models were evaluated using sensitivity analysis and were tested by using nonlinear regression to determine parameter values that are optimal, in that they provide the best match between the measured and simulated heads and flows. The different conceptual models were judged based both on the fit achieved to measured heads and spring flows, and the plausibility of the optimal parameter values. One of the conceptual models considered appears to represent the system most realistically. Any apparent model error is probably caused by the coarse vertical and horizontal discretization.A regional-scale, steady-state, saturated-zone ground-water flow model was constructed to evaluate potential regional ground-water flow in the vicinity of Yucca Mountain, Nevada. The model was limited to three layers in an effort to evaluate the characteristics governing large-scale subsurface flow. Geoscientific information systems (GSIS) were used to characterize the complex surface and subsurface hydrogeologic conditions of the area, and this characterization was used to construct likely conceptual models of the flow system. Subsurface properties in this system vary dramatically, producing high contrasts and abrupt contacts. This characteristic, combined with the large scale of the model, make zonation the logical choice for representing the hydraulic-conductivity distribution. Different conceptual models were evaluated using sensitivity analysis and were tested by using nonlinear regression to determine parameter values that are optimal, in that they provide the best match between the measured and simulated heads and flows. The different conceptual models were judged based both on the fit achieved to measured heads and spring flows, and the plausibility of the optimal parameter values. One of the conceptual models considered appears to represent the system most realistically. Any apparent model error is probably caused by the coarse vertical and horizontal discretization.
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The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.