Ground Water

Hill, Mary C.; Poeter, Eileen; Zheng, Chunmiao

doi: 10.1111/j.1745-6584.2010.00734.xpmid: 20727016

Appelo, C.A.J.; Rolle, Massimo

doi: 10.1111/j.1745-6584.2010.00732.xpmid: 20662945

Refsgaard, Jens Christian; Højberg, Anker Lajer; Møller, Ingelise; Hansen, Martin; Søndergaard, Verner

doi: 10.1111/j.1745-6584.2009.00634.xpmid: 19788560

Groundwater modeling is undergoing a change from traditional stand‐alone studies toward being an integrated part of holistic water resources management procedures. This is illustrated by the development in Denmark, where comprehensive national databases for geologic borehole data, groundwater‐related geophysical data, geologic models, as well as a national groundwater‐surface water model have been established and integrated to support water management. This has enhanced the benefits of using groundwater models. Based on insight gained from this Danish experience, a scientifically realistic scenario for the use of groundwater modeling in 2020 has been developed, in which groundwater models will be a part of sophisticated databases and modeling systems. The databases and numerical models will be seamlessly integrated, and the tasks of monitoring and modeling will be merged. Numerical models for atmospheric, surface water, and groundwater processes will be coupled in one integrated modeling system that can operate at a wide range of spatial scales. Furthermore, the management systems will be constructed with a focus on building credibility of model and data use among all stakeholders and on facilitating a learning process whereby data and models, as well as stakeholders' understanding of the system, are updated to currently available information. The key scientific challenges for achieving this are (1) developing new methodologies for integration of statistical and qualitative uncertainty; (2) mapping geological heterogeneity and developing scaling methodologies; (3) developing coupled model codes; and (4) developing integrated information systems, including quality assurance and uncertainty information that facilitate active stakeholder involvement and learning.

Reeves, Howard W.; Zellner, Moira L.

doi: 10.1111/j.1745-6584.2010.00677.xpmid: 20132323

The U.S. Geological Survey numerical groundwater flow model, MODFLOW, was integrated with an agent‐based land‐use model to yield a simulator for environmental planning studies. Ultimately, this integrated simulator will be used as a means to organize information, illustrate potential system responses, and facilitate communication within a participatory modeling framework. Initial results show the potential system response to different zoning policy scenarios in terms of the spatial patterns of development, which is referred to as urban form, and consequent impacts on groundwater levels. These results illustrate how the integrated simulator is capable of representing the complexity of the system. From a groundwater modeling perspective, the most important aspect of the integration is that the simulator generates stresses on the groundwater system within the simulation in contrast to the traditional approach that requires the user to specify the stresses through time.

Valerio, Allison; Rajaram, Harihar; Zagona, Edith

doi: 10.1111/j.1745-6584.2010.00702.xpmid: 20412319

Accurate representation of groundwater‐surface water interactions is critical to modeling low river flows in the semi‐arid southwestern United States. Although a number of groundwater‐surface water models exist, they are seldom integrated with river operation/management models. A link between the object‐oriented river and reservoir operations model, RiverWare, and the groundwater model, MODFLOW, was developed to incorporate groundwater‐surface water interaction processes, such as river seepage/gains, riparian evapotranspiration, and irrigation return flows, into a rule‐based water allocations model. An explicit approach is used in which the two models run in tandem, exchanging data once in each computational time step. Because the MODFLOW grid is typically at a finer resolution than RiverWare objects, the linked model employs spatial interpolation and summation for compatible communication of exchanged variables. The performance of the linked model is illustrated through two applications in the Middle Rio Grande Basin in New Mexico where overappropriation impacts endangered species habitats. In one application, the linked model results are compared with historical data; the other illustrates use of the linked model for determining management strategies needed to attain an in‐stream flow target. The flows predicted by the linked model at gauge locations are reasonably accurate except during a few very low flow periods when discrepancies may be attributable to stream gaging uncertainties or inaccurate documentation of diversions. The linked model accounted for complex diversions, releases, groundwater pumpage, irrigation return flows, and seepage between the groundwater system and canals/drains to achieve a schedule of releases that satisfied the in‐stream target flow.

Hanson, R. T.; Schmid, W.; Faunt, C. C.; Lockwood, B.

doi: 10.1111/j.1745-6584.2010.00730.xpmid: 20572873

The extension of MODFLOW onto the landscape with the Farm Process (MF‐FMP) facilitates fully coupled simulation of the use and movement of water from precipitation, streamflow and runoff, groundwater flow, and consumption by natural and agricultural vegetation throughout the hydrologic system at all times. This allows for more complete analysis of conjunctive use water‐resource systems than previously possible with MODFLOW by combining relevant aspects of the landscape with the groundwater and surface water components. This analysis is accomplished using distributed cell‐by‐cell supply‐constrained and demand‐driven components across the landscape within “water‐balance subregions” comprised of one or more model cells that can represent a single farm, a group of farms, or other hydrologic or geopolitical entities. Simulation of micro‐agriculture in the Pajaro Valley and macro‐agriculture in the Central Valley are used to demonstrate the utility of MF‐FMP. For Pajaro Valley, the simulation of an aquifer storage and recovery system and related coastal water distribution system to supplant coastal pumpage was analyzed subject to climate variations and additional supplemental sources such as local runoff. For the Central Valley, analysis of conjunctive use from different hydrologic settings of northern and southern subregions shows how and when precipitation, surface water, and groundwater are important to conjunctive use. The examples show that through MF‐FMP's ability to simulate natural and anthropogenic components of the hydrologic cycle, the distribution and dynamics of supply and demand can be analyzed, understood, and managed. This analysis of conjunctive use would be difficult without embedding them in the simulation and are difficult to estimate a priori.

Leake, Stanley A.; Reeves, Howard W.; Dickinson, Jesse E.

doi: 10.1111/j.1745-6584.2010.00701.xpmid: 20727017

All groundwater pumped is balanced by removal of water somewhere, initially from storage in the aquifer and later from capture in the form of increase in recharge and decrease in discharge. Capture that results in a loss of water in streams, rivers, and wetlands now is a concern in many parts of the United States. Hydrologists commonly use analytical and numerical approaches to study temporal variations in sources of water to wells for select points of interest. Much can be learned about coupled surface/groundwater systems, however, by looking at the spatial distribution of theoretical capture for select times of interest. Development of maps of capture requires (1) a reasonably well‐constructed transient or steady state model of an aquifer with head‐dependent flow boundaries representing surface water features or evapotranspiration and (2) an automated procedure to run the model repeatedly and extract results, each time with a well in a different location. This paper presents new methods for simulating and mapping capture using three‐dimensional groundwater flow models and presents examples from Arizona, Oregon, and Michigan.

Singh, Abhishek; Mishra, Srikanta; Ruskauff, Greg

doi: 10.1111/j.1745-6584.2009.00642.xpmid: 19878329

In recent years a growing understanding has emerged regarding the need to expand the modeling paradigm to include conceptual model uncertainty for groundwater models. Conceptual model uncertainty is typically addressed by formulating alternative model conceptualizations and assessing their relative likelihoods using statistical model averaging approaches. Several model averaging techniques and likelihood measures have been proposed in the recent literature for this purpose with two broad categories—Monte Carlo‐based techniques such as Generalized Likelihood Uncertainty Estimation or GLUE (Beven and Binley 1992) and criterion‐based techniques that use metrics such as the Bayesian and Kashyap Information Criteria (e.g., the Maximum Likelihood Bayesian Model Averaging or MLBMA approach proposed by Neuman 2003) and Akaike Information Criterion‐based model averaging (AICMA) (Poeter and Anderson 2005). These different techniques can often lead to significantly different relative model weights and ranks because of differences in the underlying statistical assumptions about the nature of model uncertainty. This paper provides a comparative assessment of the four model averaging techniques (GLUE, MLBMA with KIC, MLBMA with BIC, and AIC‐based model averaging) mentioned above for the purpose of quantifying the impacts of model uncertainty on groundwater model predictions. Pros and cons of each model averaging technique are examined from a practitioner's perspective using two groundwater modeling case studies. Recommendations are provided regarding the use of these techniques in groundwater modeling practice.

Ye, Ming; Pohlmann, Karl F.; Chapman, Jenny B.; Pohll, Greg M.; Reeves, Donald M.

doi: 10.1111/j.1745-6584.2009.00633.xpmid: 19788638

This study evaluates alternative groundwater models with different recharge and geologic components at the northern Yucca Flat area of the Death Valley Regional Flow System (DVRFS), USA. Recharge over the DVRFS has been estimated using five methods, and five geological interpretations are available at the northern Yucca Flat area. Combining the recharge and geological components together with additional modeling components that represent other hydrogeological conditions yields a total of 25 groundwater flow models. As all the models are plausible given available data and information, evaluating model uncertainty becomes inevitable. On the other hand, hydraulic parameters (e.g., hydraulic conductivity) are uncertain in each model, giving rise to parametric uncertainty. Propagation of the uncertainty in the models and model parameters through groundwater modeling causes predictive uncertainty in model predictions (e.g., hydraulic head and flow). Parametric uncertainty within each model is assessed using Monte Carlo simulation, and model uncertainty is evaluated using the model averaging method. Two model‐averaging techniques (on the basis of information criteria and GLUE) are discussed. This study shows that contribution of model uncertainty to predictive uncertainty is significantly larger than that of parametric uncertainty. For the recharge and geological components, uncertainty in the geological interpretations has more significant effect on model predictions than uncertainty in the recharge estimates. In addition, weighted residuals vary more for the different geological models than for different recharge models. Most of the calibrated observations are not important for discriminating between the alternative models, because their weighted residuals vary only slightly from one model to another.

Dausman, Alyssa M.; Doherty, John; Langevin, Christian D.; Sukop, Michael C.

doi: 10.1111/j.1745-6584.2010.00679.xpmid: 20132327

The present study demonstrates a methodology for optimization of environmental data acquisition. Based on the premise that the worth of data increases in proportion to its ability to reduce the uncertainty of key model predictions, the methodology can be used to compare the worth of different data types, gathered at different locations within study areas of arbitrary complexity. The method is applied to a hypothetical nonlinear, variable density numerical model of salt and heat transport. The relative utilities of temperature and concentration measurements at different locations within the model domain are assessed in terms of their ability to reduce the uncertainty associated with predictions of movement of the salt water interface in response to a decrease in fresh water recharge. In order to test the sensitivity of the method to nonlinear model behavior, analyses were repeated for multiple realizations of system properties. Rankings of observation worth were similar for all realizations, indicating robust performance of the methodology when employed in conjunction with a highly nonlinear model. The analysis showed that while concentration and temperature measurements can both aid in the prediction of interface movement, concentration measurements, especially when taken in proximity to the interface at locations where the interface is expected to move, are of greater worth than temperature measurements. Nevertheless, it was also demonstrated that pairs of temperature measurements, taken in strategic locations with respect to the interface, can also lead to more precise predictions of interface movement.