H11K-01 INVITED
Monitoring of Zero-Valent Iron Permeable Reactive Barriers: Electrical Properties and Barrier Aging
An innovative method of groundwater remediation invented in the 1990"s, Permeable Reactive Barriers, use sand-sized grains of scrap iron placed in trenches or injected under pressure to remediate a number of organic and inorganic contaminants. Monitoring the aging of these barriers becomes increasingly important as many of these barriers approach their predicted life spans. In-situ resistivity and induced polarization studies have been conducted at six barriers at four different sites: Monticello, Utah; the Denver Federal Center; Kansas City, Missouri; and East Helena, Montana. As some barriers tend to age dramatically faster than others, for this study we consider low permeability barriers as of greater age, as "old" barriers tend to loose permeability rather than exhaust reactive materials. One complicating factor is that two of the barriers studied appear to have issues related to installation. One site, the former Asarco Smelter Site near East Helena, Montana, has been instrumented with an autonomous monitoring system allowing continuous monitoring of the evolution of a relatively new (less than three years old) barrier. The barrier showed surprisingly rapid evolution over the first year of monitoring with changes in both resistivity and chargeability of tens of percent per month. In general, the electrical properties of all of the barriers studied follow a pattern. New barriers are fairly resistive with in-situ conductivity only a few times background (outside the barrier) values. Older barriers get increasingly conductive, with failed barriers showing values of over 100 S/m. The induced polarization response is more complicated. Chargeability values increase over time for young barriers, are largest for healthy barriers in the middle of their lifespan, and decrease as the barrier ages.
H11K-02
Design, implementation and results of an autonomous hydrogeophysical monitoring system to monitor subsurface flow at the Hanford 300 area
Time lapse electrical data (both self potential and electrical resistivity data) can provide information on subsurface flow, and over the last several years there has been an increase in the interest of automating hydrogeophysical data acquisition systems. Such systems require both adaptations to hardware and system setup, and a well designed computational backend allowing for the management and processing of such data. The 300 area at Hanford is the location of multiple DOE Office of Science and Environmental Management funded research efforts which seek to understand the groundwater and contaminant behavior at this site. The groundwater head distribution and resulting flow at this site is known to be strongly influenced by the adjacent Columbia river, and there is an interest in mapping out the spatiotemporal flow directions at this site. The site has been extensively characterized using electrical resistivity measurements, and the geometries and resistivities of subsurface formations are well known both from borings and geophysical characterization efforts. In addition, the overall Hanford 300 area contains 8 continuously recording wells which monitor groundwater level and conductivity at the site at 15 minute intervals, as well as adjacent monitoring stations which record river stage. An autonomous, one hundred electrode SP system was installed at the Hanford 300 area over a 300 x 300 m sub part of the site. Data from both the hydrological sensors and geophysical systems is collected automatically, and transferred to a central database server located at the Idaho National Laboratory. Once data is arrived, data qa/qc and data reduction are run automatically to create time lapse maps of self potential values. We will discuss the design, implementation and results obtained with this system (including ongoing modeling and inversion efforts for the data collected with these systems) as well as the potential of these hydrogeophysical monitoring systems to provide insights in to subsurface flow processes.
H11K-03
Combining Wireless Sensor Networks and Groundwater Transport Models: Protocol and Model Development in a Simulative Environment
Groundwater transport modeling is intended to aid in remediation processes by providing prediction of plume location and by helping to bridge data gaps in the typically undersampled subsurface environment. Increased availability of computer resources has made computer-based transport models almost ubiquitous in calculating health risks, determining cleanup strategies, guiding environmental regulatory policy, and in determining culpable parties in lawsuits. Despite their broad use, very few studies exist which verify model correctness or even usefulness, and those that have shown significant discrepancies between predicted and actual results. Better predictions can only be gained from additional and higher quality data, but this is an expensive proposition using current sampling techniques. A promising technology is the use of wireless sensor networks (WSNs) which are comprised of wireless nodes (motes) coupled to in-situ sensors that are capable of measuring hydrological parameters. As the motes are typically battery powered, power consumption is a major concern in routing algorithms. By supplying predictions about the direction and arrival time of the contaminant, the application-driven routing protocol would then become more efficient. A symbiotic relationship then exists between the WSN, which is supplying the data to calibrate the transport model, and the model, which may be supplying predictive information to the WSN for optimum monitoring performance. Many challenges exist before the above can be realized: WSN protocols must mature, as must sensor technology, and inverse models and tools must be developed for integration into the system. As current model calibration, even automatic calibration, still often requires manual tweaking of calibration parameters, implementing this in a real-time closed-loop process may require significant work. Based on insights from a previous proof-of-concept intermediate-scale tank experiment, we are developing the models, tools, and protocols necessary for a closed-loop simulation online, combining work across multiple disciplines. This simulation environment will expedite software development and a large-scale experimental aquifer will be used for further validation of the techniques. The results presented here address: setup of a WSN simulator which cooperates with transport models, development of fault detection techniques into the WSN routing protocol which are particular to this application, and planned steps in building a transport model capable of working in the WSN context.
H11K-04 INVITED
WaterML: an XML Language for Communicating Water Observations Data
One of the great impediments to the synthesis of water information is the plethora of formats used to publish such data. Each water agency uses its own approach. XML (eXtended Markup Languages) are generalizations of Hypertext Markup Language to communicate specific kinds of information via the internet. WaterML is an XML language for water observations data – streamflow, water quality, groundwater levels, climate, precipitation and aquatic biology data, recorded at fixed, point locations as a function of time. The Hydrologic Information System project of the Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) has defined WaterML and prepared a set of web service functions called WaterOneFLow that use WaterML to provide information about observation sites, the variables measured there and the values of those measurments. WaterML has been submitted to the Open GIS Consortium for harmonization with its standards for XML languages. Academic investigators at a number of testbed locations in the WATERS network are providing data in WaterML format using WaterOneFlow web services. The USGS and other federal agencies are also working with CUAHSI to similarly provide access to their data in WaterML through WaterOneFlow services. http://www.cuahsi.org/his/webservices.html
H11K-05
Deployment and Evaluation of an Observations Data Model
Environmental observations are fundamental to hydrology and water resources, and the way these data are organized and manipulated either enables or inhibits the analyses that can be performed. The CUAHSI Hydrologic Information System project is developing information technology infrastructure to support hydrologic science. This includes an Observations Data Model (ODM) that provides a new and consistent format for the storage and retrieval of environmental observations in a relational database designed to facilitate integrated analysis of large datasets collected by multiple investigators. Within this data model, observations are stored with sufficient ancillary information (metadata) about the observations to allow them to be unambiguously interpreted and used, and to provide traceable heritage from raw measurements to useable information. The design is based upon a relational database model that exposes each single observation as a record, taking advantage of the capability in relational database systems for querying based upon data values and enabling cross dimension data retrieval and analysis. This data model has been deployed, as part of the HIS Server, at the WATERS Network test bed observatories across the U.S where it serves as a repository for real time data in the observatory information system. The ODM holds the data that is then made available to investigators and the public through web services and the Data Access System for Hydrology (DASH) map based interface. In the WATERS Network test bed settings the ODM has been used to ingest, analyze and publish data from a variety of sources and disciplines. This paper will present an evaluation of the effectiveness of this initial deployment and the revisions that are being instituted to address shortcomings. The ODM represents a new, systematic way for hydrologists, scientists, and engineers to organize and share their data and thereby facilitate a fuller integrated understanding of water resources based on more extensive and fully specified information. http://www.cuahsi.org/his/odm.html
H11K-06
Development of a One-Stop Data Search and Discovery Engine using Ontologies for Semantic Mappings (HydroSeek)
Search engines have changed the way we see the Internet. The ability to find the information by just typing in keywords was a big contribution to the overall web experience. While the conventional search engine methodology worked well for textual documents, locating scientific data remains a problem since they are stored in databases not readily accessible by search engine bots. Considering different temporal, spatial and thematic coverage of different databases, especially for interdisciplinary research it is typically necessary to work with multiple data sources. These sources can be federal agencies which generally offer national coverage or regional sources which cover a smaller area with higher detail. However for a given geographic area of interest there often exists more than one database with relevant data. Thus being able to query multiple databases simultaneously is a desirable feature that would be tremendously useful for scientists. Development of such a search engine requires dealing with various heterogeneity issues. In scientific databases, systems often impose controlled vocabularies which ensure that they are generally homogeneous within themselves but are semantically heterogeneous when moving between different databases. This defines the boundaries of possible semantic related problems making it easier to solve than with the conventional search engines that deal with free text. We have developed a search engine that enables querying multiple data sources simultaneously and returns data in a standardized output despite the aforementioned heterogeneity issues between the underlying systems. This application relies mainly on metadata catalogs or indexing databases, ontologies and webservices with virtual globe and AJAX technologies for the graphical user interface. Users can trigger a search of dozens of different parameters over hundreds of thousands of stations from multiple agencies by providing a keyword, a spatial extent, i.e. a bounding box, and a temporal bracket. As part of this development we have also added an environment that allows users to do some of the semantic tagging, i.e. the linkage of a variable name (which can be anything they desire) to defined concepts in the ontology structure which in turn provides the backbone of the search engine. http://www.hydroseek.org
H11K-07
Unifying Diverse Watershed Data to Enable Analysis
The wide variety of agencies collecting, storing, and publishing hydrologic data today makes it possible for scientists to gather a tremendous amount of data about a watershed simply by using the Internet. However, availability of the data is only the first step to its use in analysis. Typically the data sets that exist across a watershed are highly heterogeneous in data format, units, types, periods of measurement, frequency of measurements, quality, etc. In this presentation we will describe the Scientific Data Server we have designed to enable the combination of data from across a watershed into a database organized using a unifying schema. This data server has been prototyped using data from the Russian River and Bear River watersheds and is currently being used to study the hydrology and characteristics of the Russian River. http://bwc.berkeley.edu
H11K-08
Enhancing the Solution of Large Monitoring Network Design Problems Using a New Epsilon- Dominance Hierarchical Bayesian Optimization Algorithm
Designing long-term monitoring (LTM) networks for contaminated groundwater is a challenging problem that has long been recognized to suffer from the "curse of dimensionality". LTM design problems are challenging multiobjective problems that have discrete decision spaces that grow exponentially as the different types of measurements, their locations, and sampling rates are considered. The scaling challenges of LTM network design problems have been discussed in the water resources literature for more than 30 years. Since the late 1990's, evolutionary algorithms (EAs) have shown promise for providing approximately optimal LTM network designs for problems of limited size and complexity. However, recent studies have highlighted that currently available algorithms do not consider that sampling decisions are often correlated due to contaminant plume structure. Current Multi-Objective Evolutionary Algorithms (MOEAs) have at best displayed quadratic computational scaling, which means that as the number of sampling decisions (l) increases linearly, the number of design evaluations required to optimize the problem grows at least quadratically - O(l2). This work is focusing on the development of a next generation MOEA that can learn and exploit the physical linkages between decision variables in LTM design applications with the goals of providing more robust performance for increased problem sizes. The proposed MOEA is termed the Epsilon-Dominance Hierarchical Bayesian Optimization Algorithm (\varepsilon-hBOA). \varepsilon-hBOA has been tested relative to the best known traditional MOEA, the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (\varepsilon-NSGAII) for solving a four-objective LTM problem. A comprehensive performance assessment of the \varepsilon-NSGAII and various configurations of the \varepsilon-hBOA have been performed for both a 25-well LTM design test case (a relatively small problem with over 33-million possible designs), and a 58-point LTM design test case (a much larger problem with over 2.88×1017 possible designs). The results from this comparison indicate that the model building capability of the \varepsilon-hBOA greatly enhances its performance relative to the \varepsilon-NSGAII, especially on large LTM design problems.