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Development of an Integrated Watershed Information Management Tool for Long-Term Facilities Stewardship at the INEEL

Background

The objective of this research is to provide DOE with Integrated Watershed Information Management Tools that integrate and leverage water and environmental management information leading to improved long-term stewardship decision making on the INEEL and within the associated watersheds. The tools and methods developed are transferable to other DOE and federal facilities and to address national/global watershed management issues. Key components of the system include data management, access and analysis tools, a Bayesian Decision Network (BDN), System Dynamics Model, and options analysis/decision support capabilities.

Work involved data collection and database development for disparate data sources in the watershed and development of disciplinary data analysis and mathematical modeling tools for the Big Lost River (BLR) and INEEL. These tools were integrated into a Integrated Watershed Information Management Tools (IWIMT). Facility and water resource managers, along with stakeholders can use these tools to help evaluate information, management alternatives, and to communicate decisions to other interested parties.

Objectives

  • A license agreement for using and distributing the software was completed.
  • Software documentation has been completed.
  • Other applications for the software were developed and are being pursued.
  • The software and examples are available at www.MapWindow.com.

Accomplishments through 2003

The following data for the BLR and INEEL have been compiled and integrated into the BLR-Data Viewer (DV): Groundwater elevation and quality, SNOTEL (SNOwpack TELemetry), snow course and other meteorological data, wildlife corridors for sensitive and focus species, surface water flow and water quality, soil erosion susceptibility, and a precipitation run-off forecasting model.

A data inventory table for the BLR-DV has been organized and updated. This table provides an example of what data are needed to support the tools and how to document and organize the data. The MapWindow software is used as the primary visualization tool, and GIS engine was re-engineered to be more efficient and stable. Software plug-ins were revised to improve system performance.

A soil erosion risk model was developed for the BLR watershed identifying key areas where soils are most susceptible to erosion causing sediment problems in the streams.

A model identifying key wildlife corridors for sensitive and important vertebrate species has been developed for the BLR watershed. This component of the tool set attempts to address terrestrial ecological sustainability for the identified key species.

The structure of the BLR BDN has been completed. The decision nodes are defined and the conditional probabilities tables have been populated. Some modifications to the INEEL portion of the BDN are still taking place to capture additional questions and decisions associated with operation of the BLR diversion on the INEEL.

The system dynamics (SD) model for surface water flow in the BLR is completed, but not calibrated. This model is used to predict stream flows based on different dam operation scenarios and its output will populate the conditional probability tables in the BDN.

A precipitation runoff forecasting BDN was completed for the upper BLR basin above Howe Ranch gauge. This model utilizes historic snow pack snow water equivalent data, flow data, and northern sea surface water temperatures to provide probabilities of upcoming run-off conditions in acre-feet based on five types of precipitation years, and can provide information regarding the amount of water expected in the coming year with an uncertainty component.

A snow-cover run-off model was completed for the Copper Basin portion of the BLR. It provides an estimate of total water run-off based on snow cover in early spring. It provides an additional prediction tool for supporting management options for the Mackay dam. It increases the accuracy and reduces the uncertainty associated with using SNOTEL data alone.

Investigators and Affiliations

  • Ron Rope, Advisory Engineer, Ecological and Cultural Resources Department, Idaho National Engineering and Environmental Laboratory, Idaho Falls, ID
  • Jerry Sehlke, Advisory Engineer, Ecological and Cultural Resources Department, Idaho National Engineering and Environmental Laboratory, Idaho Falls, ID
  • Dr. David Stevens, Utah State University, Logan, UT
  • Dr. Dan Ames, Utah State University, Logan, UT

Funding Sources
Laboratory Directed Research and Development (LDRD)

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