Cooperative State Research, Education, and Extension Service

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Cooperative State Research, Education, and
Extension Service
Grant 2001-51130-11373
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Policy PBMS strategies Model
Regulations Conservation Policies
Science Spatial/Temporal
Analyses Modeling MCL Violations
Technology Atlases GIS/SDSS Spatial Analysis
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Overall 3-year Goals (2001-2002)
Protection of Water Quality in Agroecosystems
  Science for Characterizing Water
Quality GIS for Visualizing patterns in Water
Quality Policy analysis for better utilization
of ST Integration of the above three
efforts Sharing/communicating findings
Promoting student participation  
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Overall 3-year Goals (2001-2002)
  Study Area, People, and Methods   Eastern Iowa
Watersheds Surface-water influenced public
water supplies SDWA and CWA orientation Collabor
ating organizations (IGSB (DNR), USGS, UHL,
CHEEC, USDA, ICWP, DMWW and others) Steering
Committee, Focus Groups Interns/student
researchers Website design and
development Annual Workshop/Conference
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I know of no safe depository of the ultimate
powers of society but the people themselves if
we think them not enlightened enough to exercise
their control with wholesome discretion, the
remedy is not to take it from them, but to inform
their discretion. Thomas Jefferson
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The risks that kill you are not necessarily the
risks that anger or frighten you. Sandman, 1987

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Science/Technology (Major
Components) 1. Scale and uncertainty
2. Description 3. Estimation 4.
Modeling/Explanation/Association
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Science/Technology Scale Water quality
can be studied at many different scales from a
single point (a well or a water supply), a
creek, a river, a subwatershed, a major
watershed, or the entire hydrological system.

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Science/Technology Uncertainty Almost
all attributes of water quality
vary in space and time. This
variability in observed measurements of
water quality is influenced by methodologies
such as sampling, experimental and survey
designs, and technologies ranging from
simple pH meters to complex Gas
Chromatographs and satellites.
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Science/Technology Uncertainty
Understanding uncertainty (or error in
measurement) as a function of nature and human
processes is one of the most important areas of
scientific endeavors. We can do that by
analyzing the vast amount of water quantity and
quality, exposure, toxicological, and public
perception data that has been collected and
stored over the years. What is needed is a
process by which science technology can assist
the policy process by effectively utilizing the
insights gained on uncertainty in measurements.
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Uncertainty Explained

• Sample Size ()
• Confidence Interval
• Level of Significance

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Policy Focus Group (2001-2002)

• Discuss policy issues in the context of SDWA
• Discuss SDWA implementation issues in Iowa
• Discuss issues at water quality/health interface
• Estimate potential problem areas for Iowa PWSs
• Discuss integration issues with science and GIS
• Prioritize and develop recommendations

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Public Water Supplies in Eastern and Central Iowa
Watersheds
Legend
Public Water Supply
State Boundary
Iowa River Watershed
Des Moines River Watershed
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Water Quality

• Key EPA and State Decisions
• Limited by Inconsistent and Incomplete Data
• GAO, March 2000

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Lessons Learned

• Classify, categorize, organize, and order data,
  models, and alternatives
• Develop clear and effective communication
  strategies
• Explore developing a variety of indices, similar
  to the consumer price index (or Dow Jones
  Industrial Average)

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Setting Priorities

• Who will get what?
• How are we going to decide on that in a
  democracy?
• Power/Politics/Governance

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Setting Priorities

• Environmental Context
• Should worst things come first?
• Comparative Risk Assessment
• Environmental Justice
• Pollution Prevention
• Market-based Innovation

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Other Major Debates (1990s)

• Unfunded Mandates
• Takings/Private Property Rights

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Current Debates

• Resource Allocation in Recession
• Homeland Security

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How Things Work

• Data

Hard science models
Information
Information, planning, and management science
models
Knowledge
Policies/ decisions
Social science models
Wisdom