Water Policy in the US and the EU

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Water Policy in the US and the EU K H Reckhow
and C Pahl-Wostl Part I US Total Maximum Daily
Load Program
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The Extent of the Impaired Waters Problem

• 20,000 waterbodies across America not meeting
  Clean Water Act goals established by States
• These waterbodies represent 40 of those
  assessed, including
• Over 300,000 river shore miles
• 5 million lake acres
• Approx. 36,000 TMDLs needed in 8 - 13 years

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Framework for Restoring Impaired Waters
Water Quality Standards Designated Use,
Criteria, Anti-deg.
Monitoring and Assessment
303(d) List of Impaired Waters
TMDLDetermine maximum load and allocate load
reductions among PS, NPS
Nonpoint Sources Manage via partnerships,
grants, voluntary programs
Point Sources Control via NPDES Permits
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Current Regulations

• Components of a TMDL
• Sum of allowable loads to meet State water
  quality standards
• Wasteload allocations from point sources
• Load allocations from nonpoint sources and
  natural background
• Margin of safety (MOS)

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ASSESSING THE TMDL APPROACH TO WATER QUALITY
MANAGEMENT Committee to Assess the Scientific
Basis of the Total Maximum Daily Load Approach to
Water Pollution Reduction Water Science and
Technology Board Division on Earth and Life
Studies National Research Council National
Academy Press Washington, D.C. 2001
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Neuse Estuary EutrophicationModel
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NeuBERN Bayes Net Estuary Model
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Water Quality (TMDL) Forecasting
The problem with water quality forecasting is
that were not terribly good at it.
Result Prediction uncertainty is high
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Model development is likely to proceed along the
conventional lines

• Advances in process models will likely lead to
  increasingly elaborate mechanistic descriptions,
  with improvements expected.

• More/better observational data, and advances in
  statistical techniques, will likely lead to gains
  in empirical model forecast accuracy.

However, it is hard to believe that either of
these will result in dramatic improvements
(perhaps mechanistic/statistical hybrid models
have more promise).
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So, we need to consider another approach - using
implemented actions on the real system as
learning experiments to augment/improve model
forecasts.
Adaptive Implementation We can learn while
doing that is, we can observe how the real
system (the actual waterbody) responds, and then
use that information to augment and improve the
prediction for the modeled system.
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Adaptive Implementation Bayesian Analysis
Water Quality Criterion Concentration
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Example TN in Neuse Estuary

• Prior distribution of log TN concentration
  assessed from the Bayesian SPARROW model
• TN monitoring data collected from 1992 2000
• The log TN distribution is updated using one
  years data at a time to illustrate sequential
  updating.

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Sequential Updating

• Repeated use of Bayes theorem
• Current posterior becomes prior when new data are
  available.

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Post (TMDL) Implementation Questions

• Has compliance with the water quality standard
  been achieved?

• If compliance has not been achieved, what
  pollutant reduction actions did not respond as
  predicted?