models and water-quality trading pennsylvania section american water resources association october...
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Models and Water-Quality Trading
Pennsylvania SectionAmerican Water Resources Association
October 22, 2004Harrisburg, Pennsylvania
Cy JonesAquaCura
A Brief History of Water-Quality Trading
Inspired by CAA SO2 Trading
1996 EPA Policy Statement
1996 EPA Draft Framework
2003 EPA Trading Policy
What Is Water-Quality Trading?
Conventional Approach to Pollution Control
All WWTPs in the Watershed Must Meet Their Allocations
Capital Upgrades if Necessary
Nonpoint Sources
State Encourages Voluntary Programs
Hopes for the Best
Point Sources
Water-Quality Trading
WWTPs Can Exchange “Discharge Allowances”
WWTPs Can Acquire Nonpoint Source “Credits”
Nonpoint Sources
State Encourages Voluntary Programs
Hopes for the Best
Point Sources
Can Sell Pollution Reduction “Credits”
Necessary Preconditions for Trading
Individual Sources Assigned Pollutant Allocations and Required to Reduce Loads
Permit Limits for WWTPsDefined Baselines for Nonpoint Sources
Cost-differentials Among Sources for Pollutant Reductions
Freedom for Individual Sources to Decide How to Meet Allocation
Reduce Discharged Load
Buy Credits
Types of Water-Quality Trading
Water-Quality Goals Are Set
Analysis of Available Pollution-Control MeasuresLargest ReductionsCost-effectivenessAbility to Implement Quickly
Best Sequence and Timing for Upgrades is Determined
Managed Trading
Choose Initial Upgrades that Will Produce Reductions Greater than Needed to Meet Goals
Types of Water-Quality Trading
Non-Upgraded Facilities Must Purchase Credits from Upgraded Ones
Not All Facilities May Need to Be Upgraded
Managed Trading
Additional Upgrades Are Added as Needed to Comply with Goals
Connecticut Long Island Sound Nitrogen Trading Program
Types of Water-Quality Trading
Water-Quality Goals Are Set
Mass-load Limits or Goals Are Assigned to Existing Point Source Dischargers
The Dischargers Form a Trading Association
Trading Associations
Allocations Are Aggregated into a Single Association Allocation
Membership Is Voluntary
Types of Water-Quality Trading
The Association Is Free to Meet its Allocation in any Manner it Sees Fit
The Association Is Legally Responsible for Compliance
The Association Could Acquire NPS Credits
Trading Associations
Tar-Pamlico Trading AssociationNeuse River Compliance Association
Types of Water-Quality Trading
State Gives Allocations to Individual Dischargers
Buyers and Sellers Find Each Other and Negotiate Credit Sales
Cost Considerations and Market Forces Dictate Behaviors
Market-Like Trading
No Real Examples Yet
State Sets General Rules of the Market-Place
How Can Models Be Used
in
Water-Quality Trading
???
But First...
A Journey Through the Art of Water-Quality Management
If Heisenberg Had Been a Water-Quality Manager:
Dang!
I can’t be certainof anything!
An Inventory of Uncertainty
Designated Uses
Most Are Generalized and Vague
Many Are Inappropriate
Scientifically Invalid
Unattainable
Difficult to Judge “Attainment” of Uses
There’s Never Enough Data
An Inventory of Uncertainty
Numeric Water-Quality Criteria
Proper Development Requires Extensive Data
Known or Suspected Impacts of Substance of Concern
Water Body Physical Conditions, Chemistry, and Biology
Actual Instream Impacts
Wholesale Adoption of EPA Criteria?
An Inventory of Uncertainty
Numeric Water-Quality Criteria
Wholesale Application of a Single Generic Criterion to a Variety of Water Bodies?
Are Criteria Easily Comparable to Reasonably Obtainable Monitoring Data?
There’s Never Enough Data
How Do You Interpret Compliance with a Criterion?
An Inventory of Uncertainty
Narrative Water-Quality Criteria
Don’t Even Go There!(1)
(1) Except on Rare Occasions
An Inventory of Uncertainty
Identification of Impaired Waters
Proper Assessment Requires Extensive Data
Data Quality a Concern (Use any Old Available Data)
Many Waters Listed on 303(d) Lists with Little Certainty about Actual Status
When Is a Water Body Truly Impaired?
There’s Never Enough Data
An Inventory of Uncertainty
Determining Actual Pollutant Loads
Point Sources - Not a Problem
Nonpoint Sources - Big Problem
Many Factors Affect Agricultural Loads
Soil TypeSlopeCrop TypeFertilizer Application RateWeather
Scientific Uncertainty
Non-Random Variability
Random Variability
An Inventory of Uncertainty
Determining Actual Pollutant Loads
There’s Never Enough Data
An Inventory of Uncertainty
Determining Allowable Pollutant Loads
Some Sort of Analytical Framework is Needed
A Host of Issues
Data Needs
Model Selection and Validity
Analytical Uncertainty
Prediction Reliability
Selection of Proper Design Conditions
Scientific Understanding
An Inventory of Uncertainty
Determining Allowable Pollutant Loads
There Is Never Enough Data
An Inventory of Uncertainty
Assigning Pollutant Reduction Responsibilities
Certainty of Reductions?
Point Sources - Yes
Uncertainty and Variability in NPS Loads
Nonpoint Sources - Not Usually
There’s Never Enough Data
Uncertainty and Variability in BMP Performance
An Inventory of Uncertainty
Determining Water-Quality Results
Adequate Post-Implementation Monitoring and Analysis?
There’s Never Enough Data
An Inventory of Uncertainty
The Political Context
Water-Quality Management Is Ultimately a Public and Political Process
Empirical Data Won’t Help You
Which Brings Us Back to...
The Art of Water-Quality Management
Science Cannot Really DeliverWhat the Political Process of
Water-Quality Management Demands
How Can Models Be Used
in
Water-Quality Trading
???
Water-Quality Models and Trading
Trading Should Be Evaluated Using the Same Analytic Framework Used to:
Adopt Designated Uses
Develop and Adopt Criteria to Support Uses
Determine Attainment Status
Determine Pollutant Sources
Water-Quality Models and Trading
Trading Should Be Evaluated Using the Same Analytic Framework Used to:
Relate Water Quality to Pollutant Sources and Loads
Assign Pollutant Reduction Responsibilities
Assess Water-Quality Results
Water-Quality Models and Trading
All of Our Analytic Frameworks Are Imperfect
Most [models] are little different than those developed in the1960s and 1970s for dry weather wasteload allocation…
Paul Freedman, 2001The CWA's New Clothes. In: Water Environment & Technology 13(6) 28-32
Worse yet, many models have complex detailed input requirements, so many people assume that the calculations are more precise and accurate than they really are...
and little guidance on testing model adequacy or reliability is available.
Rational Action in the Face of Uncertainty
Recognize Shortcomings in Water-Quality Management
Balance the Needs
Balance the Risks
“Sound Science”
Action
Delay or Inaction while Awaiting the Science
Wasting Scarce Resources in Implementing Needless or Ineffective Requirements
Rational Action in the Face of Uncertainty
“Allow for an iterative (or adaptive or phased) approach in cases of uncertainty or lack of success in achieving standards”
Report of the Federal Advisory Committee on the Total Maximum Daily Load (TMDL) Program 1998
Iterative Approach
Rational Action in the Face of Uncertainty
“Using the best tools and data available, we should make best estimates and take action, recognizing that the decision and action may not be final. If we work to explicitly define the range of uncertainty in our analysis, we can act within that range. Then if, as part of the TMDL, we monitor progress and later adapt our actions, we can continue to progress toward clean water.”
Paul FreedmanThe CWA's New Clothes
Adaptive Management
Rational Action in the Face of Uncertainty
“It is a process of taking actions of limited scope commensurate with available data and information to continuously improve our understanding of a problem and its solutions, while at the same time making progress toward attaining a water quality standard. Plans for future regulatory rules and public spending should be tentative commitments subject to revision as we learn how the system responds to actions taken early on.”
National Research Council, 2001Assessing the TMDL Approach to Water Quality Management
Adaptive Implementation
Cautionary Tales
A Comparison of Three Modeling Approaches:
Stow C. A., Roessler C., Borsuk, M. E., Bowen J. D., and Reckhow K. H., 2001American Society of Civil Engineers Journal of Water Resources Planning and Management. 129(4):307-314.
Comparison of Estuarine Water Quality Models for Total Maximum Daily Load Development in the Neuse River Estuary
Neuse Estuary Eutrophication Model (NEEM) A Two-dimensional, Mechanistic Model
Water Analysis Simulation Program (WASP) A Three-dimensional, Mechanistic Model
Neuse Estuary Bayesian Ecological Response Network (Neu-BERN) A Bayesian Probabilistic model
Cautionary Tales
Conclusions of the Study Team:
Stow C. A., Roessler C., Borsuk, M. E., Bowen J. D., and Reckhow K. H., 2001American Society of Civil Engineers Journal of Water Resources Planning and Management. 129(4):307-314.
Comparison of Estuarine Water Quality Models for Total Maximum Daily Load Development in the Neuse River Estuary
None of the models were deemed to be able to offer satisfactory performance for the purpose of explicit chlorophyll a predictions for all sections of the estuary.
“Even in a well-studied, data-rich system, accurate prediction is difficult.”
Models should be used in a collaborative atmosphere, with ample stakeholder involvement, to provide “quantitative guidance rather than a definitive number.”
Cautionary Tales
The Chesapeake Bay’s Pesky Pycnocline
Refined Designated Uses forChesapeake Bay and Tidal Tributary Waters
A. Cross Section of Chesapeake Bay or Tidal Tributary
B. Oblique View of the “Chesapeake Bay” and its Tidal Tributaries
Shallow Water
Open WaterDeep Water
Deep Channel
Open WaterHabitatShallow Water
Habitat
Deep Water
Deep Channel
Migratory FinfishSpawning andNursery Habitat
Source: Chesapeake Bay Program
Cautionary Tales
The Chesapeake Bay’s Pesky Pycnocline
Source: Chesapeake Bay Program at http://www.chesapeakebay.net/ecoint3a.htm
CB4 Deep Water Drives “Attainment”
CB4
Source: Chesapeake Bay Program
Cautionary Tales
The Chesapeake Bay’s Pesky Pycnocline
Some Amusing Events:
February, 2003 PSC Debates Baywide Nitrogen Load Goal
175 M lbs/Yr 99.1 % CB4 D.O. Attainment
Versus
198 M lbs/Yr 98.1 % CB4 D.O. Attainment
Cost Difference $430 M per Year
PSC Selects 175 M lbs/Yr as the Goal
99 % Attainment Is the Minimum Acceptable Level
Cautionary Tales
The Chesapeake Bay’s Pesky Pycnocline
Some Amusing Events:
Sometime Later, 2003
Bay Modelers “Tweak” the Pycnocline Definition
New Model Runs:
175 M lbs/Yr ~ 93 % CB4 D.O. Attainment
Fall, 2004 Maryland Proposes “Restoration Variance” for CB4 D.O. Standard
OOOOOPS!