The U.S. Environmental Protection Agency and its partners develop implementation plans to meet water quality standards. Climate change and other key drivers may significantly affect these plans, but are often neglected due to uncertainty. This study uses two case studies to demonstrate how Robust Decision Making (RDM) can help to address future uncertainty by identifying vulnerabilities in water quality plans and suggesting appropriate responses.

Cover: Managing Water Quality in the Face of Uncertainty
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Managing Water Quality in the Face of Uncertainty

A Robust Decision Making Demonstration for EPA's National Water Program

by Jordan R. Fischbach, Robert J. Lempert, Edmundo Molina-Perez, Abdul Ahad Tariq, Melissa L. Finucane, Frauke Hoss

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Research Questions

  1. How might Robust Decision Making help the U.S. Environmental Protection Agency and its partners to improve total maximum daily load (TMDL) implementation planning in the face of imperfect models, climate change, and other uncertainties?
  2. What assumptions about future hydrology, land use, or water quality best management practice effectiveness most often lead proposed watershed implementation plans to exceed TMDL water quality standards for the Patuxent River in Maryland or North Farm Creek tributary of the Illinois River, respectively?
  3. What types of additional investments might help reduce the vulnerabilities identified, and when might planning processes need to be reconsidered?

The U.S. Environmental Protection Agency (USEPA), together with its state and local partners, develops watershed implementation plans designed to meet total maximum daily load (TMDL) water quality standards. Uncertainty regarding the impacts of climate change, future land use, the effectiveness of best management practices, and other drivers may make it difficult for these implementation plans to meet water quality goals. But the methods and processes used to develop implementation plans typically do not address uncertainty in these key drivers of change. In this study, RAND researchers explored how Robust Decision Making (RDM) methods could help USEPA and its partners develop implementation plans that are more robust to such uncertainty. Through two pilot case studies — one on the Patuxent River in Maryland and one on the North Farm Creek tributary of the Illinois River — this study shows how analytic RDM methods can be used to identify future vulnerabilities in TMDL implementation plans and suggest appropriate responses. In both case studies, proposed plans meet their water quality goals under current assumptions, but do not meet water quality goals in many climate and other futures. The study finds that modified plans and adaptive management approaches can often reduce these vulnerabilities. Moving forward, USEPA and its partners can better manage future uncertainty by employing iterative risk management processes and adopting TMDL implementation plans that are robust and flexible.

Key Findings

Climate Change and Other Uncertainties Could Have a Significant Impact on the Success of Water Quality Plans

  • In both pilot regions studied, total maximum daily load (TMDL) watershed implementation plans expected to meet water quality standards if future climate resembles the past do not meet these standards over a wide range of plausible future climate conditions.
  • An increase in the amount of paved or impervious area cover due to future population growth, as well as worse-than-anticipated performance from stormwater best management practices (BMPs), could also lead to missing future standards.

Rainfall-Runoff Simulation Models Can Provide Useful Information for TMDL Planning Under Deep Uncertainty

  • Used within a Robust Decision Making (RDM) framework, these models can help decisionmakers explore the performance of TMDL plans across many plausible paths into the future.

Currently Available Simulation Models Are Suitable to Support RDM Analyses, but Much Could Be Done to Improve Their Utility

  • Treatment of BMP performance in the rainfall-runoff models could be improved, with better accounting for uncertainty related to BMP effectiveness.
  • Models could be streamlined to better integrate with simulations of a more complete range of biophysical and socioeconomic processes and to facilitate scanning over many possible futures.

Recommendations

  • To improve and maintain high water quality standards in changing, often difficult-to-predict conditions, USEPA and its partners will need to employ iterative risk management and rely increasingly on robust and flexible implementation plans.
  • Future analysis could help make TMDL plans more robust by considering a wider range of potential uncertainties and a richer set of response options.
  • The treatment of adaptive TMDL implementation plans could be considerably expanded.
  • Improvements to the analytic tools used in this study could significantly improve the effectiveness of the decision support available to water quality planners. Packaging such tools in more user-friendly and potentially web-accessible toolkits could help make these methods widely available to decisionmakers at the local, state, and regional levels.

Table of Contents

  • Chapter One

    Water Quality Decisions Are Challenged by Future Uncertainty

  • Chapter Two

    Analytic Tools for Robust Adaptive Water Quality Management

  • Chapter Three

    Managing Storm Water in Maryland's Patuxent River Basin with Climate and Land Use Uncertainty

  • Chapter Four

    Evaluating the Impacts of Climate Change on the Water Quality Implementation Plan for the North Farm Creek Tributary of the Illinois River

  • Chapter Five

    Implications for USEPA Water Quality Management

  • Appendix A

    Iterative Risk Management Process for Setting Water Quality Standards

  • Appendix B

    Criteria for Choosing Case Studies

  • Appendix C

    Supplemental Information for the Patuxent River Case Study

  • Appendix D

    SWAT Model Calibration and Validation

The research reported here was conducted in the RAND Environment, Energy, and Economic Development Program, a part of RAND Justice, Infrastructure, and Environment.

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