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Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Watershed reliability, resilience and vulnerability analysis under uncertainty using water quality data.

Yamen M Hoque1, Shivam Tripathi, Mohamed M Hantush

  • 1Department of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA. yhoque@purdue.edu

Journal of Environmental Management
|June 16, 2012
PubMed
Summary

This study introduces a watershed health assessment method using reliability, resilience, and vulnerability (R-R-V) analysis. Accounting for data uncertainty is crucial, as ignoring it can lead to misleading results, particularly for sediment levels.

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Area of Science:

  • Environmental Science
  • Hydrology
  • Water Resource Management

Background:

  • Water quality data are often sparse, necessitating reconstruction using surrogate variables like streamflow.
  • Assessing watershed health requires robust methods that account for data uncertainty.

Purpose of the Study:

  • To develop and apply a novel method for watershed health assessment using reliability, resilience, and vulnerability (R-R-V) metrics.
  • To quantify uncertainty in reconstructed water quality time-series and its impact on watershed status assessment.

Main Methods:

  • Employed a Bayesian algorithm based on relevance vector machine (RVM) to quantify reconstruction errors.
  • Utilized R-R-V analysis on stream water quality data from the Cedar Creek watershed (2002-2007).
  • Conducted a probabilistic assessment against established water quality thresholds.

Main Results:

  • Cedar Creek watershed generally complies with standards for pesticides, ammonia, and total phosphorus.
  • The watershed is susceptible to exceeding sediment standards, highlighting the importance of accurate data.
  • Ignoring data uncertainty led to misleading results, especially concerning sediment violations.

Conclusions:

  • The developed R-R-V method effectively assesses watershed health and the impact of stressors.
  • Probabilistic assessment considering data uncertainty is vital for reliable watershed management.
  • This approach offers a promising tool for environmental managers to evaluate watershed conditions.