Decision trees for incorporating the likelihood of excessive hydrologic alteration into hydropower-ecosystem tradeoffs
- Tufts University
Detrimental ecological impacts have often been observed or anticipated when changes in streamflow indicators exceed percent deviation thresholds believed to be ecologically critical. Yet, short pre- and post-impact flow records often make it difficult to determine whether changes exceeding tolerable thresholds are due to dam operations or natural variability. Through a hypothetical reservoir operations example, we incorporate the uncertainty of dam-induced streamflow changes into a Bayesian decision tree framework that evaluates tradeoffs between expected regrets associated with hydropower and ecology. The likelihood of over-protection (type I) and under-protection (type II) errors associated with hypothesis tests are used to compute expected hydropower and ecosystem regrets associated with dam operation decisions. We examine changes to high (annual Q5) and low flows (annual Q95) in typical years using a modified and extended nonparametric ranked-sum test that accounts for percent deviation thresholds. A multiple comparison test is then used to determine the likelihood of at least one threshold violation. An example shows that our decision-theoretic approach can lead to different dam operation recommendations than do other common methods, and highlights limitations that arise when the type I error rate is selected a priori. While we illustrate a hydropower-ecosystem tradeoff, our approach can also be applied to other multi-stakeholder reservoir and river management conflicts.
- Research Organization:
- Hydropower Foundation
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office
- DOE Contract Number:
- EE0006506
- OSTI ID:
- 1648146
- Country of Publication:
- United States
- Language:
- English
Similar Records
Agricultural vs. hydropower tradeoffs in the operation of the High Aswan Dam
Restoring the Grand Canyon Ecosystem: Evidence from Dynamic Systems Theory