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Scientific Investigations Report 2010–5159

National Water Availability and Use Pilot Program

Using Prediction Uncertainty Analysis to Design Hydrologic Monitoring Networks: Example Applications from the Great Lakes Water Availability Pilot Project

By Michael N. Fienen, John E. Doherty, Randall J. Hunt, and Howard W. Reeves

Thumbnail of and link to report PDF (10.4 MB)ABSTRACT

The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also—importantly—when designing the prediction-uncertainty paradigm.

First posted Sept 2, 2010

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Suggested citation:

Fienen, M.N., Doherty, J.E., Hunt, R.J., and Reeves, H.W., 2010, Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project: U.S. Geological Survey Scientific Investigations Report 2010–5159, 44 p.



Contents

Abstract

Introduction

Purpose and Scope

Methods

Model Description

Parameter Contributions to Prediction Uncertainty

Determining Observation Locations for Reducing Prediction Uncertainty

Discussion and Conclusions

Acknowledgments

References

Appendix 1—Derivation of PREDUNC equations in a Bayesian Context

Appendix 2—Derivation of OPR-PPR Statistics

Appendix 3—Conditions Under Which OPR-PPR and PREDUNC Give the Same Results

Appendix 4—Proof of the Athans and Schweppe Identity


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