Robert R. Mason

How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications

Streamflow data are used for important environmental and economic decisions, such as specifying and regulating minimum flows, managing water supplies, and planning for flood hazards. Despite significant uncertainty in most flow data, the flow series for these applications are often communicated and used without uncertainty information. In this commentary, we argue that proper analysis of uncertainty in river flow data can reduce costs and promote robust conclusions in water management applications. We substantiate our argument by providing case studies from Norway and New Zealand where streamflow uncertainty analysis has uncovered economic costs in the hydropower industry, improved public acceptance of a controversial water management policy, and tested the accuracy of water quality trends. We discuss the need for practical uncertainty assessment tools that generate multiple flow series realizations rather than simple error bounds. Although examples of such tools are in development, considerable barriers for uncertainty analysis and communication still exist for practitioners, and future research must aim to provide easier access and usability of uncertainty estimates. We conclude that flow uncertainty analysis is critical for good water management decisions.

A Comparison of Methods for Streamflow Uncertainty Estimation

Streamflow time series are commonly derived from stage-discharge rating curves, but theuncertainty of the rating curve and resulting streamflow series are poorly understood. While differentmethods to quantify uncertainty in the stage-discharge relationship exist, there is limited understanding ofhow uncertainty estimates differ between methods due to different assumptions and methodologicalchoices. We compared uncertainty estimates and stage-discharge rating curves from seven methods at threeriver locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a widerange of estimates, particularly for high and low flows. At the simplest site on the Isere River (France), fullwidth 95% uncertainties for the different methods ranged from 3 to 17% for median flows. In contrast,uncertainties were much higher and ranged from 41 to 200% for high flows in an extrapolated section of therating curve at the Mahurangi River (New Zealand) and 28 to 101% for low flows at the Taf River (UnitedKingdom), where the hydraulic control is unstable at low flows. Differences between methods result fromdifferences in the sources of uncertainty considered, differences in the handling of the time-varying nature ofrating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptionswhen extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of anuncertainty method requires a match between user requirements and the assumptions made by theuncertainty method. Given the signi ficant differences in uncertainty estimates between methods, we suggestthat a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.

The Evolution and Development of Improved Data Collection Methods and Mobile Networks for the Observation of Inland Hurricane Storm Surge

1U.S. Geological Survey, 415 National Center, Reston, VA 20192; PH 703-6485305; email rrmason@usgs.gov 2U.S. Geological Survey, Stephenson Center, Suite 129, 720 Gracern Road, Columbia, SC 29210; PH 803-750-6140; email pconrads@usgs.gov 3U.S. Geological Survey 3916 Sunset Ridge Road, Raleigh, N.C. 27607; PH 919571-4017; email jrobbins@usgs.gov 4U.S. Geological Survey, Peachtree Business Center, Suite 130, 3030 Amwiler Road, Atlanta GA 30360; PH 770-903-9127; email bemccall@usghs.gov 5U.S. Geological Survey, 6000 “J” Street, Placer Hall, Sacramento, CA 95819; PH 916-278-3227; ceberenb@usgs.gov