How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications. Issue 7 (20th July 2017) - McMillan, Hilary
Abstract: 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. Plain Language Summary: In this commentary, we show how analyzing uncertainty in river flow data can reduce costs and promote robust conclusions in water management applications. River flow data can contain large uncertainties but are often communicated and used without uncertainty information. We give case studies from Norway and New Zealand where flow 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 conclude that flow uncertainty analysis is critical for good water management decisions. Key Points: Ignorance of uncertainties in streamflow data can lead to suboptimal decisions and economic costs in water management applications We present three case studies where uncertainty analysis reduced costs and promoted robust conclusions We recommend development of practical tools that generate multiple streamflow time series realizations rather than simple error bounds