A Better Reservoir Model for the Millions Who Rely on Surface Water Supplies

Last Modified: Aug 10, 2018


  • Reed Palmer - Hazen and Sawyer

Utilities regularly engage in long-term water supply planning to ensure adequate supply availability, timely supply expansions, and appropriate application of demand management tools. A sound longterm water supply plan includes both a detailed evaluation of the supply available from current and planned sources and an evaluation of customer demand projections. Surface waters are used to meet the needs of over two-thirds of the population relying on public water systems and that figure is over 90% in Virginia. Yet surface waters are highly variable with many orders of magnitude separating the runoff available during floods as compared to drought events and it is critical that sufficient supply is always available. In this presentation we focus on improving estimates of supply availability for surface water systems. Expectations of supply availability for utilities relying on surface water sources involve the development of a hydrologic model for each withdrawal point. These supply models are typically based upon USGS stream gage records, but not all water supply watersheds are gaged so nearby sites are often used, assuming that runoff characteristics will be similar to those upstream of the withdrawal location. Records are then adjusted by the ratio of the drainage area between the gage and the withdrawal location. However, applying this approach without a solid understanding of where discrepancies in runoff characteristics may arise can lead to erroneous estimates of supply availability. There are many reasons such errors can arise for models, including differences between the target watershed and reference watershed in the following areas: Physiographic region of the watersheds, land cover characteristics, large disparities in drainage area, and microclimates in mountainous regions. In this paper we describe two case studies in Virginia that contain examples of each type of discrepancy and describe how each can be addressed to produce more accurate models.

For more information, please contact the author at rpalmer@hazenandsawyer.com.

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