Skill Assessment of Water Supply Forecasts for Western Sierra Nevada Watersheds

Skill Assessment of Water Supply Forecasts for Western Sierra Nevada Watersheds Downloaded from ascelibrary.org by University of California, Merced on 03/08/16. Copyright ASCE. For personal use only; all rights reserved. Brent Harrison 1 and Roger Bales, M.ASCE 2 Abstract: The western slope of the Sierra Nevada contains 13 major river basins with sufficient long-term seasonal forecast and runoff data to assess forecast skill. These seasonal forecasts are issued by the California Department of Water Resources on February 1, March 1, April 1, and May 1 of each year for the major watersheds in California. Annual average precipitation in these river basins goes from 1,500 mm in the Yuba in the north, to 600 mm in the Kern to the south. Average runoff fraction (April to July) for the various watersheds ranged from 0.1 to 0.5 with the lower-elevation watersheds, and the Kern in the south having lowest values. The difference between precipitation and runoff, an index of evapotranspiration, was highest in the lower-elevation Cosumnes and Mokelumne Basins. Approximately half of the April 1 annual forecasts had a percent bias of 15%. Skill scores for the 13 watersheds showed low scores (0.3) for forecasts in February, increasing through the forecast season to 0.8 for forecasts issued May 1, with 1.0 being a perfect forecast. Correlation skill measures, such as the Nash Sutcliffe scores, also exhibited increases in skill through the season from 0.45 in February to 0.95 in May. A linear regression between Nash Sutcliff scores and watershed elevation yielded a strong relationship with a coefficient of determination of 0.77. This relationship between higher elevation basins and greater forecast skill reflects the stronger statistical relationship between snow accumulations at index sites and seasonal runoff, versus more rainfall dominance in lower elevation watersheds. April through July runoff for each year was classified as the lower 30%, the mid 40%, and upper 30%; categorical skill measures were computed on the three runoff categories. Increases in forecast skill during the forecast season were visible in the low-flow and high-flow years versus the midflow years. Over forecasting of flow in the middle category was especially apparent early in the season, illustrated by high early season false-alarm rate and over forecasting bias. Difficulty in making accurate forecasts for midflow runoff along with the under forecast of high-runoff years and the over forecast of low-runoff years are shown to be common difficulties in runoff forecasting, especially early in the forecast season. Forecast skill is shown to be elevation dependent and can be expected to decrease with increasing temperatures. DOI: 10.1061/(ASCE)HE.1943-5584.0001327. © 2016 American Society of Civil Engineers. Author keywords: Forecast; Runoff; Skill. Introduction Runoff from the Sierra Nevada is used for agricultural, municipal, industrial, and environmental purposes. To apportion the water to its many uses, the agencies responsible rely on runoff forecasts prepared during the snow accumulation and ablation period to estimate water supplies for the remainder of the water year. These forecasts use information on snowpack, precipitation, and other hydrologic conditions to make runoff projections. In California, the Cooperative Snow Survey Program, part of California Department of Water Resources (DWR), coordinates the measurements and pre- pares the forecasts. Local agencies sometimes use these forecasts to prepare specialized forecasts for their own use. The DWR forecasts have been prepared on watersheds in California since the 1930s. The forecasts predict April through July runoff and, by extension, estimate runoff for the water year. The forecasts are issued monthly, February through May, of each year. Lecturer, Sierra Nevada Research Institute, Univ. of California, Merced, 5200 N. Lake Rd., Merced, CA 95343 (corresponding author). E-mail: bharrison3@ucmerced.edu Director, Sierra Nevada Research Institute, Univ. of California Merced, 5200 N. Lake Rd., Merced CA 95343. E-mail: rbales@ucmerced.edu Note. This manuscript was submitted on February 25, 2015; approved on October 7, 2015; published online on January 12, 2016. Discussion period open until June 12, 2016; separate discussions must be submitted for individual papers. This paper is part of the Journal of Hydrologic En- gineering, © ASCE, ISSN 1084-0699. © ASCE In preparation of these forecasts, snow water content is mea- sured in snow courses consisting of 7–10 sample points (CDEC 2014a). Runoff forecasts are based on historical relationships de- fined by regressions between April through July runoff and snow water content of the applicable snow course, precipitation to date, and preceding month’s runoff. The forecasts are issued in two parts: (1) the runoff in the April through July period, which is generally considered snowmelt; and (2) runoff for the water year, October through September. Runoff for remaining months in the winter period is estimated using relationships with runoff to date of the forecasts and precipitation accumulations. Runoff amounts for August and September are correlated with April through July runoff for forecasting purposes. Forecasts for one river basin are checked with the forecasts for adjoining basins for quality control purposes. These forecasts are used by water managers and stake- holders to commit deliveries and schedule operations. The forecasts also are used by regulatory agencies for ecosystem protection. Similar runoff forecasts are made for watersheds in other western states of the United States. The first formal evaluations of the skill of runoff forecasts in other western states were made in the late 1950s on various portions of these forecasts. Additional work to evaluate forecast skill in the western states (excluding the western Sierra Nevada) was done again in the mid 1980s. Shafer and Huddleston (1984) reviewed historical seasonal volume fore- casts based on regression techniques and found a small improve- ment in forecasting skill in recent years, but cautioned that large improvements in skill are not to be expected in the future by refin- ing regression techniques. Starting in 2002, the latest work was J. Hydrol. Eng., 04016002 J. Hydrol. Eng.