Upper Upper SacramentoSacramento

FeatherFeather

YubaYuba

AmericanAmerican

CosumnesCosumnes

MokelumneMokelumne

StanislausStanislaus

TuolomneTuolomne

San JoaquinSan Joaquin

MercedMerced

KingsKings

KaweahKaweah

TuleTule

KernKern

We explore methods that employ dynamically simulated snow water equivalent (SWE) as predictors within the regression-based seasonal streamflow forecasts used operationally. We give particular attention to the performance of our “hybrid” model early and late in the forecast season, when snow is often present only at elevations higher than observing stations, but seasonal forecasts are nonetheless useful.

Development of a Hybrid Dynamical-Statistical Model for Seasonal Streamflow ForecastsDevelopment of a Hybrid Dynamical-Statistical Model for Seasonal Streamflow ForecastsEric RosenbergEric Rosenberg11, Andrew W. Wood, Andrew W. Wood22, Anne C. Steinemann, Anne C. Steinemann11, and Dennis P. Lettenmaier, and Dennis P. Lettenmaier11

11Department of Civil and Environmental Engineering, University of Washington, Seattle, WA Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 22Colorado River Basin Forecast Center, Salt Lake City, UTColorado River Basin Forecast Center, Salt Lake City, UT

AbstractAbstract Model CalibrationModel Calibration

We are grateful to the Division of Flood Management at DWR (in particular, Adam Schneider and David Rizzardo) and to David Garen at NRCS for their invaluable assistance. Funding has been provided by NOAA and NASA.

AcknowledgementsAcknowledgements

Year-by-Year Analysis: Feather River Year-by-Year Analysis: Feather River

The 14watersheds of theSacramento(blue), SanJoaquin (green),and Tulare Lake (red) hydrologic regions. Yellow circles represent runoff forecast points for DWR. Watersheds with both light and dark colors are divided by DWR into areas of high and low elevation for purposes of snow measurement.

Regression ApproachRegression Approach

The hybrid models developed in this study employ both principal components and Z-score regression techniques. Models were calibrated on data from 1956-2005, the same calibration period used by DWR. VIC-based Z-score indices of April 1 SWE matched up well with DWR’s ground-based indices (top). Hybrid forecasts based on both Z-score and principal component methods showed improvement over those from DWR, particularly later in the snowmelt season. The skill of each method is compared by plotting the 10th and 90th percentiles of the resulting residuals in so-called “funnel plots” (bottom).

Ongoing WorkOngoing WorkThe results presented here are intended as a proof of concept. Work is underway to increase VIC model resolution to 1/16° and validate the hybrid forecasting model over water years 2006 to 2010, which include some of the driest in California’s recent history.

Water Year 1998 (Wet)

When applied to spatially distributed data, PCA provides a systematic means of determining which locations in a watershed are the best predictors of seasonal streamflow. These locations (shown in (a) and (c) for Apr 1 SWE and Oct-Mar precipitation, respectively) can then be compared with those of the snow courses (b) and precipitation gauges (d) that are used operationally. The hybrid model can thus serve to optimize the infrastructure of its more traditional counterpart.

(a) (b) (c) (d)

Study AreaStudy Area

In the western US, the two agencies responsible for streamflow forecasts are the Natural Resources Conservation Service (NRCS) and, in California, the state’s Department of Water Resources (DWR). Although both agencies employ regression, the specifics of the methodologies differ:

1. DWR issues official forecasts of Apr-Jul streamflow from Feb to May, using SWE, year-to-date precipitation, and prior runoff

as their three predictors. Standard practice involves combining like data into indices prior to entering the regression, and “future

variables” that permit use of a single equation for all forecast issue dates.

Spatial Analysis of Predictive SkillSpatial Analysis of Predictive Skill

Good Luck Steve!Good Luck Steve!

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Upper Sacramento Tuolomne Kaweah

Your commitment to excellence and dedication to your students have been an inspiration. Enjoy retired life! You will be missed.

With a population of more than 37 million and a gross product of more than a trillion dollars, California is both the nation’s most populous state and home to its largest agricultural economy, an industry that relies heavily on irrigation. The state’s high demand for water is fulfilled by a sophisticated supply system that includes the State Water Project and the Central Valley Project, which together deliver roughly 1/3 of the 34 bcm of water consumed annually statewide. The source of Project water is the Central Valley Drainage, bordering the western slopes of the Sierra Nevada, and comprising the Sacramento, San Joaquin, and Tulare

Lake hydrologic regions.

2. NRCS employs principal components regression using only data known at the time of forecast, along with cross-validation and a systematic search algorithm that optimizes variable combinations. More recently, NRCS has implemented Z-score regression as an alternative approach that facilitates the use of predictor variables with missing data.

A closer inspection of forecast progression was performed for the Feather River, the main source of water for the State Water Project, in three years of varying wetness. Results indicated that the hybrid model performed particularly well in a “wet” water year and comparably to DWR’s forecasts in a “dry” water year. Additional analysis is needed to understand the reasons for these differences.