Multi-site Seasonal Streamflow Forecast on the Upper Colorado by kimbrozic

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									  Research Proposal: Multi-site Seasonal Streamflow Forecast on the

                           Upper Colorado River Basin

                                   Cameron Bracken

                              Advisor: Balaji Rajagopalan

Abstract

A process for predicting streamflows at multiple locations upstream of the Lees Ferry

gage on the Colorado River is proposed. Lees Ferry is a crucial gage because it is

considered the boundary between the upper and lower Colorado River basins. The

method seeks to increase lead-time on spring streamflow forecasts to allow basin

managers to make more informed operating decisions. Large-scale climate predictors

such as sea surface temperature, and geopotential height will be identified by their

correlation with historical natural streamflow data. An optimal subset of predictors will

be determined and an ensemble of forecasts will be made for the Lees Ferry gage. Upon

evaluating the skill of the predictions at Lees Ferry, a disaggregation model will be used

to extend predictions to upstream gages. The forecasted streamflows can be used with an

existing decision support model.



Introduction and Motivation

Due to rapidly growing populations, water resource managers in the western United

States face increased demand on the region’s renewable water supply. In the western

United States, 44% o the renewable water supply is consumed annually while only 4% is

consumed in the rest of the country (Grantz et. al. 2005). The Colorado River provides a

crucial water source to many states including Colorado, Utah, Wyoming, Arizona, New
Mexico and California. Management of the Colorado River is a complex issue that relies

in part on the on the skilful prediction of seasonal streamflows. Skilful predictions allow

basin managers to understand the impact of operational policy on aspects of the Colorado

River system (Prairie 2006). Recent droughts have increased the importance of not only

skilful flow predictions but also long lead times.



Currently, the primary predictor for streamflow in the upper basin of the Colorado River

is winter snowpack or Snow Water Equivalent (SWE) (Grantz et. al. 2005). Predictions

based on SWE are available in January at the earliest though skilful forecasts of spring

flows are also important for planning and decision-making. A growing body of literature

supports the idea that large-scale climate features (e.g. ENSO, PDO) influence the

regional hydrology of the western United States in a significant way (Regonda et. al.

2006). The identification of large-scale climate predictors (e.g. SST, geopotential height)

offers a forecast of spring streamflows during December through February.



Lees Ferry is the boundary between the upper and lower Colorado River basin located

near the border between Utah and Arizona. The gauging station located there is of

interest in this study. A recently developed disaggregation model provides the means to

expand flow predictions at the Lees Ferry gage to make predictions at upstream gages

(Prairie et. al. 2007). A dissaggregation method captures the spatial interdependency of

the upstream gages rather than simulating them all separately.            The method is

parsemoneous because predictors need only be developed for one gage.
Objectives

The objectives of this study are to:

   1. Perform Climate diagnostics in order to identify large-scale climate predictors,

       which have an impact on the flow at the Lees Ferry gage (Grantz et. al. 2005).

   2. Using an optimal subset of predictors, provide a seasonal forecast of the

       streamflow at various lead times and evaluate the skill.

   3. Employ a disaggregation model to predict flows at gages upstream of Lees Ferry

       and evaluate the skill.

   4. The final objective of this study is to provide input to the decision support model

       for the upper Colorado River Basin though this is out of the scope of this study.

Figure 1 gives a graphical representation of the objectives of this study.




                                           Identify
                                          Predictors



                                          Seasonal
                                          Forecast



                                       Disaggregation
                                           Model



                                       Decision Support
                                            Model




                           Figure 1-Flow Chart of Objectives
Methods

This section will parallel the objectives section with descriptions of the methodology at

each step.

   1. A forecast season will first be developed based on historical data. The task of

       identifying predictors involves identifying regions of high correlation to

       seasonally averaged flows for various lead times. This will be done using the

       Climate Diagnostics Center’s (CDC) website (www.cdc.noaa.gov). The average

       predictor value over the region of highest correlation is then used in a local

       polynomial fit model, which captures local nonlinearity.           In addition to

       identifying predictors, extreme flow years will be singled out for composite

       analysis in hopes of identifying large-scale mechanisms that drive basin-scale

       hydrology.

   2. A statistical method called Generalized Cross Validation (GCV) will be used to

       determine an optimal subset of predictors (Grantz et. al. 2005). The subset of

       predictors will then be used to generate an ensemble of forecasts in the form of

       probability density functions. The skill will be evaluated using ranked probability

       skill scores and likelihood functions (Grantz et. al. 2005).

   3. The ensemble of forecasts will be run through the disaggregation model which

       may be modified slightly in order to fit the desired number of upstream gages.
Expected Schedule

The data analysis, generation of correlation and composite maps, identification of

predictors, and determining an optimal set of predictors is expected to take 1-2 weeks.

The end of June will be spent using the LOCFIT package to fit local polynomials and

generate an ensemble of forecasts. July will be spent evaluating the skill of the

predictions, using the disaggregation model to predict upstream flows, and interpreting

data. The end of July and the beginning of August will be spent compiling and analyzing

data and preparing the final report.

References

   1. Prairie, J., B. Rajagopalan, U. Lall and T. Fulp, A Stochastic Nonparametric

       Technique for Space-Time Disaggregation of Streamflows, Water Resources

       Research, 2007

   2. Regonda, S., B. Rajagopalan, M. Clark and E. Zagona, Multi-model Ensemble

       Forecast of Spring Seasonal Flows in the Gunnison River Basin, Water Resources

       Research, 42, W09494, 2006

   3. Grantz, K., B. Rajagopalan, M. Clark and E. Zagona, A Technique for

       incorporating large-scale climate information in basin-scale ensemble streamflow

       forecasts. Water Resources Research, 41, W10410, 1-13, 2005

   4. Prairie, James. Stochastic Nonparametric Framework for Basin Wide Streamflow

       and Salinity Modeling: Application for the Colorado River Basin. PhD thesis,

       University of Colorado, 2006.

								
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