Terrance Fulp

Stochastic Modeling and Simulation of the Colorado River Flows

The Colorado River is one of the most important rivers systems in the western United States. Streamflows of this river represent a significant portion o f the water supply in the region. Many river diversions and dams have been developed throughout the system in order to be able to deliver and meet the expected demands from a variety of water users. To plan and operate the river system planners and manag ers must be able to analyze and assess the effect of possible streamflow scenarios that may occur in the future. For this purpose stochastic simulation studies of the system have been undertaken. The spatial and temporal variability of streamflows in thi s river is quite complex not only because of its size but the effect of large -scale atmospheric and oceanic fluctuations. Monthly and annual data of naturalized flows at 29 sites have been analyzed to characterize the spatial and temporal variability in k ey statistics such as means, variances, covariances, skewness, and surplus and drought related statistics. T he statistical relation ships between site s are just as important as the statistical characteristics of each site. In principle a multivariate model could be applied directly to the 29 -site system but such a model will require estimating a large number of parameters , in some cases the parameters may be difficult if not impossible to obtain, and for a river network system such a direct application of a model for the entire system may not be necessarily appropriate . Therefore, the system must be simplified so that it becomes statistically tractable and consistent with a river network. For this purpose alternative modeling strategies (schemes) have been developed in literature and appropriate software are available (e.g. SAMS and SPIGOT). In this study we have applied SAMS and compared three

Stochastic Streamflow Generation Incorporating Paleo -Reconst ruction

The Colorado River Bas in experienced t he worst drought on record from 2000 -2004. Though this drought wa s unprece de nted in the observed streamflow record (1906 -present) reconstructed streamflows dating back to 1490 generated from tree -ring chronologies have shown droughts of greater magnitude and duration. Yet , the decision to adopt the informat ion from tree -ring s is not with out question. Alternate techniques to reconstruct streamflows base d on tree -ring s can display different magnitudes of past flow . Though the magnitudes are different these various reconstructions do show similar system state, i.e., wet or dry state . We present a technique to combine reconstructed streamflow state information with the observed records flow magnitude. This a achieved using a nonhomogeneous Markov chain model with kernel smoothing coupled with a K -nearest neighbor sampling al gorithm. The technique is demonstrated for the Lees Ferry stream gauge on the Colorado River. The coupled models retain t he ability to generate basic statistics similar to the observed record while also capturing the state properties of the reconstructed s treamflows.