Water availability has been of great concern in the State of Texas and many other places worldwide. During 1997-2003, pursuant to the 1997 Senate Bill 1, the Texas Commission on Environmental Quality (TCEQ), its partner agencies, and contractors developed a Water Availability Modeling (WAM) System based on the Water Rights Analysis Package (WRAP) model, developed at Texas A&M University. WAM has been widely applied in the State of Texas and because of its convenience, applications, and capabilities, it is planned to be implemented in other States and Countries. This thesis addresses different aspects of WAM, including conditional reliability modeling, firm yield analysis following classic and recently developed methodologies, evaluating the impact of different considerations on reliability analyses, simplification of complex WAM datasets and the display of WRAP results into ArcMap. Conditional reliability modeling evaluates short term diversion/storage reliabilities based on an initial storage level. WRAP-CON has been evaluated and improved, in addition a new modeling methodology has been developed, in which probabilities of occurrence for each hydrologic sequence is based on the relationship between storage and future flows. Recently developed WRAP capabilities have been evaluated, providing users new tools and increased flexibility. Some of these improvements are firm yield analysis, cycling and dual simulation. In addition to improved software, guidelines have also been developed, including a set to simplify extremely large WAM datasets, while maintaining the effect of all the other water rights in a basin.

A new model developed for the entire bathtub-shaped hazard rate curve has been evaluated as to its usefulness as a method of reliability estimation. The model is of the form: F(t) = 1 - exp - (ϴ1tL + ϴ2t + ϴ3tM) where "L" and "M" were assumed known. The estimate of reliability obtained from the new model was compared with the traditional restricted sample estimate for four different time intervals and was found to have less bias and variance for all time points. The was a monte carlo study and the data generated showed that the new model has much potential as a method for estimating reliability. (51 pages)