DISAGGREGATION PROCEDURES FOR STOCHASTIC HYDROLOGY BASED ON NONPARAMETRIC DENSITY-ESTIMATION

Synthetic simulation of streamflow sequences is important for the anal ysis of water supply reliability. Disaggregation models are an importa nt component of the stochastic streamflow generation methodology. They provide the ability to simulate multiseason and multisite streamflow sequences that preserve statistical properties at multiple timescales or space scales. In recent papers we have suggested the use of nonpara metric methods for streamflow simulation. These methods provide the ca pability to model time series dependence without a priori assumptions as to the probability distribution of streamflow. They remain faithful to the data and can approximate linear or nonlinear dependence. In th is paper we extend the use of nonparametric methods to disaggregation models. We show how a kernel density estimate of the joint distributio n of disaggregate flow variables can form the basis for conditional si mulation based on an input aggregate flow variable. This methodology p reserves summability of the disaggregate flows to the input aggregate flow. We show through applications to synthetic data and streamflow fr om the San Juan River in New Mexico how this conditional simulation pr ocedure preserves a variety of statistical artributes.