Disaggregation procedures for stochastic hydrology based on nonparametric density estimation

Abstract

Abstract. Synthetic simulation of streamflow sequences is important for the analysis of water supply reliability. Disaggregation models are an important 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 nonparametric methods for streamflow simulation. These methods provide the capability 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 this paper we extend the use of nonparametric methods to disaggregation models. We show how a kernel density estimate of the joint distribution of disaggregate flow variables can form the basis for conditional simulation based on an input aggregate flow variable. This methodology preserves summability of the disaggregate flows to the input aggregate flow. We show through applications to synthetic data and streamflow from the San Juan River in New Mexico how this conditional simulation procedure preserves a variety of statistical attributes. IntroductionA goal of stochastic hydrology is to generate synthetic streamflow sequences that are statistically similar to observed streamflow records. Such synthetic streamflow sequences are useful for analyzing reservoir operation and stream management policies. Often, multiple reservoir sites and stream sections need to be considered as part of a system operation plan, and the operating horizon may extend from a few days to several years. For proper system operation it is important that the streamflow sequences generated for the different sites and/or time periods be "compatible." Practically, this suggests that (1) the flow recorded at a downstream gage be represented as the sum of the tributary flows and channel losses/ gains, (2) the annual flow represent a sum of the monthly flows, (3) the monthly fractions of flows in wet/dry years be representative of wet/dry years respectively, and (4)

Stochastic generation of annual, monthly and daily climate data: A review

“…Recently Tarboton et al (1998) proposed a disaggregation procedure based on nonparametric density (NPD) estimation which models adaptively complex relationships between aggregate and disaggregate flows. The primary shortcoming of the NPD procedure is that it is data and computationally intensive.…”

A hybrid stochastic model for multiseason streamflow simulation

“…Following this, a split sample validation is performed on the Weber River monthly streamflows to show that the hybrid model is able to capture repeatable statistical structure present in the observed streamflows. The latter part of the paper presents a performance comparison between SPIGOT [Grygier and Stedinger, 1990], NPD [Tarboton et al, 1998], and HM in simulating historical monthly streamflows of San Juan River (New Mexico).…”

An Introduction to Probability, Statistics, and Uncertainty

“…Subsequent improvements and variations are described by Stedinger and Vogel (1984), Maheepala and Perera (1996), Koutsoyiannis and Manetas (1996) and Tarboton et al (1998).…”