A stochastic nonparametric approach for streamflow generation combining observational and paleoreconstructed data

Abstract

The Colorado River basin experienced the worst drought on record during 2000–2004. Paleoreconstructions of streamflow for the preobservational period show droughts of greater magnitude and duration, indicating that the recent drought is not unusual. The rich information provided by paleoreconstructions should be incorporated in stochastic streamflow models, enabling the generation of realistic flow scenarios required for robust water resources planning and management. However, the magnitudes of reconstructed streamflow have a high degree of uncertainty. This apparent weakness of the paleodata has made their use in water resources planning contentious, despite their availability for many decades. However, few contest the accuracy of hydrologic state (i.e., dry and wet periods). A key question is how to combine the long paleoreconstructed streamflow information of lower reliability with the shorter observational data to develop a framework for streamflow simulation. We propose a unique stochastic streamflow simulation framework combining these two data sets. This has two components: (1) a nonhomogeneous Markov chain model, developed using the paleodata, which is used to simulate the hydrologic state, and (2) a nonparametric K‐nearest neighbor (K‐NN) time series bootstrap of observational flow magnitudes conditioned on the hydrologic state, thus combining the respective strengths of the two data sets. The framework is demonstrated for the Lees Ferry, Arizona, stream gauge on the Colorado River. The simulations show the ability to reproduce relevant statistics of the observational period and generate a rich variety of wet and dry sequences for use in sustainable management of water resources.