Multi-Decadal Stochastic Streamflow Projections and 
Application to Water Resources Decision Making in the    
Colorado River Basin
		
Effective water resources planning & management requires skillful decisions on decadal timeframes. In basins 
such as the Colorado River Basin (CRB), streamflow exhibits variability that reflects teleconnections with 
climate indices such as Atlantic Multi-decadal Oscillation (AMO) & Pacific Decadal Oscillation (PDO). This 
research addresses this problem with four main contributions: It develops a conditional stochastic 
streamflow simulation model and decadal scale streamflow projections based on these climate indices, 
compares this with recently developed models, identifies and quantifies periods of unpredictability, and 
demonstrates the value of adding decadal projections to existing decision criteria in the CRB Supply and 
Demand Study.  The novel WKNN model identifies and reconstructs dominant signals in the AMO and PDO using 
wavelet analysis, simulates each using block K-Nearest Neighbor(KNN) bootstrap, then simulates the 
streamflow using a KNN bootstrap conditioned on the simulated climate forcings. Our WKNN model is compared 
with hidden Markov & wavelet based models with respect to skill of projections over a range of lead times. 
Time varying predictability of streamflow is assessed by quantifying the divergence of trajectories in the 
phase space with time, using Local Lyapunov Exponents (LLE) through a nonlinear dynamical system based 
approach. Ensembles of projections from a current time are generated by block resampling trajectories from 
the K-nearest neighbors of the current vector in the phase space. Decadal WKNN projections & time varying 
predictabilities are demonstrated to enhance existing decision criteria in the CRB Study that identify 
system vulnerability and invoke options and strategies to increase water availability or reduce demand.  
Based on projections being wet, dry or unpredictable, improved decisions may reduce cost or reduce shortage 
and are illustrated by tradeoff curves of risk of shortage vs. cost.