Wavelet‐based time series bootstrap model for multidecadal streamflow simulation using climate indicators. Issue 5 (26th May 2016)

• Record Type: Journal Article
• Authors: Erkyihun, Solomon Tassew; Rajagopalan, Balaji; Zagona, Edith; Lall, Upmanu; Nowak, Kenneth
• Is Part Of: Water resources research. Volume 52:Issue 5(2016:May)
• Page Start: 4061
• Page End: 4077
• Publication Date: 2016-05-26
• Subjects: block K‐NN -- climate indices -- wavelet -- conditional streamflow simulation; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2016WR018696
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: A model to generate stochastic streamflow projections conditioned on quasi‐oscillatory climate indices such as Pacific Decadal Oscillation (PDO) and Atlantic Multi‐decadal Oscillation (AMO) is presented. Recognizing that each climate index has underlying band‐limited components that contribute most of the energy of the signals, we first pursue a wavelet decomposition of the signals to identify and reconstruct these features from annually resolved historical data and proxy based paleoreconstructions of each climate index covering the period from 1650 to 2012. A K ‐Nearest Neighbor block bootstrap approach is then developed to simulate the total signal of each of these climate index series while preserving its time‐frequency structure and marginal distributions. Finally, given the simulated climate signal time series, a K‐Nearest Neighbor bootstrap is used to simulate annual streamflow series conditional on the joint state space defined by the simulated climate index for each year. We demonstrate this method by applying it to simulation of streamflow at Lees Ferry gauge on the Colorado River using indices of two large scale climate forcings: Pacific Decadal Oscillation (PDO) and Atlantic Multi‐decadal Oscillation (AMO), which are known to modulate the Colorado River Basin (CRB) hydrology at multidecadal time scales. Skill in stochastic simulation of multidecadal projections of flow using this approach is demonstrated. Key Points: Wavelet decomposition identifies the frequency/period of the climate signal in which the variabilities with time are significant Application of block K‐NN simulation technique on the significant wavelet signals of climate indices is skillful The conditional simulation of streamflow based on teleconnecting climate indices such as sea surface temperature is skillful