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Nowak K.,University of Colorado at Boulder | Nowak K.,Center for Advanced Decision Support for Water and Environmental Systems | Prairie J.,Bureau of Reclamation | Rajagopalan B.,University of Colorado at Boulder | And 2 more authors.
Water Resources Research | Year: 2010

Streamflow disaggregation techniques are used to distribute a single aggregate flow value to multiple sites in both space and time while preserving distributional statistics (i.e., mean, variance, skewness, and maximum and minimum values) from observed data. A number of techniques exist for accomplishing this task through a variety of parametric and nonparametric approaches. However, most of these methods do not perform well for disaggregation to daily time scales. This is generally due to a mismatch between the parametric distributions appropriate for daily flows versus monthly or annual flows, the high dimension of the disaggregation problem, compounded uncertainty in parameter estimation for multistage approaches, and the inability to maintain flow continuity across disaggregation time period boundaries. We present a method that directly simulates daily data at multiple locations from a single annual flow value via K-nearest neighbor (K-NN) resampling of daily flow proportion vectors. The procedure is simple and data driven and captures observed statistics quite well. Furthermore, the generated daily data are continuous and display lag correlation structure consistent with that of the observed data. The utility and effectiveness of this approach is demonstrated for selected sites in the San Juan River Basin, located in southwestern Colorado, and later compared with the disaggregation technique of Prairie et al. (2007) for several locations in the Colorado River Basin. © 2010 by the American Geophysical Union. Source

Nowak K.C.,University of Colorado at Boulder | Nowak K.C.,Center for Advanced Decision Support for Water and Environmental Systems | Rajagopalan B.,University of Colorado at Boulder | Rajagopalan B.,Center for Advanced Decision Support for Water and Environmental Systems | And 2 more authors.
Journal of Hydrology | Year: 2011

Traditional stochastic simulation methods that are crafted to capture measures such as mean, variance and skew fail to reproduce significant spectral properties of the observed data. A growing body of literature indicates that many geo-physical data, especially streamflow, exhibit quasi-periodic and non-stationary variability driven by large scale climate features. Thus, methods which accurately model this behavior, in particular, the time evolution of variability, frequency of wet/dry epochs, etc. are crucial for risk assessment and management of water resources. In this paper, a Wavelet based Auto Regression Modeling (WARM) framework is proposed for data with significant non-stationary spectral features. This approach has four broad steps - (i) the wavelet transform of a time series is reconstructed as several periodic components based on dominant variability frequencies, (ii) scale averaged wavelet power (SAWP) is computed for each band to capture the time varying power and the components are scaled by this, (iii) Auto Regressive (AR) models fit to the scaled components and, (iv) simulations are performed from the AR models, rescaled and combined to obtain simulations of the original time series. Step (ii) is a new and unique departure from the WARM proposed by Kwon et al. (2007). We demonstrate this approach on annual streamflow at the Lee's Ferry gauge on the Colorado River. Furthermore, this is coupled with a spatial disaggregation method to generate streamflow ensembles at multiple locations upstream. We also show that this combination captures the spectral properties at several locations in a parsimonious manner. © 2011. Source

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