HR: 11:55h
AN: H52A-06 INVITED     [Abstracts]
TI: A Stochastic Nonparametric Technique for Space-Time Disaggregation of Streamflows
AU: Rajagopalan, B
EM: balajir@colorado.edu
AF: Dept. of Civil and Env. Eng., Univ. of Colo., 428 UCB, Boulder, CO 80309 United States
AU: * Prairie, J R
EM: prairie@colorado.edu
AF: Dept. of Civil and Env. Eng., Univ. of Colo., 428 UCB, Boulder, CO 80309 United States
AU: * Prairie, J R
EM: prairie@colorado.edu
AF: Bureau of Reclamation, Univ. of Colo., Boulder, CO United States
AU: Lall, U
EM: ula2@columbia.edu
AF: Earth and Env. Eng., Columbia Univ., New York, NY United States
AB: Stochastic disaggregation models are widely used to simulate streamflows at several sites preserving their spatial dependencies. Traditional approaches to this problem involve transforming the streamflow data of each month and at every location to a Gaussian structure and subsequently fitting a linear model in the transformed space; the simulations are then back transformed to the original space. The main drawbacks of the traditional linear models are (i) the number of parameters to be estimated grows exponentially with increase in space or time components, (ii) transforming the monthly data at each location to normality can be cumbersome and may not be satisfactory and, (iii) restricted to capturing only linear dependency. The approach broadly has three steps:(i) Annual flow at the index gauge z is generated from an appropriate model (e.g., AR1 or a nonparametric model). (ii) A vector u is resampled from the conditional probability density function (pdf) f(U | Z), where U is the transformed matrix of the historic monthly flows at the index gauge. (iii) The resampled u is back transformed to obtain monthly flows at the index gauge x. (iv) A vector s' is resampled from the conditional pdf f(S' | X), where S' is the transformed matrix of the spatial locations, and the vector s of monthly flows at all the D locations is obtained upon back transformation. The resampling from the conditional pdfs is based on a K-nearest neighbor bootstrap approach. The method is extremely parsimonious, as the only parameter to estimate is K (the number of nearest neighbors to be used in resampling). Simulating space-time flow scenarios conditioned upon large-scale climate information (e.g., ENSO, etc.) can be easily achieved unlike the traditional methods. We demonstrate the utility of this methodology by applying it for space-time disaggregation of streamflows in the Upper Colorado River basin. The method appropriately captures the distributional and spatial dependency properties at all the gauges.
DE: 1833 Hydroclimatology
DE: 1860 Runoff and streamflow
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2005 Joint Assembly