A stochastic nonparametric technique for space‐time disaggregation of streamflows

Abstract

[1] Stochastic disaggregation models are used to simulate streamflows at multiple sites preserving their temporal and 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 this approach are (1) transforming marginals to Gaussian need not lead to the correct multivariate distribution particularly if the dependence across variables is nonlinear, and (2) the number of parameters to be estimated for a traditional disaggregation model grows rapidly with an increase in space or time components. We present a K-nearest-neighbor approach to resample monthly flows conditioned on an annual value in a temporal disaggregation or multiple upstream locations conditioned on a downstream location for a spatial disaggregation. The method is 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., El Niño-Southern Oscillation, etc.) can be easily achieved. 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 locations.

Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall–runoff regime

“…Details regarding the temporal disaggregation and spatial downscaling are provided in the next section. Temporal (K-NN) disaggregation (Prairie et al, 2007) a. VIC fcst 6-month lead ECHAM4. Figure 2 illustrates the experimental setup, for streamflow forecasts development, using the VIC model and the statistical model.…”

“…Daily time series of precipitation were derived from monthly time series using the temporal disaggregation technique described in Prairie et al (2007). The temporal disaggregation involved classifying monthly time series into daily time series by identifying similar monthly conditions in the historical record based on the Kernel-nearest neighbor (K-NN) approach.…”

Modeling multisite streamflow dependence with maximum entropy copula

“…It can be seen that the derivation of the maximum entropy copula is separate from that of the marginal probability distributions. Suitable marginal distributions, such as kernel density, can be selected to model the properties of streamflow of each month, such as skewness and bimodal properties, which have been well documented [Sharma et al, 1997;Prairie et al, 2007;Salas and Lee, 2010;Hao and Singh, 2012]. Thus, we omit the discussion of the marginal distributions but focus on the dependence structure modeling of multisite monthly streamflow through the maximum entropy copula.…”

“…The nonparametric model, such as kernel density method, moving block bootstrapping method, or K-nearest neighbor resampling method, does not make assumptions about the probability distribution or dependence forms and provides an alternative for stochastic simulation [Vogel and Shallcross, 1996;Sharma et al, 1997;Prairie et al, 2007;Nowak et al, 2010].…”

Disaggregation of Annual to daily Streamflows: A linear deterministic method

“…Several non-parametric techniques [6, 7, 8, and 9] have been developed to avoid the parametric limitations in generating streamflow. The non-parametric approach was utilized by [7] including Kernel density estimation approach. This approach was improved by using K-nearest neighbor (KNN) approach based on resampling model by [8].…”