Stepwise nonparametric disaggregation for daily streamflow generation conditional on hydrologic and large-scale climatic signals
Molina Tabares, José Manuel
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A stepwise nonparametric stochastic disaggregation framework to produce synthetic scenarios of daily streamflow conditional on volumes of spring runoff and large-scale ocean-atmosphere oscillations is presented. This thesis examines statistical links (i.e., teleconnections) between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a spectral analysis of climate signals to detect coherences of their behavior in the frequency domain. We explore the use of such teleconnections of selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices) in the proposed data-driven framework by means of a cross-validation-based combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. The downscaling process includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. Although the stepwise procedure may lead to a lack of preservation of the historical correlation between flows of the last day of a month and flows of the first day of the following month, we present a new and simple algorithm, based on nonparametric resampling, that overcomes this limitation. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and preserves very well the statistical characteristics, temporal dependences, and distributional properties of historical flows. We also show that both including conditional information of climatic teleconnection signals and developing the downscaling in cascades decrease significantly the mean error between synthetic and observed flow traces. The downscaling framework is tested with data from the Payette River Basin in Idaho.