Space-time Disaggregation of Streamflow Data Using K-nearest Neighbor Patterns and Optimization

Disaggregated sequences that are statistically similar to observed streamflow records are very useful for analyzing multi reservoir operation policies and river basin management. There is renewed interest in disaggregation methods as climate related issues (regional ENSO forecasts or downscaling of Climate Change Scenarios) have come to the fore. Disaggregated streamflow should preserve statistical attributes of time series across multiple sites and time scales. A new algorithm for simultaneously disaggregating monthly to weekly or daily flows at a number of sites on a drainage network is presented in this paper. The continuity of flow in time across months at each site as well as the inter-site flow pattern is preserved. The disaggregated daily flows at the multiple sites are conditioned on the spatial (across site) pattern of monthly flows at the same sites. The probability distribution of the vector of disaggregated flows conditional on the multi-site monthly flows is approximated nonparametrically using the k-nearest neighbors of the monthly spatial flow pattern. A constrained optimization problem is solved to adaptively estimate the disaggregated flows in space and time for each such neighborhood. An application to data from a tributary of the Colorado River is used to illustrate the modeling process. The daily streamflow data available at the index site was disaggregated to obtain the streamflow data at four upstream sites conditioned on monthly data available at those sites.