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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,...
In the western United States many rivers experience high salinity resulting from natural and anthropogenic sources. This impacts the water quality and hence, is closely monitored. The salinity is closely linked with streamflow quantity in that, a higher flow brings with it more salt but also provides substantial dilution to reduce the salt concentration and vice-versa during low flow regimes. Decision makers typically plan strategies for salinity mitigation and evaluate impacts of water management policy options on salinity in the basin using decision support models. These models require statistically consistent basin wide scenarios of streamflow and salinity. Recognizing this need, we develop a basin wide stochastic...