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We combined the methods and advantages of stochastic hydrology and paleohydrology to estimate 900 years of weekly flows for the North and South Saskatchewan Rivers at Edmonton and Medicine Hat, Alberta, respectively. Regression models of water‐year streamflow were constructed using historical naturalized flow data and a pool of 196 tree‐ring (earlywood, latewood, and annual) ring‐width chronologies from 76 sites. The tree‐ring models accounted for up to 80% of the interannual variability in historical naturalized flows. We developed a new algorithm for generating stochastic time series of weekly flows constrained by the statistical properties of both the historical record and proxy streamflow data, and by the necessary condition that weekly flows correlate between the end of a year and the start of the next. A second innovation, enabled by the density of our tree‐ring network, is to derive the paleohydrology from an ensemble of 100 statistically significant reconstructions at each gauge. Using paleoclimatic data to generate long series of weekly flow estimates augments the short historical record with an expanded range of hydrologic variability, including sequences of wet and dry years of greater length and severity. This unique hydrometric time series will enable evaluation of the reliability of current water supply and management systems given the range of hydroclimatic variability and extremes contained in the stochastic paleohydrology. It also could inform evaluation of the uncertainty in climate model projections, given that internal hydroclimatic variability is the dominant source of uncertainty. We combined the methods and advantages of paleo and stochastic hydrology to estimate 900 years of weekly flows for two major rivers in Alberta, CanadaWe developed a new algorithm to generate stochastic time series of weekly flows that have decadal variability not evident in the short historical flow recordA second innovation is to derive the paleohydrology from an ensemble of 100 statistically significant reconstructions at each gauge

In this paper we describe main features of a Strongly Feasible Evolution Program(SFEP) designed to solve non-linear network flow problems. The program can handle non-linearities both in the constraints and in the objective function. The solutions procedure is based on a recombination operator in which all parents in a small mating pool have equal chance of contributing their genetic material to offspring. When offspring is created with better fitness value than that of the worst parent, the worst parent is discarded from the mating pool while the offspring is placed in it. The main contributions are in the massive parallel initializationprocedure which creates only feasible solutions with simple heuristic rules that increase chances of creating solutions with good fitness values for the initial mating pool, and the gene therapy procedurewhich fixes “defective genes” ensuring that the offspring resulting from recombination is always feasible. Both procedures utilize the properties of network flows. The algorithm is capable of handling mixed integer problems with non-linearities in both constraints and the objective function. Tests were conducted on a number of previously published transportation problems with 49 and 100 decision variables, which constitute a subset of network flow problems. Convergence to equal or better solutions was achieved with often less than one tenth of the previous computational efforts.