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Reservoir storage reduces fluctuation in streamflow salinity, as inflow blends with stored water. This process is directly relevant to reservoir management, yet the methods for predicting outflow salinity from inflow and storage data are not adequately addressed. This study examined three simple methods for estimating outflow salinity on a monthly time step. The first method applies when inflow approximately equals outflow. The second method is applicable to changing reservoir storage. The third method utilizes a two-layer model in which evaporative concentration is assumed to occur in the top layer. The third method requires the full account of the water balance, including evaporation and percolation losses. All three methods require reservoir storage blend with inflow in a stepwise manner. Outflow salinity was then computed monthly as a moving average. These methods were applied to high and low storage periods up to two years at three large reservoirs (Elephant Butte, Amistad, and Falcon) located along the Rio Grande. All of the equations tested provided good estimates of outflow salinity with the standard errors of estimate ranging from 5 to 10%. When a matching factor (accounting for ungauged inflow and water levels) was used in the first or the second method, they provided the estimate of monthly outflow salinity just as accurately as did the two-layer model. The accuracy of prediction, especially by the third method, can be improved if the initial reservoir salt storage is assessed with greater accuracy. Although the third method is more descriptive, the first two methods are also useful when detailed water balance data are not available.
