Data from eight evaporation pans containing brines of different salinity and ionic composition were analyzed to quantify the effect of salinity on evaporation. The common procedure of correcting fresh water evaporation by an empirical ratio (00 of salt water to fresh water evaporation rates is shown to only approximate. A more accurate approach based on the effect of salinity on saturation vapor pressure is described. The activity coefficient of water (•) was computed based on the pan evaporation data. Various analytical methods to compute the effect of salinity on saturation vapor pressure based on ionic composition of the solution are described and applied with Dead Sea data. These approaches can be applied in many engineering applications including water balance calculations for saline lakes, salt production ponds, and evaporatio n ponds used for disposal of saline effluents.

Evaporation from a saline water body is less than that from a freshwater body because of two factors: a decrease in saturation vapor pressure and a partially compensating increase in water surface temperature. These factors are quantified by analyzing field data for eight evaporation pans containing solutions with different salinities and ionic compositions (extending the analysis of Salhotra et al. (1985)). A large saline lake (Dead Sea) is also analyzed using a one-dimensional numerical model with coupled heat, salt, water, and mechanical energy balances. Further, the use of experiments involving evaporation pans, to study the effect of salinity on lake evaporation, is discussed in light of the fact that the two water bodies have different wind speed functions and hence different temperature feedback effects. Finally, data on the direct measurement of the saturation vapor pressure from mixtures of Mediterranean and Dead Sea waters are presented and compared with results from the analyses of the evaporation pan data. Australia, 1956. Bonython, C. W., Factors determining the rate of solar evaporation in the production of salt, paper presented at the 2nd Northern Ohio Geological Society Symposium on Salt, N. Ohio Geol. Soc., Cleveland, Ohio, 1965. Calder, R., and C. Neal, Evaporation from saline lakes: A combination equation approach, J. Hydrol. Sci., 29(1), 89-97, 1984. Harbeck, G. E., The effect of salinity on evaporation, U.S. Geol. Surv. Prof Pap. 272-A, 1955. Marcus, Y., The vapor pressures of mixtures of Dead Sea water with Mediterranean water, report, , Surface heat loss from cooling ponds, Water Resour. Res., 10(5), 930-938, 1974. Salhotra, A.M., A coupled heat, salt and water balance model of evaporation and stratification in saline terminal lakes: An appli. cation to the Dead Sea, Ph.D. thesis, Dep. of Civ. Eng., Massachu.

Synchronization of chaotic low-dimensional systems has been a topic of much recent research. Such systems have found applications for secure communications. In this work we show how synchronization can be achieved in a high-dimensional chaotic neural network. The network used in our studies is an extension of the Hopfield Network, known as the Complex Hopfield Network (CHN). The CHN, also an associative memory, has both fixed point and limit cycle or oscillatory behavior. In the oscillatory mode, the network wanders chaotically from one stored pattern to another. We show how a pair of identical high-dimensional CHNs can be synchronized by communicating only a subset of state vector components. The synchronizability of such a system is characterized through simulations.

The use of contaminant transport modeling has become an integral component of the regulatory and decision process for the disposal and cleanup of hazardous wastes. Because many of the input parameters to these models are uncertain, analysis of this uncertainty and its impact on the decision process has become increasingly important. Many contaminant transport models are computationally intensive and require run times that make traditional Monte Carlo analysis impractical. This paper therefore evaluates and tests an approximate technique, the Rackwitz‐Fiessler method, that can be used when computation time prohibits the use of Monte Carlo simulation. The accuracy and efficiency of the method is assessed and compared to Monté Carlo simulation for three contaminant transport models.

Values for Vh, Vs, Vws, and Vw are calculated in Table 1 of Steinhorn [1991], for water of different salinity. Unfortunately, the subscripts appear to be mislabeled; under each column, the correct entry should be consistent with (12b). As such, the difference between Vh and V,, is small, ranging A final comment should be made concerning the feedback between temperature and salinity. It is true that for water bodies experiencing the same rate of evaporation (i.e., E•,, in (13)), temperature will decrease with increasing salinity. However, for water bodies subject to the same environmental conditions (i.e., air temperature, wind speed, humidity), the rate of evaporation decreases, and hence water temperature increases, with increasing salinity. This effect is clearly illustrated in the pan measurements summarized in Figure 2 of Salhotra et al. [ 1985]. The increase in water temperature, in turn, increases evaporation to a value between that of a freshwater body and a saline body of the same temperature. The coupling among temperature, salinity, and mass requires in general that transient thermal, salt, and mass balances be solved simultaneously in real time-varying applications [Salhotra et al., 1984]. However, the fact that the feedback is negative provides some compensation in the calculation. For example, if evaporation is overpredicted, the computed temperature will be too low and the computed salinity will be two high; both factors will result in lower predicted evaporation during future time steps. REFERENCES