This paper presents two methods for approximating the optimal groundwater pumping policy for several interrelated aquifers in a stochatic setting that also involves conjunctive use of surface water. The first method employs a policy iteration dynamic programming (DP) algorithm where the value function is estimated by Monte Carlo simulation combined with curve-fitting techniques. The second method uses a Taylor series approximation to the functional equation of DP which reduces the problem, for a given observed state, to solving a system of equations equal in number to the aquifers. The methods are compared using a four-state variable, stochastic dynamic programming model of Madera County, California. The two methods yield nearly identical estimates of the optimal pumping policy, as well as the steady state pumping depth, suggesting that either method can be used in similar applications.