The regional variability of shape parameters (such as κ for the GEV distribution) may be described by generalized least squares (GLS) regression models that allow shape parameters to be estimated from basin characteristics recognizing the sampling uncertainty in available shape estimators. Implementation of such GLS models requires estimates of the cross-site correlation of the shape parameter estimators for every pair of sites. Monte Carlo experiments provided the information needed to identify simple power approximations of the relationships between the cross correlation of estimators of skewness γ from Pearson type 3 (P3) data and of the shape parameter κ of both generalized Pareto (GP) and generalized extreme value (GEV) distributions, as functions of the intersite correlation of concurrent flows.