This project shall research the generalized (regional) skew coefficients (RegSkew) and other measures of distribution shape in and near Texas. RegSkews, which are derived by procedures integrating sample at-site skew values obtained at many stream gages, are important for peak-streamflow frequency (flood frequency) analyses because of the high sampling variability. The measures of distribution geometry are normally derived from research of United States Geological Survey (USGS) instantaneous annual peak streamflow data and ancillary watershed properties. However, identification of applicable time periods of the USGS observational record is complex and critical for execution of this research, and the USGS peak-values database provides only qualitative information to this effect. Texas is in need of new RegSkews for hydrologic design due to the currently used being out of date relative to Federal guidelines. Future flood frequency analyses shall inherently be more reliable and shall decrease uncertainties when new RegSkews are in use and in particular with the Advisory Committee on Water Information (ACWI) Expected Moments Algorithm of Bulletin 17C. Bulletin 17C currently recommends Bayesian generalized least squares (B-GLS) concepts to estimate RegSkews because B-GLS reflects the precision of available estimates, their cross correlations, and the precision of the regional model. This project shall report on the results of B-GLS for Texas. The complexity of the Texas flood hydrology, due to a broad spectrum of wide ranging climatic, rural to urban development conditions, and potential flood-flow regulation effects, requires further research of spatial and temporal trends in annual peaks and empirical distributions. Further, RegSkew and other measures of distribution shape concepts and methods that incorporate machine learning and generalized additive models shall be explored in this project to fully discern probability distribution shape and prediction for the distal tail estimation of flood frequency. The project shall also produce products and training materials suitable for self-training and inclusion in workforce development facilitated training.