Bulletin 17C recommends the Log-Pearson Type III (LP3) distribution for design-flood prediction in the United States. Nevertheless, to make the determination of flood frequency trackable, it is convenient to select a reasonable distribution. Flood records do not fit any specific known statistical distributions. The results have confirmed that the spatial prior knowledge can improve the Bayesian inference of the Log-Pearson Type III flood frequency model for watersheds with short gauge period.Ī design flood is a hypothetical peak discharge graph representation of previous knowledge of precipitation frequency in an area, which is commonly used to evaluate the construction of dams, bridges, canals, and flood damage desistance systems. The result shows that the BLP3-SP outperforms other choices of the priors for the Bayesian Log-Pearson Type III model by significantly reducing the uncertainty in the flood frequency estimation for the station with short data length. The method is validated using a case study watershed with 15 streamflow gauge stations located in the San Jacinto River Basin in Texas, US. A Markov chain Monte Carlo (MCMC) algorithm is used to estimate the posterior distribution and associated flood quantiles. To improve the flood frequency prediction for these watersheds, we propose a Bayesian Log-Pearson Type III model with spatial priors (BLP3-SP), which uses a spatial regression model to estimate the prior distribution of the parameters from nearby stations with longer data records and environmental factors. For watersheds with limited water data length, the flood frequency model, such as the Log-Pearson Type III, will have large uncertainties. Gauge stations have uneven lengths of discharge records owing to the historical hydrologic data collection efforts.

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