Probabilistic prediction in ungauged basins (PUB) based on regional parameter estimation and Bayesian model averaging

Abstract

Predictions in ungauged basins (PUB) are widely considered to be one of the fundamentally challenging research topics in the hydrological sciences. This paper couples a regional parameter transfer module with a probabilistic prediction module in order to obtain probabilistic PUB. Steps in the proposed probabilistic PUB include: (1) variable infiltration capacity-three layers (VIC-3L) model description; (2) three regional parameter transfer schemes for ungauged basins, i.e., regression analysis, spatial proximity, and physical similarity; (3) probabilistic PUB using Bayesian model averaging (BMA); and (4) performance evaluation for probabilistic PUB. The study is performed on 12 sub-basins in the Hanjiang River basin, China. The results demonstrate that the mean prediction of BMA is much closer to the observed data compared with its associated individual parameter transfer scheme (physical similarity approach), and the probabilistic predictions of BMA can effectively reduce the uncertainty in runoff PUB better than any associated individual parameter transfer schemes for two ungauged sub-basins.