Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework

• Published in: Computation Vol. 10; no. 5; p. 72
• Main Authors: Karagiannis, Georgios, Hou, Zhangshuan, Huang, Maoyi, Lin, Guang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Bayesian analysis; Bayesian inversion; Calibration; Enthalpy; General circulation models; generalized polynomial chaos; Heat flux; Hydrology; inverse modeling; Mathematical models; Maximum entropy method; Modelling; Moisture effects; Parameter estimation; Parameter identification; Polynomials; Simulation; Soil moisture; Uncertainty; uncertainty quantification; US-ARM

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for...