  The Bayesian paradigm is becoming an increasingly popular framework for
estimation and uncertainty quantification of unknown parameters in geo-physical
inversion problems. Badlands is a basin and landscape evolution forward model
for simulating topography evolution at a large range of spatial and time
scales. Our previous work presented Bayeslands that used the Bayesian paradigm
to make inference for unknown parameters in the Badlands model using Markov
chain Monte Carlo (MCMC) sampling. Bayeslands faced challenges in convergence
due to multi-modal posterior distributions in the selected parameters of
Badlands. Parallel tempering is an advanced MCMC method suited for irregular
and multi-modal posterior distributions. In this paper, we extend Bayeslands
using parallel tempering (PT-Bayeslands) with high performance computing to
address previous limitations in parameter space exploration in the context of
the computationally expensive Badlands model. Our results show that
PT-Bayeslands not only reduces the computation time, but also provides an
improvement of the sampling for multi-modal posterior distributions. This
provides an improvement over Bayeslands which used single chain MCMC that face
difficulties in convergence and can lead to misleading inference. This
motivates its usage in large-scale basin and landscape evolution models.
