High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing

Abstract

Spatially distributed hydrologic models are increasingly being used to study and predict soil moisture flow, groundwater recharge, surface runoff, and river discharge. The usefulness and applicability of such complex models is increasingly held back by the potentially many hundreds (thousands) of parameters that require calibration against some historical record of data. The current generation of search and optimization algorithms is typically not powerful enough to deal with a very large number of variables and summarize parameter and predictive uncertainty. We have previously presented a general‐purpose Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference of the posterior probability density function of hydrologic model parameters. This method, entitled differential evolution adaptive Metropolis (DREAM), runs multiple different Markov chains in parallel and uses a discrete proposal distribution to evolve the sampler to the posterior distribution. The DREAM approach maintains detailed balance and shows excellent performance on complex, multimodal search problems. Here we present our latest algorithmic developments and introduce MT‐DREAM(ZS), which combines the strengths of multiple‐try sampling, snooker updating, and sampling from an archive of past states. This new code is especially designed to solve high‐dimensional search problems and receives particularly spectacular performance improvement over other adaptive MCMC approaches when using distributed computing. Four different case studies with increasing dimensionality up to 241 parameters are used to illustrate the advantages of MT‐DREAM(ZS).