Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator

TL;DR

This paper presents a method to accelerate Bayesian inference in hydrological modeling by using a mechanistic emulator, low-discrepancy sampling, and iterative refinement, achieving accurate results with minimal model runs.

Contribution

It introduces a combined approach of mechanistic emulation, efficient sampling, and iterative design to perform Bayesian inference with significantly fewer model evaluations.

Findings

Reasonable inference results achieved with only 128 model runs.

Iterative refinement improves emulator accuracy and inference quality.

Method outperforms traditional approaches in computational efficiency.

Abstract

As in many fields of dynamic modeling, the long runtime of hydrological models hinders Bayesian inference of model parameters from data. By replacing a model with an approximation of its output as a function of input and/or parameters, emulation allows us to complete this task by trading-off accuracy for speed. We combine (i) the use of a mechanistic emulator, (ii) low-discrepancy sampling of the parameter space, and (iii) iterative refinement of the design data set, to perform Bayesian inference with a very small design data set constructed with 128 model runs in a parameter space of up to eight dimensions. In our didactic example we use a model implemented with the hydrological simulator SWMM that allows us to compare our inference results against those derived with the full model. This comparison demonstrates that iterative improvements lead to reasonable results with a very small design data set.