A History Matching Approach for Calibrating Hydrological Models

TL;DR

This paper introduces a modified history matching method to efficiently calibrate hydrological models, significantly improving their accuracy in replicating observed rainfall-runoff data.

Contribution

It presents a novel history matching approach tailored for time-series hydrological model calibration, demonstrated through simulations and real case studies.

Findings

30% reduction in root mean squared error for one case study

26% improvement in peak percent threshold statistics

Significant enhancement in model calibration accuracy

Abstract

Calibration of hydrological time-series models is a challenging task since these models give a wide spectrum of output series and calibration procedures require significant amount of time. From a statistical standpoint, this model parameter estimation problem simplifies to finding an inverse solution of a computer model that generates pre-specified time-series output (i.e., realistic output series). In this paper, we propose a modified history matching approach for calibrating the time-series rainfall-runoff models with respect to the real data collected from the state of Georgia, USA. We present the methodology and illustrate the application of the algorithm by carrying a simulation study and the two case studies. Several goodness-of-fit statistics were calculated to assess the model performance. The results showed that the proposed history matching algorithm led to a significant improvement, of 30% and 14% (in terms of root mean squared error) and 26% and 118% (in terms of peak percent threshold statistics), for the two case-studies with Matlab-Simulink and SWAT models, respectively.