  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.
