Abstract. Automated calibration of complex deterministic water quality
models with a large number of biogeochemical parameters can reduce
time-consuming iterative simulations involving empirical judgements of model
fit. We undertook autocalibration of the one-dimensional
hydrodynamic-ecological lake model
DYRESM-CAEDYM, using a Monte Carlo sampling (MCS) method, in order to test
the applicability of this procedure for shallow, polymictic Lake Rotorua (New
Zealand). The calibration procedure involved independently minimizing the
root-mean-square error (RMSE), maximizing the Pearson correlation coefficient
(r) and Nash–Sutcliffe efficient coefficient (Nr) for comparisons
of model state variables against measured data. An assigned number of
parameter permutations was used for 10 000 simulation iterations. The
“optimal” temperature calibration produced a RMSE of 0.54 ∘C,
Nr value of 0.99, and r value of 0.98 through the whole water
column based on comparisons with 540 observed water temperatures collected
between 13 July 2007 and 13 January 2009. The modeled bottom dissolved oxygen
concentration (20.5 m below surface) was compared with 467 available
observations. The calculated RMSE of the simulations compared with the
measurements was 1.78 mg L−1, the Nr value was 0.75, and the
r value was 0.87. The autocalibrated model was further tested for an
independent data set by simulating bottom-water hypoxia events from 15
January 2009 to 8 June 2011 (875 days). This verification produced an
accurate simulation of five hypoxic events corresponding to
DO < 2 mg L−1 during summer of 2009–2011. The RMSE was
2.07 mg L−1, Nr value 0.62, and r value of 0.81, based on
the available data set of 738 days. The autocalibration software of
DYRESM-CAEDYM developed here is substantially less time-consuming and more
efficient in parameter optimization than traditional manual calibration which
has been the standard tool practiced for similar complex water quality
models.
Auto-calibration of a one-dimensional hydrodynamicecological
model (DYRESM 4.0-CAEDYM 3.1) using a
Monte Carlo approach: simulations of hypoxic events in
a polymictic lake
