Abstract. Complex, software-intensive, technically advanced, and
computationally demanding models, presumably with ever-growing realism and
fidelity, have been widely used to simulate and predict the dynamics of the
Earth and environmental systems. The parameter-induced simulation crash
(failure) problem is typical across most of these models despite
considerable efforts that modellers have directed at model development and
implementation over the last few decades. A simulation failure mainly occurs
due to the violation of numerical stability conditions, non-robust
numerical implementations, or errors in programming. However, the existing
sampling-based analysis techniques such as global sensitivity analysis (GSA)
methods, which require running these models under many configurations of
parameter values, are ill equipped to effectively deal with model failures.
To tackle this problem, we propose a new approach that allows users to cope
with failed designs (samples) when performing GSA without rerunning the
entire experiment. This approach deems model crashes as missing data and
uses strategies such as median substitution, single nearest-neighbor, or
response surface modeling to fill in for model crashes. We test the
proposed approach on a 10-parameter HBV-SASK (Hydrologiska Byråns Vattenbalansavdelning modified by the second author for educational purposes) rainfall–runoff model and a
111-parameter Modélisation
Environmentale–Surface et Hydrologie (MESH) land surface–hydrology model. Our results show that
response surface modeling is a superior strategy, out of the data-filling
strategies tested, and can comply with the dimensionality of the model,
sample size, and the ratio of the number of failures to the sample size.
Further, we conduct a “failure analysis” and discuss some possible causes
of the MESH model failure that can be used for future model improvement.
Variance Based Sensitivity Analysis of FLake Lake Model for Global Land Surface Modeling
