Prior to model calibration, uncertainty and sensitivity analyses were performed to identify parameters that have a significant impact on the model results. Initial parameter estimates were determined based on the literature or were conservatively estimated if data were unavailable (Table , S9). The parameter uncertainty was described with uniform distributions spanning ±50% around their initial estimates and was propagated through Monte Carlo simulations with Latin Hypercube Sampling (1,000-20,000 samples). Key metrics for model prediction performance were defined as the normalized root mean squared errors (NRMSEs, Equation S1)