A progressive segmented optimization algorithm for calibrating time-variant parameters of the snowmelt runoff model (SRM)

We applied and compared the SOA and PSOA algorithms to the Snowmelt Runoff Model (SRM) in simulating snow-melt streamflow for the Manasi River basin, northwest of China, during snowmelt seasons of 2001-2012. The study showed: (1) PSOA can effectively calibrate the time-variant model parameters while avoiding too much computational time caused by a significant increase of parameter dimensionality. (2) PSOA outperforms SOA for both single-snowmelt-season and multi-snowmelt-season simulations. (3) For single-snowmelt-season simulation, the length of the sub-period has an apparent effect on model performance, the shorter the sub-period is, the better the model performance will be, when the model is calibrated using the PSOA method. (4) For multi-snowmelt-season simulation, an over-short sub-period may cause overfitting problems in some cases such as the situation of taking Nash-Sutcliffe efficiency (NSE) as the objective function. A compromised length of sub-period and objective function may have to be chosen as a trade-off among evaluation criteria and between the importance of calibration and validation.