Automatic Calibration of the Continuous HMS-SMA Rainfall-Runoff Model using the Metaheuristic Algorithm (Case Study: Kasilian Basin)

Rainfall-runoff simulation models can be used in many water resources applications such as flood control, drought management. Although modeling is both continuous and single-event, continuous modeling has been less important in our country. In continuous models, more hydrological parameters are involved in comparison with single-event models, although this leads to more complicated modeling, but instead of a more realistic conditions of the hydrological system of the watershed will be illustrated and, in continuous systems, the surface water status can be monitored over a long period of time. Single-event models simulate only one incident, hence the moisture content between rainfall events is not considered, in contrast to continuous models of longer periods for estimating the response of the hydrologic information of the basin considered throughout the length of the rainfall events and between them (Lastoria, 2008). The American Hydrological Engineers Center (HEC), along with continuous hydrologic modeling, added the Soil Moisture Accounting (SMA) soil moisture content algorithm based on the PRMS model to the HMS software (Bennett, 1998). In this research, the aim is to provide an automatic calibration model based on the anion colony for the HMS-SMA soil moisture model. In this continuous model, the multiplicity of the considered parameters of the model, in addition to causing the difficulty of calibration by the method of trial and error, which also allows the automatic calibration of the software package to fail. For this purpose, in this research, by selecting a continuous HMS-SMA rainfall-runoff model, an external optimization program (Anion Cluster Algorithm (ACOR)) was used to overcome the weakness.