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Influence analysis of peak rate factor in the flood events’ calibration process using HEC–HMS

  • Christian CahyonoEmail author
  • Wanny K. Adidarma
Original Article
  • 3 Downloads

Abstract

In flood management, analysis and modelling are needed, especially for the analysis of the occurrence of floods and decision making. One method for analyzing flood events can use the HEC–HMS software. In HEC–HMS, there are three sub-models, namely, losses, transform, and baseflow. In the development of the use of the HEC–HMS model, it still utilizes sub-model transform, where the peak rate factor (PRF) value is constant at 484 in modelling. The value of PRF is very dependent on the slope of the land (basin slope), where the value of this parameter is very varied depending on the physical condition of the area under study. Because of the fact, where the value of PRF should be varied depends on the slope in modelling stage; while the reality is still constant, this research is done. The research result shows that the role of PRF makes the modelling carried out using HEC–HMS produce comparable results as indicated by the objective function values in two study areas. The following is the comparison of the objective function of the Selorejo watershed in the March 2007 flood event between constant PRF 484 with variations in the values of PRF, RMSE (0.63 m; 0.59 m), CORREL (0.977; 0.976), and DELTAPEAK (43.04%; 41.46%). Whereas in the PDA Cipasang watershed, Nash–Sutcliffe (0.818; 0.820), RMSE (66.20 m3/s; 65.91 m3/s), CORREL (0.916; 0.917), and DELTAPEAK (15.55%; 15.35%). To utilize the variation of PRF into the model, based on the results of this study, the sub-model used is SCS Curve Number for Losses, SCS Units Hydrograph for Transform, and Recession Constant for Baseflow. It can be concluded that by including the influence of variations in PRF values resulting not a better model but only a slight improvement which is insignificant and a more complete model.

Keywords

Analysis of flood occurrence Peak rate factor Basin slope Objective function 

Notes

Supplementary material

40808_2019_625_MOESM1_ESM.xlsx (142 kb)
Supplementary material 1 (XLSX 141 kb)
40808_2019_625_MOESM2_ESM.xlsx (1.2 mb)
Supplementary material 2 (XLSX 1253 kb)
40808_2019_625_MOESM3_ESM.xlsx (2.1 mb)
Supplementary material 3 (XLSX 2177 kb)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Parahyangan Catholic UniversityBandungIndonesia
  2. 2.Parahyangan Catholic University Post-Graduate ProgramBandungIndonesia

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