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Examination of the Applicability of a Model for Predicting Ice Sheet Thickne

Author(s): Yu Inami; Hiroshi Yokoyama; Yasuhiro Yoshikawa; Hiroki Yabe

Linked Author(s): Hiroshi Yokoyama

Keywords:

Abstract: By considering the Hokkaido Ice Jam Disaster of March 2018, the Civil Engineering Research Institute for Cold Region developed a program for calculating the thickness of river ice in collaboration with Associate Professor Yasuhiro Yoshikawa of the Kitami Institute of Technology. In this study, we first calculated the changes in ice thickness using the ice jam prediction program for the Kenufuchi River in Hokkaido, Japan, and discussed the results. The program was confirmed to reproduce the time-series of phenomena from river ice formation at subfreezing temperatures to melting at above-freezing temperatures. The program also demonstrated that the time at which the calculated river ice thickness suddenly decreases can be regarded as the start of an ice jam. Next, the possibility of using the ice jam prediction program in the field was discussed by comparing the calculation results with the results of field observation conducted on the Kenufuchi River on March 12,2018. This comparison revealed that predicted time of ice jam occurrence roughly agreed with the actual time of ice jam occurrence estimated from field observations. Therefore, it is suggested that the ice jam prediction program can be roughly predict the time of ice jam occurrence. Finally, the accuracy of the ice jam prediction program was examined by comparing its calculation results with those of the one-dimensional unsteady flow calculation model CERI1D, developed by Associate Professor Yasuhiro Yoshikawa of Kitami Institute of Technology. The ice jam prediction program was found to be accurate enough for practical use, although the accuracy is slightly lower than that of CERI1D, which is a commonly used program for calculating river ice thickness fluctuations. From the viewpoint of usability at field offices, the ice jam prediction program may be regarded as an effective tool for detecting ice jams.

DOI:

Year: 2020

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