A method of constructing a dynamic chart depth model for coastal areas

PeerJ. 2023 Jul 20:11:e15616. doi: 10.7717/peerj.15616. eCollection 2023.

Abstract

The depth is important for vessel navigation at sea. Currently, most vessels use electronic navigation charts to navigate at sea. In coastal areas, especially close to shallow water areas, the dynamic change of the water level is very important to safe navigation. Ships calculate the change of water level by using up-to-date tide tables, to obtain the dynamic water depth in the channels. However, the depth caused by the tide and non-tidal components may reach several meters in some seas, causing the dynamic depth below the safety depth, which can easily lead to grounding of vessels stranding accidents. The channel is regularly dredged to achieve navigational depth. Without regular dredging, the offshore non-channel area becomes the common area of ship grounding. The dynamic chart depth model studied in this article can provide real-time depth, which serves the ships navigation in the non-channel. The model incorporates the chart depth and the dynamic water levels on the same reference datum. The chart depth is from the electronic navigational chart depth. The dynamic water levels are constructed by the simulated tidal levels and continuous series of nontidal residual. We then designed a deviation correction method to reduce the discrepancy of the simulated tidal level with the actual water level, including datum offset correction and residual water level correction. Finally, by merging the revised dynamic water levels with the electronic navigational chart depth, we obtained the dynamic chart depth model of the study region.

Keywords: Depth datum; Dynamic chart depth; Residual water level; Water level correction.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents*
  • Oceans and Seas
  • Water*

Substances

  • Water

Grants and funding

This work was supported by the Shenzhen Science and Technology Program (Grant 20220818095816035, Grant JSGG20220831103800001); the Shenzhen Excellent Science and Technology Innovation Talents Cultivation Program (Grant RCBS20221008093252090); the Youth Fund Program of Shenzhen Polytechnic (Grant 6022310015K); the Scientific Research Launch Program of Shenzhen Polytechnic (Grant 6022312051K); and the Guangming Laboratory Open Program (Grant GML-KF-22-21). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.