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Impact of climate changes on the Santos Harbor, São Paulo State (Brazil)

Alfredini P., Arasaki E., Pezzoli A., Fournier J.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2013

Vol. 7, no. 4

609--617

Santos Harbor Area (SHA) in Sao Paulo Coastline (Brazil) is the most important marine cargo transfer terminal in the Southern Hemisphere. A long term relative tidal level variability assessment shows a consistent response to relative sea level rise. A wave data base Wave Watch III was compared with a long term wave data-base generated by the ERA40-ECMWF (2003), both local validated. The current bed level of SHA Outer Channel is -15.00 m (Chart Datum or, in abbreviation, CD), maintained by dredging. According to the cargo throughput forecast, in 2025, the Access Channel will have to be deepened to level of -17.00 m. The feasibility of that choice is discussed from a technical, economical and conceptual navigation point of view in that context. A data set found from a scale model of the whole area of Santos Bay, Estuary and nearby beaches, showed the impact of maritime climate changes upon the coastal area. In the previous researches developed by the authors, it was demonstrated that the wave climate, the tides and tidal currents affect harbor and coastal structures maintenance, beaches stability, tidal inlet, sediment transport, saline intrusion and wetlands. Considering the increasing of the sea hazards and the high values of the infrastructures in that coastline, it is necessary to mitigate the risks. Hence, based on the results obtained by the authors, are highlighted guidelines strategies suggested for Access Channels dimensions, wharves free-board, jetties dimensions, dredging rates, rigid and flexible littoral defenses and land protection against flooding (including wetlands).

The Role of Operational Ocean Forecasting in E-Navigation

Graff J.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2008

Vol. 2, no. 3

259--262

Advances in ocean modelling have led to improved performance for operational ocean forecasting and the availability of continuously reliable forecast information for certain ocean regions of the world. Although such forecasts are being increasingly adopted into a wide range of services across the maritime industry they have not yet been considered as candidates to supplement or to substitute conventional tide tables for navigation use. The issue is important in the context of climate change and the added uncertainty now placed on the use of conventional tide table for navigation in complex coastal waters. In the context of e-navigation it is timely to begin to explore the issue and examine how such forecasts might be used and adopted. This requires closer connectivity between ocean forecasting and navigation communities and the involvement of overarching organisations such as IMO and I GOOS. This paper raises the issue and opens the debate.

A Required Data Span to Detect Sea Level Rise

Niedzielski T., Kosek W.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2008

Vol. 2, no. 2

143--147

Altimetric measurements indicate that the global sea level rises about 3 mm/year, however, in various papers different data spans are adopted to estimate this value. The minimum time span of TOPEX/Poseidon (T/P) and Jason-1 (J-1) global sea level anomalies (SLA) data required to detect a statisti-cally significant trend in sea level change was estimated. Seeking the trend in the global SLA data was per-formed by means of the Cox-Stuart statistical test. This test was supported by the stepwise procedure to make the results independent of the starting data epoch. The probabilities of detecting a statistically significant trend within SLA data were computed in the relation with data spans and significance levels of the above-mentioned test. It is shown that for the standard significance level of 0.05 approximately 5.5 years of the SLA data are required to detect a trend with the probability close to 1. If the seasonal oscillations are removed from the combined T/P and J-1 SLA data, 4.3 years are required to detect a statistically significant trend with a probability close to 1. The estimated minimum time spans required to detect a trend in sea level rise are ad-dressed to the problem of SLA data predictions. In what follows, the above-mentioned estimate is assumed to be minimum data span to compute the representative sample of SLA data predictions. The forecasts of global mean SLA data are shown and their mean prediction errors are discussed.