PREDICTING SARGASSUM WASHING ASHORE IN THE LESSER ANTILLES.

Transcription

1 PREDICTING SARGASSUM WASHING ASHORE IN THE LESSER ANTILLES. JEAN-PHILIPPE MARÉCHAL 1*, CHUANMIN HU OMMM (Marine Institute of Martinique NGO), 14 rue Chery Rosette, Fond Lahaye, Schoelcher, Martinique, French West Indies. 2. Optical Oceanography Laboratory, College of Marine Science, University of South Florida, 140 Seventh Avenue South, St. Petersburg, FL 33701, USA.

2 Unprecedented events of Sargassum influx in the Caribbean No previous record (people/literature) New ecosystem dynamic in the Central Atlantic Environmental Economical consequences H 2 S NH 3 human health issues Administration - Politics mobilisation Early warning & alerts Collection of algae Valorisation of biomass

3 Can we predict / anticipate Sargassum beaching? early warning and alert system Sargassum Watch System (SaWS): provides a set of tools to monitoring and tracking Sargassum in near-real time using satellite imagery and currents numerical models Data & Hu, C., et al. (2016), Sargassum watch warns of incoming seaweed, Eos, 97, doi: /2016eo058355

4 Floating Sargassum satellite signature NASA s Aqua satellite carries a suite of instruments designed primarily to study the water cycle. (NASA image by Marit Jentoft-Nilsen) MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra MODIS + Aqua MODIS view the entire Earth's surface every 1 to 2 days images every day

5 Region of interest 2 1

6 Sargassum specific AFAI index Hu (2009) MODIS AFAI: Alternate Floating Algae Index (in reflectance units) to detect ocean surface features such as Sargassum, green macroalgae, and cyanobacteria. AFAI = 1km product. Detects subtle ocean surface features Other criteria = shape of the rafts, persistence in time, period of the year

7 Limits: AFAI saturates under sun glint, clouds, or thick aerosols Clouds cover Sun glint False signal 10 km mask around islands and continent Other high resolution satellites are better for precise detection: Landsat, Sentinel

8 RGB MODIS image May 21 st, 2015 AFAI image May 21 st, 2015

9 Web portal: online available products Data available in near real time (MODIS, VIIRS) Several images every day

10 Data N, 59.2W Speed: cm/s N/S (positive N, negative S): cm/s E/W (positive E, negative W): cm/s SaWS for the eastern Caribbean KML file downloadable MODIS AFAI image displayed in Google Earth + HYCOM current vectors

11 Image analysis Original AFAI image Isolation of likely Sargassum signal (ImageJ) Combination of Sargassum signal + Hycom surface current vectors

12 Guadeloupe case study and early warning alert system

13 May 21 st 2015 METHODOLOGY AFAI image analysis 50 km Sargassum signal isolation 208 km GPS coordinates Distance from coast Surface current average speed and orientation Date of observation Risk assessment & Forecast 30 cm.sec -1 ALERTS

14 May November 2015 = 46 alerts for Guadeloupe 37 accounted for single-day alerts and were linked to 23 beaching reports Regional Health Agency Reports Risk (in Date Image Date Forecast Actual Date days) Error 21/05/ ,8 21,8 22-0,2 0 = accordance forecast/actual + = early arrival - = late arrival Probability for Sargassum rafts detected by MODIS to reach the coastline = 62%

15 Conclusions on predicting Sargassum WASHING ASHORE in the Lesser Antilles. For each alert there was an average 60% chance that drifting Sargassum reach the exposed coastline. According to our results, error was very low and average predictions quite precise. For longer drifting periods (rafts detected over 150 km from the shore), it is likely that our accuracy decreases. While these results are encouraging, it was not verifiable whether Sargassum washed ashore on the date of field observation or had accumulated on shore over the previous days. To reduce prediction errors, it seems reasonable to restrain short term washingashore forecasts to detection within a geographical limit of km maximum off the coast.

16 Future work (PSB-CARIB project) Sargassum prediction trial (D. Johnson - USM) 06/30/2016 Predictions Back-tracking

17 Long term series analysis: (Wang & Hu, 2016) Dynamic and processes (PSB-CARIB project) ?

18 Conclusions Simple, fast and reliable method with online available free products Can be implemented by non-technical staff No automatic detection: requires ~ 1h/day Large scale detection, but useful to predict local events (within km detection off the coast) Limits: clouds, sun glint, coastal waters interference Useful for historical and ecological understanding Develop automated image analysis coupled with GIS alerts release. Research pilot program: PSB-CARIB Predicting Sargassum Blooms in the Caribbean (Funding: ADEME 972 CTM DEAL 971)

19 Thank you for your attention