This monthly bulletin is produced by the Tanzania Fisheries Research Institute (TAFIRI). The bulletin provides monthly satellite based on general oceanographic observations of the Tanzanian ocean waters. The issue focuses on remote sensing are sea surface temperature, chlorophyll-a concentration and an overview of ocean productivity in Tanzanian ocean waters
Importance of Fisheries in Tanzania
The fisheries industry is a major source of income and a key component of the livelihoods of Tanzania coastal communities. Also, fish proteins are essential for food security and nutrition throughout Tanzania. However, all these benefits are threatened by unsustainable utilization of fish resources due to illegal, unregulated and unreported fishing, coupled with increasing sea surface temperature as a result of global climate change, which will affect the chlorophyll concentration of the ocean water hence it affects the biological production in the ocean.
1.0 SEA SURFACE TEMPERATURE (SST)
Satellite measurement of SST is made by sensing the ocean radiation, which indicates the temperature of the top millimeter of the ocean surface. Monitoring of SST is an essential contributor to climate studies and climate change monitoring, including EL Niño/La Niña cycles, through its strong influence on wind and ocean currents pattern, or rainfall distribution and intensity.
1.1 Sea Surface Temperature (SST)
Sea surface temperature (SST) is the temperature of the top millimeter of the ocean’s surface. SST reflects the storage of thermal energy in the upper mixed layer of the oceans. The SST maps/graphs display the temperature variation during the period of the particular month. Warmer colors such as yellows and reds represent higher temperatures, while cooler ones such as green and blue represent lower temperatures.
1.2 Sea Surface Temperature Climatology (SST Climatology): Means a long term average temperatures of that month for several years/periods.
1.3 Sea Surface Temperature Anomaly (SST Anomaly): Is the difference between the observed SST and the reference value or long-term average climatological SST, these anomalies are calculated on a monthly basis. SST anomaly maps/graphs compare temperatures in a given month to the long-term average temperature of that month. Sea surface temperature anomalies have practical applications to fisheries and coastal waters management, including coral reef monitoring and prediction of red tides or other harmful algal blooms.
A positive anomaly indicates that the observed temperature was warmer than the reference value, while a negative anomaly indicates that the observed temperature was cooler than the reference value. Figure 4 below shows the SST Anomaly of Tanzanian ocean waters for August 2016.
2.0 CHLOROPHYLL-A CONCENTRATION
Nutrients, such as nitrogen and phosphorous, are not directly measurable with satellite observations. However, satellites can measure chlorophyll concentration from phytoplankton in the sea. As plankton form, the base of the marine food chain, the plankton levels in an area indicate the health of the fish stock.
Chlorophyll-a is the light-harvesting pigment found in marine microscopic photosynthetic plants, known as phytoplankton. Its concentration is widely used as an index of phytoplankton biomass and is also used as a proxy for primary production. Chlorophyll absorbs most visible light but reflects some green and near-infrared light. By measuring what kind of light is absorbed and reflected, the satellite can measure chlorophyll concentrations in the ocean, thus providing valuable insights into the health of the ocean.
2.2 Sea Surface Chlorophyll-a Climatology: Means a long term average chlorophyll-a of that month for several years/periods. Blue shows chlorophyll-a that were less than average, light blue shows near-average chlorophyll-a and yellow to red shows where chlorophyll-a were more than average. The chlorophyll-a climatology for the month of August 2003 to 2015 was as shown in the marked area with a white ring, figure 6 below
2.3 Sea Surface Chlorophyll-a Anomaly: Is the difference between the observed chlorophyll-a and the reference value or long-term average (climatological chlorophyll), these anomalies are calculated on a monthly basis. Figure 7 below shows the average chlorophyll-a concentration anomaly for August 2016.
3 3. OCEAN PRODUCTIVITY
Chl-a and SST anomalies provide essential information for policy makers in designing or revising fisheries management tools such as the location and extension of no-take zones or the location and timing of seasonal closures or the establishment of fishing quotas. For the month of August, the anomaly maps (Figure 1) it is clear that chlorophyll-a level off
Tanzania is markedly higher than average for that time of year. Coupled with lower than usual SST, this is an early indication that fish stocks will increase. Hence during this month, the fishery's productivity is expected to increase.
For SST, a climatological reference was derived from the GLORYS2 model reanalysis from the Mercator Ocean over the period 1992-2009. For the current month, daily model hindcasts, also from the Mercator Ocean have been used with a spatial resolution of about 27 km. The main advantages of reanalysis datasets are the completeness with respect to observations (remote sensing and in-situ data) and the performance of the interpolation method (assimilation). These provide a spatially and temporally continuous field that can be viewed as the “best estimate” of the ocean state for the given period.
Chlorophyll Level 3 Standard Mapped Image (SMI) dataset was used from the Moderate-resolution Imaging Spectrometer (MODIS) data, with a spatial resolution of 4 km. The Level 3 SMI products are image representations of binned data products.
Chl-a Chlorophyll a
Dipole A local pair of anomalies, one positive and the other negative
DSFA Deep Sea Fishing Authority
EEZ Exclusive Economic Zone
Geoid A reference surface corresponding to the equilibrium water level of the stationary ocean
GLORYS GLobal Ocean ReanalYsis and Simulations
JRC Joint Research Centre
MESA Monitoring for Environment and Security for Africa
MODIS Moderate-resolution Imaging Spectrometer
MOI Mauritius Oceanography Institute
SMI Standard Mapped Image
SST Sea Surface Temperature
SWIO South West Indian Ocean