This scenario has been provided by CSIR
and NEVANTROPIC regarding the analysis of Coastal Water Quality in
Mozambique.
The goal of the scenario is to investigate the
temporal dynamics of chlorophyll a concentrations in the coastal
waters of Mozambique near large river mouths and a potential link
with the outbreak of a waterborne disease, namely cholera. Changes in
chlorophyll a concentrations are driven directly by factors such as
the availability of nutrients, and temperature. Rainfall for example
most likely play an indirect role as it leads to nutrient run-off
from the land especially during the warm summer months. Chl-a
concentrations close to or in river mouths and estuaries show
different temporal characteristics when compared to areas in the
middle of the Mozambique Channel.
*Studies done in other parts of
the world have shown either a direct link between Vibrio cholerae,
the bacterium responsible for cholera outbreaks and phytoplankton
(using chl-a concentrations as an indicator of phytoplankton
dynamics), or an indirect link between phytoplankton and V.cholerae
where high chl-a concentrations are most likely to be indicative of
high zooplankton concentrations. Zooplankton has been shown to be
closely linked with V.cholerae.
Scenario Steps
Scenario Name
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Engineering Use
Cases
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Specialization of Use Cases
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WQ-M-01.
A Scientist
discovers
and selects relevant environmental information (CHL-a concentration,
rainfall and SST data)
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01.1.
Scientist
access a
GEOSS catalogue to
discover the available
environmental
datasets
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01.2.
Scientist
sends
a query to the
GEOSS catalogue
based on the parameters of interest (CHL-a, rainfall and SST)
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01.3.
Scientist
selects
three
datasets (i.e.
Chl-a, rainfall and SST))
based on the list of available
environmental
datasets
returned by the query.
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WQ-M-02.
Scientist
extracts time series for selected datasets
of Mozambique, or land area) and a study period
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02.1.
Scientist
defines
the study area for
all datasets
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02.2.Scientist
defines
the study period for
all datasets.
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02.3.
Scientist
gets all time
series
(one for each datasets
selected
at 01.3) computed by a
GEOSS
service.
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WQ-M-03.
A
scientist
pre-processes
the time
series
(for instance, change temporal resolution so that the datasets can be
correlated)
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03.1.
If the temporal resolution of the
time
series
are different, scientist
access
a GEOSS
service
to simulate processed
time series
with same temporal resolution
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WQ-M-04.
A scientist
correlates
the processed
time series
and get statistical
indices
describing the relationship
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04.1.
Scientist
activates
a
GEOSS
service
to correlate the processed
time series
of CHL-a concentration, rainfall and SST
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04.2.
Scientist
gets
a statistical
file containing
statistical information and data (mean and standard deviation for
each processed
time series;
correlation coefficient, determination coefficient, significance
tests between SST and chl-a and rainfall and chl-a processed
time series)
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Legend:
AIP-3 Services in blue
AIP-3 Products in red
AIP-3 Actors in orange
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