Coastal Water Quality - Mozambique

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
Engineering Use Cases
 Specialization of Use Cases

WQ-M-01. A Scientist discovers and selects relevant environmental information (CHL-a concentration, rainfall and SST data)

01.1. Scientist access a GEOSS catalogue to discover the available environmental datasets

01.2. Scientist sends a query to the GEOSS catalogue based on the parameters of interest (CHL-a, rainfall and SST)


 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.


 WQ-M-02. Scientist extracts time series for selected datasets of Mozambique, or land area) and a study period


02.1. Scientist defines the study area for all datasets

 02.2.Scientist defines the study period for all datasets.


02.3. Scientist gets all time series (one for each datasets selected at 01.3) computed by a GEOSS service.

 WQ-M-03. A scientist pre-processes the time series (for instance, change temporal resolution so that the datasets can be correlated)


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


WQ-M-04. A scientist correlates the processed time series and get statistical indices describing the relationship


 04.1. Scientist activates a GEOSS service to correlate the processed time series of CHL-a concentration, rainfall and SST


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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