CONCLUSIONS AND RECOMMENDATIONS - Integrating GIS, Remote Sensing, and Mathematical Modelling for Surface Water Quality Management - page 199

remote sensing analysis and the extraction of the main parameters TSM and CHL-a. It could
also be applied using any satellite imagery for water quality modelling.
The inclusion of an advanced ecological model within the computational framework of the
surface water quality management system is a recommended to be considered in the future.
The current study has focused on the water quality modelling of basic parameters and the
screening of eutrophication models. For better management of the lake water system it is
important to focus on detailed ecological modelling, taking into consideration the detailed
modelling of phytoplankton species, the modelling of the vegetation buffer zone around the
water body and also the areas of fish ponds. The three components of the lake system
explained in this research should be linked together in a more advanced and integrated
hydrodynamic-ecological model. This will give access to more knowledge about the
downstream area of the watershed, which could be modelled holistically as a wetland region.
Conducting a detailed error propagation and uncertainty analysis that includes the different
models and tools of the computational framework is highly recommended. Water quality and
eutrophication modelling is associated with a high level of uncertainty due to the complex
chemical and biological processes involved. This is in addition to the various possible sources
of uncertainty that have been discussed in this study. Another important issue is that the use of
remote sensing data to compensate for the lack of in situ measurements creates another
accumulation of uncertainty associated with the application of the remote sensing. Therefore
access to the acquisition of specific in situ measurements and more frequent remote sensing
data can be used to explore the inherent uncertainties in order to improve model validation.
Using the developed models for forecasting water quality is another important
recommendation for future research. The modelling tools developed in this research and the
integrated remote sensing tools have been mainly used to calibrate and verify models that are
to be used for the prediction of water quality parameter concentrations. But these modelling
and remote sensing tools could also be used for forecasting the water quality status of the lake
system for any near future interventions within the watershed. The forecasting will depend on
detailed remote sensing analytical tools to reduce the model uncertainty. Time series data sets
could then be used for short term forecasting of TSM and CHL-a in the lake. Different
forecasting methodologies could be applied and tested for best performance.