Zimbabwe's water resources are under pressure from both point and non-point sources of pollution hence the need for regular and synoptic assessment. In-situ and laboratory based methods of water quality monitoring are point based and do not provide a synoptic coverage of the lakes. This paper presents novel methods for retrieving water quality parameters in Chivero and Manyame lakes, Zimbabwe, from remotely sensed imagery. Remotely sensed derived water quality parameters are further validated using in-situ data. It also presents an application for automated retrieval of those parameters developed in VB6, as well as a web portal for disseminating the water quality information to relevant stakeholders. The web portal is developed, using Geoserver, open layers and HTML. Results show the spatial variation of water quality and an automated remote sensing and GIS system with a web front end to disseminate water quality information.
Harare the capital city of Zimbabwe and its satellite towns (commonly known as Greater Harare) are seated in the Upper Manyame sub-catchment that supplies its water resources. Chivero and Manyame Lakes are the major sources of water for Greater Harare which are also situated downstream of the sub-catchment. It follows that any land use related activity in the Upper Manyame sub-catchment will affect the water status of the lakes. The Lakes are now rated among Zimbabwe and the world's most polluted inland water bodies (Chigonda, 2011; Chawira et al., 2013) and Chivero is rated among the world's most polluted lakes (Herald, 2012). The leading sources of pollution in these lakes have been identified by Kibena et al. (2013) as sewage (raw or partially treated) from sewer treatment plants as well as urban runoff. According to Buka et al. (2014), all the major sewage treating plants have been releasing their effluent into the local river system. Industries release their waste effluent into the river system. Leaching of nutrients from urban agricultural activities is also affecting the nutrient content of the lakes. Masere et al. (2012) and Kibena et al. (2013) supports the fact that the water quality of Upper Manyame has been impaired over the years.
A massive rise in urban population was realized over the past years
(20–34
Water quality monitoring involves the retrieval of water quality parameters from the water for water management purposes (Dept. of Ecology State of Washington, 2012; EMA, 2014). The current methods of water quality monitoring in Zimbabwe involves only the use of in-situ and laboratory based methods (EMA, 2012). These methods are costly, tedious, time consuming and do not provide complete spatial coverage of the water bodies and the water quality parameters of concern. This leaves the nation and the responsible authorities in particular with no option but to find and implement more efficient integrated water response strategies.
The applicability of remote sensing in retrieval of water quality variables, use of programming in automating GIS and remote sensing tasks and sharing of GIS products over the world wide web network allows for the development of an integrated and automated system for monitoring water quality and disseminating the results in near real time.
In this study we develop a near real-time water quality monitoring system for Chivero and Manyame lakes of Zimbabwe. This is achieved through identifying suitable algorithms that can determine water quality parameters and develop an application, which computes water quality parameters and present them on a web GIS.
Chivero and Manyame Lakes are situated in the Upper Manyame sub-catchment of
Zimbabwe which also contains the Greater Harare. The Surface area for
Chivero and Manyame is about 26.3 and
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Map of study area (first: Upper Manyame in Zimbabwe, second: Lakes Chivero and Manyame in Upper Manyame).
Remote sensing and field based (in-situ) data were applied. Field based
water quality data was obtained from in situ measurements for Chivero at
three selected sampling points by Dlamini (2012). Landsat 5–7
Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM
Landsat Imagery was downloaded for April and September (1986, 1990, 2000, 2005, 2012 and 2015) from the USGS glovis website. Image preprocessing was done through the conversion from Digital Numbers (DN) to spectral radiance by implementing the Chander et al. (2009) algorithm using the ENvironment for Visualizing Images (ENVI) software. The water quality parameters were computed in ILWIS. The computed raster maps were resampled to a common georefrence and the lake are extracted. The water quality parameters were validated using the point to pixel method (Chander et al., 2009).
The authors used formulae in Shafique et al. (2002) and Dlamini (2012) as shown in Table 1. Several formulas applied before in tropical lakes have been tested and it is the formula from the above authors that yielded better correlation with ground based measurements and thus has been adopted in this study.
Algorithms for retrieval of water quality parameters from Landsat images.
(Shafique et al., 2002; Dlamini, 2012)NB B1–B7 refers to Band 1 to Band 7 of Landsat TM/ETM
ILWIS software was used for automating retrieval.Making use of of ILWIS's DDE server capabilities and VB6's DDE client capabilities, the automation of water quality parameters computation was achieved using VB6. A client program to ILWIS was programmed in VB6 also using ILWIS scripting commands (ILWIS, 2013; Microsoft, 2015). The application provides navigation tools for the user to navigate through the host computer and and to select the folder containing the images and click a command button so that the automated computations begin.
Geoserver was installed and the necessary set up done. The web interface and client to the data was programmed using HTML, JavaScript and PHP scripting languages, as well as the OpenLayers mapping library. It was designed in such a way to allow the user to enter date and parameter of interest, and then the program would retrieve the specified parameter at the specified date and present it as a map.
The research presents four successfully determined water quality parameters;
Chlorophyll
Spatial variations of Chlorophyll
The minimum value for Chl
There is a general increase in the pollution levels with respect to turbidity as it is evident on the diagrams by the increase in the intensity of red on the maps. The year 1995 is noted as an exception in the trend. Generally, the minimum value of the parameter increased from 1.2 NTU in 1986 to 3.6 NTU in 2015, whilst the maximum parameter value increased from 3.2 to 5.8 NTU throughout the study period. The spatial and temporal variations of turbidity are shown on Fig. 3. Turbidity values of less than 5 NTU are accepted by the Zimbabwean effluent standards. So, the water quality changed from an acceptable status of 3.2 to 5.8 NTU a status beyond the accepted.
Spatial variations of Turbidity 1986 and 2015.
The minimum values of total phosphorus varied from 0.00 to 0.91
Spatial variations of Total Phosphorus 1986 and 2015.
Figure 5 shows the spatial and temporal variation of TSM. The variations of
water pollution status with respect to TSM were actually retrogressive in
that from 1986 to 2015 the maximum and minimum values varied from 3.25
to 1.77
Spatial variations of Total Suspended Matter 1986 and 2015.
Table 2 is a summary of the water quality parameters, showing the maximum and minimum of each parameter from 1986 to 2015. The parameters show that the lake is polluted and pollution generally increased with time.
Summary of the estimated water quality variables.
Using data obtained from Dlamini (2012) and February 2012 Landsat imagery, the remotely sensed parameters were compared to in-situ derived data from three lake sampling points which are L4, L6, and L9.
The remotely sensed remote sensing values for the four sites show a
deviation of 0.021, 0.02 and 0.018
Validation of Chlorophyll
The remotely sensed values of total phosphorus show deviations of 0.543,
0.528 and 0.530 for L4, L6 and L9 site respectively. Hence an average of
Validation of total phosphorus.
The determination of parameters was successfully automated, from the stage
of importing them up to the finished water quality maps. This was achieved
using the vb6 application with the interface shown on Fig. 6.It is the
back-end application which the personnel responsible for water quality data
dissemination will be using to retrieve water quality parameters from
satellite imagery. The interface allows the user to navigate to a directory
where a satellite image is stored and by clicking the locate button and then
the proceed button that comes on top of the locate button in about
10
Interface of the backend application for automated remote sensing and GIS.
The web accessible interface to water quality data.
The dissemination of water quality data to the web was achieved using Geoserver accessing imagery in a file folder and OpenLayers. The client main webpage provides the user with three dropdown menus, the other one to select year, another month and the other one to select the parameter which the user wants to view. There is also a submit button which the user uses to submit their query to the host and retrieve the desired water quality parameter.
The buttons captioned “about us”, “database”and “contact us” lead to the respective web pages as labelled.
Mouse location on the lakes is also shown by coordinates in WGS84 UTM, on the bottom right end of the map section. Steissberg et al. (2010) developed a web accessible repository similar to the one presented in this paper for Lake Tahoe, South Nevada. However, theirs allowed users to make downloads of the water quality products, functionality not provided for here. Another web GIS system comparable to this one was developed by Li et al. (2006), however this one was for exploration and visualisation of ice in Arctic waters.
Stakeholders that could benefit from this system include the Environmental management Agency (EMA), Zimbabwe National Water Authority (ZINWA), the Upper Manyame sub-Catchment Council (UMSCC) and those responsible for pollution. ZINWA for decision making as they mainly deal with surface water abstraction. EMA and local authorities can use this to raise awareness to the public on how much their activities are affecting the environment for effective clean up and environmental campaigns.
Three conclusions were drawn.
Four water quality parameters were successfully determined that is
Chlorophyll The retrieval of water quality parameters can be automated by combining
computer programming and remote sensing tools. In this work the
determination of all the above-mentioned parameters were automated and the
results from the automated process were found to be tallying with those
obtained when the processes were executed manually. Water quality data can
be made accessible to the public easily and on a near real time basis using
web GIS technology. There is therefore a need to apply remote sensing and GIS tools in water
quality monitoring in order to reduce the costs of monitoring. This will
also provide information of the spatial variations of the key water
parameters and do away with the statistical assumptions of sampling. Spatial
variations of water quality parameters can also put water treatment
organizations in a better position by knowing lake areas for water withdrawal
from at any given time and reduce treatment costs. The water quality
parameter computing application eliminates human error in processing.
Data may be accessed by emailing our first author.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Understanding spatio-temporal variability of water resources and the implications for IWRM in semi-arid eastern and southern Africa”. It is a result of the IAHS Scientific Assembly 2017, Port Elizabeth, South Africa, 10–14 July 2017. Edited by: Jean-Marie Kileshye-Onema Reviewed by: Colleta Tundu and Nyaradzayi Mawango