Development and Testing of an ICT-based Decision Support System for effective Management of Small Reservoirs: the case of Gwanda district, Zimbabwe
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Small reservoirs represent one of the most important sources of water for livelihoods in the Mzingwane catchment, which constitutes the entirety of the Limpopo basin in Zimbabwe, because of semi-arid conditions that prevail in the area. Despite the water reforms that, among other things, were meant to improve water-based livelihoods, little attention has been paid to systematic management of small reservoirs as illustrated by disparate and in some cases nonexisting information. Recent developments in Information and Communication Technologies (ICTs) provide opportunities for identification and characterization of small reservoirs, which is critical to effective local water resource management. This is because non-ICT methods are worthwhile, but they are time and cost ineffective. The study therefore sought to develop and test an ICT-based tool for effective management of small reservoirs. The study built upon previous work that demonstrated the possibility of identifying small reservoirs and estimating their capacities using satellite images and GIS. The objective of this study was to integrate the output information – small reservoir location, capacity, river or subcatchment, reliability, and status – in decision making processes by using Gwanda district located in southwest Zimbabwe as a case study. The study focused on identifying and characterising small reservoirs, and building up a database of little physical requirements. Identification was done through GIS processing of Landsat TM 4-5 images of February-March and April-May 2009. Finding recent images of the period around September for the dry season period was a major limitation. Fields surveys were done for ground truthing and for further identification and characterisation (name, place, capacity, turbidity and chlorophyll-a validation) of the reservoirs. Documents reviews were carried out to explore the contents of the existing databases. A total of 256 small reservoirs were identified and their status characterised in the district. The distribution of the capacities among the three subcatchments (Shashe, Lower and Upper Mzingwane) was found to be proportional to their surface area coverage over the district. Capacities were found to vary widely from around 4,000 m3 to over 650,000 m3; with the majority of them being around 30,000 m3. The total capacity in the district was estimated to 17 million m3 from which ward 23, one of the 24 rural wards in the district, accounted for more than 40% of the total capacity. Reservoirs with capacities less than 52,222 m3 were found susceptible to drying up in the dry season. Only 32% of the small reservoirs could reach the next rainy season. Seven reservoirs were characterised as highly turbid indicating a poor catchment protection, 79 moderately turbid, and 170 clear or less turbid. However, among the clear ones, 23 were characterised as affected by excessive floating vegetation probably due to nutrient leaching (mainly from fertilisers) in the catchment. A database was designed and integrated the results of the study with other attributes of some small reservoirs that were documented in organisations’ records. Finally, a Decision Support System was built from the results in a web application interface to provide a basis for informed decisionmaking concerning small reservoirs, especially in relation to their capacities, locations, and status, via facilitated information query, update, and visualisation. The tool provides a basis for effective collaboration between local institutions (for both based on hydrological and administrative boundaries) and responsible decision-making that needs information in various areas. These areas include surface water resources assessment, ability of small reservoirs to reach the next rainy season, and interventions required in catchment to ensure the sustainability of a small reservoir. The study demonstrated the possibility to use ICTs in effective small reservoirs management as a resources and time-efficient way to continually acquire, and integrate information.