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Following complaints about water shortages in some areas of Gweru, the paper assessed the availability of enabling capacities to efficiently and sufficiently deliver water to the residents of Gweru in line with the existing level of demand. The key inspected capacity aspects were infrastructure, human resources, finances and physical availability of raw water at source. Purposively selected informants from Zimbabwe National Water Authority (ZINWA) and Gweru city council (GCC) provided data on the infrastructural, financial and human resources situation of GCC. Dam levels data for Gweru’s three supply dams were obtained from ZINWA records through the assistance of Sanyati Catchment Hydrologist. The raw water availability was assessed with the aid of Mann–Kendall test using a 10 years period data set from 2003 to 2012 for trend analysis. Findings revealed that the water sources were not experiencing major changes in water levels, to be precise, the changes were insignificant. However, given the increase in population and demand, any slightest negative change in the supply chain would further widen the gap between supply and demand. The study also uncovered that GCC had challenges in terms of the infrastructure, mainly due to financial constraints. Notably, GCC was not experiencing high staff turnover, but it was however, seriously under staffed and failing to effectively monitor water use in the city. Therefore, strong financial injection is required to support staff and resuscitate the reticulation system. Given the perpetual water shortages in Gweru and the status quo in the supply chain, water demand management strategies, wastewater use and consumer education are consequently proposed as measures that would ensure continuous water supply for all needs in the city. 相似文献
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In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R2 ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error. 相似文献
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