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Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin

机译:参数和输入数据不确定估计,用于评估林蛙河流域两个副盆地的水资源

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摘要

The demand for water resources is rapidly growing, placing more strain onaccess to water and its management. In order to appropriately manage waterresources, there is a need to accurately quantify available water resources.Unfortunately, the data required for such assessment are frequently far fromsufficient in terms of availability and quality, especially in southernAfrica. In this study, the uncertainty related to the estimation of waterresources of two sub-basins of the Limpopo River Basin – the Mogalakwena inSouth Africa and the Shashe shared between Botswana and Zimbabwe – isassessed. Input data (and model parameters) are significant sources ofuncertainty that should be quantified. In southern Africa water use data areamong the most unreliable sources of model input data because availabledatabases generally consist of only licensed information and actual use isgenerally unknown. The study assesses how these uncertainties impact theestimation of surface water resources of the sub-basins. Data on farmreservoirs and irrigated areas from various sources were collected and usedto run the model. Many farm dams and large irrigation areas are located inthe upper parts of the Mogalakwena sub-basin. Results indicate that water useuncertainty is small. Nevertheless, the medium to low flows are clearlyimpacted. The simulated mean monthly flows at the outlet of the Mogalakwenasub-basin were between 22.62 and 24.68 Mm3 per month whenincorporating only the uncertainty related to the main physical runoffgenerating parameters. The range of total predictive uncertainty of the modelincreased to between 22.15 and 24.99 Mm3 when water use datasuch as small farm and large reservoirs and irrigation were included. For theShashe sub-basin incorporating only uncertainty related to the main runoffparameters resulted in mean monthly flows between 11.66 and 14.54 Mm3. The range of predictive uncertainty changed to between 11.66and 17.72 Mm3 after the uncertainty in water use informationwas added.
机译:对水资源的需求迅速增长,放置更多的压力进入水及其管理。为了适当地管理水资源,需要准确地量化可用的水资源。不幸的是,这种评估所需的数据往往远非在可用性和质量方面,特别是在南部非洲。在这项研究中,与水估计有关的不确定性林帕河流域两个子盆地的资源 - Mogalakwena南非和博茨瓦纳和津巴布韦之间分享的仓库是评估。输入数据(和模型参数)是重要的来源应该量化的不确定性。在南部非洲的水使用数据是在最不可靠的模型输入数据中,因为可用数据库通常仅由许可信息和实际使用组成一般是未知的。该研究评估了这些不确定性如何影响亚盆地地表水资源估算。农场上的数据收集和使用来自各种来源的水库和灌溉区域运行模型。许多农场和大型灌溉区域都位于Mogalakwena子盆地的上部。结果表明用水不确定性很小。然而,媒体到低流量清楚受影响。模拟平均每月流动在Mogalakwena的出口处次级盆地每月22.62和24.68mm3之间仅包含与主要物理径流相关的不确定性生成参数。模型的总预测不确定性范围水使用数据时增加到22.15和24.99mm3之间包括小型农场和大型水库和灌溉。为了Shashe子盆地仅包含与主要径流相关的不确定性参数导致11.66和14.54mm之间的平均每月流量。预测性不确定性范围变为11.66之间水使用信息不确定性后17.72mm3加入。

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