Water availability and agricultural demand: An assessment framework using global datasets in a data scarce catchment, Rokel-Seli River, Sierra Leone

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• 作者：Christopher K. Masafu^a ; ^{masafu@gmail.com} ; Mark A. Trigg^b ; Richard Carter^c ; Nicholas J.K. Howden^d
• 关键词：Hydrological model ; CROPWAT ; IHACRES ; Water resources assessment framework ; Global climate data
• 刊名：Journal of Hydrology: Regional Studies
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：8
• 期：Complete
• 页码：222-234
• 全文大小：2048 K
• 卷排序：8

文摘

The proposed assessment framework is aimed at application in Sub-Saharan Africa, but could also be applied in other hydrologically data scarce regions. The test study site was the Rokel-Seli River catchment, Sierra Leone, West Africa.Study focusWe propose a simple, transferable water assessment framework that allows the use of global climate datasets in the assessment of water availability and crop demand in data scarce catchments. In this study, we apply the assessment framework to the catchment of the Rokel-Seli River in Sierra Leone to investigate the capabilities of global datasets complemented with limited historical data in estimating water resources of a river basin facing rising demands from large scale agricultural water withdrawals. We demonstrate how short term river flow records can be extended using a lumped hydrological model, and then use a crop water demand model to generate irrigation water demands for a large irrigated biofuels scheme abstracting from the river. The results of using several different global datasets to drive the assessment framework are compared and the performance evaluated against observed rain and flow gauge records.New hydrological insightsWe find that the hydrological model capably simulates both low and high flows satisfactorily, and that all the input datasets consistently produce similar results for water withdrawal scenarios. The proposed framework is successfully applied to assess the variability of flows available for abstraction against agricultural demand. The assessment framework conclusions are robust despite the different input datasets and calibration scenarios tested, and can be extended to include other global input datasets.