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Assessment of irrigation performance by remote sensing in the Naivasha Basin, Kenya

Njuki, S.M. (Sammy Muchiri)

Titre : Assessment of irrigation performance by remote sensing in the Naivasha Basin, Kenya.

Auteur : Njuki, S.M. (Sammy Muchiri)

Etablissement de soutenance : University of Twente International Institute for Geo-Information Science and Earth Observation (ITC)

Grade : Master of Science in Geo-Information Science and Earth Observation 2016

Résumé
Irrigation performance assessment is vital for effective water management especially in water- scarce areas. In addition, it provides information needed to monitor crop water use and related productivity in irrigation command areas. However, irrigation performance assessment is hampered by lack of necessary ground data more so in developing countries. Advances in remote sensing technology and its applications have reduced over reliance on ground data. This has led to a tremendous improvement in irrigation performance assessment and monitoring in ground data scarce areas. This research utilizes information derived fr om remote sensing to assess irrigation performance in the commercial irrigation farms in the Naivasha basin, Kenya for the year 2014 . It aims at quantifying irrigation consumption by crops by use of remote sensing derived actual evapotranspiration . Consequently, irrigation efficiency is assessed based on the derived irrigation consumption and irrigation water abstraction data. The Surface Energy Ba lance System (SEBS) model was used together with MODIS land surface temperature, variables derived from Landsat 8, meteorological data and MODIS monthly evapotranspiration product to obtain monthly evapotranspiration estimates at 30 m spatial resolution. On the other hand, CHIRPS rainfall product was combined with gauge rainfall data and information derived from land use and land cover to derive monthly effective precipitation maps. Monthly irrigation consumption was then computed from the difference between the two maps. Monthly irrigation efficiency was finally obtained by comparing the monthly irrigation consumption to the monthly water abstraction data. Four farms were considered and the total amount of irrigation consumption in the year 2014 , was found to be 4 364 680 m 3 . The highest amount of irrigation consumption (471 147 m 3 ) was obtained in July and the lowest (275 467 m 3 ) in September. Irrigation efficiency was computed for Vegpro Gorge farm only. An average irrigation efficiency of 71% was obtained for 2014. Highest irrigation efficiency (88.5%) was in March with the lowest (44.1%) being in September. High irrigation efficiencies were obtained for the wet and the dry months with low efficiencies being obtained for the transition months between wet and dry seasons. It was concluded that reliance on rainfall data only in irrigation scheduling led to low irrigation efficiencies during transition months . This is because the effect of soil moisture storage is not taken into account in irrigation scheduling thus excess irrigation water is supplied . It is recommended to incorporate indicators such as the aridity index in irrigation scheduling to improve the efficiency of the irrigation system.

Mots clés : Irrigation performance assessment, remote sensing, S EBS, Naivasha , irrigation efficiency , Landsat 8, MODIS, CHIRPS.

Version intégrale (ITC)

Page publiée le 29 janvier 2018