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Estimating areal rainfall over Lake Victoria and its basin using ground-based and satellite data
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL.
School of Engineering, Makerere University, P. O. Box 7062, Kampala, Uganda.
2012 (English)In: Journal of Hydrology, ISSN 0022-1694, E-ISSN 1879-2707, Vol. 464, 401-411 p.Article in journal (Refereed) Published
Abstract [en]

A gridded monthly rainfall dataset having a spatial resolution of 2 km and covering the period 1960–2004 was derived for the Lake Victoria basin. The lake and its basin support more than 30 million people and also contribute substantially to the River Nile flow. The major challenge in the estimation of the Lake Victoria water balance is the estimation of the rainfall over the lake, which is further complicated by the varying quality and spatial coverage of rain-gauge data in the basin. In this study, these problems were addressed by using rain-gauge data for 315 stations around the basin and satellite-derived precipitation data from two products to derive a monthly precipitation dataset for the entire basin, including the lake. First, the rain-gauge data were quality controlled. Thereafter short gaps were filled in the daily data series which resulted in 9,429 additional months of data. Two spatial interpolation methods were used for generating the gridded rainfall dataset and the universal kriging method performed slightly better than the inverse distance weighting method. The enhancement of rainfall over the lake surface was addressed by estimating a relationship between rain-gauge and satellite data. Two satellite rainfall products, TRMM 3B43 and PERSIANN were compared to the interpolated monthly rain-gauge data for the land part of the basin. The bias in the TRMM 3B43 rainfall estimates was higher than the bias for PERSIANN but its correlation was higher with a better representation of the intra-annual variability. The TRMM 3B43 product showed an enhancement of lake rainfall over basin rainfall of 33% while the PERSIANN product gave a much higher enhancement of up to 85%.

Place, publisher, year, edition, pages
Elsevier, 2012. Vol. 464, 401-411 p.
Keyword [en]
Lake Victoria, precipitation, spatial interpolation, inverse distance weighting, universal kriging, TRMM 3B43, PERSIANN
National Category
Oceanography, Hydrology, Water Resources
Research subject
Hydrology
Identifiers
URN: urn:nbn:se:uu:diva-179067DOI: 10.1016/j.jhydrol.2012.07.024ISI: 000309783700031OAI: oai:DiVA.org:uu-179067DiVA: diva2:543256
Available from: 2012-08-07 Created: 2012-08-07 Last updated: 2017-12-07Bibliographically approved

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Kizza, MichaelWesterberg, IdaRodhe, Allan

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