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Assessment of global reanalysis precipitation for hydrological modelling in data–scarce regions: a case study of Kenya

Wanzala, M. A., Ficchi, A., Cloke, H. L. ORCID:, Stephens, E. M., Badjana, H. M. and Lavers, D. A. (2022) Assessment of global reanalysis precipitation for hydrological modelling in data–scarce regions: a case study of Kenya. Journal of Hydrology: Regional Studies, 41. 101105. ISSN 2214-5818

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To link to this item DOI: 10.1016/j.ejrh.2022.101105


Study region 19 flood prone catchments in Kenya, Eastern Africa Study focus Flooding is a major natural hazard especially in developing countries, and the need for timely, reliable, and actionable hydrological forecasts is paramount. Hydrological modelling is essential to produce forecasts but is a challenging task, especially in poorly gauged catchments, because of the inadequate temporal and spatial coverage of hydro-meteorological observations. Open access global meteorological reanalysis datasets can fill in this gap, however they have significant errors. This study assesses the performance of four reanalysis datasets (ERA5, ERA-Interim, CFSR and JRA55) over Kenya for the period 1981–2016 on daily, monthly, seasonal, and annual timescales. We firstly evaluate the reanalysis datasets by comparing them against observations from the Climate Hazards group Infrared Precipitation with Station. Secondly, we evaluate the ability of these reanalysis datasets to simulate streamflow using GR4J model considering both model performance and parameters sensitivity and identifiability. New hydrological insights for the region While ERA5 is the best performing dataset overall, performance varies by season, and catchment and therefore there are marked differences in the suitability of reanalysis products for forcing hydrological models. Overall, wetland catchments in the western regions and highlands of Kenya obtained relatively better scores compared to those in the semi-arid regions, this can inform future applications of reanalysis products for setting up hydrological models that can be used for flood forecasting, early warning, and early action in data scarce regions, such as Kenya.

Item Type:Article
Divisions:Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:105043


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