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Multi-site stochastic modelling of daily rainfall in Uganda

Abstract The availability of precipitation records in Uganda has significantly decreased since the 1970s, severely limiting the data available for hydrological modelling. This problem has been addressed in this study by using the generalized linear modelling (GLM) framework to develop stochastic daily rainfall models that have the capability for extending and infilling historic data sets. We have used a relatively sparse raingauge network in the Kyoga basin (in the Upper Nile) to reproduce spatial and temporal patterns of precipitation by fitting occurrence (logistic) and amounts (gamma) models as functions of dominant seasonal, climate and geographical controls. The GLMs were able to reproduce rainfall properties when predefined climatic zones are modelled independently. We were not able to represent the spatial variability of the equatorial climate with one integrated basin scale model, however this is not thought to preclude application to basin scale hydrological modelling. Citation Kigobe, M., McInty...
- Imperial College London United Kingdom
- University of Queensland Australia
- University College London United Kingdom
- University of Queensland Australia
Rainfall, Generalized linear models, Stochastic models, Climate change, Uganda
Rainfall, Generalized linear models, Stochastic models, Climate change, Uganda
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).37 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
