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Rainfall intensity‒duration‒frequency (IDF) curves are essential for hydrological design and flood management, yet their development in Uganda is limited by scarce subdaily rainfall data. This research regionalized the IDF coefficients using physical and climatic predictors. IDF coefficients (a, b, c) were derived from observed hourly rainfall and from disaggregated daily rainfall using aligned time distribution (TD) factors.
You are already at the latest version This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods; Quantile Mapping (QM), Linear Transformation (LT), Delta Multiplicative (DM), and Polynomial Regression (PR), were evaluated using daily rainfall data from four gauged stations (Gulu, Soroti, Jinja, Mbarara).
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Martin Okirya Martin Okirya Scilit Preprints.org Google Scholar * and JA Du Plessis JA Du Plessis Scilit Preprints.org Google Scholar Department of Civil Engineering, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa * Author to whom correspondence should be addressed.
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