Application of non-parametric regression in estimating missing daily rainfall data Online publication date: Wed, 18-Sep-2019
by Rachel Makungo; John Ogony Odiyo
International Journal of Hydrology Science and Technology (IJHST), Vol. 9, No. 3, 2019
Abstract: Most daily rainfall time series data are too short and/or possess missing records hindering them to perform reliable and meaningful analyses. Robust locally weighted scatter smoother non-parametric regression approach (NPR), with tricube weighting function was used to estimate missing daily rainfall data in the upper reaches of Nzhelele and Luvuvhu River Catchments in Limpopo Province of South Africa. The approach proposed in this study has not yet been widely applied for estimating missing rainfall data. Model performance ranged from acceptable to excellent. Graphical fits of observed and estimated rainfall data showed a general agreement. Scatter plots indicated that there was no definite pattern of underestimation and overestimation of peak rainfall events. Scatter points for low rainfall values were closer to the best fit line showing good agreement between observed and simulated rainfall values for most of the stations. The study showed that NPR effectively estimated missing rainfall data.
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