Document Type : Original Article
Authors
1
Department of Physical Geography, University of Sistan and Baluchestan, Zahedan, Iran.
2
Master's student, Department of physical Geography, Faculty of Geography and Environmental Planning, University of Sistan and Baluchestan, Iran
3
Professor, Department of physical Geography, Faculty of Geography and Environmental Planning, University of Sistan and Baluchestan, Iran
4
Assistant Professor, Department of Statistics, Faculty of Mathematics, Statistics and Computer Science, University of Sistan and Baluchestan, Iran
10.22125/iwe.2023.422204.1761
Abstract
Precipitation is a parameter related to climate and research on it is facing problems due to the lack of continuous data. Accurate planning and management of water resources depends on the availability of continuous and accurate precipitation data at meteorological stations. Hence, estimation of missing rainfall data is important in order to obtain more reliable results. Various imputation methods have been proposed and developed by researchers to estimate missing values in daily and monthly rainfall data. In most situations, spatial interpolation techniques such as normal ratio and inverse distance methods are used for estimating missing rainfall values at a particular target station based on the available rainfall values recorded at the neighboring stations. Moreover, these two methods are found to be very useful in the case where the neighboring stations are very close and highly correlated with the target stations. In this study, several modifications and improvements have been proposed to these methods in order to estimate the missing rainfall values at the target station using the information from the nearby stations. The methods have been tested with different percentages of missing rainfall values and also with a radius range of 75 km to 150 km in the catchment area of South Balochestan. The result indicate that the performance of these modified methods improved the estimation of missing rainfall values at the target station based on the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R).
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