The impact of data reconstruction methods on climate indicators

Document Type : Original Article

Authors

1 Associate Professor, Department of Irrigation and Reclamation Engineering, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 agrometeorology

Abstract

The importance of choosing appropriate methods for data reconstruction is clearly evident. Given the increasing spread of climate change and its effects on plants and other living organisms, the need for accurate and reliable data is felt more than ever. Therefore, researchers and decision-makers should pay special attention to analyzing and selecting data reconstruction methods so that they can take steps towards optimal resource management and reducing the negative effects of climate change. The aim of this study was to investigate the effect of reconstructing random statistical gaps in the daily minimum and maximum temperature data of the Kohrang station (base station) using the multiple linear regression method and with the help of neighboring stations. For this purpose, 5 to 50 percent (with an interval of 5 percent) of the daily minimum and maximum temperature data at the Kohrang station were randomly considered statistical gaps. Then, data gaps were calculated using the multiple linear regression method and the estimation error was determined for different percentages of statistical gaps. Reconstruction errors were reviewed and evaluated on a monthly and annual basis.

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History

  • Receive Date: 09 January 1404
  • Accept Date: 09 January 1404

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