Assessment of precipitation data from Asfazari national database in runoff evaluating and regional drought monitoring

Document Type : Research Article

Authors

1 Water Resources Engineering, Abouraihan College, University of Tehran

2 Associate Assistant, Department of Water Resources Engineering, Tarbiat Modares University

3 Associate Professor of Climatology University of Kurdistan Faculty of Natural Resources Department of Climatology Iran

Abstract

Due to high spatiotemporal resolution of gridded data, reanalysis precipitation databases, have many applications in climate prediction, climate change modeling, water resources management and hydrological modeling, especially in areas without observational data. Therefore, in the present study, hydrological simulation was evaluated by SWAT model and spatial data mining for drought monitoring with SPI and SDI indexes over Maharlu Lake and assessing the temporal sense of Asfazari national database by observational stations as a reference on the spatial extent. The results showed high accuracy of Asfazari database in simulating of runoff in comparison with simulated runoff with observation database. The coefficient of determination and Nash efficiency presented the average accuracy of 0.6 in the simulation. During the cold and rainy seasons, the performance of this database is higher than is the warm season. During rainy months of year, the correlation coefficient between observation and Asfazari is about 0.85, and the POD index is more than 0.9. Furthermore, subsequently the accuracy of monitoring drought by Asfazari is too high, so it can be said that the Asfazari database can be used as a reliable database in runoff simulating and drought monitoring, especially in areas with poor or no sufficient precipitation data.

Keywords

Main Subjects

References

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Iranian journal of Ecohydrology
Volume 5, Issue 1
April 2018
Pages 99-110

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History

  • Receive Date: 22 June 2017
  • Revise Date: 15 September 2017
  • Accept Date: 04 October 2017
  • First Publish Date: 21 March 2018
  • Publish Date: 21 March 2018

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