Evaluation of satellite precipitation products for estimating heavy precipitation on the Caspian coast

Document Type : Research/Original/Regular Article

Authors

1 Graduated M.Sc. Student/ Department of Water Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

2 Graduated M.Sc. Student/ Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran

3 Professor/ Department of Water Engineering, Faculty of Agriculture and Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

Introduction
Climate changes and global warming have caused changes in weather patterns and the occurrence of serious events in different regions of the earth. Therefore, it is necessary to predict destructive floods, especially in coastal areas, to announce flood warnings and control them. Satellite precipitation estimation systems often generate data with global coverage that can provide information in areas where data from other sources are not available. Satellite retrieval and estimation systems, rain gauges, and radar networks complement each other regarding coverage and rainfall monitoring capabilities. Satellite precipitation estimation systems often produce data with global coverage. One of the main advantages of satellite-based precipitation products is the ease of real-time data availability and high spatial-temporal resolution. Currently, various satellite precipitation products such as GPM, TRMM, GSMap, CMORPH, CHIRPS, etc. are available to the public.
Materials and Methods
In this study, the southwest coastline of Caspian Sea, Gilan province is considered as the study area and the accuracy of four satellite-based products namely CHIRPS, GPIM-IMERG, PERSIANN CDR, TRMM-3B42V7 is compared in order to estimate the heavy precipitation from 2017 to 2021. Estimates are performed using categorical indices including PC, CSI, BIAS, and HSS and statistical criteria of correlation coefficient (Corr) and normalized root mean square error (nRMSE).
Results and Discussion
Evaluations and statistical criteria are conducted by comparing estimated satellite precipitation with ground stations through definitive indicators as well as the height of each station and the average of all stations respectively. The results show that in the PC index in eight stations, all of which are located at an altitude of fewer than 40 m above sea level and on the Caspian coast, the product IMERG scores higher than other products. The weakest performance in the PC index at the threshold of more than 5 mm belongs to TRMM and PERSIANN-CDR, which have the lowest scores jointly in eight stations. According to the results obtained from CSI, IMERG product had better performance in all stations except the Manjil station. Additionally, evaluation of the height of stations and the results obtained from this index demonstrate the fact no relationship was found between station height and product performance. In the Bias quality index, in IMERG and CHIRPS products, the bias of each station is directly related to its height, so that in higher stations, rainfall is overestimated, and vice versa in lower altitudes, the precipitations have been underestimated less than the amount of observation.
Conclusion
The IMERG product has performed better than the other three products in all categorical indices and correlation criteria. Finally, to evaluate and analyze heavy precipitation in the coastal strip of Gilan province and studies related to precipitation estimation, it is suggested that the IMERG product be used as a priority. The analysis carried out in this study, from the collection of satellite precipitation bases in estimating heavy precipitation in coastal areas, reveals the need to study and investigate this issue as much as possible in future research.

Keywords

Main Subjects

References

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Water and Soil Management and Modelling
Volume 2, Issue 4
December 2022
Pages 107-122

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History

  • Receive Date: 12 July 2022
  • Revise Date: 04 August 2022
  • Accept Date: 04 August 2022

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