Evaluating precipitation estimates over Jazmurian Basin using satellite images and ground-based observations

Document Type : Original Article


1 Assistant Professor, Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran

2 Assistant Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Lorestan University, Iran

3 Assistant Professor, Department of Natural Science, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran


In recent years, remote sensing technology has been considered as a useful tool to estimation of precipitation and its spatial-temporal variations. The current study aims to estimate the amount of annual precipitation in the Jazmurian basin using remote sensing and ground-based observed data. For this purpose, the mean annual precipitation of the study region for the period of 1998 to 2017 was calculated by Thiessen method. Besides, the CHIRPS, PERSIANN-CDR and TRMM satellite products for the same period, were processed using Google Earth Engine. The results showed that based on ground observed data, the mean annual precipitation (1998-2017) of the Jazmurian basin is approximately 124 mm. The corresponding values of TRMM, CHIRPS and PERSIANN-CDR annual precipitation products, were approximately 139, 99.5 and 154 respectively. The spatial pattern of precipitation revealed that the amount of precipitation decreased from the west to the east of the basin and the lowest precipitation values are observed in central and eastern regions of Jazmurian basin. In general, TRMM provided more reliable estimations (correlation coefficient = 0.88 and lowest RMSE), therefore it can be considered as an alternative for observed data especially in areas where weather stations are limited and sparse.


Ahrari, A.H. Virtual satellite image processing engine. 2019. Kelid Amozesh publication. 242 pp. (In Farsi).
Almazroui, M. 2011. Calibration of TRMM rainfall climatology over Saudi Arabia during 1998–2009. Atmospheric Research, 99(3–4): 400-414.
Asgari, Gh., Porbagheri, M., Mobaraki, Z. 2008. Validation of Precipitation Data Obtained from TRMM Satellite    Using  Auto  Meteorological Stations. Geomatics Conference, Tehran. (In Farsi)
Ashouri, H., Hsu, K., Sorooshian, S., Braithwaite, D.K., Knapp, K.R., Cecil, L.D., Nelson, B.R., Prat, O.P. 2015. PERSIANN-CDR: Daily Precipitation Climate Data Record from Multi-Satellite Observations for Hydrological and Climate Studies. Bulletin of the American Meteorological Society, 96: 69–83.
Chen, C., Yu, Z., li, l., yang, C. 2011. Adaptability Evaluation of TRMM Satellite Rainfall and Its Application in the Dongjiang River Basin, Procedia Environmental Sciences, 3rd International Conference on Environmental Science and Information Application Technology (ESIAT 2011), 10: 396-402.
Fang, J., Yang, W., Luan, Y., Du, J., Lin, A., Zhao, L. 2019. Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China. Atmospheric Research, 223: 24-38.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, Sh., Husak, G., Rowland J., Harrison, L., Hoell, A., Michaelsen, J. 2015. The climate hazards infrared precipitation with stations a new environmental record for monitoring extremes. Scientific Data 2, 150066. doi:10.1038/sdata.2015.66
Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., Verdin, A.P. and Pedreros, P. 2014. A quasi-global precipitation time series for drought monitoring. US Geological Survey Data Series 832.
Guo, H., Bao, A., Liu, T., Chen, Ch., Ndayisaba, F. 2016. Evaluation of PERSIANN-CDR for Meteorological Drought Monitoring over China. Remote Sensing. 8(5), 379, https://doi.org/10.3390/rs8050379
Haji Mirrahimi, M., Feizizade, B. 2008. Accuracy of ground radar and TRMM data in precipitation estimation. Geomatics Conference, Tehran. (In Farsi)
Henderson, D. S., C. D. Kummerow, D. A. Marks, and W. Berg, 2017. A regime-based evaluation of TRMM oceanic precipitation biases. Journal of Atmospheric and Oceanic Technology, 34(12): 2613–2635.
Huffman, G. J., Bolvin, D. T., Nelkin, E.J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman K.P., Stocker, E.F. 2007. The TRMM Multi-satellite Precipitation Analysis: Quasi-Global, Multi-Year, Combined-Sensor Precipitation Estimates at Fine Scale. Journal of Hydrometeorology, 8 (1): 38-55.
Ioannidou, M. P., Kalogiros, J. A., Stavrakis, A. K. 2016. Comparison of the TRMM Precipitation Radar rainfall estimation with ground-based disdrometer and radar measurements in South Greece. Atmospheric Research, 181: 172–185.
Javanmard, S., Tajbakhsh, S., Badagh Jamali, J. 2018. The estimation of type and amount rainfall using remote sensing techniques. 18th Iranian Geophysical Conference, 335-339. (In Farsi)
Javanmard, S., Yatagai, A., Nodzu, M.I., Bodagh -Jamali,      J.,      Kawamoto,         H.         2010. Comparing high-resolution gridded precipitation data with satellite rainfall estimates of TRMM 3B42 over Iran. Advances in Geosciences, 25: 119-125.
Katiraie-Boroujerdy, P.S., Akbari Asanjan, A., Hsu, K-l., Sorooshian, S. 2017. Inter-comparison of PERSIANN-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales. Atmospheric Research. doi:10.1016/j.atmosres.2017.04.005.
Katiraie-Boroujerdy, P-S., Nasrollahi, N., Hsu, K-l., Sorooshian, S. 2013. Evaluation of satellite-based precipitation estimation over Iran. Journal of Arid Environments, 97: 205-219.
Katsanos, D., Retalis, A., Michaelides, S. 2016. Validation of a high-resolution precipitation database (CHIRPS) over Cyprus for a 30-year period. Atmospheric Research, 169, 459–464. https://doi.org/10.1016/j.atmosres.2015.05.015.
Madadi, Gh., Hamze, S., Noruzi, A.K. 2015. Precipitation Estimation Using TRMM Satellite Data. National Congress of Irrigation and Drainage. (In Farsi)
Matkan, A.A., Shakiba, A.R., Ashorlo, D., Badagh Jamali, J., Mohamadian, V. 2009. Ability of combined infrared and microwave passive remote sensing data and estimation of rainfall and flood monitoring. Iranian remote sensing and GIS, 1(2): 31-44. (In Farsi)
Mianabadi, A., Alizade, A., Sanaeinejad, H., Banayan, M., Faridhosseini, A.R. 2013. The statistic assessment of CMORPH model output for precipitation estimation over the northeast of Iran (Case study: north Khurasan province). Journal of water and soil, 27(5): 919-927. (In Farsi)
Moazami, S., Golian, S., Kavianpour, M.R., Hong, Y., 2013. Comparison of PERSIANN and V7 TRMM multi-satellite precipitation analysis (TMPA) products with rain gauge data over Iran. International Journal of Remote Sensing 34(22): 8156-8171. http://dx.doi.org/10.1080/ 01431161.2013.833360.
Niazi, Y., Talebi, A., Mokhtari, M.H., Vazifedost, M. 2018. Spatio-Temporal Analysis of the Accuracy of TRMM Satellite Data to Estimate the Severity of a Drought Based on Precipitation in Central Iran. Physical Geography Research, 50(1): 69-85. (In Farsi)
Omidvar, K., Fenodi, M., Banivaheb, A.R. 2013. Investigation of TRMM Satellite Rainfall data with Groundwater Stations Case Study: Synoptic Stations of Khorasan Razavi Province. First National Meteorological Conference, Kerman. (In Farsi)
Prakash, S., Mitra, A. K., Pai, D.S., AghaKouchak, Ar. 2016. From TRMM to GPM: How well can heavy rainfall be detected from space?. Advances in Water Resource, 88: 1-7.
Rasoli, A.K., Erfanian, M., Sari saraf, B., Javan, Kh. 2016. Comparative evaluation of estimated TRMM rainfall values and recorded precipitation of ground stations in Lake Urmia Basin. Geographic Space, 16(540): 195-217. (In Farsi)
Sadeghi, M., Akbari Asanjan, A., Faridzad, M., Afzali Gorooh, V., Nguyen, P., Hsu, K., Sorooshian, S., Braithwaite, D. 2019. Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale. Remote Sensing,11(23): 2755.
Saeidizand, R., Sabet-ghadam, S., Tarnavsky, E., Pierleoni, A. 2018. Evaluation of CHIRPS rainfall estimates over Iran. Quarterly Journal of the Royal Meteorological Society:1–10. https://doi.org/10.1002/qj.3342.
Shirvani, A., Fakharizade, E. 2014. Comparison of ground based observation of precipitation with TRMM satellite estimations in Fars Province. Journal of Agricultural Meteorology, 2(2): 1-15. (In Farsi)
Sorooshian, S., Hsu, K., Braithwaite,  D.,  Ashouri, H. 2014. NOAA Climate Data Record (CDR)of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1. Doi:10.7289/V51V5BWQ.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., Hsu, K. 2018. A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Inter-comparisons. Reviews of Geophysics, 56(1): 79-107.
Tao, W-K., Lang, S., Olson, W. S., Meneghini, R., Yang, S., Simpson, J., Kummerow, C., Smith E., Halverson, J. 2001. Retrieved Vertical Profiles of Latent Heat Release Using TRMM Rainfall Products for February 1988. Journal of applied Meteorology, 40 (6): 957-982.
Upadhyaya, S., Ramsankaran, R. 2014. Review of Satellite Remote Sensing Data Based Rainfall Estimation Methods. Proceedings of Hydro 2013 International, Iit Madras, India. 1-15.
Zeng, Q., Wang, Y., Chen, L., Wang, Z., Zhu, H., Li, B. 2018. Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013. Remote Sensing, 10(2): 168.