1.Abdollahi, B., Hosseini-Moghari, S.M., and Ebrahimi, K. 2017. Assessment of Satellite Precipitation Data from TRMM 3B42RT V7 and CMORPH in Order to Estimate Precipitation in Gorganroud Basin-Iran, J. Water. Manage. Sci. Eng. 11: 36. 55-68. (In Persian)
2.Akbari, M., Ownegh, M., Asgari, H.R., Sadoddin, A., and Khosravi, H. 2016. Drought Monitoring based on the SPI and RDI Indices under Climate Change Scenarios (Case Study: Semi-Arid
Areas of West Golestan Province). ECOPERSIA. 4: 1585-1602.
4.Alibakhshi, S.M., Farid Hossini, A.R., Davari, K., Alizadeh, A., and Munyka, H. 2017. Statistical comparison between IMERG and TMPA 3B42V7 products at the level of three GPM and TRMM precipitation data Case study: Kashafrood catchment, Razavi Khorasan province. Iranian J. Nat. Resour. 4: 69. 963-981. https://doi.org/
10.22059/jrwm.2017.61194. (In Persian)
5.Anjum, M.N., Ding, Y., Shangguan, D., Ijaz, M.W., and Zhang, S. 2016. Evaluation of high-resolution satellite-based real-time and post-real-time precipitation estimates during 2010 extreme flood event in Swat River Basin, Hindukush region. Adv. Meteorol. 1-8.
http://dx.doi.org/10.1155/2016/2604980.
6.Azari, M., Moradi, H.R., Saghafian, B., and Faramarzi, M. 2013. Assessment of Hydrological Effects of Climate Change in Gourganroud River Basin. J. Water Soil. 27: 3. 537-547. (In Persian)
7.Golestan province Regional Water Company. 2016. Integrated Water Resources Studies Update Update Report for Gharasu and Gorganrood River Basin. 247p. (In Persian)
8.Guo, H., Chen, S., Bao, A., and Hu, J. 2015. Inter-comparison of high-resolution satellite precipitation products over Central Asia,” Remote Sens. 7: 6. 7181-7211.
https://doi.org/10.3390/rs70607181.
9.Guo, H., Chen, S., Bao, A., Behrangi, A., Hong, Y., Ndayisaba, F., and Stepanian, P.M. 2016. Early assessment of integrated multi-satellite retrievals for global precipitation measurement over China. Atmos. Res. 176: 121-133.
10.Hou, A.Y., Kakar, R.K., Neeck, S., Azarbarzin, A.A., Kummerow, C.D., Kojima, M., and Iguchi, T. 2014. The global precipitation measurement mission. B. AM. Meteorol. Soc.95: 5. 701-722.
11.Hsu, K. 1997. Precipitation estimation from remotely sensed information using artificial neural networks,” J. Appl. Meteorol. Clim. 36: 1176-1190.
https:// doi.org/10.1175/1520-0450 http://trmm. gsfc.nasa.gov (1/06/2016 available access date).
12.Huffman, G.J., Adler, R.F., and Bolvin, D.T. 2007. The TRMM Multi-Satellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol. 8: 1. 38-55.
https://doi.org/10.1175/JHM560.1.
13.Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P., and Yoo, S.H. 2015. NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG). Algorithm theoretical basis document, Nat. Aero. Space Admin. 4: 1-30.
14.Joyce, R.J., Janowiak, J.E., Arkin, P.A., and Xie, P. 2004. CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at the high spatial and temporal resolution, J. Hydrometeorol. 5: 3. 487-503.
https://doi.org/ 10.1175/ 1525-7541.
15.Khwarazmi, S. 2013. Validation of microwave satellite rain rate algorithms based on observations. M.Sc. Thesis. The University of Hormozgan. Iran. 121p. (In Persian)
16.Kidd, C., and Huffman, G. 2011. Global precipitation measurement. Meteorological Applications. 18: 3. 334-353.
https:// doi.org/10.1002/met.284. (In Persian)
17.Kim, K., Park, J., Baik, J., and Choi, M. 2017. Evaluation of topographical and seasonal features using GPM IMERG and TRMM 3B42 over Far-East Asia. Atmos. Res. 187: 95-105.
18.Kubota, T., Shige, S., Hashizume, H., and Aonashi, K. 2007. Global precipitation map using satellite-borne microwave radiometers by the GSMaP project: production and validation,” IEEE T. Geosci. Remote Sens.45: 7. 2259-2275.
https://doi.org/ 10.1109/TGRS.2007.895337.
19.Liechti, T., Matos, G.C., Pedro, J., Boillat, J.L., and Schleiss, A. 2012. Comparison and evaluation of satellite-derived precipitation products for hydrological modeling of the Zambezi River Basin. Hydrol. Earth Syst. Sci.16: 489-500.
20.Li, N., Tang, G., Zhao, P., Hong, Y., Gou, Y., and Yang, K. 2017. Statistical assessment and hydrological utility of the latest multi-satellite precipitation analysis IMERG in the Ganjiang River basin. Atmos. Res. 183: 212-223.
21.Liu, J., Zhang, W., and Nie, N. 2018. Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China, Adv. Meteorol. 1: 1-13.
https://doi.org/ 10.1155/2018/3491960.
22.Modaresi, F., Araghinejad, S.H., Ebrahimi, K., and Kholghi, M. 2010. Regional Assessment of Climate Change Using Statistical Tests: Case Study of Gorganroud-Gharehsou Basin, J. Water Soil. 24: 3. 476-489.
23.Mohammadi, R., Dastorani, M.T., Akbari, M., and Ahani, H. 2019. The impacts of magnetized water treatment of different morphological and physiological factors of plant species
in the arid regions, Water Supply.19: 6. 1587-1596.
https://doi.org/ 10.2166/ws.2019.027.
24.Mosaedi, A., Ghabaei Sough, M., Sadeghi, S.H., Mooshakhian, Y., and Bannayan, M. 2017. Sensitivity analysis of monthly reference crop evapotranspiration trends in Iran: a qualitative approach, Theor. Appl. Climatol. 128: 3. 857-873.
25.Ning, S., Wang, J., Jin, J., and Ishidaira, H. 2016. Assessment of the latest GPM-era high-resolution satellite precipitation products by comparison with observation gauge data over the Chinese Mainland. Water. 8: 11. 481.
26.O’h, S., and Kirstetter, P.E. 2018. Evaluation of diurnal variation of GPM IMERG‐derived summer precipitation over the contiguous US using MRMS data. Q. J. R. Meteorol. Soc. 144: 1.270-281.
https://doi.org/10.1002/qj.3218.
27.Prakash, S., Mitra, A.K., AghaKouchak, A., Liu, Z., Norouzi, H., and Pai, D.S. 2016. A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. J. Hydrol. 556: 865-876. https://10.1016/j.jhydrol.2016.01.029.
28.Sahlu, D., Nikolopoulos, E.I., Moges, S.A., Anagnostou, E.N., and Hailu, D. 2016. First evaluation of the Day-1 IMERG over the upper Blue Nile basin. J. Hydrometeorol. 17: 11. 2875-2882.
29.Sharifi, E., Saghafian, B., and Steinacker, R. 2016a. Performance evaluation of the latest generation of high temporal-spatial resolution satellite precipitation products. National Conference on Water Resources Management, University of Kurdistan. 10p.
30.Sharifi, E., Steinacker, R., and Saghafian, B. 2016b. Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results”. Remote Sens. 8: 2. 1-25.
31.Sorooshian, S., Hsu, K.L., Gao, X., Gupta, H.V., Imam, B., and Braithwaite, D. 2000. Evaluation of PERSIAN system satellite-based estimates of tropical rainfall,” B. AM. Meteorol. Soc. 81: 2035-2046.
https://doi.org/ 10.1175/ 1520-0477.
32.Tao, J., Hua, Y., Rui, L., Tairong, H., and Jianfeng, W. 2014. Applicability analysis of the TRMM precipitation data in the Sichuan-Chongqing region,”
Prog. Phys. Geog. 33: 10. 1375-1386.
https://doi.org/10.11820/dlkxjz.2014.10.009.
33.Tan, M.L., and Duan, Z. 2017. Assessment of GPM and TRMM precipitation products over Singapore. Remote Sens. 9: 7. 720.
34.Tan, M.L., and Santo, H. 2018. Comparison of GPM IMERG, TMPA 3B42, and PERSIAN-CDR satellite precipitation products over Malaysia. Atmos. Res. 202: 63-76. http://dx.doi. org/
10.1016/j.atmosres.2017.11.006.
35.Tang, G., Ma, Y., Long, D., Zhong, L., and Hong, Y. 2016a. Evaluation of GPM Day-1 IMERG and TMPA Version-7 legacy products over Mainland China at multiple spatiotemporal scales. J. Hydrometeorol. 17:5.1407-1423.
http://dx.doi.org/ 10. 1175/ JHM-D-15-0081.1.
36.Tang, G., Zeng, Z., Long, D., Guo, X., Yong, B., Zhang, W., and Hong, Y. 2016b. Statistical and hydrological comparisons between TRMM and GPM level-3 products over a mid-latitude basin: Is day-1 IMERG a good successor for TMPA 3B42V7? J. Hydrometeorol. 17: 1. 121-137.