Comparison of two high-resolution gridded precipitation data sets at the upstream of the Maroun dam in Iran

Document Type : Research Paper


1 PhD Candidate, Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahwaz, Iran

2 Professor, Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahwaz, Iran

3 Khuzestan Water and Power Organization, Ahwaz, Iran

4 Assistant professor, Faculty of Civil, Water and Environmental Sciences, Shahid Beheshti University of Tehran, Tehran, Iran


Satellite-based precipitation estimations are important and necessary because they are used to compensate the limited rain measurements in areas where there is no continuous monitoring of rainfall due to the dispersion of rain ague networks. Satellite-based precipitation estimation systems can provide information in areas where rainfall data are not available. Therefore, the accuracy of this type of data is very important. In this study, rainfall data of two long-term satellite data sets (FARSI-CDR and PERSIANN-CCS) at the upstream of Maroun Dam (Dehno, Ghale-Raeesi, Idenak, Margoon stations) during 2003-2014 were used and evaluated on daily, monthly, seasonally and annually basis. The results show that the annual precipitation of each dataset is underestimated in all stations, but the PERSIANN-CCS model compare to the PERSIANN-CDR has better estimations for annual observations. For estimation of seasonal precipitation, the results indicate that the PERSIANN-CCS model is better than the other one for rainfall estimation and rainfall detection. For estimation of monthly and daily precipitation, the results indicate that PERSIANN-CDR data are more appropriate than the other data set. Also, regarding to POD (probability of detection) and FAR (False alarm rate) estimated data, It was found that according to POD index, PERSIANN-CCS precipitation daily data and according to FAR, daily precipitation data of PERSIANN-CDR model have better performance in detecting rainy and non-rainy days.


Main Subjects

Abdollahi, B., Hosseini moghari, M., & Ebrahimi, K. (2017). Evaluation of CMORPH and TRMM 3B42RT V7 satellite data in order to estimate rainfall in the Gorganroud Basin. Iran Watershed Engineering and Scinces, 36, 55–68. (In Farsi)
Adjei, K. A., Ren, L., & Appiah-adjei, E. K. (2012). Validation of TRMM Data in the Black Volta Basin of Ghana, (May), 647–654.
Ashouri, H., Hsu, K. L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., et al. (2015). FARSI-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bulletin of the American Meteorological Society, 96(1), 69–83.
Bitew, M. M., & Gebremichael, M. (2011). Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrology and Earth System Sciences, 15(4), 1147–1155.
Dezfuli, D., Hosseini moghari, M., & Ebrahimi, K. (2016). Comparison of TRMM-3B42 V7 and FARSI satellite data with observations of ground stations (Case study: Gorganroud Basin). Journal of Soil and Water Sciences, 76, 85–98. (In Farsi)
Duan, Z., Liu, J., Tuo, Y., Chiogna, G., & Disse, M. (2016). Evaluation of eight high spatial resolution gridded precipitation products in Adige Basin (Italy) at multiple temporal and spatial scales. Science of the Total Environment, 573, 1536–1553.
Eghtedari, M., Iran Nejad, P., Vazife doost, M., Bazrafshan, J., & Ghahraman, N. (2018). Comparison of spring rainfall from four products networked and simulated by RegCM and their evaluation with observations in Qazvin Plain. Iran-Water Resources Research, 14(4), 32–44. Retrieved from (In Farsi)
Einfalt, T., Arnbjerg-Nielsen, K., Golz, C., Jensen, N.-E., Quirmbach, M., Vaes, G., & Vieux, B. (2004). Towards a roadmap for use of radar rainfall data in urban drainage. Journal of Hydrology, 299(3–4), 186–202.
Fujihara, Y., Yamamoto, Y., Tsujimoto, Y., & Sakagami, J.-I. (2014). Discharge Simulation in a Data-Scarce Basin Using Reanalysis and Global Precipitation Data : A Case Study of the White Volta Basin. Journal of Water Resource and Protection, 06(6), 1316–1325.
Gao, F., Zhang, Y., Chen, Q., Wang, P., Yang, H., Yao, Y., & Cai, W. (2018). Comparison of two long-term and high-resolution satellite precipitation datasets in Xinjiang, China. Atmospheric Research, 212, 150–157.
Hong, Y., Hsu, K.-L., Sorooshian, S., & Gao, X. (2004). Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System. Journal of Applied Meteorology, 43(12), 1834–1853.
Javanmard, S., Yatagai, A., Nodzu, M. I., Bodaghjamali, 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.
Jia, S., Zhu, W., Lu, A., & Yan, T. (2011). A statistical spatial downscaling algorithm of TRMM precipitation based on NDVI and DEM in the Qaidam Basin of China. Remote Sensing of Environment, 115(12), 3069–3079.
Katiraie-Boroujerdy, P. S., Akbari Asanjan, A., Hsu, K. lin, & Sorooshian, S. (2017). Intercomparison of FARSI-CDR and TRMM-3B42V7 precipitation estimates at monthly and daily time scales. Atmospheric Research.
Lashkari, A., Banayan, M., Koochaki, A., & Alizade, A. (2016). Investigation of the feasibility of using the AgMERRA database for the production of incomplete and missing data in synoptic station data (Case study: Mashhad Plain). Water and Soil Journal, 1749–1758. (In Farsi)
Li, M., & Shao, Q. (2010). An improved statistical approach to merge satellite rainfall estimates and raingauge data. Journal of Hydrology, 385(1–4), 51–64.
Maggioni, V., Meyers, P. C., & Robinson, M. D. (2016). A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era. Journal of Hydrometeorology, 17(4), 1101–1117.
Mahrooghy, M., Anantharaj, V. G., Younan, N. H., Aanstoos, J., & Hsu, K.-L. (2012). On an Enhanced FARSI-CCS Algorithm for Precipitation Estimation. Journal of Atmospheric and Oceanic Technology, 29(7), 922–932.
Sharifi, E., Steinacker, R., & Saghafian, B. (2016). Assessment of GPM-IMERG and other precipitation products against gauge data under different topographic and climatic conditions in Iran: Preliminary results. Remote Sensing, 8(2), 135.
Tan, M. L., & Duan, Z. (2017). Assessment of GPM and TRMM precipitation products over Singapore. Remote Sensing, 9(7), 720.
Tan, M. L., & Santo, H. (2018). Comparison of GPM IMERG, TMPA 3B42 and FARSI-CDR satellite precipitation products over Malaysia. Atmospheric Research, 202, 63–76.
Tan, M. L., Ibrahim, A. L., Duan, Z., Cracknell, A. P., & Chaplot, V. (2015). Evaluation of six high-resolution satellite and ground-based precipitation products over Malaysia. Remote Sensing, 7(2), 1504–1528.
Tao, H., Fischer, T., Zeng, Y., & Fraedrich, K. (2016). Evaluation of TRMM 3B43 precipitation data for drought monitoring in Jiangsu Province, China. Water (Switzerland), 8(6), 221.
Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., & De Roo, A. (2013). Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Journal of Hydrology, 499, 324–338.
Xie, P., & Xiong, A. Y. (2011). A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses. Journal of Geophysical Research Atmospheres, 116(21).
Xu, R., Tian, F., Yang, L., Hu, H., Lu, H., & Hou, A. (2017). Ground validation of GPM IMERG and trmm 3B42V7 rainfall products over Southern Tibetan plateau based on a high-density rain gauge network. Journal of Geophysical Research, 122(2), 910–924.
Yuan, F., Zhang, L., Wah Win, K. W., Ren, L., Zhao, C., Zhu, Y., et al. (2017). Assessment of GPM and TRMM multi-satellite precipitation products in streamflow simulations in a data sparse mountainous watershed in Myanmar. Remote Sensing, 9(3), 302.
Zhong, R., Chen, X., Lai, C., Wang, Z., Lian, Y., Yu, H., & Wu, X. (2018). Drought monitoring utility of satellite-based precipitation products across mainland China. Journal of Hydrology.