Evaluation of drought characteristics based on combined global precipitation and runoff products datasets across Iran's sub basins

Document Type : Research Paper


1 Department of Water Science and Engineering, Arak University, Arak, Iran

2 water resources-Arak university


Recently, with the advancement of technology, extensive facilities have been provided for monitoring climate data with different resolution. Therefore, the aim of this research is to investigate the combination of precipitation databases and simultaneous monitoring of meteorological and hydrological droughts on a monthly time scale using the Combined Drought Index (CDI) at sub basins of Iran. To this end, observational data (100 synoptic stations on a daily scale) and global precipitation and runoff databases including ERA5, MRRRA2, GRUN, GLDAS and TERRA (with different spatial resolution on a monthly scale) were collected and extracted during the period of 1987-2019. Then, based on the aridity index, the climatic classification of the stations and sub basins has been done and the entropy weight method (EW) was employed to combine databases and indices. The accuracy of the datasets was assessed based on Kling Gupta (KGE) and Standardized Mean Square Error (NRMSE) metrics. The results showed that the combination of precipitation databases in the Hyper-arid, Arid, Semi-arid and Humid climates reduced the error index by 32, 10, 24 and 26%, respectively, compared to individual databases. In the 3-month scale, the sub basin of Sefidroud, Lake Namak, and Talesh, and in the 12-month scale, the sub basin of Lake Urmia, Lake Namak, Sefid Rud, Hamon Jazmourian, Hamon Mashkel, South Baluchistan, and parts of Aras River have faced with drought with more severity, duration, and peak values of drought. It should be said that, on average, in both scales, the middle part of the country has less drought severity and duration. The extent of drought in the river basins of Hyper-arid, Arid and Semi-arid climates in the scale of 3 and 12 months on average is 45 and 53%, and 70 and 40% in Humid climates. Based on these findings, it can be concluded that the combining precipitation databases enhances the accuracy compared to using global single databases. Additionally, drought in the humid climate shows higher intensity, duration, and extent in the short-term scale compared to the long-term scale.


Main Subjects

Evaluation of drought characteristics based on combined global precipitation and runoff products datasets across Iran's sub basins



Due to the high cost of installation and maintaining gauges, specific locations worldwide may have limited or no weather stations, and the available records might be incomplete or cover shorter time periods. To address the data scarcity in developing regions, various institutions have developed alternative sources of global gridded datasets. However, selecting an appropriate database and index for drought monitoring poses challenges and underscores the significance of combining databases and indices. The objective of this research is to assess global products, their composite nature, and their effectiveness in monitoring drought characteristics such as severity, duration, peak, and extent, utilizing the compound drought index.

Material and Methods

For this purpose, the daily meteorological data were collected from 100 synoptic stations around the Iran during 1987-2019. Thiessen polygons method was used to extend the monthly time series of precipitation and potential evapotranspiration in synoptic stations to sub basin scale (to classify them based on Aridity index). Then, the five products including ERA5، MRRRA2، GRUN, GLDAS، TERRA with 0.5×0.5 spatial resolution were used to extract monthly precipitation and runoff. Then, for each pixel, precipitation (four products) and runoff (four products) datasets were composed. In the other words, one data as representative of precipitation and runoff was obtained that the non-parametric standardized precipitation and runoff index were calculated based on them. Then, the composite drought index (CDI) was developed and characterized drought at the short and long time scale (3 and 12-month scale). The Entropy weight method was used to compose datasets and indices. To extract drought characteristics, theory run was used and the drought extent was obtained based on the number of pixel located in each sub basin. To assess the performance of products, across Iran's sub basins on monthly scale, Kling-Gupta efficiency (KGE) and Normalized Root Mean Square Error (NRMSE), of each product were calculated at the sub basin scale.


The results indicate that the accuracy of each dataset varied over years and climates. However, both ERA5 and TERRA datasets exhibited high performance in all climates. Furthermore, combining multiple datasets demonstrated improved performance across all climates, particularly in the Hyper-arid climate. As the time scale increased, drought characteristics such as severity and duration  increased, too. In the Hyper-arid, Arid, and Semi-arid climates, the severity and duration were approximately 1.7, while in the humid climate, it was around 0.8 (long-term to short-term timescale). The peak of drought did not show significant changes. Mild and extreme drought levels accounted for approximately 14% of drought occurrences during the study period across all climates. The variation in drought extent indicated an average increase in the Hyper-arid, Arid, and Semi-arid climates (from 45% to 53%) with an increase in the time scale.


In summary, this study presented an evaluation of both individual and combined precipitation datasets. The research findings demonstrate the applicability of utilizing precipitation data from appropriate global products, taking into account temporal and spatial considerations, and characterizing drought using a composite drought index. Based on the results of this study, it is recommended to utilize these datasets, their combination, and the composite index for drought monitoring purposes.


Beersma, J. J., & Buishand, T. A. (2004). Joint probability of precipitation and discharge deficits in the Netherlands. Water Resources Research, 40(12),1-12.
Eghdami, M., & Barros, A.P. (2019). Extreme orographic rainfall in the eastern Andes tied to cold air intrusions. Frontiers in Environmental Science, 7, 101.
Erfani, A., Babaeian, I., & Entezari, A. (2020). ERA-Interim. Journal of Climate Research, 1398(38), 77-92.
Fahimirad, Z., & Shahkarami, N. (2021). The Impact of Climate Change on Hydro-Meteorological Droughts Using Copula Functions. Water Resources Management, 35(12), 3969-3993.
Farahmand, A., & AghaKouchak, A. (2015). A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76, 140-145.
Faridzad, M., Yang, T., Hsu, K., Sorooshian, S., & Xiao, C. (2018). Rainfall frequency analysis for ungauged regions using remotely sensed precipitation information. Journal of Hydrology, 563, 123–142.
Fooladi, M., Golmohammadi, M. H., Rahimi, I., Safavi, H. R., & Nikoo, M. R. (2023). Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran. Expert Systems with Applications, 221, 119788.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., ... & Zhao, B. (2017). The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14), 5419-5454.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., & Gudmundsson, L. (2019). GRUN:an observation-based global gridded runoff dataset from 1902 to 2014. Earth System Science Data, 11(4), 1655-1674.
Hao, Z., & AghaKouchak, A. (2013). Multivariate standardized drought index: a parametric multi-index model. Advances in Water Resources, 57, 12-18.
Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., ... & Zuo, H. (2018). Operational global reanalysis: progress, future directions and synergies with NWP. European Centre for Medium Range Weather Forecasts. Reading, UK.
Hoseeni, Z. S., Moghaddasi, M., & Paimozd, S. (2022). Accuracy assessment of ECMWF datasets in prediction of climate data and drought monitoring of Garechai basin of Markazi Province. Iranian Journal of Soil and Water Research, 53(4), 715-732 (In persion).
Hosseini, Z. S., Moghaddasi, M., & Paimozd, S. (2023). Simultaneous Monitoring of Different Drought Types Using Linear and Nonlinear Combination Approaches. Water Resources Management, 37(3), 1125-1151.
Hosseini-Moghari, S.M., Araghinejad, S., & Ebrahimi, K. (2018). Spatio-temporal evaluation of global gridded precipitation datasets across Iran. Hydrological Sciences Journal ,63 (11), 1669–1688.
Kim, H., Watanabe, S., Chang, E. C., Yoshimura, K., Hirabayashi, J., Famiglietti, J., & Oki, T. (2017). Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1). Data Integration and Analysis System (DIAS).
Li, Q., Li, P., Li, H. & Yu, M. (2015). Drought assessment using a multivariate drought index in the Luanhe River basin of Northern China. Stochastic Environmental Research and Risk Assessment, 29(6), 1509-1520.
McKee, T.B., Doesken, N.J. & Kleist, J. (1993, January). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, California, USA. 17:179-183.
Mohammadi Ghaleni, M., & Sharafi, S. (2022). Evaluation of CRU TS4. 05 and ERA5 Datasets Accuracy to Precipitation, Temperature and Potential Evapotranspiration in Different Climates across Iran. Iranian Journal of Irrigation & Drainage, 16(5), 879-890 (In persion).
Morid, S., Smakhtin, V. and Moghaddasi, M. (2006). Comparison of Seven Meteorological Indices for Drought in Iran. International Journal of Climatology, 26, 971-985.
Motevali Bashi Naeini, E., Akhoond-Ali. A.M., Radmanesh, F., Koupai, J.A, & Soltaninia, S. (2021). Comparison of the Calculated Drought Return Periods Using Tri-variate and Bivariate Copula Functions under Climate Change Condition. Water Resources Management, 35(14), 4855-4875.
Naderi, K., Moghaddasi, M., & Shokri, A. (2022). Drought Occurrence Probability Analysis Using Multivariate Standardized Drought Index and Copula Function Under Climate Change. Water Resources Management, 36(8), 2865-2888.
Nazeri Tahroudi, M., Ramezani, Y., De Michele, C. & Mirabbasi, R. (2020). A new method for joint frequency analysis of modified precipitation anomaly percentage and streamflow drought index based on the conditional density of copula functions. Water Resources Management, 34(13), 4217-4231.
Palmer, W.C. (1965). Meteorological Drought. Department of Commerce, Weather Bureau., Washington, DC, 58 pp.
Pom´eon, T., Jackisch, D., & Diekkrüger, B. (2017). Evaluating the performance of remotely sensed and reanalyzed precipitation data over west Africa using HBV light. Journal of Hydrology, 547, 222–235.
Rienecker, M.M., Suarez, M.J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M.G., Schubert, S.D., Takacs, L., & Kim, G.-K. (2011). MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24, 3624–3648.
Rodell, M., Houser, P. R., Jambor, U. E. A., Gottschalck, J., Mitchell, K., Meng, C. J., ... & Toll, D. (2004). The global land data assimilation system. Bulletin of the American Meteorological Society, 85(3), 381-394.
Saemian, P., Tourian, M. J., AghaKouchak, A., Madani, K., & Sneeuw, N. (2022). How much water did Iran lose over the last two decades?. Journal of Hydrology: Regional Studies, 41, 101095.
Salman, S.A., Shahid, S., Ismail, T., Al-Abadi, A.M., Wang, X.j., & Chung, E.S. (2019). Selection of gridded precipitation data for Iraq using compromise programming. Measurement ,132, 87–98.
Shukla, S., & Wood, A. W. (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2).
Su, F., Hong, Y., & Lettenmaier, D.P. (2008). Evaluation of TRMM multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata basin. Journal of Hydrometeorology, 9 (4), 622–640.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., & Hsu, K.L. (2018). A review of global precipitation data sets: Data sources, estimation, and intercomparisons. Reviews of Geophysics, 56 (1), 79–107.
Tsakiris, G., & Vangelis, H. J. E. W. (2005). Establishing a drought index incorporating evapotranspiration. European Water, 9(10), 3-11.
 Tsiros, I. X., Nastos, P., Proutsos, N. D., & Tsaousidis, A. (2020). Variability of the aridity index and related drought parameters in Greece using climatological data over the last century (1900–1997). Atmospheric Research, 240, 104914.
United Nations Educational, Scientific and Cultural Organization. (1979). Map of the world distribution of arid regions: map at scale 1:25,000,000 with explanatory note, MAB Technical Notes 7. UNESCO, Paris.
Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of Climate, 23(7), 1696-1718.
Won, J., Choi, J., Lee, O. & Kim, S. (2020). Copula-based Joint Drought Index using SPI and EDDI and its application to climate change. Science of the Total Environment, 744, 140701.
Xu, H., Xu, C.Y., Chen, S., & Chen, H. (2016). Similarity and difference of global reanalysis datasets (WFD and APHRODITE) in driving lumped and distributed hydrological models in a humid region of China. Journal of Hydrology, 542, 343–356.
Yang, J., Chang, J., Wang, Y., Li, Y., Hu, H., Chen, Y., ... & Yao, J. (2018). Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index. Journal of Hydrology, 557, 651-667.
Yevjevich, V. M. (1967). Objective approach to definitions and investigations of continental hydrologic droughts, An (Doctoral dissertation, Colorado State University. Libraries).
Zhu, G. Y., He, L. J., Ju, X. W., & Zhang, W. B. (2018). A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEA. European Journal of Operational Research, 265(3), 813-828.