Drought Monitoring and Trend Analysis by Using Rainfall Products ERA5, CHIRPS, and PERSIANN-CDR Rainfall Products in Iran

Subject Areas : Drought in meteorology and agriculture

Milad Nouri ¹ (Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.)
Shadman Veysi ² (Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.)

Received: 2023-06-07 Accepted : 2023-08-20 Published : 2023-06-22

Keywords: data-scarce areas, Alternate datasets, climatic variabilities, trend detection,

Abstract :

Background and Aim: The scarcity of data poses a significant challenge for drought studies. Alternative datasets are created to supplement existing data sources. Despite the inherent uncertainties associated with alternative datasets, the gridded datasets provide long-term, spatially-continuous weather data, making them suitable for drought assessment under climate changes. Several studies have been conducted to characterize dry spells across Iran using both point datasets and gridded products. However, most of these studies have focused primarily on identifying errors in absolute values of drought indices and drought detection.Method: In the present study, we evaluated the performance of three gridded datasets in characterizing droughts across different climatic conditions in Iran. The datasets under consideration were the fifth generation of the European Centre for Medium-Range Weather Forecasts (ERA5), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). The Standardized Precipitation Index in 3-, 6-, and 12-month scales (i.e., SPI3, SPI6, and SPI12) was applied. The precipitation observations were obtained from the Iran Meteorological Organization (IRIMO) for 35 sites spanning the period from 1988 to 2017. The با توجهstudy sites covered a range of climatic conditions, including hyper-arid, arid, semi-arid, and humid/semi-humid regions. To analyze the long-term trend in precipitation, two statistical methods, namely, the Sen’s slope estimator (SSE) and the Mann-Kendall non-parametric test (MKZ) were employed.Results: Results revealed that the gridded datasets performed poorly in detecting dry months and estimating SPI values in humid/semi-humid regions. However, ERA5 estimated SPI3, SPI6, and SPI12 with sufficient accuracy in more than the two-third of arid and semi-arid areas. Moreover, ERA5 detected dry months accurately based on SPI12 in the majority of arid and semi-arid cases. Specifically, ERA5 accurately detected severe and long-lasting dry events that occurred in drylands during the periods of 1998-2001 and 2007-2009. These intense dry epochs detected by ERA5 have had significant negative impacts on the agricultural sectors in the Middle East, highlighting the critical need for accurate drought monitoring and management. However, CHIRPS and PERSIANN-CDR performed poorly in estimating SPI and detecting dry months in arid and semi-arid regions. Furthermore, ERA5 provided reliable estimates of the significance and direction of the slope of SPI3, SPI6, and SPI12 in more than half of arid and semi-arid regions, while CHIRPS and PERSIANN-CDR yielded inaccurate estimates in most areas. However, in some cases where SPI values and drought months were not accurately modeled, the significance and direction of slopes were estimated accurately. These findings suggest that while inaccurate SPI estimates from gridded datasets may indicate limitations in their skill to characterize drought; they do not necessarily imply their unsuitability for trend analysis and climate change assessments.Conclusion: The results suggest that ERA5 outperformed the other alternate datasets evaluated in terms of estimating SPI values, detecting drought events, and estimating the significance and slope of SPI in drylands. As such, ERA5 precipitation products may be suitable for drought characterization and monitoring under climate change in drought-prone arid and semi-arid regions of Iran.

References:

Abarghouei, H. B., Zarch, M. A. A., Dastorani, M. T., Kousari, M. R., & Zarch, M. S. (2011). The survey of climatic drought trend in Iran. Stochastic environmental research and risk assessment, 25(6), 851-863.

Abebe, S. A., Qin, T., Yan, D., Gelaw, E. B., Workneh, H. T., Kun, W., Liu, S., & Dong, B. (2020). Spatial and Temporal Evaluation of the Latest High-Resolution Precipitation Products over the Upper Blue Nile River Basin, Ethiopia. Water, 12, 3072. https://doi.org/10.3390/w12113072

Abramowitz, M., & Stegun, I. A. (1964). Handbook of mathematical functions: with formulas, graphs, and mathematical tables. Courier Corporation.

Bannayan, M., Sanjani, S., Alizadeh, A., Lotfabadi, S., & Mohamadian, A. (2010). Association between climate indices, aridity index, and rainfed crop yield in northeast of Iran. Field Crops Research, 118(2), 105-114.

Bazrafshan, O., Zamani, H., & Shekari, M. (2019). A copula‐based index for drought analysis in arid and semi‐arid regions of Iran. Natural Resource Modeling, 33, e12237. https://doi.org/10.1111/nrm.12237

Bouaziz, M., Medhioub, E., & Csaplovisc, E. (2021). A machine learning model for drought tracking and forecasting using remote precipitation data and a standardized precipitation index from arid regions. Journal of Arid Environments, 189, 104478. https://doi.org/10.1016/j.jaridenv.2021.104478

Dai, A. (2011). Drought under global warming: a review. Wiley Interdisciplinary Reviews: Climate Change, 2(1), 45-65. https://doi.org/10.1002/wcc.81

Darand, M., Amanollahi, J., & Zandkarimi, S. (2017). Evaluation of the performance of TRMM Multi-satellite Precipitation Analysis (TMPA) estimation over Iran. Atmospheric Research, 190, 121-127. https://doi.org/10.1016/j.atmosres.2017.02.011

Darand, M., & Pazhoh, F. (2022). Spatiotemporal changes in precipitation concentration over Iran during 1962–2019. Climatic Change, 173, 25. https://doi.org/10.1007/s10584-022-03421-z

Dashtpagerdi, M. M., Kousari, M. R., Vagharfard, H., Ghonchepour, D., Hosseini, M. E., & Ahani, H. (2015). An investigation of drought magnitude trend during 1975–2005 in arid and semi-arid regions of Iran. Environmental Earth Sciences, 73(3), 1231-1244. https://doi.org/10.1007/s12665-014-3477-1

Dee, D. P., Balmaseda, M., Balsamo, G., Engelen, R., Simmons, A. J., & Thépaut, J. N. (2014). Toward a Consistent Reanalysis of the Climate System. Bulletin of the American Meteorological Society, 95(8), 1235-1248. https://doi.org/10.1175/bams-d-13-00043.1

Edwards, D. C., & McKee, T. B. (1997). Characteristics of 20th Century drought in the United States at multiple time scales, Atmospheric Science Paper No. 634, Climatology Report No. 97-2. C. Department of Atmospheric Science Colorado State University Fort Collins, USA.

Funk, C., Peterson, P., Landsfeld, M., Davenport, F., Becker, A., Schneider, U., Pedreros, D., McNally, A., Arsenault, K., Harrison, L., & Shukla, S. (2020). Algorithm and Data Improvements for Version 2.1 of the Climate Hazards Center’s InfraRed Precipitation with Stations Data Set. In V. Levizzani, C. Kidd, D. B. Kirschbaum, C. D. Kummerow, K. Nakamura, & F. J. Turk (Eds.), Satellite Precipitation Measurement: Volume 1 (pp. 409-427). Springer International Publishing. https://doi.org/10.1007/978-3-030-24568-9-23.

Gao, F., Zhang, Y., Ren, X., Yao, Y., Hao, Z., & Cai, W. (2018). Evaluation of CHIRPS and its application for drought monitoring over the Haihe River Basin, China. Natural Hazards, 92(1), 155-172. https://doi.org/10.1007/s11069-018-3196-0

Ghozat, A., Sharafati, A., & Hosseini, S. A. (2022). Satellite-based monitoring of meteorological drought over different regions of Iran: application of the CHIRPS precipitation product. Environmental Science and Pollution Research, 29, 36115-36132. https://doi.org/10.1007/s11356-022-18773-3

Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., & Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. Science, 327(5967), 812-818.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., & Thépaut, J. N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999-2049. https://doi.org/10.1002/qj.3803

Kam, J., Min, S.-K., Park, C.-K., Kim, B.-H., & Kug, J.-S. (2022). Human Contribution to 2020/21-like Persistent Iran Meteorological Droughts. Bulletin of the American Meteorological Society, 103(12), E2930-E2936. https://doi.org/10.1175/bams-d-22-0149.1

Karimi, M. and Heidari, S. (2023). Variability and trend of changes in the severity-area of drought and wet in Iran. Journal of Natural Environmental Hazards 12, 129-150. [in Persian]

Kazemzadeh, M., Noori, Z., Alipour, H., Jamali, S., Akbari, J., Ghorbanian, A., & Duan, Z. (2022). Detecting drought events over Iran during 1983–2017 using satellite and ground-based precipitation observations. Atmospheric Research, 269, 106052. https://doi.org/https://doi.org/10.1016/j.atmosres.2022.106052

Keikhosravi‐Kiany, M. S., Masoodian, S. A., Balling, R. C., & Darand, M. (2021). Evaluation of Tropical Rainfall Measuring Mission, Integrated Multi‐satellite Retrievals for GPM, Climate Hazards Centre InfraRed Precipitation with Station data, and European Centre for Medium‐Range Weather Forecasts Reanalysis v5 data in estimating precipitation and capturing meteorological droughts over Iran. International Journal of Climatology, 42, 2039-2064. https://doi.org/10.1002/joc.7351

McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, Anaheim, California,17–22 January.

Miller, D. E., Wang, Z., Li, B., Harnos, D. S., & Ford, T. (2021). Skillful Subseasonal Prediction of United States Extreme Warm Days and Standardized Precipitation Index in Boreal Summer. Journal of Climate, 34, 5887-5898. https://doi.org/10.1175/jcli-d-20-0878.1

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885-900. https://doi.org/https://doi.org/10.13031/2013.23153

Mukherjee, S., Mishra, A., & Trenberth, K. E. (2018). Climate Change and Drought: a Perspective on Drought Indices. Current Climate Change Reports, 4(2), 145-163. https://doi.org/10.1007/s40641-018-0098-x

Nguyen, P., Ashouri, H., Ombadi, M., Hayatbini, N., Hsu, K.-L., & Sorooshian, S. (2020). PERSIANN-CDR for Hydrology and Hydro-climatic Applications. In V. Levizzani, C. Kidd, D. B. Kirschbaum, C. D. Kummerow, K. Nakamura, & F. J. Turk (Eds.), Satellite Precipitation Measurement: Volume 2 (pp. 993-1012). Springer International Publishing. https://doi.org/10.1007/978-3-030-35798-6_26

Nouri, M., & Homaee, M. (2018). On modeling reference crop evapotranspiration under lack of reliable data over Iran. Journal of Hydrology, 566, 705-718. https://doi.org/10.1016/j.jhydrol.2018.09.037

Nouri, M., & Homaee, M. (2020). Drought trend, frequency and extremity across a wide range of climates over Iran. Meteorological Applications, 27, e1899. https://doi.org/10.1002/met.1899

Nouri, M., & Homaee, M. (2021a). Contribution of soil moisture variations to high temperatures over different climatic regimes. Soil and Tillage Research, 213, 105115. https://doi.org/https://doi.org/10.1016/j.still.2021.105115

Nouri, M., & Homaee, M. (2021b). Spatiotemporal changes of snow metrics in mountainous data-scarce areas using reanalyses. Journal of Hydrology, 603, 126858. https://doi.org/10.1016/j.jhydrol.2021.126858

OCHA. (2000). United Nations technical mission on the drought situation in the Islamic republic of Iran (UN Mission Report, Issue).

Sadat Hoseeni, Z., 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, 715-732. [in Persian]

Sadeghi, A., Kamgar-Haghighi, A., Sepaskhah, A., Khalili, D., & Zand-Parsa, S. (2002). Regional classification for dryland agriculture in southern Iran. Journal of Arid Environments, 50(2), 333-341.

Salami, H., Shahnooshi, N., & Thomson, K. J. (2009). The economic impacts of drought on the economy of Iran: An integration of linear programming and macroeconometric modelling approaches. Ecological Economics, 68(4), 1032-1039. https://doi.org/10.1016/j.ecolecon.2008.12.003

Santos, C. A. G., Brasil Neto, R. M., Nascimento, T., Silva, R. M. D., Mishra, M., & Frade, T. G. (2021). Geospatial drought severity analysis based on PERSIANN-CDR-estimated rainfall data for Odisha state in India (1983-2018). Science of the Total Environment, 750, 141258.

Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. Journal of the American Statistical Association, 63(324), 1379-1389. https://doi.org/10.1080/01621459.1968.10480934

Sheffield, J., Wood, E. F., Pan, M., Beck, H., Coccia, G., Serrat‐Capdevila, A., & Verbist, K. (2018). Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions. Water Resources Research, 54(12), 9724-9758. https://doi.org/10.1029/2017wr022437

Tabari, H., Abghari, H., & Hosseinzadeh Talaee, P. (2012). Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran. Hydrological Processes, 26(22), 3351-3361. https://doi.org/10.1002/hyp.8460

Taghizadeh, E., Ahmadi-Givi, F., Brocca, L., & Sharifi, E. (2021). Evaluation of satellite/reanalysis precipitation products over Iran. International Journal of Remote Sensing, 42(9), 3474-3497. https://doi.org/10.1080/01431161.2021.1875508

Trigo, R. M., Gouveia, C. M., & Barriopedro, D. (2010). The intense 2007–2009 drought in the Fertile Crescent: Impacts and associated atmospheric circulation. Agricultural and Forest Meteorology, 150(9), 1245-1257.

UNEP. (1997). World atlas of desertification. Arnold, Hodder Headline, PLC.

Vicente‐Serrano, S. M., Domínguez‐Castro, F., Reig, F., Tomas‐Burguera, M., Peña‐Angulo, D., Latorre, B., Beguería, S., Rabanaque, I., Noguera, I., Lorenzo‐Lacruz, J., & El Kenawy, A. (2022). A global drought monitoring system and dataset based on ERA5 reanalysis: A focus on crop‐growing regions. Geoscience Data Journal, 00, 1-14. https://doi.org/10.1002/gdj3.178

Wang, J., Zhang, Q., Zhang, L., Wang, Y., Yue, P., Hu, Y., & Ye, P. (2022). The global pattern and development trends & directions on the drought monitoring research from 1983 to 2020 by using bibliometric analysis. Bulletin of the American Meteorological Society. Wright, B., & Cafiero, C. (2011). Grain reserves and food security in the Middle East and North Africa [journal article]. Food Security, 3(1), 61-76. https://doi.org/10.1007/s12571-010-0094-z

Xiong, W., Tang, G., Wang, T., Ma, Z., & Wan, W. (2022). Evaluation of IMERG and ERA5 Precipitation-Phase Partitioning on the Global Scale. Water, 14(7). https://doi.org/10.3390/w14071122

Yue, S. and Wang, C. (2004). The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series. Water Resources Management, 18, 201-218.