Drought Monitoring and Trend Analysis by Using Rainfall Products ERA5, CHIRPS, and PERSIANN-CDR Rainfall Products in Iran
Subject Areas : Drought in meteorology and agricultureMilad Nouri 1 , Shadman Veysi 2
1 - Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
2 - Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
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.
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