Drought Assessment Using Gridded Data Sources in Data-Poor Areas with Different Aridity Conditions

Milad Nouri ()
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Milad Nouri: Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 11, No 7, 4327-4343

Abstract: Abstract This study evaluated the accuracy of alternative satellite and reanalysis datasets in estimating the Standardized Precipitation Evapotranspiration Index (SPEI). Eight alternate datasets consisting of the gridded precipitation data acquired from IMERG, TRMM, ERA5-Land (ERA5L), and GLDAS and the reference evapotranspiration (ETo) computed by using the ERA5L and GLDAS reanalysis data were exploited. The results revealed that the combination of IMERG precipitation and ERA5L-estimated ETo (IME) outperformed the other alternative data sources in simulating drought severity. The IME also accurately simulated dry months and the length of the most extended dry spells for the majority of the studied regions. This highlights the potential benefits of integrating data sources, which appear to yield better results compared to using single-source datasets. The SPEI estimated by the alternative datasets showed poor agreement with the SPEI calculated based on meteorological records in the hyper-arid/arid areas. However, better results were achieved for the semi-arid and sub-humid/humid conditions. Moreover, the long-term droughts were simulated with higher accuracy relative to short- and mid-term dry epochs. The errors in ETo estimates contribute more significantly to the errors in SPEI for the hyper-arid/arid and semi-arid regions. However, the errors in precipitation products had a greater influence on the errors in SPEI estimates in the sub-humid/humid environments. These findings suggest that improving the quality of reanalysis data used for modeling ETo is likely to considerably enhance the accuracy of SPEI estimates in the semi-arid and arid/hyper-arid environments. Overall, this study provides a reliable guide for drought identification under data limitation.

Keywords: Data integration; Drylands; Satellite retrievals; Raster datasets; Reanalysis (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11269-023-03555-4

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