A hybrid model to predict the hydrological drought in the Tarim River Basin based on CMIP6
Author:
Funder
the Shanghai Sailing Program
Publisher
Springer Science and Business Media LLC
Subject
Atmospheric Science
Link
https://link.springer.com/content/pdf/10.1007/s00382-023-06791-x.pdf
Reference53 articles.
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3. Awange JL, Mpelasoka F, Goncalvesm RM (2016) When every drop counts: analysis of droughts in Brazil for the 1901–2013 period. Sci Total Environ 566:1472–1488
4. Barker LJ, Hannaford J, Chiverton A, Svensson C (2016) From meteorological to hydrological drought using standardised indicators. Hydrol Earth Syst Sci 20:2483–2505
5. Belayneh A, Adamowski J, Khalil B, Quilty J (2016) Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction. Atmos Res 172:37–47
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