Climate indices and hydrological extremes: Deciphering the best fit model
The present work comprehensively reviews all the pertinent large-scale climate indices used to analyse the hydrological extremes in India; along with various non-linear models, which have utilized long-term past precipitation data, and global climate indices to produce forecasts at different tempora...
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| Vydáno v: | Environmental research Ročník 215; číslo Pt 2; s. 114301 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.12.2022
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| Témata: | |
| ISSN: | 0013-9351, 1096-0953, 1096-0953 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | The present work comprehensively reviews all the pertinent large-scale climate indices used to analyse the hydrological extremes in India; along with various non-linear models, which have utilized long-term past precipitation data, and global climate indices to produce forecasts at different temporal scales. We specifically enumerated various statistical operations that may provide better precision at modelling efficiency. Further, in the quest to discover the best-fit modelling technique for the Indian scenario, we compared various modelling techniques applied to decipher hydroclimatic tele-connections between extreme hydrological variables and the large-scale climate indices. Our analyses suggest that the global atmospheric phenomena have performed better than the traditional geospatial models pertaining to the accurate prediction of precipitation extremes for India. We also confirmed that the use of large-scale climate indices to predict the local scale hydrological dynamics had been steadily increasing owing to the advantage associated with it. We conclude that wavelet-based non-linear models are a better fit, and large-scale climate indices based hydrological extremes prediction is an essential requirement for deciphering the esoteric nature of the Indian monsoon. The present work aims to contribute towards efficient water resources management under the pre-text of Indian hydrological extremes, which will be crucial and critical day by day for boosting Indian rain-dependent agriculture, as well as water supply and security.
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•Climate indices-based prediction correlates better than linear ANN models.•Global atmospheric model performs better than the traditional geospatial models.•Non-linear wavelet models are better at explaining esoteric Indian monsoon system.•Integration of combination of indices enhances the modelling efficiency. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0013-9351 1096-0953 1096-0953 |
| DOI: | 10.1016/j.envres.2022.114301 |