Meteorological drought analysis using Standardized Precipitation Index over Luni River Basin in Rajasthan, India
The study aimed to monitor the spatial extent and severity of drought events in the Luni River Basin using Standardized Precipitation Index (SPI), and hence, the long-term monthly precipitation records of 39 rain gauge stations (1973–2016) were used in the study. Both the long-term (24, 12, and 9 mo...
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Veröffentlicht in: | SN applied sciences 2020-09, Vol.2 (9), p.1530, Article 1530 |
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Sprache: | eng |
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Zusammenfassung: | The study aimed to monitor the spatial extent and severity of drought events in the Luni River Basin using Standardized Precipitation Index (SPI), and hence, the long-term monthly precipitation records of 39 rain gauge stations (1973–2016) were used in the study. Both the long-term (24, 12, and 9 months) and short-term (6, 3, and 1 month) SPI were calculated to recognize the drought events and the percentage of the area covered by the severe drought conditions. The nonparametric Mann–Kendall test was performed for trend analysis in drought events to investigate the consistency of drought events. The frequency results of drought events revealed that Jalore station was the highest drought frequency station, while the lowest drought frequency was observed in Vijaynagar station. The annual SPI result showed that the following years witnessed major drought events: 1981, 1984, 1985, 1988, 1989, 1991, 1993, 1999, 2000, 2004, 2005, and 2008. Besides, the results of the Mann–Kendall test showed that a substantial portion of the eastern basin experienced an increase in the intensity of drought, while the western basin experienced a decrease in the severity of the drought. The comprehensive analysis is indicative of climate change, and there is a possibility that such droughts would become more common in the future in the Luni River Basin. The results of this study would help planners to develop sound policy on water resources and also assist in forecasting systems to provide advance warnings. |
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ISSN: | 2523-3963 2523-3971 |
DOI: | 10.1007/s42452-020-03321-w |