Drought monitoring over the Indian state of Tamil Nadu using multitudinous standardized precipitation evapotranspiration index
DOI:
https://doi.org/10.14719/pst.4653Keywords:
SPEI, SPI, agricultural drought, evapotranspirationAbstract
Droughts significantly impact agriculture and water resources in Tamil Nadu, India, making precise monitoring essential for effective response and mitigation. Traditional drought indices, like the Standardized Precipitation Index (SPI), rely solely on precipitation data and may overlook other critical factors. The Standardized Precipitation Evapotranspiration Index (SPEI) addresses this by incorporating temperature and precipitation data, offering a more comprehensive assessment of drought conditions, especially under changing climate scenarios. This study utilized daily temperature and precipitation records from NASA's Prediction of Worldwide Energy Resources (POWER) project, covering 1991 to 2024. Potential evapotranspiration (PET) was calculated using the Thornthwaite method, and the water balance was derived by aggregating monthly precipitation and PET data, which was then fitted to a log-logistic probability distribution (1). SPEI values were standardized to create a drought severity index, validated through comparisons with SPI and the Enhanced Vegetation Index (EVI) from MODIS data. Temporal analysis revealed significant year-to-year variability in drought conditions, with 2021 experiencing the most severe drought. The extreme droughts of 2019, 2020 and 2021 highlighted the need for adaptive drought management strategies due to their substantial impacts on agriculture and water resources. Spatial analysis identified the northwestern and southern regions of Tamil Nadu as more vulnerable to drought. Strong correlations between SPEI, SPI and EVI validated SPEI's effectiveness as a drought monitoring tool. The study emphasizes the importance of advanced indices like SPEI for precise drought monitoring and recommends integrating SPEI with real-time data and remote sensing technologies for improved drought prediction.
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Copyright (c) 2024 Janarth S, Jagadeeswaran R, Pazhanivelan S, Balaji Kannan, Ragunath K P, Sathiyamoorthy N K
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