Meteorological drought
Based on a 1-month SPI, meteorological drought was examined (SPI-1). Since SPI-1 and rainfall have a strong connection, using it to correlate the meteorological drought makes more sense. SPI-1 was calculated between 1981 and 2023. But the conditions of the drought that happened in 2019, 2020, 2021, 2022 and 2023 are the main focus of this study. SPI when calculated at 1- month tine scale (Fig 2a) shows no negative value on precipitation for the whole Tamil Nadu. The lowest value of SPI is 0.40 and highest value is 18.91. The highest is found in Nilgiris district and part of Coimbatore, Tirupur and Erode districts. The SPI value of all the districts of Tamil Nadu at different time scales were presented in Table 3 that the SPI in 1-month time scale plays very significant role in any vulnerability studies for accurate prediction of any events. Besides SPI was also calculated on 3, 6, 9, 12 months’ time scale. The SPI value of SP1 3 ranges from -0.79 to 2.00, SPI 6 ranges from -1.04 to 1.77, SP1 9 ranges from 0.10 to -2.11 and SPI 12 ranges from -5.87 to 0.29 as shown in Fig 2 a, b, c, d and e.
Enhanced vegetation index (EVI)
The EVI is a vegetation index that is obtained from remote sensing data, usually from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images. It assesses the density and overall health of the vegetation cover. EVI is a helpful indication for the impact of drought on ecosystems because decreased vegetation density may be a sign of stress brought on by inadequate supply of water. EVI is more robust in regions with aerosols, clouds, or other atmospheric difficulties since it is made to reduce the effects of atmospheric influences. In regions with high biomass, where NDVI may saturate, EVI is frequently seen to be more appropriate. The value of EVI ranges from -1 to +1 (Jensen, 2016). Where -1 indicates non -vegetated or sparsely vegetated area, 0 indicates areas with very sparse or stressed vegetation cover and +1 indicates the area with dense and healthy vegetation cover. Here in this research EVI for Kharif season is considered for five years
i.e.) from 2019 to 2023 (Table 4) and it is correlated with the SPI at various time scales. In this study it is found that in all the years the EVI was high in western Ghats area throughout Tamil Nadu in all the five years
kharif season (Fig 3 a,b,c,d and e). To reduce the perplexing impact of soil reflectance upon vegetation signal, especially in areas with low plant cover, EVI integrates a soil correction factor
(Huete et al., 1994). Effectively capturing changes in vegetation condition and biomass depends on this. Time series analysis and dataset comparisons are made easier by EVI, which partially corrects for variations in spectral response across different satellite sensors (
Liu and Huete, 1995). This is especially helpful for long-term monitoring projects that use information from several satellites.
Correlation between SPI and EVI
A more thorough understanding of drought conditions can be obtained by relating SPI and EVI because they account for both vegetation and meteorological responses to moisture availability. The correlation may operate as follows:
Negative SPI and decreased EVI
A reduction in EVI, which indicates stressed or decreased vegetation as a result of water limitations, could be associated with a negative SPI, which implies lower-than-average precipitation.
Positive SPI and increased EVI
An increase in EVI, which indicates better vegetative health because of adequate water availability, could be associated with a positive SPI, which implies higher-than-average precipitation.
The correlation between agricultural drought and weather conditions
Droughts caused by weather patterns and agricultural practices typically. It exists because rainfall gradually decreases without directly reducing the soil’s water content. As shown in Fig 4, The correlation between SPI1 and EVI for the year 2020 is with 0.57 with 99.9% level of significance in other words we can say that this has a strong correlation coefficient also EVI for the year 2019 have correlation coefficient value of 0.20 and other EVI of the year 2021, 2022, 2023 does not have correlation, from the values we can understand that the 1-month rainfall deficit have maximum effect on agricultural drought for these kharif season.
When SPI at 3 months’ time interval is correlated with the EVI of kharif seasons of the above said years
i.e.) 2019- 2023, it is found that there is correlation of SPI - 3 with EVI 2019. EVI 2022 and 2023 with 0.21, 0.16 and 0.26 respectively. This indicates that the SPI with the 3-month time interval does not have much influence on much on vegetation at a place. SPI with 6-month interval is well correlated with EVI - 2022 with correlation coefficient of 0.39 with 0.1 % level of significance, in other words with 99% level of significance EVI 2022 is well correlated with SPI -6. EVI of kharif season 2019 is correlated with coefficient value of 0.28 with 5% level of significance.
Also, the relation between the EVI with the precipitation with 9 months interval was also tested. It is found that this 9-month interval have effect on the year 2019, 2022 and 2023 with the correlation interval of 0.30 with 5% level of significance, 0.35 with 5 % level of significance for the year 2022 and 2023. With SPI of 12-month time scale all the EVI (2019 to 2023) of kharif season was negatively correlated with 99.99 % level of significance with -0.54 (2019), -0.20 (2020), -0.62 (2021), -0.74 (2022) and -0.57 (2023). This indicates that the 12 months interval of precipitation have more stress on vegetation and it can negatively impact the agriculture activities leading to crop failures.
Reason for positive and negative correlation
Positive correlation
The most logical scenario is this particular one. In general, healthier vegetation (higher EVI) is correlated with more precipitation (positive SPI). This is due to the fact that sufficient moisture is essential for plant growth and verdancy. So, there will probably be a positive association between locations with wetter periods and an increase in EVI.
Negative correlation
There are circumstances in which an excessive amount of precipitation might harm vegetation and cause a negative association. Here are a few instances:
Flooding
Extremely high SPI levels may be a sign of flooding, which lowers EVI and damages plants.
Nutrient leaching
Even when there is plenty of moisture, heavy rain can remove vital nutrients from the soil, preventing plant growth and reducing EVI.
Plant community changes
In certain habitats, more precipitation may be advantageous to water-loving plants with lower EVI values than the dominant species during dry spells. There may be a bad association as a result of this change in plant communities.