Environment Pollution and Climate Change
Open Access

Drought, Precipitation, The Standardized Precipitation Index, Marathwada Region, Drought years

• This study is about evaluating the variation year wise of meteorological drought (variable taken is precipitation) in Marathwada Region, Maharashtra.

• SPI6 and SPI12 are the two-time series taken for comparison long term drought.

• R-programming software is used in this paper.

• Drought years have been considered in 60 years of data and compared in two time series mentioned above.

Climate change is a natural process, but human-caused increases in GHGs, which modify the climatic system, have triggered more sudden changes and influenced the recurrence of extreme (Drought, Flood etc.) climatic events. Extreme weather events, such as drought, are becoming more common as a result of climate change. Drought intensity is expected to worsen in several places of the world. Drought is notoriously difficult to identify and explain due to its insidious nature. Droughts are widely acknowledged as a natural environmental disaster that has drawn multidisciplinary attention from many sectors of science. Water shortage and demand have increased globally as a result of population growth and industrial expansion. Climate change, for example, has exacerbated the water scarcity (Vogt, 2017). The fourtype drought classification method is based on the nature of water scarcity. According to this classification, meteorological, hydrological, and agricultural droughts are all environmental droughts, and are characterised as periods of inadequate rainfall, groundwater and river flow, and soil moisture. Socioeconomic drought is the fourth type of drought, and it is caused by water resource systems failing to fulfil demand. Droughts are long-term events that affect huge areas and result in significant human deaths and economic losses. Droughts are natural occurrences that are certain to occur again. The gravity of their socioeconomic consequences has prompted extensive research.

Drought is caused by the persistence of atmospheric circulation patterns that fail to deliver needed precipitation, whether they are continuous or intermittent. These are the most common and dangerous in semi-arid environments. Their economic effects can be concentrated locally, but as they spread across the economy, they become diffused. Historically, drought buffering has been achieved through industry diversification, risk spreading, crop insurance, aid, and other methods. Sectors that aren’t as well-known, such as cattle farming and farm implement manufacture, might be severely impacted. Existing economic systems are capable of coping with common dry spells. The biggest havoc is caused by extreme events. The chronic deterioration and loss of topsoil in semi-arid areas is unabated and less evident [1].

SPI expresses actual rainfall as a standardized deviation from the rainfall probability distribution function, and as a result, the index has gained popularity in recent years as a potential drought indicator that allows for comparisons across area and time [2]. Long-term precipitation data is needed to estimate the probability distribution function (gamma distribution), which is then transformed to a normal distribution with a mean of zero and a standard deviation of one. The longer the reference period used to determine the distribution parameters, the more accurate the results will be. SPI values are reported in standard deviations, with positive values indicating more precipitation than the median and negative values indicating less precipitation. A station’s SPI should preferably be determined using at least 30 years of historical data. Only the monthly time scale should be used to calculate SPI. For an accurate SPI calculation and explanation with expertise, fitting relevant statistical distributions to time series rainfall data is crucial [2]. However, in many States, the lack of long-term Block level quality data is a restraint in computing SPI (Table 1).

Table 1: SPI values.

Study area and data used

The goal of this study was to look into the varying drought conditions across the sub-divisions of Marathwada, Maharashtra, India. Jalna, Aurangabad, Parbhani, Hingoli, Nanded, Latur, Osmanabad, and Beed are the eight Maharashtra districts that make up the Marathwada region. This region is found in the upper Godavari basin, spanning 17° 35’ North latitude to 20° 41’ North latitude and 74° 40’ East longitude to 78° 16’ East longitude. Maharashtra’s central region is known as Marathwada.

The Marathwada region is located in the semi-arid tropics, with the highest maximum temperature of 43°C in May and the lowest minimum temperature of 11°C in December. The south-west monsoon is the region’s primary source of precipitation, with an average annual rainfall of 890 mm and 48 wet days in Parbhani. The majority of the state is located in the Western Ghat rain shadow area, with annual average precipitation ranging between 600 and 700mm (Figure 1).

Figure 1: Index map of Marathwada Region.

Standardized Precipitation Index

The SPI is a precipitation-based drought measure created by McKee et al [3]. The adaptability and ease of use of SPI are its strengths. It can be calculated on a variety of time scales and intervals [4]. SPI is a probabilistic index calculated by fitting a gamma distribution to a long-term precipitation time record [5,6]. Using the equal probability transformation, this distribution is transformed into a normal distribution.

Positive SPI numbers indicate humid circumstances with more precipitation than the median, whilst negative SPI values indicate dry conditions with less precipitation. Using different periods, the SPI can also be used to represent several drought types [7]. Since drought conditions were analysed using the SPI with time periods up to six months, the SPI-1, SPI-3, and SPI-6 imply meteorological, agricultural, and hydrological droughts, respectively Abdulah [8]. Table 2

Table 2: Number of drought months for SPI 12.

shows drought severity levels in terms of SPI values. For any of the time scales, the values define the occurrence of a drought event at various severity levels. Drought happens when the SPI number remains negative for an extended period of time. Its positive value indicates that there is no drought [9].

Results and discussion

According to study results, there are six droughts i.e., 1973, 1985, 1986, 1992, 2016 which taken into consideration (Table 3-7). Two groups were divided of 60years i.e., 1961-1990 and 1991- 2020 called as group 1and group 2 respectively.

Table 3: Severity in 1973 for four seasons in Marathwada region (Drought Year 1973).

Table 4: Severity in 1985 for four seasons in Marathwada region (Drought Year 1985).

Table 5: Severity in 1986 for four seasons in Marathwada region (Drought Year 1986).

Table 6: Severity in 1992 for four seasons in Marathwada region (Drought Year 1992).

Table 7: Severity in 2016 for four seasons in Marathwada region (Drought Year 2016).

Table 8: Number of drought months for SPI 6.

Table 9: Severity in 1973 for four seasons in Marathwada region (Drought Year 1973).

Table 10: Severity in 1985 for four seasons in Marathwada region (Drought Year 1985).

Table 11: Severity in 1986 for four seasons in Marathwada region (Drought Year 1986).

Table 12: Severity in 1992 for four seasons in Marathwada region (Drought Year 1992).

Table 13: Severity in 2016 for four seasons in Marathwada region (Drought Year 2016).

In SPI 12, group 1 has more numbers of drought months than group 2 which can be clearly seen in table 5, 10 and Table 7. Group 1 has extremely dry months almost equal to moderately dry months whereas in group 2 moderately dry months are exponentially high than extremely dry months. That is why, drought years are more from group 1. Now in SPI 6, group 1 has more effect like in SPI12 but Extremely dry months are less than moderately dry months. Here severely dry months and extremely dry months are equivalent to each other. We can see gradual severity in SPI6 where in SPI 12 exponentially less and Dmore severity can be seen.

In SPI12, Drought year 1973, Jalna, Hingoli and Parbhani are most affected districts due to the drought whereas aurangabad is less affected drought district. In 1985, Osmanabad has most severity as aurangabad and beed follows. From all drought years 1986 has most horrible drought till now. All the districts were affected where Jalna tops it following all the remaining districts. 1992 is also considered as drought year but the severity was not at its peak but consistent effect was there. 2016 was a little bit severe than 1992 but still not as severe as 1986.

In SPI6, As the difference of drought years in SPI12 and SPI6 should be known, the same drought years are considered. There is consistent severity in all the districts. A little change can be seen but not significant change. In 1973, start of the year is extremely dry following start of the year of 1986. As all the districts are similarly severe difference cannot be known. Similarly in 1992 and 2016 are same severe as mentioned above (Table 8-13).

• To deal with drought suffering, one must first comprehend its features, such as its potential duration, intensity, severity, frequency, and aerial extent (spatial distribution). In assessing the hydrology of extreme events, the choice of time step is very crucial.

• The SPI is a method that can be used to identify dry spells in the Marathwada region. Drought is a recurring phenomenon in the studied region as a result of below-normal precipitation.

• During the 1980s, however, a series of severe droughts resulted in huge economic losses. In a case study based around 1986, one of the most severe drought years during the above dry period, the performance and monitoring capabilities of the index (SPI) were explored. Because different features of drought can be examined using multiple time frames, the SPI can be calculated with several time scales i.e., SPI12 and SPI6 giving the index flexibility for monitoring.

• According to study results, there are six droughts i.e., 1973, 1985, 1986, 1992, 2016 which taken into consideration. In Drought year 1973, Jalna, Hingoli and Parbhani are most affected district In 1985, Osmanabad has most severity as aurangabad and beed follows. From all drought years 1986 has most horrible drought till now. All the districts were affected where Jalna tops it following all the remaining districts.

• In SPI6, As the difference of drought years in SPI12 and SPI6 should be known, the same drought years are considered. There is consistent severity in all the districts.

Acknowledgements

The department of civil engineering of Maulana Azad National Institute of Technology, Bhopal for providing their support and encouragement.

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