The extremes of long term distribution of rainfall in drought hit hot semiarid agro ecoregion of Yavatmal district, Maharashtra have significant implications on rainfed cotton productivity and socioeconomic activity of farming community. In the present approach, the occurrence of week wise dry spells during crop calendar were examined with the computation of standardized precipitation Index(SPI) covering the period of 1971 to 2005. The standard week wise rainfall analysis further showed that 27^th to 35^th week have mean of 43±35.9 to 65±93.2 mm with coefficient of variation of 83.6 to 141.4 per cent. The week wise SPI values for 35 years were categorized into 6 classes such as near normal dry periods occurring 63 % followed by 19.73% of normal wet periods, 5.71% of severely wet periods, 4.72% of moderately wet periods, 4.28% of extremely wet periods and 2.41% of moderately dry weeks. The occurrence of prolonged dry spells with mean of 38 to 41.36 % in 36th to 44 th week during critical cotton growth stages (branching and flowering stages) often coincides with reduction in lint yield. The study demonstrated that SPI considered as valuable tool to construct historical events of dry spells and its impact on soil water content in shrink-swell soils in drought hit areas.

Keywords: rainfall variability, dry spells, semi arid, Yavatmal, cotton growing

Drought indices such as Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are important tools to monitor and to assess drought. The SPI has an advantage over others because of rainfall data alone and its variable time scale to describe drought conditions.¹ and calculated for 3, 6, 12, 24, and 48^th month scales to reflect the temporal behaviour of the impact. SPI was applied to monitor the intensity and spatial extension of droughts at different time scales in South Africa.² and in Thessaly region of Greece.^3,4 carried out a study on temporal and spatial analyses of meteorological drought using SPI and hydrological drought based on theory of runs. Similarily the severity and spatial pattern of drought in Purilia district, West Bengal was studied using SPI by Moumita Palchaudhuri and Sujata Biswas.⁵ Many studies have been done on the impact of changing precipitation pattern in India under climate variability and its regional pattern.^6–9 Dry spell in Indian summer monsoon season causes to decrease in food production. Precipitation hardly follows a typical normal distribution for the whole duration of the year, therefore standardized precipitation index (SPI) has been computed to overcome this limitation for analyzing the wet and dry spell of precipitation (Shahid, 2009). There is scanty of long term rainfall analysis of rainfed cotton belt of Yavatmal district and in workingout the wet and dry spells during crop growing season using SPI. Hence, an attempt has been made using to analyze spatial pattern of meteorological drought using SPI and analysed the seasonal changes of soil water dynamics in cotton growing shrink-swell soils of Yavatmal district, Maharashtra.

Study area

Yavatmal district in the eastern vidharbha region of Maharashtra state (19⁰26’ to 20⁰42’N latitude and 77⁰18’ to 79⁰98’E longitude) consists of masses of Deccan trap basaltic plateaus with very steep sides and ridges or into flat-topped or pointed hills broken by broad valleys and partially surrounded by plains in north west and southern half with east–west ranges of hills. This district comes under Deccan Plateau, Hot Semi-Arid Eco-Region (6) of Western Maharashtra plateau and hot moist semi-arid eco - subregion (Figure 1) Mandal et al.¹⁰ The mean annual rainfall ranges from 1,125mm of rain in eastern parts of Wani to 889 mm in western parts of Darwah and 1099.5 mm in central portion of Yavatmal showing an increasing trend as one proceeds from West to East. Yavatmal district has per capita ecologically productive land (ha) of about 0.16 ha with an ecological deficit of 0.84ha (534%). The water balance diagram of Yavatmal, Figure 1 shows that the black soils in the region are saturated with water and kept close to field capacity from June to August. It was observed in field surveys of this region and also 35 years of climatic data that soil water recovers from short dry spells with the receival of intermittent rains and restore excess water in the top soil. Delayed sowing after 15^th July resulted in drastic reduction in productivity (up to 40–50 and needs to be irrigated at 50–70% depletion of available soil moisture. In black cotton soils ‘protective irrigation’ must be provided for every 20 days, if rains fail, especially during the boll development stage.

Figure 1 Water balance of Yavatmal district.

Computation of SPI (Standardized precipitation index)

The SPI is the number of standard deviations that the observed value would deviate from the long-term mean, for a normally distributed random variable. The SPI calculation for any location is based on the long-term precipitation record that is fitted to a probability distribution, which is then transformed into a normal distribution so that the mean SPI for the location and desired period is zero. The mathematical equation used to compute SPI as given under:-

Where, Π stands for SPI, X for rainfall, and σ for standard deviation of X with subscript i signifying the location and superscript c the time scales (monthly or seasonal). Over bar on X indicates mean climatology. Negative values of SPI indicate dry atmospheric condition or less rainfall while SPI less than −1 indicates drought. The event ends when the SPI becomes positive. Each drought event, therefore, has a duration defined by its beginning and end, and intensity for each month that the event continues. The positive sum of the SPI for all the months within a drought event can be termed the drought’s “magnitude”as 2.0+extremely wet, 1.5-1.99 –very wet, 1.0-1.49 –moderately wet, -0.99 to +0.99 near normal, -1.0 - -1.49 moderately dry , -1.5 - -1.99 severely dry and>-2.0 extremely dry Drought is indicated by SPI values of −1 or below.¹¹

Rainfall analysis

The thirty five years of rainfall data during the period of 1971 to 2005 was used to calculate monthly mean and standard deviations of rainfall. The data shows that August and July receives peak rainfall (250 to 320mm) with maximum deviation from mean. The rainfall begins in June and retreats in second week of October. The mean rainfall during September and October is 50 to 200 mm with maximum variations from the mean. Tehsil wise average rainy days and average rainfall (mm) is recored as mean rainfall of 1180 mm with 62 average rainy days in Yavatmal (Northern side) to minimum of 587 mm of rainfall in Ner with 47 rainy days (Table 1). The major crops are cotton (52% of total area ) followed by Jower (22%) and Redgram (6.6%). The monthly mean rainfall (mm) data shows that June, July and August are peak rainy months and receeds after September onwards. Standard week wise rainfall and rainy days shows that 27^th to 34^th week have mean amount of rainfall of 43.03 mm to 54.25 mm with CV of 35.98 to 53.9%. The mean rainy days for these weeks are four for 27^th week to 3.8 for 34^th week with CV of 50 to 52%. The gradual decrease in mean amount of rainfall from 36^th to 45^th week with CV of 137.5 to 280.47 %. Similar trends are noticed for rainy days with mean less than 1 from 43 to 45^th week and CV of 180 to 237% (Table 2).

Precipitation index

The semiarid Yavatmal district, precipitation is quite variable and shows fluctuations in its distribution and quantity that have significant implications on agricultural productivity, food security, land use as well as ecological impacts. The week-wise SPI values were calculated during crop calendar for 35 years (1971 to 2005) and categorized into 6 classes (Table 3). The near normal dry periods are 63 % followed by 19.73% of normal wet periods, 5.71% of severely wet periods, 4.72% of moderately wet periods, 4.28% of extremely wet periods and 2.41 % of moderately dry weeks. The occurrence of the normal dry weeks exceeding more than 18 out of 26 weeks in 11 years and nine years with 8 wet weeks during 25^th to 35^th standard week of cotton growing period in the region with severe water stress for 40^th to 45^th standard week. The decadal rainfall data shows that there is gradual increase of number of occurence of dry weeks from 1971 to 2005. It is observed from the data that there is increase of dry weeks more than 15 in 5 years from 1970 to 1980 but increased to 7 years from 1981 to 1990 and 9 years from 1991 to 2000 .The dry weeks from 2001 to 2005 is almost normal drought like situations in the district.

Seasonal factors influencing soil water dynamics

The climate in Yavatmal district is hot Semi-arid eco-region with four seasons such as: hot season (March and extends up to the first week of June), south west monsoon season (June 2^nd week to till the1st week of September), post monsoon season (2^nd week of September to 1^st week of December) and cold season (2^nd week of December to till February) with the mean daily minimum temperature at about 13⁰C. The cold waves over northern India sometimes affect the district and the minimum temperature may drop to about 5⁰C. It is reported that the monsoon sowing, 24^th to 25^th meteorological week (15 June to 5 July) is optimum for cotton sowing in the region with minimum of 30 to 40 % of rainfall. The shallow, medium deep and deep shrink-swell soils account for 35, 50 and 15% of total area with an average water content (AWC) ranging from 100–500 mm/m, a slope of 0.5–3%, well to moderately drained, with a soil depth ranging from 0.6 to 0.9 m and a pH of between 7.0–8.2 . The water retention curve of shrink-swell soils were similar with respect to depth of layers showing decreasing soil water content with the increase of water suction applied. The water content decreased at 800kPa as the curves of cambic and slickensided horizons intersect with each other with soil water values of 25 to 50% from 1 to 800 kPa. At field capacity the water suction for top A horizon is 20 kPa but of 30 kPa for cambic and slicken sided zones (Figure 2). Depending upon the irregularities in soil water movement under hot semiarid climate, the shrink-swell soils experiences the period of wet stage and alternate wet - dry stages during cotton growing season. These soils have 12.7% of mean plant available water content with standard deviation of 2.95 % and negative relation with CaCO₃ content (r=- 0.41*, significant at 1% level). The clay and fine clay has a significant positive relation with water held at-33 kPa and -1500 kPa.¹² The plant available water can be approximated with multiple regression equation with R² value of 0.29, and F value of 2.25.

Figure 2 Soil water retention characteristic curves for four shrink- swell soil series of Yavatmal district.

Plant available water (%) = - 6.39 + 0.156 (sand) + 0.283 (silt %) + 0.189 (clay %) – 0.433 (organic carbon g/kg) – 0.022 (calcium carbonate, g/kg)

The dry stage is often with at least 15 days of dry spells after September 15^th and have rainfall of less than 20 % of total precipitation which often coincide with flowering and boll development stages of cotton. During this period, the top A horizon has low soil water content and reaches to wilting point. The stored water in subsoils is used by crop in times delay in rains and often experiences drought like situations with prolonged dry spells. The water stress occurs when cotton attains early bloom stage and is highly sensitive to water stress. The water content of shrink-swell soils in the region varies from the zone of 0-40cm at the rate of 14 to 32 % to 24 to 32% from the zone of 40-110cm and 28 to 32% from the zone of, 110-260cm.¹³ The change in soil water at 0-20cm in the cotton growing season (from June to February) is main supplying water source to support seedling emergence to primordial branching stage and sensitive to water stress that ultimately influence lint yield. Late sowing because of the late arrival of the monsoon leads to a significant yield loss due to terminal moisture stress as well as low temperature effect in the months of December and January.¹⁴

In the present approach, the occurrence of week wise dry spells during cotton crop calendar were examined with the computation of standardized precipitation Index(SPI) for the period of 1971 to 2005. The weekwise rainfall analysis revealed that 63 % of crop season experiences near normal dry periods with prolonged dry spells in 36^th to 44^th week during critical growth stages (branching and flowering stages). The study demonstrated that SPI considered as valuable tool to construct historical events of dry spells in drought hit areas and had strong influence on change in soil water at 0-20cm. It was observed that there is increase of dry weeks more than 15 in 5 years from 1970 to 1980 but increased to 7 years from 1981 to 1990, 9 years from 1991 to 2000 and normal drought like situation from 2001 to 2005.

Authors wish to express their sincere thanks to The Head and his staff Regional Agricultural Research Station, Yavatmal, PDKV, Akola for providing agrometeorological data to carry out analysis of rainfall in the region.

I hereby declare that there is no financial interest and any other conflict of interests exist in this project work done by me at NBSS&LUP, Nagpur under the Institute approve project.

1. McKee TB, Doesken NJ, Kleist J. The relationship of drought frequency and duration of time scales. Eighth Conference on Applied Climatology, American Meteorological Society. 1993;179‒186.
2. Rouault M, Richard Y. Intensity and Spatial Extension of Drought in South Africa at Different time Scale. Water SA. 2003;29(4).
3. Loukas A, Vasiliades L. Probabilistic analysis of Drought spatiotemporal characteristics in Thessaly region, Greece. Natural Hazards and Earth System Sciences. 2004; 4(5/6):719–731.
4. Edossa DC, Babel MS Gupta AD. Drought Analysis in the Awash River Basin, Ethiopia. Water Resource Management. 2010;24(7):1441–1460.
5. Moumita P, Sujata B. Analysis of Meteorological Drought Using Standardized Precipitation Index – A Case Study of Puruliya District, West Bengal, India. International Journal of Environmental, Ecological, Geological and Mining Engineering. 2013;7(3):119‒126.
6. Ashok K, Saji NH. On the impact of ENSO and Indian Ocean dipole events on subregional Indian summer monsoon rainfall. Nat Hazards. 2009;42(2):273–285.
7. Ghosh S, Luniya V, Gupta A. Trend analysis of Indian summer monsoon rainfall at different spatial scales. Atmos Sci Lett. 2009;10(4):285–290.
8. Goswami BN, Venugopal V, Sengupta D, et al. Increasing trend of extreme rain events over India in a warming environment. Science. 2006; 314(5804):1442–1445.
9. Pal I, Al-Tabbaa A. Regional changes in extreme monsoon rainfall deficit and excess in India. Dynam Atmos Ocean. 2009;49:206–214.
10. Mandal C, Mandal DK, Srinivas CV, et al. Soil climatic database for crop planningin India. National Bureau of Soil Survey & Land Use Planning (ICAR). 1999; 994p.
11. Edwards DC, McKee TB. Characteristics of 20th Century drought in the United States at multiple time scales. Climatology Report Number 97-2, Department of Atmospheric Science, Colorado State University, Fort Collins. 1997;429–449.
12. Bhaskar BP, Dipak Sarkar, Bobade SV, et al. Land resource evaluation for optimal land use plans in cotton growing yavatmal district, Maharashtra. The Ecoscan. 2011;1:251‒259.
13. Meng K, Zhang X, Sui Y, et al. Analysis of water characteristics of black soils over long term experimental researches in North East China. Bulg J Plant Physiol. 2004;30(3-4):111‒120.
14. Hebbar KB, Venugopalan MV, Rao MRK, et al. Effect of sowing dates and fertilizer levels on phenology, growth and yield of cotton. Indian J Plant Physiol. 2003;7(4):380–383.