Statistical analysis of the relationship between summer monsoon precipitation extremes and foodgrain yield over India

Abstract

ABSTRACT:The Indian economy largely depends on agriculture which is highly influenced by the spatio-temporal variability of precipitation. Kharif and rabi are the two main crop-growing seasons which require major proportion of rainfall. Increase in heavy precipitation events, however, can have adverse effects on the crops. This study, therefore, is mainly focused on understanding the variation of extremes in precipitation during summer monsoon season and its impact on kharif foodgrain yield over India. For this, several objectively defined indices of observed precipitation extremes, in terms of frequencies and intensities, have been computed for the period 1951-2003 using daily gridded data of 1°latitude ×1°longitude resolution. Efforts have also been made to prepare the climatology of extremes in precipitation to determine their basic characteristics over the Indian region.Correlation analysis reveals that a large part of the country exhibits positive relationship between kharif foodgrain yield and the indices of extreme precipitation, significant at 5% level. Correlations with the indices of frequencies defined as seasonal count of days when rainfall exceeds 85th, 75th, and 65th percentiles (and 30, 20, and 10 mm) show that spatial extent and strength of the positive relationship decreases with increase in threshold values. This indicates that very heavy daily rainfall is less useful than the moderate daily rainfall. Strong negative relationship between the yield and indices of extreme precipitation observed over heavy precipitation areas of northeastern parts of the country indicate that very heavy daily rainfall have an adverse effect on crop. The study also points out that increase in heavy rainfall activities during July are favourable for the yield, but correlations become weak and statistically insignificant during the month of August. Individual crops show similar results as that obtained from aggregate kharif foodgrain, however, magnitude of the correlations varies from crop to crop.

Rising Precipitation Extremes across Nepal

“…Similarly, one of the worst winter droughts of the country in 2008/2009 reduced yield of wheat and barley by 14% and 17%, respectively, leading to severe food shortage in 66% of rural households in the worst hit far-and mid-western hill and mountain regions [25]. Such intense precipitation and extreme dryness (droughts) negatively impact the yield of both cash and cereal crops [26], and in turn, the livelihood of around 60% of the total Nepalese population directly dependent on agriculture [27]. Therefore, analyzing the precipitation extremes and their time evolution under prevailing climatic changes is of paramount importance for ensuring food and water security in Nepal and developing a region-specific disaster management strategy.…”

Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

“…Various approaches exist for studying Indian monsoon dynamics, such as numerical modeling using, for instance, Atmospheric General Circulation Models (AGCMs) (Krishna Kumar, 2005;Waliser et al, 2003), statistical analysis of observational or reanalysis data (Revadekar and Preethi, 2012;Rajkumari and Narasimha, 1996), or recurrence analysis to detect regime transitions (Marwan et al, 2009 or coupling directions with intersystem recurrence networks (Feldhoff et al, 2013).…”

“…NCEP/NCAR: reanalysis data (Kalnay et al, 1996 2010; Gozolchiani et al, 2008Gozolchiani et al, , 2011Yamasaki et al, 2009;Paluš et al, 2011;Barreiro et al, 2011;Deza et al, 2013Deza et al, , 2014Martin et al, 2013;Tirabassi and Masoller, 2013). While most of these studies are focused on global climate networks of temperature fields and precipitation (Donges et al, 2009a, b;Tsonis and Roebber, 2004;Tsonis et al, 2006;Gozolchiani et al, 2011;Yamasaki et al, 2008Yamasaki et al, , 2009Scarsoglio et al, 2013), others consider smaller, regional networks that focus on a specific climate phenomenon of interest, such as El Niño Gozolchiani et al, 2008), Rossby waves (Wang et al, 2013), continental rainfall in Germany (Rheinwalt et al, 2012), the South American Monsoon System (SAMS) (Boers et al, 2013), and the Indian Summer Monsoon (Malik et al, 2010(Malik et al, , 2011Rehfeld et al, 2012). In the work by Rehfeld et al (2012) on Indian Summer Monsoon (ISM) dynamics, a paleoclimate network approach has revealed a strong influence of the ISM on the East Asian Summer monsoon during the late Holocene period, but with varying strength according to the warm vs. cold epochs.…”

Assessment of Kalpana-1 Rainfall Product over Indian Meteorological Sub-Divisions During the Summer Monsoon Season

“…India receives about 80 % of its annual rainfall from the southwest or summer monsoon spanning from June to September which has paramount socio-economic impacts (Gadgil 2003;Gadgil and Srinivasan 2010;Revadekar and Preethi 2010;Prakash et al 2014). The Indian summer monsoon rainfall (ISMR) exhibits a regionally heterogeneous variability at different time scales such as interannual, seasonal, intraseasonal, etc.…”