Integrating rainfall probability and moisture availability index for crop planning during kharif rice (Oryza sativa) in eastern Indo-Gangetic basin

Abstract

In the eastern region of Indo-Gangetic basin (EIGB), India, more than 80% of the rice (Oryza sativa L.) cultivated area is rainfed and monsoon rainfall is the critical and deciding factor for rice production. The districts comprising Bihar, West Bengal, Uttar Pradesh, Madhya Pradesh and Jharkhand form the eastern Indo-Gangetic basin in India, broadly representing 10 agro-ecological sub-regions and one/two/three representative rain gauge sites depending on the size of the region in each sub-region was considered for analysis. The purpose of this study is to find out rainfall availability at different probability levels of 60, 70, 80 and 90% at weekly intervals during the crop season using Gamma distribution and also to find out initial and conditional probability of occurrence of dry and wet weeks and the probability of occurrence of two and three consecutive dry and wet weeks through Markov chain modeling approach. Weekly moisture availability index (MAI) was computed for 15 stations during the crop period, i e 18th Standard meteorological week (SMW) to 44th SMW to explore the weekly water availability (WA) and water demand (WD) of the area. The mean annual rainfall vary from 880.1 mm with a standard deviation of 213.6 mm at Jhansi (4.4 subregion) to 1589.6 mm at Dhanbad (12.3 sub-region) with a standard deviation of 460.3 mm. The eastern part of the EIGB receives more rainfall compared to the other part of the region. For Allahabad (western IGB) site, it is noticed that at 70% probability level, MAI is higher (>0.5) from 28th weeks and continued up to 35th week. However, the MAI never reaches > 1.0 in any of weeks. This indicates the crop will be always in moist situation for seven weeks and in the remaining weeks the crop will be under stress condition. But for Krishnanagar (Eastern IGB) site, the MAI is more from 25th to 35th week. Weekly values of MAI at different probability levels will be highly useful for planners at different risk levels.