The Pharma Innovation Journal 2018; 7(10): 716-722 ISSN (E): 2277- 7695 ISSN (P): 2349-8242 NAAS Rating: 5.03 Spatial and temporal variability of rainfall in Khordha TPI 2018; 7(10): 716-722 © 2018 TPI district of Odisha www.thepharmajournal.com Received: 27-08-2018 Accepted: 29-09-2018 S Pattanayak, BS Rath, S Pasupalak, A Baliarsingh, AKB Mohapatra, S Pattanayak A Nanda and GS Panigrahi Department of Agricultural Meteorology, OUAT, Abstract Bhubaneswar, Odisha, India Seasonal variability of rainfall in different blocks of Khordha district is of utmost importance in this existing scenario of changing climate and temperature. The present study has been undertaken using BS Rath Department of Agricultural rainfall data of the district for 25 years (1993-2017). Seasonal rainfall has been characterised under four Meteorology, OUAT, seasons-Winter, Summer, South west monsoon and north east monsoon which contributes around 1.5%, Bhubaneswar, Odisha, India 10.5%, 72.7% and 15.3% respectively to the mean annual rainfall of the district. Year wise variability of rainfall revealed that year 2013 has received the maximum annual rainfall (1873 mm) in 105 rainy days S Pasupalak and year 1996 has received least annual rainfall (778 mm) in 80 numbers of rainy days. Trend line Department of Agricultural analysis of annual and seasonal rainfall and rainy days visualised a decreasing trend in number of rainy Meteorology, OUAT, days whereas as slightly decreasing trend in annual rainfall and slightly increasing trend in seasonal Bhubaneswar, Odisha, India rainfall. Extreme rainfall events of different blocks were different depending upon the highest amount of rainfall they received in the last 25 years. Markov Chain probability model determined the initial and A Baliarsingh conditional probabilities of dry and wet weeks at 20 mm threshold limit. Initial land preparations can be Department of Agricultural done in the standard meteorological weeks having more than 30% of initial probability of wet weeks (21- Meteorology, OUAT, 23 SMW) and sowing or transplanting can be done in the SMWs having more than 50% of initial Bhubaneswar, Odisha, India probability of wet weeks (24-26 SMW). PET is also an important factor in assessing the drought AKB Mohapatra condition and for better crop water management planning. PET calculated by modified Penman method Department of Agricultural is of more concern and it has been found out from the graph that the post monsoon period (October to Meteorology, OUAT, December) and pre monsoon period (January to May) is having water deficit condition whereas monsoon Bhubaneswar, Odisha, India period (June to September) is having water surplus condition. The surplus water needs to be stored for efficient utilisation in the future. A Nanda Department of Agricultural Keywords: Variability, Markov chain, SMW, PET, modified penman, surplus Meteorology, OUAT, Bhubaneswar, Odisha, India Introduction GS Panigrahi Odisha is an agriculture dominant state where about 70% of the citizens depend on agriculture Department of Agricultural for their livelihood. Therefore crop failure and yield loss creates havoc among the farmers. Meteorology, OUAT, Hence, the crops can be saved by analysing the rainfall variability and deciding the probability Bhubaneswar, Odisha, India of dry and wet spells using Markov chain probability model before sowing of the crops. Initial field preparation, planning of crop sowing/planting and carrying out all agricultural operations in an area requires the details of distribution of rainfall, onset and withdrawal of rainy season and periods of dry and wet spells. Markov chain probability model helps to study the chances of occurrences of dry and wet spells (Pandharinath, 1991). It can be used for analyzing rainfall data to obtain specific information needed for crop planning and for carrying out agricultural operations. Water surplus conditions also prevail during monsoon period and so the surplus water can be used in future during water stress condition in rabi season. Rainfall is one of the substantial weather indicators of climate change. Spatial and temporal variability of rainfall not only provides the trend of the weather condition of an area but also helps the farmer to know when to perform the agricultural operations in the field. It is of utmost importance while preparing the agro met advisory services (AAS) keeping in view the trend of rainfall in a particular area. Rising temperature across the globe results in change in precipitation and atmospheric moisture through a more active hydrological cycle, leading to increase in water holding capacity throughout the atmosphere at a rate of about 7% per °C (Hossain et al., 2014) [2]. Rising temperatures in combination with rainfall anomalies due to Correspondence climate change strongly influence soil moisture. Therefore, the study has been undertaken for S Pattanayak Department of Agricultural analysing the spatial and temporal variability of rainfall patterns in Khordha district of Odisha. Meteorology, OUAT, Bhubaneswar, Odisha, India ~ 716 ~ The Pharma Innovation Journal Materials and methods daily data series and were used for estimation of initial and The study has been conducted for Khordha district of Odisha conditional probability analysis based on Markov chain for rainfall characterisation and probability analysis which probability model as described by Pandarinath, 1991. In this lies between 19⁰55’ to 20⁰25’N Latitude and 84⁰55’ to method, 20 mm or more rainfall in a week is considered as 86⁰5’E Longitude. Khordha has normal rainfall of 1408 mm wet week otherwise dry as the previous researchers with maximum and minimum temperature of 42.2 and 11.1 (Pandarinath, 1991; Dash and Senapati, 1992) [4, 1] also used degree Celsius respectively. The district comprises of 10 20 mm as the threshold value. Initial, and conditional blocks namely Balianta, Balipatna, Banapur, Begunia. probability analysis for 52 SMWs were made by using Bhubaneswar, Bolagarh, Chilika, Jatani, Khordha & Tangi. equations from 1-6. All the weather data including maximum temperature (Tmax), minimum temperature (Tmin), morning and evening relative Initial probability humidity (RH1, RH2), bright sunshine hours (BSH), wind P (D) = F (D)/N (Eq. 1) speed (WS) and evaporation (E) has been collected from the P (W) = F (W)/N (Eq. 2) Department of Agricultural Meteorology, OUAT, BBSR for the period 1993-2017 (25 years). Block wise rainfall data of Where, P (D) = probability of the week being dry, F (D) = Khordha has been obtained from SRC site of Govt. of Odisha frequency of dry weeks, P (W) = probability of the week for the same period. being wet, F(W) = frequency of wet weeks, and N = total number of years of data being used. A. Temporal Variability of Rainfall and Rainy days Daily rainfall data of Khordha district for 25 years (1993- Conditional probabilities 2017) have been used to analyse the temporal variability. P (DD) = F (DD)/F (D) (Eq. 3) Year wise annual rainfall and rainy days along with Standard P (WW) = F (WW)/F (W) (Eq. 4) deviation and coefficient of variation have also been P (WD) = 1 – P (DD) (Eq. 5) estimated. Trend lines have also been shown in the respective P (DW) = 1 – P (WW) (Eq.6) graphs. Where, P(DD) = probability of a week being dry preceded by B. Spatial Variability of Rainfall another dry week, F(DD) = frequency of dry week preceded Block wise daily rainfall data have been used for spatial by another dry week, P(WW) = probability of a week being variability of rainfall. Mean annual, seasonal and monthly wet preceded by another wet week, F(WW) = frequency of a rainfall variability have been worked out by analysing rainfall wet week preceded by another wet week, P(WD) = block wise using Weather cock. “Rainy Day.exe” module has probability of a wet week preceded by a dry week, and been used to analyse the rainfall data. P(DW) = probability of a dry week preceded by a wet week. Annual Rainfall and Rainy days E. Calculation of Potential Evapotranspiration According to India Meteorological Department, a day with Weather parameters like maximum and minimum temperature rainfall equal to or more than 2.5 mm is termed as a rainy day (⁰C), relative humidity (%), rainfall (mm), bright sunshine for Indian region. Mean annual rainfall and rainy days has hours (hr), wind speed (m/s) and evaporation (mm/day) have been calculated for different blocks using “Rainy day.exe” been used to calculate the potential evapotranspiration (PET) module of Weather Cock model. Standard deviation (SD) and using ‘PET Calculator’ designed by CRIDA, Hyderabad. The Co-efficient of variance (CV) are also being calculated by PET calculator calculates PET in seven different methods using statistical equation. namely Modified Penman, Hargreaves, Turc, Blaney Criddle, Christiansen, PET Open Pan and Penman Monteith. The study Seasonal Rainfall and Rainy days is more concerned with Modified Penman method as it is the The whole year in Odisha condition is categorised into four most trusted one and largely followed. Year wise mean seasons – Winter which includes January, February and monthly PET has been calculated to plot a graph with annual March; Summer consisting of April and May; South West precipitation. monsoon consisting of June, July, August and September; North East monsoon which includes October, November and Results and discussion December. The seasonal rainfall and rainy days along with 1. Year wise annual rainfall and rainy days SD and CV have been calculated for the same using the above Annual rainfall of Khordha district has been recorded highest said model. (1873 mm) in 2013 and lowest (778 mm) in 1996 (Table 1). Whereas, most number of rainy days have been observed in C. Extreme Rainfall Events the year 1998 (127 days) and least in 2009 (77 days). The extreme rainfall events of ten different blocks have been Variability of the district annual rainfall comes around 19% found out using the daily rainfall data of the blocks of last 25 and rainy days around 13%.