Evaluation of Markov Chain Model for Forecasting Precipitation of Uttarakhand Districts
Journal Title: International Journal of Agriculture Innovations and Research - Year 2018, Vol 6, Issue 5
Abstract
This paper intends to evaluate the Markov Chain model for forecasting rainfall of Uttarakhand state for each district separately. District wise interpolated daily rainfall data for the period 1971 – 2011 was collected from the NICRA (National Initiative on Climate Resilient Agriculture) website http://www.nicra-icar.in/nicrarevised/index.php/tools services. Instat climatic software version 3.37 is used to calculate the Markov chain probability of rainy day, rainy day preceded by a rainy day, rainy day preceded by a rainy day and rainy day preceded by a dry day. It is also used to study the probability of getting dry spells of duration 5 and 7 days during the monsoon period. This probability is used to model the 75% probable amount of rainfall that can occur on a given day using the Markov chain model. The model is validated for the period 2002 – 2011 by using various evaluation methods like skill scores, NMSE and MBE. Evaluation tests showed that the average skill score of the forecast is 76% while highest skill score is calculated for Udham Singh Nagar (88%) and lowest for Tehri Garhwal (63%) district. The rainfall event forecast during the months July and August was better compared to June and September months. The deviation percent is more than 20% for Bageshwar, Dehradun, Nainital, Rudraprayag, Tehri and US Nagar districts. It can be concluded that the Markov Chain model can be used for forecasting precipitation but the forecast accuracy depends on solely the reliability of the past rainfall data provided to the model.
Authors and Affiliations
G. Pranuthi, et al.
Keywords
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