Probability Distributions Models to Optimize the Rainfall in Karnataka State

Author: Vinushree R., Ashish Baluni, Megha J. and Chetan Mahadev Rudrapur

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Abstract

The daily rainfall data (1988-2018) were analysed to extrapolate maximum rainfall in Dharwad district of Karnataka state, India. Newer run-off model was formulated to study the different cropping patterns@ different significant levels. Absolute rainfall data were divided into 23 sets viz. one- annual and four seasons (June to September) were Standardized based on different meteorological weeks (23-39rd SMW). The model findings showed that, the annual maximum daily rainfall IQR ranged between (23.14-83.44) mm indicated with large amount of fluctuation of rainfall because due to periodic changing of the weather parameters and global warming. Asper the models diagnostic checking, the runoff- probability forecasted distribution model was found to be best fit to optimize the absolute daily rainfall. Our formulated model will be very useful for the agricultural scientists for designing of cropping patterns and also it can be served as navigation tool for the meteorologists, agricultural policy planners and researchers for the holistic development of soil conservation strategies and irrigation purpose in the developing countries

Keywords

Maximum daily rainfall, Probability distributions, Kolmogorov-Smirnov test

Conclusion

Summing of the results concludes that, due to uneven rainfall Agricultural scientists can unable to formulate the cropping patterns in different agro climatic zones at National level. Every year, periodic hitting of cyclones and global warming, the agriculture usual production will be deteorating to achieve sustainable goals. In this programmatic approach, the analytical intervention is very important to elucidate the problem at the early stage. As such being the case, weather and rainfall mathematical /statistical model will be necessary to fill the gap by means of development of new- algorithms of rainfall . An overall essence of the above model we predict the absolute minimum rainfall at the greatest accuracy with reduced errors in the Indian context. The developed runoff-predictive models successfully integrated multiple meteorological and geographic parameters, thereby enhancing the accuracy of rainfall estimation and optimizing decision-making in agriculture. The findings underscore the model's value in forecasting rainfall-dependent agricultural outcomes, managing irrigation resources, and contributing to climate-resilient policy development

References

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How to cite this article

Vinushree R., Ashish Baluni, Megha J. and Chetan Mahadev Rudrapur (2024). Probability Distributions Models to Optimize the Rainfall in Karnataka State. Biological Forum – An International Journal, 16(7): 341-347