Extreme rainfall analysis of Andhra Pradesh using a probability distribution model :  A regional estimate

Guhathakurta, Pulak; Menon, Preetha; Dikshit, S. K.; Sable, Sweet

doi:10.54302/mausam.v56i4.1032

Cited by 4 publications

(2 citation statements)

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“…These were followed by Malnad districts Shivamogga, Chikkamagaluru, and Kodagu (104.3, 80.6, and 102.8 mm, respectively), and the remaining four interior districts, Hassan, Mysuru, Davanagere, and Ballari (41.0, 41.9, 21.7, and 25.3 mm, respectively) received lower rainfall during the crop growth period. Many studies have indicated the impact of such an unequal spatial distribution of rainfall [37][38][39][40] on the yield variability of rainfed crops. However, crops such as rice are capable of producing yields under irrigated situations, and rainfall distribution has a meagre impact, although in Karnataka only a few rice-growing districts are under irrigation (Ballari and Mandya), and in the remaining districts rice is grown in rainfed conditions.…”

Section: Observed Variability In Rainfall In the Study Districtsmentioning

confidence: 99%

Weather-Based Statistical and Neural Network Tools for Forecasting Rice Yields in Major Growing Districts of Karnataka

et al. 2023

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Two multivariate models were compared to assess their yield predictability based on long-term (1980–2021) rice yield and weather datasets over eleven districts of Karnataka. Simple multiple linear regression (SMLR) and artificial neural network models (ANN) were calibrated (1980–2019 data) and validated (2019–2020 data), and yields were forecasted (2021). An intercomparison of the models revealed better yield predictability with ANN, as the observed deviations were smaller (−37.1 to 21.3%, 4% mean deviation) compared to SMLR (−2.5 to 35.0%, 16% mean deviation). Further, district-wise yield forecasting using ANN indicated an underestimation of yield, with higher errors in Mysuru (−0.2%), Uttara Kannada (−1.5%), Hassan (−0.1%), Ballari (−1.5%), and Belagavi (−15.3%) and overestimations in the remaining districts (0.0 to 4.2%) in 2018. Likewise, in 2019 the yields were underestimated in Kodagu (−0.6%), Shivamogga (−0.1%), Davanagere (−0.7%), Hassan (−0.2%), Ballari (−5.1%), and Belagavi (−10.8%) and overestimated for the other five districts (0.0 to 4.8%). Such model yield underestimations are related to the farmers’ yield improvement practices carried out under adverse weather conditions, which were not considered by the model while forecasting. As the deviations are in an acceptable range, they prove the better applicability of ANN for yield forecasting and crop management planning in addition to its use for regional agricultural policy making.

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Section: Observed Variability In Rainfall In the Study Districtsmentioning

confidence: 99%

Weather-Based Statistical and Neural Network Tools for Forecasting Rice Yields in Major Growing Districts of Karnataka

et al. 2023

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“…The results show that these regions have higher instability of thunderstorm events on thunderstorm days and higher moisture availability in the region during the entire premonsoon season. Suppression is higher on nonstormy days and limits the occurrence of convective activity on certain days [12][13][14][15][16][17].…”

Section: Realized Weather On January 14 2022mentioning

confidence: 99%

Extreme Hydrological Events in January 2022: A Case Study over Telangana and Andhra Pradesh State, India

Sunanda

Prayek²,

Soni³

et al. 2023

IJECC

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The study will look at extreme rainfall events in Andhra Pradesh and Telangana, where both the monsoon and post-monsoon seasons significantly contribute to rainfall. The winter season 2022 was highlighted by unusual rainfall in some districts of Andhra Pradesh and Telangana in the 2nd week of January 2022, causing significant damage. In this paper, we identify the observational aspects, main synoptic system, physical process, and thermodynamic features leading to such unusual rainfall during the winter season. According to the study's findings, a hailstorm was recorded over Telangana state on January 13, 2022, heavy rain fell in Telangana due to the most active western disturbance as a cyclonic circulation over Rajasthan and Pakistan; on January 14, 2022, heavy to very heavy rain fell in Vizianagaram (16 cm) due to the convergence of winds from the Arabian Sea and Bay of Bengal at lower tropospheric levels over east and adjoining central India and a trough from north interior Karnataka to north interior Odisha. The diurnal and temporal pattern of the hail storm and heavy rainfall over the Andhra Pradesh and Telangana States shows that late evenings are most conducive for the occurrence of extreme rainfall events due to the accumulation of precipitable water.

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Extreme Rainfall Analysis Using Extreme Value (EV-I) Distribution Based on L-Moment Approach

Kumar

Thakural

Patra

et al. 2022

Smart Technologies for Energy, Environment and Sustainable Development, Vol 1

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Extreme rainfall analysis of Andhra Pradesh using a probability distribution model : A regional estimate

Cited by 4 publications

References 0 publications

Weather-Based Statistical and Neural Network Tools for Forecasting Rice Yields in Major Growing Districts of Karnataka

Weather-Based Statistical and Neural Network Tools for Forecasting Rice Yields in Major Growing Districts of Karnataka

Extreme Hydrological Events in January 2022: A Case Study over Telangana and Andhra Pradesh State, India

Extreme Rainfall Analysis Using Extreme Value (EV-I) Distribution Based on L-Moment Approach

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