A review on extreme weather events and livelihood in Odisha, India

Patel, Sangram Kishor; Mathew, Bincy; Nanda, Ankit; Nayak, Hiralal

doi:10.54302/mausam.v70i3.258

Cited by 12 publications

(2 citation statements)

References 14 publications

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“…Odisha was the most vulnerable state in India in terms of climate extremes, as demonstrated by [35] From 1951 to 2010, the state experienced 35 years of floods, 22 years of droughts, and 8 years of cyclones. Consequently, it is necessary and relevant to develop studies and monitoring in this agroeconomic state, such as those of [36], [37], [38] , [39] and [40].…”

Section: Introductionmentioning

confidence: 99%

Drought Analysis and Forecasting in Odisha using Machine Learning Techniques

Neena Uthaman

2024

cana

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Drought is a natural phenomenon that damages agricultural land severely. The severity of drought must be reduced to decrease its impact on agricultural productivity. The study of drought was carried out for the state Odisha which experienced drought 8 times during the last 20 years due to failure of monsoon. Analysis for the data was explored by explorative analysis.The drought forecasting was carried out using machine learning techniques like the Auto-regressive model (AR), Long Short-Term Memory (LSTM), and Auto-regressive Integrated Moving Average (ARIMA) using daily rainfall data collected for 28 years (1993-2020). Further using this data each district was categorised into four different categories namely Flood (FL), No Drought (ND), Moderate Drought (MD), and Severe Drought (SD). To classify the districts after forecasting, classification models were used like Support Vector Classifier (SVC) and Naïve Bayes. The results of the forecasting model as well as the classification model were compared. It becomes important to forecast drought for proper planning and management of the water resource system to decrease the damage due to such calamities. This study is valuable for the government, farmers, and other stakeholders to understand the pattern and reason behind the severity of drought to take relevant precautionary measures and improve decisions and facilities to tackle such natural calamities.

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Section: Introductionmentioning

confidence: 99%

Drought Analysis and Forecasting in Odisha using Machine Learning Techniques

Neena Uthaman

2024

cana

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“…A substantial decline in mean Indian summer monsoon rainfall (ISMR) and wet days (rainfall ≥ 1 mm; 7%-14%) over Indo Gangetic Plains under RCP8.5 scenario is suggested under RegCM4 projections. Several other studies in India and other countries are conducted on extreme indices to find out the occurrence and frequency of extreme events [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The purpose of this study was to analyze the ETCCDI recommended extreme temperature indices and to assess the trends of indices".…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Changes in Temperature Extremes in Vamsadhara River Basin, Odisha, India

Kumari

Thomas

Chandola

2023

IJECC

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With an emphasis on extreme events, several climate change indices that were derived from daily temperature data were computed and studied. Odisha is a coastal state of India which is severely prone to extreme events. With climate change, extreme events are also increasing in the world especially in coastal regions. Vamsadhara river basin which is situated at Odisha is selected for the study. The objective of this study was to compute and analyze the Expert Team on Climate Change Detection and Indices (ETCCDI) extreme indices using CLIMPACT software. 14 temperature indices were used to analyze the signals of climate change in the study area. The IMD data for the period 1961-2022 has been used for the study. The trend detection of indices was done through Mann-Kendall test and magnitude of trend was calculated using Sen’s Slope estimator. The result showed that warm days (TX90), Diurnal temperature range (DTR) and Warm Spell Duration Index (WSDI) are increasing with 1%, 10% and 1% level of significance respectively. This indicates that indices associated with warming are increasing in river basin. The cold night (TX10) indices found to be decreasing with 10% level of significance and TNx showed a negative trend at 10% level of significance.

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