Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Drought monitoring for RABI season in upper Krishna river basin using remote sensing and GIS
    (Asian Association on Remote Sensing Sh1939murai@nifty.com, 2015) Chandran, C.; Dodamani, B.M.; Reddy, K.; Naseela, E.K.
    In this study, the upper Krishna river basin, lying in the state of Maharashtra has been chosen as study area. Two drought indices, SPI and NDVI, representing meteorological and agricultural droughts respectively, were calculated and analysed for the study area for a study period of 2000-2012. Using ArcGIS maps of the two types of droughts have been created to represent the spatial extent of the droughts. Further analysing the two indices, relevant relationships have been obtained between them.
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    Assessment of meteorological drought return periods over a temporal rainfall change
    (Springer Science and Business Media Deutschland GmbH, 2021) Datta, R.; Pathak, A.A.; Dodamani, B.M.
    Investigation of the rainfall homogeneity along with bivariate frequency analysis of drought considering change points in long-term annual precipitation series has been carried out in this study. Nonparametric Pettitt’s test was applied for detecting change points of annual precipitation series at different grid locations over the Ghataprabha River Basin. Depending on the results of change point analysis, we divided the entire period of 1950–2013 into two subperiods: from 1950 to 1980 and 1981 to 2013. Characterization of meteorological drought is performed with the help of the Standardized Precipitation Index (SPI) at a time scale of three months for the period before the change point (1950–1980), after the change point (1981–2013) and for the entire period of 1950–2013. Three Archimedean copulas, namely Clayton, Gumbel–Houggard, and Frank, were tested for joint distribution modeling. The Akaike’s and Bayesian information criteria have been implemented for selecting the best copula; the Gumbel–Hougaard copula performed comparatively better for all three periods. Drought return periods were calculated using the joint distribution of drought characteristics. The study gives valuable insight into drought risk management on a regional scale. © Springer Nature Singapore Pte Ltd 2021.
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    Forecasting of Meteorological Drought Using Machine Learning Algorithm
    (Springer Science and Business Media Deutschland GmbH, 2022) Kikon, A.; Deka, P.C.
    Drought forecasting is one of the crucial tools for the water management system, and understanding the different climatic variables affecting the occurrences of drought is a major scientific challenge. In this study, drought forecasting is done for the Peninsular region of India using different machine learning algorithms. A meteorological drought index known as Standardized Precipitation Index (SPI) which is dependent on the precipitation is taken into account for analysis. The SPI with a different timescale for 3-, 6-, 9-, 12-month were calculated from 1958–2017 for 60 years. SPI is a function of precipitation and the trend of rainfall followed may be found to be similar in some regions. Two different models, GA-ANFIS and GRNN were compared in this study. The results obtained from the statistical performance assessment of the models were compared with each other. For different timescale, there is a variation in its evaluation metrics. Comparing the performance assessment of the two different models, it is noticeable that the performance assessment of the statistics of the GA-ANFIS model outperformed GRNN model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.