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Tamil Nadu Agricultural University (2018)

Enhancing drought prediction by integrating near real time vegetative fraction in numerical models

Cuba P.

Titre : Enhancing drought prediction by integrating near real time vegetative fraction in numerical models

Auteur : Cuba P.

Université de soutenance : Tamil Nadu Agricultural University

Grade : Doctor of Philosophy (PhD) in 5Agriculture) Meteorology 2018

Résumé partiel
A study was conducted at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore during the academic year 2016-2018 to enhance the seasonal prediction of drought by incorporating near real time vegetative fraction in numerical models. The drought forecasting framework was developed to estimate the occurrence of agricultural and meteorological drought by incorporating high resolution topographic dataset into WRF model to downscale CFSV2 analysis dataset. The second set of experiment was carried out by integrating near real time Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) with CFSV2 analysis data at 0.5° resolution into numerical weather prediction model WRF. The model simulations were carried out for peninsular India at 18 km horizontal resolution and Tamil Nadu and adjoining states at 6 km. This approach demonstrates the creation of forecasts up to several months ahead of time. Using these variables the drought indices for particular blocks in Tamilnadu representing various zones were analysed. An integrated assessment were made between natural climatic fluctuations (ENSO and IOD) and Agricultural drought indices for prognostication of drought.

Présentation et version intégrale (Shodhganga)

Page publiée le 18 avril 2021