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Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani (2018)

Drought characterization under climate change for sustainable crop planning in marathwada region

Waikar, Aniket Udaykumar

Titre : Drought characterization under climate change for sustainable crop planning in marathwada region

Auteur : Waikar, Aniket Udaykumar

Université de soutenance : Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani

Grade : Doctor of Philosophy in Irrigation and drainage Engineering 2018

Résumé partiel
Drought is caused due to sudden and significant deficiency in precipitation, resulting in acute water scarcity, remarkable shortage of soil moisture and reduced crop yields. Droughts cannot be avoided, but they can be predicted and monitored to alleviate their adverse impacts. Droughts are classified into meteorological, agricultural, hydrological, and socioeconomic droughts. While meteorological drought occurs mainly due to deficient precipitation, agricultural drought is developed due to the impact of meteorological and hydrological droughts.Severity, duration and spatial extent of agricultural droughts vary from place to place and time to time depending on varios other factors such as late arrival and/or early retreat of the monsoon, duration of dry spell and lack of irrigation water. A comprehensive analysis of the spatial and temporal extension of droughts can help to develop drought-monitoring signals based on relevant drought indices. Rainfall is one of the important climatic variables that largely determine the occurrence of drought and also influences the growth and development of vegetation reflecting in NDVI. In the present study an attempt has been made to analyse rainfall pattern of Marathwada region comprising of 46 stations on annual and monthly basis using historical meteorological data for the years 1981 to 2016. The trend analysis of rainfall and evaporation of study area was performed on annual and monthly basis using Mann Kendall Test and Sen’s slope estimator. The meteorological drought indices namely SPI, NMI, Deciles, ND and PN were determined by analysing daily rainfall data. The probability analysis of SPI as drought indicator was carried out by using different probability distribution functions to identify the best fit probability distribution. The drought frequency and return period were estimated using Weibull’s method

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