Dear Siva Kumar,

Some useful info is presented below:

Over recent years, the frequency and intensity of droughts have increased and there has been a large drying trend over many parts of the world. Consequently, drought monitoring using big data analytic has gained an explosive interest. Droughts stand among the most damaging natural disasters. It threatens agricultural production, ecological environment, and socio-economic development. For this reason, early warning, accurate evaluation, and efficient prediction are an emergency especially for the nations that are the most menaced by this danger. There are numerous emerging studies addressing big data and its applications in drought monitoring. In fact, big data handle data heterogeneity which is an additive value for the prediction of drought, it offers a view of the different dimensions such as the spatial distribution, the temporal distribution and the severity detection of this phenomenon. Big data analytic and drought are introduced and reviewed in this paper. Besides, this review includes different studies, researches and applications of big data to drought monitoring. Challenges related to data life cycle such as data challenges, data processing challenges and data infrastructure management challenges are also discussed. Finally, we conclude that big data analytic can be beneficial in drought monitoring but there is a need for statistical and artificial intelligence-based approaches.

https://www.researchgate.net/publication/343992806_A_review_of_drought_monitoring_with_big_data_Issues_methods_challenges_and_research_directions

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University of Nebraska -

Lincoln DigitalCommons@University of Nebraska -

Lincoln Drought Mitigation Center Faculty Publications Drought --

National Drought Mitigation Center

2016

Handbook of Drought Indicators and Indices Mark Svoboda University of Nebraska - Lincoln, [email protected] Brian Fuchs National Drought Mitigation Center, [email protected]

Integrated Drought Management Programme (IDMP)

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Global integrated drought monitoring and prediction system

https://www.nature.com/articles/sdata20141

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• DOI:10.1023/B:NHAZ.0000035020.76733.0B
• Corpus ID: 2213498

Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA

• T. Tadesse, D. Wilhite, +2 authors S. Goddard
• Published 1 September 2004
• Environmental Science
• Natural Hazards

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought.

https://www.semanticscholar.org/paper/Drought-Monitoring-Using-Data-Mining-Techniques%3A-A-Tadesse-Wilhite/def1af36bcec9fd2ceb9a54eb6056a2fc5f5b068

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The FAO ASIS uses the Agricultural Stress Index (combination of temperature and vegetation anomaly) for monitoring drought. At this link, you can find data and full documentation.

https://www.fao.org/giews/earthobservation/index.jsp?lang=en