Simple Short-Term Probabilistic Drought Prediction Using Mediterranean Teleconnection Information

Timely forecasts of the onset or possible evolution of droughts is an important contribution to mitigate their manifold negative effects; therefore, in this paper, we propose a mathematically-simple drought forecasting framework gaining Mediterranean Sea temperature information (SST-M) to predict dr...

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Bibliographic Details
Published in:Water resources management 2018-10, Vol.32 (13), p.4345-4358
Main Authors: Bateni, Mohammad Mehdi, Behmanesh, Javad, Bazrafshan, Javad, Rezaie, Hossein, De Michele, Carlo
Format: Article
Language:English
Subjects:
Applications
Atmospheric Sciences
Autocorrelation
Civil Engineering
Data processing
Drought
Drought index
Earth and Environmental Science
Earth Sciences
Environment
Frameworks
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrology
Hydrology/Water Resources
Lake basins
Lakes
Markov analysis
Markov chains
Mathematical models
Meteorology
Modelling
Sciences of the Universe
Sea surface
Sea surface temperature
Seasonal variations
Seasonality
Statistics
Teleconnections (meteorology)
Temperature (air-sea)
Two dimensional models
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Summary:Timely forecasts of the onset or possible evolution of droughts is an important contribution to mitigate their manifold negative effects; therefore, in this paper, we propose a mathematically-simple drought forecasting framework gaining Mediterranean Sea temperature information (SST-M) to predict droughts. Agro-metrological drought index addressing seasonality and autocorrelation (AMDI-SA) was used in a Markov model in Urmia lake basin, North West of Iran. Markov chain is adopted to model drought for joint occurrence of different classes of drought severity and sea surface temperature of Mediterranean Sea, which is called 2D Markov chain model. The proposed model, which benefits suitability of Markov chain models for modeling droughts, showed improvement results in prediction scores relative to classic Markov chain model not including SST-M information, additionally.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-018-2056-8