A monitoring and prediction system for compound dry and hot events

Bibliographic Data

ID15545883
AuthorsZengchao Hao (0000-0001-7666-7053, Beijing Normal University, corresponding author), Fanghua Hao (0009-0007-7686-1359, Beijing Normal University, corresponding author), Youlong Xia (NOAA Environmental Modeling Center), Vijay P Singh (0000-0003-1299-1457, Texas A&M University), Xuan Zhang (0000-0003-2929-2126, Beijing Normal University)
Year2019
Volume14
Issue11
Pages114034-114034
Publication date2019-10-15
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ab4df5
OpenAlexW2981277091
LanguageEN
Citations received3
References cited60

Compound dry and hot events (i.e. concurrent or consecutive occurrences of dry and hot events), which may cause larger impacts than those caused by extreme events occurring in isolation, have attracted wide attention in recent decades. Increased occurrences of compound dry and hot events in different regions around the globe highlight the importance of improved understanding and modeling of these events so that they can be tracked and predicted ahead of time. In this study, a monitoring and prediction system of compound dry and hot events at the global scale is introduced. The monitoring component consists of two indicators (standardized compound event indicator and a binary variable) that incorporate both dry and hot conditions for characterizing the severity and occurrence. The two indicators are shown to perform well in depicting compound dry and hot events during June–July–August 2010 in western Russia. The prediction component consists of two statistical models, including a conditional distribution model and a logistic regression model, for predicting compound dry and hot events based on El Niño–Southern Oscillation, which is shown to significantly affect compound events of several regions, including northern South America, southern Africa, southeast Asia, and Australia. These models are shown to perform well in predicting compound events in large regions (e.g. northern South America and southern Africa) during December–January–February 2015–2016. This monitoring and prediction system could be useful for providing early warning information of compound dry and hot events

Climatology · Early warning system · Geography · Meteorology · Warning system · Climate variability and models · Computer Science · Environmental Science · Hydrology and Drought Analysis · Meteorological Phenomena and Simulations · Geology

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