NDLI: Drought prediction in Apalachicola–Chattahoochee–Flint River Basin using a semi-Markov model

Content Provider	Springer Nature Link
Author	Nnaji, Gideon A. Clark, Clayton J. Chan Hilton, Amy B. Huang, Wenrui
Copyright Year	2016
Abstract	The Markov models widely used in hydrology are not adequate for drought analysis because they are independent of previous processes in dealing with associated significant autocorrelations of hydrological events. Therefore, use of semi-Markov model becomes more realistic for studying droughts processes due to dynamics of the system. An embedded Markov-based model was developed to assess chances of occurrence of hydrological droughts in which waiting times of the series were defined explicitly. This presents a more global parametric method to define drought duration compared to those frequently used. This model was applied to monthly streamflow series of Apalachicola River, Chattahoochee River and Flint River in Apalachicola–Chattahoochee–Flint (ACF) River Basin, located in southeastern USA. The streamflow conditions below the mean resulting to Near Drought and Critical Drought conditions were considered crucial. Drought occurrence probabilities and corresponding flows indicate a 42 % chance of Near Drought condition and 18 % chance of Critical Drought condition, with transition time of about 1.8 months. The model results were validated using last ten-year data series. Correlation coefficient (r) and root-mean-square error statistics demonstrate that the model can predict Near Drought and Critical Drought conditions with high accuracy, resulting in errors less than 5 % statistical significance. The method is capable of preserving longer memory persistence of historic flow trends in the ACF River Basin, and gives an effective recursive equation of defining occurrence of droughts. The semi-Markov model developed in this work will provide valuable lead in estimating similar drought indices in other related river systems.
Starting Page	267
Ending Page	297
Page Count	31
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	82
Issue Number	1
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-02-10
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Markov process Autocorrelation Embedded Markov chain Waiting times Semi-Markov process Drought prediction Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

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