Quantifying Changes in Reconnaissance Drought Index using Equiprobability Transformation Function

Abolfazl Mosaedi (), Hamid Zare Abyaneh, Mohammad Ghabaei Sough and S. Samadi

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2015, vol. 29, issue 8, 2469 pages

Abstract: The Reconnaissance Drought Index (RDI) is obtained by fitting a lognormal probability density function (PDF) to the ratio of accumulated precipitation over potential evapotranspiration values (α k ) at different time scales. This paper aims to address the question of how a probability distribution may fit better to the α k values than a lognormal distribution and how RDI values may change in shorter (i.e.,3-month, and 6-month) and longer (i.e., 9-month, and annual) time scales during 1960–2010 period over various climate conditions (arid, semi-arid, and humid) in Iran. For this purpose, the series of RDI were initially computed by fitting a lognormal PDF to the α k values and the Kolmogorov–Smirnov (K-S) test was implemented to choose the best probability function in different window sizes from 3 to 12-months. The corresponding RDI values for the best distribution were then deriven based on an equiprobability transformation function. The differences between RDI values (the lognormal (RDI log ) and the best (RDI App ) distributions) were compared based on Nash-Sutcliffe efficiency (NSE) criterion. The results of goodness of fit test based on threshold value in the K-S test showed that the goodness of fit in the lognormal distribution may not be rejected at 0.01 and 0.05 significance levels while may only be rejected in a short term (Apr.-Jun.) period at humid station (Rasht station), and three-month (Oct.-Dec. and Apr.-Jun.) and six-month (Apr.-Sep.) periods in semi-arid station (Shiraz station) at significance levels of 0.10 and 0.20, correspondingly. Further a difference between RDI log and RDI App performed that RDI values may change if the best distribution employs and this may therefore lead to significant discrepant and/or displacement of drought severity classes in the RDI estimation. Copyright Springer Science+Business Media Dordrecht 2015

Keywords: Reconnaissance drought index (RDI); Equiprobability transformation function; Probability density function; Drought monitoring (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11269-015-0944-8

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