Drought Monitoring by Reconnaissance Drought Index (RDI) in Iran

Abstract

Drought is one of the most important natural hazards in Iran and frequently affects a large number of people, causing tremendous economic losses, environmental damages and social hardships. Especially, drought has a strong impact on water resources in Iran. This situation has made more considerations toward the study and management of drought. The present study is focused on two important indices; SPI and RDI, for 3, 6, 9, 12, 18 and 24 months time scales in 40 meteorological synoptic stations in Iran. In the case of RDI computation, potential evapotranspiration was an important factor toward drought monitoring. So, evapotranspiration was calculated by Penman-Monteith equation. The correlation of RDI and SPI was also surveyed. Drought severity maps for SPI and RDI were also presented in the driest year (1999)(2000). The present results have shown that the correlation of SPI and RDI was more considerable in the 3, 6 and 9 months than longer time scales. Furthermore, drought severity maps have shown that during 1999-2000, the central, eastern and south-eastern parts of Iran faced extremely dry conditions. While, according to SPI and RDI trends, other parts of the country suffered from severe drought. The SPI and RDI methods showed approximately similar results for the effect of drought on different regions of Iran. Since, RDI resolved more climatic parameters, such as evapotranspiration, into account which had an important role in water resource losses in the Iranian basins, it was worthwhile to consider RDI in drought monitoring in Iran, too.

Growth response of different tree species (oaks, beech and pine) from SE Europe to precipitation over time

“…Part of the analysis was presented in Stojanović et al (2015a). Higher response coefficients between TRW chronologies and climate variables were observed for PDSI (Alley, 1984), SPI 12 (Guttman, 1999;McKee et al, 1993), SPEI 12 (Beguería et al, 2013;Vicente-Serrano et al, 2010), and RDI 12 (Asadi Zarch et al, 2011;Tsakiris et al, 2007).…”

Probabilistic Analysis of Drought Spatiotemporal Characteristics in the Beijing-Tianjin-Hebei Metropolitan Area in China

“…According to AsadiZarch et al [35], the Reconnaissance Drought Index (RDI) was characterized as a general meteorological index for the drought assessment. The RDI is considered in three ways: the initial value (αk), normalized RDI (RDIn) and standardized RDI (RDIst).…”

“…According to Tsakiris et al (2008) [41] and also Asadi Zarch et al (2011) [35], it has been proved that the calculation of RDIst could be performed better by fitting the gamma probability density function (pdf) to the given frequency distribution of αk, following the procedure described below. Like SPI computation using the gamma approach, this method tends to solve the problem of calculating RDIst for the small time scales, e.g., monthly, which may include zero precipitation values (αk = 0), for which Equation (3) could not be applied [41].…”

Spatial variability of SPI and RDIst drought indices applied to intense episodes of drought occurred in Rio de Janeiro State, Brazil

“…Furthermore, several scientific papers related to drought have been published in Asia, especially in China (Zhang and Jia, 2013;Chen et al, 2014;Yu et al, 2014). According to these articles, the increase of severe drought has threatened food security, as well as caused socio-economic losses to the country.…”

“…It behaves in a generally similar way to the SPI and therefore the interpretation of the results is alike since the same thresholds as SPI can be used (Tsakiris and Vangelis, 2005). It has been used to characterize drought in different regions of the world using rainfall and ET data, and interesting results have been achieved by Tsakiris et al (2007), Pashiardis and Michaelides (2008), Zarch et al (2011), Tigkas et al (2013 and Shah et al (2013).…”