Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data

Abstract

Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices (SPI (Standardized Precipitation Index), PNI (Percent of Normal Index), DI (Deciles index), EDI (Effective drought index), CZI (China-Z index), MCZI (Modified CZI), RAI (Rainfall Anomaly Index), and ZSI (Z-score Index)) calculated from the station-observed precipitation data and the AgMERRA gridded precipitation data to assess historical drought events during the period 1987-2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index (DDI) for comparing the intensities of different drought categories in each year of the study period (1987-2010). In general, the correlations among drought indices calculated from the AgMERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated (R 2 =0.99). Furthermore, the higher correlations (R 2 =0.99) were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the AgMERRA precipitation data and from the station-observed precipitation data, we suggest that the AgMERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the AgMERRA precipitation data may be used for the data-lacking areas.

Parallel Analysis of Monthly Precipitation Series and Drought Indices in Croatia (1951-2010)

“…where Γ(α) is the gamma function; p (mm) is the amount of precipitation (p > 0); α is the shape parameter (α > 0); and β is the scale parameter (β > 0). If the SPI is computed at a timescale of δ-month, the variable to be analysed is the monthly precipitation, for δ = 1, and the cumulative precipitation in periods of δ consecutive month, for δ > 1 (Salehnia et al, 2017). In the paper, the SPI6 was calculated as this is a recommended timescale for agriculture drought (Hayes et al, 1999) and it can be associated with the lack of precipitation that impacts the water level in rivers and lakes (Espinosa et al, 2019).…”

“…There are many types of droughts, due to their origin -meteorological, agricultural, hydrological, ground-water, stream-flow and socio-economic drought (Lloyd-Hughes and Saunders, 2002; Espinosa, 2019). The meteorological drought, that is caused by precipitation deficiency over some prolonged period (Martins et al, 2012;Potop et al, 2014;Salehnia et al, 2017), will be analysed in this paper. In Europe, the drought is a usual phenomenon in the southern (Mediterranean area) and southeastern part of the continent, but there were droughts in 1989, 1991 and 2003 that had impacted all the areas of Europe (Blenkinsop and Fowler, 2007;Tadić et al, 2015).…”

Simulation of meteorological drought using exponential smoothing models: a study on Bankura District, West Bengal, India

“…Using the input data, deviation of total rainfall (x) from longterm rainfall mean (x) is estimated by the software. After completion of the process, that total deviation is divided by the standard deviation of rainfall ( δ) which is basically as follows [45,46]:…”

Yukarı Gediz Havzası Yağışlarının Meteorolojik Kuraklık Analizleri

“…Bu indekslerden, meteorolojik kuraklığı tanımlamada sadece yağış değerlerini kullanan standartlaştırılmış yağış indeksi (SYİ) yöntemi dünyada sıklıkla kullanılır hale gelmiştir [6][7][8][9]. Dünya genelinde farklı kıtalardaki 10 ülkede bulunan toplam 12 bölgede gözlenmiş yağışla , farklı meteorolojik kuraklık indekslerini karşılaştırılmış ve bunlardan SYİ yönteminin diğer yöntemlere kıyasla meteorolojik kuraklığı tanılamakta daha tutarlı olduğu gösterilmiştir [7].…”