1. s a Drought Indices for Zagreb-Grič Observatory
and Drought Damages in Croatia
Krešo Pandžić, Tanja Likso
Meteorological and Hydrological Service, Grič 3, Zagreb
1. Introduction
Drought indices are quatitative indicators of climate variation and change. Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) were developed for representation of abnormal wetness and dryness conditions by Palmer (1965) and Guttman (1999), respectively. Penzar (1976) and Cindrić et al. (2015) applied both PDSI and SPI, respectively, on secular time series of Zagreb-Grič Observatory, capial of Croatia. A comparison of two indices is considered here as well as a reationship with drought damages in Croatia.
2. Methodology and data
PDSI and SPI have been calculated using free software in FORTRAN 77. A slight deviation from orginal Palmer (1965) algorithm for PDSI is an use of Eagleman (1967) algorithm for calculation of potential evapotranspiration. Cited algorithm for PDSI has been applied on mothly scale using monthly precipitation amounts, average mothly air temperature and average air humidity data for Zagreb-Grič Observtory for the period For calculation of SPI for 1-, 2-, 3-, 6-, 9-, 12- and 24-month periods, indicated as SPI1, SPI2, SPI3, SPI6, SPI9, SPI12 and SPI24, only corresponding cumulative precipitation data amounts have been used. Computation of SPI involves fitting a gamma probability density function to a given frequency distribution of precipitation totals for the station considered (McKee et al., 1993; Mihajlović, 2006).
3. Results
9-month period SPI (SPI9) and monthly scale PDSI for Zagreb-Grič Observatory for the period are represented in Figure 1. They have similar time patterns and negative trends, stronger for PDSI because of gobal warming i.e. because of potential evapotranspiration increase (Pandžić et al., 2009). The year 2012 was one of the the driest year since begining obsevation at Zagreb-Grič Observatory what is especialy emphasized in the case of PDSI. Figure 2, representing scatter diagrams between SPI1, SPI9, SPI24 and PDSI, respectively, indicates the strongest similarity between SPI9 and PDSI. Higher time resolution of SPI1, SPI9 and PDSI is shown in Figure 3 for te period Again SPI9 and PDSI express similar patterns although drought periods are more clear emphasised in the case of PDSI than in the case of SPI9. In both cases a high correlation is shown with drought damages for Croatia shown in Figure 4. s a 1
Figure 1. SPI9 and PDSI trends for Zagreb for period
Figure 2. Scatter diagrams between SPI1, SPI9, SPI24 and PDSI,
for Zagreb, respectively. F
Figure 3. SPI1, SPI9 and PDSI for Zagreb for
Figure 4. Damages (in Million Euros) due to drought in Croatia during the period 2000–2012
References
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