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1. chinaXiv:202006.00236 [pdf]

Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran

Subjects: Geosciences >> History of Geosciences

Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (R2>0.63; R2, determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature (R2>0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.

submitted time 2020-06-22 From cooperative journals:《Journal of Arid Land》 Hits4592Downloads242 Comment 0

2. chinaXiv:201711.00373 [pdf]

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

Subjects: Geosciences >> Geography

Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoringthe drought is a vital and important part of predicting and analyzing drought impacts. Because no singleindex can represent all facets of meteorological drought,二took a multi-index approach for droughtmonitoring 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 (Effectivedrought index), CZI (China-Z index), MCZI (i}Modified CZI), Rr1I (Rainfall Anomaly Index), and ZSI(Z一score Index)) calculated from the station-observed precipitation data and the r}gi}MERRr} griddedprecipitation data to assess historical drought events during the period 1987-?010 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 AgMERRr1 precipitation data were higher than those derived from the station-observed precipitation data. r1ll 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 (B2=0.99). Furthermore, the higher correlations (B2=0.99) were also found between CZI and MCZI, and between ZSI and Rr1I. r1ll indices were able to track drought intensity but EDI and Rr1I showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the AgMERRr1 precipitation data and from the station-observed precipitation data.MERRr1 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 :}gi}IERR:} precipitation data may be used for the data-lacking are as.

submitted time 2017-11-07 From cooperative journals:《Journal of Arid Land》 Hits806Downloads320 Comment 0

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