Keywords: reconnaissance drought index, RDI, standardised precipitation index, SPI, uncertainty, ECHO–G, A1 emission scenario, LARS–weather generator, LARS–WG, drought prediction, climate change, Iran, downscaling parameters, temperature, rainfall, drought severity, droughts
Quantitative assessment and prediction of drought under climate change impact in Birjand region, Iran
Drought as a natural hazard causes high amount of damages to farmers, government and different people in societies. Protecting against this phenomena can lead to decline the damage. In order to reach this aim, in the present study, A1 emission scenario, ECHO–G model and LARS–weather generator (LARS–WG) statistical downscaling method are used for downscaling parameters of minimum and maximum temperature and rainfall. Using two drought indices, standardised precipitation index (SPI) and reconnaissance drought index (RDI), the uncertainty of generated data is computed to assess the model abilities. The drought indices were estimated and assessed in different scales. Then, the frequency analyses of maximum and average characteristics of drought severity in different return periods were performed. The results indicated that SPI and RDI have similar trends in recognising classified range of drought severity. Of course, RDI index identified and assessed drought with high intensity. The frequency analysis in this station showed that the basin had strong sensitivity to these characteristics in different return periods.