Journal of Watershed Management Research

Extended Abstract
Background: Global warming has increased the frequency of extreme precipitation events across the globe during the past decades. Information about precipitation and its forecast in various hydrological applications is important in terms of economic studies and risk assessment, such as estimating the maximum probable flood and engineering and management of water resources. One of the important parameters that changes with climate change is probable maximum precipitation (PMP), defined as the maximum probable precipitation depth in a certain duration, such as 24 hours, at a single site. There is no comprehensive study on the impact of climate change on PMP in Iran. This study fills the mentioned gap in the country by generating the precipitation depths for future periods in Iran. As a result of climate change, PMP and the negative consequences of such precipitation are expected to increase until the end of the century. As a result, studying the effect of climate change on extreme precipitation and PMP of different stations in Iran can be very important and practical.
Methods: In this study, the effects of future climate changes on PMP values ​​in different climates in Iran were investigated using two distinct scenarios, namely SSP1-2.6 and SSP5-8.5 (optimistic and pessimistic scenarios, respectively), and the HadGEM3-GC31-LL model from the output of the latest report of the Intergovernmental Panel on Climate Change (IPCC), i.e., CMIP6 report. The output of this model and scenarios was downscaled using the LARS-WG model. The statistical measures of the correlation coefficient (r) and Root Mean Square Error (RMSE) were used to evaluate the performance of the LARS-WG model. Two statistical methods, generalized Hershfield and Hershfield-DESA and 30-year data (1993-2022) related to 13 synoptic stations located in different climates across Iran, were used to estimate the PMP values.
Results: The results of the simulation of precipitation and temperature in the base period (1993-2022) using the Lars-WG model indicate that the correlation coefficient (r) for precipitation ranges between 0.95 and 0.99, and for minimum and maximum temperatures is equal to 0.99 in all 13 synoptic stations in Iran. The range of RMSE for precipitation is between 1.22 and 16.29 mm, and for minimum and maximum temperatures is in the range of 0.18-0.42 and 0.14-0.32 °C, respectively. Therefore, the Lars-WG model has an acceptable performance in simulating the climate variables in the base period at all stations. The effect of climate change on the average annual precipitation is increasing in all regions of Iran, and the highest range of increase in the next 20-year period (2021-2040) is at Isfahan with a +61.18% increase compared to the base period and under the pessimistic scenario (SSP5-8.5). The maximum annual 24-hour precipitation will increase in most areas with arid climates and decrease in per-humid climates (Bandar Anzali and Astara). The maximum annual 24-hour precipitation of the stations in the base period ranges from 25 mm in Tabas to 214 mm in Bandar Anzali. In the future period, the annual maximum 24-hour precipitation will increase in Isfahan, Tabas, Kashan, Birjand, Zahedan, Ardabil, and Yasuj under both scenarios, and in Khorramabad under the SSP5-8.5 scenario. A decrease in the maximum annual 24-hour precipitation is predicted in Tehran, Mashhad, Tabriz, BandareAnzali, and Astara under both scenarios and in Khorramabad under the SSP1-2.6 scenario. The largest increase and decrease in maximum 24-hour precipitation is predicted with a +55.70% increase in Yasouj and a -26.92% decrease in Bandar Anzali, respectively, compared to the base period. The highest ratio of PMP to maximum annual 24-hour precipitation is estimated in arid climates. According to the Desa method, the highest ratio of PMP to the maximum 24-hour precipitation in the future will be at Zahedan under both scenarios. The biggest increase in this ratio will be in Mashhad. According to the generalized Hershfield method, the value of PMP in all stations has been estimated many times higher than that of the modified Desa method. According to the modification of the frequency factor (K_m) in the Desa method, the estimated values seem more expected according to this method. The amount of PMP will increase in most synoptic stations in Iran. The highest PMP increase in Zahedan under the SSP1-2.6 scenario and decrease under both scenarios are predicted only in Bandar Anzali. The percentage of PMP changes in the future will be higher in most arid and semi-arid climates than in humid climates.
Conclusion: The general results of this study indicate that the average annual precipitation will increase in all climates of Iran under the effect of climate change. In addition, the maximum annual 24-hour precipitation will increase in most regions with arid climates. This amount will decrease in per-humid climates. The generalized Hershfield method overestimates PMP values, but the values estimated using the Hershfield-DESA method are more expected. In general, the PMP will increase in most synoptic stations in Iran in the future. The percentage of PMP changes in the future will be higher in arid and semi-arid climates than in humid climates. Therefore, it is necessary to pay attention to the areas prone to adverse consequences from probable floods resulting from such precipitation. The results of the present research can be used by managers and decision-makers in engineering and water resources management issues to anticipate the effects of climate change on extreme precipitation and maximum probable precipitation in different cities and regions of Iran.