چكيده لاتين :
As an undeniable environmental phenomenon, climate change can be defined as a reversible
change or variability in the average climate and its relevant variables, including the temperature, precipitation,
humidity, climate patterns, wind, radiation, etc., which lasts for a long period of time. Located in a special
geographical location that suffers from insufficient precipitation, Iran faces inappropriate distribution of rainfall
temporally and spatially. On the other hand, the world seems to be facing new challenges in terms of water
resources. Moreover, the most important consequence of the change in the hydrological cycle is the tendency
toward extreme events such as torrential rains, widespread droughts, and in some cases, regional wetlands. In
this regard, it can be said that the frequency and severity of floods are among the terrible or deadly natural
disasters brought about by climate change. Therefore, it is necessary to determine the best probability for the
distribution of flood discharges, measure the best probability distribution for management and planning in cases
where climate change occurs, and finally assess the frequency and severity of floods in Iran.
Materials and methods: To analyze the floods' frequency and severity under different climate change scenarios,
the minimum and maximum values of precipitation and temperature were measured using the CanESM2 general
circulation model under RCP2.6 and RCP8.5 climate change scenarios and the SDSM4.2.9 linear multiple
regression downscaling model. Then, the collected precipitation data was processed and analyzed and the flood
pattern was simulated for future periods via NetSTORM software (separating the rainfall from the hourly data).
Moreover, the HEC-HMS model was used to simulate floods in basic and future periods. Accordingly, the SCS
method, the Clark unit hydrograph method, and the Muskingum method were calibrated and evaluated to
calculate the infiltration, convert the rainfall to runoff, and rout the river, respectively. Finally, to analyze the
floods' frequency and severity, the probabilistic distribution function was fitted for the future periods' baseline
data and propagation scenarios using the SMADA software for different statistical distributions (normal, twoand
three-parameter log-normal, Pearson type III, Log-Pearson Type III and Gumbel), followed by the selection
of the best-fit distribution model based on the RMSE and MSE tests.
Results: The results of the climatic model showed that under the RCP2.6 and RCP8.5 scenarios, the maximum
temperature rate would increase in 2011-2055-and 2056-2100 by 3.02˚C, 3.27˚C, and 3.2˚C, and 5.47˚C,
respectively. Furthermore, the minimum temperature rate would increase in the same periods by 0.62, 0.87, and
1.1 and 2.82 degrees Celsius, respectively. However, the monthly precipitation data did not reveal any specific
trend throughout the future periods. The Emamah watershed's data concerning the flood discharge and maximum daily precipitation rate during
the study period (1999-2019) were used to select flood and pervasive events. The predicted data were then
analyzed under RCP2.6 and RCP8.5 scenarios at five separate periods. Finally, the selected events' data were
imported to the HEC-HMS model and simulated. After selecting the flood events with the highest magnitude
compared to other events, they were decomposed into one-hour or fewer rainfall events using the NETSTORM
model. Then the flood discharge values were calculated for the base and future periods and their probabilistic
distribution function was obtained through the SMADA software. Finally, the Pearson type III distribution, the
best distribution among normal, two-and three-parameter lognormal, Pearson type 3, Log-Pearson Type 3, and
Gamble distributions were selected for each base and future time series using the goodness-of-fit test.
According to the results of the best frequency distribution, flood values were estimated with return periods of 2,
10, 25, 50, 100, and 200 years. Moreover, one third of the floods with a 2-year return period witnessed an
increase in discharge rate compared to the base period. However, the discharge rate decreased or remained
unchanged in floods with other return periods. On the other hand, floods estimated with 10 and 25-year return
periods were increased in two-thirds of the periods, the highest increase of which occurred in the second period
under the RCP8.5 scenarios by 12.68 and 25.76 percent, respectively. It should also be noted that the highest
chances of increase in flood occurrence with a return period of 200 years belonged to the second period by
56.12% increase rate and 10.07 m2 discharge rate under the RCP8.5 scenarios.
Discussion and Conclusion: throughout the next hundred years, climate change would experience significant
changes in precipitation patterns, leading to the risks of severe floods and droughts. Moreover, the results of the
analysis and study of climate change indicated that the temperature increasing trend in the periods under the
studied scenarios and that the biggest increase belonged to the RCP8.5 scenarios. It was also found that the
temperature rate would increase more in the period 2056-2100 compared to the 2011-2055 period. However, the
results of precipitation simulation under the scenarios did not show a definite trend for the future periods, with
the precipitation increasing and decreasing in different months of the year. On the other hand, the simulation of
basin floods for the future periods and the comparison of peak discharge values within the future and the
observation periods indicated a change in the regime of river flood discharges. Accordingly, the maximum
discharge rate increased in the constant returns period. Furthermore, the discharge rates significantly increased in
the maximum constant flow period with an increase in the return period.
