Rainfall-Runoff Modeling to Predict Maximum Daily Flow under Climate Change Conditions

One of the most important challenges in recent years is the issue of climate change and its effects on water resources, which is considered one of the main areas of research around the world. Two obvious signs of climate change are changes in the mean and extreme values of climate variables that can increase the Probability of flood occurrence. Therefore, it is necessary to study the effect of climate change on floods in the future.

In this study, using the observed daily data of precipitation and temperature in the period 1961-2005 synoptic and climatological stations inside and around the Bakhtiary catchment, the effect of climate change was investigated. To predict future precipitation and temperature, the data of the canESM2 general circulation model under RC 2.6 and RCP 8.5 scenarios for climate variables for the period 2006 to 2100 were used. The effective criteria in downscaling were selected according to the correlation between the predictor and predictands variables from the NCEP-NCAR reanalysis database. Then the MORDOR-SD semi-distributed models were used to evaluate the effects of climate change on runoff. This model was calibrated and validated using daily rainfall, temperature, and observation data at Tang-e-Panj Bakhtiary station for the period 1976-2006. Applying the results of changes in precipitation and temperature data obtained from the SDSM model under the two scenarios mentioned and reproducing them in large numbers by nonparametric bootstrap method and replacing the resulting maximum daily events in the hydrological model to achieve maximum daily flood and probability made it possible. At the end of the simulation and calculation of the cumulative distribution function (CDF) of the simulated events, the maximum daily discharge values in different return periods were calculated.

The results showed that, temperature and precipitation parameters under RCP 2.6 and RCP 8.5 scenarios with average evaluation criteria MAE = 1.29, RMSE = 1.96 and NASH = 0.56 for precipitation decreased by an average of 5.4 to 11% compared to the base period for temperature with evaluation criteria of MAE= 2.27, RMSE= 2.71 and NASH 0.92 = from 6.5 to 10.4% increase compared to the base period. The results of the Nash-Sutcliffe and Kling-Gupta evaluation criteria are 0.76 and 0.83, respectively, which shows the acceptable ability of the hydrological model in the simulation. The results of the study showed that the maximum daily discharges simulated using scenario 8.5 in different return periods show higher values than the maximum daily discharges in scenario 2.6 but there is no significant change compared to the maximum daily data in the base period.

In general, it can be said that the use of climate data in the future for planning in the field of integrated water resources management and prevention of natural hazards such as extreme floods and droughts can be a useful tool for planning managers.