Climate change has impacted all phenomena in the hydrologic cycle, especially extreme events. General circulation models (GCMs) are used to investigate climate change impacts but because of their low resolution, downscaling methods are developed to provide data with high enough resolution for regional studies from GCM outputs. The performance of rainfall downscaling methods is commonly acceptable in preserving average characteristics, but they do not preserve the extreme event characteristics especially rainfall amount and distribution. In this study, a novel downscaling method called synoptic statistical downscaling model is proposed for daily precipitation downscaling with an emphasis on extreme event characteristics preservation. The proposed model is applied to a region located in central Iran. The results show that the developed model can downscale all percentiles of precipitation events with an acceptable performance and there is no assumption about the similarity of future rainfall data with the historical observations. The outputs of CCSM4 GCM for two representative concentration pathways (RCPs) of RCP4.5 and RCP8.5 are used to investigate the climate change impacts in the study region. The results show 40% and 30% increase in the number of extreme rainfall events under RCP4.5 and RCP8.5, respectively.

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Simulation of the interaction between an urban drainage system (UDS) and surface flow is a challenging issue. Conventional rainfall–runoff models are only able to simulate the flow in UDSs without considering the interaction between surface runoff and UDS. In this study, SWMM (storm water management model), as a well‐known urban drainage simulation model, and a two‐dimensional overland flow model developed based on the cellular automata approach, have been coupled for UDS simulation. In the proposed model, sewer flows are one‐dimensionally simulated using the SWMM model and at the flooded manholes, the surcharge hydrographs are determined. Then the two‐dimensional overland flow model is used for routing the surcharged flows. The coupled model could consider the complex interaction between the drainage system and the overland flow. The proposed model has been used in a study area located in Tehran, capital of Iran. The results show that almost 40% of surcharged flow returns back to the UDS, which caused 1.5% increase in the UDS's outflow volume. This can greatly affect the UDS performance downstream of the study area. The implementation results show the model's capability in simulation of flood behaviour as well as the urban inundated area with acceptable accuracy and much less computational effort. Copyright © 2017 John Wiley & Sons, Ltd.

Climate change has a dramatic effect on the hydrologic variables including extreme rainfall amounts. To evaluate the climate change effects, general circulation models (GCMs) have been developed. However, due to the daily temporal scale of GCM outputs which could be insufficient for some hydrological studies, disaggregation models are introduced. The available disaggregation models which are almost useful in producing time series of finer scale than a day, cannot accurately estimate some statistical characteristics such as extreme events. The method of fragments (MOF) is one of the disaggregation models which considers daily rainfall as the only input. In the present study, in addition to daily rainfall, other influential factors on the rainfall distribution during a day such as weather variables and sub‐daily characteristics have been considered to improve the disaggregation results especially extreme events estimation in the MOF model. The two introduced approaches have been examined for a case study in Tehran, Iran and indicated that weather variables and sub‐daily characteristics are effective in the daily rainfall disaggregation during the dry and wet seasons, respectively. These approaches seem to be much better than the basic MOF in sub‐daily rainfall disaggregation. Hence, the modified disaggregation approaches have been used to evaluate the climate change impacts on the sub‐daily rainfall distribution. The obtained results indicated an increase in the extreme value statistics such as mean and standard deviation of the 95th percentile data compared with the historical ones.

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