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.

中文翻译：

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次日降雨分解分段修正法评价气候变化对极端降雨量的影响

气候变化对包括极端降雨量在内的水文变量有显着影响。为了评估气候变化的影响，已经开发了大气环流模型（GCM）。然而，由于 GCM 输出的每日时间尺度可能不足以进行某些水文研究，因此引入了分解模型。可用的分解模型几乎可用于生成比一天更精细的时间序列，但无法准确估计某些统计特征，例如极端事件。碎片法（MOF）是一种将日降雨量作为唯一输入的分解模型。在本研究中，除了每日降雨量外，其他影响一天降雨分布的因素，如天气变量和次日特征，已被考虑改进分解结果，特别是 MOF 模型中的极端事件估计。已在伊朗德黑兰对这两种引入的方法进行了案例研究，并表明天气变量和次日特征分别在旱季和雨季的每日降雨分解中有效。这些方法似乎比次日降雨分解中的基本 MOF 要好得多。因此，改进的分解方法已被用于评估气候变化对次日降雨分布的影响。