چكيده لاتين :
A methodology is presentedfor the stochastic generation ofdaily rainfall which accounts
for changes to the climatic inputs. The focus of the study is an example catchment in Iran. The
methodology addresses the inability ofGCMs to provide suitable future scenarios for the time and
space scales requiredfor a water resource impact assessmentfor a small catchment. One stochastic
model for rainfall (Neyman-Scott Rectangular Pulses, NSRP, model) is used to generate daily
rainfall sequences and then validated using historic records. For present climate conditions, the
NSRP model is fitted to observed rainfall statistics. GCM outputs are then downscaled using
regressions between atmospheric circulation indices (ACIs) and rainfall statistics. The
relationships are then used to predict the rainfall statistics for future conditions using GCMoutputs.
In this respect, climate change impacts are studied and assessed in this paper. Generated rainfall
scenario can then be used as inputs to a rainfall-runoffmodel in order to generate daily streamflow
data which is not investigated here.
