|dc.description.abstract||The purpose of this dissertation is to enhance the applicability and the accuracy
of the Poisson cluster stochastic rainfall generators.
Firstly, the 6 parameters of the Modified Bartlett-Lewis Rectangular Pulse
(MBLRP) stochastic rainfall simulation model were regionalized across the contiguous
United States. Each of the parameters of MBLRP model estimated at 3,444 National
Climate Data Center (NCDC) rain gages was spatially interpolated based on the
Ordinary Kriging technique to produce the parameter surface map for each of the 12
months of the year. Cross-validation was used to assess the validity of the parameter
maps. The results indicate that the suggested maps reproduce well the statistics of the
observed rainfall for different accumulation intervals, except for the lag-1
autocorrelation coefficient. The estimated parameter values were also used to produce
the maps of storm and rain cell characteristics.
Secondly, the relative importance of the rainfall statistics in the generation of
watershed response characteristics was estimated based on regression analyses using the
rainfall time series observed at 1099 NCDC rain gages. The result of the analyses was
used to weigh the rainfall statistics differently in the parameter calibration process of MBLRP model. It was observed that synthetic rainfall time series generated weighing
the precipitation statistics according to their relative importance outperformed those
generated weighing all statistics equally in predicting watershed runoff depths and peak
flows. When all statistics were given the same weight, runoff depths and peak flows
were underestimated by 20 percent and 14 percent, respectively; while, when the statistics were
weighed proportionally to their relative importance, the underestimation was reduced to
4 percent and 3 percent, which confirms the advantage of weighing the statistics differently. In
general, the value of the weights depends on the hydrologic process being modeled.
Lastly, a stochastic rainfall generation model that can integrate year-to-year
variability of rainfall statistics is suggested. The new framework consists of two parts.
The first part generates the short-term rainfall statistics based on the correlation between
the observed rainfall statistics. The second part generates the rainfall time series using
the modified Bartlett-Lewis rectangular pulse model based on the simulated rainfall
statistics. The new approach was validated at 104 NCDC gages across the United States
in its ability to reproduce rainfall and watershed response characteristics. The result
indicates that the new framework outperformed the traditional approach in reproducing
the distribution of monthly maximum rainfall depths, monthly runoff volumes and
monthly peak flows.||en