UNCERTAINTY REDUCTION IN RUNOFF ESTIMATION USING RAINFALL DATA
Ahmed Mohamed Lotfy Youssef Nasr;
Abstract
In hydrological modeling, there are several sources of uncertainties
associated with runoff estimation. Rainfall data is frequently
represented in modeling as an average value over the studied
watershed. As the rainfall data is the primary input of hydrological
modeling, it is considered the most crucial source of uncertainty related
to runoff estimation. Consequently, increasing the degree of confidence
of the errors associated with estimating the average value of rainfall
would definitely reduce the uncertainty in the computed runoff. This is
not linked to the errors resulted from hydrological models themselves
where they depend on other parameters.
The current dissertation therefore contributes to reduce the uncertainty
in runoff estimation by using the rainfall data. The aim of this research
associated with runoff estimation. Rainfall data is frequently
represented in modeling as an average value over the studied
watershed. As the rainfall data is the primary input of hydrological
modeling, it is considered the most crucial source of uncertainty related
to runoff estimation. Consequently, increasing the degree of confidence
of the errors associated with estimating the average value of rainfall
would definitely reduce the uncertainty in the computed runoff. This is
not linked to the errors resulted from hydrological models themselves
where they depend on other parameters.
The current dissertation therefore contributes to reduce the uncertainty
in runoff estimation by using the rainfall data. The aim of this research
Other data
| Title | UNCERTAINTY REDUCTION IN RUNOFF ESTIMATION USING RAINFALL DATA | Authors | Ahmed Mohamed Lotfy Youssef Nasr | Issue Date | 2018 |
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