First Advisor

Hamid Moradkhani

Date of Publication

Spring 7-20-2015

Document Type


Degree Name

Master of Science (M.S.) in Civil & Environmental Engineering


Civil and Environmental Engineering




Hydrologic models -- Columbia River Watershed -- Evaluation, Sensitivity theory (Mathematics), Soil moisture -- Columbia River Watershed -- Mathematical models.



Physical Description

1 online resource (ix, 54 pages)


Global Sensitivity Analysis (GSA) approach helps to identify the effectiveness of model parameters or inputs and thus provides essential information about the model performance. The effects of 14 parameters and one input (forcing data) of the Sacramento Soil Moisture Accounting (SAC-SMA) model are analyzed by using two GSA methods: Sobol' and Fourier Amplitude Sensitivity Test (FAST). The simulations are carried out over five sub-basins within the Columbia River Basin (CRB) for three different periods: one-year, four-year, and seven-year. The main parameter sensitivities (first-order) and the interactions sensitivities (second-order) are evaluated in this study. Our results show that some hydrological processes are highly affected by the simulation length. In other words, some parameters reveal importance during the short period simulation (e.g. one-year) while other parameters are effective in the long period simulations (e.g. four-year and seven-year). Moreover, the reliability of the sensitivity analysis results is compared based on 1) the agreement between the two sensitivity analysis methods (Sobol' and FAST) in terms of highlighting the same parameters or input as the most influential parameters or input and 2) how the methods are cohered in ranking these sensitive parameters under the same conditions (sub-basins and simulation length). The results show that the coherence between the Sobol' and FAST sensitivity analysis methods. Additionally, it is found that FAST method is sufficient to evaluate the main effects of the model parameters and inputs. This study confirms that the Sobol' and FAST methods are reliable GSA methods that can be applied in different scientific applications. Finally, as a future work, we suggest to study the uncertainty associated with the sensitivity analysis approach regarding the reliability of evaluating different sensitivity analysis methods.


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