Integrated sensitivity analysis, calibration, and uncertainty propagation analysis approaches for supporting hydrological modeling

The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.

Regional flood frequency analysis for Newfoundland and Labrador using the L-Moments index-flood method

The L-moments based index-flood procedure had been successfully applied for Regional Flood Frequency Analysis (RFFA) for the Island of Newfoundland in 2002 using data up to 1998. This thesis, however, considered both Labrador and the Island of Newfoundland using the L-Moments index-flood method with flood data up to 2013. For Labrador, the homogeneity test showed that Labrador can be treated as a single homogeneous region and the generalized extreme value (GEV) was found to be more robust than any other frequency distributions. The drainage area (DA) is the only significant variable for estimating the index-flood at ungauged sites in Labrador. In previous studies, the Island of Newfoundland has been considered as four homogeneous regions (A,B,C and D) as well as two Water Survey of Canada's Y and Z sub-regions. Homogeneous regions based on Y and Z was found to provide more accurate quantile estimates than those based on four homogeneous regions. Goodness-of-fit test results showed that the generalized extreme value (GEV) distribution is most suitable for the sub-regions; however, the three-parameter lognormal (LN3) gave a better performance in terms of robustness. The best fitting regional frequency distribution from 2002 has now been updated with the latest flood data, but quantile estimates with the new data were not very different from the previous study. Overall, in terms of quantile estimation, in both Labrador and the Island of Newfoundland, the index-flood procedure based on L-moments is highly recommended as it provided consistent and more accurate result than other techniques such as the regression on quantile technique that is currently used by the government.