Advancing Hydrologic Model Evaluation and Identification using Multiobjective Calibration, Sensitivity Analysis, and Parallel Computation


This thesis work has comprehensively compared, developed, and implemented tools for advancing the evaluation and identification of hydrologic models including the lumped conceptual Sacramento Soil Moisture Accounting (SAC-SMA) model coupled with a snow accumulation and ablation model (SNOW-17), the distributed conceptual Research Distributed Hydrologic Model (HL-RDHM), and a semi-distributed version of the physical Penn State Integrated Hydrologic Model (PIHM). The model evaluation and identification tools addressed in this thesis include evolutionary multiobjective optimization algorithms and several sensitivity analysis methods implemented for distributed parallel computing systems. This thesis work was partitioned into four component studies. Study 1 assesses the efficiency, effectiveness, reliability, and ease-of-use of state-of-the-art evolutionary multiobjective optimization (EMO) tools when calibrating the SAC-SMA and the PIHM. This research proposes and demonstrates a formal metrics-based methodology for algorithm evaluation that clearly demonstrates their relative strengths and weaknesses. Understanding the relative strengths and weaknesses of the currently available EMO algorithms was important for Study 2 in which two parallelization schemes were developed to improve EMO algorithms’ performance in terms of their computational cost, their ability to identify high quality solutions and their robustness on a variety of applications including computer science test functions, hydrologic model calibration, and long-term groundwater monitoring design. Beyond EMO algorithmic improvements, model evaluation and identification also requires a detailed understanding of hydrologic simulations’ sensitivities to guide model improvement, advance calibration strategies, and enhance our understanding of the key observations and processes controlling model behavior. Study 3 compares the repeatability, robustness, efficiency, and ease-of-implementation of four sensitivity analysis (SA) methods ranging from local analysis using parameter estimation software (PEST) to global approaches including regional sensitivity analysis (RSA), analysis of variance (ANOVA), and Sobol’s method. The four SA tools were applied to the fully lumped SAC-SMA coupled with SNOW-17 using different model time steps and watershed locations. The results show that lumped model parameter sensitivities are heavily impacted by the choice of analysis method, model time interval, and local watershed characteristics. Study 4 extends Study 3 to advance distributed hydrologic model evaluation and identification using Sobol’s variance decomposition method since it was shown to be more robust and interpretable relative to the other sensitivity analysis methods tested. Study 4 demonstrates a methodology that balances the computational constraints posed by global sensitivity analysis with the need to fully characterize the HL-RDHM’s sensitivities. The model’s sensitivities were assessed for long-term (annual and monthly) as well as short-term (events) forecasting periods. Overall, the results reveal that storage variations, spatial trends in forcing, cell-connectivity, and cell proximity to the gauged outlet are the four primary factors that control the HL-RDHM’s behavior. This study suggests that operational forecasts would benefit from the joint use of a robust sensitivity analysis framework directly integrated into new calibration methodologies. Overall, this thesis advances the analysis, formulation, and solution of hydrologic model evaluation and identification problems using multiple performance objectives and state-of-the-art algorithms implemented to exploit high-performance computing.