ADVANCING HYDROLOGIC MODEL EVALUATION AND IDENTIFICATION USING MULTIOBJECTIVE CALIBRATION, SENSITIVITY ANALYSIS, AND PARALLEL COMPUTATION

Open Access
- Author:
- Tang, Yong
- Graduate Program:
- Civil Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- January 24, 2007
- Committee Members:
- Patrick M Reed, Committee Chair/Co-Chair
Christopher J Duffy, Committee Member
Thorsten Wagener, Committee Member
Hangsheng Lin, Committee Member - Keywords:
- Hydrologic model
model calibration
sensitivity analysis
multiobjective optimization
evolutionary algorithm
parallel computation - Abstract:
- 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 Hydrology Laboratory Research Modeling System (HL-RMS), and a semi-distributed version of the physical Penn State Integrated Hydrologic Model (PIHM). The 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 used in calibrating the SAC-SMA and PIHM. This research proposes a formal metrics-based methodology for algorithm evaluation. 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, solution quality and robustness on a variety of applications including test functions, hydrologic model calibration, and long-term groundwater monitoring design. 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 time scales and watershed locations. The results show that model parameter sensitivities are heavily impacted by the choice of analysis method, model time interval, and local physical characteristics. Study 4 extends Study 3 to advance distributed hydrologic model evaluation and identification using Sobol¡¯s variance decomposition method. Study 4 demonstrates a methodology that balances the computational constraints posed by global sensitivity analysis with the need to fully characterize the HL-RMS's sensitivities. The model sensitivities were assessed for long-term (annual and monthly) as well as short-term (events) forecasting periods using approaches allowing model users to visualize spatial sensitivities with the aid of GIS tool. 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.