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 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.