Conceptual hydrologic modeling : insights into Bayesian analysis, model development, and predictions in ungauged basins
Water is a fundamental resource, essential for human activities and underpinning economic and environmental sustainability. As such, management of water resources is a critical task, even more so in this time of climate uncertainty and change. Though hydrologic models have long been used to address water resource management, there is a growing desire to develop more reliable, transferable, and physically meaningful hydrologic models in light of data scarcity and uncertainty. In this dissertation, the three primary topics of (1) statistical implementation of models under uncertainty, (2) hydrologic model development, and (3) model applicability/transferability under data scarcity are considered. As a result of this research, we have: (1) developed methods for improved statistical implementation of hydrologic models through the development of a formal likelihood function for ephemeral catchments, the creation of a framework for identifying adequate likelihood functions across catchment conditions, and the introduction of a time domain transformation that was not previously used in hydrologic modeling; (2) developed a new model of catchment hydrologic connectivity based on extensive empirical observations that was shown to be consistent with both external and internal hydrologic variables, as well as transferable; and (3) introduced a new hierarchical Bayesian approach that was shown to accurately quantify uncertainty at an ungauged catchment based on pooled information from similar gauged catchments. This dissertation is divided into six key chapters/manuscripts that each address various aspects involved in improving hydrologic model performance, whose cumulative contribution results in a better understanding of how to construct, implement, and apply hydrologic models to lead to improved water resource management and planning problems of societal importance.