Addressing Variability in Hydrologic Systems Using Efficient Uncertainty Quantification

The scale and complexity of environmental and earth systems introduce an array of uncertainties that need to be systematically addressed. In numerical modeling, the ever-increasing complexity of representation of these systems confounds our ability to resolve relevant uncertainties. Specifically, the numerical simulation of the governing processes involve many inputs and parameters that have been traditionally treated as deterministic. Considering them as uncertain with traditional approaches introduces a large computational burden, stemming from the requirement of a prohibitive number of model simulations. Furthermore, within hydrology, most catchments are sparsely monitored, and there are limited, disparate types of data available to confirm the model's behavior. Here I present a blueprint of a general, computationally efficient approach to uncertainty quantification for complex hydrologic models, taking advantage of recent methodological developments. The framework is used in two basic science problems in hydrology. First, it is applied to the problem of combining heterogeneous data sources representing different physical processes to infer physical parameters for the complex hydrologic model tRIBS-VEGGIE. The inference provides a probabilistic interpretation of bulk soil characteristics and related hydraulic properties for an experimental watershed in central Amazonia. These parameters are then used to propagate uncertainty in hydrologic response to an array of quantities of interest through tRIBS-VEGGIE and determine their sensitivity to uncertain model inputs. Second, the framework is used to explore landscape controls mediated by subsurface hydrologic dynamics on the distribution of vegetative traits in a mature Amazon rainforest. This study features a large parameter set as uncertain across three different soil types and three layers of vegetation, explicitly incorporating interactions between subsurface moisture and vegetation biophysical function. Vegetative performance is examined using a hypothesized cost-benefit approach between vegetation carbon uptake and hydraulic effort required to maintain long-term production. The research enables model-driven inference using a disparate set of observed hydrologic variables including stream discharge, water table depth, evapotranspiration, soil moisture, and gross primary production from the Asu experimental catchment near Manaus, Brazil. Computationally inexpensive model surrogates are constructed and shown to mimic solution of the complex hydrologic model tRIBS-VEGGIE with a high skill. The two applications demonstrate the flexibility of the framework for hydrologic inference in watershed with sparse, irregular observations of varying accuracy. Significant computational savings imply that problems of greater computational complexity and dimension can be addressed. Furthermore, the framework simultaneously yields probabilistic representation of model behavior, robust parameter inference, and sensitivity analysis without the need for greater investment in computational resources.PHDEnvironmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147605/1/dwellem_1.pd