Urban areas -- often characterized by their impervious surfaces -- have a disproportionate impact on the hydrologic regimes of associated river systems and their water quality. Over the past century, enormous amounts of scientific research and funding has been allocated towards the effective management of urban runoff. Despite these investments, however, it continues to confound engineering solutions, causing pollution, flooding, and habitat destruction. These challenges are compounded by the need to plan for non-stationarity in climate, shifts in hydrologic regimes and land use, and inter-dependencies between ecological, hydrological, and human systems. The first chapter of the dissertation provides relevant background on the challenges associated with effectively managing the hydrologic impacts of urbanization. These challenges arise from some of the assumptions that have driven management decisions over the past century, namely: (1) that production of urban runoff is dominated by overland flow across impervious surfaces; and (2) that overland flow in urban areas is determined by the total quantity of impervious area, rather than their patterns with interspersed previous areas, and characteristics of these pervious areas.
The second chapter of the dissertation explores the first of these assumptions, and asks: What are the implications of different hydrologic processes for the production of urban stormwater and its management? The assumption that runoff in urban areas is driven by impervious surfaces has dominated our understanding and management of urban catchments for decades. Through a literature review and theoretical framework, this chapter identifies the range and drivers of hydrologic processes in urban settings, characterizes their associated spatial and temporal scales, and shows how a mismatch in process and management scales can lead to unintended outcomes. It offers guidance for adaptation of the current `risk-based' approach for managing urban runoff across the different runoff processes and scales.
The third chapter of the dissertation explores the second assumption, and asks: How does landscape variability impact the spatial production of urban runoff? This chapter uses a combination of hydrologic modeling, machine learning, and geospatial analysis to determine the extent to which different landscape factors moderate runoff contribution from impervious areas. Results show that impervious surface contribution to runoff (or, `hydrologic connectivity of impervious areas', HCIA), is controlled by spatial variability of pervious area characteristics and temporal variability in pervious area conditions and rainfall. To enable such analysis in practice and for urban planning purposes, this chapter presents a geospatial tool for estimation of HCIA at watershed scales.
The fourth chapter of the dissertation explores the implications of the second assumption for the predictive accuracy of semi-distributed hydrologic models, and asks: How do land cover characteristics and climate variability impact calibration and predictive accuracy of semi-distributed runoff models? Semi-distributed models represent landscape heterogeneity, such as pervious and impervious patterns, with unobservable effective parameters. Through comparative `virtual experiments', this chapter demonstrates that the predictive accuracy of a widely used urban hydrological model (SWMM) can be affected by calibrated parameter dependence on soil, storm, and landcover characteristics. The inter-dependencies between the forcing parameters and calibration parameters can result in significant prediction error when a calibrated model is applied to predict runoff from novel climate and landcover conditions.
The research presented in this dissertation will help municipalities and flood managers identify applicable policies, design standards, and planning mechanisms for urban runoff management. It also points to a need for a better understanding of the process (or processes) by which runoff is generated, the effects of human alteration and management on these processes, and the sensitivity of such processes to ongoing changes in climate, land use, and management.
