Hydrology and biogeochemistry are coupled in all systems. However, human decision-making regarding hydrology and biogeochemistry are often separate, even though decisions about hydrologic systems may have substantial impacts on biogeochemical patterns and processes. The overarching question of this dissertation was: How does hydrologic engineering interact with the effects of nutrient loading and climate to drive watershed nutrient yields? I conducted research in two study systems with contrasting spatial and temporal scales. Using a combination of data-mining and modeling approaches, I reconstructed nitrogen and phosphorus budgets for the northeastern US over the 20th century, including anthropogenic nutrient inputs and riverine fluxes, for ~200 watersheds at 5 year time intervals. Infrastructure systems, such as sewers, wastewater treatment plants, and reservoirs, strongly affected the spatial and temporal patterns of nutrient fluxes from northeastern watersheds. At a smaller scale, I investigated the effects of urban stormwater drainage infrastructure on water and nutrient delivery from urban watersheds in Phoenix, AZ. Using a combination of field monitoring and statistical modeling, I tested hypotheses about the importance of hydrologic and biogeochemical control of nutrient delivery. My research suggests that hydrology is the major driver of differences in nutrient fluxes from urban watersheds at the event scale, and that consideration of altered hydrologic networks is critical for understanding anthropogenic impacts on biogeochemical cycles. Overall, I found that human activities affect nutrient transport via multiple pathways. Anthropogenic nutrient additions increase the supply of nutrients available for transport, whereas hydrologic infrastructure controls the delivery of nutrients from watersheds. Incorporating the effects of hydrologic infrastructure is critical for understanding anthropogenic effects on biogeochemical fluxes across spatial and temporal scales.
Natural gas development in the Northern Appalachian region has skyrocketed dramatically over the past decade. Correspondingly to the unprecedented growth rate of the natural gas industry, population health risks have shifted dramatically in response to both aerial and water pollution. With energy as a key input in all sectors of Appalachian life, the Pennsylvania region serves as a fascinating case study where clusters of unconventional gas drilling wells intersect varying population densities and governing laws to create different levels of health risks. Studies have found that horizontal hydraulic fracking corresponds to an increased risk of upper respiratory symptoms (URS), low birth weights, premature births, and certain cancers (White et al., 2009). Also, zoning and local planning laws are policy tools local governments can use to directly influence community wellbeing (Diez-Roux, 2011). This study will focus on the spatial relationship between upper respiratory symptoms (URS), a key volatile health benchmark, and the zoning/planning laws that the Oil and Natural Gas Industry must adhere to. Our project seeks to provide a preliminary understanding of the interplay between different natural gas zoning laws and the resulting health implication risks that appear in the Marcellus shale region of Pennsylvania. This is necessary to appropriately regulate and monitor hydraulic fracking. To get a better understanding of this phenomenon, spatial autocorrelation and analysis of variance statistics are integrated to generate a surface-level understanding of areas impacted by natural gas development. To guide the creation of our models, we geographically process the unconventional well locations, upper respiratory symptom health utilization, and zoning law data to develop insights that policymakers can take into consideration. Regionally, natural gas has become an integrated part of the energy sector and a driver of local economic development. The patterns drawn from this assessment provide a novel way of understanding the population health risks posed by different zoning ordinance models.