INTEGRATED ASSESSMENT OF ANTHROPOGENIC, CLIMATE, AND POLICY INDUCED CHANGES OF PHOSPHORUS EXPORT IN THE UNITED STATES LAURENTIAN GREAT LAKES WATERSHEDS

Anthropogenic activities have increased phosphorus (P) loading in tributaries to the Laurentian Great Lakes resulting in eutrophication in small bays to most notably, Lake Erie. Changes to surface water quality from P loading have resulted in billions of dollars in damage and threaten the health of the world’s largest freshwater resource. To understand the factors affecting P delivery with projected increasing urban lands and biofuels expansion, two spatially explicit models were coupled. The coupled models predict that the majority of the basin will experience a significant increase in urban area P sources while the agriculture intensity and forest sources of P will decrease. Changes in P loading across the basin will be highly variable spatially. Additionally, the impacts of climate change on high precipitation events across the Great Lakes were examined. Using historical regression relationships on phosphorus concentrations, key Great Lakes tributaries were found to have future changes including decreasing total loads and increases to high-flow loading events. The urbanized Cuyahoga watersheds exhibits the most vulnerability to these climate-induced changes with increases in total loading and storm loading , while the forested Au Sable watershed exhibits greater resilience. Finally, the monitoring network currently in place for sampling the amount of phosphorus entering the U.S. Great Lakes was examined with a focus on the challenges to monitoring. Based on these interviews, the research identified three issues that policy makers interested in maintaining an effective phosphorus monitoring network in the Great Lakes should consider: first, that the policy objectives driving different monitoring programs vary, which results in different patterns of sampling design and frequency; second, that these differences complicate efforts to encourage collaboration; and third, that methods of funding sampling programs vary from agency to agency, further complicating efforts to generate sufficient long-term data to improve our understanding of phosphorus into the Great Lakes. The dissertation combines these three areas of research to present the potential future impacts of P loading in the Great Lakes as anthropogenic activities, climate and monitoring changes. These manuscripts report new experimental data for future sources, loading and climate impacts on phosphorus.

Probabilistic-based hurricane risk assessment and mitigation considering the potential impacts of climate change

Studies are suggesting that hurricane hazard patterns (e.g. intensity and frequency) may change as a consequence of the changing global climate. As hurricane patterns change, it can be expected that hurricane damage risks and costs may change as a result. This indicates the necessity to develop hurricane risk assessment models that are capable of accounting for changing hurricane hazard patterns, and develop hurricane mitigation and climatic adaptation strategies. This thesis proposes a comprehensive hurricane risk assessment and mitigation strategies that account for a changing global climate and that has the ability of being adapted to various types of infrastructure including residential buildings and power distribution poles. The framework includes hurricane wind field models, hurricane surge height models and hurricane vulnerability models to estimate damage risks due to hurricane wind speed, hurricane frequency, and hurricane-induced storm surge and accounts for the timedependant properties of these parameters as a result of climate change. The research then implements median insured house values, discount rates, housing inventory, etc. to estimate hurricane damage costs to residential construction. The framework was also adapted to timber distribution poles to assess the impacts climate change may have on timber distribution pole failure. This research finds that climate change may have a significant impact on the hurricane damage risks and damage costs of residential construction and timber distribution poles. In an effort to reduce damage costs, this research develops mitigation/adaptation strategies for residential construction and timber distribution poles. The costeffectiveness of these adaptation/mitigation strategies are evaluated through the use of a Life-Cycle Cost (LCC) analysis. In addition, a scenario-based analysis of mitigation strategies for timber distribution poles is included. For both residential construction and timber distribution poles, adaptation/mitigation measures were found to reduce damage costs. Finally, the research develops the Coastal Community Social Vulnerability Index (CCSVI) to include the social vulnerability of a region to hurricane hazards within this hurricane risk assessment. This index quantifies the social vulnerability of a region, by combining various social characteristics of a region with time-dependant parameters of hurricanes (i.e. hurricane wind and hurricane-induced storm surge). Climate change was found to have an impact on the CCSVI (i.e. climate change may have an impact on the social vulnerability of hurricane-prone regions).

IMPACTS OF CLIMATE CHANGE ON SOIL MICROORGANISMS IN NORTHERN HARDWOOD FORESTS

As global climate continues to change, it becomes more important to understand possible feedbacks from soils to the climate system. This dissertation focuses on soil microbial community responses to climate change factors in northern hardwood forests. Two soil warming experiments at Harvard Forest in Massachusetts, and a climate change manipulation experiment with both elevated temperature and increased moisture inputs in Michigan were sampled. The hyphal in-growth bag method was to understand how soil fungal biomass and respiration respond to climate change factors. Our results from phospholipid fatty acid (PLFA) analyses suggest that the hyphal in-growth bag method allows relatively pure samples of fungal hyphae to be partitioned from bacteria in the soil. The contribution of fungal hyphal respiration to soil respiration was examined in climate change manipulation experiments in Massachusetts and Michigan. The Harvard Forest soil warming experiments in Massachusetts are long-term studies with 8 and 18 years of +5 °C warming treatment. Hyphal respiration and biomass production tended to decrease with soil warming at Harvard Forest. This suggests that fungal hyphae adjust to higher temperatures by decreasing the amount of carbon respired and the amount of carbon stored in biomass. The Ford Forestry Center experiment in Michigan has a 2 x 2 fully factorial design with warming (+4-5 °C) and moisture addition (+30% average ambient growing season precipitation). This experiment was used to examine hyphal growth and respiration of arbuscular mycorrhizal fungi (AMF), soil enzymatic capacity, microbial biomass and microbial community structure in the soil over two years of experimental treatment. Results from the hyphal in-growth bag study indicate that AMF hyphal growth and respiration respond negatively to drought. Soil enzyme activities tend to be higher in heated versus unheated soils. There were significant temporal variations in enzyme activity and microbial biomass estimates. When microbial biomass was estimated using chloroform fumigation extractions there were no differences between experimental treatments and the control. When PLFA analyses were used to estimate microbial biomass we found that biomass responds negatively to higher temperatures and positively to moisture addition. This pattern was present for both bacteria and fungi. More information on the quality and composition of the organic matter and nutrients in soils from climate change manipulation experiments will allow us to gain a more thorough understanding of the mechanisms driving the patterns reported here. The information presented here will improve current soil carbon and nitrogen cycling models.

Evaluation of flood risk in response to climate variability

Standard procedures for forecasting flood risk (Bulletin 17B) assume annual maximum flood (AMF) series are stationary, meaning the distribution of flood flows is not significantly affected by climatic trends/cycles, or anthropogenic activities within the watershed. Historical flood events are therefore considered representative of future flood occurrences, and the risk associated with a given flood magnitude is modeled as constant over time. However, in light of increasing evidence to the contrary, this assumption should be reconsidered, especially as the existence of nonstationarity in AMF series can have significant impacts on planning and management of water resources and relevant infrastructure. Research presented in this thesis quantifies the degree of nonstationarity evident in AMF series for unimpaired watersheds throughout the contiguous U.S., identifies meteorological, climatic, and anthropogenic causes of this nonstationarity, and proposes an extension of the Bulletin 17B methodology which yields forecasts of flood risk that reflect climatic influences on flood magnitude. To appropriately forecast flood risk, it is necessary to consider the driving causes of nonstationarity in AMF series. Herein, large-scale climate patterns—including El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO)—are identified as influencing factors on flood magnitude at numerous stations across the U.S. Strong relationships between flood magnitude and associated precipitation series were also observed for the majority of sites analyzed in the Upper Midwest and Northeastern regions of the U.S. Although relationships between flood magnitude and associated temperature series are not apparent, results do indicate that temperature is highly correlated with the timing of flood peaks. Despite consideration of watersheds classified as unimpaired, analyses also suggest that identified change-points in AMF series are due to dam construction, and other types of regulation and diversion. Although not explored herein, trends in AMF series are also likely to be partially explained by changes in land use and land cover over time. Results obtained herein suggest that improved forecasts of flood risk may be obtained using a simple modification of the Bulletin 17B framework, wherein the mean and standard deviation of the log-transformed flows are modeled as functions of climate indices associated with oceanic-atmospheric patterns (e.g. AMO, ENSO, NAO, and PDO) with lead times between 3 and 9 months. Herein, one-year ahead forecasts of the mean and standard deviation, and subsequently flood risk, are obtained by applying site specific multivariate regression models, which reflect the phase and intensity of a given climate pattern, as well as possible impacts of coupling of the climate cycles. These forecasts of flood risk are compared with forecasts derived using the existing Bulletin 17B model; large differences in the one-year ahead forecasts are observed in some locations. The increased knowledge of the inherent structure of AMF series and an improved understanding of physical and/or climatic causes of nonstationarity gained from this research should serve as insight for the formulation of a physical-casual based statistical model, incorporating both climatic variations and human impacts, for flood risk over longer planning horizons (e.g., 10-, 50, 100-years) necessary for water resources design, planning, and management.

The effects of a changing climate on root respiration of woody plants in sugar maple forests and northern peatlands

Global climate change might significantly impact future ecosystems. The purpose of this thesis was to investigate potential changes in woody plant fine root respiration in response to a changing climate. In a sugar maple dominated northern hardwood forest, the soil was experimentally warmed (+4 °C) to determine if the tree roots could metabolically acclimate to warmer soil conditions. After one and a half years of soil warming, there was an indication of slight acclimation in the fine roots of sugar maple, helping the ecosystem avoid excessive C loss to the atmosphere. In a poor fen northern peatland in northern Michigan, the impacts of water level changes on woody plant fine root respiration were investigated. In areas of increased and also decreased water levels, there were increases in the CO2 efflux from ecosystem fine root respiration. These studies show the importance of investigating further the impacts climate change may have on C balance in northern ecosystems.

Using water balance models to approximate the effects of climate change on spring catchment discharge : Mt. Hanang, Tanzania

This project addresses the potential impacts of changing climate on dry-season water storage and discharge from a small, mountain catchment in Tanzania. Villagers and water managers around the catchment have experienced worsening water scarcity and attribute it to increasing population and demand, but very little has been done to understand the physical characteristics and hydrological behavior of the spring catchment. The physical nature of the aquifer was characterized and water balance models were calibrated to discharge observations so as to be able to explore relative changes in aquifer storage resulting from climate changes. To characterize the shallow aquifer supplying water to the Jandu spring, water quality and geochemistry data were analyzed, discharge recession analysis was performed, and two water balance models were developed and tested. Jandu geochemistry suggests a shallow, meteorically-recharged aquifer system with short circulation times. Baseflow recession analysis showed that the catchment behavior could be represented by a linear storage model with an average recession constant of 0.151/month from 2004-2010. Two modified Thornthwaite-Mather Water Balance (TMWB) models were calibrated using historic rainfall and discharge data and shown to reproduce dry-season flows with Nash-Sutcliffe efficiencies between 0.86 and 0.91. The modified TMWB models were then used to examine the impacts of nineteen, perturbed climate scenarios to test the potential impacts of regional climate change on catchment storage during the dry season. Forcing the models with realistic scenarios for average monthly temperature, annual precipitation, and seasonal rainfall distribution demonstrated that even small climate changes might adversely impact aquifer storage conditions at the onset of the dry season. The scale of the change was dependent on the direction (increasing vs. decreasing) and magnitude of climate change (temperature and precipitation). This study demonstrates that small, mountain aquifer characterization is possible using simple water quality parameters, recession analysis can be integrated into modeling aquifer storage parameters, and water balance models can accurately reproduce dry-season discharges and might be useful tools to assess climate change impacts. However, uncertainty in current climate projections and lack of data for testing the predictive capabilities of the model beyond the present data set, make the forecasts of changes in discharge also uncertain. The hydrologic tools used herein offer promise for future research in understanding small, shallow, mountainous aquifers and could potentially be developed and used by water resource professionals to assess climatic influences on local hydrologic systems.

PARTICIPANT IMPACTS FROM A PARTICIPATORY WATER MODELING WORKSHOP IN THE SONORA RIVER BASIN, MEXICO

Much of the research in the field of participatory modeling (PM) has focused on the developed world. Few cases are focused on developing regions, and even fewer on Latin American developing countries. The work that has been done in Latin America has often involved water management, often specifically involving water users, and has not focused on the decision making stage of the policy cycle. Little work has been done to measure the effect PM may have on the perceptions and beliefs of decision makers. In fact, throughout the field of PM, very few attempts have been made to quantitatively measure changes in participant beliefs and perceptions following participation. Of the very few exceptions, none have attempted to measure the long-term change in perceptions and beliefs. This research fills that gap. As part of a participatory modeling project in Sonora, Mexico, a region with water quantity and quality problems, I measured the change in beliefs among participants about water models: ability to use and understand them, their usefulness, and their accuracy. I also measured changes in beliefs about climate change, and about water quantity problems, specifically the causes, solutions, and impacts. I also assessed participant satisfaction with the process and outputs from the participatory modeling workshops. Participants were from water agencies, academic institutions, NGOs, and independent consulting firms. Results indicated that participant comfort and self-efficacy with water models, their beliefs in the usefulness of water models, and their beliefs about the impact of water quantity problems changed significantly as a result of the workshops. I present my findings and discuss the results.

Assessment of Flood Risk Under Future Climate Conditions

Global climate change is predicted to have impacts on the frequency and severity of flood events. In this study, output from Global Circulation Models (GCMs) for a range of possible future climate scenarios was used to force hydrologic models for four case study watersheds built using the Soil and Water Assessment Tool (SWAT). GCM output was applied with either the "delta change" method or a bias correction. Potential changes in flood risk are assessed based on modeling results and possible relationships to watershed characteristics. Differences in model outputs when using the two different methods of adjusting GCM output are also compared. Preliminary results indicate that watersheds exhibiting higher proportions of runoff in streamflow are more vulnerable to changes in flood risk. The delta change method appears to be more useful when simulating extreme events as it better preserves daily climate variability as opposed to using bias corrected GCM output.