Abstract
Estimates of flood risk involve using the statistical moments of peak flow series data, i.e. mean, standard deviation, to estimate the parameters of a distribution. The fitted distribution relates the probability of exceedance to a peak flow discharge. The fitted distribution, or flood frequency curve, is then used to inform the design of structures and water management and planning. However, flood risk estimation requires key assumptions, some of which have come under scrutiny in recent years. The first, stationarity, assumes that the moments used to fit a probability distribution are time invariant; e.g. the mean is constant throughout the observed record. The second assumption, homogeneity, is defined as the spatial invariance of flood moments. Homogeneity is a critical assumption when using regional information to inform a statistic of interest. In regional flood frequency estimation, commonly employed techniques such as quantile regression, regional skew estimation and the index flood method work under the assumption of homogeneity. Homogeneity assumes that all sites within a defined region will have the same flood frequency curve, indicating that given the same climatic disturbance, all sites will behave similarly. However as climate and landscape change, these assumptions can be violated. Alterations to precipitation and temperature have occurred, producing subsequent changes to associated flood risk. Landscape changes have also altered how runoff is translated through the watershed, ultimately impacting peak flows. This analysis considers several cases under which the assumptions of flood risk are violated. Chapter 1 analyzes the regional water balance, defined by the Baseflow Index (BFI), on the flood frequency curve indexed at key return periods. Chapter 2 assesses how storage in a case study watershed, the Suwannee River Basin, impacts the assumptions of homogeneity. Results from Chapter 2 guide the development of a new regional skew for the Suwannee River Basin which is documented in Chapter 3. Finally, Chapter 4 addresses using hydrological models to assess flood risk. First, different bias correction methods are applied to peak flows modeled using the Soil and Water Assessment Tool (SWAT), and then the impacts of climate and landscape change on these peak flows are compared to determine how each uniquely alter flood risk.
This analysis considers several cases under which the assumptions of flood risk are violated. The first two chapters deal with current estimates of flood risk and how storage effects the assumptions of homogeneity. The last chapter deals with analyzing future flood risk, and how plausible changes to the land scape and climate would alter this risk.
Chapter 1 analyzes the role of basin scale watershed storage and how it differentially impacts the flood frequency curve. The flood frequency curve is partitioned into select exceedance probabilities and the Baseflow Index, or BFI, is regressed onto peak flows represented at each exceedance probability across 4,270 sites in the US. Results show that BFI, defined as a metric that considers the hydrogeology and storage capabilities of a watershed, is most influential at lower exceedance probabilities altering peak flows by as much as 16%. This could have implications for the stationarity of flood moments, as extreme alterations to baseflow which can happen on short timescales, could significantly alter flood risk.
Chapter 2 addresses how karst terrain affects the assumptions of homogeneity used in regional techniques. Localized floodwater losses common in karst terrain can alter the propagation of peak flows. A case study from the Suwannee River Basin in northern Florida partitions the basin into the karst (lower basin), and non karst (upper basin) sites. The evolution of streamflow is analyzed and it is shown that as rivers cross over into karst terrain, the flood series skew is altered. In light of these previous results, a new regional skew is developed for the Suwannee River Basin.
Finally, Chapter 3 looks at the comparative effects of land use and climate change across watersheds in the northeastern US. First, bias correction techniques that are used to adjust Global Climate Model (GCM) precipitation are used on peak flow series generated using the rainfallrunoff model, Soil and Water Assessment Tool (SWAT). Three different methods are compared in their ability to match the simulated to observed series moments and how sensitive each method is based on the time frame in which they were derived. For peak flow series data, it is shown that the linear method, which only adjusts the mean of peak flow series data is the best method based on the aforementioned criteria. The bias corrections are then applied to peak flow series data from 2 watersheds simulated under 8 different plausible climate change scenarios. Results are compared to those in which only land use change was altered. Results indicate that for already altered watersheds, climate change poses the most significant factor in altering flood risk, whereby in a dominantly agricultural watershed, land use change would be a more critical factor.
Original language  American English 

Qualification  Ph.D. 
Awarding Institution 

Supervisors/Advisors 

DOIs  
State  Published  Jan 1 2016 
Externally published  Yes 
Keywords
 Flood Risk
 Hydrology
 Climate Change
 Landscape Change
 Homogeneity
Disciplines
 Civil Engineering
 Geology