In the United States, the floodplain maps used in the administration of the National Flood Insurance Program are created and maintained by the Federal Emergency Management Agency. Currently, a nationwide map modernization program is underway to convert the existing paper floodplain maps into a digital format, while continuing to improve the maps and expand the scope of the studies. The flood zones depicted on these maps are developed through engineering studies, using a variety of accepted methods to model and predict flood-prone areas. These methods are classified as detailed, limited detailed, or approximate, corresponding to varying levels of expense and accuracy. Current flood map revision activities across the nation typically consist of developing new hydraulic models, or reusing existing hydraulic model results in conjunction with new, more detailed LiDAR terrain models. This research develops a profile synthesis method for redelineation of approximate flood boundaries, and evaluates the method's performance and usability. The profile synthesis method is shown to perform reliably on simple floodplain geometry, recreating a water surface profile based only on its floodplain boundaries. When applied to a real-world floodplain studied in a previous flood insurance study, the profile synthesis method is shown to perform adequately, with results comparable to an approximate hydraulic model developed in HEC-RAS. Methods similar to this profile synthesis method for reuse of existing approximate zone boundaries have not been widely documented or evaluated; nevertheless, methods such as this are believed to be common in the revision of approximate zone flood boundaries. As such, this work explores concepts which will be of interest to individuals actively involved in flood map revision and modernization. / Master of Science

Flood plain mapping has become an increasingly important part of flood plain management. Flood plain mapping employs mapping software and hydraulic calculation packages to efficiently map flood plains. Modelers often utilize automation software to develop the complex geometries required to reduce the time to develop hydraulic models. The Utah Inundation Mapping System (UTIMS) is designed to reduce the time required to develop complex geometries for use in flood plain mapping studies. The automated geometries developed by UTIMS include: flood specific river centerlines, bank lines, flow path lines, cross sections and areal averaged n-value polygons. UTIMS thus facilitates developing automated input to US Army Corps of Engineer's HEC-RAS software. Results from HEC-RAS can be imported back to UTIMS for display and mapping. The user can also specify convergence criteria for water surface profile at selected locations along the river and thus run UTIMS and HEC-RAS iteratively till the convergence criterion is met. UTIMS develops a new flood specific geometry file for each iteration, enabling an accurate modeling of flood-plain. Utilizing this robust and easy to operate software within the GIS environment modelers can significantly reduce the time required to develop accurate flood plain maps. The time thus saved in developing the geometries allows modelers to spend more time doing the actual modeling and analyzing results. The time thus saved can also result in faster turn around and potential cost cutting in flood-plain modeling work. In this paper the authors describe UTIMS capabilities, compare them with other available software, and demonstrate the UTIMS flood plain automation process using a case study.

<p>Flooding is one of the most devastating and frequently occurring natural phenomena in the world. Due to the adverse impacts of floods on the life and property of humans, it is crucial to investigate the best flood modeling approaches for delineation of floodplain areas. Conventionally, different hydrodynamic models are used to identify the floodplain areas. However, the high computational cost, and the dependency of these models on detailed input datasets limit their application for large scale floodplain mapping in data-scarce regions. Recently, a new floodplain mapping method based on a hydrogeomorphic feature, named Height Above Nearest Drainage (<i>HAND</i>), has been proposed as a successful alternative for fast and efficient floodplain mapping at the large scale. The overall goal of this study is to improve the performance of <i>HAND</i>-based method by overcoming its current limitations. The main focus will be on extending the application of the <i>HAND</i>-based method to data-scarce environments. To achieve this goal, regionalization techniques are integrated with the floodplain models at the regional and continental scales. Considering these facts, four research objective are established to (1) Develop a regression model to create 100-year floodplain maps at a regional scale (2) Develop a classification framework for creating 100-year floodplain maps for the Contiguous United States (3) Develop a new version of the <i>HAND</i>-based method for creating probabilistic 100-year floodplain maps, and (4) Propose a general regionalization framework for transferring information from data-rich basins to data-scarce environments. </p> <p> </p> <p>In the first objective, the state of North Carolina is selected as the study area, and a regression model is developed to regionalize the available 100-year Flood Insurance Rate Maps (FIRMs) to the data-scarce regions. The regression model is an exponential equation with three independent variables including the average slope, the average elevation, and the main stream slope of the watershed. The results show that the estimated floodplains are within the expected range of accuracy of C>0.6 and F>0.9 for majority of watersheds located in the mid-altitude regions, but it overpredicts and underpredicts in the flat and mountainous regions respectively. </p> <p> </p> <p>The second objective of this research extends the spatial application of the <i>HAND</i>-based method to the entire United States by proposing a new classification framework. The proposed framework classifies the watersheds into three groups by using seven watershed characteristics related to the topography, climate and land use. The validation results show that the average error of floodplain maps is around 14% which demonstrate the reliability and robustness of the proposed framework for continental floodplain mapping. In addition to the acceptable accuracy, the proposed framework creates the floodplain maps for any watershed within the United States. </p> <p> </p> <p>The <i>HAND</i>-based method is a deterministic modeling approach to floodplain mapping. In the third objective, the probabilistic version of this method is proposed. Using a probabilistic approach to floodplain mapping provides more informative maps. In this study, a flat watershed in the state of Kansas is selected as the case study, and the performance of four probabilistic functions for floodplain mapping is compared. The results show that a linear function with one parameter and a gamma function with two parameters are the best options for this study area. It is also shown that the proposed probabilistic approach can reduce the overpredictions and underpredictions made by the deterministic <i>HAND</i>-based approach. </p> <p> </p> <p>In the fourth objective, a new regionalization framework for transferring the calibrated environmental models to data-scarce regions is proposed. This framework aims to improve the current similarity-based regionalization methods by reducing the subjectivity that exists in the selection of basin descriptors. Using this framework for the probabilistic <i>HAND</i>-based method in the third objective, the floodplains are regionalized for a large set of watersheds in the Central United States. The results show that “vertical component of centroid (or latitude)” is the dominant descriptor of spatial variabilities in the probabilistic floodplain maps. This is an interesting finding which shows how a systematic approach can help to explore the hidden descriptors for regionalization. It is demonstrated that using common methods, such as correlation coefficient calculation, or stepwise regression analysis, will not reveal the critical role of latitude on the spatial variability of floodplains.</p>

This research presents methods of creating digital maps for floodplain delineation and flood probability studies and storing them in a geodatabase. Methods for creating a geodatabase for water resources outside of a GIS are presented. The geodatabase follows the ArcHydro data model. Methods are also shown for creating digital flood maps and storing them in the geodatabase. These flood maps, defining the floodplain boundary and flood probability, are stored in a digital format ready for use in a FEMA flood hazard project, allowing for better archival methods and reproducibility.

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