<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7709255 |
| Date | 10 June 2019 |
| Creators | Keighobad Jafarzadegan (5929811) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Floodplain_Mapping_in_Data-Scarce_Environments_Using_Regionalization_Techniques/7709255 |
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