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A New Overland Flow Accumulation Algorithm with Enhanced Adaptability for Terrain Surface and Its Application in Distributed Hydrological Modeling

The simulation of overland flow accumulation is critical for drainage network extraction, soil moisture monitoring, and hydrological modeling, etc. A variety of flow accumulation algorithms have been developed, but the complex and variable terrain has undermined their predictive accuracy. In my dissertation, a new flow accumulation algorithm (SAPC) is proposed that applies different flow distribution schemes to divergent and convergent flow scenarios with respect to slope, aspect, and plan curvature. Flow accumulation for the divergent scenario is slope-driven in the sense that flow distributed to the downslope neighboring cells is proportional to the slope values, and the weight of slope varies with plan curvature, making the SAPC algorithm adaptable to the variation of terrain surface. For the convergent scenario, flow accumulation is determined by aspect and all the water in the center cell is distributed in the same direction in two dimensions. Comparisons between the SAPC algorithm and the other algorithms show that flow accumulations estimated by the SAPC algorithm are closer to the true values for artificial surfaces, and the generated flow pathways are more balanced and smoother without serious artifacts for natural terrain surfaces. The SAPC algorithm is further integrated into the WetSpa Extension model to simulate hydrological responses at the outlet of the Bull Creek watershed for the 100-year tropical storm Hermine occurring in September 2010. The WetSpa Extension model provides both the semi-distributed and the fully-distributed modeling options. The fully-distributed WetSpa Extension model predicts a higher amount of surface runoff and thus the peak flow approaches more to the observed value than that predicted by the semi-distributed model. Flow accumulation is an important spatial parameter involved in hydrological modeling, and specifically it affects flow routing. Incorporating the SAPC algorithm into the WetSpa Extension model helps to obtain a hydrograph that aligns closer to the observed high flow region and more importantly, the model is able to provide the correct time to peak, otherwise there is half an hour of delay in the time to peak when SAPC algorithm is not used. Statistics demonstrate that the SAPC algorithm enables the WetSpa Extension model to be less biased, more confident and efficient. The significance of this dissertation lies in its provision of the possible ways to enhance the adaptability of flow accumulation algorithm to the varying terrain surfaces, and to improve hydrological modeling results through the more accurate and reliable flow accumulation predictions. This interdisciplinary study, which involves terrain analysis, hydrological modeling, and geographic information science (GIS), stresses the importance of location in describing physical features and processes that is usually the focus of geographical investigation. / A Dissertation submitted to the Department of Geography in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester 2019. / March 29, 2019. / Distributed hydrological modeling, Flow accumulation algorithm, Geographic information science, Python, WetSpa Extension model / Includes bibliographical references. / Victor Mesev, Professor Directing Dissertation; Christopher J. Coutts, University Representative; Xiaojun Yang, Committee Member; Tingting Zhao, Committee Member.

Identiferoai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_709078
ContributorsGao, Xinyu (author), Mesev, Victor (Professor Directing Dissertation), Coutts, Christopher (University Representative), Yang, Xiaojun (Committee Member), Zhao, Tingting (Committee Member), Florida State University (degree granting institution), College of Social Sciences and Public Policy (degree granting college), Department of Geography (degree granting departmentdgg)
PublisherFlorida State University
Source SetsFlorida State University
LanguageEnglish, English
Detected LanguageEnglish
TypeText, text, doctoral thesis
Format1 online resource (154 pages), computer, application/pdf

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