Graduation Semester and Year
Summer 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Civil Engineering
Department
Civil Engineering
First Advisor
Nick Z. Fang
Second Advisor
Daniel, Li
Third Advisor
Jessica, Eisma
Fourth Advisor
Adnan, Rajib
Abstract
The increasing frequency and severity of flood events globally necessitate more accurate and reliable hydrologic models to inform flood risk management and mitigation strategies. Traditional hydrologic models have often relied on the simplification of uniformly distributed rainfall across watersheds, a method that fails to account for the spatial variability inherent in real-world storm events. This dissertation addresses this critical gap by applying Depth-Area Reduction Factors (DARFs) in hydrologic modeling, specifically focusing on the Trinity and Neches River Basins in Texas. DARFs are used to adjust point rainfall data to reflect the reduction in rainfall intensity as storm areas increase, thereby providing a more realistic representation of rainfall distribution within hydrologic models.
This research leverages a newly developed DARF catalog, derived from an unprecedented dataset of over 28,000 historical storms, representing the most comprehensive effort to date in this field. The study aims to validate the effectiveness of these DARFs by comparing hydrologic simulations that incorporate DARFs against those using uniformly distributed rainfall and historical DARF studies such as the Water Hydrology Agency (WHA) benchmarks. To achieve this, the study conducted over 500 HEC-HMS simulations across different storm durations, return periods, and DARF percentiles, making it one of the most extensive applications of DARFs in hydrologic modeling to date.
The findings of this dissertation confirm that the application of DARFs significantly enhances the accuracy of flood predictions. Models incorporating DARFs showed a stronger correlation with observed flood events, as evidenced by higher R-squared values and more consistent peak discharge predictions when compared to uniformly distributed rainfall models. The research also reveals that different DARF percentiles, durations, and return periods have a substantial impact on hydrologic modeling outcomes, with higher percentiles providing more conservative estimates of peak discharges, which are crucial for flood risk mitigation in high-risk areas.
Furthermore, this study developed an innovative, automated Python-based framework to streamline the process of integrating DARFs into hydrologic models, reducing the potential for human error and allowing for the efficient management of the complex interactions between various model parameters. This automation was critical in handling the scale and scope of the simulations required for this research, involving a total of approximately 5,000 simulations to ensure robustness and reliability of the results.
The research also underscores the importance of regionalization in hydrologic modeling. By focusing on the specific climatic and geographical conditions of the Trinity and Neches River Basins, the study demonstrates the value of region-specific DARFs in improving flood prediction accuracy. This regional approach not only enhances the relevance of the findings for local flood risk management but also provides a framework that can be adapted to other regions with similar hydrologic characteristics.
In conclusion, this dissertation makes a significant contribution to the field of hydrologic modeling by validating a comprehensive DARF catalog and demonstrating its superiority over traditional uniformly distributed rainfall models. The results of this study have important implications for the future of flood risk management in Texas and potentially other regions, where the adoption of DARFs could lead to more accurate and reliable flood predictions, ultimately contributing to safer and more resilient communities. Future research directions include the integration of temporal rainfall variability, the expansion of DARF applications to other watersheds, and the exploration of machine learning techniques for optimizing DARF parameters.
Keywords
Flood Frequency Analysis, Python, Automation, HMS, HEC-HMS, Elliptical Storm
Disciplines
Civil Engineering | Hydraulic Engineering | Other Civil and Environmental Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Ahmadzadeh, Mohamad, "Flood Frequency Analyses Using Depth-Area Reduction Factors (DARFs) in Texas" (2024). Civil Engineering Dissertations. 498.
https://mavmatrix.uta.edu/civilengineering_dissertations/498
Included in
Civil Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons