UA cloudflare authentication

Theses and Dissertations

Permanent URI for this collection

https://ir.ua.edu/handle/123456789/508

Browse

Browsing Theses and Dissertations by Author "Abbaszadeh, Peyman"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A Metropolitan-Scale Framework for Urban Flood Inundation Modeling and Ensemble-Based Analysis
    (University of Alabama Libraries, 2026) Samadi, Aylar; Moradkhani, Hamid
    Urban flooding presents a growing challenge in metropolitan regions due to the combined effects of intense rainfall, complex drainage networks, and heterogeneous socioeconomic vulnerability. Flood inundation modeling in urban areas is essential for understanding hazard dynamics, exploring ensemble flood scenarios, and interpreting spatially variable damage patterns. This dissertation develops a metropolitan-scale framework for urban flood inundation modeling and ensemble-based analysis built upon a topography-based flood modeling approach, applied to pluvial and fluvial flood processes and extended through synthetic rainfall generation, ensemble flood inundation simulation, and regime-based damage interpretation.The first chapter introduces a DEM-based pluvial flood modeling framework designed for large-scale applications. The approach adopts a dual-drainage representation in which the storm drainage system response to intense rainfall is simulated using SWMM. Pluvial flooding is represented through two primary contributions: surcharge from the drainage system derived from SWMM outputs and direct rainfall effects on the surface. These inputs are incorporated into a topography-based surface routing module to generate maximum flood depth inundation maps.The second chapter extends this framework to a unified pluvial–fluvial modeling structure that represents the combined influence of storm drainage response, direct rainfall, and riverine overflow. In this chapter, the modeling formulation is expanded to generate dynamic flood inundation maps over time in addition to maximum flood depth maps. By incorporating both pluvial and fluvial processes within a consistent modeling approach, the unified framework provides an improved representation of flood evolution in urban areas influenced by major river systems.Due to the limited availability of historical extreme rainfall events, the third chapter introduces a generative modeling approach to expand the range of plausible rainfall forcing scenarios. A Generative Adversarial Network (GAN) is employed to produce spatiotemporally coherent synthetic rainfall sequences that support ensemble-based flood analysis. These rainfall sequences are used to generate hourly flood inundation ensembles, enabling probabilistic and scenario-based characterization of flooding beyond single deterministic events.Finally, the ensemble flood simulations are leveraged to develop a regime-based diagnostic framework for interpreting extreme urban flood damage. Flood behavior is classified according to persistence and inundation depth during flooded hours, revealing distinct hazard regimes. By examining interactions between hazard dynamics and baseline socioeconomic–infrastructural vulnerability, the analysis identifies hazard-dominant, vulnerability-dominant, and compound damage areas, providing a structured perspective on spatial variability in urban flood impacts.
  • Thumbnail Image
    Item
    Toward hyper-resolution hydrologic data assimilation systems for improved predictions of hydroclimate extremes
    (University of Alabama Libraries, 2020) Abbaszadeh, Peyman; Moradkhani, Hamid; University of Alabama Tuscaloosa
    Over the past decades, tropical storms and hurricanes in the Southeast United States have become more frequent and intense, mainly due to the effects of climate change. They often produce torrential rains that may result in catastrophic floods depending on hydrologic, geomorphologic and orographic characteristics of the region. Although hydrological models are widely used to provide estimates of such floods, their predictions most often are not perfect as the models suffer either from inadequate conceptualization of underlying physics or non-uniqueness of model parameters or inaccurate initialization. Data Assimilation (DA) based on Particle Filtering (PF) has been recognized as an effective and reliable mean to integrate the hydrometeorological observations from in-situ stations and remotely sensed sensors into hydrological models for enhancing their prediction skills while accounting for the associated uncertainties. Although recent developments in DA theory and remote sensing technologies have made significant progress in enhancing the performance of the hydrologic models, their usefulness are subject to some inherent limitations that may result in inaccurate and imprecise model predictions, especially in the case of an extreme event such as flooding. This dissertation is an attempt to identify these limitations and address those by conducting four studies. The first tackles a fundamental problem associated with the utilization of remotely sensed observations in hydrologic data assimilation applications. The two and third are progressive studies that address two conceptual/theoretical problems of using particle filtering approach in hydrologic studies. As a result, the fourth study demonstrates the effectiveness and usefulness of the developments in all three studies in improving the hyper-resolution hydrologic model predictions over a region in the Southeast Texas where heavy rainfall from Hurricane Harvey caused deadly flooding.