DSpace Repository :: Browsing by Author "Moftakhari, Hamed"

Browsing by Author "Moftakhari, Hamed"

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Application of Deep Learning to Estimate Mean River Cross-Sectional Depth
(University of Alabama Libraries, 2021) Raney, Arthur Austin; Cohen, Sagy; University of Alabama Tuscaloosa
Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their abilities to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7%. Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered.
Assessing Extreme Coastal Water Levels: Statistical Models for Nearshore Flood Hazard Prediction
(University of Alabama Libraries, 2024) Boumis, Georgios; Moftakhari, Hamed
Amid impacts of climate variability and change on coastal communities, which are increasingly at risk due to their expansion over time, accurately assessing and predicting nearshore flood hazard is crucial for protecting lives, infrastructure, and economic activity. This dissertation addresses this critical need through a series of interconnected studies that enhance our understanding of extreme coastal water levels through innovative statistical modeling techniques. In this work, I use a combination of frequentist and Bayesian statistics to tackle the challenges of non-stationarity, spatial correlation, predictive accuracy and uncertainty, as well as multi-driver interdependence when modeling probability of nearshore flood hazard. Specifically, I first employ a pseudo-global dataset of revised sea-level rise projections that incorporate previously overlooked physical processes, such as ice-cliff collapse and ice-shelf hydrofracturing. This dataset is used to apply non-stationary classical extreme value theory to measurements of still water level (SWL) from around the globe, enabling predictions of coastal flood hazard over the coming decades under various CO2 concentration scenarios. I then shift focus to the stochastic component of SWL, i.e., storm surge, along the Gulf Coast of the United States (US). In particular, I develop a Bayesian hierarchical model to estimate storm surge hazard, including at ungauged coasts, capturing the spatial interconnectivity between nearby coastal areas by incorporating atmospheric reanalysis data. Next, I apply, for the first time, the novel metastatistical extreme value distribution to storm surge observations along the East, Gulf and West Coast of the US. I compare its accuracy in predicting out-of-sample storm surge hazard with that of traditional extreme value theory, and demonstrate implications, particularly in determining design storm surge height for construction of coastal defenses. In the final chapter, I examine estuarine environments where nearshore flood hazard can result from a combination of interdependent drivers, rather than from an elevated sea level alone, and search for evidence of (non-)stationary dependence. I analyze observations of river discharge and storm surge, along with climate teleconnections, to estimate and compare parameters for both stationary and dynamic multivariate distributions using Bayesian methods. Finally, I illustrate the impact of non-stationarity on the quantification of bivariate coastal flood hazard.
Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States
(Nature Portfolio, 2023) Yarveysi, Farnaz; Alipour, Atieh; Moftakhari, Hamed; Jafarzadegan, Keighobad; Moradkhani, Hamid; University of Alabama Tuscaloosa
The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks. Introduces a precise, machine-learning-based Socio-Economic-Infrastructure Vulnerability index for natural hazards that uncovers stark variations in vulnerability at the block level emphasizing crucial information for risk-informed decision making.
Climatic and Anthropogenic Influences on Food and Water Security
(University of Alabama Libraries, 2024) Rathore, Lokendra Singh; Kumar, Mukesh
Ensuring food security for the growing global population is one of the pressing challenges of the 21st century. The interconnectedness of food and water systems necessitates an integrated assessment of food security in conjunction with water security. This dissertation focuses on food and water systems and their interaction with climate and anthropogenic factors. Specifically, it has the following research objectives: (1) to quantify the sensitivity of crop yields to droughts for major crops and investigate the association of various climate and anthropogenic factors on the change in crop yield loss risk, (2) to elucidate the potential controlling drivers and their influence on contrasting corn acreage trends between Midwestern and Southeastern United States, (3) understand and quantify the unsustainable virtual water flows inherent in crop trade, and (4) to assess the impact of anthropogenic influences such as irrigation expansion and reservoir operations on water scarcity. To address the outlined objectives, a range of statistical and process-based modeling and data analytics are utilized. The first objective is achieved by developing a copula-based drought yield loss risk index for crop-producing counties. The results indicate that, historically, the growing-season droughts have become drier in only 52% of the county growing period instances over the cropped regions of the US. Additionally, crop risks from droughts vary significantly both spatially and temporally. Only about 55% of the sites experiencing drier droughts have seen an increase in yield risk from these droughts. Ancillary climatic variables, such as kill degree days and vapor pressure deficit, as well as anthropogenic factors, such as fertilizer application, significantly mediate the changes in yield risks under drier drought conditions. These findings challenge the notion that droughts are uniformly becoming drier in crop-growing regions over time and that more intense droughts will necessarily lead to increased crop yield risks. For the second objective, historical profit from corn farming per acre between Midwestern and Southeastern US was intercompared. Findings indicate that both hydroclimatology and anthropogenic interventions, such as irrigation expansion, can have a significant influence on crop acreage dynamics. Furthermore, economic incentives for farmers to expand irrigation can potentially stem crop acreage losses. To address the third objective, a virtual water flow sustainability assessment is performed by leveraging food trade and water footprint datasets. The findings identify that one-third of the virtual water flow in the United States due to cereal and milled grain trade is unsustainable as it originates from water-scarce regions. The fourth objective involves using a process-based hydrological model to develop a water demand and consumption-based water scarcity estimate, vis-à-vis scenarios of irrigation expansion and alternative reservoir routing schemes. The results show a negative impact of rainfed to irrigation-fed transition on urban water scarcity, indicating that poorly managed, extensive irrigation expansion can affect an additional 16 million urban population. Overall, the dissertation demonstrates the intricate connection between food and water security, highlighting their vulnerability and ways to mitigate them. Furthermore, it addresses the methodological challenges involved in assessing food and water security. The insights gained from this research can help support more informed decision-making to secure our water and food resources.
Enhancing Short-Term Operational Water Management of Urban Water Supply Systems Amid Persistent Streamflow Deficits
(University of Alabama Libraries, 2024) Aziz, Danyal; Burian, Steven
Streamflow deficits disrupt the regular performance of a water supply system and require management actions to prevent or reduce negative impacts. The effects on water supply exacerbate with increasing severity and duration of the deficit. Characterization of streamflow deficit severity, duration, and frequency (SDF) and organization of the information into forms and workflows useable for water management would help water utilities to mitigate the impacts of persistent low flows. This dissertation adapts the concept of streamflow deficit SDF information for urban water supply management and illustrates the impact of streamflow deficits across historical severity and duration on urban water supply system performance. This research demonstrates the use of the SDF information in a novel method called the retro-prospective approach to help water managers operationalize the streamflow deficit in preparing for low-flow conditions. Using Salt Lake City (SLC) in Utah as a case study, the research consists of three key parts. The first part analyzes historical data from the three primary creeks supplying SLC to characterize annual and multi-year streamflow deficits, aiming to determine if these deficits have or are changing. The second part concentrates on developing streamflow deficit SDF information and converting that into a time domain through the retro-prospective approach. This enables the application of the streamflow deficit hydrograph representing a specific historical event in impact analysis. The final part applies the approach to evaluate SLC's water supply system performance. The SDF information is used with a water systems model to evaluate the SLC water supply vulnerability to a range of deficit scenarios. The final part examines whether the system is more vulnerable to severe, shorter-term deficits compared to milder, longer-term ones. Results showed streamflow deficits over multi-year durations occurring more frequently, however, no change in the magnitude of these deficits was observed. The water supply system showed higher vulnerability to duration than the severity of deficits. Specifically, streamflow deficit of 7-year duration and a 100-year return period was identified as the critical event with the highest impact. The research outcomes include a characterization of streamflow deficit SDFs and its usage for operational management in urban water supply systems.
Extreme heat events heighten soil respiration
(Nature Portfolio, 2021) Anjileli, Hassan; Huning, Laurie S.; Moftakhari, Hamed; Ashraf, Samaneh; Asanjan, Ata Akbari; Norouzi, Hamid; AghaKouchak, Amir; University of California Irvine; California State University Long Beach; University of Alabama Tuscaloosa; Concordia University - Canada; Universities Space Research Association (USRA); University of California System
In the wake of climate change, extreme events such as heatwaves are considered to be key players in the terrestrial biosphere. In the past decades, the frequency and severity of heatwaves have risen substantially, and they are projected to continue to intensify in the future. One key question is therefore: how do changes in extreme heatwaves affect the carbon cycle? Although soil respiration (Rs) is the second largest contributor to the carbon cycle, the impacts of heatwaves on Rs have not been fully understood. Using a unique set of continuous high frequency in-situ measurements from our field site, we characterize the relationship between Rs and heatwaves. We further compare the Rs response to heatwaves across ten additional sites spanning the contiguous United States (CONUS). Applying a probabilistic framework, we conclude that during heatwaves Rs rates increase significantly, on average, by similar to 26% relative to that of non-heatwave conditions over the CONUS. Since previous in-situ observations have not measured the Rs response to heatwaves (e.g., rate, amount) at the high frequency that we present here, the terrestrial feedback to the carbon cycle may be underestimated without capturing these high frequency extreme heatwave events.
From Hazard to Impact: Smart Flood Forecast System and Damage Prediction
(University of Alabama Libraries, 2022) Alipour, Atieh; Moradkhani, Hamid; University of Alabama Tuscaloosa
Floods are among the most devastating natural hazards across the globe, and it isexpected to be escalated in the future mostly due to the warming climate. Depside tremendousnational governments' protection efforts, they still result in the loss of lives and properties.Understanding flood dynamics and developing fast and reliable flood forecasting systems areessential for mitigating the associated risks and implementing proactive risk managementstrategies. This dissertation is an attempt to characterize flood hazard and enhance flood riskassessment by leveraging enhanced statistical approaches and recent physical models. The firstfour chapters present previous peer-reviewed studies and are as follows:The first chapter is on assessing flash flood characteristics including frequency, duration,and intensity in addition to their associated property damages. The second chapter presents asystematic framework that considers a variety of features explaining different components ofrisk and examines multiple machine learning methods to predict flash flood damage. In chapterthree, we assess the sensitivity of the HEC-RAS 2D to its configuration factors and parameters.In chapter four, we are developing a new tropical cyclone scaling system that uses Copulas forcategorizing Tropical Cyclones (TCs) based on the likelihood of a given set of severity forrainfall, surge, and wind speed. Finally, in chapter five, we present a systematic framework thatuses Deep Learning (DL) algorithms, and hydrodynamic models to generate probabilistic floodwater levels at different locations along coastal rivers.
Integration of Physically-Based and Data-Driven Modeling Approaches for Compound Coastal Flood Hazard Assessment Under Uncertainties
(University of Alabama Libraries, 2021) Muñoz, David Fernando; Moftakhari, Hamed; University of Alabama Tuscaloosa
Flood hazard assessment is an essential component of risk and disaster management that helps identify areas exposed to flooding as well as support decision making, and emergency response. Floods can result from isolated, concurrent, or successive drivers of (non-) extreme origin (e.g., fluvial, pluvial, and oceanic) and so put society and the environment at constant risk. Specifically, a combination of either concurrent or successive flood drivers with potential impacts larger than those from isolated drivers is defined as compound flooding (CF). Contemporary studies in compound flood hazard assessment (CFHA) and modeling have focused on simulating inundation extent, water depth, and velocities at local or regional scale. However, those studies often neglect inherent uncertainties associated with forcing data, observations, model parameters, and model structure. A comprehensive analysis of these uncertainty sources is thus imperative, but it requires advanced statistical techniques such as data assimilation (DA) to adequately account for error propagation in compound flood modeling. Chapters 1 to 4 present previous peer-review studies oriented towards a better characterization of uncertainty in CFHA. Those studies include the following research topics: (i) analysis of wetland elevation error and correction of coastal digital elevation models, (ii) compound effects of wetland elevation error and uncertainty from flood drivers, (iii) effects of model selection and model structure error on total water level prediction, and (iv) long-term wetland dynamics associated with urbanization, sea level rise, and hurricane impacts. Chapter 5 presents a cost-effective approach based on deep learning (DL) and data fusion (DF) techniques that enables efficient estimation of exposure to compound coastal flooding at regional scale. Chapter 6 presents a DA scheme based on the Ensemble Kalman Filter (EnKF) technique and hydrodynamic modeling to improve water level (WL) predictions and CFHA in coastal to inland transition zones where pluvial, fluvial, and coastal processes interact. The last section of this dissertation summarizes the main findings of these studies and discusses future research areas that are worth exploring in the context of CFHA.
A Regional Perspective to Coastal Food, Energy, and Water Nexus Along the United States' Gulf Coast
(University of Alabama Libraries, 2023) Lewis, Michael Matthew; Moftakhari, Hamed
The interconnectedness of Food, Energy, and Water (FEW) components has mainly been studied inland even though many coastal communities are major producers and consumers of FEW. The United States' Gulf coast is an example of this; it is among the wettest regions in the nation with considerable contributions to the agriculture sector. It also hosts infrastructure for oil extraction, refinery, and other forms of fossil fuels, making it a crucial component of the energy sector. Here, we quantify the main components of FEW Nexus within coastal systems utilizing several governmental and open-source databases and highlight how various components of the system interact with each other. Special focus is given to water consumption and usage by food and energy with the agricultural sector estimated to consume about 1,260 billion gallons annually, and thermoelectric energy production consuming 64 billion gallons annually. Production of fossil fuels and electricity generation and their interactions are also quantified. Future population estimates are also mapped on the county/parish level, which can provide insight into future demand for the aforementioned resources/products. Quantifying current FEW connections enables further research into how changes like population or sea level rise will impact the nexus and the service to coastal communities.
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.
Unraveling Compound Extremes: Tropical Storm, Drought, Heat Wave, and Wildfire
(University of Alabama Libraries, 2022) Song, Jae Yeol; Moradkhani, Hamid; University of Alabama Tuscaloosa
Climate and weather extremes are occurring more frequently and intensively due to climate change. Over the past decades, research relating to compound and cascading climate extremes are gaining more attention. These events can occur in complex combination which threats the human society and cause huge damage to the environment and economy. On the other hand, there are cases when the risk or damage of climate extremes are neutralized by another extreme event. Therefore, it is essential to understand the characteristics of climate extremes and need to investigate on the compound effects of the extremes.This dissertation is an attempt to identify the effect and behavior of different climate extremes including tropical storms, agricultural drought, heat wave, and wildfire. Here I conduct a rigorous spatiotemporal analysis to different cases of concurrent extreme events. First, I assess the joint likelihood of precipitation and wind-speed from landfalling tropical storm and then integrate the vulnerability of affected communities to depict the risk and damage from tropical storm events. Second, I involve the storm surge and a larger sample size of the landfalling tropical storms to depict the risk and damage from them. Third, I examine the concurrent moment of drought and tropical storms to seek whether the agricultural droughts are ameliorated or exacerbated after tropical storm landfall. Finally, I investigate the relationship between heat wave and wildfire using probabilistic approaches.
Wastewater Access and Affordability Challenges in the U.S.: the Current Situation and Proposed Solutions for Equitable Access to Safely Managed Sanitation
(University of Alabama Libraries, 2023) Maxcy-Brown, Jillian; Elliott, Mark A
The United Nations has declared it a human right to have access to affordable and reliable safely managed sanitation (wastewater management), but despite decades of global efforts, there are still millions of people across the world, including estimated 8.4 million residents of high-income countries, for whom this has not been realized. In the United States, reports indicate that populations in rural and urban areas are relying on unsafely managed wastewater treatment which is a threat to both public and environmental health. The objectives of this dissertation were to investigate the scope and impacts of inadequate wastewater management for onsite and decentralized systems, to evaluate the mechanisms utilized to collect household level wastewater data, and to develop a methodology to equitably evaluate wastewater access affordability for sewered and unsewered residents in the U.S. This research found there is relatively widespread usage of straight pipes, failing septic tank systems, cesspools, failing outhouses, bucket latrines, open defecation and incomplete indoor plumbing across the U.S. in areas with challenging climate and geological conditions, low annual household incomes, and/or populations of residents experiencing homelessness. The dissertation explains the challenges to understanding the extent of unsafely managed sanitation, factors that limit access to safely managed sanitation, and barriers to addressing these wastewater issues. These include, but are not limited to, the limitations in comprehensive data collection and accessible funding mechanisms, the impacts of structural racisms, and the specific challenges faced by households, small communities, and people experiencing homelessness. The research reveals the limitations of existing datasets to comprehensively collect household level wastewater data that captures these nationwide inequities. The dissertation also establishes a novel methodology for mapping statewide wastewater access affordability based on the diverse range of system expenses and household annual incomes in Alabama. In addition to these findings, the dissertation proposes a suite of recommendations for long-term solutions that will enable all residents to have reliable and affordable access to safely managed sanitation within the funding and regulatory context of the U.S.

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