Research – Hamed Moftakhari Rostamkhani

Coastal Hydrology Lab
RESEARCH PROJECTS

𝐑𝐚𝐩𝐢𝐝 𝐢𝐧𝐭𝐞𝐧𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐭𝐫𝐨𝐩𝐢𝐜𝐚𝐥 𝐜𝐲𝐜𝐥𝐨𝐧𝐞𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐆𝐮𝐥𝐟 𝐨𝐟 𝐌𝐞𝐱𝐢𝐜𝐨 𝐢𝐬 𝐦𝐨𝐫𝐞 𝐥𝐢𝐤𝐞𝐥𝐲 𝐝𝐮𝐫𝐢𝐧𝐠 𝐦𝐚𝐫𝐢𝐧𝐞 𝐡𝐞𝐚𝐭𝐰𝐚𝐯𝐞𝐬

Radfar, S., Moftakhari, H., Moradkhani, H. (2024)

Tropical cyclones can rapidly intensify under favorable oceanic and atmospheric conditions. This phenomenon is complex and difficult to predict, making it a serious challenge for coastal communities. A key contributing factor to the intensification process is the presence of prolonged high sea surface temperatures, also known as marine heatwaves. However, the extent to which marine heatwaves contribute to the potential of rapid intensification events is not yet fully explored. Here, we conduct a probabilistic analysis to assess how the likelihood of rapid intensification changes during marine heatwaves in the Gulf of Mexico and northwestern Caribbean Sea. Approximately 70% of hurricanes that formed between 1950 and 2022 were influenced by marine heatwaves. Notably, rapid intensification is, on average, 50% more likely during marine heatwaves. As marine heatwaves are on the increase due to climate change, our findings indicate that more frequent rapid intensification events can be expected in the warming climate.

𝐍𝐚𝐭𝐮𝐫𝐞-𝐛𝐚𝐬𝐞𝐝 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐚𝐬 𝐛𝐮𝐟𝐟𝐞𝐫𝐬 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐜𝐨𝐚𝐬𝐭𝐚𝐥 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐟𝐥𝐨𝐨𝐝𝐢𝐧𝐠: 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐩𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐟𝐨𝐫 𝐩𝐫𝐨𝐜𝐞𝐬𝐬-𝐛𝐚𝐬𝐞𝐝 𝐦𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐨𝐟 𝐡𝐚𝐳𝐚𝐫𝐝 𝐦𝐢𝐭𝐢𝐠𝐚𝐭𝐢𝐨𝐧

Radfar, S., Mahmoudi, S., Moftakhari, H., Mckelvey, T., Bilskie, M. V., Collini, R., Alizad, K., Cherry, J. A., Moradkhani, H. (2024)

The paper explores how nature-based solutions (NbS) can mitigate coastal compound flooding by reviewing process-based modeling studies. It identifies key challenges such as managing complex environments, computational costs, and the lack of experts and data.
It suggests ways to improve NbS characterization within compound flooding models and discusses uncertainties in numerical modeling, aiming to bridge the gap between research findings and practical application.

𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐟𝐨𝐫 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐜𝐨𝐚𝐬𝐭𝐚𝐥 𝐟𝐥𝐨𝐨𝐝 𝐫𝐢𝐬𝐤 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐚 𝐰𝐚𝐫𝐦𝐢𝐧𝐠 𝐜𝐥𝐢𝐦𝐚𝐭𝐞: 𝐚 𝐜𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐆𝐮𝐥𝐟 𝐂𝐨𝐚𝐬𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐔𝐧𝐢𝐭𝐞𝐝 𝐒𝐭𝐚𝐭𝐞𝐬

Lewis M, Moftakhari H and Passalacqua P (2024)

The study investigates the complex issue of compound coastal flooding (CF) in the Gulf Coast of the United States, focusing on how factors like heavy rainfall, storm surges, and river discharge combine and are intensified by climate change and sea-level rise. This is particularly examined in Southeast Texas and South Alabama. A two-fold approach is used: firstly, a statistical analysis is conducted to understand the changing patterns and interactions among CF drivers. Secondly, a review of current flood resilience policies is performed to evaluate how well they address the complexities of CF events. The findings highlight significant gaps in current policies, underscoring the need for updated flood resilience strategies that consider the non-stationary, multi-dimensional, and non-linear nature of CF.

𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡𝐢𝐧𝐠 𝐅𝐥𝐨𝐨𝐝 𝐓𝐡𝐫𝐞𝐬𝐡𝐨𝐥𝐝𝐬 𝐟𝐨𝐫 𝐒𝐞𝐚 𝐋𝐞𝐯𝐞𝐥 𝐑𝐢𝐬𝐞 𝐈𝐦𝐩𝐚𝐜𝐭 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧

Mahmoudi, S., Moftakhari, H. R., Muñoz, D.F., Sweet, W., Moradkhani, H. (2024)

Sea level rise (SLR) affects coastal flood regimes and poses serious challenges to flood risk management, particularly on ungauged coasts. To address the challenge of monitoring SLR at local scales, we propose a high tide flood (HTF) thresholding system that leverages machine learning (ML) techniques to estimate SLR and HTF thresholds at a relatively fine spatial resolution (10 km) along the United States’ coastlines. The proposed system, complementing conventional linear- and point-based estimations of HTF thresholds and SLR rates, can estimate these values at ungauged stretches of the coast. Trained and validated against National Oceanic and Atmospheric Administration (NOAA) gauge data, our system demonstrates promising skills with an average Kling-Gupta Efficiency (KGE) of 0.77. The results can raise community aware- ness about SLR impacts by documenting the chronic signal of HTF and providing useful information for adaptation planning. The findings encourage further application of ML in achieving spatially distributed thresholds.

𝐍𝐨𝐧𝐥𝐢𝐧𝐞𝐚𝐫 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐨𝐟 𝐒𝐞𝐚‐𝐋𝐞𝐯𝐞𝐥 𝐑𝐢𝐬𝐞 𝐚𝐧𝐝 𝐒𝐭𝐨𝐫𝐦 𝐓𝐢𝐝𝐞 𝐀𝐥𝐭𝐞𝐫 𝐄𝐱𝐭𝐫𝐞𝐦𝐞 𝐂𝐨𝐚𝐬𝐭𝐚𝐥 𝐖𝐚𝐭𝐞𝐫 𝐋𝐞𝐯𝐞𝐥𝐬: 𝐇𝐨𝐰 𝐚𝐧𝐝 𝐖𝐡𝐲?

Moftakhari, H. R., Muñoz, D.F., Akbari Asanjan, A., Aghakouchak, A. Moradkhani, H., Jay, D. (2024)

The study investigates how sea-level rise (SLR) and storm tides interact to increase the risk of flooding in coastal areas, using global tidal data to understand these complex dynamics. It highlights that these interactions can significantly amplify extreme sea levels, making some coastal communities more vulnerable than previously estimated. It introduces the concept of “Potential Maximum Storm Tide” (PMST) as a measure to capture these nonlinear interactions and their impact on flood hazards. The findings suggest that by mid-century, the median PMST could be 20% larger, indicating that current flood risk assessments may underestimate future hazards by up to four times in certain locations.

𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐓𝐫𝐨𝐩𝐢𝐜𝐚𝐥 𝐂𝐲𝐜𝐥𝐨𝐧𝐞 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐁𝐚𝐬𝐞𝐝 𝐨𝐧 𝐂𝐮𝐦𝐮𝐥𝐚𝐭𝐢𝐯𝐞 𝐋𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐈𝐦𝐩𝐚𝐜𝐭𝐬

Sohrabi, M., Moftakhari, H. R., & Moradkhani, H. (2023)

In recent decades, there has been a notable increase in climate-related disasters, highlighting the need for proactive measures. One specific concern is the potential for tropical cyclones (TCs) to create compound flood risks by triggering multiple drivers that lead to flooding. Typically, we rely on synthetic scenarios to assess this risk, but this might not be the most efficient approach, as such method demands significant computational resources and not all those scenarios are relevant for flood risk assessment at a specific point of interest along the coastline. To address this challenge, a novel dependence-informed sampling scheme has been introduced that utilizes a metric called the Cumulative Likelihood of Potential Impact (CLPI). CLPI serves as an index to help identify TCs that are more likely to cause severe flooding at a point of interest. Importantly, CLPI can efficiently rank hurricane scenarios based on their potential impact without the need for complex hydrodynamic simulations. A validation study conducted along the Texas coast, USA, using historical TC data, underscores the practicality of the proposed dependence-informed sampling scheme with CLPI. It enhances the accuracy of flood forecasting, providing reliable information at a reduced cost. This advancement improves the efficiency of analyzing flooding patterns associated with selected storms using CLPI. Furthermore, CLPI can play a crucial role in providing essential information to vulnerable communities, potentially saving lives and reducing the risk of damage to coastal infrastructure.

𝐂𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐄𝐟𝐟𝐞𝐜𝐭𝐬 𝐨𝐟 𝐅𝐥𝐨𝐨𝐝 𝐃𝐫𝐢𝐯𝐞𝐫𝐬, 𝐒𝐞𝐚 𝐋𝐞𝐯𝐞𝐥 𝐑𝐢𝐬𝐞, 𝐚𝐧𝐝 𝐃𝐫𝐞𝐝𝐠𝐢𝐧𝐠 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬 𝐨𝐧 𝐕𝐞𝐬𝐬𝐞𝐥 𝐍𝐚𝐯𝐢𝐠𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐖𝐞𝐭𝐥𝐚𝐧𝐝 𝐈𝐧𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬.

Muñoz, D.F., Moftakhari, H. R., Kumar, M. & Moradkhani, H. (2022)

The study investigates how dredging activities, combined with sea level rise and flood factors, impact vessel movement and flood risks in coastal and wetland areas, specifically focusing on Mobile Bay, Alabama. Through hydrodynamic simulations, it explores the balance between maintaining navigable waterways for economic benefits and the environmental consequences on flood risks and wetland ecosystems. Findings reveal that while dredging increases navigational clearances, it also influences flood patterns and wetland inundation, suggesting a need for integrated management approaches that consider both economic and environmental impacts. The study emphasizes the importance of environmentally-friendly solutions in the context of increasing cargo transportation demands.

𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐮𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐢𝐞𝐬 𝐢𝐧 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐟𝐥𝐨𝐨𝐝 𝐡𝐚𝐳𝐚𝐫𝐝 𝐚𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭: 𝐓𝐡𝐞 𝐯𝐚𝐥𝐮𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 𝐚𝐬𝐬𝐢𝐦𝐢𝐥𝐚𝐭𝐢𝐨𝐧

Muñoz, D.F., Abbaszadeh, P., Moftakhari, H. R., & Moradkhani, H. (2022)

The study introduces a Data Assimilation (DA) scheme using the Ensemble Kalman Filter technique combined with hydrodynamic modeling to enhance the accuracy of water level predictions and flood hazard maps, specifically targeting compound flood events in coastal to inland transition zones. This approach effectively reduces uncertainties in compound flood hazard assessment (CFHA) by accounting for various sources of error, including model parameters and interactions among different flood drivers. By applying this method to historical compound flood events, such as Hurricane Harvey and Hurricane Sandy, the research demonstrates significant improvements in predicting peak water levels and generating more accurate flood hazard maps, thereby proving the value of DA in CFHA.

𝐅𝐫𝐨𝐦 𝐥𝐨𝐜𝐚𝐥 𝐭𝐨 𝐫𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐟𝐥𝐨𝐨𝐝 𝐦𝐚𝐩𝐩𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐝𝐚𝐭𝐚 𝐟𝐮𝐬𝐢𝐨𝐧 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬

Muñoz, D.F., Muñoz, P., Alipour, A., Moftakhari, H. R., Moradkhani, H., & Mortazavi, B. (2021)

This paper evaluates a deep learning framework combining convolutional neural networks (CNNs) and data fusion techniques to create maps of compound flooding (CF) along the southeast Atlantic coast of the U.S., using a mix of satellite imagery, radar data, and elevation models. The approach aims to improve large-scale flood mapping and support hydrodynamic model calibration, offering a cost-effective solution for assessing flood exposure, especially in areas lacking data. Achieving high accuracy (97%) and excellent agreement with existing flood guidance and post-flood data, this method demonstrates its potential for enhancing flood risk management and planning in coastal regions.

𝐅𝐮𝐬𝐢𝐧𝐠 𝐌𝐮𝐥𝐭𝐢𝐬𝐨𝐮𝐫𝐜𝐞 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐄𝐬𝐭𝐢𝐦𝐚𝐭𝐞 𝐭𝐡𝐞 𝐄𝐟𝐟𝐞𝐜𝐭𝐬 𝐨𝐟 𝐔𝐫𝐛𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧, 𝐒𝐞𝐚 𝐋𝐞𝐯𝐞𝐥 𝐑𝐢𝐬𝐞, 𝐚𝐧𝐝 𝐇𝐮𝐫𝐫𝐢𝐜𝐚𝐧𝐞 𝐈𝐦𝐩𝐚𝐜𝐭𝐬 𝐨𝐧 𝐋𝐨𝐧𝐠-𝐓𝐞𝐫𝐦 𝐖𝐞𝐭𝐥𝐚𝐧𝐝 𝐂𝐡𝐚𝐧𝐠𝐞 𝐃𝐲𝐧𝐚𝐦𝐢𝐜𝐬

Muñoz, D.F., Muñoz, P., Alipour, A., Moftakhari, H. R., Moradkhani, H., & Mortazavi, B. (2021)

This research investigates the decline of wetlands in the Mobile Bay area, Alabama, due to urbanization, sea level rise, and hurricanes since 1984, using a sophisticated model combining convolutional neural networks and data fusion for accurate land cover classification. The model demonstrates high accuracy, especially in identifying woody and emergent wetland types, and reveals a concerning trend of wetland loss at a rate of -1106 m² per year, highlighting the need for effective conservation strategies.

𝐂𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐄𝐟𝐟𝐞𝐜𝐭𝐬 𝐨𝐟 𝐅𝐥𝐨𝐨𝐝 𝐃𝐫𝐢𝐯𝐞𝐫𝐬 𝐚𝐧𝐝 𝐖𝐞𝐭𝐥𝐚𝐧𝐝 𝐄𝐥𝐞𝐯𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐧 𝐂𝐨𝐚𝐬𝐭𝐚𝐥 𝐅𝐥𝐨𝐨𝐝 𝐇𝐚𝐳𝐚𝐫𝐝 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭

Muñoz, D.F., Moftakhari, H. R., and Moradkhani, H. (2020)

The study uses a combined method of statistical analysis and hydrodynamic modeling to examine how different flood causes and corrections to wetland elevation in digital models affect flood hazard maps in Savannah, Georgia. This approach helps improve the accuracy of predicting flood extents and velocities during compound flooding events. By comparing these findings with the actual impacts of Hurricane Matthew in 2016, the research demonstrates the importance of including wetland elevation adjustments for more precise flood hazard assessments, showing significant differences in flood predictions with these corrections.

𝐀𝐝𝐣𝐮𝐬𝐭𝐢𝐧𝐠 𝐄𝐦𝐞𝐫𝐠𝐞𝐧𝐭 𝐇𝐞𝐫𝐛𝐚𝐜𝐞𝐨𝐮𝐬 𝐖𝐞𝐭𝐥𝐚𝐧𝐝 𝐄𝐥𝐞𝐯𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐎𝐛𝐣𝐞𝐜𝐭-𝐁𝐚𝐬𝐞𝐝 𝐈𝐦𝐚𝐠𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬, 𝐑𝐚𝐧𝐝𝐨𝐦 𝐅𝐨𝐫𝐞𝐬𝐭 𝐚𝐧𝐝 𝐭𝐡𝐞 2016 𝐍𝐋𝐂𝐃

Muñoz, D. F., Cissell, J., & Moftakhari, H. R. (2019)

This study introduces a method to update the mapping of emergent herbaceous wetlands to their current state and automate the correction of salt marsh elevation errors using a combination of object-based image analysis, random forest techniques, and Landsat imagery. The process improves the accuracy of digital elevation models (DEMs) in coastal areas by addressing vertical errors common in LiDAR measurements. The methodology was applied to wetlands in Weeks Bay, Alabama, Savannah Estuary, Georgia, and Fire Island, New York, achieving high accuracy in land cover classification and elevation correction, which is validated with real-time kinematic elevation data. This approach offers a scalable solution for accurate flood inundation mapping in estuarine systems