Browsing by Author "Shirzaei, Manoochehr"

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    Assessment of Future Flood Hazards for Southeastern Texas: Synthesizing Subsidence, Sea-Level Rise, and Storm Surge Scenarios
    Miller, Megan M.; Shirzaei, Manoochehr (2021-04-28)
    Recent hurricanes highlight shortcomings of flood resilience plans in Texas that can worsen with climate change and rising seas. Combining vertical land motion (VLM) with sea-level rise (SLR) projections and storm surge scenarios for the years 2030, 2050, and 2100, we quantify the extent of flooding hazards. VLM rates are obtained from GNSS data and InSAR imagery from ALOS and Sentinel-1A/B satellites. VLM is resampled and projected on LIDAR topographic data, then multiple inundation and flooding scenarios are modeled. By the year 2100, over 76 km(2) are projected to subside below sea level. Subsidence increases the area of inundation over SLR alone by up to 39%. Under the worst-case composite scenario of an 8-m storm surge, subsidence, and the SLR RCP8.5, the total affected area is 1,156 km(2). These models enable communities to improve flood resiliency plans.
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    Big Remote Sensing Data and Machine Learning for Assessing 21st Century Flooding and Socioeconomic Exposures
    Sherpa, Sonam Futi (Virginia Tech, 2023-04-28)
    Over the past decades, we have seen escalating costs associated with the direct socioeconomic impacts of hydrometeorological events and climate extremes such as flooding, rising sea levels due to climate change, solid earth changes, and other anthropogenic activities. With the increasing population in the era of changing climate, the number of people suffering from exposure to extreme events and sea level rise is expected to increase over the years. To develop resilience plans and mitigation strategies, hindcast exposure models, and calculate the insurance payouts, accurate maps of flooding extent and socioeconomic exposure at management-relevant resolution (102m) are needed. The growing number and continually improving coverage of Earth-observing satellites, an extensive archive of big data, and machine learning approaches have transformed the community's capacity to timely respond to flooding and water security concerns. However, in the case of flood extent mapping, most flood mapping algorithms estimate flood extent in the form of a binary map and do not provide any information on the uncertainty associated with the pixel class. Additionally, in the case of coastal inundation from sea level rise, most future projections of sea-level rise lack an accurate estimate of vertical land motion and pose a significant challenge to flood risk management plans. In this dissertation, I explore an extensive archive of available remotely sensed space-borne. synthetic aperture radar (SAR) and interferometric SAR measurements for 1) Large-scale flood extent mapping and exposure utilizing machine learning approaches and Bayesian framework to obtain probabilistic flood maps for the 2019 flood of Iran and 2018 flood of India and 2) Assessment of relative sea-level rise flooding for coastal disaster resilience in the Chesapeake Bay. Lastly, I investigate how climate change affects hydrology and cryosphere to 3) understand cryosphere-climate interaction for hazard risk and water resources management.
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    Country-wide flood exposure analysis using Sentinel-1 synthetic aperture radar data: Case study of 2019 Iran flood
    Sherpa, Sonam Futi; Shirzaei, Manoochehr (2021-11-24)
    Extreme precipitation and flooding often lead to human and economic losses. However, the high-resolution nationwide flooding exposure data are scarce. Availability of all-weather space-borne SAR satellite data can potentially improve the ability to generate high-resolution flood map extent and exposure globally. In Iran, flooding is a major concern, given the socioeconomic vulnerabilities and increased likelihood of climate extremes. Iran experienced extreme flooding during January to March 2019, attributed to significant precipitation during October 2018 to March 2019, which is well above the long-term averages for 1999-2019. Using Pettitt and Mann Kendall tests, nationwide precipitation records were identified by significant decreasing and increasing trends in north and south, respectively. Utilizing 673 Sentinel-1 SAR intensity images, we applied a fast-marching algorithm for image segmentation in combination with a Bayesian framework to obtain high-resolution probabilistic flood maps. We found, 22, 9, and 15 states in January, February, and March, respectively, experienced flooding that covered >15% of their area with high flooded area percent in the northwestern and southeastern region. We estimated that >15, >11.32, and >11.33 million people were exposed to floods in January, February, and March, respectively. Our datasets inform flooding models and management efforts under increasing climate extremes and changing land use and cover.
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    Disappearing cities on US coasts
    Ohenhen, Leonard O.; Shirzaei, Manoochehr; Ojha, Chandrakanta; Sherpa, Sonam F.; Nicholls, Robert J. (Nature Research, 2024-03-06)
    The sea level along the US coastlines is projected to rise by 0.25–0.3 m by 2050, increasing the probability of more destructive flooding and inundation in major cities. However, these impacts may be exacerbated by coastal subsidence— the sinking of coastal land areas—a factor that is often underrepresented in coastal-management policies and long-term urban planning. In this study, we combine high-resolution vertical land motion (that is, raising or lowering of land) and elevation datasets with projections of sea-level rise to quantify the potential inundated areas in 32 major US coastal cities. Here we show that, even when considering the current coastal-defence structures, further land area of between 1,006 and 1,389 km² is threatened by relative sea-level rise by 2050, posing a threat to a population of 55,000–273,000 people and 31,000–171,000 properties. Our analysis shows that not accounting for spatially variable land subsidence within the cities may lead to inaccurate projections of expected exposure. These potential consequences show the scale of the adaptation challenge, which is not appreciated in most US coastal cities.
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    Disruptive Role of Vertical Land Motion in Future Assessments of Climate Change-Driven Sea-Level Rise and Coastal Flooding Hazards in the Chesapeake Bay
    Sherpa, Sonam Futi; Shirzaei, Manoochehr; Ojha, Chandrakanta (American Geophysical Union, 2023-04)
    Future projections of sea-level rise (SLR) used to assess coastal flooding hazards and exposure throughout the 21st century and devise risk mitigation efforts often lack an accurate estimate of coastal vertical land motion (VLM) rate, driven by anthropogenic or non-climate factors in addition to climatic factors. The Chesapeake Bay (CB) region of the United States is experiencing one of the fastest rates of relative sea-level rise on the Atlantic coast of the United States. This study uses a combination of space-borne Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), Light Detecting and Ranging (LiDAR) data sets, available National Oceanic and Atmospheric Administration (NOAA) long-term tide gauge data, and SLR projections from the Intergovernmental Panel on Climate Change (IPCC), AR6 WG1 to quantify the regional rate of relative SLR and future flooding hazards for the years 2030, 2050, and 2100. By the year 2100, the total inundated areas from SLR and subsidence are projected to be 454(316–549)–600(535𝐴𝐴–690) km² for Shared Socioeconomic Pathways (SSPs) 1–1.9 to 5–8.5, respectively, and 342(132–552)–627(526–735) 𝐴𝐴 km2 only from SLR. The effect of storm surges based on Hurricane Isabel can increase the inundated area to 849(832–867)–1,117(1,054–1,205) km² under different VLM and SLR scenarios. We suggest that accurate estimates of VLM rate, such as those obtained here, are essential to revise IPCC projections and obtain accurate maps of coastal flooding and inundation hazards. The results provided here inform policymakers when assessing hazards associated with global climate changes and local factors in CB, required for developing risk management and disaster resilience plans.
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    Earth Observation Data-Driven Assessment of Local to Regional, Contemporary, and Emerging Coastal Environmental Security Challenges
    Ohenhen, Osadebamwen Leonard (Virginia Tech, 2024-09-25)
    Coastal zones are hotspots of global environmental changes. Worldwide, coastal environments face multiple, interactive stressors caused by both natural and anthropogenic impacts on climatic, oceanographic, ecological, and socio-economic processes such as sea level rise, storm surges, hurricanes, land subsidence, and population growth. The coastal U.S. is highly vulnerable to many of these climate and human-induced stressors. Over the past three decades, sea levels have risen by about 0.1 m along the U.S. coasts, with an additional projected increase of 0.2 to 0.3 m by 2050, and up to 2.0 m by the end of the century. The rise in sea levels will cause tides and storm surges to reach further inland, significantly altering flood regimes in coastal cities. By 2050, potentially damaging coastal flooding is expected to occur ten times as often compared to a baseline for the start of the 21st century. Moreover, these changes along the U.S. coastlines vary regionally and locally due to either positive or negative changes in land elevation over time (i.e., vertical land motion (VLM)). Lowering of land elevation (i.e., land subsidence) exacerbates sea level rise and the risk of inundation along coastal zones, presenting significant security challenges to coastal ecosystems, infrastructure, and populations. These dynamic and interacting stressors necessitate continuous monitoring to inform effective mitigation and adaptation strategies. Earth observation data allows for accurate, high-resolution, and continuous measurements of changing coastline. Despite the increasing availability of Earth observation data, current methods for monitoring VLM along coastlines lack the necessary spatial resolution and continuous coverage to accurately assess localized surface elevation changes. In this dissertation, I introduce a framework to jointly invert interferometric synthetic aperture radar (InSAR) and global navigation satellite systems (GNSS) data to provide semi-continuous measurement (50 m spatial resolution) of VLM for the contiguous U.S. coasts from 2007 – 2020. Combining the VLM dataset with projected sea level rise using different scenarios, I estimate flood hazards exposure for 32 major U.S. coastal cities by 2050, demonstrating that current measurements and frameworks underestimate flood vulnerability in several cities by not accounting for local and regional high-resolution VLM data. Next, I evaluate the possible drivers of land subsidence, exploring the relationship between spatio-temporal dynamic VLM and groundwater withdrawal from aquifers in major U.S. cities. Additionally, I assess the hazards and risks of land subsidence to infrastructure and wetlands along U.S. coasts. Finally, I extend this analysis beyond the U.S. coastline, investigating how land subsidence is linked to the incessant occurrence of building collapses in Lagos, Africa's most populous coastal city.
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    Generative AI tools can enhance climate literacy but must be checked for biases and inaccuracies
    Atkins, Carmen; Girgente, Gina; Shirzaei, Manoochehr; Kim, Junghwan (Springer Nature, 2024-04)
    In the face of climate change, climate literacy is becoming increasingly important. With wide access to generative AI tools, such as OpenAI’s ChatGPT, we explore the potential of AI platforms for ordinary citizens asking climate literacy questions. Here, we focus on a global scale and collect responses from ChatGPT (GPT-3.5 and GPT-4) on climate change-related hazard prompts over multiple iterations by utilizing the OpenAI’s API and comparing the results with credible hazard risk indices.Wefind a general sense of agreement in comparisons and consistency in ChatGPT over the iterations. GPT-4 displayed fewer errors than GPT-3.5. Generative AI tools may be used in climate literacy, a timely topic of importance, but must be scrutinized for potential biases and inaccuracies moving forward and considered in a social context. Future work should identify and disseminate best practices for optimal use across various generative AI tools.
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    Groundwater Volume Loss in Mexico City Constrained by InSAR and GRACE Observations and Mechanical Models
    Khorrami, Mohammad; Shirzaei, Manoochehr; Ghobadi-Far, Khosro; Werth, Susanna; Carlson, Grace; Zhai, Guang (American Geophysical Union, 2023-03)
    Groundwater withdrawal can cause localized and rapid poroelastic subsidence, spatially broad elastic uplift of low amplitude, and changes in the gravity field. Constraining groundwater loss in Mexico City, we analyze data from the Gravity Recovery and Climate Experiment and its follow-on mission (GRACE/FO) and Synthetic Aperture Radar (SAR) Sentinel-1A/B images between 2014 and 2021. GRACE/FO observations yield a groundwater loss of 0.85-3.87 km(3)/yr for a region of similar to 300 x 600 km surrounding Mexico City. Using the high-resolution interferometric SAR data set, we measure >35 cm/yr subsidence within the city and up to 2 cm/yr of uplift in nearby areas. Attributing the long-term subsidence to poroelastic aquifer compaction and the long-term uplift to elastic unloading, we apply respective models informed by local geology, yielding groundwater loss of 0.86-12.57 km(3)/yr. Our results suggest Mexico City aquifers have been depleting at faster rates since 2015, exacerbating the socioeconomic and health impacts of long-term groundwater overdrafts.
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    Hidden vulnerability of US Atlantic coast to sea-level rise due to vertical land motion
    Ohenhen, Leonard O.; Shirzaei, Manoochehr; Ojha, Chandrakanta; Kirwan, Matthew L. (Nature Research, 2023-04-11)
    The vulnerability of coastal environments to sea-level rise varies spatially, particularly due to local land subsidence. However, high-resolution observations and models of coastal subsidence are scarce, hindering an accurate vulnerability assessment. We use satellite data from 2007 to 2020 to create high-resolution map of subsidence rate at mm-level accuracy for different land covers along the ~3,500 km long US Atlantic coast. Here, we show that subsidence rate exceeding 3mm per year affects most coastal areas, including wetlands, forests, agricultural areas, and developed regions. Coastal marshes represent the dominant land cover type along the US Atlantic coast and are particularly vulnerable to subsidence. We estimate that 58 to 100% of coastal marshes are losing elevation relative to sea level and show that previous studies substantially underestimate marsh vulnerability by not fully accounting for subsidence.
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    Joint Inversion of GNSS and GRACE for Terrestrial Water Storage Change in California
    Carlson, Grace; Werth, Susanna; Shirzaei, Manoochehr (American Geophysical Union, 2022-03)
    Global Navigation Satellite System (GNSS) vertical displacements measuring the elastic response of Earth's crust to changes in hydrologic mass have been used to produce terrestrial water storage change ( increment TWS) estimates for studying both annual increment TWS as well as multi-year trends. However, these estimates require a high observation station density and minimal contamination by nonhydrologic deformation sources. The Gravity Recovery and Climate Experiment (GRACE) is another satellite-based measurement system that can be used to measure regional TWS fluctuations. The satellites provide highly accurate increment TWS estimates with global coverage but have a low spatial resolution of similar to 400 km. Here, we put forward the mathematical framework for a joint inversion of GNSS vertical displacement time series with GRACE increment TWS to produce more accurate spatiotemporal maps of increment TWS, accounting for the observation errors, data gaps, and nonhydrologic signals. We aim to utilize the regional sensitivity to increment TWS provided by GRACE mascon solutions with higher spatial resolution provided by GNSS observations. Our approach utilizes a continuous wavelet transform to decompose signals into their building blocks and separately invert for long-term and short-term mass variations. This allows us to preserve trends, annual, interannual, and multi-year changes in TWS that were previously challenging to capture by satellite-based measurement systems or hydrological models, alone. We focus our study in California, USA, which has a dense GNSS network and where recurrent, intense droughts put pressure on freshwater supplies. We highlight the advantages of our joint inversion results for a tectonically active study region by comparing them against inversion results that use only GNSS vertical deformation as well as with maps of increment TWS from hydrological models and other GRACE solutions. We find that our joint inversion framework results in a solution that is regionally consistent with the GRACE increment TWS solutions at different temporal scales but has an increased spatial resolution that allows us to differentiate between regions of high and low mass change better than using GRACE alone.
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    Measuring, modelling and projecting coastal land subsidence
    Shirzaei, Manoochehr; Freymueller, Jeffrey; Tornqvist, Torbjorn E.; Galloway, Devin L.; Dura, Tina; Minderhoud, Philip S. J. (2021-01)
    Measuring coastal subsidence is essential to evaluating hazards associated with sea-level rise. This Review discusses the processes driving coastal subsidence, space-borne and land-based measurement techniques, as well as models for simulating observed subsidence and predicting future trends. Coastal subsidence contributes to relative sea-level rise and exacerbates flooding hazards, with the at-risk population expected to triple by 2070. Natural processes of vertical land motion, such as tectonics, glacial isostatic adjustment and sediment compaction, as well as anthropogenic processes, such as fluid extraction, lead to globally variable subsidence rates. In this Review, we discuss the key physical processes driving vertical land motion in coastal areas. Use of space-borne and land-based techniques and the associated uncertainties for monitoring subsidence are examined, as are physics-based models used to explain contemporary subsidence rates and to obtain future projections. Steady and comparatively low rates of subsidence and uplift owing to tectonic processes and glacial isostatic adjustment can be assumed for the twenty-first century. By contrast, much higher and variable subsidence rates occur owing to compaction associated with sediment loading and fluid extraction, as well as large earthquakes. These rates can be up to two orders of magnitude higher than the present-day rate of global sea-level rise. Multi-objective predictive models are required to account for the underlying physical processes and socio-economic factors that drive subsidence.
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    A novel machine learning and deep learning semi-supervised approach for automatic detection of InSAR-based deformation hotspots
    Tiwari, Ashutosh; Shirzaei, Manoochehr (Elsevier, 2024-02)
    Over the past two decades, Interferometric synthetic aperture radar (InSAR) has been invaluable for studying earth surface deformation and related effects. Deformation maps generated through multi-temporal InSAR processing methods are however difficult to interpret accurately by general individual users, decision-makers, and non-domain experts owing to the volume, variety, and velocity they are produced. This paper proposes a semi-supervised machine learning based information mining approach to simplify these deformation maps and detect hotspots by extracting prominent signals from time series deformation. The approach initially combines two machine learning based clustering methods named time series k-means (TSKM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to derive clusters with unique spatiotemporal deformation behavior, using time series deformation output generated from Wavelet-based InSAR (WabInSAR) method. Clustering results generated from this unsupervised machine learning approach are later used as training labels to develop two deep learning models, one using long short term memory (LSTM) networks alone and another using a combination of LSTM and single-layer perceptron for supervised training. The developed LSTM and LSTM + Perceptron models efficiently learn from the cluster labels, reaching an accuracy of 97.3 %. Further, the deep learning models significantly reduce the computational time from orders of days (∼5) to hours (∼2) while training and from hours to minutes during prediction. We evaluate the developed approach over Los Angeles, a highly challenging area affected by umpteen deformation events that are challenging to categorize. The outcome of the proposed approach produces hotspots of deforming areas in Los Angeles, providing a generalized and more precise picture of events, much appreciable to non-domain experts. The approach can augment any of the multi-temporal InSAR processing chains and is applicable to different deformation prone sites, aiding in derivation of deformation hotspots from time series deformation maps.
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    Novel Multitemporal Synthetic Aperture Radar Interferometry Algorithms and Models Applied on Managed Aquifer Recharge and Fault Creep
    Lee, Jui-Chi (Virginia Tech, 2024-02-09)
    The launch of Sentinel-1A/B satellites in 2014 and 2016 marked a pivotal moment in Synthetic Aperture Radar (SAR) technology, ushering in a golden era for SAR. With a revisit time of 6–12 days, these satellites facilitated the acquisition of extensive stacks of high-resolution SAR images, enabling advanced time series analysis. However, processing these stacks posed challenges like interferometric phase degradation and tropospheric phase delay. This study introduces an advanced Small Baseline Subset (SBAS) algorithm that optimizes interferometric pairs, addressing systematic errors through dyadic downsampling and Delaunay Triangulation. A novel statistical framework is developed for elite pixel selection, considering distributed and permanent scatterers, and a tropospheric error correction method using smooth 2D splines effectively identifies and removes error components with fractal-like structures. Beyond geodetic technique advancements, the research explores geological phenomena, detecting five significant slow slip events (SSEs) along the Southern San Andreas Fault using multitemporal SAR interferometric time series from 2015-2021. These SSEs govern aseismic slip dynamics, manifesting as avalanche-like creep rate variations. The study further investigates Managed Aquifer Recharge (MAR) as a nature-engineering-based solution in the Santa Ana Basin. Analyzing surface deformation from 2004 to 2022 demonstrates MAR's effectiveness in curbing land subsidence within Orange County, CA. Additionally, MAR has the potential to stabilize nearby faults by inducing a negative Coulomb stress change. Projecting into the future, a suggested 2% annual increase in recharge volume through 2050 could mitigate land subsidence and reduce seismic hazards in coastal cities vulnerable to relative sea level rise. This integrated approach offers a comprehensive understanding of geological processes and proposes solutions to associated risks.
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    Observing Drought-Induced Crustal Loading Deformation Around Lake Mead Region via GNSS and InSAR: A Comparison With Elastic Loading Models
    Zehsaz, Sonia (Virginia Tech, 2023-09-22)
    Lake Mead, the largest reservoir in the United States along the Colorado River on the border between the states of Nevada and Arizona, is one of the nation's most important sources of freshwater. As reported by the U.S. drought monitor (USDM), the entire region has been experiencing recurring severe to extreme droughts since the early 2000s, which have further intensified during the past two years. The drought-driven water deficit caused Lake Mead's water volume to decrease to approximately one-third of its capacity, creating a water crisis and negatively affecting soil and groundwater storage across the region. Water deficits have further reduced the mass of water loading on the Earth's crust, causing it to elastically deform. I observe this process from the ground by recording the vertical land motion occurring at Global Navigation Satellite System (GNSS) stations, or from space via Interferometric Synthetic Aperture Radar (InSAR) technology. In this study, I analyze vertical deformation observations from GNSS sites and multi-temporal InSAR analysis of Sentinel-1A/B to investigate the contribution of water mass changes in lake, soil, and groundwater to the deformation signal. To achieve this, I remove the effects of glacial isostatic adjustment and non-tidal mass loads from GNSS/InSAR observations. Our findings indicate that recent drought periods led to a notable uplift near Lake Mead, averaging 7.3 mm/year from 2012 to 2015 and an even larger rate of 8.6 mm/year from 2020 to 2023. Further, I provide an estimate of the expected vertical crustal deformation in response to well-known changes in lake and soil moisture storage. For that, I quantify hydrological loads through two different loading models. These include the application of Green's functions for an elastic, layered, self-gravitating, spherical Earth, and the Love load numbers from the Preliminary Reference Earth Models (PREMs), as well as elastic linearly homogeneous half-space Earth models. I further test various load models against the GNSS observations. Our research further investigates the impact of local crustal properties and evaluates the output of several elastic loading models using crustal properties and different model types under non-drought and drought conditions. For future studies, I suggest a comprehensive analysis of the deformation field InSAR data. Also, rigorous monitoring of groundwater levels is essential to accurately predict changes in water masses based on deformation. In addition, for each data set, I suggest implementing an uncertainty analysis to assess the predictability of groundwater level changes based on vertical loading deformation observed by INSAR/GNSS data around the region. Obtaining such estimates will provide valuable insight into the dynamic interactions of the local aquifers with Lake Mead.
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    On the Origin of Orphan Tremors and Intraplate Seismicity in Western Africa
    Olugboji, T.; Shirzaei, Manoochehr; Lu, Yingping; Adepelumi, A. A.; Kolawole, F. (2021-09-20)
    On September 5-7, 2018, a series of tremors were reported in Nigeria's capital city, Abuja. These events followed a growing list of tremors felt in the stable intraplate region, where earthquakes are not expected. Here, we review available seismological, geological, and geodetic data that may shed light on the origin of these tremors. First, we investigate the seismic records for parent location of the orphan tremors using a technique suitable when a single-seismic station is available such as the Western Africa region, which has a sparse seismic network. We find no evidence of the reported tremors within the seismic record of Western Africa. Next, we consider the possibility of a local amplification of earthquakes from regional tectonics, reactivation of local basement fractures by far-field tectonic stresses, post-rift crustal relaxation, landward continuation of oceanic fracture zones, or induced earthquakes triggered by groundwater extraction. Our assessments pose important implications for understanding Western Africa's intraplate seismicity and its potential connection to tectonic inheritance, active regional tectonics, and anthropogenic stress perturbation.
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    Persistent impact of spring floods on crop loss in U.S. Midwest
    Shirzaei, Manoochehr; Koshmanesh, Mostafa; Ojha, Chandrakanta; Werth, Susanna; Kerner, Hannah; Carlson, Grace; Sherpa, Sonam Futi; Zhai, Guang; Lee, Jui-Chi (Elsevier, 2021-10-20)
    Climate extremes threaten global food security, and compound events, such as late spring heavy and warmer rainfall over snow and subsequent flooding, exacerbate this vulnerability. Despite frequent occurrences in recent years, a quantitative understanding of the compound weather events' impacts remains elusive. Here, we use Synthetic Aperture Radar data from Sentinel-1 and normalized difference vegetation index data from MODIS satellites to map the spring 2019 U.S. Midwest flood extent and evaluate its impact on crop loss. We find a statistically significant association between flooded counties and those with plant greenup delay, while the correlation between flood area percent and amount of green-up delay remains weak, albeit reliable. An analysis of the stream gage time series and crop loss records shows that during the past ∼70 years, ∼43% of spring large discharges are associated with widespread crop loss. We also find an increase in streams' discharge frequency and magnitude across the Midwest, indicating the possibility of a future increase in crop loss due to spring flooding. This study highlights the importance of Earth-observing satellite data for developing climate adaptation and resilience plans.
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    Potential Link Between 2020 Mentone, West Texas M5 Earthquake and Nearby Wastewater Injection: Implications for Aquifer Mechanical Properties
    Tung, Sui; Zhai, Guang; Shirzaei, Manoochehr (2021-02-16)
    The M5 Mentone earthquake that occurred on March 26, 2020, was the largest event recorded over the last 2 decades in West Texas within the Delaware Basin, a U.S. major petroleum-producing area. Also, numerous hydrofracturing and wastewater disposal wells are spread across this region. Within a 30 km distance to mainshock, eight class-II injection wells for industrial wastewater disposal target the deep porous Ellenburger aquifer at an average rate of 1.36 x 10(6) barrel (BBL) per month during 2012-2020. Poroelastic models of fluid diffusion show these nearby injectors collectively imparted up to 80.5 kPa of Coulomb stress at the mainshock location, capable of triggering this M5 event. Assuming the Mentone event occurs when pore-pressure increase is maximum, the time delay between peak injection and the M5 occurrence corresponds with an optimal permeability of 6.76 x 10(-14) m(2) for the Ellenburger aquifer layer, in agreement with independent estimates.
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    Remote Sensing of 21st Century Water Stress for Hazard Monitoring in California
    Carlson, Grace Anne (Virginia Tech, 2023-02-02)
    California has experienced an unusually dry past two decades punctuated by three intense multi-year droughts from 2007-2010, 2012-2015, and 2020-2022. A portion of the water lost during these two decades is due to intense groundwater overdraft of the Central Valley Aquifer. This groundwater overdraft has led to poroelastic compaction of the aquifer system and subsidence of the land surface. Water mass loss also causes elastic deformation of the solid Earth, an opposite and smaller amplitude response than the poroelastic deformation of aquifer systems. These mass changes can disturb the regional stress field, which may influence earthquake activity. Both the elastic and poroelastic deformation responses can be observed using satellite-based geodetic tools including Global Navigation Satellite System (GNSS) station displacements and Interferometric Synthetic Aperture Radar (InSAR). In this dissertation, I model aquifer-system compaction at depth using InSAR-based vertical land motion during the 2007-2010 drought and evaluate hazards related to Earth fissures, tensional cracks that form at the edges of subsidence zones. Next, I forward-calculate the predicted elastic deformation response to groundwater mass loss over the same period and calculate crustal stress change to evaluate what, if any, impact this has on seismicity in California. In addition to modeling deformation caused by water storage change, I also introduce a new method to jointly invert elastic vertical displacements at GNSS stations with water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) to solve for water storage changes from 2003-2016 over California. Finally, I expand on this joint inversion framework to include poroelastic deformation measured using InSAR over the Central Valley aquifer-system to solve for a change in water storage and groundwater storage over water years 2020-2021, the most recent drought period in California.
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    Slowly but surely: Exposure of communities and infrastructure to subsidence on the US east coast
    Ohenhen, Leonard; Shirzaei, Manoochehr; Barnard, Patrick L. (Oxford University Press, 2024-01-02)
    Coastal communities are vulnerable to multihazards, which are exacerbated by land subsidence. On the US east coast, the high density of population and assets amplifies the region's exposure to coastal hazards. We utilized measurements of vertical land motion rates obtained from analysis of radar datasets to evaluate the subsidence-hazard exposure to population, assets, and infrastructure systems/facilities along the US east coast. Here, we show that 2,000 to 74,000 km² land area, 1.2 to 14 million people, 476,000 to 6.3 million properties, and >50% of infrastructures in major cities such as New York, Baltimore, and Norfolk are exposed to subsidence rates between 1 and 2 mm per year. Additionally, our analysis indicates a notable trend: as subsidence rates increase, the extent of area exposed to these hazards correspondingly decreases. Our analysis has far-reaching implications for community and infrastructure resilience planning, emphasizing the need for a targeted approach in transitioning from reactive to proactive hazard mitigation strategies in the era of climate change.
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    Spatiotemporal Groundwater Storage Dynamics and Aquifer Mechanical Properties in the Santa Clara Valley Inferred From InSAR Deformation Over 2017-2022
    Ghobadi-Far, Khosro; Werth, Susanna; Shirzaei, Manoochehr; Burgmann, Roland (American Geophysical Union, 2023-11-22)
    We used Interferometric Synthetic Aperture Radar (InSAR)-derived vertical land motion (VLM) timeseries during 2017–2022 to examine the compounding impacts of natural and anthropogenic processes on groundwater dynamics in the Santa Clara Valley (SCV). VLM strongly correlates (>0.75) with groundwater level in both unconfined and confined aquifers. We show that VLM in SCV is mainly driven by groundwater dynamics in deep aquifer layers below 120 m. Our results show that during the most recent drought from March 2019 to November 2021, Santa Clara County subsided up to 30 mm due to groundwater depletion, three times as large as average seasonal amplitude of VLM. Owing to the managed aquifer recharge, the region has been able to avoid unrecoverable land subsidence. We utilize InSAR data to calibrate storage coefficient and lag time related to delayed response of clay interbeds to groundwater level changes, which further serves to estimate groundwater volume loss in confined aquifer units during drought.