Browsing by Author "Werth, Susanna"

Now showing 1 - 11 of 11
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    Aging dams, political instability, poor human decisions and climate change: recipe for human disaster
    Shirzaei, Manoochehr; Vahedifard, Farshid; Sadhasivam, Nitheshnirmal; Ohenhen, Leonard; Dasho, Oluwaseyi; Tiwari, Ashutosh; Werth, Susanna; Azhar, Mohammed; Zhao, Yunxia; Nicholls, Robert J.; AghaKouchak, Amir (Springer Nature, 2025-01-16)
    In Derna, Libya, a record-breaking storm and subsequent dam failures on September 10, 2023, caused over 11,000 deaths. Analyzing satellite data from 2016–2023, we found 1.8 mm/yr of differential settlement in dams contributed to their failure, and flooding damaged ~8570 buildings. We argue that the interplay of aging infrastructure, political instability, climate change, and human decisions drove this disaster, stressing the need for a holistic ‘healthcare’ management approach to prevent future catastrophes.
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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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    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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    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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    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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    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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    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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    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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    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.
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    Subsidence-Derived Volumetric Strain Models for Mapping Extensional Fissures and Constraining Rock Mechanical Properties in the San Joaquin Valley, California
    Carlson, Grace; Shirzaei, Manoochehr; Ojha, Chandrakanta; Werth, Susanna (2020-09)
    Large-scale subsidence due to aquifer-overdraft is an ongoing hazard in the San Joaquin Valley. Subsidence continues to cause damage to infrastructure and increases the risk of extensional fissures.Here, we use InSAR-derived vertical land motion (VLM) to model the volumetric strain rate due to groundwater storage change during the 2007-2010 drought in the San Joaquin Valley, Central Valley, California. We then use this volumetric strain rate model to calculate surface tensile stress in order to predict regions that are at the highest risk for hazardous tensile surface fissures. We find a maximum volumetric strain rate of -232 microstrain/yr at a depth of 0 to 200 m in Tulare and Kings County, California. The highest risk of tensile fissure development occurs at the periphery of the largest subsiding zones, particularly in Tulare County and Merced County. Finally, we assume that subsidence is likely due to aquifer pressure change, which is calculated using groundwater level changes observed at 300 wells during this drought. We combine pressure data from selected wells with our volumetric strain maps to estimate the quasi-static bulk modulus, K, a poroelastic parameter applicable when pressure change within the aquifer is inducing volumetric strain. This parameter is reflective of a slow deformation process and is one to two orders of magnitude lower than typical values for the bulk modulus found using seismic velocity data. The results of this study highlight the importance of large-scale, high-resolution VLM measurements in evaluating aquifer system dynamics, hazards associated with overdraft, and in estimating important poroelastic parameters.