Browsing by Author "Sherpa, Sonam Futi"

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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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    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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    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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    An Unsupervised Probabilistic Method for Large Scale Flood Mapping: Exploring Archive of Sentinel-1A/B Satellites over India
    Sherpa, Sonam Futi (Virginia Tech, 2021-04-30)
    Synthetic aperture radar (SAR) imaging provides an all-weather sensing technique that is suitable for near-real-time mapping of disasters such as floods. In this article, I use SAR data acquired by Sentinel-1A/B satellites to investigate a flood event that affected the Indian state of Kerala in August 2018. I apply a Bayesian approach to generate probabilistic flood maps, which contain for each pixel its probability to be flooded rather than binary flood information. I find that the extent of the flooded area begins to increase throughout Kerala after August 8, with the highest values on August 9 and August 21. I observe no apparent correlation between the spatial distributions of the flooded areas and the rainfall amounts at the district level of the study area. Instead, larger flooded areas are in the districts of Alappuzha and Kottayam, located in the downstream floodplain of the Idduki dam, which released a significant volume of water on August 16. The lack of apparent correlation is likely due to two reasons: first, there is often some delay between the rainfall event and the flooding, especially for rather large catchments where flood waves need some time to reach floodplains from higher elevations. Second, rainfall is more abundant at overhead catchments (hills and mountains), whereas flood occurs further downstream in the floodplains. Further comparison of our SAR-based flood maps with optical data and flood maps produced by moderate resolution imaging spectroradiometer highlights the advantages of our data and approach for rapid response purposes and future flood forecasting.