{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/14546"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/14546","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"Trends in Extreme Precipitation: Identifying Historical and Projected Patterns Using Multi-Source Precipitation Datasets","abstract":"Understanding past precipitation trends and the likelihood of such trends persisting or changing in the future is crucial to optimize water demands, designing infrastructure, and preparing a climate-resilient society. This dissertation investigates historical and projected changes (magnitude, frequency, and intensity) in annual, seasonal, and extreme precipitation across different regions in the world by leveraging datasets from a range of sources: Earth observations (i.e., satellite remote sensing), models of earth system science (e.g., atmospheric reanalysis and community earth system models, CESM), as well as ground-based measurements. Precipitation patterns are evaluated in terms of temporal trends (and their statistical significance) and spatial patterns from the regional (Southern Mid-Atlantic and High Mountain Asia) to the continental scale (i.e., Contiguous United States). This work adopts hydroclimatic extreme indices from ETCCDI (Expert Team on Climate Change Detection and Indices) to characterize precipitation trends and the relative contribution of extreme events to the total precipitation. At a regional scale, using 40 years (1980-2018) of high-resolution reanalysis data from the North American Land Data Assimilation V2 (NLDAS-2, 12 km/ hourly), a significant (0.1 significance level) increase in annual precipitation (+3~+5 mm/year) is identified over Northern Virginia accompanied with an increase in summer precipitation. An annual increase (at 0.05 significance level) in extreme events (95th and 99th percentiles) is also identified in the southern Mid-Atlantic region. An investigation into the proportion of annual precipitation occurring on wet and extremely wet days (95th and 99th) also indicates a significant increase. Next, this dissertation evaluated the projected changes until the end of the 21st century in the probability distribution of hydroclimate extreme indices across the National Climate Assessment (NCA) regions in the contiguous US. This was performed using a large ensemble simulation (70 members) in a new scenario-matrix-architecture (Shared Socioeconomic Pathway 3, SSP3-7.0) of CESM v2 (100km/daily, 2015-2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). A projected increase in the northeastern regions in spring and winter and consistent drying in the Midwest summer precipitation were identified. Next, spatiotemporal patterns of both precipitation averages and extremes were identified over a region characterized by complex terrain, i.e., HMA, during 1990-2018. However, finding a reliable dataset in this region can be challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Therefore, evaluating the quality and reliability of different precipitation products before analyzing their trends and patterns is fundamental. A comprehensive assessment of high-resolution satellite-based, model reanalysis precipitation estimates and their blended product (ensemble) was conducted using ground observations from a suite of rain gauge networks at different elevation ranges. The last chapter of this dissertation transitions from pattern-based analyses to an event-based analysis that focuses on the heavy downpours that triggered devastating floods in Pakistan in the summer of 2022. Results confirm the singularity of this event with abnormal daily rain rates compared to climatology in western Balochistan and large anomalies across Pakistan’s southern provinces after August 16th. Outputs from this dissertation provide insights into the changing distribution of precipitation extremes, which are likely to trigger hydroclimatic hazards. In conclusion, this dissertation highlights the importance of assessing changes in extreme precipitation patterns across scales and from a variety of data sources to improve our understanding of the changing water cycle and enhance regional resilience to extreme climatic events.","abstract_html":"Understanding past precipitation trends and the likelihood of such trends persisting or changing in the future is crucial to optimize water demands, designing infrastructure, and preparing a climate-resilient society. This dissertation investigates historical and projected changes (magnitude, frequency, and intensity) in annual, seasonal, and extreme precipitation across different regions in the world by leveraging datasets from a range of sources: Earth observations (i.e., satellite remote sensing), models of earth system science (e.g., atmospheric reanalysis and community earth system models, CESM), as well as ground-based measurements. Precipitation patterns are evaluated in terms of temporal trends (and their statistical significance) and spatial patterns from the regional (Southern Mid-Atlantic and High Mountain Asia) to the continental scale (i.e., Contiguous United States). This work adopts hydroclimatic extreme indices from ETCCDI (Expert Team on Climate Change Detection and Indices) to characterize precipitation trends and the relative contribution of extreme events to the total precipitation. At a regional scale, using 40 years (1980-2018) of high-resolution reanalysis data from the North American Land Data Assimilation V2 (NLDAS-2, 12 km/ hourly), a significant (0.1 significance level) increase in annual precipitation (+3~+5 mm/year) is identified over Northern Virginia accompanied with an increase in summer precipitation. An annual increase (at 0.05 significance level) in extreme events (95th and 99th percentiles) is also identified in the southern Mid-Atlantic region. An investigation into the proportion of annual precipitation occurring on wet and extremely wet days (95th and 99th) also indicates a significant increase. Next, this dissertation evaluated the projected changes until the end of the 21st century in the probability distribution of hydroclimate extreme indices across the National Climate Assessment (NCA) regions in the contiguous US. This was performed using a large ensemble simulation (70 members) in a new scenario-matrix-architecture (Shared Socioeconomic Pathway 3, SSP3-7.0) of CESM v2 (100km/daily, 2015-2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). A projected increase in the northeastern regions in spring and winter and consistent drying in the Midwest summer precipitation were identified. Next, spatiotemporal patterns of both precipitation averages and extremes were identified over a region characterized by complex terrain, i.e., HMA, during 1990-2018. However, finding a reliable dataset in this region can be challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Therefore, evaluating the quality and reliability of different precipitation products before analyzing their trends and patterns is fundamental. A comprehensive assessment of high-resolution satellite-based, model reanalysis precipitation estimates and their blended product (ensemble) was conducted using ground observations from a suite of rain gauge networks at different elevation ranges. The last chapter of this dissertation transitions from pattern-based analyses to an event-based analysis that focuses on the heavy downpours that triggered devastating floods in Pakistan in the summer of 2022. Results confirm the singularity of this event with abnormal daily rain rates compared to climatology in western Balochistan and large anomalies across Pakistan’s southern provinces after August 16th. Outputs from this dissertation provide insights into the changing distribution of precipitation extremes, which are likely to trigger hydroclimatic hazards. In conclusion, this dissertation highlights the importance of assessing changes in extreme precipitation patterns across scales and from a variety of data sources to improve our understanding of the changing water cycle and enhance regional resilience to extreme climatic events.","abstract_has_math":false,"creators":["Dollan, Ishrat"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023","date_published":"2023","updated_at":"2026-07-27T19:51:56Z","subjects":["Climate models","extreme precipitation","High Mountain Asia","Satellite estimates","spatiotemporal patterns","trends"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/14546"],"render_values":[{"text":"hdl:1920/14546","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Climate models","extreme precipitation","High Mountain Asia","Satellite estimates","spatiotemporal patterns","trends"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/14546"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["Understanding past precipitation trends and the likelihood of such trends persisting or changing in the future is crucial to optimize water demands, designing infrastructure, and preparing a climate-resilient society. This dissertation investigates historical and projected changes (magnitude, frequency, and intensity) in annual, seasonal, and extreme precipitation across different regions in the world by leveraging datasets from a range of sources: Earth observations (i.e., satellite remote sensing), models of earth system science (e.g., atmospheric reanalysis and community earth system models, CESM), as well as ground-based measurements. Precipitation patterns are evaluated in terms of temporal trends (and their statistical significance) and spatial patterns from the regional (Southern Mid-Atlantic and High Mountain Asia) to the continental scale (i.e., Contiguous United States). This work adopts hydroclimatic extreme indices from ETCCDI (Expert Team on Climate Change Detection and Indices) to characterize precipitation trends and the relative contribution of extreme events to the total precipitation. At a regional scale, using 40 years (1980-2018) of high-resolution reanalysis data from the North American Land Data Assimilation V2 (NLDAS-2, 12 km/ hourly), a significant (0.1 significance level) increase in annual precipitation (+3~+5 mm/year) is identified over Northern Virginia accompanied with an increase in summer precipitation. An annual increase (at 0.05 significance level) in extreme events (95th and 99th percentiles) is also identified in the southern Mid-Atlantic region. An investigation into the proportion of annual precipitation occurring on wet and extremely wet days (95th and 99th) also indicates a significant increase. Next, this dissertation evaluated the projected changes until the end of the 21st century in the probability distribution of hydroclimate extreme indices across the National Climate Assessment (NCA) regions in the contiguous US. This was performed using a large ensemble simulation (70 members) in a new scenario-matrix-architecture (Shared Socioeconomic Pathway 3, SSP3-7.0) of CESM v2 (100km/daily, 2015-2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). A projected increase in the northeastern regions in spring and winter and consistent drying in the Midwest summer precipitation were identified. Next, spatiotemporal patterns of both precipitation averages and extremes were identified over a region characterized by complex terrain, i.e., HMA, during 1990-2018. However, finding a reliable dataset in this region can be challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Therefore, evaluating the quality and reliability of different precipitation products before analyzing their trends and patterns is fundamental. A comprehensive assessment of high-resolution satellite-based, model reanalysis precipitation estimates and their blended product (ensemble) was conducted using ground observations from a suite of rain gauge networks at different elevation ranges. The last chapter of this dissertation transitions from pattern-based analyses to an event-based analysis that focuses on the heavy downpours that triggered devastating floods in Pakistan in the summer of 2022. Results confirm the singularity of this event with abnormal daily rain rates compared to climatology in western Balochistan and large anomalies across Pakistan’s southern provinces after August 16th. Outputs from this dissertation provide insights into the changing distribution of precipitation extremes, which are likely to trigger hydroclimatic hazards. In conclusion, this dissertation highlights the importance of assessing changes in extreme precipitation patterns across scales and from a variety of data sources to improve our understanding of the changing water cycle and enhance regional resilience to extreme climatic events."]},{"key":"dc:title","label":"Title","values":["Trends in Extreme Precipitation: Identifying Historical and Projected Patterns Using Multi-Source Precipitation Datasets"]}]}],"canonical_facts":{"dc:date.issued":["2023"],"dc:description.other":["Understanding past precipitation trends and the likelihood of such trends persisting or changing in the future is crucial to optimize water demands, designing infrastructure, and preparing a climate-resilient society. This dissertation investigates historical and projected changes (magnitude, frequency, and intensity) in annual, seasonal, and extreme precipitation across different regions in the world by leveraging datasets from a range of sources: Earth observations (i.e., satellite remote sensing), models of earth system science (e.g., atmospheric reanalysis and community earth system models, CESM), as well as ground-based measurements. Precipitation patterns are evaluated in terms of temporal trends (and their statistical significance) and spatial patterns from the regional (Southern Mid-Atlantic and High Mountain Asia) to the continental scale (i.e., Contiguous United States). This work adopts hydroclimatic extreme indices from ETCCDI (Expert Team on Climate Change Detection and Indices) to characterize precipitation trends and the relative contribution of extreme events to the total precipitation. At a regional scale, using 40 years (1980-2018) of high-resolution reanalysis data from the North American Land Data Assimilation V2 (NLDAS-2, 12 km/ hourly), a significant (0.1 significance level) increase in annual precipitation (+3~+5 mm/year) is identified over Northern Virginia accompanied with an increase in summer precipitation. An annual increase (at 0.05 significance level) in extreme events (95th and 99th percentiles) is also identified in the southern Mid-Atlantic region. An investigation into the proportion of annual precipitation occurring on wet and extremely wet days (95th and 99th) also indicates a significant increase. Next, this dissertation evaluated the projected changes until the end of the 21st century in the probability distribution of hydroclimate extreme indices across the National Climate Assessment (NCA) regions in the contiguous US. This was performed using a large ensemble simulation (70 members) in a new scenario-matrix-architecture (Shared Socioeconomic Pathway 3, SSP3-7.0) of CESM v2 (100km/daily, 2015-2100) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). A projected increase in the northeastern regions in spring and winter and consistent drying in the Midwest summer precipitation were identified. Next, spatiotemporal patterns of both precipitation averages and extremes were identified over a region characterized by complex terrain, i.e., HMA, during 1990-2018. However, finding a reliable dataset in this region can be challenged by several factors, including the lack of ground observations, the diverse climate zones, and the sharp orographic gradients. Therefore, evaluating the quality and reliability of different precipitation products before analyzing their trends and patterns is fundamental. A comprehensive assessment of high-resolution satellite-based, model reanalysis precipitation estimates and their blended product (ensemble) was conducted using ground observations from a suite of rain gauge networks at different elevation ranges. The last chapter of this dissertation transitions from pattern-based analyses to an event-based analysis that focuses on the heavy downpours that triggered devastating floods in Pakistan in the summer of 2022. Results confirm the singularity of this event with abnormal daily rain rates compared to climatology in western Balochistan and large anomalies across Pakistan’s southern provinces after August 16th. Outputs from this dissertation provide insights into the changing distribution of precipitation extremes, which are likely to trigger hydroclimatic hazards. In conclusion, this dissertation highlights the importance of assessing changes in extreme precipitation patterns across scales and from a variety of data sources to improve our understanding of the changing water cycle and enhance regional resilience to extreme climatic events."],"dc:identifier":["hdl:1920/14546"],"dc:subject":["Climate models","extreme precipitation","High Mountain Asia","Satellite estimates","spatiotemporal patterns","trends"],"dc:title":["Trends in Extreme Precipitation: Identifying Historical and Projected Patterns Using Multi-Source Precipitation Datasets"],"dc:type":["Dissertation"]},"updated_at":"2026-07-27T19:51:56Z"}