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The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s of kilometers) at high spatial resolutions (1 m) pertinent to coastal hazard assessments and planning. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the Puget Sound region of Washington State (USA) and the greater Salish Sea. The modeling system provides coastal planners with projections of storm hazards and flood exposure for recurring flood events, spanning the annual to 1-percent annual chance of flooding, necessary to manage public safety and the prioritization and cost-efficient protection of critical infrastructure and valued ecosystems. The tool is applied and validated for Whatcom County, Washington, and includes a cross-shore profile model (XBeach) and overland flooding model (SFINCS) and is nested in a regional tide–surge model and wave model. Despite uncertainties in boundary conditions, hindcast simulations performed with the coupled model system accurately identified areas that were flooded during a recent storm in 2018. Flood hazards and risks are expected to increase exponentially as the sea level rises in the study area of 210 km of shoreline. With 1 m of sea-level rise, annual flood extents are projected to increase from 13 to 33 km2 (5 and 13% of low-lying Whatcom County) and flood risk (defined in USD) is projected to increase fifteenfold (from 14 to USD 206 million). PS-CoSMoS, like its prior iteration in California (CoSMoS), provides valuable coastal hazard projections to help communities plan for the impacts of sea-level rise and storms. [ABSTRACT FROM AUTHOR]

Extreme water-level recurrence estimates for a complex estuary using a high-resolution 2D model and a new method for estimating remotely generated sea level anomalies (SLAs) at the model boundary have been developed. The hydrodynamic model accurately resolves the dominant physical processes contributing to extreme water levels across the Washington State waters of the Salish Sea, including the relative contribution of remote SLA and other non-tidal residual processes that drive extreme water levels above the predicted tide. The model's predictions have errors of less than 15 cm (<5% of 3–4 m tidal range) at eight tide gauge locations across the model domain. The influence of remote SLAs at the seaward boundary of the model was implemented using a multivariate regression of readily available and locally relevant wind, sea surface temperature, and pressure anomaly data, combined with El Niño Index data (R2 = 0.76). The hydrodynamic model simulations using the remote SLA predictor compared well with simulations using the widely used data-assimilative global ocean model HYCOM SLA data (root mean square difference of 5.5 cm). Extreme water-level recurrence estimates with and without remote SLA show that remote forcing accounts for 50–60% of the total water level anomaly observed along Salish Sea shorelines. The resulting model simulations across decadal timescales provide estimates of extreme water level recurrence across the Salish Sea, capturing climate variability important to long-term coastal hazard planning. This approach has widespread applications for other complex estuarine systems. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is transforming coastal science from a "data-poor" field into a "datarich" field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change have outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the bestmonitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earthobserving satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and everexpanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, that is, (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer vision to fully leverage this massive treasure trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earthobserving satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems. [ABSTRACT FROM AUTHOR]

Moraine successions in glaciated valleys south of Mount Everest provide evidence for at least eight glacial advances during the late Quaternary. Cosmogenic radionuclide (CRN) surface exposure dating of moraine boulders defines the timing of each glacial advance and refines the previous glacial chronologies. The CRN data show that glaciation was most extensive during the early part of the last glacial (marine oxygen isotype stage [MIS] 3 and earlier), but limited during MIS 2 (the global Last Glacial Maximum) and the Holocene. A previously assumed Neoglacial advance is dated to 3.6 [+ or -] 0.3 ka and the CRN dates confirm a glacial advance ca. 1 ka. These results show that glaciations on the south side of Everest were not synchronous with the advance of Northern Hemisphere ice sheets, yet glaciations within the Himalaya, the world's highest mountain belt, were synchronous during the late Quaternary. The existence of glacial advances during times of increased insolation suggests that enhanced moisture delivered by an active south Asian summer monsoon is largely responsible for glacial advances in this part of the Himalaya. These data allow us to quantify the importance of global climate change and monsoon influence on glaciation in the Himalaya. Keywords: cosmogenic radionuclides, Himalaya, glacial geology, geochronology, monsoon, Everest.