Kai Parker

Using high-resolution, ensemble-based modeling of the CMIP6 High Resolution Model Intercomparison Project, this study evaluates potential changes to extreme coastal water levels and waves across the Salish Sea, the largest estuary on the North American west coast. Results of a well-validated (water level error ~10 cm) regional hydrodynamic and wave model indicate that extreme water levels (e.g., 30-year return period) are projected to increase across the region, while lower-magnitude events (e.g., annual) are projected to slightly decrease. Wave height projections are spatially heterogeneous but generally decrease in southern basins and increase in the far north basins. Seasonal decomposition reveals a modest decrease in winter water levels and an increase in summer water levels. Non-linear interactions between sea level rise (SLR) and hydrodynamic response were assessed across seven SLR scenarios (0–3 m), revealing localized amplification effects up to 10 cm, though these remain minor relative to the SLR signal. Overall, uncertainty across the CMIP6 ensemble is high in this region, rendering most modeled changes statistically non-significant and emphasizing the need for studies to consider an ensemble when looking at climate projections. The dominance of tidal forcing in extreme water levels, coupled with high inter-model variability, indicates that near-term climate-induced changes to oceanic drivers are unlikely to significantly alter coastal hazards in the Salish Sea by 2050. These findings support the use of present-day conditions as a proxy for near-future planning and emphasize SLR as the primary contributor to changing future coastal risk in the Salish Sea.

Using high-resolution, ensemble-based modeling of the CMIP6 High Resolution Model Intercomparison Project, this study evaluates potential changes to extreme coastal water levels and waves across the Salish Sea, the largest estuary on the North American west coast. Results of a well-validated (water level error ~10 cm) regional hydrodynamic and wave model indicate that extreme water levels (e.g., 30-year return period) are projected to increase across the region, while lower-magnitude events (e.g., annual) are projected to slightly decrease. Wave height projections are spatially heterogeneous but generally decrease in southern basins and increase in the far north basins. Seasonal decomposition reveals a modest decrease in winter water levels and an increase in summer water levels. Non-linear interactions between sea level rise (SLR) and hydrodynamic response were assessed across seven SLR scenarios (0–3 m), revealing localized amplification effects up to 10 cm, though these remain minor relative to the SLR signal. Overall, uncertainty across the CMIP6 ensemble is high in this region, rendering most modeled changes statistically non-significant and emphasizing the need for studies to consider an ensemble when looking at climate projections. The dominance of tidal forcing in extreme water levels, coupled with high inter-model variability, indicates that near-term climate-induced changes to oceanic drivers are unlikely to significantly alter coastal hazards in the Salish Sea by 2050. These findings support the use of present-day conditions as a proxy for near-future planning and emphasize SLR as the primary contributor to changing future coastal risk in the Salish Sea.

Climate change is expected to impact hydrological regimes worldwide, including the Pacific Northwest of the United States. This study investigates how climate change will affect river discharge in the Puget Sound region of the State of Washington, with a focus on King and Pierce Counties. We simulated river discharge under historical and future conditions using the physically based, spatially distributed wflow_sbm hydrological model, which was calibrated and validated against U.S. Geological Survey discharge records. Future forcing was based on an ensemble of six high-resolution CMIP6 climate models, which were bias corrected using empirical quantile mapping. The results indicate a decrease in summer discharges (5–10%) and an increase in winter discharges (5–10%) across the study region. The high discharges (90th percentile) are projected to increase in winter, and the low discharges are projected to decrease in summer, due to shifts in precipitation regimes, snowpack hydrology, and evapotranspiration. However, variability between individual CMIP6 models often exceeds the magnitude of ensemble mean changes, underscoring substantial uncertainty in climate projections and the importance of including multiple climate models in climate change analysis. Furthermore, model consensus increased with elevation, which could be the result of the higher elevation areas being driven by less diverse hydrological processes. These findings highlight potential challenges for regional water management, ecosystem health, and flood risk mitigation in the Puget Sound region under future climate conditions.

A 38-year hindcast water-level product is developed for the US Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water-level modeling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modeled water-level components. Corrected water-level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water-level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water-level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water-level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water-level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

A 38-year hindcast water level product is developed for the United States Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

A 38-year hindcast water level product is developed for the United States Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones in coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are thirty times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more area will be flooded, regardless if that flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

Climate change impacts on extreme water levels (WLs) at two United States Pacific Northwest estuaries are investigated using a multicomponent process-based modeling framework. The integrated impact of climate change on estuarine forcing is considered using a series of sub-models that track changes to oceanic, atmospheric, and hydrologic controls on hydrodynamics. This modeling framework is run at decadal scales for historic (1979–1999) and future (2041–2070) periods with changes to extreme WLs quantified across the two study sites. It is found that there is spatial variability in extreme WLs at both study sites with all recurrence interval events increasing with further distance into the estuary. This spatial variability is found to increase for the 100-year event moving into the future. It is found that the full effect of sea level rise is mitigated by a decrease in forcing. Short-recurrence-interval events are less buffered and therefore more impacted by sea level rise than higher-return-interval events. Finally, results show that annual extremes at the study sites are defined by compound events with a variety of forcing contributing to high WLs.