Geology topics

Salme Ellen Cook

Publications and source records attributed to Salme Ellen Cook.

4 recordsLinked to original sources

USGS2026

Hazard potential of compound flooding from rainfall, storm surge, and groundwater in coastal New York and Connecticut

Compound flood events, the co-occurrence of multiple flood drivers, can result in flood hazard potential exceeding that of any single driver alone. To evaluate compound flooding in a semi-urbanized coastal area, historical records dating back to 1970 are used to study the co-occurrences of high precipitation, storm surge, and shallow groundwater conditions along the coastlines of New York and Connecticut. Joint return periods for coincident precipitation-surge events were computed using statistical dependence models and compared to the assumption of independence as a ratio, referred to here as a return period adjustment. Results indicate distinct seasonality where compound events in the area disproportionately occur in the cold season between October and April. Return period adjustments range from a factor of 1 to almost 9, demonstrating the range in precipitation-storm surge dependence across the study area. Across all 24 station triad locations, groundwater levels were elevated during times of precipitation- surge co-occurrence, reflecting the tendency for coastal storms and shallow groundwater conditions to co-occur seasonally. The result is a pseudo-trivariate compound flood hazard score and corresponding hazard map that integrates dependence between daily precipitation-surge events and overall monthly groundwater levels (as a precondition) into a relative compound hazard score. The location with the highest compound flood hazard score is on the south shore of Long Island, as well as locations across coastal Connecticut where groundwater levels compound the co-occurrence of heavy precipitation and storm surge.

Connecticut, New York↗

USGS2025

Present and future coastal flooding hazard for Long Island, NY and Long Island Sound (NY/CT), USA

Coastal flooding and the associated damages due to storms are increasing with sea level rise around the world, with regional variability in the severity of impacts., Researchers and resource managers need to better understand and predict the future shifts in coastal flooding due to these processes to plan for resilient and sustainable communities. Here we present an analysis of long-term historical records of water levels, tides, and modeled present-day wave climatologies, to characterize the present-day inundation extent in Long Island Sound and Long Island, NY. To understand the potential future changes in inundation extent, we provide a similar analysis of future climate projections of non-tidal residuals (storm surge) for the year 2050 and compare these projections with our present-day results. We examine both the magnitude of relatively frequent events with a 0.99 annual exceedance probability to more extreme events with a 0.01 annual exceedance probability (or the 1 in 100-year event). This range of events is relevant for local managers to understand the spatial variability in coastal inundation, in addition to planning for larger more catastrophic events.

EarthArXiv↗

USGS2024

Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost

Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L −1 Cl − isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere-Wave-Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process-based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.

Limnology and Oceanography↗

USGS2023

A numerical investigation of the mechanisms controlling salt intrusion in the Delaware Bay Estuary

Salinity intrusion in coastal systems is mainly controlled by freshwater inflows. However, extreme events like drought, low-pressure storms, and longer-term sea level rise can exacerbate the landward salt migration and threaten economic infrastructure and ecological health. Along the eastern seaboard of the United States, approximately 13 million people rely on the water resources of the Delaware River basin. Salinity intrusion is actively managed through river discharge targets to suppress the propagation of the salt front (∼0.52 daily averaged psu line). The purpose of this study is to examine the mechanisms controlling the location of the salt front in the Delaware Bay estuary using a calibrated three-dimensional hydrodynamic model, the Coupled Ocean Atmosphere Wave and Sediment Transport modeling system. This study explored how river discharge, tidal motions, interactions with bathymetric and topographic features, and meteorological events affected the location of the salt front. The model was forced with tides, subtidal water levels, bulk atmospheric conditions, and waves. Compared with the observationally derived location of the salt front line, the model captured the major dynamics throughout the year and performed particularly well during times of low discharge, when salinity intruded up estuary at a constant rate of 0.4 km /day . The daily average salt front moved almost 16 km (10 mi) within a neap-spring tidal cycle, and low-pressure storm systems were found to move the daily averaged salt front by 13–16 km in one event.

Delaware, New Jersey, Pennsylvania↗