Amelia Snyder
Amelia Snyder is a Data Scientist for the USGS Water Resources Mission Area
Science and Products
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Hawaii, 1980-2021
This data release contains 15 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) modeling application forced with Daymet version 4 (Rosa and others, 2024) from 1980 through 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Hawaii. The following fluxes and...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Puerto Rico, 1950-21
This data release contains 15 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) modeling application forced with Daymet version 4 (LaFontaine and others, 2024) from 1950 through 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Puerto Rico. The following...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System version 1.1 forced with CONUS404-BA, 1980-2021
This data release contains 15 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) version 1.1 modeling application forced with CONUS404-BA (Markstrom and others, 2024) from January 1980 through September 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of the...
A deep learning model and associated data to support understanding and simulation of salinity dynamics in Delaware Bay
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...
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...
Authors
Galen Agnew Gorski, Salme Ellen Cook, Amelia Marie Snyder, Alison P. Appling, Theodore Paul Thompson, Jared David Smith, John C. Warner, Simon Nemer Topp
Science and Products
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Hawaii, 1980-2021
This data release contains 15 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) modeling application forced with Daymet version 4 (Rosa and others, 2024) from 1980 through 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Hawaii. The following fluxes and...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Puerto Rico, 1950-21
This data release contains 15 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) modeling application forced with Daymet version 4 (LaFontaine and others, 2024) from 1950 through 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Puerto Rico. The following...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System version 1.1 forced with CONUS404-BA, 1980-2021
This data release contains 15 variables from the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS) version 1.1 modeling application forced with CONUS404-BA (Markstrom and others, 2024) from January 1980 through September 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of the...
A deep learning model and associated data to support understanding and simulation of salinity dynamics in Delaware Bay
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...
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...
Authors
Galen Agnew Gorski, Salme Ellen Cook, Amelia Marie Snyder, Alison P. Appling, Theodore Paul Thompson, Jared David Smith, John C. Warner, Simon Nemer Topp