Amelia Snyder
Amelia Snyder is a Data Scientist for the USGS Water Resources Mission Area
Science and Products
Daily twelve-digit hydrologic unit code aggregations of snow water equivalent, soil moisture, and actual evapotranspiration estimates from the National Hydrologic Model Precipitation Runoff Modeling System forced with CONUS404-BA Daily twelve-digit hydrologic unit code aggregations of snow water equivalent, soil moisture, and actual evapotranspiration estimates from the National Hydrologic Model Precipitation Runoff Modeling System forced with CONUS404-BA
This data release contains three 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 1st, 1980 through September 25th, 2021 that are summarized to a twelve-digit hydrologic unit code for the spatial extent of the conterminous...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Alaska, 1980-2021 Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Alaska, 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 (Koczot and others, 2025) from 1980 through 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Alaska.The following flux and...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Hawaii, 1980-2021 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 16 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, 2025) 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, 1950-2021 (ver. 2.0, June 2025) Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Puerto Rico, 1950-2021 (ver. 2.0, June 2025)
This data release contains 16 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 January 1950 through December 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Puerto Rico...
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 (version 2.0, April 2025) 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 (version 2.0, April 2025)
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 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 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 Gorski, Salme Ellen Cook, Amelia Marie Snyder, Alison P. Appling, Theodore Paul Thompson, Jared David Smith, John C. Warner, Simon Nemer Topp
NHM-Assist NHM-Assist
NHM-Assist is a collection of python workflows presented in Jupyter notebooks for evaluating, running and interpreting National Hydrologic Model (NHM) domains using pywatershed. NHM-Assist allows users to: evaluate hydrofabric element connections such as hydrologic response unit connections to streamflow segments, segment routing order, and gage placement accuracy; display NHM domain...
Science and Products
Daily twelve-digit hydrologic unit code aggregations of snow water equivalent, soil moisture, and actual evapotranspiration estimates from the National Hydrologic Model Precipitation Runoff Modeling System forced with CONUS404-BA Daily twelve-digit hydrologic unit code aggregations of snow water equivalent, soil moisture, and actual evapotranspiration estimates from the National Hydrologic Model Precipitation Runoff Modeling System forced with CONUS404-BA
This data release contains three 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 1st, 1980 through September 25th, 2021 that are summarized to a twelve-digit hydrologic unit code for the spatial extent of the conterminous...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Alaska, 1980-2021 Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Alaska, 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 (Koczot and others, 2025) from 1980 through 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Alaska.The following flux and...
Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Hawaii, 1980-2021 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 16 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, 2025) 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, 1950-2021 (ver. 2.0, June 2025) Monthly twelve-digit hydrologic unit code aggregations of the National Hydrologic Model Precipitation-Runoff Modeling System modeling application for Puerto Rico, 1950-2021 (ver. 2.0, June 2025)
This data release contains 16 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 January 1950 through December 2021 that are summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of Puerto Rico...
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 (version 2.0, April 2025) 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 (version 2.0, April 2025)
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 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 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 Gorski, Salme Ellen Cook, Amelia Marie Snyder, Alison P. Appling, Theodore Paul Thompson, Jared David Smith, John C. Warner, Simon Nemer Topp
NHM-Assist NHM-Assist
NHM-Assist is a collection of python workflows presented in Jupyter notebooks for evaluating, running and interpreting National Hydrologic Model (NHM) domains using pywatershed. NHM-Assist allows users to: evaluate hydrofabric element connections such as hydrologic response unit connections to streamflow segments, segment routing order, and gage placement accuracy; display NHM domain...