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 Cook, Amelia Snyder, Alison Appling, Theodore Thompson, Jared Smith, John Warner, Simon 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 Cook, Amelia Snyder, Alison Appling, Theodore Thompson, Jared Smith, John Warner, Simon 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...