Richard McDonald (Former Employee)
Science and Products
Filter Total Items: 18
Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System for Puerto Rico, Geospatial Fabric version 1.0, and Daymet version 4 Atmospheric Forcings, 1950-2021 Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System for Puerto Rico, Geospatial Fabric version 1.0, and Daymet version 4 Atmospheric Forcings, 1950-2021
This data release contains inputs for and outputs from hydrologic simulations for Puerto Rico using the Precipitation Runoff Modeling System (PRMS) version 5.2.1, the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018), National Hydrologic Geospatial Fabric version 1.0 (Viger and Bock, 2014), and the Daymet version 4 (Thornton et. al., 2020) atmospheric forcing...
Hawai'i National Hydrologic Model (NHM) application,1980–2021 Hawai'i National Hydrologic Model (NHM) application,1980–2021
This data release contains inputs for and outputs from hydrologic simulations for the Hawai‘i (HI) domain using the Precipitation Runoff Modeling System (PRMS) version 5.2.1.1 for the precalibration, by Hydrologic Response Unit (byHRU) release, and by Point Of Interest Observation (byPOIobs) release using the USGS National Hydrologic Model infrastructure (NHM; Regan and others, 2018)...
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 CONUS404 bias adjusted application, 1979-2021 Monthly twelve-digit hydrologic unit code aggregations of the CONUS404 bias adjusted application, 1979-2021
This data release contains accumulated precipitation data from the CONUS404 climate forcing variable subset for hydrologic models, downscaled to 1 km and bias-adjusted for precipitation and temperature (CONUS404-BA; Zhang and others, 2024) from January 1980 through September 2021 that is summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of...
Filter Total Items: 40
A Lagrangian particle-tracking approach to modelling larval drift in rivers A Lagrangian particle-tracking approach to modelling larval drift in rivers
The migration of larval fish from spawning to rearing habitat in rivers is not well understood. This paper describes a methodology to predict larval drift using a Lagrangian particle-tracking (LPT) model with passive and active behavioural components loosely coupled to a quasi-three-dimensional hydraulic model. In the absence of measured larval drift, a heuristic approach is presented...
Authors
Richard R. McDonald, Jonathan M. Nelson
Remote sensing of tracer dye concentrations to support dispersion studies in river channels Remote sensing of tracer dye concentrations to support dispersion studies in river channels
In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance...
Authors
Carl J. Legleiter, Richard R. McDonald, Jonathan M. Nelson, Paul J. Kinzel, Ryan L. Perroy, Donghae Baek, Il Won Seo
New methods for predicting and measuring dispersion in rivers New methods for predicting and measuring dispersion in rivers
To develop a better predictive tool for dispersion in rivers over a range of temporal and spatial scales, our group has developed a simple Lagrangian model that is applicable for a wide range of coordinate systems and flow modeling methodologies. The approach allows dispersion computations for a large suite of discretizations, model dimensions (1-, 2-, or 3-dimensional), spatial and...
Authors
Jonathan M. Nelson, Richard R. McDonald, Carl J. Legleiter, Paul J. Kinzel, Travis Terrell Ramos, Yutaka Higashi, Il Won Seo, Donghae Baek, Du Han Lee, Yonguk Ryu
Estimating floodwater depths from flood inundation maps and topography Estimating floodwater depths from flood inundation maps and topography
Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide...
Authors
Sagy Cohen, G. Robert Brakenridge, Albert Kettner, Bradford Bates, Jonathan M. Nelson, Richard R. McDonald, Yu-Fen Huang, Dinuke Munasinghe, Jiaqi Zhang
Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California
Vast sections of the Sacramento River have been listed as critical habitat by the National Marine Fisheries Service for green sturgeon spawning (Acipenser medirostris), yet spawning is known to occur at only a few specific locations. This study reveals the range of physical habitat variables selected by adult green sturgeon during their spawning period. We integrated fine-scale fish...
Authors
Megan T. Wyman, Michael J. Thomas, Richard R. McDonald, Alexander R. Hearn, Ryan D. Batt, Eric D. Chapman, Paul J. Kinzel, J. Tobey Minear, Ethan A. Mora, Jonathan M. Nelson, Matthew D. Pagel, A. Peter Klimley
Using remotely sensed data to estimate river characteristics including water-surface velocity and discharge Using remotely sensed data to estimate river characteristics including water-surface velocity and discharge
This paper describes a project combining field studies and analyses directed at providing an assessment of the accuracy of remotely sensed methods for determining river characteristics such as velocity and discharge. In particular, we describe a remote sensing method for surface velocities using mid-wave thermal camera videography combined with image analysis. One of the critical...
Authors
Jonathan M. Nelson, Paul J. Kinzel, Carl J. Legleiter, Richard R. McDonald, Brandon Overstreet, Jeffrey S. Conaway
Science and Products
Filter Total Items: 18
Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System for Puerto Rico, Geospatial Fabric version 1.0, and Daymet version 4 Atmospheric Forcings, 1950-2021 Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System for Puerto Rico, Geospatial Fabric version 1.0, and Daymet version 4 Atmospheric Forcings, 1950-2021
This data release contains inputs for and outputs from hydrologic simulations for Puerto Rico using the Precipitation Runoff Modeling System (PRMS) version 5.2.1, the USGS National Hydrologic Model infrastructure (NHM, Regan and others, 2018), National Hydrologic Geospatial Fabric version 1.0 (Viger and Bock, 2014), and the Daymet version 4 (Thornton et. al., 2020) atmospheric forcing...
Hawai'i National Hydrologic Model (NHM) application,1980–2021 Hawai'i National Hydrologic Model (NHM) application,1980–2021
This data release contains inputs for and outputs from hydrologic simulations for the Hawai‘i (HI) domain using the Precipitation Runoff Modeling System (PRMS) version 5.2.1.1 for the precalibration, by Hydrologic Response Unit (byHRU) release, and by Point Of Interest Observation (byPOIobs) release using the USGS National Hydrologic Model infrastructure (NHM; Regan and others, 2018)...
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 CONUS404 bias adjusted application, 1979-2021 Monthly twelve-digit hydrologic unit code aggregations of the CONUS404 bias adjusted application, 1979-2021
This data release contains accumulated precipitation data from the CONUS404 climate forcing variable subset for hydrologic models, downscaled to 1 km and bias-adjusted for precipitation and temperature (CONUS404-BA; Zhang and others, 2024) from January 1980 through September 2021 that is summarized to a monthly time step and a twelve-digit hydrologic unit code for the spatial extent of...
Filter Total Items: 40
A Lagrangian particle-tracking approach to modelling larval drift in rivers A Lagrangian particle-tracking approach to modelling larval drift in rivers
The migration of larval fish from spawning to rearing habitat in rivers is not well understood. This paper describes a methodology to predict larval drift using a Lagrangian particle-tracking (LPT) model with passive and active behavioural components loosely coupled to a quasi-three-dimensional hydraulic model. In the absence of measured larval drift, a heuristic approach is presented...
Authors
Richard R. McDonald, Jonathan M. Nelson
Remote sensing of tracer dye concentrations to support dispersion studies in river channels Remote sensing of tracer dye concentrations to support dispersion studies in river channels
In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance...
Authors
Carl J. Legleiter, Richard R. McDonald, Jonathan M. Nelson, Paul J. Kinzel, Ryan L. Perroy, Donghae Baek, Il Won Seo
New methods for predicting and measuring dispersion in rivers New methods for predicting and measuring dispersion in rivers
To develop a better predictive tool for dispersion in rivers over a range of temporal and spatial scales, our group has developed a simple Lagrangian model that is applicable for a wide range of coordinate systems and flow modeling methodologies. The approach allows dispersion computations for a large suite of discretizations, model dimensions (1-, 2-, or 3-dimensional), spatial and...
Authors
Jonathan M. Nelson, Richard R. McDonald, Carl J. Legleiter, Paul J. Kinzel, Travis Terrell Ramos, Yutaka Higashi, Il Won Seo, Donghae Baek, Du Han Lee, Yonguk Ryu
Estimating floodwater depths from flood inundation maps and topography Estimating floodwater depths from flood inundation maps and topography
Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide...
Authors
Sagy Cohen, G. Robert Brakenridge, Albert Kettner, Bradford Bates, Jonathan M. Nelson, Richard R. McDonald, Yu-Fen Huang, Dinuke Munasinghe, Jiaqi Zhang
Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California Fine-scale habitat preference of green sturgeon (Acipenser medirostris) within three spawning locations in the Sacramento River, California
Vast sections of the Sacramento River have been listed as critical habitat by the National Marine Fisheries Service for green sturgeon spawning (Acipenser medirostris), yet spawning is known to occur at only a few specific locations. This study reveals the range of physical habitat variables selected by adult green sturgeon during their spawning period. We integrated fine-scale fish...
Authors
Megan T. Wyman, Michael J. Thomas, Richard R. McDonald, Alexander R. Hearn, Ryan D. Batt, Eric D. Chapman, Paul J. Kinzel, J. Tobey Minear, Ethan A. Mora, Jonathan M. Nelson, Matthew D. Pagel, A. Peter Klimley
Using remotely sensed data to estimate river characteristics including water-surface velocity and discharge Using remotely sensed data to estimate river characteristics including water-surface velocity and discharge
This paper describes a project combining field studies and analyses directed at providing an assessment of the accuracy of remotely sensed methods for determining river characteristics such as velocity and discharge. In particular, we describe a remote sensing method for surface velocities using mid-wave thermal camera videography combined with image analysis. One of the critical...
Authors
Jonathan M. Nelson, Paul J. Kinzel, Carl J. Legleiter, Richard R. McDonald, Brandon Overstreet, Jeffrey S. Conaway