Adrienne M Marshall
Colorado School of Mines
|
Assistant Professor
Subject Areas: | Hydrology, hydrologic modeling, snow hydrology, boreal hydrology, hydropower |
Recent Activity
ABSTRACT:
This data resource supports Marshall & Grubert (2021). The study presents operational hydropower parameters calculated based on six years of hourly data from 158 dams, and estimates power generation in these hours based on monthly hydropower generation data reported to the Energy Information Administration (EIA). More details on calculated parameters are available in the associated manuscript. The data are distributed as a Shiny application. The application can be run locally by downloading the data package, accessed at: https://adrienne-marshall.shinyapps.io/hydropower/, or users can use the data included for their own analyses (further documentation in the readme.md further down this page).
References:
Marshall, A. M., & Grubert, E. (2022). Hydroelectricity modeling for low-carbon and no-carbon grids: Empirical operational parameters for optimization and dispatch models. Earth’s Future, n/a(n/a), e2021EF002503. https://doi.org/10.1029/2021EF002503
ABSTRACT:
This dataset support a manuscript in which we describe SHAW model calibration using a Generalized Likelihood Uncertainty Estimation (GLUE) approach at four boreal forest sites in interior Alaska. Two sites (Smith Lake 1 and Smith Lake 2) are permafrost-underlain black spruce sites. The other two are upland deciduous sites that were burned at varying intervals prior to the study period (US-Rpf and UP1A). Meteorological, vegetation, and soils data were obtained from a variety of resources, as detailed in the manuscript. Acceptable parameter sets are determined based on calibration to soil moisture and evaporation at these sites. Model instances with these parameter sets are then run with two downscaled climate models in order to evaluate the relative sensitivity of soil moisture and evapotranspiration to parameter selection, GCM, and climate change. These data include tabular summary results and model inputs and outputs for both observed meteorological forcings and GCM forcings for the accepted parameter sets. More details are in the manuscript, and file structures for the included data are described in the readme.md file (see below).
Please see related resources for full manuscript details.
Contact
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Website | https://people.mines.edu/adriennemarshall/ |
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Resource | 2 |
App Connector | 0 |

Created: Feb. 26, 2021, 6:58 p.m.
Authors: Marshall, Adrienne M · Link, Timothy · Gerald Flerchinger · Melissa Lucash
ABSTRACT:
This dataset support a manuscript in which we describe SHAW model calibration using a Generalized Likelihood Uncertainty Estimation (GLUE) approach at four boreal forest sites in interior Alaska. Two sites (Smith Lake 1 and Smith Lake 2) are permafrost-underlain black spruce sites. The other two are upland deciduous sites that were burned at varying intervals prior to the study period (US-Rpf and UP1A). Meteorological, vegetation, and soils data were obtained from a variety of resources, as detailed in the manuscript. Acceptable parameter sets are determined based on calibration to soil moisture and evaporation at these sites. Model instances with these parameter sets are then run with two downscaled climate models in order to evaluate the relative sensitivity of soil moisture and evapotranspiration to parameter selection, GCM, and climate change. These data include tabular summary results and model inputs and outputs for both observed meteorological forcings and GCM forcings for the accepted parameter sets. More details are in the manuscript, and file structures for the included data are described in the readme.md file (see below).
Please see related resources for full manuscript details.

Created: June 22, 2021, 10:05 p.m.
Authors: Marshall, Adrienne M · Grubert, Emily
ABSTRACT:
This data resource supports Marshall & Grubert (2021). The study presents operational hydropower parameters calculated based on six years of hourly data from 158 dams, and estimates power generation in these hours based on monthly hydropower generation data reported to the Energy Information Administration (EIA). More details on calculated parameters are available in the associated manuscript. The data are distributed as a Shiny application. The application can be run locally by downloading the data package, accessed at: https://adrienne-marshall.shinyapps.io/hydropower/, or users can use the data included for their own analyses (further documentation in the readme.md further down this page).
References:
Marshall, A. M., & Grubert, E. (2022). Hydroelectricity modeling for low-carbon and no-carbon grids: Empirical operational parameters for optimization and dispatch models. Earth’s Future, n/a(n/a), e2021EF002503. https://doi.org/10.1029/2021EF002503