Work Description
Title: Land surface hydrology data for the North American Great Lakes region Open Access Deposited
Attribute | Value |
---|---|
Methodology |
|
Description |
|
Creator | |
Depositor |
|
Contact information | |
Discipline | |
Funding agency |
|
Other Funding agency |
|
Keyword | |
Citations to related material |
|
Resource type | |
Last modified |
|
Published |
|
Language | |
DOI |
|
License |
(2022). Land surface hydrology data for the North American Great Lakes region [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/hry7-kv95
Relationships
- This work is not a member of any user collections.
Files (Count: 2; Size: 90 GB)
Thumbnailthumbnail-column | Title | Original Upload | Last Modified | File Size | Access | Actions |
---|---|---|---|---|---|---|
![]() |
ReadMe_LandSurfaceHydrologyGreat...s.txt | 2022-12-12 | 2022-12-12 | 3.19 KB | Open Access |
|
![]() |
regridcon0.05_NOAHMP_9km_Jun2016...ly.nc | 2022-12-09 | 2022-12-09 | 90 GB | Open Access |
|
Date: 27 September, 2022
DATASET TITLE: Land surface hydrology data for the North American Great Lakes region
DATASET CREATORS: Samar Minallah, Allison L. Steiner
DATASET CONTACT: Samar Minallah (minallah@umich.edu)
Research funding sources:
(1) National Science Foundation Grant OCE-1600012
(2) University of Michigan Rackham Predoctoral Fellowship
TO CITE DATA
Minallah, S. and Steiner, A.L. Land surface hydrology data for the North American Great Lakes region [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/hry7-kv95
RESEARCH OVERVIEW
The North American Great Lakes region is one of the largest freshwater ecosystems in the world. The land surface hydrology of the Great Lakes watershed regulates the ecosystem water availability, lake levels, vegetation dynamics, and agricultural practices.
In this work, we analyzed the terrestrial water budget using the NOAH-MP land surface model and quantified the dominant processes that contribute to the variability in the land surface hydrology. The quantification of variability sources is important to improve water budget estimates for the diverse hydroclimate of the Great Lakes basin and to assess any shift in the future regimes.
METHODOLOGY
This data was produced using the NOAH-MP land surface model (LSM) within the WRF-Hydro V5.2.0 modeling system. We used the NOAA National Water Model (NWM) configuration for the parameterization of the land surface processes. The runoff scheme was "TOPMODEL with groundwater" for these simulations. We ran the LSM at 9 km resolution and the coupled WRF-Hydro terrain routing at 900 m, with a 90 m hydrologically conditioned DEM for surface elevation. After a 10-year treadmill spin-up, we simulated the Great Lakes basin over the 2017 - 2020 hydrological years (October 2016 - September 2020). We obtained the atmospheric forcing from the North American Mesoscale (NAM) 12 km, 6-hourly analysis.
SOFTWARE SPECIFICATION:
- WRF-Hydro is a numerical hydrologic model written in Fortran. To execute WRF-Hydro, prospective users must download the program's source codes from https://ral.ucar.edu/projects/wrf_hydro/model-code and install the model on a Linux cluster following the instruction on the same website.
- The Linux system that WRF-Hydro is installed on must have the NETCDF library installed.
FILES CONTAINED HERE:
This data contains the model simulations output from NOAH-MP. The variables included are enslisted in the link below under the "LDASOUT_DOMAIN" output.
https://ral.ucar.edu/sites/default/files/public/projects/wrf-hydro/technical-description-user-guide/outputvariablematrix-v5.2.0.pdf (Link access date: 9/27/2022)
The data coverage is from 2016-06-01 to 2020-10-31 at daily timestep. The model output is regridded to a 0.05 degree rectilinear (lat/lon) grid using the conservative remapping method (cdo remapcon). The spatial extent is roughly from 32.5-52 deg N and 95.5-72 deg W.
The model output is stored in a signle netcdf4 file "regridcon0.05_NOAHMP_9km_Jun2016_Oct2020_daily.nc". File size is approximately 96 GB.
Use and Access:
This data set is made available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).