Please wait for the process to complete.
Data for a comparison of national water model retrospective analysis snow outputs at SNOTEL sites across the Western U.S.
||This resource does not have an owner who is an active HydroShare user. Contact CUAHSI (firstname.lastname@example.org) for information on this resource.|
|Resource type:||Collection Resource|
|Storage:||The size of this resource is 2.1 KB|
|Created:||Feb 06, 2021 at 12:01 a.m.|
|Last updated:|| Jul 09, 2022 at 8:12 p.m.
|Citation:||See how to cite this resource|
|+1 Votes:||Be the first one to this.|
|Comments:||No comments (yet)|
The HydroShare resources in this collection contain the data and scripts used for: Garousi-Nejad, I. and Tarboton, D. (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States", Hydrological Processes, https://doi.org/10.1002/hyp.14469.
Abstract from the paper:
This study compares the US National Water Model (NWM) reanalysis snow outputs to observed snow water equivalent (SWE) and snow‐covered area fraction (SCAF) at snow telemetry (SNOTEL) sites across the Western United States SWE was obtained from SNOTEL sites, while SCAF was obtained from moderate resolution imaging spectroradiometer (MODIS) observations at a nominal 500 m grid scale. Retrospective NWM results were at a 1000 m grid scale. We compared results for SNOTEL sites to gridded NWM and MODIS outputs for the grid cells encompassing each SNOTEL site. Differences between modelled and observed SWE were attributed to both model errors, as well as errors in inputs, notably precipitation and temperature. The NWM generally under‐predicted SWE, partly due to precipitation input differences. There was also a slight general bias for model input temperature to be cooler than observed, counter to the direction expected to lead to under‐modelling of SWE. There was also under‐modelling of SWE for a subset of sites where precipitation inputs were good. Furthermore, the NWM generally tends to melt snow early. There was considerable variability between modelled and observed SCAF as well as the binary comparison of snow cover presence that hampered useful interpretation of SCAF comparisons. This is in part due to the shortcomings associated with both model SCAF parameterization and MODIS observations, particularly in vegetated regions. However, when SCAF was aggregated across all sites and years, modelled SCAF tended to be more than observed using MODIS. These differences are regional with generally better SWE and SCAF results in the Central Basin and Range and differences tending to become larger the further away regions are from this region. These findings identify areas where predictions from the NWM involving snow may be better or worse, and suggest opportunities for research directed towards model improvements.
Order to follow the developed scripts:
1. Notebook to get the indices of National Water Model grid cells containing SNOTEL sites
2. Notebook for retrieval of National Water Model Retrospective run results at SNOTEL sites
3. Notebooks for post-processing the retrieved National Water Model Retrospective run results and inputs at SNOTEL sites
4. Notebook for retrieval of precipitation, air temperature, and snow water equivalent measurements at SNOTEL sites
6. Notebooks for combining the National Water Model results/inputs with observations from SNOTEL and MODIS at SNOTEL sites
7. Notebooks for visualizations reported at A Comparison of National Water Model Retrospective Analysis Snow Outputs at SNOTEL Sites Across the Western U.S.
Resource Level Coverage
|This resource is referenced by||Garousi-Nejad, I. and Tarboton, D. (2022), "A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States", Hydrological Processes, https://doi.org/10.1002/hyp.14469|
|The content of this resource is derived from||NOAA National Water Model Reanalysis at https://console.cloud.google.com/storage/browser/national-water-model-v2?pli=1|
|The content of this resource is derived from||Omernik Ecoregions level III (Omernik & Griffith, 2014) available from the Commission for Environmental Corporation at http://www.cec.org/north-american-environmental-atlas/terrestrial-ecoregions-level-iii/|
|The content of this resource is derived from||Hall, D. K. and G. A. Riggs. 2016. MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6. [NDSI_Snow_Cover]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MODIS/MOD10A1.006, accessed through Google Earth Engine|
|The content of this resource is derived from||USDA Natural Resources Conservation Service Snow Telemetry SNOTEL Network, https://www.nrcs.usda.gov/wps/portal/wcc/home/aboutUs/snowProgramOverview/|
|The content of this resource is derived from||National Center for Environmental Prediction, NOAA (2022). National Water Model v2 physiographic input, retrieved from https://www.nco.ncep.noaa.gov/pmb/codes/nwprod/nwm.v2.0.4/parm/domain/, accessed Nov 30, 2020, saved in https://www.hydroshare.org/resource/1b66a752b0cc467eb0f46bda5fdc4b34/|
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|Utah Water Research Laboratory||Graduate Student Research Assistantship for I Garousi-Nejad|
|National Science Foundation||Scalable Capabilities for Spatial Data Synthesis||ACI-1548562|
|National Science Foundation||Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis||OAC‐1664119|
|National Science Foundation||Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis||OAC-1664061|
|National Science Foundation||Collaborative Research: SI2-SSI: Cyberinfrastructure for Advancing Hydrologic Knowledge through Collaborative Integration of Data Science, Modeling and Analysis||OAC-1664018|
People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.
|Arezoo RafieeiNasab||National Center for Atmospheric Research|
|High Performance Computing (CHPC)||University of Utah|
|David Gochis||National Center for Atmospheric Research|
|WRF‐Hydro research team||National Center for Atmospheric Research|