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GES DISC – USGS collaboration for hydrological assimilation model data

The Geo Data Portal offers a region-specific analysis capability

GES DISC – USGS collaboration for hydrological assimilation model data

GES DISC - USGS collaboration makes NLDAS and GLDAS hydrological data more compatible with Geographical Information System (GIS) analyses.

GES DISC – USGS collaboration for hydrological assimilation model data

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides several different temporal and spatial resolutions of North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) hydrological data. The data can be directly accessed using the Mirador data search and download system. The data can also be visualized and analyzed with the NASA Giovanni system. These data sets, including such variables as rain rate, soil moisture, surface runoff, snowfall rate, and total evapotranspiration, are particularly useful for regional hydrological studies. The data can be used to analyze the similarities and differences between surface observations, satellite observations, and model data.

The usefulness of NLDAS and GLDAS data has now been enhanced by a recent collaboration between the NASA GES DISC and the United State Geological Survey (USGS). The collaboration makes NLDAS and GLDAS areal statistics data available for download via the USGS Geo Data Portal (GDP). Users can access and acquire the data from GDP for specific geographical areas, such as states or countries, or they can input their own area of interest as a “shapefile” and acquire the data for just that specific area. This enhancement makes the data more compatible with Geographical Information Systems (GIS) and more easily incorporated into GIS analyses.

The availability of the NLDAS and GLDAS data is described in the GES DISC news article, NLDAS and GLDAS data sets accessible through the USGS GDP, with an example on how to order and download data with the GDP.

As a demonstration of the difference between a simple analysis of NLDAS data performed with the NASA Giovanni system and with the GDP, the dramatic impact of Hurricane Irene in 2011 on the state of Vermont was selected. Extremely heavy rainfall from Irene caused large amounts of damage in Vermont, including washed out roads, destroyed covered bridges, and collapsed homes. Irene’s flood waters even caused the isolation of several of Vermont’s picturesque towns for several days. The town of Rochester, Vermont was cut off for nearly a week, where the normally placid Nason Brook became a raging torrent that sliced into its own banks and caused damage to the local cemetery, where several caskets were unearthed and carried away. 

 

Pictures of Irene's aftermath in Vermont

Washed-out bridge near Rochester, VT, with temporary footbridge.

Washed-out bridge near Rochester, VT, with temporary footbridge.   (Click to see full size.)  Photo courtesy Belvi Designs.
 
Damage to Woodlawn Cemetery near Rochester, VT.
 
Damage to Woodlawn Cemetery near Rochester, VT. Unearthed caskets can be seen in the eroded creekbed.  Image courtesy of Al Cooper.
 
Irene’s flood waters near East Pittsford, VT
 
Irene’s flood waters near East Pittsford, VT.  (Click to view full size.) Image courtesy AP/Tony Talbot.
 
 

Comparing the use of NLDAS data in Giovanni and in the USGS GDP

 
To demonstrate the difference between the analysis results from Giovanni and the USGS GDP, the “surface runoff (non-infiltrating)” data parameter is used. The images shown above demonstrate the extreme amount of runoff from Irene’s torrential rains in Vermont. Two Giovanni maps of the NLDAS hourly surface runoff data parameter during the storm’s passage on August 28, 2011 show the flood event in progress (Fig. 1).
 

Hurricane Irene Surface runoff at 15ZHurricane Irene surface runoff 19Z

 
Figure 1. NLDAS hourly surface runoff from the passage of Hurricane Irene over Vermont on August 28, 2011. (a) Surface runoff at 15Z, showing substantial amount of runoff in both southern Vermont and southern New Hampshire. (b) Surface runoff at 19Z, when the peak runoff amount was located near Rochester, VT. At this time, most of the runoff was within Vermont, with some occurring in northeastern New York.  (Click on each image to view the full size version.)
 
The area shown in Figure 1 was used to generate an area-averaged time-series of surface runoff using Giovanni (Fig. 2).
 
                  Time-series of NLDAS surface runoff plotted in Giovanni, averaged over the area shown in Figure 1. 
 
Figure 2. Time-series of NLDAS surface runoff plotted in Giovanni, averaged over the area shown in Figure 1.  (Click on the plot to view the full size version.)
 
Areas of interest in Giovanni can only be defined for rectangular regions, but the USGS GDP allows the use of state boundaries. The same period of time used for Figure 2 was submitted to the USGS GDP, and the state of Vermont was designated as the area of interest. The GDP output consisted of a Microsoft Excel spreadsheet with the hours and corresponding average NLDAS hourly surface runoff values. This output allowed the generation of a time-series plot in MS Excel, shown in Figure 3. 
Time-series of NLDAS surface runoff plotted in Excel with output from the USGS GDP, averaged over the area of the state of Vermont.
Figure 3. Time-series of NLDAS surface runoff plotted in Excel with output from the USGS GDP, averaged over the area of the state of Vermont.  (Click on the plot to view the full size version.)
 
Comparison of these two time-series plots indicates subtle differences that are due to the different areas used for each plot. When the surface runoff from Irene was nearly contained within the boundaries of the state (e.g., Fig. 1b, 19Z), the average surface runoff value calculated for the larger area used for the Giovanni plot is lower than the value calculated for just the area of the state used for the plot based on the GDP output. Thus, Figure 3, which shows the period of highest runoff lasting almost seven hours, provides a better depiction of Irene’s surface runoff peak within the state of Vermont.
 
This example shows that the USGS GDP should provide more accurate results than does Giovanni, for areas of interest with irregular boundaries. This aspect of the USGS GDP should be desirable to users who want to analyze the NLDAS or GLDAS data in GIS applications. As noted earlier, users can also provide their own regional shapefiles as input to the GDP, including the boundaries of cities, counties, provinces, watersheds, or countries.
 
Note:  "Z" refers to "Zulu Time" or Greenwich Mean Time (GMT).  Vermont is in the GMT -5 time zone, which means that 15Z is 10:00 AM. 

Questions or comments? Email the NASA GES DISC Help Desk: gsfc-help-disc@lists.nasa.gov

 

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Last updated: Mar 28, 2014 01:08 PM ET
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