Basin-scale interpolation of Snow Water Equivalent using PRISM, SNOTEL and MODIS
Meredith C. Payne1 (mpayne@coas.oregonstate.edu) and Anne W. Nolin2 (nolina@geo.oregonstate.edu) ●
1
College of Oceanic and Atmospheric Sciences ● 2Department of Geosciences ● Oregon State University
1. Introduction
Snow Water Equivalent (SWE) is the amount of water a snowpack would yield if it melted
completely. Estimating SWE of a seasonal snowpack is especially important when making
runoff estimates and other water management judgements (1). Traditionally, SWE has
been estimated through manual survey methods, and extrapolated to entire basin areas
through interpolation techniques that require a statistical approach, such as the binary regression tree (2-4). However, such data are often not available, or more often, are not
abundant enough for such an approach. In this project I have attempted to combine SWE
measurements from SNOTEL stations and snow course surveys with satellite remote
sensing data from the MODIS Terra instrument and modelled monthly precipitation abundance from the PRISM project in order to attain SWE estimates for the Clear Lake Topographic watershed in the Western Cascades, Oregon (Fig 1). In doing so, I am addressing
the following hypothesis: In watersheds where direct SWE measurements are scarce, the
combination of PRISM, MODIS, and SNOTEL data yields a spatially significant SWE estimate, appropriate for use in summer melt and runoff calculations.
2. Study Site: Clear Lake Basin, Western Cascades, Oregon
Fig 1. The Clear Lake Basin is found in the McKenzie
River Watershed of the Western Cascades, Oregon. It
has an area of ~239 km2. The region is characterized by
rugged, heavily dissected volcanic terrain into which many
natural springs flow from the underlying basaltic aquifer.
Snow typically accumulates in the basin from NovemberMarch each year, with a peak SWE date that occurs on or
around April 1st.
OREGON
N
Santiam Junction
SNOTEL station
Clear Lake Watershed
MODIS image extent
Hogg Pass
McKenzie River Watershed
Elevation (m)
High : 3150.6
3. Methodology cont.: SNOTEL data compilation
SNOwpack TELemetry (SNOTEL) data and available snow course data for water years (October-September) 20002005 and ranging from 43.8-44.6° N latitude were downloaded as text files from the NRCS FTP site. They were then
imported and tabulated in Microsoft Excel, in order to determine the peak SWE dates (maximum snowpack just
before melting begins) and measurements. These data provided a groundtruth to measure the success of the remote
sensing SWE values (see Error Analysis section). Due to the paucity of the data, determining SWE by interpolation
of the SNOTEL points was difficult, therefore measurements from the Hogg Pass SNOTEL station (elev. 1451 m)
within the Clear Lake basin were used to weight raster data products.
3. Methodology cont.: A GIS-based model to calculate SWE
The Modelbuilder application within ESRI’s ArcGIS desktop v. 9.1 was used to create a 3-part model for the calculation of SWE at the study site (Fig. 3).
Part 1 transforms the PRISM data set from an ASCII file into an ESRI grid in GCS WGS 1972 projection and clips that file to the MODIS image extent.
Part 2 of the model resamples the PRISM data cell size (4 km) to MODIS cell size (463 m). A mask was built by converting the MODIS fractional snow
cover (SCA) image to a binary image, where pixels with less than 50% snow are mapped as snow-free. The mask was then applied to the PRISM data
in order to distinguish rain from snow precipitation. The masked PRISM data were then clipped to the extent of the study site basin. Part 3 creates a
snow index by normalizing the PRISM study site raster and weighting it with the Hogg Pass SNOTEL SWE values to yield a basin SWE raster.
Part 2
Part 1
3. Methodology cont.: MODIS image processing
In order to ensure the greatest spatial resolution, cloud-free, near-nadir MODIS images were desired. Dates of
near-nadir Terra orbit tracks were determined through use of the Terra-track archive at
ssec.wisc.edu/datacenter/terra/NA.html. Appropriate daily MOD09 level 2G surface reflectance 500m V005 images
were downloaded from the EOS data gateway. These images, in native HDF format, were then pre-processed with
the MODIS reprojection tool (MRT) in order to subset them to 43-45° N and -123--121° W, to reproject them into UTM
zone 10 N with WGS 1984 datum, and to convert them into geoTIFF files. Multiband geoTIFF files were then imported into RSI ENVI digital image processing software where the least cloudy images closest to the peak SWE date
were determined. ENVI was then used to construct snow covered area (SCA) maps. (Fig 2). The SCA are fractional,
and were derived by linear spectral unmixing (LSU). Discussion of the LSU algorithm is beyond the scope of this
poster, however it is governed by the following equation:
DN c =
N
∑
n =1
n DN n,c
+ Ec
Where DNc is reflectance in channel c
N number of endmembers
Fc fraction of endmember n
DNn,c reflectance of endmember n in channel c
Ec error in channel c of the fit of N spectral endmembers.
Part 3
The model was divided into three
phases in order to reconcile datum
conflicts between PRISM data
(GCS, WGS 1972) and MODIS
data (UTM zone 10 N, WGS
1984). The model imports and
resamples PRISM data to MODIS
grid cell size, and masks them
using the MODIS SCA images.
This masking disseminates snow
from rain in the PRISM data. Finally, the model calculates appropriate weights based on SNOTEL
point data for Hogg Pass station,
and applies them to the
PRISM/MODIS normalized snow
indices, yielding study site basin
SWE rasters. The model was run
in entirety for each water year from
2000-2005. These rasters are
shown below in the results section.
Low : 110.2
4. Results: Fig. 4 SWE maps of Clear Lake Basin 2000-2005
2000
2001
2002
Fig. 2. Full MODIS image extent rasters (43-45° N, -123--121° W) for Row a) MODIS 6-4-2 false color
RGB images, Row b) MODIS SCA maps, Row c) PRISM data rasters, and Row d) PRISM data masked
by MODIS binary masks.
22
Santiam
Junction
Hogg Pass
(
/
20
Jump Off
Joe
0
15 30
Kilometers
3. Methodology: Data Products
Data T
Type
yp
Data Description
p
SNOTEL
Daily digital snow pillow SWE
measurements
Snow Course
MODIS
PRISM
Total Basin SWE = 90.67x106 m3
126
60
Total Basin SWE = 166.25x106 m3
Data Source
Natural Resources
Conservation Service
(
(NRCS)
)
Bimonthly manual snowpack
Natural Resources
measurements converted to SWE Conservation Service
(
(NRCS)
)
MODIS Surface-Reflectance
EOS Data Gateway;
Product (MOD 09), computed
Land Processes Disfrom the MODIS Level 1B land tributed Active Arbands 1, 2, 3, 4, 5, 6, and 7 (cen- chive Center (LPtered at 648 nm, 858 nm, 470
DAAC)
nm, 555 nm, 1240 nm, 1640
nm, and 2130 nm, respectively).
The product is an estimate of the
surface spectral reflectance for
each band as it would have been
measured at ground level if there
were no atmospheric scattering
or absorption.
Monthly Precipitation grids incorporating elevation into the
modeled estimates
Total Basin SWE = 114.31x106 m3
2003
URL
http://www.wcc.nrcs.
usda.gov/snow/
Total Basin SWE = 256.69x106 m3
2004
Total Basin SWE = 68.06x106 m3
2005
Total Basin SWE = 6.90x106 m3
5. Conclusions & Error Analysis
2000
2001
2002
Year
2000
2001
2002
2003
2004
2005
http://www.wcc.nrcs.
usda.gov/snowcourse/
http://edcimswww.
cr.usgs.gov/pub/imswelcome/
Spatial Climate Analy- http://www.ocs.orst.
sis Service, Oregon
edu/prism/products/
State University
y
Modeled SNOTEL SWE on
Basin
MODIS image
SNOTEL SWE on
SWE
date (mm)
PRISM date (mm)
Jump
Jump
Hogg Santiam Off Hogg Santiam Off
Pass Jct
Jct
m3
Joe Pass
Joe
2003
SNOTEL SWE on MODIS image SNOTEL SWE on PRISM date
date (mm)
(mm)
MODELED SWE at MODIS
extent (mm)
2004
2005
MODELED SWE at
MODIS extent (mm)
Hogg Santiam Jump
Pass Jct
Off Joe
90670000 1069 523
460 1154.82 393.40 390.86 1069 0
0
114310000 572 0
0
704.01
494.92 0
0
572 529
256690000 1367 544
640 1370.56 563.45 664.97 1367 0
166250000 686 132
89
68060000 803 406
424 705.58 0
197.97 803 0
0
6900000
25
55.84 224 0
0
224 8
Estimated
Normalized
basin
basin SWE
SWE (m3)
9.07E+07
1.26E+08
1.14E+08
1.23E+08
2.57E+08
1.42E+08
1.66E+08
1.66E+08
6.81E+07
5.48E+07
6.90E+06
2.77E+07
Fig. 5 Range
maps showing the difference between
MODIS SCA
and PRISM
index rasters
for the study
site basin.
Estimated SWE values do not correspond well with actual measurement values from individual SNOTEL stations. Indeed, uncertainty associated with raster values are half of the original grid cell size (e.g. 2 km for PRISM data). Hence, as calculation area
increases, so does the accuracy of the technique. Error between MODIS SCA and PRISM SWE rasters generally fell within the
0-0.5 m range (Fig. 5 above). Accuracy again increased over the entire MODIS image extent (Fig. 2). Other sources of error included disseminating snow from rain using the MODIS binary mask, and choosing appropriate endmembers upon which to base
the LSU process during SCA map generation.
609.14 71.07
223.35 60.91
1253.38
22.84 686 683.49 666.51
Fractional
MODIS
% Difference
MODIS
SCA with
from fractional
SCA
binary mask
SCA
1.55E+08
1.34E+08
1.91E+01
1.73E+10
1.36E+08
9.93E+01
1.56E+08
1.39E+08
8.75E+00
1.84E+08
1.82E+08
9.64E+00
1.10E+08
5.80E+07
4.90E+01
5.33E+07
2.09E+07
4.81E+01
% Difference
from masked
SCA
6.33E+00
9.42E+00
2.65E+00
8.68E+00
5.46E+00
2.46E+01
Although quantitative comparisons were not made over
the entire MODIS image
extent, qualitative comparison
between the MODIS SCA
and masked estimated SWE
rasters (Fig. 2 rows b and d)
show good agreement.
Hence, as calculation area increases, the accuracy of the
technique appears to as well.
6. References
1. C. Tague, G. E. Grant, Water Resources Research 40 (Apr 28, 2004).
2. N. P. Molotch, M. T. Colee, R. C. Bales, J. Dozier, Hydrological Processes 19, 1459 (Apr 30,
2005).
3. J. Erxleben, K. Elder, R. Davis, Hydrological Processes 16, 3627 (Dec 30, 2002).
4. K. Elder, W. Rosenthal, R. E. Davis, Hydrological Processes 12, 1793 (Aug-Sep, 1998).