Evaluation of the representativeness of snow water equivalent sensors in the Rio Grande headwaters
1Noah
U53A-0701
1
0
1
2
Wyoming
3
N
Utah
Colorado
Arizona
New
Mexico
45
107 W
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder; 2School of Engineering, University of California, Merced
Introduction
10 20 30 km
38 N
Del Norte
Gauge
6
P. Molotch and 2Roger C. Bales
SNOTEL
30 m
The representativeness of measured snow
water equivalent (SWE) at six SNOTEL stations
in the Rio Grande headwaters was assessed
using detailed observations of snow depth and
density, remotely sensed data, binary
regression tree models, and a spatially
distributed snowmelt model.
snow
pit
Scaling point observations of SWE to the
resolution of model-elements and remotely
sensed data is one of the most pressing
challenges in mountain hydrology as up-scaling
is needed to estimate initial conditions and to
evaluate remote sensing retrieval algorithms.
This work aims to improve the scalability of
current observations and develop an unbiased
and systematic approach for future network
design.
1 km
Methods
Results: Maximum Accumulation
physiographics
Snow depth & density
data collected around
the SNOTEL sites on 2227 April & 3-12 April
2002 were interpolated
using regression tree
models. Daily snowmelt
was computed for each
30-m grid element
throughout the melt
season.
wind exposure
srad04<228.5
elevation
slope
srad04>228.5
74
24
elev<3286.5
elev>3286.5
ndvi<2.5
ndvi>2.5
100
67
ndvi<1.5
13
50
srad04<193.5
ndvi<-10.5
ndvi>1.5
srad04>193.5
ndvi>-10.5
62
76
122
93
0
71
102
solar
snow
albedo
temp.
relative
humidity
47
ndvi<7.5
ndvi>7.5
Snowmelt Model Inputs
longwave
67
SWE
63
35
2 km
4 km
Results: Ablation Season
Daily SWE distribution, estimated from the snowmelt
model, showed that representativeness at Upper San
Juan deteriorated in a short time period, particularly
WRT the 4 and 16 km2 areas. SNOTEL SWE values
are indicated on selected cumulative probability
functions with black stars.
DOY 2001
DOY 2002
Results: Optimal Site Location
Optimal observation areas (shaded in black) around 4
SNOTEL sites (white rings) are areas with the lowest
cumulative absolute deviance from the mean
regression-tree-modeled snow depth. Only the Wolf
Creek SNOTEL site was within the optimal area.
a
Slum
4 km2
cumulative probability
16 km2
3000
April deviance
= 0.61 cm
April and May
deviance = 7 cm
solar radiation, W m
elevation, m
Sx, degrees
NDVI
slope, degrees
aspect, degrees
-2
Slumgullion
min. max. STEL
218 226 215
3514 3530 3518
4
26
13
-0.01 0.40 0.01
8
25
9
83
91
65
Upper San
min. max.
218 223
2935 3152
-21
0
0.02 0.29
1
31
220 352
c 3420
WC
April deviance = 8 cm
3180
d
Lily
2960 3080
3210
3330
1 km2
April deviance
= 1 cm
39
106
35
100
94
26
31
88
22
82
17
76
13
70
64
58
9
4
0
The relationship between observed snow depth at the SNOTEL
sites and the snow depth distribution of the surrounding areas
were fairly consistent in 2001 & 2002; significant positive bias
was observed at the Slumgullion site.
Juan
STEL
227
3089
-3
0.07
6
195
2001
Slum
USJ
2002
WC
Lilly
Slum
USJ
WC
Conclusions
3300
snow water equivalent, cm
Regression trees were used to identify the physiographic attributes of
optimal site locations. Bold font indicates that the value at the SNOTEL
sites are outside the range of values at the optimal site location. The Lily
Pond site (not shown) is also not optimally located.
b
USJ
April deviance = 2 cm
3300
3390
3450
120 130 140 150 160 170 100 110 120 130 140
initial
condition
temp.
BATS
GOES
RH
Veg.
Veg. TOPORAD
USJ April 2002
USJ April 2001
112
20
slope<12.5 srad04<223.5
slope>12.5
srad04>223.5
3300
3390
3450
N
62
NDVI
SWE distribution was relatively consistent in 2001 and 2002.
The Upper San Juan SNOTEL site (white rings) is located in an
east-west oriented valley; north (south)-facing slopes to the
south (north) experience greater (lower) SWE accumulation.
Other sites showed repeating snow depth patterns as well,
with SNOTEL sites located in anomalously high accumulation
areas in some cases.
SWE biases at the sites, caused by biases in solar radiation, elevation, aspect and slope, were not consistent from site to site. On
average, less than 2.4% of each study area satisfied the physiographic criteria of optimality for future observations. This research
has shown that SNOTEL SWE data are often unrepresentative of their surrounding areas (i.e. 1, 4, 16 km2). The spatial and
temporal signatures of the observation biases identified in this research will improve the utility of current observations. Lastly, the
methodology presented here provides the basis for designing ground-based observation networks in support of spatial applications.
Acknowledgements. This work was supported by a CIRES visiting fellowship, the NSF STC for the Sustainability of Hydrology and Riparian Areas (SAHRA), and
by the Climate Assessment for the Southwest (CLIMAS). Walter Rosenthal, Bert Davis and Ceretha McKenzie provided technical assistance. Mike Gillespie and
Tom Pagano provided information about the SNOTEL sites. Logistical support was provided by Davey Pitcher / the Wolf Creek Ski Area. Jennifer Hamblen,
Kevin Dressler, Steven Fassnacht, Sean Helfrich, Derek Lampkin, and others contributed to the field data collection.
Lilly