Partitioning of precipitation into rain and snow in distributed hydrologic simulations
in the Western Cascades, Oregon, USA.
Edwin P. Maurer1, Jasmine Cetrone2, Socorro Medina2, and Clifford Mass2
1. Civil Engineering Department, Santa Clara University, Santa Clara, CA 95053-0563
2. Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195-1640
AMS Annual Meeting 2004
Poster Session 3: Poster P3.5
3
ABSTRACT
One of the greatest challenges in hydrologic modeling in areas with significant orographic influences is accurate simulation of the
precipitation fields, since this drives the streamflow response. In the northwest United States, where most of the precipitation occurs
during the cool season, another major factor in streamflow simulation is the determination of whether the precipitation is falling as rain
or snow, since these strongly influence the timing of the resulting runoff. The partitioning of precipitation in distributed hydrologic models
into rain, snow, or a mixture of the two is often based on surface air temperature, since this is included in the station observation records
that provide the precipitation and other meteorological data used to force the model. This study examines the adequacy for hydrologic
modeling of using surface air temperature to determine this partitioning of rain and snow in the Santiam River basin, Oregon. The
western slopes of the Cascade mountain range in Oregon, specifically an area including the South Fork of the Santiam River, was the
geographical focus for the second phase of the research effort dubbed Improvement of Microphysical PaRameterization through
Observational Verification Experiment (IMPROVE-2). This intensive field observation campaign was carried out from 26 November
through 22 December 2001, with measurements used to perform comprehensive verification of cloud and precipitation microphysical
processes parameterized in mesoscale models. Included in the suite of IMPROVE-2 observations were both scanning and vertically
pointing radar. While scanning radar observations in areas of complex terrain, such as the western Cascades, are problematic due to
ground clutter and beam blocking, vertically pointing radar does not suffer from this. We show that, by replacing the surface air
temperature-based algorithm in a distributed hydrologic model with a freezing level determined with S-band radar supplemented by
other observations, significant improvement in the simulated hydrograph can be obtained.
1
Meteorology in South Santiam Basin during IMPROVE Observation Period
Examination of P/T/SWE relationships at 2 SNOTEL sites in basin
JUMP_OFF_JOE
LITTLE_MEADOWS
Sample reflectivity data from the NOAA/ETL Sband vertically pointing radar. The data shown are
for 2215 UTC 13 December - 0115 UTC 14
December 2001. Note the bright band in red, the top
of which is typically associated with 0°C
temperatures. (Houze and Medina, 2002)
Elevation and surface air temperature at each pixel (interpolated from
observations with a lapse rate of -5.5 °C/km) were combined with the radarobserved freezing level to illustrate the variability in surface temperatures
associated with rain and snow. At each time step, the radar detected 0° level was
projected across the basin. The surface air temperatures for pixels with elevations
within 10m of this 0° level, were taken as Surface Air Temperatures for Snow/Rain
samples of the minimum surface air
Inferred from Radar 0°C Level
temperature at which any rain occurs,
Tmin(rain). At a distance 300 meters
below the 0° level all melt is assumed
complete, and pixels with elevations
close to this are representative of the
maximum surface air temperature at
which snow occurs, Tmax(snow). A linear
mixture is assumed between these levels.
Focus of This Study
In many hydrologic models, determination of precipitation type is indexed to surface air temperature, and the
selection of the maximum snow and minimum rain thresholds are chosen empirically, by calibration or using
published values (e.g. U.S. Army Corps of Engineers, 1956), or are selected arbitrarily (e.g., Bowling et al.,
2003). Some models use one fixed temperature as a division between rain and snow, rather than using a
range with mixed (frozen and solid) precipitation (e.g. Bicknell et al., 2002). The NWSRFS implementation
of the Sacramento model (Office of Hydrologic Development, 2002) is rare in allowing the incorporation of
freezing level data.
Observed 0° Level Based on Bright Band
Identification
Minimum during
IMPROVE-2 period
Tmin(Rain)
-9.7
-0.6
-4.9
Tmax(Snow)
-6.7
1.7
-2.4
This example, from
LITTLE_MEADOWS,
highlights 2 periods where
the air temperature
indexing and radar 0°C
levels can give different
results. Here two periods
are highlighted where the
freezing level is well
above the station elevation
(indicating rain), while the
surface air temperature is
below zero (indicating at
least partial snow).
The following questions take advantage of the availability of radar-based freezing level observations in the
study region to look for opportunities for improving streamflow simulations in regions of complex topography
and strong orographic influence:
1) How well do surface temperature-based methods work for determining whether precipitation is falling as
rain, snow, or a mixture?
Net decrease in snow water equivalent (swe)
Net accumulation of snow water equivalent
2) Does the radar-detected 0°C level differ substantially from the air-temperature-based method?
Observations show:
• Surface air temperature is not a good indicator of whether precipitation is falling as rain or snow.
• This is especially evident for lower elevation JUMP_OFF_JOE site, closer to valley bottom, where
there is essentially no correlation between air temperature during a precipitation even and whether
snow is accumulating or melting.
• Even at LITTLE_MEADOWS, closer to the ridge, at air temperatures between 2° and 4°C during
this period, air temperature is a poor indicator of precipitation type.
• There is a wide discrepancy between the 2 locations in the air surface temperatures associated
with both rain and snow, indicating the use of one index for the basin could be problematic.
3) Can the observed radar-based 0°C level be used to improve streamflow simulations during the events
studied during IMPROVE-2?
2
IMPROVE-2 Overview and River Basin for Study
IMPROVE is aimed at
comprehensively checking and
improving the parameterization
schemes currently implemented in
the Penn State/NCAR Mesoscale
Model (MM5), a mesoscale model
that has been extensively used for
both research and operational
forecasting. The primary goal of
IMPROVE is to utilize quantitative
measurements of cloud microphysical
parameters in a variety of mesoscale
features to improve the
representation of cloud and
precipitation processes in mesoscale
models. The IMPROVE-2 field study,
focused on orographic clouds and
precipitation in the Oregon Cascade
Mountains, was conducted 26
November through 22 December
2001. For more details, see:
http://improve.atmos.washington.edu/
4
South Santiam River Basin
The IMPROVE-2 domain overlaps largely with the South Santiam River basin, shown here, which has a total basin
area of 1,440 km2. This catchment provides a spatial integrator for the observed and modeled precipitation, and a
valuable validation tool for assessing precipitation fields simulated by forecast models.
5
Effect of Precipitation Type Determination on Hydrologic Simulations
RMSE for peak events (observed flows > 60 m3/s)
Trial #1 – Using air temperature-based indexing of 2°C Tmax(snow) and
0°C Tmin(rain) (following Bowling, et al., 2003; Office of Hydrologic
Development, 2002)
Gauge 14185000
Gauge 14185900
Trial #1
43
57
Trial #2
40
40
Trial #3
39
39
Using the radar detected 0°C level, either directly (Trial #3) or
combining it with the basin DEM and surface air temperatures to
estimate a Tmax(snow) and Tmin(rain) (Trial #2) produces simulated
improved hydrographs compared to literature-based Tmax(snow)
and Tmin(rain) values (Trial #1), as reflected in the RMSE values
above. Trial #2 achieves most of the decrease in RMSE, implying
that “calibrating” Tmax(snow) and Tmin(rain) using radar data may
be possible and beneficial to hydrologic simulations.
Trial #2 – Using air temperature-based indexing, as in Trial #1, but with
average vales inferred from radar-detected level (see box 3 above):
-2.4°C Tmax(snow) and -4.9°C Tmin(rain)
The South Santiam basin, during the IMPROVE-2
period, included many observational assets. The
subset of observations used in this study included
a vertically pointing S-Band radar, daily and hourly
cooperative observer stations, SNOTEL stations,
precipitation gauges installed for this study
(labeled “IMPROVE”), and USGS streamflow
gauges, shown on this map.
Vertically-pointing S-Band
Radar
Daily Cooperative Obs.
Hourly Cooperative Obs.
SNOTEL Station
IMPROVE Precipitation
USGS Stream Gauge
The average 700 - 925 mb wind speed for the entire
IMPROVE-2 period is 13.5 ms-1, thus applying the 0°
level at the S-Band location to the entire basin
introduces at most a 1 hour timing error for any point
in the basin on average.
Given the above differences in air temperature-based versus radar-based discrimination
of rain and snow, we investigate the sensitivity of streamflow simulations in
IMPROVE to incorporation of freezing level data, using the DHSVM model
(Wigmosta et al., 1994), modified to ingest freezing level data.
The simulation of snow at the 2 SNOTEL sites in the basin is more
problematic. The use of the variable radar-detected freezing level
(trial #3) improves the snow water equivalent simulations at these
points compared to trial #1, while trial #2 is not a consistent
improvement. None of the trials could reproduce the complete
removal of snow at the Jump_off_Joe site, indicating that other
local factors are important.
Trial #1
JUMP_OFF_JOE
Trial #3 – Using elevation indexing: Observed 0° Level Based on
Bright Band Identification (see plot in Box 3 above), varies with time:
Trial #2
LITTLE_MEADOWS
Maximum during
Average during
IMPROVE-2 period IMPROVE-2 period
Summary
• Based on surface observations of air temperature and
snow accumulation, the surface air temperature is not a
strong indicator of accumulation or melt of snowpack
(or whether precipitation type is rain or snow).
• The first method of partitioning precipitation into rain
and snow used radar detected 0° elevation as that below
which snow begins to melt; 300 m below this elevation
marked that below which all precipitation is rain.
• A second method combined radar data with the
elevations and surface air temperatures at each pixel and
time step in the basin. Populations of Tmin(rain) and
Tmax(snow) (see Box 3 for definitions) were derived, the
average of which provided temperature indices for
partitioning precipitation into rain, snow, or a mixture.
• The use of the second method in a hydrologic model
(see Trial 2, Box 4) produced measurable improvements
in simulated peak flows over using values of Tmin(rain)
and Tmax(snow) from literature.
• Using the first method (see Trial 3, Box 4) produced
further improvements in the simulation of one flood
peak, but the incremental improvement over using the
second method was small.
• At two locations where snow was observed, the
simulations by the hydrologic model were better with
the first method than the second, though local effects
complicate the accurate simulation of snow at one site.
REFERENCES
Bicknell, B.R., J.C. Imhoff, J.L. Kittle Jr., A.S. Donigian, Jr. and R.C. Johanson. 1997. Hydrological
Simulation Program -- FORTRAN, User's Manual for Version 11. EPA/600/R-97/080. U.S. EPA, National
Exposure Research Laboratory, Athens, GA.
Bowling, L.C., D.P. Lettenmaier, B. Nijssen, L.P. Graham, et al. 2003, Simulation of high latitude
hydrological processes in the Torne-Kalix basin: PILPS Phase 2(e) 1: Experiment description and
summary intercomparisons, Journal of Global and Planetary Change, 38(1-2), 1-30.
Trial #3
Houze, R.A. and S. Medina, 2002: comparison of orographic precipitation in MAP and IMPROVE II, In:
Preprints: 10th Conference on mountain meteorology and MAP meeting, Park City, UT, 17-21 June, 2002.
Office of Hydrologic Development, 2002: National Weather Service Forecast System Model User Manual,
Section 3.3-RSNWELEV, National Weather Service.
U.S. Army Corps of Engineers, 1956: Summary Report of the snow investigations – Snow Hydrology,
North Pacific Division, Portland, OR, June 1956.
Wigmosta, M.S., L.W. Vail, and D.P. Lettenmaier, 1994, A distributed hydrology-soil-vegetation model for
complex terrain, Water Resour. Res. 30, 1665-1679.