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