Transcript Partitioning precipitation into rain and snow for event

Partitioning precipitation into rain
and snow for event-based
hydrologic modeling in the Pacific
Northwest U.S.
Edwin Maurer
Civil Engineering Department
Santa Clara University
[email protected]
Ed Maurer
H51G-04
Motivation
Precipitation type can drive
flood simulations
Determination of type in
hydrology models is dubious
Unique data presents
opportunities to improve
precipitation type
determination with radar
Potential for transferability
Ed Maurer
Primary Questions
•
•
•
Do surface temperature-based methods work
adequately for determining whether
precipitation is falling as rain, snow, or a
mixture?
Can using a reflectivity from a verticallypointing radar be used to improve this, and
ultimately streamflow simulations?
Can information on derived rain-snow
partitioning be transferred to neighboring
watersheds?
Ed Maurer
Area of Focus
Improvement of Microphysical
PaRameterization through
Observational Verification
Experiment (IMPROVE-2).
Intensive field observation
campaign: 26 Nov- 22 Dec
2001
IMPROVE-2 domain overlaps
with South Santiam River
basin: total basin area of
1,440 km2.
Ed Maurer
South Santiam River Basin
High orographic influence
Winter storms include mix of
rain and snow
Ground-based Meteorological
Observations:
•Hourly P
•Co-op Stations
•SNOTEL
•IMPROVE P
•USGS
•Radar
Ed Maurer
Surface Air Temperature for
Rain-Snow Determination
JUMP OFF JOE
LITTLE MEADOWS
Each 6-hourly observation where P>0
1. determine change in swe
2. find P, Tavg
3. Plot d(swe)/d(P) vs. T
11/25 12/01 12/07 12/13 12/19
11/25 12/01 12/07 12/13 12/19
Accumulation
• T is not a good indicator
of accumulation or melt
• Probably not good
indicator of P type
Ed Maurer
Melt
Scenarios for Precipitation Type
Determination
Three scenarios:
1. Base Case – published T thresholds
(0.0 °C and 0.7 °C)
2. Alternative 1 – 0°C level from
Radar Data
3. Alternative 2 – Radar-derived T
thresholds
Ed Maurer
Vertically Pointing Radar Data –
Reflectivity Data
Observed 0° Level Based on
Bright Band Identification
• NOAA/ETL S-band vertically pointing
radar
• Sample from 2215 UTC 13 Dec- 0115
UTC 14 Dec 2001
• Bright band in red, the top is
associated with 0°C temperatures.
• Approx. 300 meter thickness
Ed Maurer
11/25 12/01 12/07 12/13 12/19
Alternative 1: Using Radar Detected
Melting Layer in Hydrologic Model
Snow at land surface
0°C level – Melting begins
Radar-detected bright band
Melt complete
Rain below bright band
Ed Maurer
Alternative 2: Radar-derived surface
air temperature index
Surface air temperature at
pixels set to Tmin(rain)
Radar-detected bright band
Surface air
temperature at pixels
set to Tmax(snow)
Ed Maurer
Alternative 2: Using radar to set air
temperature thresholds
Basin average surface air temperatures for
snow/rain inferred from radar 0°C level
Dynamic variability
of radar-derived
Tmax(snow) and
Tmin(rain)
Average over basin and time
period shows values outside
published range
Average over period
Ed Maurer
Minimum
Maximum
Average
Tmin(Rain)
-9.7
-0.6
-4.9
Tmax(Snow)
-6.7
1.7
-2.4
Stream Flow Simulation
DHSVM implemented with:
•150 m spatial resolution
•3-hour time step
•Gridded observed meteorology
Gauge 14185900
elev. 320 m
Gauge 14185000
elev. 230 m
Gauges selected based on:
•observed data for period
•no effects from dams
Ed Maurer
Improvement in simulated
hydrographs
Base Case
RMSE for flows over 50 m3/s
Gauge 14185000 Gauge 14185900
Alt. 1
Alt. 2
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12/01
Ed Maurer
12/07
12/13
12/19
Base Case
38
46
Alternative 1
36
34
Alternative 2
37
35
• In all cases, improvement is seen over
the base case, esp. peaks 3, 4, 5.
• 26% reduction in RMSE for gauge in
higher elevation basin
• Temperature index derived from radar
data achieves most of improvement
seen in direct use of radar freezing
level
Snow Simulations at SNOTEL site
Base Case
•Simulated SWE at Little
Meadows SNOTEL site,
upstream of Gauges 14185900
Alt. 1
•Alt. 1 shows dramatic
improvement over base case
•Alt. 2, while better than Base
Case later, substantially
overestimates melt in
intermediate period
Alt. 2
11/25
Ed Maurer
12/01
12/07
12/13
12/19
Transferring methods to neighboring
watershed
Gauge 14182500
elev. 200 m
Gauge 14178000
elev. 485 m
Gauge 14185900
Gauge 14185000
Ed Maurer
Changes at transferred sites
Base Case
RMSE for flows over 50 m3/s (14182500) and
40 m3/s (14178000)
Gauge 14182500 Gauge 14178000
Alt. 1
Alt. 2
11/25
12/01
Ed Maurer
12/07
12/13
12/19
Base Case
44
64
Alternative 1
44
59
Alternative 2
47
75
• Higher elevation basin sees minor
benefit using radar-detected 0o level
• Increasing from ~45 to ~80 km
appears beyond the transfer range for
“calibrated” temperature index for
Tmax(snow) and Tmin(rain)
Radar as a calibration tool
Base Case
• Apply to same period of
previous year:
11/25/2000-12/19/2000
shown as shaded region
Alt. 2
RMSE for flows over 10 m3/s
Gauge 14185000 Gauge 14185900
Base Case
18
12
Alternative 2
16
9.5
Radar-derived Tmax(snow) and Tmin(rain) derived using
December 2001.
Decrease RMSE for same period in 2000 by 20% at
higher elevation gauge
Ed Maurer
Conclusions
• Surface air temperature is not a good indicator of
precipitation type
• Radar-detected freezing levels can improve P
partitioning into rain/snow in hydrologic simulations
• Tmax(snow) and Tmin(rain) derived from radar-detected 0°C
levels achieve much of the benefit of direct use of
freezing levels for concurrent period
• Benefits are not realized when transferring to other
basins
• Derived Tmax(snow) and Tmin(rain) show some promise in
transferring to same period and basin in previous year
Ed Maurer