Ed Maurer Partitioning precipitation into rain and snow for event-based hydrologic modeling in the...
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![Page 1: Ed Maurer Partitioning precipitation into rain and snow for event-based hydrologic modeling in the Pacific Northwest U.S. Edwin Maurer Civil Engineering.](https://reader035.fdocuments.net/reader035/viewer/2022062804/56649f435503460f94c63d81/html5/thumbnails/1.jpg)
Ed Maurer
Partitioning precipitation into rain Partitioning precipitation into rain and snow for event-based and snow for event-based
hydrologic modeling in the Pacific hydrologic modeling in the Pacific Northwest U.S. Northwest U.S.
Edwin Maurer
Civil Engineering DepartmentSanta Clara University [email protected]
H51G-04
![Page 2: Ed Maurer Partitioning precipitation into rain and snow for event-based hydrologic modeling in the Pacific Northwest U.S. Edwin Maurer Civil Engineering.](https://reader035.fdocuments.net/reader035/viewer/2022062804/56649f435503460f94c63d81/html5/thumbnails/2.jpg)
Ed Maurer
MotivationMotivation
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
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Ed Maurer
Primary QuestionsPrimary 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 vertically-pointing radar be used to improve this, and ultimately streamflow simulations?
• Can information on derived rain-snow partitioning be transferred to neighboring watersheds?
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Ed Maurer
Area of FocusArea 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.
![Page 5: Ed Maurer Partitioning precipitation into rain and snow for event-based hydrologic modeling in the Pacific Northwest U.S. Edwin Maurer Civil Engineering.](https://reader035.fdocuments.net/reader035/viewer/2022062804/56649f435503460f94c63d81/html5/thumbnails/5.jpg)
Ed Maurer
South Santiam River BasinSouth 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
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Ed Maurer
Surface Air Temperature for Surface Air Temperature for Rain-Snow Determination Rain-Snow Determination
Accumulation
Melt
• T is not a good indicator of accumulation or melt
• Probably not good indicator of P type
JUMP OFF JOE LITTLE MEADOWS
Each 6-hourly observation where P>01. determine change in swe2. find P, Tavg3. Plot d(swe)/d(P) vs. T
11/25 12/01 12/07 12/13 12/1911/25 12/01 12/07 12/13 12/19
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Ed Maurer
Scenarios for Precipitation Type Scenarios for Precipitation Type DeterminationDetermination
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
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Ed Maurer
Vertically Pointing Radar Data – Vertically Pointing Radar Data – Reflectivity DataReflectivity Data
• 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
11/25 12/01 12/07 12/13 12/19
Observed 0° Level Based on Bright Band Identification
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Ed Maurer
Alternative 1: Using Radar Detected Alternative 1: Using Radar Detected Melting Layer in Hydrologic ModelMelting Layer in Hydrologic Model
Radar-detected bright band
0°C level – Melting begins
Snow at land surface
Rain below bright band
Melt complete
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Ed Maurer
Alternative 2: Radar-derived surface Alternative 2: Radar-derived surface air temperature indexair temperature index
Radar-detected bright band
Surface air temperature at pixels set to Tmin(rain)
Surface air temperature at pixels set to Tmax(snow)
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Ed Maurer
Alternative 2: Using radar to set air Alternative 2: Using radar to set air temperature thresholdstemperature thresholds
Minimum Maximum Average
Tmin(Rain) -9.7 -0.6 -4.9
Tmax(Snow) -6.7 1.7 -2.4
Average over period
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
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Ed Maurer
Stream Flow SimulationStream Flow Simulation
Gauge 14185900elev. 320 m
Gauge 14185000elev. 230 m
Gauges selected based on:•observed data for period•no effects from dams
DHSVM implemented with:•150 m spatial resolution•3-hour time step•Gridded observed meteorology
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Ed Maurer
Improvement in simulated Improvement in simulated hydrographshydrographs
Gauge 14185000 Gauge 14185900
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
Base Case
Alt. 1
Alt. 2
RMSE for flows over 50 m3/s
11/25 12/01 12/07 12/13 12/19
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Ed Maurer
Snow Simulations at SNOTEL siteSnow Simulations at SNOTEL site
•Simulated SWE at Little Meadows SNOTEL site, upstream of Gauges 14185900
•Alt. 1 shows dramatic improvement over base case
•Alt. 2, while better than Base Case later, substantially overestimates melt in intermediate period
Base Case
Alt. 1
Alt. 2
11/25 12/01 12/07 12/13 12/19
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Ed Maurer
Transferring methods to neighboring Transferring methods to neighboring watershedwatershed
Gauge 14185900
Gauge 14185000
Gauge 14182500elev. 200 m
Gauge 14178000elev. 485 m
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Ed Maurer
Changes at transferred sitesChanges at transferred sites
Gauge 14182500 Gauge 14178000
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)
RMSE for flows over 50 m3/s (14182500) and 40 m3/s (14178000)
Base Case
Alt. 1
Alt. 2
11/25 12/01 12/07 12/13 12/19
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Ed Maurer
Radar as a calibration toolRadar as a calibration tool
•Apply to same period of previous year:
11/25/2000-12/19/2000shown as shaded region
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
RMSE for flows over 10 m3/s
Alt. 2
Base Case
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Ed Maurer
ConclusionsConclusions
• 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