integration of snodas data products and the prms model – an evaluation of streamflow simulation...
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Integration of SNODAS Data Products and the
PRMS Model – An Evaluation of Streamflow
Simulation and Forecasting CapabilitiesGeorge Leavesley1, Don Cline2,
Tom Carroll2, Lauren Hay1, and Roland Viger1
1USGS, Denver, CO; 2NOHRSC, Chanhassen, MN
Focus Issue
The distribution of point precipitation measurements for streamflow simulation and forecasting.
Concerns: Spatial and temporal availability and
variability Measurement error and missing data Ungauged basins …
Meteorological Variable Forecast Methodologies
- Historic data as analog for the future
Ensemble Streamflow Prediction (ESP)
-Synthetic time-series
Weather Generator
- Atmospheric model output
Dynamical Downscaling
Statistical Downscaling
Ensemble Streamflow Prediction
Using history as an analog for the future
Simulate to today
Predict future using historic data
Probability of exceedence
NOAA
USGS
BOR
ESP Forecast Error Sources
UncorrectedClimate Data
Corrected Climate Data
Hunter Creek nr Aspen, Colorado
Hunter
Midway
No Name
Gage Trans-mountain Diversion
PointsHRUs
Forecasting at Internal Nodes
Precipitation Interpolation Methods
Inverse distance weighting Kriging Multiple linear regression Climatological multiple linear
regression Locally weighted polynomial k nearest neighbor …
XYZ Distribution
San Juan Basin
Observation Stations 37
XYZ Spatial Redistribution of Precip and Temperature
1. Develop Multiple Linear Regression (MLR) equations (in XYZ) for PRCP, TMAX, and TMIN by month using all appropriate regional observation stations.
XYZ Spatial
Redistribution
2. Daily mean PRCP, TMAX, and TMIN computed for a subset of stations (3) determined by the Exhaustive Search analysis to be best stations
3. Daily station means from (2) used with monthly MLR xyz relations to estimate daily PRCP, TMAX, and TMIN on each HRU according to the XYZ of each HRU
Precip and temp stations
Z
PR
CP
2. PRCPmru = slope*Zmru + intercept
where PRCPmru is PRCP for your modeling response unit
Zmru is mean elevation of your modeling response unit
x
One predictor (Z) example for distributing daily PRCP from a set of stations:
1. For each day solve for y-intercept
intercept = PRCPsta - slope*Zsta
where PRCPsta is mean station PRCP and
Zsta is mean station elevation
slope is monthly value from MLRs Plot mean station elevation (Z)
vs. mean station PRCP
Slope from monthly MLR used to find the
y-intercept
XYZ Methodology
Predicted and Predicted and Measured StreamflowMeasured Streamflow
Animas Basin, Animas Basin, CO 1990 - 2005CO 1990 - 2005
PREDICTEPREDICTEDDMEASUREMEASUREDD
ESP - Animas River @ Durango
010002000
30004000500060007000
80009000
10000
4/3
/20
05
4/1
7/2
00
5
5/1
/20
05
5/1
5/2
00
5
5/2
9/2
00
5
6/1
2/2
00
5
6/2
6/2
00
5
7/1
0/2
00
5
7/2
4/2
00
5
8/7
/20
05
8/2
1/2
00
5
9/4
/20
05
9/1
8/2
00
5
Str
ea
mfl
ow
(c
fsd
)1982
1983
1987
1991
1992
1993
1994
1995
1997
1998
2002
2003
2004
2005
ESP Animas River @ Durango
0
2000
4000
6000
8000
10000
120004/
3/20
05
4/17
/200
5
5/1/
2005
5/15
/200
5
5/29
/200
5
6/12
/200
5
6/26
/200
5
7/10
/200
5
7/24
/200
5
8/7/
2005
8/21
/200
5
9/4/
2005
9/18
/200
5
Str
eam
flo
w (
cfsd
)
1981
1982
1983
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2005 ESP Forecast
Forecast Period 4/3 – 9/30
Made 4/2/2005
All historic years
Only el nino years
Observed 2005
Animas Animas Basin Basin Snow-Snow-
covered covered Area Area Year 2000Year 2000SimulateSimulate
dd
MeasurMeasured ed
(MODI(MODIS S
SatellitSatellite)e)
Error Range <= Error Range <= 0.10.1
PRMSPRMSSNODASNODA
SS
swe swe (in)(in) PRMSPRMS
SNODASNODASS
PRMS and SNODAS PRMS and SNODAS Basin Average Basin Average
Snowpack Water Snowpack Water Equivalent (SWE)Equivalent (SWE)
Models
Ground-based Snow Data
METAR, SNOTEL, CADWR, HADS, NWS Coop, etc.
Airborne Snow Water
Equivalent Satellite Snow
Cover Data
GOES, AVHRR, SSM/I, MODIS
NEXRAD Radar Data
Numerical Weather Model
Data
Eta, RUC2, MAPS
NOHRSC Database Management
System
Data ingest, quality control, pre-processing
Data and Product Archive
NOHRSC Snow Data Assimilation System
Energy-and-mass-balance snow modeling and
observed snow data assimilation
Product Generation and
Distribution
Elements:
Daily National Snow Analyses:
(water equivalent, snow
depth, temperature, sublimation,
condensation, snow melt)
Formats:
Interactive map, time-series plots, text discussions, alphanumeric and gridded products
Distribution:
NOHRSC Web Site, AWIPS, direct FTP,
NSIDC, NCDC
NOHRSC Operations
NSA ProductGeneration
Interactive MapsDigital DataDiscussions
NSA ProductGeneration
Interactive MapsDigital DataDiscussions
TemperatureRelative Humidity
Wind SpeedSolar Radiation
Atmos. RadiationPrecipitation
Precipitation Type
Hourly InputGridded Data (1 km)
Hourly InputGridded Data (1 km)
Soils PropertiesLand Use/Cover
Forest Properties
Static GriddedData (1 km)
Static GriddedData (1 km)
Snow Energy and Mass Balance Model
Snow Energy and Mass Balance Model
Blowing Snow ModelBlowing Snow Model
Radiative Transfer ModelRadiative Transfer Model
State Variables forMultiple Vertical
Snow & Soil LayersSnow Water Equivalent
Snow DepthSnow Temperature
Liquid Water ContentSnow Sublimation
Snow Melt
State Variables forMultiple Vertical
Snow & Soil LayersSnow Water Equivalent
Snow DepthSnow Temperature
Liquid Water ContentSnow Sublimation
Snow Melt
NOHRSC Snow Modeling Framework
1
1
Data Assimilation2
3
Snow Observations
Snow Water Equivalent
Snow Depth
Snow Cover
Snow Observations
Snow Water Equivalent
Snow Depth
Snow Cover
NOHRSC Snow Model Physics
National Snow Analyses (NSA)
High-resolution Daily and Hourly Gridded Snow Data Sets of Fused Model and Observations
• Snow Water Equivalent
• Snow Density
• Snow Sfc. Temperature
• Snow Avg. Temperature
• Snow Melt
• Sublimation
• Snow Wetness
Local Information (1 km2)
Continental U.S. Information
• Snow Depth
• Archived at NCDC, NSIDC, and NDFD (soon)
Data Products
Interactive Maps
Time Series Plots
Text Discussions
Snow Information Products
PRMS
PRMS Snowpack Energy Balance Components
Animas River Basin, Animas River Basin, COCO
Animas Basin SWE - Animas Basin SWE - 2004 2004
SNODASSNODAS PRMSPRMS
April April 11
May May 11
(in.(in.))
Animas Basin SWE Animas Basin SWE - 2005- 2005
SNODASSNODAS PRMSPRMS(in.(in.
))
April April 11
May May 11
SWE_diff = SNODAS - SWE_diff = SNODAS - PRMSPRMS
SWE Difference on SWE Difference on Selected HRUsSelected HRUs
PRMSPRMS
OBSOBS
Q Q (cfs)(cfs) AnimasAnimas PRMSPRMS
OBSOBS
PRMSPRMSSNODASNODA
SS
melt melt (in)(in)
PRMSPRMSSNODASNODA
SS
PRMSPRMSSNODASNODA
SS
swe swe (in)(in) PRMSPRMS
SNODASNODASS
PRMSPRMSOBSOBS
No No UpdateUpdate
Selected Selected UpdateUpdate
Daily Daily UpdateUpdate
Update PRMS SWE with SNODAS Update PRMS SWE with SNODAS SWESWEAnimas BasinAnimas Basin
East Fork Carson Basin, East Fork Carson Basin, CACA
Predicted and Predicted and Measured StreamflowMeasured Streamflow
East Fork Carson East Fork Carson Basin, CA 1990 - 2005Basin, CA 1990 - 2005
PREDICTEPREDICTEDDMEASUREMEASUREDD
SNODASSNODAS PRMSPRMS(in.(in.))
East Fork Carson SWE - East Fork Carson SWE - 20042004
April April 11
May May 11
East Fork Carson SWE - East Fork Carson SWE - 20052005
SNODASSNODAS PRMSPRMS
April April 11
May May 11
(in.(in.))
Q Q (cfs)(cfs)
PRMSPRMS
OBSOBSEast F. East F. CarsonCarson
PRMSPRMSSNODASNODA
SS
melt melt (in)(in)
swe swe (in)(in)
PRMSPRMSSNODASNODA
SS
No No UpdateUpdate
March 1 March 1 UpdateUpdate
April 1 April 1 UpdateUpdate
Update PRMS SWE with SNODAS Update PRMS SWE with SNODAS SWESWEEast Fork Carson East Fork Carson BasinBasin
Skykomish Basin, WASkykomish Basin, WA
(in.(in.))
April April 11
May May 11
SNODASSNODAS PRMSPRMS
Skykomish Basin SWE - Skykomish Basin SWE - 20042004
(in.(in.))
April April 11
May May 11
Skykomish Basin SWE - Skykomish Basin SWE - 20052005
SNODASSNODAS PRMSPRMS
Q Q (cfs)(cfs)
PRMSPRMS
OBSOBSSkykomiSkykomi
shsh
melt melt (in)(in) PRMSPRMS
SNODASNODASS
swe swe (in)(in) PRMSPRMS
SNODASNODASS
A work in progress (Sample of 2 basins). Remotely sensed measures of SCA are
valuable, but the combined products of SCA and SWE from SNODAS provide a needed extra dimension for modeling.
Similar mean daily melt rates in PRMS and SNODAS can result from different spatial HRU melt rates.
Update of PRMS SWE may be possible when distributional patterns of SNODAS SWE are similar.
DISCUSSION AND CONCLUSIONS
The weaknesses of a climatological multiple linear regression precipitation distribution method was demonstrated
Work is continuing to identify the most robust precipitation distribution methods for different climatic and physiographic regions and will build on the SNODAS product.
DISCUSSION AND CONCLUSIONS
Working with the NRCS and
NWS to develop a Modular Modeling System
forecasting toolbox using
MMS/OMS and PRMS
TOOL PITCH Parameterizer (GIS Weasel)
DISCUSSION AND CONCLUSIONS
TOOL PITCH Parameterizer (GIS Weasel) Downsizer
DISCUSSION AND CONCLUSIONS
TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator
DISCUSSION AND CONCLUSIONS
TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca)
DISCUSSION AND CONCLUSIONS
TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca) Visualizer
DISCUSSION AND CONCLUSIONS
TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca) Visualizer Analyzer
DISCUSSION AND CONCLUSIONS
Statistical and graphical sensitivity and uncertainty analysis tools
TOOL PITCH Parameterizer (GIS Weasel) Downsizer Interpolator Optimizer (Luca) Visualizer Analyzer Terminator
DISCUSSION AND CONCLUSIONS
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