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