Development of Gridded QPE Datasets for Mountainous Area Distributed Hydrologic Modeling
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Development of Gridded QPE Datasets for Mountainous Area Distributed Hydrologic Modeling Mike Smith 1 , Feng Ding 1, 2 , Zhengtao Cui 1, 3 , Victor Koren 1 , Naoki Mizukami 1, 3 , Ziya Zhang 1, 4 , Brian Cosgrove 1 , David Kitzmiller 1 , and John Schaake 1,5

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Development of Gridded QPE Datasets for Mountainous Area Distributed Hydrologic Modeling

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Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

Development of Gridded QPE Datasets for Mountainous Area Distributed Hydrologic Modeling

Mike Smith1, Feng Ding1, 2, Zhengtao Cui1, 3, Victor Koren1,

Naoki Mizukami1, 3, Ziya Zhang1, 4, Brian Cosgrove1,

David Kitzmiller1, and John Schaake1,5

1Office of Hydrologic Development, National Weather Service

National Oceanic and Atmospheric Administration

2Wiley Information Systems Group

3MHW

4University Corporation for Atmospheric Research

5Riverside Technology, Inc.

2010 EWRI Conference Providence Rhode Island May 17-21


Overview

Overview

  • Purpose

  • Methodology

  • Data QC Issues

  • Results

  • Conclusions


Purpose

Purpose

  • Develop and test a method to generate gridded gauge-only quantitative precipitation estimates (QPE) to support NWS R&D and operational river forecasting

    • Leverage RFC tools and data

    • Multi-year duration

    • Hourly time step

    • 4km scale

    • Data QC


Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

NCDC

Hourly

Daily

Methodology for Gauge-Only Gridded QPE

  • Data Analysis

  • -Check data consistency – double mass analysis

  • - Generate monthly station means

  • - Estimate missing data using station means

  • - Disaggregate all daily data to hourly values

    • - Non-disaggregated daily obs put into one hour

    • Manual QC: Fix ‘non-disaggregated’ values

    • - Uniformly distribute remaining daily values

SNOTEL

Daily

  • Generate QPE Grids

  • - Use NWS Multi-Sensor Precip. Estimator (MPE)

    • ‘Gauge-only’ option

    • Uses PRISM monthly climatology grids

    • Uses single optimal estimation (Seo et al., 1998, J. Hydrology)

Hourly Point

Time Series


Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

Comptonville

Methodology 2

North Fork American River

Bowman Dam 67.5”

N. Bloomfield 54.6”

Ind. Cr. 33.8

Deer Cr. Forebay 72.6”

Ind. Lake 47”

Ind. Camp 34.67

Lake Spaulding 75.6”

Blue Canyon 64

Grass Valley

Sagehen Cr. 32.5

CSS Lab 70.7”

Donner 38.9”

Gold Run 55.3”

Colfax 48.3”

Truckee 33.1”

Soda Springs 60.7”

Truckee # 2 34.8”

Iowa Hill 59.5”

Squaw Valley 69.4”

Forest Hill 55.6”

Hell Hole 47”

Ward Cr. 70.7”

Georgetown 54.5”

Auburn 37”

Blodget Ex. Forest 64”

Rob’s Peak 56.3”

Legend

NCDC

Hourly

NCDC

Daily

CSS Lab

SNOTEL

Donner

Soda Springs

20K30

48332

42467


Qpe derivation north fork american river

Methodology 3

QPE Derivation North Fork American River

  • Generate hourly 4km QPE grids 1980 – 2006

  • Use PRISM 1961-1990 gridded monthly climatology

  • Based on 36 NCDC and SNOTEL stations

  • Three cases (227,760 grids each case!)

    • No correction of non-distributed daily observations (312 cases > 0.5 in)

    • Correction of non-distributed daily observations and other errors

    • Repeat No. 2 with 1971-2000 PRISM climatology

  • Hydrologic analysis

    • Run distributed model for 1988 to 2006

    • Generate hourly streamflow simulation for each case

    • Compute statistics compared to observed streamflow

    • Water balance analysis


  • Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Example of Data Errors

    Data QC Issues 1

    Missing Flags: Foresthill changed from zero to -998 to agree with Georgetown

    *= Missing accumulation;

    wrongly coded as -999 in

    data file: should be -998


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Data QC

    Issues 2

    Impact of Data Errors on Hourly Gridded QPE

    Non-disaggregated daily value

    at Lake Spaulding station

    Max grid value

    4.59 in

    00Z

    1/22/2000

    = Snotel

    D

    = Daily

    H

    = Hourly


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Results 1

    Distributed Model

    Hourly Streamflow Simulation Statistics

    Compared to Observed Flow

    10/1988 – 9/2006


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    1. No Data QC

    ‘61-’90 PRISM

    2. Data QC

    ‘61-’90 PRISM

    3. Data QC

    ‘71- ‘00 PRISM

    Results 2

    Accumulated Streamflow Simulation Error, mm

    Monthly Cumulative Error, mm


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Jan 22, 2000

    4.59 in

    Results 3

    Hydrographs for 3 Cases

    1. No Data QC

    ’61-’90 PRISM

    2. Data QC

    ’61-’90 PRISM

    3. Data QC

    ’71-’00 PRISM

    Observed

    Flow

    Time

    January 16-30, 2000


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Results 4

    Water Balance Analysis


    Conclusions

    Conclusions

    • Methodology is sound

    • Hourly time step simulations require intensive data QC

    • Data errors not readily seen in streamflow simulation statistics

    • Automated procedure to correct wrong data flags would streamline the process


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Next Steps

    1990

    1991

    1992

    1993

    1994

    1995

    1996

    1997

    1998

    1999

    2000

    2001

    2002

    2003

    2004

    2005

    2006

    2007

    2008

    HMT QPE Data Processing for Use in DMIP 2

    ‘Advanced’ DMIP 2 Data: Multi-year time series of gridded data comprised of

    1) ‘Basic’ data and 2) Processed and gridded HMT data for each IOP

    Step 2:

    Extend ‘Basic’ Data: gridded precip.

    and temp. from NCDC, Snotel sites

    Step 1:

    ‘Basic’ DMIP 2 Data: Time series of gridded precipitation

    and temperature from NCDC, Snotel sites to Dec. 2002;

    -Represent what the RFC uses for current

    Forecast operations.

    -Used for the initial lumped and distributed

    DMIP 2 simulations in the western basins.

    Gridded Precipitation

    for each IOP replaces Basic Data

    Analysis of Data

    ESRL, NSSL, OHD

    Step 3

    Note: the time scale describes the attributes of the time series,

    not the schedule for processing the HMT data. The HMT observations

    will be processed after each campaign and inserted into

    the Basic Data time series.

    HMT-West

    Observations

    Gathered

    1

    2

    3

    Year


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Thank you!


    Extra slides

    Extra slides


    Dmip 2 western basin experiments

    DMIP 2 Western Basin Experiments

    • NCEP/EMC: J. Dong

    • HRC: K. Georgakakos

    • U. Washington: J. Lundquist with DHSVM

    • CEMAGREF: V. Andreassian

    • UCI: Sorooshian

    • U. Illinois: Sivapalan

    • U. Bologna: E. Todini


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    North Fork

    American River


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    NCDC

    Hourly

    Daily

    Methodology for Gauge-Only Gridded QPE

    Precipitation

    Preprocessor

    -Data QC:

    -Double mass analysis

    -Suspect values

    -Generate monthly station

    means

    Mean Areal Precip. Processor

    - Generate mean areal precip time series

    - Check data consistency – double mass analysis

    - Estimate missing data using station means

    - Disaggregate all daily data to hourly values

    - Non-disaggregated daily obs put into one hour

    - Write out hourly time series for all stations

    SNOTEL

    Daily

    -Manual QC: Fix ‘non-disaggregated’

    daily precipitation values

    -Script to uniformly distribute remaining

    daily values

    Hourly Point

    Time Series

    • Multi-Sensor Precip. Estimator (MPE)

    • Uses PRISM monthly climatology grids

    • Uses single optimal estimation in interpolation

    • Generate gauge-only 4km gridded QPE


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    00Z

    1/22/2000

    = Snotel

    D

    = Daily

    H

    = Hourly


    Map3 computational sequence

    MAP3 Computational Sequence

    • Read in data and corrections

    • Applies consistency corrections to observed data

    • Estimates missing hourly data using only other hourly stations.


    Map3 computational sequence continued

    MAP3 Computational Sequencecontinued

    • Time distribute observed daily amounts into hourly values based on surrounding hourly stations.

      • Procedure uses 1/d2 weighting for surrounding hourly stations.

      • If all hourly stations = 0, then all precipitation is put in last hour of the daily station. Hour of the observation time. NFAR example

    • Estimate missing daily amounts using both hourly and daily gages; time distribute these amounts

      -If all estimators are missing, then uses 0.0

    • Generates file of station and group accumulated precipitation for IDMA

    • IDMA

      • -Compute correction factors

      • -Preliminary check of correction factors

      • -Insert correction factors into input file

      • -Re-run MAP3 for final check of consistency

    • Applies weights to station for each area

    • Computes hourly MAP time series

    • Sums to selected time interval, e.g., 3hr, 6hr.


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Spatial Extent of DMIP2 American Precipitation Grid


    Development of gridded qpe datasets for mountainous area distributed hydrologic modeling

    Observed

    Schaake old

    Schaake New

    OHD no data QC

    OHD Data QC

    Jan 22, 2000

    Corrected 116.58 mm

    in one hour at

    Lake Spaulding.

    Corrected Foresthill:

    changed zero to -998 Jan 18

    to agree with Georgetown.

    Corrected Georgetown data

    to agree with NCDC paper

    records (-998 not -999 on Jan

    15-17)


    Dmip 2 western basin experiments1

    “DMIP 2” Western Basin Experiments

    • HMT experiments 2005-2006 data

    • Freezing level, precipitation type

    • Value of ‘gap’ filling radar QPE.


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