Use of multi model super ensembles in hydrology
Download
1 / 56

Use of - PowerPoint PPT Presentation


  • 332 Views
  • Updated On :

Use of Multi-Model Super-Ensembles in Hydrology. Martyn Clark * Steven Markstrom. Lauren Hay George Leavesley. Roland Viger U.S. Geological Survey Water Resources Discipline National Research Program * University of Colorado - Boulder. Hydrologic Simulation. Inputs Time series data

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Use of ' - medwin


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Use of multi model super ensembles in hydrology l.jpg

Use of Multi-Model Super-Ensembles in Hydrology

Martyn Clark*

Steven Markstrom

Lauren Hay

George Leavesley

Roland Viger

U.S. Geological Survey

Water Resources Discipline

National Research Program

* University of Colorado - Boulder


Hydrologic simulation l.jpg
Hydrologic Simulation

  • Inputs

    • Time series data

      • Precipitation, Minimum + Maximum Temperature

    • Parameters (static information)

      • Spatial characteristics

      • Non-spatial characteristics

  • Modeling Software


Sources of error l.jpg
Sources of Error

  • State of the system:

    • observed != simulated

  • Error in:

    • Inputs

      • Time series data

      • Parameters

    • Modeling Software


  • Optimization of model l.jpg
    Optimization of Model

    • Standard technique:

      • adjustment of parameters

        • Spatial characteristics

        • Non-spatial characteristics

      • “Fitting” simulated hydrograph to the observed hydrograph


    Optimization of model5 l.jpg
    Optimization of Model

    • Standard technique:

      • adjustment of parameters

        • Spatial characteristics

        • Non-spatial characteristics

      • “Fitting” simulated hydrograph to the observed hydrograph

    • Ignores numerous other sources of error!


    Sources of error6 l.jpg
    Sources of Error

    • Inputs

      • Time series data

        • Weather Stations


    Sources of error7 l.jpg
    Sources of Error

    • Inputs

      • Time series data

        • Weather Stations

          • Measurement inaccuracy

          • Measurement bias

          • Measurement drift


    Sources of error8 l.jpg
    Sources of Error

    • Inputs

      • Time series data

        • Weather Stations

          • Measurement inaccuracy

          • Measurement bias

          • Measurement drift

        • Global or Regional Climate Model inputs


    Sources of error9 l.jpg
    Sources of Error

    • Inputs

      • Time series data

        • Weather Stations

          • Measurement inaccuracy

          • Measurement bias

          • Measurement drift

        • Global or Regional Climate Model inputs

          • Model accuracy (timing, volume, extremes)

          • Spatial scale

          • Temporal scale


    Sources of error10 l.jpg
    Sources of Error

    • Inputs

      • Time series data

        • Weather Stations

          • Measurement inaccuracy

          • Measurement bias

          • Measurement drift

        • Global or Regional Climate Model inputs

          • Model accuracy (timing, volume, extremes)

          • Spatial scale

          • Temporal scale

        • Representation & Distribution

          • Does this data describe what’s “hitting the ground”?


    Sources of error11 l.jpg
    Sources of Error

    • Inputs

      • Time series data

      • Parameters

        • Spatial characteristics


    Sources of error12 l.jpg
    Sources of Error

    • Inputs

      • Time series data

      • Parameters

        • Spatial characteristics

          • Quality of GIS layers

          • Quality of algorithms

          • Quality of GIS delineation techniques


    Sources of error13 l.jpg
    Sources of Error

    • Inputs

      • Time series data

      • Parameters

        • Spatial characteristics

          • Quality of GIS layers

            (is my soil info accurate enough?)

          • Quality of algorithms

            (is my GIS using my soils data correctly?)

          • Quality of GIS delineation techniques

            (are my model’s geographic feature concepts appropriately represented in the GIS?)


    Sources of error14 l.jpg
    Sources of Error

    • Inputs

      • Time series data

      • Parameters

        • Spatial characteristics

        • Non-spatial characteristics


    Sources of error15 l.jpg
    Sources of Error

    • Inputs

      • Time series data

      • Parameters

        • Spatial characteristics

        • Non-spatial characteristics

          • adjustment factors for Time series data

            coefficients for measurement error & bias correction

            distribution of climate data to land surface units

             Modeling Response Units (MRUs)


    Sources of error16 l.jpg
    Sources of Error

    • Inputs

    • Modeling Software


    Sources of error17 l.jpg
    Sources of Error

    • Inputs

    • Modeling Software

      • Model concepts valid?

      • In setting of the application area?

      • Are selected processes successfully integrated?


    Sources of error18 l.jpg
    Sources of Error

    • Inputs

    • Modeling Software

    • Optimization technique

      • “fitting” the simulated hydrograph to the observed


    Sources of error19 l.jpg
    Sources of Error

    • Inputs

    • Modeling Software

    • Optimization technique

      • “fitting” the simulated hydrograph to the observed

        • How is this measured?

          Is chosen statistic appropriate?

          Is a single statistic appropriate?

          Is this statistics appropriate for the entire cycle of hydrologic response?


    Optimization of model20 l.jpg
    Optimization of Model

    • Standard technique:

      • adjustment of parameters

        • Based on single statistic over entire period


    Optimization of model21 l.jpg
    Optimization of Model

    • Standard technique:

      • adjustment of parameters

        • Based on single statistic over entire period

          Seems incomplete!


    Super ensemble study l.jpg
    Super-Ensemble Study

    • Joint effort:

      • USGS

      • University of Colorado – Boulder

    • Funded by:

      • NOAA

      • University of Colorado

      • USGS (barely)


    Super ensemble study purpose l.jpg
    Super-Ensemble Study: purpose

    • Systematically evaluate alternative components for hydrologic modeling

    • Develop optimized modeling configurations

    • Produce map-based database of configurations to support field staff


    Super ensemble study approach l.jpg
    Super-Ensemble Study: approach

    • Specify approximately 15 different model permutations

    • Select 2 watersheds from each Hydrologic Landscape Unit

    • Develop input climate time series data

    • Automate delineation & parameterization of geographic features

    • Automate Sensitivity & Optimization Analyses


    Super ensemble study tools l.jpg
    Super-Ensemble Study: tools

    • Modular Modeling System (MMS)

    • Climate processing methods

    • GIS Weasel

    • MOGSA & MOCOM

      • Multi-object sensitivity and optimization tools

      • University of Arizona


    Super ensemble study mms l.jpg

    PROCEDURES

    Super Ensemble Study:MMS

    # Modules in MMS

    X X X

    Input Data

    Climate Processing

    Solar Radiation

    Potential Evapotranspiration

    Snow

    Soil

    Subsurface

    Groundwater

    X X X

    X X

    X X

    X X

    X X X




    Climate processing methods l.jpg
    Climate Processing Methods

    Produces time series values for each MRU

    • Basin Average

    • Inverse Distance

    • Nearest Neighbor

    • Thiessen Polygons

    • XYZ

    • Local Polynomial Regression

    • Artificial Neural Networks


    Basin selection l.jpg
    Basin Selection

    • 2 basins from each HLU

      approximately 70 for first iteration

    • Each basin part of Hydrologic Climate Data Network (HCDN)

    • Drainage area

      > 50 km2

      < 3000 km2


    Slide31 l.jpg

    Hydrologic Landscape Units (HLUs)

    • Land surface form

    • Climate

    • geology





    Gis weasel l.jpg
    GIS Weasel

    • Simplifies the creation of spatial information for modeling

    • Provides tools to:

      Delineate

      Parameterize

      relevant spatial features


    Gis weasel36 l.jpg
    GIS Weasel

    • Still have to insert a nice plug for da weasel…


    Gis weasel example delineation methodology l.jpg
    GIS Weasel:ExampleDelineationMethodology


    Slide38 l.jpg

    METHODOLOGY

    Basin

    Setup

    “Uncalibrated” Watershed Model

    Optimize

    Volume

    Optimize

    Timing


    Slide39 l.jpg

    METHODOLOGY

    Basin

    Setup

    Basin

    Setup

    “Uncalibrated” Watershed Model

    Optimize

    Volume

    Optimize

    Timing

    • Data set compilation (temperature, precipitation, DEM, Q)

    • Basin delineation

    • GIS Weasel

    • XYZ parameterization


    Slide40 l.jpg

    METHODOLOGY

    Identify and

    calibrate the ET parameters by

    comparing “observed”

    and simulated monthly mean

    PET

    out of hydrologic model

    Basin

    Setup

    Basin

    Setup

    August Monthly Mean PE

    “Uncalibrated” Watershed Model

    Optimize

    Volume

    Optimize

    Timing

    Get a Water Balance

    Calibrate ET and climate station choice


    Slide41 l.jpg

    METHODOLOGY

    Basin

    Setup

    “Uncalibrated” Watershed Model

    Optimize

    Volume

    Optimize

    Timing

    Get a Water Balance

    Calibrate ET and climate station choice

    Find ‘best’ climate station sets


    Slide42 l.jpg

    Developed at U. of AZ:

    MOGSA – Multi Objective

    Generalized

    Sensitivity

    Analysis

    Determines parameter

    sensitivity

    METHODOLOGY

    METHODOLOGY

    Basin

    Setup

    Uncalibrated Watershed Model

    “Uncalibrated” Watershed Model

    Optimize

    Volume

    Optimize

    Timing

    Identify and optimize sensitive parameters


    Slide43 l.jpg

    Developed at U. of AZ:

    MOGSA – Multi Objective

    Generalized

    Sensitivity

    Analysis

    Determines parameter

    sensitivity

    Developed at U. of AZ:

    MOCOM – Multi-Objective

    COMplex

    Evolution

    Solves the multi-objective

    optimization problem

    METHODOLOGY

    METHODOLOGY

    Identify and optimize sensitive parameters

    Basin

    Setup

    Uncalibrated Watershed Model

    “Uncalibrated” Watershed Model

    Optimize

    Volume

    Optimize

    Timing


    Slide44 l.jpg

    Multi-Objective

    FD: Driven

    FQ: Quick

    FS: Slow

    FD

    FQ

    FS

    FS

    Peak/Timing

    Baseflow

    Quick recession

    (See Boyle et al., WRR, 2000)


    Anticipated products l.jpg
    Anticipated Products

    • Linking of physical processes

      • Atmospheric

      • Watershed

      • Two-way interaction (eventually)

    • Development of Super-ensemble approach

    • Physically-based watershed models that need limited interactive calibration


    Anticipated products46 l.jpg
    Anticipated Products

    • Regionalization (spatial maps) of:

      • Climate:

        • recommended sources variables

        • processing methods

        • parameters

      • Recommendations for place-specific model selection/configuration

      • Pareto sets of optimized parameters

      • Confidence and error figures


    Limitations l.jpg
    Limitations

    • Study deals with limited modeling question

      • Volume & timing of streamflow

      • Watershed scale (50-3000 km2)

      • Daily time step

    • Limited number of physical process algorithms tested

    • Limited number of watersheds featured

      • Automation will enable broader (nationwide) application


    Timeline l.jpg
    Timeline

    • Dare we make these predictions?


    Work completed l.jpg
    Work Completed

    • Climate processing

      • 4 of 7 methods implemented

      • Station observations selected for all test basins

        • Records clean

      • Regional and Global Climate Model outputs assembled

    • GIS

      • Delineation of geographic features automated

      • Parameterization of geographic features automated

      • Spatial data layers assembled

      • Processing complete


    Work completed50 l.jpg
    Work Completed

    • Hydrologic science modules assembled

    • MOGSA & MOCOM established


    Contact information l.jpg
    Contact Information

    • Staff

      • George Leavesley (project chief)

      • Lauren Hay

      • Steve Markstrom

      • Roland Viger

      • Martyn Clark

    • URLs

      • http://wwwbrr.cr.usgs.gov/mms

      • http://wwwbrr.cr.usgs.gov/weasel

    [email protected]

    [email protected]

    [email protected]

    [email protected]

    [email protected]



    Climate processing l.jpg
    Climate Processing

    • Need to be able to distribute:

      • From:

        • stations

        • grid points

      • To:

        • individual Modeling Response Unit (MRU)


    Climate processing xyz overview l.jpg
    Climate Processing:XYZ overview

    • Multiple Linear Regression (MLR) equations

      • Developed for:

        • Precipitation

        • Temperature, Maximum

        • Temperature, Minimum

      • Based on:

        • X

        • Y

        • Z

      • Monthly

      • Explains variation in observation across stations

    Same relationship between stations and MRUs

    (use MRU X,Y,Z in MLR)


    Climate processing statistical downscaling sds overview l.jpg
    Climate Processing:Statistical Downscaling (SDS) overview

    • Output from Global Climate Model (GCM)

      • National Center for Environmental Prediction (NCEP) model

    • Averaged to a point

      (e.g. basin centroid)

    • Distributed to MRUs

      • XYZ methodology


    Climate processing dynamical downscaling dds overview l.jpg
    Climate Processing:Dynamical Downscaling (DDS) overview

    • Uses Regional Climate Model

      • RegCM2

      • Seeded with NCEP output

    • Averaged to a point

      (e.g. basin centroid)

    • Distributed to MRUs

      • XYZ methodology


    ad