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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

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use of multi model super ensembles in hydrology

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
Hydrologic Simulation
  • Inputs
    • Time series data
      • Precipitation, Minimum + Maximum Temperature
    • Parameters (static information)
      • Spatial characteristics
      • Non-spatial characteristics
  • Modeling Software
sources of error
Sources of Error
  • State of the system:
      • observed != simulated
  • Error in:
    • Inputs
      • Time series data
      • Parameters
    • Modeling Software
optimization of model
Optimization of Model
  • Standard technique:
    • adjustment of parameters
      • Spatial characteristics
      • Non-spatial characteristics
    • “Fitting” simulated hydrograph to the observed hydrograph
optimization of model5
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
Sources of Error
  • Inputs
    • Time series data
      • Weather Stations
sources of error7
Sources of Error
  • Inputs
    • Time series data
      • Weather Stations
        • Measurement inaccuracy
        • Measurement bias
        • Measurement drift
sources of error8
Sources of Error
  • Inputs
    • Time series data
      • Weather Stations
        • Measurement inaccuracy
        • Measurement bias
        • Measurement drift
      • Global or Regional Climate Model inputs
sources of error9
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
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
Sources of Error
  • Inputs
    • Time series data
    • Parameters
      • Spatial characteristics
sources of error12
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
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
Sources of Error
  • Inputs
    • Time series data
    • Parameters
      • Spatial characteristics
      • Non-spatial characteristics
sources of error15
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
Sources of Error
  • Inputs
  • Modeling Software
sources of error17
Sources of Error
  • Inputs
  • Modeling Software
    • Model concepts valid?
    • In setting of the application area?
    • Are selected processes successfully integrated?
sources of error18
Sources of Error
  • Inputs
  • Modeling Software
  • Optimization technique
    • “fitting” the simulated hydrograph to the observed
sources of error19
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
Optimization of Model
  • Standard technique:
    • adjustment of parameters
      • Based on single statistic over entire period
optimization of model21
Optimization of Model
  • Standard technique:
    • adjustment of parameters
      • Based on single statistic over entire period

Seems incomplete!

super ensemble study
Super-Ensemble Study
  • Joint effort:
    • USGS
    • University of Colorado – Boulder
  • Funded by:
    • NOAA
    • University of Colorado
    • USGS (barely)
super ensemble study purpose
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
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
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

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
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
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

Hydrologic Landscape Units (HLUs)

  • Land surface form
  • Climate
  • geology
gis weasel
GIS Weasel
  • Simplifies the creation of spatial information for modeling
  • Provides tools to:

Delineate

Parameterize

relevant spatial features

gis weasel36
GIS Weasel
  • Still have to insert a nice plug for da weasel…
slide38

METHODOLOGY

Basin

Setup

“Uncalibrated” Watershed Model

Optimize

Volume

Optimize

Timing

slide39

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

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

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

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

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

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
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
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
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
Timeline
  • Dare we make these predictions?
work completed
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
Work Completed
  • Hydrologic science modules assembled
  • MOGSA & MOCOM established
contact information
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

george@usgs.gov

lhay@usgs.gov

markstro@usgs.gov

rviger@usgs.gov

clark@vorticity.colorado.edu

climate processing
Climate Processing
  • Need to be able to distribute:
    • From:
      • stations
      • grid points
    • To:
      • individual Modeling Response Unit (MRU)
climate processing xyz overview
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
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
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
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