Use of Multi-Model Super-Ensembles in Hydrology

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Use of Multi-Model Super-Ensembles in Hydrology

• Lauren Hay
• George Leavesley

Martyn Clark Steven Markstrom
Roland Viger U.S. Geological Survey Water
Resources Discipline National Research Program
University of Colorado - Boulder
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Hydrologic Simulation

• Inputs
• Time series data
• Precipitation, Minimum Maximum Temperature
• Parameters (static information)
• Spatial characteristics
• Non-spatial characteristics
• Modeling Software

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Sources of Error

• State of the system
• observed ! simulated
• Error in
• Inputs
• Time series data
• Parameters
• Modeling Software

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Optimization of Model

• Standard technique
• adjustment of parameters
• Spatial characteristics
• Non-spatial characteristics
• Fitting simulated hydrograph to the observed
  hydrograph

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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!

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Sources of Error

• Inputs
• Time series data
• Weather Stations

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Sources of Error

• Inputs
• Time series data
• Weather Stations
• Measurement inaccuracy
• Measurement bias
• Measurement drift

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Sources of Error

• Inputs
• Time series data
• Weather Stations
• Measurement inaccuracy
• Measurement bias
• Measurement drift
• Global or Regional Climate Model inputs

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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

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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 whats hitting the
  ground?

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Sources of Error

• Inputs
• Time series data
• Parameters
• Spatial characteristics

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Sources of Error

• Inputs
• Time series data
• Parameters
• Spatial characteristics
• Quality of GIS layers
• Quality of algorithms
• Quality of GIS delineation techniques

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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 models geographic feature
  concepts
  appropriately represented in the GIS?)

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Sources of Error

• Inputs
• Time series data
• Parameters
• Spatial characteristics
• Non-spatial characteristics

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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)

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Sources of Error

• Inputs
• Modeling Software

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Sources of Error

• Inputs
• Modeling Software
• Model concepts valid?
• In setting of the application area?
• Are selected processes successfully integrated?

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Sources of Error

• Inputs
• Modeling Software
• Optimization technique
• fitting the simulated hydrograph to the
  observed

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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?

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Optimization of Model

• Standard technique
• adjustment of parameters
• Based on single statistic over entire period

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Optimization of Model

• Standard technique
• adjustment of parameters
• Based on single statistic over entire period
• Seems incomplete!

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Super-Ensemble Study

• Joint effort
• USGS
• University of Colorado Boulder
• Funded by
• NOAA
• University of Colorado
• USGS (barely)

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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

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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

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Super-Ensemble Study tools

• Modular Modeling System (MMS)
• Climate processing methods
• GIS Weasel
• MOGSA MOCOM
• Multi-object sensitivity and optimization tools
• University of Arizona

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Super Ensemble StudyMMS
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
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Super Ensemble StudyMMS
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Super Ensemble StudyMMS
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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

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Basin Selection

• 2 basins from each HLU
• approximately 70 for first iteration
• Each basin part of Hydrologic Climate Data
  Network (HCDN)
• Drainage area
• gt 50 km2
• lt 3000 km2

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Hydrologic Landscape Units (HLUs)

• Land surface form
• Climate
• geology

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Hydrologic Landscape Units (HLUs)
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Basin Selection
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Basin Selection
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GIS Weasel

• Simplifies the creation of spatial information
  for modeling
• Provides tools to
• Delineate
• Parameterize
• relevant spatial
  features

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GIS Weasel

• Still have to insert a nice plug for da weasel

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GIS WeaselExampleDelineationMethodology
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METHODOLOGY
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1. Data set compilation (temperature, precipitation,
   DEM, Q)
2. Basin delineation
3. GIS Weasel
4. XYZ parameterization

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Identify and calibrate the ET parameters by
comparing observed and simulated monthly mean
PET out of hydrologic model
Get a Water Balance Calibrate ET and climate
station choice
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Get a Water Balance Calibrate ET and climate
station choice Find best climate station sets
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METHODOLOGY
Developed at U. of AZ MOGSA Multi Objective
Generalized
Sensitivity Analysis
Determines parameter
sensitivity
Identify and optimize sensitive
parameters
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METHODOLOGY
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
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Peak/Timing
Baseflow
Quick recession
(See Boyle et al., WRR, 2000)
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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

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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

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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

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Timeline

• Dare we make these predictions?

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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

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Work Completed

• Hydrologic science modules assembled
• MOGSA MOCOM established

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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_at_usgs.gov lhay_at_usgs.gov
markstro_at_usgs.gov rviger_at_usgs.gov clark_at_vorticit
y.colorado.edu
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Thank You
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Climate Processing

• Need to be able to distribute
• From
• stations
• grid points
• To
• individual Modeling Response Unit (MRU)

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Climate ProcessingXYZ 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)
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Climate ProcessingStatistical 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

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Climate ProcessingDynamical 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