Long-lead streamflow forecasts: 2. An approach based on ensemble climate forecasts

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Long-lead streamflow forecasts 2. An approach
based on ensemble climate forecasts
Andrew W. Wood, Dennis P. Lettenmaier, Alan .F.
Hamlet University of Washington NWS/OGP
Climate Prediction Assessments Workshop Alexandria
, VA Oct, 2002
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Overview

• Research Objective
• To produce monthly to seasonal snowpack,
  streamflow, runoff soil moisture forecasts for
  continental scale river basins
• Underlying rationale/motivation
• Global numerical weather prediction / climate
  models exploit SST atmosphere teleconnections
• Hydrologic models add soil-moisture / snowpack
  influence on future hydrologic conditions and
  streamflow (persistence)

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Previous Experimental Applications
Columbia River Basin Summer 2001 drought
East Coast, Summer 2000 drought
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UW Experimental West-wide hydrologic prediction
system

• climate model T P output
• NCEP, NSIPP, CCM, MPI
• ESP as baseline
• (note not using official tercile forecasts, yet)

Real-time Ensemble Forecasts

1. Downscaling
2. VIC hydrologic simulations

Ensemble Hindcasts (for bias-correction and
preliminary skill assessment)

• West-wide forecast products
• streamflow
• soil moisture, snowpack
• tailored to application sectors
• fire, power, recreation

current work
ESP extended streamflow prediction (unconditiona
l climate forecasts run from current hydrologic
state)
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Simulations
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Bias Example NCEPGlobal Spectral Model (GSM)
Bias is removed at the monthly GSM-scale from the
meteorological forecasts
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GSM Bias Example (cont)for one cell over Ohio
River basin
biases in monthly Precip Temperature can be too
large for use as hydrologic simulation inputs
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UW climate model downscaling and bias adjustment
approach

1. bias correction - percentile-based mapping of
   model output to climate model-scale observations
   (i.e., spatially averaged, temporally aggregated)
   
2. downscaling - interpolation of monthly anomalies
   to 1/8 degree, application to long term 1/8
   degree observed means
3. disaggregation by resampling observed daily
   sequences

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GSM Bias Example (cont)after procedure, most
monthly biases removed
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Comparison with Dynamical Downscaling
PCM DOE Parallel Climate Model (2.8 degree
resolution) RCM PNNL Regional Climate Model
(1/2 degree resolution)
used HISTORICAL climate scenario from PCM, for
20 year period
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Downscaling Method Comparisons
Domain and Model resolutions
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Downscaling Method Comparisons
Precipitationdownscaled vs. observed(1975-1995
averages)
Methods PCM vs RCM I Interpolation SD
Statistical (Spatial) Downscaling
alone BCSD Bias-correction and SD
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Downscaling Method Comparisons
Temperaturedownscaled vs. observed(1975-1995
averages)
OBS
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Downscaling Method Comparisons Columbia River
Basin Averages (1975-95)
hydrology based on downscaled vs.
observed (1975-1995 averages)
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Downscaling Method Comparisons Streamflow
based ondownscaled vs. observed P
T(1975-1995 averages)
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Summary Comments about Approach

• Climate-hydrology forecast model system has
  potential
• only if model biases are addressed
• should be compared with current forecast
  practices, and with other experimental approaches
• performs as well as dynamical downscaling
  approach, and is simpler to implement
• Critical needs
• access to quality met data during spin-up period
• ability to demonstrate / assess skill
  quantitatively, hopefully aided by what we learn
  from retrospective assessments (hindcasts)

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Sample Results from Recent Work

• Current Objectives
• Implement climate-hydrology model forecast
  system over western U.S. domain
• Assess skill of approach with respect to
  traditional standards such as ESP and
  climatology, using retrospective analysis

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Recent results hindcast analysis

• Columbia R. Basin
• basin-averaged anomalies
• GSM 6-month hindcasts
• JAN initiation date
• (shown last 5 years of
• retrospective analysis period)

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Recent results streamflow
RMSE-Skill Score JAN forecast of FEB-JUL flow

• Columbia R. Basin hindcast analysis
• GSM- and ESP-derived ensembles
• for 1979-1999, all years
• using RMSE-skill score wrt.
• climatology
• Results
• Both ensembles show skill (from initial
  conditions), but ESP outperforms GSM in most
  locations
• (in figure, larger circle higher skill)
• Explanation
• Poor precipitation simulation
• in GSM JAN forecasts

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THE FOLLOWING SLIDES MAY BE OF INTEREST DEPENDING
WHAT YOU WANT TO DISCUSS
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NRCS Collaboration

• with WCC-NRCS-USDA
• Comparison of retrospective forecast performance
  for
• 30 Basins
• Forecast Target Period
• Arizona Jan-May
• Central Apr-Jul
• North Apr-Sept
• Issue dates
• 1st of Jan, Feb, Mar, Apr

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Continuing ResearchDevelop a framework for use
with ESP, multiple models
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Progress and schedule
task current FALL 2002 2003
domain Columbia (CRB) California Colorado, Great Basin, Rio Grande
hindcast ensemble analysis NCEP, ESP NCEP, ESP NSIPP, CCM, MPI
real time ensemble forecast NCEP, ESP, NSIPP, CCM, MPI
multi-model ensemble test for CRB, NCEPESP all domain / all models
official tercile forecasts NCEP, (ESP), NSIPP, CCM, MPI
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Experimental Forecasting ApproachDownscaling-Dis
aggregationTest