"streamflow prediction in river rhine: exploring combinations of bias-correcting forcing and...
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Streamflow prediction in River Rhine: Exploring combinations of bias-correcting forcing and bias-correcting flow
Jan Verkade (Deltares and Delft University of Technology)
James Brown (NOAA-NWS-OHD and UCAR)
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
Motivation and research questions
Biases/uncertainty in predicted forcing used for streamflow prediction:• NWP models are skillful, but biased (mean, spread,..)• This bias/uncertainty propagates from forcing to flow• Bias-correction of precipitation is complex• Ultimately, flow bias-correction is always needed
Key research questions:
1. What is the signal from bias-correction of forcing in streamflow?
2. Is this signal maintained after bias-correction of flow, i.e. is forcing correction needed?
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
Research design
Raw forcing (T,P)
Hydrologic model
Raw flow
Ensemble verification
B-C forcing (T,P)
Hydrologic model
Raw forcing (T,P)
Hydrologic model
Scenario 1 Scenario 2 Baseline
B-C streamflow
B-C streamflow
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
Data kindly provided by Florian Pappenberger @ ECMWF
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
Observed forcing data: E-OBS dataset
Downloadable from KNMI @ http://eca.knmi.nl/
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
Bias-correction of temperature, precipitation and flow
The random variables (one time/location):
• Predictand Y = observed temp/precip/flow. Assumed unbiased!
• Potential predictors X = {X1,…,X5,…, Xm}; biased.
The bias-corrected forecast:
How to parameterize for T and P?
• Parsimonious model (subject to skill!)
• Model the statistical dependence (“traces”)
yxXxXyYxxyF mmm ],...,|Pr[)|( 11,...,1
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
Bias-correction of temperature, precipitation and flow (2/2)
Temperature• normal regression: linear regression in normal space
Precipitation• logistic regression: linear regression in logistic space
Streamflow:• Krzysztofowicz approach: Hydrologic Uncertainty Processor• Prior: unconditional climatology• Posterior: distribution of flow conditional on ensemble mean
).)(,(]|Pr[ 10 yyY XXX
.1
1]|Pr[
10 XeyY X
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
B-C: preservation of space-time dependencies
How to parameterize dependence?• Space-time patterns of T and P• Cross-variable dependence in T and P• Critical for streamflow prediction
Empirical approach• Based on “Schaake shuffle” (Clark et al.)• Shuffle the bias-corrected ensemble members to preserve rank-
ordering of the raw ensemble members
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
• Skill of T correction
• CRPSS = “% gain” over raw forecast
• ~20-60% gain
• Gradual decline with lead time
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
• Skill of P correction
• ~20-30% gain
• Faster drop after 24 hour lead time
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
• Skill of P correction for > “1-in-10 day” observed P amount
• ~small gain or loss
• Failure of logistic regression to remove conditional bias (under-prediction of large P)
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
• Skill of S for T and P correction.
• ~-10% to +10%
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
• Skill of S for > “1-in-10” day observed S, with T and P correction.
• ~-40% to +20%
• Loss of skill at long lead times.
• Caution when “correcting” high P at long lead times!
December 8, 2011Verkade and Brown – Bias-correcting forcings and flow
Next steps
Q1: “What is the signal from bias-correction of forcing in streamflow?”:• Some way towards answering that question• Need to establish why skilful forcing correction is not consistently
translating into flow skill.
• Could it be due to the space-time and cross-variable dependence (“Schaake Shuffle”)?
• Try Brown and Seo (2011) approach to conditional bias (bias-penalized kriging)
Next, we’ll focus on Q2:• Is the signal from forcing bias-correction lost following flow bias-
correction?
Questions?
(slides available from slideshare.net/janverkade)
Contact:• [email protected], twitter.com/janverkade• [email protected]