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"Streamflow prediction in River Rhine: Exploring combinations of bias-correcting forcing and bias-correcting flow
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"Streamflow prediction in River Rhine: Exploring combinations of bias-correcting forcing and bias-correcting flow


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  • Basin: Rhine basin (approx. 160e3 km2), focus on Moselle sub-basin Available modeling system: HBV rainfall runoff model at daily time step Within a Delft-FEWS forecast production system (CHPS)
  • 3164 “issued” reforecasts between 1991 and 2010 1/10th-by-1/10th degree 30 days leadtime  we use 10 days only
  • E-OBS is a daily gridded observational dataset for precipitation, temperature and sea level pressure in Europe. The full dataset covers the period 1950-01-01 until 2011-06-30. It has originally been developed as part of the ENSEMBLES project (EU-FP6) and is now maintained and elaborated as part of the EURO4M project (EU-FP7).
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    • 1. 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)
    • 2. 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:
      • What is the signal from bias-correction of forcing in streamflow?
      • Is this signal maintained after bias-correction of flow , i.e. is forcing correction needed?
    • 3. 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
    • 4.  
    • 5. Data kindly provided by Florian Pappenberger @ ECMWF
    • 6. Observed forcing data: E-OBS dataset Downloadable from KNMI @
    • 7. 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”)
    • 8. 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
    • 9. 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
    • 10.
      • Skill of T correction
      • CRPSS = “% gain” over raw forecast
      • ~20-60% gain
      • Gradual decline
      • with lead time
    • 11.
      • Skill of P correction
      • ~20-30% gain
      • Faster drop after 24 hour lead time
    • 12.
      • 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)
    • 13.
      • Skill of S for T and P correction.
      • ~-10% to +10%
    • 14.
      • 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!
    • 15. 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?
    • 16. Questions?
      • (slides available from
      • Contact:
      • [email_address]