"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).

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

  • 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)
  • 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?
  • 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
  •  
  • Data kindly provided by Florian Pappenberger @ ECMWF
  • Observed forcing data: E-OBS dataset Downloadable from KNMI @ http://eca.knmi.nl/
  • 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”)
  • 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
  • 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
    • Skill of T correction
    • CRPSS = “% gain” over raw forecast
    • ~20-60% gain
    • Gradual decline
    • with lead time
    • Skill of P correction
    • ~20-30% gain
    • Faster drop after 24 hour lead time
    • 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)
    • Skill of S for T and P correction.
    • ~-10% to +10%
    • 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!
  • 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:
    • jan.verkade@deltares.nl, twitter.com/janverkade
    • [email_address]