Streamflow prediction in River Rhine: Exploring combinations of bias-correcting  forcing  and bias-correcting  flow Jan Ve...
Motivation and research questions <ul><li>Biases/uncertainty in predicted  forcing  used for streamflow prediction: </li><...
Research design Raw forcing (T,P) Hydrologic model Raw  flow Ensemble verification B-C forcing (T,P) Hydrologic model Raw ...
 
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 <ul><li>The random variables (one time/location): </li></ul><ul><ul...
Bias-correction of temperature, precipitation and flow (2/2) <ul><li>Temperature </li></ul><ul><li>normal regression: line...
B-C: preservation of space-time dependencies <ul><li>How to parameterize dependence? </li></ul><ul><li>Space-time patterns...
<ul><li>Skill of T correction </li></ul><ul><li>CRPSS = “% gain” over raw forecast  </li></ul><ul><li>~20-60% gain </li></...
<ul><li>Skill of P correction </li></ul><ul><li>~20-30% gain </li></ul><ul><li>Faster drop after 24 hour lead time </li></ul>
<ul><li>Skill of P correction for > “1-in-10  </li></ul><ul><li>day” observed P amount </li></ul><ul><li>~small gain or lo...
<ul><li>Skill of S for T and P correction. </li></ul><ul><li>~-10% to +10% </li></ul>
<ul><li>Skill of S for > “1-in-10” day observed S, with T and P correction. </li></ul><ul><li>~-40% to +20% </li></ul><ul>...
Next steps <ul><li>Q1: “What is the signal from bias-correction of  forcing  in streamflow?”: </li></ul><ul><li>Some way t...
Questions? <ul><li>(slides available from slideshare.net/janverkade) </li></ul><ul><li>Contact: </li></ul><ul><li>jan.verk...
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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

    1. 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. 2. Motivation and research questions <ul><li>Biases/uncertainty in predicted forcing used for streamflow prediction: </li></ul><ul><li>NWP models are skillful, but biased (mean, spread,..) </li></ul><ul><li>This bias/uncertainty propagates from forcing to flow </li></ul><ul><li>Bias-correction of precipitation is complex </li></ul><ul><li>Ultimately, flow bias-correction is always needed </li></ul><ul><li>Key research questions: </li></ul><ul><li>What is the signal from bias-correction of forcing in streamflow? </li></ul><ul><li>Is this signal maintained after bias-correction of flow , i.e. is forcing correction needed? </li></ul>
    3. 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
    5. 6. Observed forcing data: E-OBS dataset Downloadable from KNMI @ http://eca.knmi.nl/
    6. 7. Bias-correction of temperature, precipitation and flow <ul><li>The random variables (one time/location): </li></ul><ul><ul><li>Predictand Y = observed temp/precip/flow. Assumed unbiased! </li></ul></ul><ul><ul><li>Potential predictors X = {X1,…,X5,…, Xm}; biased. </li></ul></ul><ul><li>The bias-corrected forecast: </li></ul><ul><li>How to parameterize for T and P? </li></ul><ul><ul><li>Parsimonious model (subject to skill!) </li></ul></ul><ul><ul><li>Model the statistical dependence (“traces”) </li></ul></ul>
    7. 8. Bias-correction of temperature, precipitation and flow (2/2) <ul><li>Temperature </li></ul><ul><li>normal regression: linear regression in normal space </li></ul><ul><li>Precipitation </li></ul><ul><li>logistic regression: linear regression in logistic space </li></ul><ul><li>Streamflow: </li></ul><ul><li>Krzysztofowicz approach: Hydrologic Uncertainty Processor </li></ul><ul><li>Prior: unconditional climatology </li></ul><ul><li>Posterior: distribution of flow conditional on ensemble mean </li></ul>
    8. 9. B-C: preservation of space-time dependencies <ul><li>How to parameterize dependence? </li></ul><ul><li>Space-time patterns of T and P </li></ul><ul><li>Cross-variable dependence in T and P </li></ul><ul><li>Critical for streamflow prediction </li></ul><ul><li>Empirical approach </li></ul><ul><li>Based on “Schaake shuffle” (Clark et al.) </li></ul><ul><li>Shuffle the bias-corrected ensemble members to preserve rank-ordering of the raw ensemble members </li></ul>
    9. 10. <ul><li>Skill of T correction </li></ul><ul><li>CRPSS = “% gain” over raw forecast </li></ul><ul><li>~20-60% gain </li></ul><ul><li>Gradual decline </li></ul><ul><li>with lead time </li></ul>
    10. 11. <ul><li>Skill of P correction </li></ul><ul><li>~20-30% gain </li></ul><ul><li>Faster drop after 24 hour lead time </li></ul>
    11. 12. <ul><li>Skill of P correction for > “1-in-10 </li></ul><ul><li>day” observed P amount </li></ul><ul><li>~small gain or loss </li></ul><ul><li>Failure of logistic regression to remove conditional bias (under-prediction of large P) </li></ul>
    12. 13. <ul><li>Skill of S for T and P correction. </li></ul><ul><li>~-10% to +10% </li></ul>
    13. 14. <ul><li>Skill of S for > “1-in-10” day observed S, with T and P correction. </li></ul><ul><li>~-40% to +20% </li></ul><ul><li>Loss of skill at long lead times. </li></ul><ul><li>Caution when “correcting” high P at long lead times! </li></ul>
    14. 15. Next steps <ul><li>Q1: “What is the signal from bias-correction of forcing in streamflow?”: </li></ul><ul><li>Some way towards answering that question </li></ul><ul><li>Need to establish why skilful forcing correction is not consistently translating into flow skill. </li></ul><ul><ul><li>Could it be due to the space-time and cross-variable dependence (“Schaake Shuffle”)? </li></ul></ul><ul><ul><li>Try Brown and Seo (2011) approach to conditional bias (bias-penalized kriging) </li></ul></ul><ul><li>Next, we’ll focus on Q2: </li></ul><ul><li>Is the signal from forcing bias-correction lost following flow bias-correction? </li></ul>
    15. 16. Questions? <ul><li>(slides available from slideshare.net/janverkade) </li></ul><ul><li>Contact: </li></ul><ul><li>jan.verkade@deltares.nl, twitter.com/janverkade </li></ul><ul><li>[email_address] </li></ul>

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