Seasonal Hydrometeorological Ensemble Prediction System: Forecast of Irrigation Potentials in Denmark

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Seasonal Hydrometeorological Ensemble Prediction System: Forecast of Irrigation Potentials in Denmark Diana Lucatero 1*, Henrik Madsen 2, Karsten H. Jensen 1, Jens C. Refsgaard 3, Jacob Kidmose 3 1 University of Copenhagen, Copenhagen, Denmark 2 DHI, Horsholm, Denmark 3 GEUS, Copenhagen, Denmark HEPEX 10th Anniversary Workshop 26 June 2014 Maryland, USA Dias 1

OUTLINE HydroCast Project and Seasonal Forecasting Irrigation permissions in Denmark ECMWF Seasonal meteorological EPS System 4 For the near future Summary and Conclusions Dias 2

HydroCast Project and Seasonal Forecasting Objective: Develop and test new forecasting and DA tools that combine different data sources with meteorological and hydrological modelling to provide probabilistic hydrological forecasts Dias 3

Irrigation permissions in Denmark How are the extraction for irrigations permits now defined? Impact of -10% on median of yearly minimum streamflow Issued for 15 years Areas where extraction must be reduced due to exceeded impact on streamflow How are they expecting to define them? Dias 4

ECMWF Seasonal meteorological EPS System 4 TIME AVAILABILITY - 20 years reforecast data, 1990-2013 - Forecast every month (01/MM/YYYY) - 7 month forecast FIELDS AVAILABLE 167 2 meter temperature, 6h, (deg K). Solar Radiation 228 Precipitation, 24 h, accumulated (mm). ENSEMBLES AVAILABLE 51 members (02,05,08,11) 15 members (rest) 24 April 2014 DMI, Copenhagen Dias 5

ECMWF Seasonal meteorological EPS System 4 EPS -0.75 deg, -80 km northing (approx) Observations 10 km - Precipitation rate (mm/day) -Temperature (deg C) -Reference Evapotranspiration (mm/day) 24 April 2014 DMI, Copenhagen Dias 6

ECMWF Seasonal meteorological EPS System 4: Downscaling, bias correction Linear Scaling of precipitation and temperature (Teutschbein and Seibert, 2012) -> Corrects biases on the mean -> Simplest approach, straightforward to implement 24 April 2014 DMI, Copenhagen Dias 7 -> Monthly mean of daily precipitation, observed values m = 1, 2,..., 12 -> Monthly mean of daily precipitation, forecast values -> Raw forecast of daily precipitation -> Corrected forecast of daily precipitation

ECMWF Seasonal meteorological EPS System 4: PRECIPITATION Jan Feb Mar Apr May Jun Jul Aug Dias 8 Sep Oct Nov Dec

ECMWF Seasonal meteorological EPS System 4: TEMPERATURE Jan Feb Mar Apr May Jun Jul Aug Dias 9 Sep Oct Nov Dec

ECMWF Seasonal meteorological EPS System 4: Skill assessment Reliability Diagrams Brier Skill Score -> BSsys4 vs Bsclim Climatology -> 20 year observed data Verification experiments -Verification A: Raw vs corrected ensembles, different lead times - Verification B: Corrected ensembles, different seasons Probability thresholds -> a) <= below T b) >= above T (dry, cold) (wet, warm) - Verification C: Corrected ensembles, different regions in Denmark (west, east) Dias 10

BSS BSS Department of Geography & Geology ECMWF Seasonal meteorological EPS System 4: Skill assessment PCP uncorrected PCP corrected 0.05 0 BSS Brier Skill Score for ECMWF S4 corrected not corrected -0.05-0.1-0.15-0.2 1 2 3 4 5 6 7 Lead time (Month) T uncorrected T corrected 0.4 0.3 0.2 BSS Brier Skill Score for ECMWF S4 corrected not corrected 0.1 0-0.1-0.2-0.3-0.4 Dias 11-0.5-0.6 1 2 3 4 5 6 7 Lead time (Month)

ECMWF Seasonal meteorological EPS System 4: Skill assessment, corrected PCP dry PCP wet T cold T warm Dias 12

For the near future Meteo Bias + Skill assessment Hydro modelling Risk Analysis Decision making for irrigation permissions -Other sophisticated bias correction methods? Dias 13

Conclusions and Summary Assessment of meteorological inputs: - Improvement of bias corrected ensembles - Good skill for the first month lead time, decreases as time evolves - Good skill for winter and spring precipitation, not so much for summer and autumn - Good skill for temperature, except warm temperatures during summer (further investigation) Combination of skillful meteo inputs + better initial conditions for better forecasting - Analysis of the influence of both How do we translate probabilistic information about discharge and groundwater levels into efficient irrigation permit amounts? - Decision making process - Meeting and continuous feedback between the researh team and with potential users Dias 14

Thank you! QUESTIONS? Dias 15

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