The hydrological forecasting platform GEM-Hydro within the … · 2020-05-13 · The hydrological...

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The hydrological forecasting platform GEM-Hydro within the GWF project V. Vionnet 1,2 , V. Fortin 2 and M. Dimitrijevic 2 1 Centre for Hydrology, University of Saskatchewan, Canada 2 Meteorological Research Division, Environment and Climate Change Canada

Transcript of The hydrological forecasting platform GEM-Hydro within the … · 2020-05-13 · The hydrological...

Page 1: The hydrological forecasting platform GEM-Hydro within the … · 2020-05-13 · The hydrological forecasting platform GEM-Hydro within the GWF project V. Vionnet1,2, V. Fortin2 and

The hydrological forecasting platform GEM-Hydro

within the GWF project

V. Vionnet1,2, V. Fortin2 and M. Dimitrijevic2

1 Centre for Hydrology, University of Saskatchewan, Canada2Meteorological Research Division, Environment and Climate Change Canada

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Outline

• GEM-Hydro: a forecasting system based on coupled numerical models for water cycle prediction

• Example: Great Lakes and St. Lawrence watershed

• GEM-Hydro for GWF

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GEM NWP system

• Global 25-km 240-h forecasting system

• North American 10-km 48-h forecasting system

• Canadian 2.5-km48-h forecasting system

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Atmosphericmodel: GEM

Water-surface model

Land-surfacemodelGEM-Surf

GEM-based

surface prediction system

Precip.

Evap.ISBAorSVS

offline mode

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Atmosphericmodel: GEM

Water-surface model

Land-surfacemodel

Routingmodel

GEM-

Hydro

GEM-based

hydrological prediction system

Precip.

Evap.

Runoff

ISBAorSVS

offline mode

WATROUTE

Gaborit et al. (2017, HESS, Accepted); Durnford et al. (2017, BAMS, Accepted)

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Advantages of using coupled

environmental models

• Accuracy and consistency gains:

– Effects of interactions between the atmosphere and the surface (lakes, land, …)

– Evaporation and evapotranspiration are the same in the atmosphere, hydrology and lake models

• Efficiency gains:

– Latency: environmental predictions are available at the sametime as the weather forecast

– Robustness: 24/7 support at almost no additional cost– Maintenance: a single land-surface model to maintain

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WATROUTE

routing model (1km)

GEM LAM (10 km)

atmospheric model

(ISBA land-surface scheme)

NEMO+CICE (2 km)

ocean-ice model

GEM RDPS (10 km)

atmospheric model

NEMO+CICE (2 km)

ocean-ice model

over the Great Lakes

Data assimilation

system (EnVAR)

Water Cycle Prediction System for

the Great Lakes and St. Lawrence

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WATROUTE

routing model (1km)

GEM LAM (10 km)

atmospheric model

(ISBA land-surface scheme)

NEMO+CICE (2 km)

ocean-ice model

over the Great Lakes

In production since

June 2016:

• 2 runs per day

• 3.5 day forecasts

Water Cycle Prediction System for

the Great Lakes and St. Lawrence

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Streamflow analysis cycle2017-06-22 06Z - 2017-06-24 06Z

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Streamflow analysis cycle2017-06-22 06Z - 2017-06-26 06Z

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GEM-Hydro within GWF

• GEM-Hydro over Southern Alberta

• Region strongly impacted by the June 2013 flood

• Many challenges for hydrology including upstream mountainous region

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Operational configuration

• 1-km grid spacing domain covering South Alberta

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Operational configuration

• 1-km grid spacing domain covering South Alberta

• Includes South-Saskatchewan river watershed

• Runs:– Coupled: GEM 1km

(-> 48h)– Offline: Ensemble

(REPS) 10->1 km (-> 96h)

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Experimental configuration

• 1-km grid spacing domain covering South Alberta

• 250-m grid spacing domain covering the Bow river

watershed

• Focus on mountain

hydrology1-km domain

250-m domain

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Experimental configuration

• 1-km grid spacing domain covering South Alberta

• 250-m grid spacing domain covering the Bow river

watershed

• Runs:– Coupled: GEM 250

m (-> 24h)– Offline: GEM 1->

0.25 km (-> 48h)

250-m domain

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Experimental configuration

A configuration to tackle major challenges in mountain

hydrology:

• Precipitation: – Spatial variability and phase change: GEM 1km, 250 m – Precipitation analysis in mountainous terrain: CaPA 1km

• Snowpack: – Model complexity (single- and multi-layer schemes in SVS)– Subgrid spatial variability (coll. Coldwater Lab.)– Data assimilation (SWE, SCA, …)

• 1st test : Rain on Snow Event during the June 2013 flood

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Thank you for your attention!

Questions?