water cycle prediction at the regional scale: on the importance of being consistent
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Water cycle prediction at the regional scale: on the importance of being consistent. Vincent Fortin, Pierre Pellerin Meteorological Research Division. Al Pietroniro, André Méthot Meteorological Service of Canada. Canadian Meteorological Centre: more than tomorrow's weather!. - PowerPoint PPT PresentationTRANSCRIPT
Water cycle prediction at the regional scale: on the importance of being consistent
Vincent Fortin, Pierre PellerinMeteorological Research Division
Al Pietroniro, André MéthotMeteorological Service of Canada
Page 2 – April 20, 2023
Canadian Meteorological Centre:more than tomorrow's weather!
Page 3 – April 20, 2023
Applications of hydrological and hydrodynamic modelling
• Adaptive management of watersheds
• Optimization of hydropower production
• Flood warning
• Search and rescue
• Predicting impacts on habitat of changes in water level
• NWP and land-surface model verification
Page 4 – April 20, 2023
Coupled modelling system for hydrological prediction
GEMatmospheric
model
Page 5 – April 20, 2023
Coupled modelling system for hydrological prediction
GEMatmospheric
model
4DVAR/EnKFdata
assimilation
4DVAR/EnKFdata
assimilation
Page 6 – April 20, 2023
Coupled modelling system for hydrological prediction
Land-surface scheme(CLASS,
ISBA, SVS)
GEMatmospheric
model
4DVAR/EnKFdata
assimilation
4DVAR/EnKFdata
assimilation
Page 7 – April 20, 2023
Coupled modelling system for hydrological prediction
Land-surface scheme(CLASS,
ISBA, SVS)
GEMatmospheric
model
WATROUTErouting model
4DVAR/EnKFdata
assimilation
4DVAR/EnKFdata
assimilation
Page 8 – April 20, 2023
CaLDAS:EnKF data assimilation
CaLDAS:EnKF data assimilation
Coupled modelling system for hydrological prediction
Land-surface scheme(CLASS,
ISBA, SVS)
GEMatmospheric
model
WATROUTErouting model
4DVAR/EnKFdata
assimilation
4DVAR/EnKFdata
assimilation
Page 9 – April 20, 2023
Coupled modelling system for hydrological prediction
Land-surface scheme(CLASS,
ISBA, SVS)
GEMatmospheric
model
WATROUTErouting model
NEMO modelfor the ocean
and large lakes
4DVAR/EnKFdata
assimilation
4DVAR/EnKFdata
assimilation
CaLDAS:EnKF data assimilation
CaLDAS:EnKF data assimilation
Page 10 – April 20, 2023
Coupled modelling system for hydrological prediction• Components can be run either coupled or offline, with
prescribed forcings
Land-surface scheme(CLASS,
ISBA, SVS)
GEMatmospheric
model
WATROUTErouting model
NEMO modelfor the ocean
and large lakes
MESH:Modélisation Environnementale de laSurface et de l'Hydrologie
Page 11 – April 20, 2023
Why not simply drive surface and hydrology models with observations?
• Required observations are generally not all available
• Forecasting becomes nearly impossible
• Accuracy of short-term forecasts can approach or even surpasses that of observations
– snowfall observations
• Working within an integrated system makes it possible for hydrologists to actively contribute to the improvement of all components
Page 12 – April 20, 2023
Saskatchewan
NorthernTerritories
Toronto
Central Quebec
It works because weather forecasting is not so difficult• Landscape • Atmosphere
Central Quebec
Toronto
NorthernTerritories
Saskatchewan
Page 13 – April 20, 2023
Not only is weather forecasting easy, it is improving
Page 14 – April 20, 2023
Not only is weather forecasting easy, it is improving
• Major improvements to the data assimilation system
• The ISBA land-surface model replaces the force-restore scheme
• Major improvements to the data assimilation system
• The ISBA land-surface model replaces the force-restore scheme
Page 15 – April 20, 2023
GEM vs reanalysis products
• Many hydrologists already use reanalysis products (NCEP, NARR, MERRA, ERA-40, WATCH, Era-interim)
• For many applications where ~10 years or less of data is required, operational NWP outputs (e.g. GEM) provide higher resolution (up to 2.5 km for GEM HRDPS) and better skill (especially for surface variables)
• For short-term hydrological forecasting applications, past atmospheric forcings are used only to calibrate the hydrological model and obtain initial conditions
– NWP forecasts are required to obtain streamflow forecasts– by using the same data source for model calibration and
forecasting, we can bypass the NWP post-processing step
Page 16 – April 20, 2023
The Canadian Precipitation Analysis (CaPA) can be used to improve GEM precipitation
• Optimal interpolation technique used to merge gauges, radar and satellite data with a background provided by the GEM NWP model
• Fully automated quality control
• 6-h and 24-h accumulations
• North American domain
• 10 km resolution
• Early (T+1h) and late (T+7h) analyses
• Operational since April 2011 http://weather.gc.ca/analysis
24-h analysis valid 2014-08-15@12Z
Page 17 – April 20, 2023
Great Lakes / St. Lawrence testbed
• Demonstrate benefits of coupled numerical models
• WMO RFDP proposal in preparation
• Already included in:– Canada/Québec St.
Lawrence Action Plan (SLAP): Environmental prediction working group
– EC/NOAA MOU: close collaboration with the Great Lakes Environmental Research Laboratory
Superior
Michigan-Huron
Erie
Ontario
Page 18 – April 20, 2023
Coupled modelling systemfor the Great Lakes• Configuration used for recently published results
Land-surface schemesCLASS or
ISBAat 15 km
GEM RDPS 15 kmatmospheric model
2 integrations per day
WATROUTErouting model
at 15 km
2 km NEMO model for the Great Lakes
UU,VV,TT,HUP0,FB,FI,PR
Q,TQ
RFF,RCH
MESH:
Page 19 – April 20, 2023
Coupled modelling systemfor the Great Lakes• Configuration to be implemented operationnally
(sorry, no results to show yet):
Land-surface scheme
SVSat 2 km
GEM HRDPS 2.5 kmatmospheric model
4 integrations per day
WATROUTErouting model
at 1 km
2 km NEMO model for the Great Lakes
UU,VV,TT,HUP0,FB,FI,PR
Q,TQ
RFF,RCH
MESH:
Page 20 – April 20, 2023
Predicting net basin suppliesto Lake Superior with GEM+ISBA
• Overlake evaporation(-E)
• Net precipitation (P-E)
• Net basin supplies(NBS=P-E+R)
• Resid: residual calculation of NBS from lake levels obs. and lake outflow
Deacu et al. (2012)J. Hydromet.
World's largest lake by area:- Lake area: 82 000 km²- Watershed: 128 000 km²
Page 21 – April 20, 2023
Predicting net basin suppliesto the Great Lakes with GEM+ISBA
• REGN: from GEM model outputs at 15km
• GLERL LakeP: assessment by NOAA/GLERL from near-shore obs. of precip., temperature, humidity, wind and streamflow
• Resid: residual calculation from lake levels obs.
Deacu et al. (2012)J. Hydromet.
Page 22 – April 20, 2023
Simulating Great Lakes physical behaviour using GEM+NEMOWater level change [m] Surface temperature [C] Ice fraction
Surfacecurrrents [m/s]
Surfacetemperature [C]
Dupont et al. (2012) WQRJC
Page 23 – April 20, 2023
Streamflow simulation for subwatersheds (CLASS LSS)
Haghnegabar et al. (2014), Atmosphere-Ocean
Grand River at Iona, MI (4571 km2)
Black River at Watertown, NY (3000 km2)
(b)
(a)
Page 24 – April 20, 2023
How did we get there?
• Monitoring activities dedicated to improving the model
• Parsimonious landscape parameterizations
• Coordinated model development
Page 25 – April 20, 2023
Monitoring activities dedicated to improving the model
Research basins Flux towers
Page 26 – April 20, 2023
Parsimonious landscape parameterizations, calibrated parameters
• Grouped Response Units (Kouwen et al., 1993)
– identify important landscape features
– within a grid cell, only keep track of areal coverage of each GRU
– assign one parameter set to each GRU
• WATDRAIN hillslope model (Soulis et al., 2011)
– takes slope into account in land-surface, hydrology and atmospheric models
– influences runoff but also soil moisture and evaporation
Page 27 – April 20, 2023
Coordinated model development
• Working as an integrated team on atmospheric, hydrologic and ocean model development by sharing key components:
– land-surface model– turbulent flux calculations– computing infrastructure
• Using streamflow and water level observations for atmospheric prediction:– to verify NWP forecasts– to tune the water balance of land-surface schemes– eventually, to estimate deep soil moisture
• Assessing the impacts of improvements to one component on the environmental prediction system as a whole
Page 28 – April 20, 2023
Overlake evaporation prediction
• Deacu, Fortin et al. (2012), Journal of Hydrometeorology
Average latent heat flux, winter 2011 (W/m²)
GEM 15km GEM 10km OAFlux
Lake Superior supplies200
150
100
50
0
W/m²
Page 29 – April 20, 2023
Conclusions
• At the regional scale, feedbacks to the atmosphere cannot be ignored: if you are using an atmospheric model product for precipitation and you want to close the water balance using a hydrological model, then you should worry about evapotranspiration computed by the atmospheric model as well
• Hydrologists and meteorologists have much to gain by collaborating– high-resolution land-surface modelling and data assimilation systems developed
by the NWP community are evolving and improving quickly– land-surface models used by the NWP community often lack some basic
hydrological processes and need to be calibrated
• Be prepared:– NWP systems already provide forecasts of sufficient quality to drive hydrological
models for both hindcasting and forecasting at the regional scale– NWP systems will soon provide gridded runoff fields of comparable quality– running coupled models is becoming more and more affordable: water resources
engineers will soon be running such systems from their basement!
• Systems like MESH offer a good starting point