r&d plans for water cycle prediction, hydrological modelling … - ec...

R&D Plans for Water Cycle Prediction,

Hydrological Modelling and Forecasting

Vincent Fortin

Recherche en prévision numérique environnementale

Meteorological Research Division

Current team

Research scientists

• Vincent Fortin

• Murray Mackay

Research assistants

• Dominique Bourdin

• Milena Dimitrijevic

• Dorothy Durnford

• Sarah Dyck

Meteorologist

• Guy Roy

Post-docs

• Nasim Alavi (NSERC VF)

• Darwin Brochero (U. Laval)

• Christopher Subich (NSERC VF)

Graduate students

• Mabrouk Abaza (U. Laval)

• Kian Abbasnezhadi (U. Manitoba)

• Haesun Choi (U. Manitoba)

• Marie Courbariaux (AgroParisTech)

• Bruce Davison (McGill)

• Bruce Friesen (U. Manitoba)

Main Acronyms and Model Names

• CaPA: Canadian Precipitation Analysis

• CaLDAS: Canadian Land Data Assimilation System

• WATROUTE: not an acronym, but a routing model

• Land surface schemes:

– CLASS: Canadian Land Surface Scheme

– sloped CLASS: CLASS + horizontal flow parameterization

– ISBA: Interaction Soil-Biosphère-Atmosphère

– SVS: Soil and Vegetation Simulator

• SPS: CMC’s surface prediction system (LSS driver)

• MESH: Modélisation Environnementale – Surface et Hydrologie =

SPS + WATROUTE

• standalone MESH: sloped CLASS + WATROUTE in a standalone

application (no dependencies on CMC, runs on Linux and Windows)

R&D plan for

numerical hydrological prediction

• Continue to improve the Canadian Precipitation Analysis

• Rely on SPS platform for operational applications

• Take advantage of other CMC products:

– CaLDAS: soil moisture and temperature, snow depth, SWE

– High-resolution deterministic surface prediction system

– Meteorological ensemble prediction system

• Introduce more hydrology in land-surface models used

for weather forecasting (coupled modelling)

• Assimilate streamflow observations

• Provide boundary conditions:

– for NEMO ocean model (Great Lakes, Gulf, oceans)

– for H2D2 river hydrodynamic model

Outline

• Canadian Precipitation Analysis (CaPA)

• Hydrological modelling infrastructure

• Hydrological modelling R&D

• Hydrological forecasting R&D

• Water cycle prediction R&D

CaPA precipitation analysis system

• Near real-time quantitative precipitation estimation (QPE)

• Merges different sources of information on precipitation

• Uses background field from the GEM NWP model due to low

network density in most of Canada

• Optimal interpolation technique (aka residual kriging)

• Fully automated quality control algorithm

Surface network

Atmospheric model

Satellite

observations

RADAR

– 22 January 2014

24h precipitation for Sept. 18, 2013

Analysis

– 22 January 2014

Verification over June-August 2013

• Radar vs no radar, Prairies

CaPA priorities

• Assimilation of radar QPE

• Real-time bias correction of solid precipitation

measurements

• Assimilation of climate stations and cooperative

networks

Hydrological Modelling

Infrastructure

• Based on the coupling of existing numerical models

• Major components:

– land-surface model

– surface and

subsurface flow

parameterization

– simple river and

lake routing model

– bucket model

for groundwater

– efficient driver for

large grids

Land-surface

sheme

GEM

atmospheric

model

Routing model

Coupling with

GEM can be

one-way (offline)

or two-wayMESH

- 11 -

Quick comparison of

standalone MESH vs SPS+WATROUTE

Characteristic Standalone MESH SPS

LSS Sloped CLASS 3.6, PDMROF, PBSM,

Preliminary infiltration into frozen soils

Sloped CLASS 3.6, ISBA, SVS, VIC

Routing WATROUTE WATROUTE

Platforms Any recent Linux or Windows platform Linux and IBM P7 (only runs at CMC for now,

remote access is possible)

Forcing Can use CMC hourly forcing (GEM and

CaPA) or data from other sources, but

all data needs to be interpolated to the

model grid

Uses CMC operational products at their native

resolution (built-in spatial and temporal

interpolation and downscaling), as long as they are

in RPN standard file format

Land-surface

parameters

All parameters are in config files, can

be calibrated using external software,

e.g. Ostrich

Many parameters are found in look-up tables, can

be changed but code needs to be recompiled

Topology for routing SHED file, typically obtained using

Green Kenue

SHED file, typically obtained by processing

HydroSHEDS data (SRTM based)

Scalability Runs on a single CPU Highly scalable, MPI support for both

computations and I/O

Tiling Built-in support for arbitrary tiles Predefined tiles (vegetated, glacier, open water,

frozen water, urban)

Priorities for Hydrological Modelling

Infrastructure

• Validation of sloped CLASS 3.6 implementation in SPS

against standalone MESH

• Merging standalone MESH and SPS development

streams

• First operational implementation over the Great Lakes

• Coupling of SPS to HYDROTEL, as part of the St.

Lawrence Action Plan

- 13 -

Hydrological Modelling R&D

- 14 -

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 supplies

200

150

100

50

0

W/m²

- 15 -

Streamflow prediction, Great Lakes

(sloped CLASS + WATROUTE)

Haghnegahdar, A., B. Tolson, B. Davison, F. Seglenieks, E. Klyszejko, E.D. Soulis, V. Fortin and L.S. Matott (2013). Calibrating

Environment Canada's MESH Modelling System over the Great Lakes Basin. Atmosphere-Ocean, submitted for publication.

- 16 -

Great Lakes Runoff Intercomparison

Project - Lake Michigan (GRIP-M)

- 17 -

Priorities for


• Imroving the hydrology in SVS, largely based on

CLASS’s GRDRAN and WATROF subroutines

• Stream temperature modelling

• Great Lakes Runoff Intercomparison Project

Hydrological

Forecasting R&D

• Prévision d'ensemble du débit émise le 30-09-2010,

Rivière Chaudière à Saint-Lambert (20 membres)

enveloppe de l'ensemble

membre de l'ensemble

débit obserbé

ensemble forecast

observed total rainfall x

tiré de Abaza et al. (2012)

GEM est en retard mais

fini par avoir la bonne quantité

La prévision de débit montre le même retard mais permet quand même

d'émettre une alerte d'inondation avec une probabilité de 70%

inond. mineure: ~50%

risque d'inondation majeure

le 2 octobre: ~5%

inondation moyenne: ~15%

EnKF performance vs human expert

• Rivière au Saumon

Ass. manuelle par un expert

Aucune assimilation des débits



Ass. auto. des débits (EnKF)



Pour des prévisions 36h à l’avance ou plus,

le système automatique est aussi efficace qu’un humain

Priorities for Hydrological

Forecasting R&D

• Monthly ensemble forecasting system for the Great

Lakes and St. Lawrence (~1500 km² / 30 days / 20

members)

• High-resolution deterministic forecasting system for the

Pacific Cordillera (~1 km² / 2-3 days)

• Ensemble Kalman Filter for streamflow assimilation

Water Cycle Prediction R&D

H2D2: Suivi des niveaux, des

masses d'eau et des polluants

MESH:

débits en rivière

GEM:

conditions atmosphériques

NEMO: température, courants

niveau et couvert de glace des lacs

– 22 January 2014

Hydrological prediction using MESH:

International Upper Great Lakes Study

Monthly water supply to Lake Superior Jun 04 – May 09

Impact of lakes on the weather:

Winter precipitation – DJF 05-09

Models improved

thanks to monitoring

of evaporation

mm

/day

MESH prediction (P-E+Runoff)

Observations Loss through evaporation (-E)

Net precipitation (P-E)

- 24 -

Prediction of monthly NBS

P

E

R

- 25 -

- 26 -

Current areas where CMC’s MESH

system is being implemented

- 27 -

Longer term developments:

the Arctic basin

from Gustafsson et Isberg (2013)

Priorities for


• Deterministic two-way coupled atmosphere, surface,

hydrology, lake, and ice forecasts

• Daily analysis of components of the water cycle for the

Great Lakes and St. Lawrence (precipitation,

evaporation, soil moisture, snow water equivalent,

runoff, streamflow)

• Canada-wide assessment of these components

• Runoff prediction for the Arctic basin

Canadian Precipitation Analysis (CaPA)

Vincent Fortin



CaPA precipitation analysis system

• Near real-time quantitative precipitation estimation (QPE)

• Merges different sources of information on precipitation

• Uses background field from the GEM NWP model due to low

network density in most of Canada

• Optimal interpolation technique (aka residual kriging)

• Fully automated quality control algorithm

Surface network

Atmospheric model

Satellite

observations

RADAR

– 22 January 2014


Observations

– 22 January 2014


Observations + radar

– 22 January 2014


Analysis

– 22 January 2014


Forecast

– 22 January 2014


Analysis

– 22 January 2014


• Radar vs no radar, Canada-wide

– 22 January 2014



A more quantitative look at bias

Carrera, Marco L., Stéphane Bélair, Vincent Fortin, Bernard Bilodeau, Dorothée Charpentier, Isabelle Doré, 2010: Evaluation of

Snowpack Simulations over the Canadian Rockies with an Experimental Hydrometeorological Modeling System. J. Hydrometeor, 11,

1123–1140.

ANG vs Nipher or RCS obs.

Winter 2010-2011, Prairies Equitable threat score Partial mean

Assimilation of

bias-corrected

observations

(Nipher+Geonor):

Assessment of

bias and skill

largely depends

on what is chosen

as “truth”

GEM

CaPA

Obs (Nipher)

ANG

vs Nipher

ANG

vs RCS

New C-SPICE site at Forêt

Montmorency Observations émises le 21-01-14 à 7h

Précipitations reçues Neige (cm) Pluie (mm)

Depuis le dernier relevé 0 0

Totales* 252 118

Présente au sol 90 --

* Depuis le 30/10/13

Hydrological modelling infrastructure

Vincent Fortin



Hydrological modelling

infrastructure

• Based on the coupling of existing numerical models

• Major components:

– land-surface model

– surface and

subsurface flow

parameterization

– simple river and

lake routing model

– bucket model

for groundwater

– efficient driver for

large grids

Land-surface

sheme

GEM

atmospheric

model

Routing model

Coupling with

GEM can be

one-way (offline)

or two-wayMESH

- 44 -

MESH

- 45 -

MESH

SPS

WATROUTE

- 46 -

SPS: Surface Prediction System

- 47 -

Quick comparison of

standalone MESH vs SPS+WATROUTE

Characteristic Standalone MESH SPS

LSS Sloped CLASS 3.6, PDMROF, PBSM,

Preliminary infiltration into frozen soils

Sloped CLASS 3.6, ISBA, SVS, VIC

Routing WATROUTE WATROUTE

Platforms Any recent Linux or Windows platform Linux and IBM P7 (only runs at CMC for now,

remote access is possible)

Forcing Can use CMC hourly forcing (GEM and

CaPA) or data from other sources, but

all data needs to be interpolated to the

model grid

Uses CMC operational products at their native

resolution (built-in spatial and temporal

interpolation and downscaling), as long as they are

in RPN standard file format

Land-surface

parameters

All parameters are in config files, can

be calibrated using external software,

e.g. Ostrich

Many parameters are found in look-up tables, can

be changed but code needs to be recompiled

Topology for routing SHED file, typically obtained using

Green Kenue

SHED file, typically obtained by processing

HydroSHEDS data (SRTM based)

Scalability Runs on a single CPU Highly scalable, MPI support for both

computations and I/O

Tiling Built-in support for arbitrary tiles Predefined tiles (vegetated, glacier, open water,

frozen water, urban)

- 48 -

A new community modelling

paradigm for MESH

• Now • Vision

MSC MSC


Vincent Fortin



- 50 -

Improving the modelling of

hydrological processes in SVS

- 51 -

Parameterization for field capacity

and lateral flow

• Soulis, Craig, Fortin et Liu (2011), Hydrol. Proc.

WATDRAIN

Field capacity at 4 feet, estimated or measured

Fie

ld c

ap

acity p

red

icte

d

- 52 -

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 supplies

200

150

100

50

0

W/m²

- 53 -

Carrera et al., 2010: Evaluation of

Snowpack Simulations over the Canadian

Rockies with an Experimental

Hydrometeorological Modeling System. J.

Hydrometeor, 11, 1123–1140.

Impact of horizontal resolution

SWE evaluation vs

snow pillow data

• Grid size matters

• Simple downscaling

helps (e.g. constant

lapse-rate)

- 54 -

Use of hydrological observations

for model verification

Bow River at Calgary

mean annual flow (▀)

confirms results

obtained using

precipitation gauges

and snow pillows

Carrera et al., 2010: Evaluation of

Snowpack Simulations over the Canadian

Rockies with an Experimental

Hydrometeorological Modeling System. J.

Hydrometeor, 11, 1123–1140.

- 55 -

Streamflow prediction, Great Lakes

(sloped CLASS + WATROUTE)

Haghnegahdar, A., B. Tolson, B. Davison, F. Seglenieks, E. Klyszejko, E.D. Soulis, V. Fortin and L.S. Matott (2013). Calibrating

Environment Canada's MESH Modelling System over the Great Lakes Basin. Atmosphere-Ocean, submitted for publication.

- 56 -

Great Lakes Runoff Intercomparison

Project - Lake Michigan (GRIP-M)

Hydrological Forecasting R&D

Vincent Fortin



Prévision d'ensemble

• Prévision d'ensemble du débit émise le 30-09-2010,

Rivière Chaudière à Saint-Lambert (20 membres)

enveloppe de l'ensemble

membre de l'ensemble

débit observé

ensemble forecast

observed total rainfall x

tiré de Abaza et al. (2012)

GEM est en retard mais

fini par avoir la bonne quantité

La prévision de débit montre le même retard mais permet quand même

d'émettre une alerte d'inondation avec une probabilité de 70%

inond. mineure: ~50%

risque d'inondation majeure

le 2 octobre: ~5%

inondation moyenne: ~15%

Ensemble vs

deterministic forecasting

• CRPS vs MAE

Velázquez, J.A., Th. Petit, A. Lavoie, M.-A.

Boucher, R. Turcotte, V. Fortin et F. Anctil (2009).

An evaluation of the Canadian global

meteorological ensemble prediction system for

short-term hydrological forecasting, Hydrology and

Earth System Sciences, 13: 2221-2231.



Ass. auto. des débits (EnKF)



Pour des prévisions 36h à l’avance ou plus,

le système automatique est aussi efficace qu’un humain


Vincent Fortin



Great Lakes Environmental Prediction System Deacu, D., V. Fortin, E. Klysejko, C. Spence et P.D. Blanken (2012). Predicting the Net Basin Supply of the Great Lakes

with a Hydrometeorological Model. Journal of Hydrometeorology, 13(6): 1739-1759.

Dupont, F., P. Chittibabu, V. Fortin, Y.R. Rao et Y. Lu (2012). Assessment of a NEMO-based hydrodynamic modelling

system for the Great Lakes. Water Quality Research Journal of Canada, 47(3-4): 198-214.

H2D2: Suivi des niveaux, des

masses d'eau et des polluants

MESH:

débits en rivière

GEM:

conditions atmosphériques

NEMO: température, courants

niveau et couvert de glace des lacs

- 64 -

– 22 January 2014

Hydrological prediction using MESH:

International Upper Great Lakes Study

Monthly water supply to Lake Superior Jun 04 – May 09

Impact of lakes on the weather:

Winter precipitation – DJF 05-09

Models improved

thanks to monitoring

of evaporation

mm

/day

MESH prediction (P-E+Runoff)

Observations Loss through evaporation (-E)

Net precipitation (P-E)

- 66 -

Prediction of monthly NBS

P

E

R

- 67 -

- 68 -

Current areas where CMC’s MESH

system is being implemented

- 69 -

Longer term developments:

the Arctic basin

from Gustafsson et Isberg (2013)

- 70 -

Arctic-HYPE

Model performance (NSE, monthly average)

WATCH Forcing Data (ERA 40 corrected with CRU+GPCC ) 1961-1988

NSE = tradeoff between variance,

correlation and bias

- 71 -

Arctic-HYPE

Arctic and Cold Regions challenges

Many lakes in some areas

Permafrost landscape

Palsa bogs

Inland sinks

Meandring and

human activity

Greenland still no routing

Ice plugs

Glaciers

Winter

evaporation

Generating Geophysical files for

SPS+hydrology

CCRN meeting

Montreal

January 21, 2014

Dorothy Durnford, Vincent Fortin

– 22 January 2014

Process overview

1. Generation of regular fields

– SPS

2. Generation of extra fields

– hydrology

• Single script calculates 1) and/or 2)

– 22 January 2014

1. Generation of SPS fields

• A. Preparation of domain’s grid

• B. Generation of SPS geophysical fields

– 22 January 2014

1A. Preparation of domain’s grid

• From gem_settings.nml

• Requires grille

– specified version of GEM

• Few minutes

– 22 January 2014

1A. gem_settings.nml:

Great Lakes/St Lawrence at 2 arcmin

&grid

Grd_typ_S = 'LU’,

Grd_ni = 795,

Grd_nj = 420,

Grd_iref = 1,

Grd_jref = 1,

Grd_latr = 39.0,

Grd_lonr = -94.5,

Grd_dx = 0.0333332,

Grd_dy = 0.0333332,

/

start of grid description

lat-lon grid

# points: x-direction

# points: y-direction

ref. point: x-direction index

ref. point: y-direction index

ref. point: latitude

ref. point: longitude

grid spacing (deg): x-direction

grid spacing (deg): y-direction

end of grid description

– 22 January 2014

1B. Generation of SPS geophysical

fields

• 4 categories:

– topography

– masks

– vegetation

– soils

• Multiple databases

– resolutions from 1 km

– covering Canada to global

– Can combine datasets with specified priority

• Requires GenPhysX, ARMNLIB and CMC databases

• Several hours

– 22 January 2014

1B. SPS geophysical fields:

Great Lakes/St Lawrence at 2 arcmin Topography: elevation Mask: ocean/continent

Veg: evergreen needle-leaf trees Soil: fraction of sand, level 1

– 22 January 2014

2. Generation of hydrological fields

1. Average subgrid-scale topographical slope (SLOP)

2. Drainage density (DRND)

• Multiple databases

– resolutions from 25 m

– covering Canada and U.S.

– Can combine datasets with specified priority

• Requires GenPhysX, ARMNLIB and CMC databases

• Several hours/days

– 22 January 2014

2.1. Generation of hydrological field:

SLOP

• Definitions:

The slope is the magnitude of the gradient

– gradient: perpendicular to the topography contours

– slope: maximum change in elevation

– slope: > 0

• Calculation:

For each database grid cell within the domain grid cell

– calculate the slope (degrees)

– calculate their average (degrees)

– convert from degrees to m/m

– 22 January 2014

2.1. Hydrological geophysical field, SLOP:


Average subgrid-scale topographical slope (SLOP): m/m

– 22 January 2014

2.1. Generation of hydrological field:

DRND

• Definition wrt a grid cell:

– m/m2

– from zero to infinity (zero)

total length of rivers/streams + total perimeters of bodies of water

grid cell area - total area of bodies of water

– 22 January 2014

2.1. Hydrological geophysical field, DRND:


Drainage density (DRND): mm/m2

From HydroSHEDS data

to MESH shed fields

CCRN meeting

Montreal

January 21, 2014

Dorothy Durnford, Milena Dimitrijevic, Vincent Fortin

- 85 -

Process overview

1. Preparation of input fields

2. Determination of essential coordinates

3. Generation of MESH shed fields

- 86 -

1. Preparation of input fields

• Script

• Format conversion of HydroSHEDS data:

– .bil to .fst

– flow directions (DIR), elevations (DEM)

– Requires ARMNLIB and SPI

• Generation of ocean-continent mask (MG)

– same domain and grid

– Requires ARMNLIB and CMC databases

• Few hours

• Done for Canada (from 35N) at 30 arc seconds

- 87 -

Nechako Res., BC: input fields

DIR DEM

MG

- 88 -

2. Determination of essential coordinates

• Manual

• Of outlet and all reaches (any point)

• (x,y) values

• Domain relative

• Uses SPI

• Few minutes

- 89 -

Nechako Res., BC: outlet

MG DIR

SPI

- 90 -

3. Generation of MESH shed fields

• Fortran program

• Input:

– DIR, DEM, MG

– (x,y) values of outlet, each reach

– names, values of constant parameter fields

• Output:

– shed fields: rank, next, reach, drainage area …

– constant fields: flz, pwr, r1n, aa2, aa3, aa4 …

– extras: bver, tour

• Requires ARMNLIB environment

• Minutes: inland watershed

• Days: tidal regions (manual intervention)

- 91 -

Nechako Res., BC: shed fields

RANK

REAC

outlet

reach 1

reach 2

reach 3 reach 4

- 92 -

Nechako Res., BC: extras

outlet

BVER

TOUR

reach 3

reach 4

reach 2

reach 3

reach 4 reach 2

reach 1

- 93 -

– 22 January 2014


• Radar vs no radar, Canada-wide

– 22 January 2014



– 22 January 2014


• Impact of radar QPE post-processing, Canada-wide

– 22 January 2014


• Impact of radar QPE post-processing, Prairies

• Known negative bias in the gauge measurement of solid precipitation

• Standard precipitation gauge used in the Canadian Reference Climate

Stations (RCS) only measures 50% of total snowfall at wind speeds of 5 m/s

• CaPA throws out observed precipitation from SYNO stations when 2m wind

speed > 3 m/s and Ta < 0°C

• No automated (RCS) data used when Ta < 0°C and Ws > 0

Solid Precipitation Issues Work done in collaboration with Craig Smith, CRD

Catch Efficiency of Geonor-SA vs. Wind Speed

0.00

0.20

0.40

0.60

0.80

1.00

1.20

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0

Wind Speed (m/s)

Ca

tch

Eff

icie

nc

y

CE = e - 0.15Ws

r2 = 0.68

Geonor

Objective and Methods

• Objective: use bias adjusted RCS Geonor data (DJF 2010/2011) for selected

locations on the Canadian Prairies for CaPA verification

• Daily RCS Geonor precipitation adjusted using: CE=e-0.15Ws when maximum

daily Ta < -2°C (cold snow)

• 11 climate stations as a start

Alberta Saskatchewan

Manitoba

• AO: Assimilation of all observations

• OP: AO + strict quality control

(operational configuration)

• AN: OP + assimilation of bias-adjusted manual

Nipher obs

• ANG: AN + assimilation of bias-adjusted Geonor obs

CaPA Configurations

Catch Efficiency vs Wind Speed: Nipher

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10

Wind Speed (2m) m/s

CE

(N

iph

er/

DF

IR)

(Goodison et al 1998)

Assimilation

of unbiased

observations only:

Slightly more

skill than forecast,

little bias

Equitable threat score Partial mean

All stations

assimilated:

Negative bias

Less skill than

forecast

GEM

CaPA

Obs (Nipher)

AO and OP vs Nipher obs.

Winter 2009-2010, Canada

AO

vs Nipher

OP

vs Nipher

Assimilation

of bias-corrected

Nipher obs:

Makes almost no

difference

Equitable threat score Partial mean

GEM

CaPA

Obs (RCS w/BC)

Assimilation

of unbiased

observations only:

Slightly more

skill than forecast,

large bias

OP and AN vs RCS obs.

Winter 2010-2011, Prairies

OP

vs RCS

AN

vs RCS

ANG vs Nipher or RCS obs.

Winter 2010-2011, Prairies Equitable threat score Partial mean

Assimilation of

bias-corrected

observations

(Nipher+Geonor):

Assessment of

bias and skill

largely depends

on what is chosen

as “truth”

GEM

CaPA

Obs (Nipher)

ANG

vs Nipher

ANG

vs RCS

ANG vs Nipher + RCS obs.

Winter 2010-2011, Prairies

GEM

CaPA

Obs (RCS w/BC + Nipher)

Verification

against both

bias-corrected

Nipher and Geonor

observations

ANG

vs Nipher

+ RCS

r&d plans for water cycle prediction, hydrological modelling … - ec...

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