an overview of the clever model

An Overview of the CLEVER Model:the real-time flood forecasting system for BC–– Challenges, science, operations, history and uncertainties

Charles LuoBC River Forecast Centre, FLNRORD

April 29, 2021

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Contents1. Why the CLEVER Model – Challenges of real-time flood forecasting in BC 2. Model structure3. Science: Distributed open channel routing – An improved kinematic wave

scheme4. Science: Lumped watershed routing – An improved temperature-index

snowmelt model5. Codes and user interfaces6. Input data and data assimilation7. Model calibration – flexibility to adapt to climate changes8. Producing forecasts – informative and easy to read9. Model history – developing, expanding and upgrading10. Post freshet review of model performance - statistics and flooding events11. Modeling uncertainties – understanding limitations12. Types of modeled stations

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Area (km2):----------------------------------------British Columbia: 947,900=91% of the following states:-------------------------------------Washington: 184,827 Oregon: 254,806 Idaho: 216,443 Montana: 380,800 -------------------------------------Total: 1,036,876

Model Area: 700,401 (km2, 74% of BC)

Model stations: 311

1.Why the CLEVER Model – Challenges of real-time flood forecasting in BC

Challenge 1.Supper large areas

Total length of rivers in BC = 42,150 km > earth’s circumference

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0200400600800

100012001400160018002000

02-12 02-13 02-14 02-15 02-16 02-17

PEACE RIVER AT HUDSON HOPE (07EF001)

Disc

harg

e(m

3 /s)

Date (MM-DD in 2016)

Q increasing by 1200 m3/s in 3 hours!!!

Highly regulated basins

Lakes


Water level only stations

Climate pattern variability Human/natural disturbances

Mountain pine beetle infests

Wildfires

Sparce climate/flow stations

Challenge 2. Tremendous heterogeneity

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Stantec Consulting Ltd, 2014. River Forecast Performance Measures Development ProjectFinal Report, Alberta Environment and Sustainable Resource Development, Edmonton, AB

<=4

Challenge 3. Very limited resources

European CERN Computer

17


Observed

Climate & Flow

Data

Latest

Challenge 4. Very limited time for modeling (about 90 minutes each day)

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Challenge 1.Supper large areas

Challenge 2. Tremendous heterogeneity

Challenge 3. Very limited resources

Challenge 4. Very limited time for modeling (about 90 minutes each day)

Channel Links Evolution EfficientRouting (CLEVER) Model

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2. Model structure – watershed simplification

4

32

1

Flow gauge station

Watershed

Watershed outlet

River

River

River

4

32

1

Sub-basin 1

Sub-basin 2

Sub-basin 3

Sub-basin 4

Channel link

Channel link

4

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1

20

2. Model structure – Watershed routing

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2. Model structure – Open channel routing

Space (x)

Tim

e (t) x=i-1 x=i

t=j

t=j-1

S0

S0

Ai-1, j

Qi-1, j

Ai-1, j-1

Qi-1, j-1

Ai, j

Qi, j

Ai, j-1

Qi, j-1

Improved kinematic wave scheme

22

2. Model structure – Model flow chart

Read watershed parameters

Read observed

and forecast climate data

Read observed flow data

Groundwater release, evapo-

transpirtation and infiltration

Daily climate data

distribution to hourly data

An improved temperature-

index snowmelt model

Net water input

Instantaneous unit hydrograph

Open channel routing from basin

center to outlet

Lake basin routing:Level – Volume –

Discharge

Lumped Watershed Routing Sub-model

Regulated basin routing:

Extending observed trend

Distributed Open Channel Routing Sub-model

Err<=Min

No

Calculate flux limiter φ

Calculate V

Calculate A

Yes

An improved

kinematic wave

scheme

Iteration

Calculate boundary conditions

Calculate initial

conditions

Read inflowRead

channel link parameters

Call

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3. Science: Distributed open channel routing –– An improved kinematic wave scheme

0200400600800

100012001400160018002000

02-12 02-13 02-14 02-15 02-16 02-17

PEACE RIVER AT HUDSON HOPE (07EF001)

Disc

harg

e(m

3 /s)

Date (MM-DD in 2016)

Highly regulated hydrograph

𝜕𝜕𝑄𝑄𝜕𝜕𝑥𝑥

+𝜕𝜕𝐴𝐴𝜕𝜕𝑡𝑡

= 0

𝑆𝑆0 =𝑛𝑛2𝑄𝑄2

𝐴𝐴2𝑅𝑅 ⁄4 3

𝐴𝐴𝑖𝑖,𝑗𝑗 =∆𝑡𝑡 𝑄𝑄𝑖𝑖−1,𝑗𝑗 + 𝑄𝑄𝑖𝑖−1,𝑗𝑗−1 − 𝑄𝑄𝑖𝑖,𝑗𝑗−1 + ∆𝑥𝑥 𝐴𝐴𝑖𝑖,𝑗𝑗−1 + 𝐴𝐴𝑖𝑖−1,𝑗𝑗−1 − 𝐴𝐴𝑖𝑖−1,𝑗𝑗

∆𝑡𝑡𝑉𝑉𝑖𝑖,𝑗𝑗 + ∆𝑥𝑥

𝑉𝑉𝑖𝑖,𝑗𝑗 =1𝑛𝑛

𝑆𝑆0𝑅𝑅𝑖𝑖,𝑗𝑗⁄2 3𝑄𝑄𝑖𝑖.𝑗𝑗 = 𝑉𝑉𝑖𝑖,𝑗𝑗𝐴𝐴𝑖𝑖,𝑗𝑗

𝐴𝐴𝑖𝑖,𝑗𝑗𝑘𝑘 =

∆𝑡𝑡 𝑄𝑄𝑖𝑖−1,𝑗𝑗 + 𝑄𝑄𝑖𝑖−1,𝑗𝑗−1 − 𝑄𝑄𝑖𝑖,𝑗𝑗−1 + ∆𝑥𝑥 𝐴𝐴𝑖𝑖,𝑗𝑗−1 + 𝐴𝐴𝑖𝑖−1,𝑗𝑗−1 − 𝐴𝐴𝑖𝑖−1,𝑗𝑗

∆𝑡𝑡 𝑉𝑉𝑖𝑖,𝑗𝑗𝑘𝑘−1 + ∆𝑥𝑥

Finite difference with the Preissmann scheme:

500

1000

1500

2000

2500

0 20 40 60 80 100 120

OBS (07FD010) LUO_SCN1

Disc

harg

e(m

3 /s)

Time (hour)

𝑟𝑟𝑖𝑖,𝑗𝑗 =𝑄𝑄𝑖𝑖−1,𝑗𝑗−1 − 𝑄𝑄𝑖𝑖−1,𝑗𝑗−2

𝑄𝑄𝑖𝑖−1,𝑗𝑗 − 𝑄𝑄𝑖𝑖−1,𝑗𝑗−1𝜑𝜑 𝑟𝑟 = 𝑚𝑚𝑚𝑚𝑥𝑥 0,𝑚𝑚𝑚𝑚𝑛𝑛 1, 𝑟𝑟

𝐴𝐴𝑖𝑖,𝑗𝑗𝑘𝑘 =

∆𝑡𝑡𝑄𝑄𝑖𝑖−1,𝑗𝑗 + ∆𝑥𝑥𝐴𝐴𝑖𝑖,𝑗𝑗−1


+ 𝜑𝜑 𝑟𝑟𝑖𝑖𝑗𝑗∆𝑡𝑡 𝑄𝑄𝑖𝑖−1,𝑗𝑗−1 − 𝑄𝑄𝑖𝑖,𝑗𝑗−1 + ∆𝑥𝑥 𝐴𝐴𝑖𝑖−1,𝑗𝑗−1 − 𝐴𝐴𝑖𝑖−1,𝑗𝑗


Minmod Flux limiter

Kinematic wave of Saint Venant Equation:

V unknow -> Iteration:

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3. Science: Distributed open channel routing –– An improved kinematic wave scheme

Luo, C. 2021. Comparing Five Kinematic Wave Schemes for Open-Channel Routing for Wide-Tooth-Comb-Wave Hydrographs. Journal of Hydrologic Engineering, ASCE, 26 (4).

28

4. Science: Lumped watershed routing – An improved temperature-index snowmelt model

Time Efficiency

Simplex=y

Temperature-index method: 𝑴𝑴 = 𝑴𝑴𝒇𝒇 𝑻𝑻𝒊𝒊 − 𝑻𝑻𝒃𝒃M: snowmelt𝑀𝑀𝑓𝑓: melt factor𝑇𝑇𝑖𝑖: air temperature 𝑇𝑇𝑏𝑏: base temperature snow starts to melt

A forest stand

A small basin such as:Kanaka Creek

A=49 km2Water balance equation:𝑊𝑊 = 𝑅𝑅 + 𝑀𝑀 + 𝐺𝐺 − 𝐸𝐸 − 𝐼𝐼

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4. Science: Lumped watershed routing – An improved temperature-index snowmelt model

FRASER RIVER AT MCBRIDEA = 5,164 km2

Temperature-index on watershed-scale using hourly time step:

𝑴𝑴 = 𝒄𝒄𝒂𝒂𝒄𝒄𝒅𝒅𝑴𝑴𝒇𝒇 𝑻𝑻𝒊𝒊 − 𝑻𝑻𝒃𝒃 𝜷𝜷

𝑐𝑐𝑎𝑎: snow covering correction factor during the snowpack receding period (<=1):

𝑐𝑐𝑎𝑎 =𝑆𝑆𝑊𝑊𝐸𝐸𝑖𝑖

𝑆𝑆𝑊𝑊𝐸𝐸𝑚𝑚𝑎𝑎𝑚𝑚

𝛼𝛼

𝑐𝑐𝑑𝑑: Ordinal day correction factor (<=1)𝛼𝛼: snowpack covering area receding power𝛽𝛽: temperature power, which could be >,=,<1

0

0.2

0.4

0.6

0.8

1

1.2Cd_BAKR Cd_SJBC Cd_WSRD Cd_NAZK

Ord

inal

day

cor

rect

ion

fact

orCd

Date (YYYY-MM-DD)

Energy for melt is nonlinear to T

T usually lags and attenuate in daytime

Hourly time step shorter than time needs for snow to melt as estimated

Large portion of radiation absorbed by vegetation for photosynthesis

Whentemperatures increase

significantlyand rapidly

Underestimateslope of rise

(flatter)

0

2

4

6

8

10

12

14

0 10 20 30 40

β=0.75 β=1 β=1.1

Snow

mel

t (m

m)

T (oC)

Mf=0.25, ca=1, cd=1

Overestimateslope of rise

(steeper)

Improved

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5. Codes and user interface

5 modules12,492 lines

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5. Codes and user interfaces

Model control interface

34

5. Codes and user interfaces

Watershed interface

Input data recording

area

Watershed parameter panel Hydrograph area

Output data recording

area

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6. Input data and data assimilation

ECCC’s MPOML climate dataObserved

climate data

ECCC climate stations

BC fire weather stations

Automated snow weather

stations

Daily max & min temperatures & 24 hour total rainfall

36


Forecast climate data

Canadian Meteorological Centre’s (CMC) Numerical Weather Prediction (NWP) -GRIB2 format

◾Regional Deterministic

Prediction System -RDPS (dx=10 km, dt=3h, Time=0 to

62h)

Locations of 368 climate stations

(about 20 minutes)

Downscaleto

◾Global Deterministic

Prediction System -GDPS (dx= 25 km, dt =3h, Time used= 63

to 240h)

Map of CMC’s10 forecasts of daily average T and daily Phttp://bcrfc.env.gov.bc.ca/freshet/FORECAST_CMC_MAP.pdf

161 files to download (about 20 minutes)

http://bcrfc.env.gov.bc.ca/freshet/FORECAST_CMC_MAP.pdf

37


Observed Hydrometric

data

Water Survey of Canadian DataMart hydrometric data

Hourly+

Daily

Different time step

Obviously wrong data

Irregularly missing

Completely missing

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In the model:Dily climate data are distributed to

hourly data

40

7. Model calibration – flexibility to adapt to climate changes

42

7. Model calibration – flexibility to adapt to climate changes

Hydrographs

Observed & estimated discharges/water levels

Model parameters

43

8. Producing forecasts – informative and easy to read

46

8. Producing forecasts – informative and easy to read

330m3/s

Forecast hydrograph

56

9. Model history – developing, expanding and upgrading

The Channel Links Evolution Efficient Routing (CLEVER) Model

2013 Planning, researching and coding the first version of the CLEVER Model(M)

32 stn

2014 Test running the CLEVER Model (Moderate water year)

51 stn

2015 Starting operational running the CLEVER Model (Low water year)

71 stn

2016 Operational running the CLEVER Model (Very low water year)

74 stn

2017 Significant increase of modeled stations (Moderate water year)

102 stn

2018 Operational running the CLEVER Model (High water year)

108 stn

2019 Providing CSV files of hourly forecast for users to download (Low water y)

119 stn

2020 Significant expanding modeling scope (Extensive flooding year)

266 stn

2021

1. Massive upgrading, including feature allowing multi-users to run the model in parallel to improve time efficiency.

2. First paper about the CLEVER Model published in an international peer reviewed journal (J. Hydro. Eng., ASCE)

311 stn

10. Post freshet review of model performance– statistics and flooding events

𝐶𝐶𝑒𝑒 = 1 −∑𝑗𝑗=1𝑚𝑚 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜

𝑗𝑗 − 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚𝑗𝑗 2

∑𝑗𝑗=1𝑚𝑚 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜𝑗𝑗 − 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜

2

𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜 =1𝑚𝑚�𝑗𝑗=1

𝑚𝑚

𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜𝑗𝑗

Coefficient of model efficiency (𝐶𝐶𝑒𝑒)

Coefficient of determination (𝐶𝐶𝑑𝑑)

Percentage volume difference (𝑑𝑑𝑉𝑉)

𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚 =1𝑚𝑚�𝑗𝑗=1

𝑚𝑚

𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚𝑗𝑗

𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜2 =1𝑚𝑚�𝑗𝑗=1

𝑚𝑚

𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜𝑗𝑗 2

𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚2 =1𝑚𝑚�𝑗𝑗=1

𝑚𝑚

𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚𝑗𝑗 2

𝐶𝐶𝑑𝑑 = 1 −∑𝑗𝑗=1𝑚𝑚 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜

𝑗𝑗 − 𝑚𝑚 � 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚𝑗𝑗 + 𝑏𝑏

2


2

𝑚𝑚 = ��𝑃𝑃 − 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜 � 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚2 − 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚2

𝑏𝑏 = 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜 − 𝑚𝑚 � 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚

𝑑𝑑𝑉𝑉 = 100 × ⁄𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚 − 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜

�𝑃𝑃 =1𝑚𝑚�𝑗𝑗=1

𝑚𝑚

𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜𝑗𝑗 � 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚

𝑗𝑗

Statistic analysis for model calibration

Statistic analysis for forecasts

Relative mean absolute error (𝐸𝐸𝑟𝑟𝑎𝑎)

𝐸𝐸𝑟𝑟𝑎𝑎 = �100 ×1𝑚𝑚�𝑗𝑗=1

𝑚𝑚

𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚𝑗𝑗 − 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜

𝑗𝑗 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜

𝑟𝑟2 =∑𝑗𝑗=1𝑚𝑚 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜

𝑗𝑗 − 𝑄𝑄𝑜𝑜𝑏𝑏𝑜𝑜 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚𝑗𝑗 − 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚

2


2∑𝑗𝑗=1𝑚𝑚 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚

𝑗𝑗 − 𝑄𝑄𝑜𝑜𝑖𝑖𝑚𝑚2

r squared (𝑟𝑟2)

62

Statistic analysis:

63


After freshet review (AFR) reports as of 2017:

1. Changes of modeling scope2. Statistics of model calibration and forecasts3. Model performance during flooding events4. Recommendations for model improvements

65


Forecast errors of FRASER RIVER AT HOPE (08MF005)

0

5

10

15

20

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10

Forecast Error (%)

2015 2016 2017 2018 2019 2020 Ave

Forecast Error (%) StatisticsYear Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day 10 AveErr RSQU2015 0.6 2.1 3.5 4.2 5.1 6.2 7.8 9.0 10.4 11.2 6.0 0.7382016 1.0 2.6 3.9 5.1 6.4 7.7 9.8 12.1 14.2 15.1 7.8 0.4442017 0.9 2.6 3.8 4.7 5.2 5.6 6.1 6.5 7.6 9.1 5.2 0.7722018 1.4 4.0 6.2 7.9 9.2 10.5 12.1 13.3 14.7 16.0 9.5 0.6062019 1.1 3.2 5.0 6.3 6.9 8.1 9.1 10.0 12.2 13.1 7.5 0.5542020 1.2 3.8 5.6 7.3 8.9 10.6 12.2 13.9 16.1 17.6 9.7 0.497Ave 1.0 3.0 4.7 5.9 7.0 8.1 9.5 10.8 12.5 13.7 7.6 0.602

67


Review of Granby May 10, 2018 Flood (3:55 p.m. PST) (Observed Q peak = 524 m3/s)

68


http://bcrfc.env.gov.bc.ca/freshet/cleverm_ref/ChilcotinFlood2019July_lr.pdf

(78 pages)

http://bcrfc.env.gov.bc.ca/freshet/cleverm_ref/ChilcotinFlood2019July_lr.pdf

Download April 24, 2021

72

11. Modeling uncertainties – understanding limitations

First category of modeling uncertaintiescoming input data

Observed flow data: gauge issues and missing data

Observed climate data: missing and

interpolation/ extrapolation

Forecast climate data (ECCC CMC NWP Grib2

Regional MD 10km/ global MD 25km) and

downscaling to clim. st

Missing data

75

11. Modeling uncertainties – understanding limitations

Second category of modeling uncertaintiescoming from the model itself

Model intrinsic limitations

Simplifying a basin into a single node

Using daily data to distribute into hourly

Lack of overland flow routing

Simplifying hydrologic cycle: Evp, Inf, GD, SM

Model operational limitations

Parameters calibrated in previous years may have changed without

recent hydrologic indicators

Watershed routing

Open channel routing

Kinematic wave simplification of SV Eq.

Finite difference solution of KW Eq.

Simplification of cross-section into a rectang.

Lack of data of channel dimensions (42,150 km)

Too many parameters to calibrate – wrong combination of

parameters for correct results

Too short time for model calibration – lower accuracy

Modelers’ experience

76

12. Types of modeled stations

The only type of stations included in the model when developed and in early stage of operation (watersheds were split originally based on WSC stations)

1. After April 2018 West Road River/Nazko River flood, the inactive WSC station NAZKO RIVER ABOVE MICHELLE CREEK (08KF001) was added because of flooding concerns.

2. After July 2019 Chilcotin River Flood, WSC eliminated the CHILCOTIN RIVER BELOW BIG CREEK (08MB005), which was kept in the model as an inactive station for better model calibration.

3. PEACE RIVER AT HUDSON HOPE (07EF001) became inactive in 2019 and removed from the model in 2020. It was restored in the model as an inactive station in 2021 for better model calibration.

4. Because of flooding concerns, the SOMASS RIVER NEAR ALBERNI (INACTIVE STATION) (08HB017) was included when VI basins were included in the model as of 2020.

The only Non-WSC station is the LILLOOET RIVER FSR (08MG00A) (BC station ID: 08MG0001), an important station for Pemberton.

77

Liard River,

A=104,000 km2

Split into 20 sub-basins with routing stations and YT stations

12. Types of modeled stations

Estimated station

an overview of the clever model

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