A high resolution glacier modelwith debris effects in Bhutan Himalaya

Orie SASAKI
Kanae Laboratory
2018/02/08 (Thu)Research flow

2

Multiple climate data at high elevations
Precipitation, air temperature and etc.
Initial glacier data
Case2

Case1

Temperature index
glacier model

Energy balance glaciermodel with debris effect

Runoff from glaciers (Case1,2)Hydrological modelRiver discharge (Case1,2)

Research flow

3

Multiple climate data at high elevations
Precipitation, air temperature and etc.
Initial glacier data
Case2

Case1

Temperature index
glacier model

Energy balance glaciermodel with debris effect

Runoff from glaciers (Case1,2)Hydrological modelRiver discharge (Case1,2)

4Energy Balance Model
Shortwave
Longwave Latent heat
Radiation
Radiationflux

Energy Flux for Melting

Sensibleheat flux

Debris
Effects

5

Debris on Glaciers
No Debris

Debris covered glacierDebris：sand gravel, rocks

Supraglacial debris affects glacier melting rate significantly.
(e.g. Mattson et al.,1993)

Ex) Rocks inhibits ice melting(Photo: ©Florian Mair, 2010)

6

Debris on Glaciers
Effects of debris on glacier melts
No DebrisThin DebrisThick Debris

Albedo is high

Albedo become low

Heat insulation effectmeltmeltmelt

GlacierGlacier

Glacier

Accelerate

Suppress

Melting rate

Accelerate

Debris thickness
is important !

Clean ice

Same as clean ice

Suppress

Thin intermediate ThickDebris
thickness

7

Energy Balance Model
Shortwave
Longwave Latent heatRadiation
Radiation
flux

Sensibleheat flux

Debris
Effects

Energy Flux for Melting

Objective
Estimating glacier melts by a glacier model
based on energy balance with debris effects

1.Development of debris information data
2.Model structure

8

1. Development ofdebris information data9Necessary parameters
To estimate debris effect,・Thickness [m]・Thermal conductivity [Wm-1 K-1]
DebrisGlacier?
Ground observation is theonly way(Zhang et al., 2011)

It is unrealistic to measure these parameters
on a large scale.

10

How to get necessaryparameters
DebrisGlacier?

Thermal resistance (TR)of debris layer [m2 K W-1]
debris thickness
TR =thermal conductivity（Nakawo and Young, 1981, 1982)

TR can be estimated from Satellite
Data.

Photo: ©NASA

Photo: ©NASA

11Previous study
Some studies estimated thermal resistanceof debris from satellite data.
Target Region

Number ofsatellite images

Suzuki et al., 2007

Lunana region

11Zhang et al., 2011

Hailugou glacier

2Fujita et al., 2014

Trambau glacier

8

Distribution of thermal resistanceon debris at Trambau glacier(Fujita et al., 2014)

Estimate distribution ofthermal resistance of debris
on Bhutan Glaciers12Data for analysis
Details
Data

Data

Spatial Res. Time Res.

PeriodBand 2~730m

16 days2013-2017

Band 10 (TIR)

100m

16 days2013-2017

Reanalysis data
(Radiation, Air temp,Humidity, Wind speed)

0.75°3hourly

2013-2017

AW3D30Elevation data
(ALOS PRISM)

30mーー

RGI 6.0

Glacier Outline dataVectorーーLandsat 8

ERA-Interim

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Data for analysis
Details
Data
Landsat 8

Data

Spatial Res. Time Res.

PeriodBand 2~730m

16 days2013-2017

Band 10 (TIR)

100m

16 days2013-2017

Multi-temporal 208 data set
Reanalysis data
2013-2017
ERA-Interim(Radiation, Air temp,
3hourly
0.75°
Humidity,Satellite
Wind speed)
Earth Observation
AW3D30RGI 6.0

Elevation 8data
Landsat(ALOS PRISM)
Glacier Outline data

Photo: ©NASA30m

ーーVectorーー(Landsat, 2013/11/13)

14Data for analysis
Details
Data
Landsat 8

ERA-Interim

Data

Spatial Res. Time Res.

PeriodBand 2~730m

16 days2013-2017

Band 10 (TIR)

100m

16 days2013-2017

Reanalysis data
(Radiation, Air temp,Humidity, Wind speed)

0.75°3hourly

2013-2017

Elevation data
30mーー
(ALOSPRISM)
ERA-Interim : Reanalysis climate data by ECMWF

AW3D30RGI 6.0

Glacier Outline data

Vectorーー・Downward shortwave
and longwave radiation
・Air temperature
・Relative humidity
・Wind speed(Fig. from ERA-Interim Web page)

15

Data for analysis
Details
Data
Landsat 8ERA-Interim

Data

Spatial Res. Time Res.

PeriodBand 2~730m

16 days2013-2017

Band 10 (TIR)

100m

16 days2013-2017

Reanalysis data
(Radiation,Air temp, ALOS
3hourly0.75°
AW3D30:World 3D
- 30m 2013-2017
Humidity, Wind speed)
Multi-temporal 208 data sets

Elevation Data (30m resolution)

AW3D30Elevation data
(ALOS PRISM)

30m

ーー

RGI 6.0

Glacier Outline data

Vectorーー7500m
3500m
Photo: ©JAXA

ALOS ‘PRISM’ (JAXA)

16

Data for analysis
Details
Data

Data

Spatial Res. Time Res.

PeriodBand 2~730m

16 days2013-2017

Band 10 (TIR)

100m16 days

2013-2017

Reanalysis data
(Radiation, Air temp,Humidity, Wind speed)

0.75°3hourly

2013-2017

AW3D30Elevation data
(ALOS PRISM)

30m

ーー

RGI 6.0

Glacier Outline dataVectorーーLandsat 8ERA-Interim

Multi-temporal 208 data sets

17

Calculation of TR
Thermal Resistance (TR) [m2 K W-1]

Assumption (2)Linear temperature profile in debris
Surface temp.
Melting temp.

Assumption (1)Melting condition

Remote sensing

𝑇𝑠 − 𝑇𝑖
𝑇𝑠 − 𝑇𝑖∴ 𝑇𝑅 ==𝑄𝑔
𝑅𝑛 + 𝐿𝐸 + 𝐻
Remote sensing

𝑄𝑔 :Heat flux into glacier [Wm-2]
𝑅𝑛 :Net Radiation [Wm-2]
𝐿𝐸 :Latent heat flux [Wm-2]
𝐻 :Sensible heat flux [Wm-2]

Remote Sensing + Reanalysis data

18

Flow of calculation

Data Set 208
Data Set 3
Data Set 2
Data Set 1

Landsat 8Band 2, 4~7, 10

AlbedoAW3D30(Elevation Data)

Climate data
（Downward radiation, Air temp.,
Wind speed, Relative humidity）

Surface Temperature

Latent Heat/Sensible Heat Flux

Net Radiation

Thermal Resistance
208 data

Wide-area Map ofThermal Resistance

Thermal Resistance on glaciers
90m-resolution

RGI 6.0
Outline of Glaciers

19

Flow of calculation① TR②Large area map of TR

No. 208
No. 3
No. 2
No. 1

Eliminatecloud/snow

Thermal Resistance[Km2/W]

Glacier Outline③Extract glacier area

90m resolution distribution map of thermal resistance20
How to eliminate cloud and snow
① Cloud and snow makes TR lower⇒ select maximum value② Interannual variation can be neglected

Thermal Resistance(m2 K W -1)
0.07

0

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Results

Thermal Resistance(× 10-2 m2 K W-1)
Clean
3–4
0 – 0.1
4-5
0.1 - 0.5
Thin5-6
0.5 - 11-22-36-7> 7 Thick

GoogleMap

22

Results

Thermal Resistance(× 10-2 m2 K W-1)
Clean
3–4
0 – 0.1
4-5
0.1 - 0.5
Thin5-6
0.5 - 1
1-2
2-3

Classification
Clean ice
DebrisGlacial ponds

6-7> 7 Thick

Classification by
Landsat 8(Kraaijenbrink et al.,2017)

GoogleMap

23

Results

Thermal Resistance(× 10-2 m2 K W-1)
Clean
3–4
0 – 0.1
Thin
4-5
0.1 - 0.55-6
0.5 - 1
1-2
2-3

Classification
Clean ice
DebrisGlacial ponds

6-7> 7 Thick

・Several large glaciers hasmuch debris.
・There are small patches which
has erroneous high TR value.
(should be corrected)

GoogleMap24

2. Model StructureBase Model
Glacier Model by Fujita et al., 2014
(Fujita model)
・ Developed for Trambau glacier（located in Nepal Himalaya）
・ Energy Balance Model with Debris effect

25

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Base Model
Glacier Model by Fujita et al., 2014
(Fujita model)
・ Developed for Trambau glacier（located in Nepal Himalaya）
・ Energy Balance Model with Debris effect・ Glacier area is divided into (i) Clean ice and(ii) Debris-covered ice.
By using Landsat data
(Kraaijenbrink et al., 2017)(i) Clean ice

Thermal Resistance
90m res.
(ii) Debris-covered ice

27

Model Structure
Mass balance was calculated in clean ice part anddebris-covered part separately.
(i) Clean ice

50m elevation band

Each grid is sorted into 50melevation band.
Mass balance was calculated
in each band.

(ii) Debris-covered ice

90m×90m grid

Mass balance was calculated
in each 90m grid.

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Model Structure
Thermal Resistance, Albedo Newly Developed Climate data
(0.5° resolution)(90 m resolution)
Snow albedo

Snow fall

Energy Balance Model
Clean ice
Debris-covered ice
One year after snowfall
Glacier ice
50m elevation band

90m×90m grid

Accumulation

Melting

Mass Balance

Runoff29

Glacier area evolution
Area change is calculated from mass change.
Step 1. Initial value
Initial area 𝑨𝟎 : from RGI6.0
𝛾
Initial volume 𝑽𝟎 : 𝑉0 = 𝑐𝑣 𝐴0(Volume-Area scaling, Bliss et al., 2013)

Parameters
Mountain glaciers:
𝑐𝑣 = 0.2055 𝑚3−2𝛾 , 𝛾 = 1.375
Ice caps:
𝑐𝑣 = 1.7026 𝑚3−2𝛾 , 𝛾 = 1.250

Step 2. Calculation of mass balance
Mass balance 𝑩𝒊 :calculated for each grid(debris) or elevation band(clean ice)

Repeat for N years

Step 3. Set new area and volume
𝑽𝒕 = 𝑉𝑡−1 + 1Τ𝜌𝑖𝑐𝑒 ∙ σ𝑛𝑖=1 𝐵𝑖 𝐴𝑖
𝑨𝒕 = 𝑉𝑡 Τ𝑐𝑣 1Τ𝛾Parameters𝜌𝑖𝑐𝑒 ： Density of ice = 900 𝑘𝑔𝑚−3

Calibration method
Local model
Local

Large
scale

Calibration by using ground observation data of each glacierGlobal Glacier Models (~2015)
Extrapolating limited direct observation data（Radic and Hock, 2011; Radic et al., 2013; Hirabayashi et al., 2010, 2013;
Marzeion et al., 2012; Bliss et al., 2014）

Global Glacier Model (Huss et al., 2015)
Extrapolating is problematic.
・ Direct observation data is restricted to rather small glaciers
Each individual glacier’s mass balance ∆𝑴𝒈 is assumed tobe same as the average regional mass balance ∆𝑴𝒓𝒆𝒈∆𝑴𝒈 = ∆𝑴𝒓𝒆𝒈

30

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Calibration Flow
Calibration method:

Model Simulation (2003-2009)∆𝑴𝒈 = ∆𝑴𝒓𝒆𝒈∆𝑴𝒓𝒆𝒈 : Average regional massbalance in whole Asia (2003-2009)(Gardner et al., 2013)⇒Observation data of Bhutan glacierwill improve model performance.

𝒄𝒑𝒓𝒆𝒄
Adjustment ofprecipitation∆𝑻𝒂𝒊𝒓
Adjustment oftemperature

Calibration period: 2003-2009
Calibration parameters:
𝑐𝑝𝑟𝑒𝑐 ： Precipitation ratio [%]（0.8<𝑐𝑝𝑟𝑒𝑐 <2.0）∆𝑇𝑎𝑖𝑟 : Air temperature [℃]
Initial value:
𝑐𝑝𝑟𝑒𝑐 = 1.0, ∆𝑇𝑎𝑖𝑟 = 0.0

Repeat∆𝑴𝒈 = ∆𝑴𝒓𝒆𝒈 ?

NoYes∆𝑴𝒈 = ∆𝑴𝒓𝒆𝒈 ?
YesFinal parameter setNo32

Model Structure
Thermal Resistance, Albedo(90 m resolution)

Newly Developed Climate data
(0.5° resolution)

Snow albedo

New
GlacierArea
VolumeSnow fall

Energy Balance Model
Clean ice
Debris-covered ice
One year after snowfall
Glacier ice50m elevation band

90m×90m grid

Accumulation

Melting
Volume-Area scaling

Mass Balance

RunoffResearch flow

33

Multiple climate data at high elevations
Precipitation, air temperature and etc.
Initial glacier data
Case2

Case1

Temperature index
glacier model

Energy balance glaciermodel with debris effect

Runoff from glaciers (Case1,2)Hydrological modelRiver discharge (Case1,2)

River Discharge (H08)

34

Newly Developed
Climate data

Runoff from Glaciers(Case1, Case2)
Input

Input
Reservoir operation

 Consisted of 7 modules

Crop growthLandsurface

Waterwithdrawal Use 2 modules・River・Land surface

RiverEnv. water(Hanasaki et al., 2008)

Virtual water

Model “H08”OutputRiver discharge(Case1, Case2)

Two type of riverdischarge will be obtained.
(from case1 & case2)

Uncertainty range

35Summary The distribution of thermal resistance of debris on
glaciers has been detected in Bhutan by using
remote-sensing data.
 A glacier model with energy balance and debris
effects was developed.

 Observation data of Bhutan glaciers will improvemodel performance.

Next Steps➢ Historical and future simulation of glacier runoff for allglaciers in Bhutan (Glacier model).
➢ Simulation of river discharge including the effects ofglacier melts (H08).

Thank you for your kind attention