The changing Himalayan Cryosphere and likely consequences: an overview (GFCS)

Dr. Parmanand Sharma National Centre for Antarctic and Ocean Research

Ministry of Earth Sciences

Snow - SWE, depth, extent, state, density, snowfall, solid precipitation, albedo

- in-situ climate & synoptic (manual, auto), weather radar, remote sensing Lake and River

- Reservoirs, Discharge, snow/ice melt contribution, GLOF - in-situ , remote sensing

Glaciers - mass balance (accumulation/ablation), thickness, area, length (geometry), firn temperature, snowline/equilibrium line, snow on ice - ground-based (in-situ), remote sensing

Frozen Ground/Permafrost - soil temperature/thermal state, active layer thickness, borehole temperature, extent, snow cover - in-situ (manual, auto), remote sensing (new)

HKH Cryosphere Component

http://commons.wikimedia.org/wiki/File:High_Asia_Mountain_Ranges.jpg?uselang=en-gb NASA/Rupert Pupkin

This NASA satellite image shows large areas of Asian mountains covered by glaciers

http://commons.wikimedia.org/wiki/File:High_Asia_Mountain_Ranges.jpg?uselang=en-gbhttp://commons.wikimedia.org/wiki/File:High_Asia_Mountain_Ranges.jpg?uselang=en-gbhttp://commons.wikimedia.org/wiki/File:High_Asia_Mountain_Ranges.jpg?uselang=en-gb

The Hindu Kush Himalayan (HKH) region, often referred to as the Third Pole, contains the world’s greatest areal extent and volume of permanent ice and permafrost outside the polar regions.

The Hindu-Kush Karakoram Himalaya (HKH), covering a glacierized area of �40 000 km2 (Bolch and others, 2012),

This vast accumulation of snow and glaciers acts as a natural water reserve, which serves as a water source for approximately 2.0 billion inhabitants in the 10 major Asian river basins downstream.

Nearly all the great rivers of Asia begin in the High Mountain of Asia

Shares of national populations living in HKH region water basins

The Himalayan cryosphere is a critically important component of the earth system, affecting the energy balance, atmospheric circulation, freshwater storage, the storage, and release of large quantities of greenhouse gases, economy, infrastructure, health, and indigenous and non-indigenous livelihoods, culture and identity. Following components of the Himalayan Cryosphere are subjected to dramatic change due to global warming: Climate Snow /Ice Water Permafrost

Change in Climate: recent paleo-reconstructions show that current Himalayan summer temperatures are higher than at any time in the past 2000 years

Temperature rise per decade (°C 10 y-1) Global North

since 1901 0.07 0.08

since1960 0.13 0.24

Absolute temperature rise (°C) Global North

since 1901 0.75 0.89

since1960 0.63 1.12

North Hemisphere warming is now 0.24 °C decade-1 since 1960, the double than the Global warming (0.13 °C decade-1).

Temperature and precipitation trends Himalaya during 1987-2011

Summer and winter temperature have significantly increase during last few decades Reduction in summer and winter precipitation are not statistically significant however trend is negative

Rising temperatures, especially at higher altitudes, are playing a role in rapid glacier melt; Consequences results: increased frequency and magnitude of associated extreme weather events, such as Flash flood Glacier Lakes Outburst Flood(GLOF) shifts in monsoon patterns availability of water for agriculture, domestic needs, industry, and hydropower

Mass balance , Western Himalaya

Bahuguna et al 2015

HIMALAYA Glaciers Retreat 2001-2010

Indus basin Western Himalayas (Indian region) which contains 33,382 km2 of glacierised area, and have 3398 number of Glaciers (GSI Report, 2009)

Basin Number of Glaciers Time period Retreat (loss)

Chenab 359 1962-2004 21%

Parbati 88 1962-2004 22%

Baspa 19 1962-2004 19%

Ravi Basin 285 1971-2010/2013 4.7 ± 4.1 %

Beas 50 1972-2006 22.3%

GSI 2009; Kulkarni et al 2005; Kulkarni et al 2007

0102030405060708090

100

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

% sn

ow c

over

ed a

rea

Period of observation

Mean monthly Snow cover area

Observations Period of observation

Trend

Maximum/ Highest snow

cover area

March, 2005 & 2007

-n/a-

Trend 2001-2014 Increasing

Seasonal snow cover area

2001-2014 Increasing

Spatio-temporal variations in snow cover area

Chart1

2000200020002000

2001200120012001

2002200220022002

2003200320032003

2004200420042004

2005200520052005

2006200620062006

2007200720072007

2008200820082008

2009200920092009

2010201020102010

2011201120112011

2012201220122012

Winter

Spring

Summer

Autumn

Period of observation

Area in percentage

Seasonal snow cover area

70.6832386364

60.7228535354

19.3762626263

57.6107954545

85.3991477273

66.9248737374

20.8390151515

49.6704545455

91.5203598485

70.4381313131

31.7367424242

62.3162878788

82.9886363636

71.1130050505

21.1382575758

46.8863636364

82.5880681818

60.8244949495

25.3882575758

79.7017045455

83.4678030303

77.5214646465

33.9621212121

49.6325757576

94.5383522727

64.7430555556

23.4034090909

59.2784090909

87.6505681818

65.1142676768

25.384469697

70.4933712121

86.5255681818

66.726010101

28.8371212121

69.4242424242

85.7627840909

72.4109848485

39.9526515152

80.8446969697

87.2613636364

72.5233585859

32.4147727273

73.6950757576

81.7698863636

73.4652777778

27.1268939394

40.6496212121

67.734375

74.8080808081

24.6628787879

27.9431818182

snowcover_terra_baspa

Julian dayperiod of observationSnow covered pixels0

11-Jan2000200120022003200420052006200720082009201020112012Total number of pixels

99-Jan3655412341484050432034303863344539514478438730724550

1717-Jan3842430628024218391138642899283536424535397539124550

2525-Jan37294054204157239313517270533693942414437514550

332-Feb3525450616714153442444223514388741913663392840724550

4110-Feb3333413341724499376443403635416539573349449241704550

4918-Feb2555445641214482208644984091438440484194345934584550

5726-Feb4201447744914363416140574238423440354459420541444550

655-Mar44504207454540864300435743074474437440854397357742794550

7313-Mar42433991450043334104444341324306420338314218443042704550

8121-Mar44254350450443483858433243944550424243364081438843644550

8929-Mar4529447241413682442944534495408338773786424841824550

976-Apr42544535441044333805428041264139389541014206426640114550

10514-Apr40474146417740563452421639164022423343713647418840254550

11322-Apr38083904394343963352405939723677428640663900419538464550

12130-Apr35483888447638912893401940543493388537614305401040184550

1298-May33923585394236864167352635753123349737453416381238324550

13716-May32723300351134293356369032903030317335713290341435464550

14524-May26872964360231822806356123522906319431903249340635604550

1531-Jun24282698262331602932367625193620318928933050280631684550

1619-Jun2064248929011860331420252828220529202985360227444550

16917-Jun1868209621131643315417491980160722042930268927904550

17725-Jun1521197513541408287724891563138626582517219725644550

1853-Jul1112121511971523953139116361807126219814550

19311-Jul107491799397666719881084114583120982064130613044550

20119-Jul4136065159546661317764115975215231661105811934550

20927-Jul5895457609511062905505772722102064780212294550

2174-Aug49676470483718613238657746699968698338754550

22512-Aug6373364828298219461313107434880645710127994550

23320-Aug17093130896699389182764068979113097634550

24128-Aug27071555499588684249746089495394615714764550

2495-Sep8007732094603147490911413266631346123811625204550

25713-Sep100384311267721097787110097114683211761114110194550

26521-Sep11771116303310821309270411971026124930921725142615514550

27329-Sep18571808286614751855262515011515366027163096147315964550

2817-Oct2410269913792416259314993354333026452860123016974550

28915-Oct13381636270013374353258513103150282536542753226624224550

29723-Oct13731483282815954239239023843866296033002657176324964550

30531-Oct1199134026642324350923402116325227632862434029894550

3138-Nov12053533256212883446228522643079288424883448150821924550

32116-Nov36983263337110333289217219752951263144313181168023294550

32924-Nov34032330261438143031214041492891400339653361136214794550

3372-Dec4272152624012623306115913470275831203719301512954550

34510-Dec3985121021822029379218254156209130403155281931404550

35318-Dec3590392224384098311518424328427230252483268926434550

36126-Dec3682447432323294318937533925419942103653235111894550

3253412040934216196026164192342341873663426510954550

analysis

total number of pixelsperiod of observationssnow covered pixelspercentage snow covered areaarea in sq.kilometersMean monthly snow covered area (in number of pixels)

4550Jan-0000020002001200220032004200520062007200820092010201120122000200120022003200420052006200720082009201020112012

4550Feb-004450981113january0368842472666324842183912344832183788415541093702winter77893910079139089181040964952943960899745

4550Mar-004363961091february4450357444034218441135924301411042894031410039334013spring668736775782669853712716734797798808823

4550Apr-00369981925march4363433744714314386243714276437341064036407343334207summer213229349233279374257279317439357298271

4550May-00279661699april3699388141354007346639553879357939753986381740513930Autumn634546685516877546652775764889811447307

4550Jun-00152133380may2796298732453257303136422720318531853218319632093425winter71859283838395888786878268

4550Jul-0064314161june1521196619182123153226361947183116472355256024382520spring61677071617865656772737375

4550Aug-0056913142july64370874393064513838059637441409131010001150summer19213221253423252940322725

4550Sep-00134630336august569499101568410379239616726369498581264640Autumn58506247805059706981744128

4550Oct-00127928320september1346154424311177166921771324171724272916211113181466Seasons

4550Nov-00379183948october1279199826891636388724002019333728583076330021322459winterDecember to march

4550Dec-00362880907november3791237327952490312719683198286732514038318614460springapril to june

4550Jan-01368881922december3628343229863409301425094150349636163239303120170summerjuly to september

4550Feb-01357479894AutumnOctober and november

4550Mar-014337951084Mean monthly snow covered area in square kilometers2000-2012

4550Apr-013881859702000200120022003200420052006200720082009201020112012average of mean monthly snow cover

4550May-01298766747january09221062666812105597886280594710391027925854

4550Jun-01196643492february11138941101105411038981075102710721008102598310031027std deviation

4550Jul-0170816177march1091108411181078966109310691093102610091018108310521060281

4550Aug-0149911125april92597010341002867989970895994996954101398396871

4550Sep-01154434386may69974781181475891168079679680579980285679043

4550Oct-01199844500june38049248053138365948745841258964060963051948

4550Nov-01237352593july16117718623216134620124118635232825028823960

4550Dec-01343275858august14212525417125923124016815923721531616020698

4550Jan-024247931062september33638660829441754433142960772952832936645469

4550Feb-024403971101october32050067240997260050583471576982553361563656

4550Mar-024471981118november94859369962378249280071781310107963610664136

4550Apr-024135911034december90785874785275462710388749048107585040741185

4550May-02324571811265

4550Jun-02191842480260

4550Jul-0274316186

4550Aug-02101522254

4550Sep-02243153608

4550Oct-02268959672

4550Nov-02279561699

4550Dec-022986667472000-20012001-20022002-20032003-20042004-20052005-20062006-20072007-20082008-20092009-20102010-20112011-2012

4550Jan-03266659666october320500672409972600505834715769825533

4550Feb-034218931054november9485936996237824928007178131010796361

4550Mar-034314951078december9078587478527546271038874904810758504

4550Apr-034007881002january9221062666812105597886280594710391027925

4550May-03325772814february894110110541103898107510271072100810259831003

4550Jun-03212347531march10841118107896610931069109310261009101810831052

4550Jul-0393020232april970103410028679899708959949969541013983

4550Aug-0368415171

4550Sep-03117726294

4550Oct-03163636409

4550Nov-03249055623

4550Dec-03340975852

4550Jan-04324871812

4550Feb-044411971103

4550Mar-04386285966

4550Apr-04346676867

4550May-04303167758

4550Jun-04153234383

4550Jul-0464514161

4550Aug-04103723259

4550Sep-04166937417

4550Oct-04388785972

4550Nov-04312769782

4550Dec-04301466754

4550Jan-054218931055

4550Feb-05359279898

4550Mar-054371961093

4550Apr-05395587989

4550May-05364280911

4550Jun-05263658659

4550Jul-05138330346

4550Aug-0592320231

4550Sep-05217748544

4550Oct-05240053600

4550Nov-05196843492

4550Dec-05250955627

4550Jan-06391286978

4550Feb-064301951075

4550Mar-064276941069

4550Apr-06387985970

4550May-06272060680

4550Jun-06194743487

4550Jul-0680518201

4550Aug-0696121240

4550Sep-06132429331

4550Oct-06201944505

4550Nov-06319870800

4550Dec-064150911038

4550Jan-07344876862

4550Feb-074110901027

4550Mar-074373961093

4550Apr-07357979895

4550May-07318570796

4550Jun-07183140458

4550Jul-0796321241

4550Aug-0767215168

4550Sep-07171738429

4550Oct-07333773834

4550Nov-07286763717

4550Dec-07349677874

4550Jan-08321871805

4550Feb-084289941072

4550Mar-084106901026

4550Apr-08397587994

4550May-08318570796

4550Jun-08164736412

4550Jul-0874416186

4550Aug-0863614159

4550Sep-08242753607

4550Oct-08285863715

4550Nov-08325171813

4550Dec-08361679904

4550Jan-09378883947

4550Feb-094031891008

4550Mar-094036891009

4550Apr-09398688996

4550May-09321871805

4550Jun-09235552589

4550Jul-09140931352

4550Aug-0994921237

4550Sep-09291664729

4550Oct-09307668769

4550Nov-094038891010

4550Dec-09323971810

4550Jan-104155911039

4550Feb-104100901025

4550Mar-104073901018

4550Apr-10381784954

4550May-10319670799

4550Jun-10256056640

4550Jul-10131029328

4550Aug-1085819215

4550Sep-10211146528

4550Oct-10330073825

4550Nov-10318670796

4550Dec-10303167758

4550Jan-114109901027

4550Feb-11393386983

4550Mar-114333951083

4550Apr-114051891013

4550May-11320971802

4550Jun-11243854609

4550Jul-11100022250

4550Aug-11126428316

4550Sep-11131829329

4550Oct-11213247533

4550Nov-11144632361

4550Dec-11201744504

4550Jan-12370281925

4550Feb-124013881003

4550Mar-124207921052

4550Apr-12393086983

4550May-12342575856

4550Jun-12252055630

4550Jul-12115025288

4550Aug-1264014160

4550Sep-12146632366

4550Oct-12245954615

analysis

Period of observations

% snow covered area

Mean monthly SCA of Baspa basin, 2000-2012

Sheet1

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Period of observations

Snow covered area in sq.kms

Mean Montly snow covered area of Baspa Basin, 2000 - 2012

281.3673449707281.3673449707

71.242776497971.2427764979

43.266448242743.2664482427

47.717098343947.7170983439

60.385532797460.3855327974

98.334597296798.3345972967

69.348818041169.3488180411

56.045952914956.0459529149

135.5212103234135.5212103234

185.4388650371185.4388650371

265.3037622639265.3037622639

259.6512314995259.6512314995

Period of observations

Area in sq.kms

Mean monthly variation in snow covered area, 2000-2012

Winter

Spring

Summer

Autumn

Period of observation

Area in square kilometers

Seasonal SCA, 2000-2012

2000-2001

2001-2002

2002-2003

2003-2004

2004-2005

2005-2006

2006-2007

2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

Area in square kilometers

Composite average snow covered area, 2000- 2012

octoberoctoberoctoberoctoberoctoberoctoberoctoberoctoberoctoberoctoberoctoberoctober

novembernovembernovembernovembernovembernovembernovembernovembernovembernovembernovembernovember

decemberdecemberdecemberdecemberdecemberdecemberdecemberdecemberdecemberdecemberdecemberdecember

januaryjanuaryjanuaryjanuaryjanuaryjanuaryjanuaryjanuaryjanuaryjanuaryjanuaryjanuary

februaryfebruaryfebruaryfebruaryfebruaryfebruaryfebruaryfebruaryfebruaryfebruaryfebruaryfebruary

marchmarchmarchmarchmarchmarchmarchmarchmarchmarchmarchmarch

aprilaprilaprilaprilaprilaprilaprilaprilaprilaprilaprilapril

2000-2001

2001-2002

2002-2003

2003-2004

2004-2005

2005-2006

2006-2007

2007-2008

2008-2009

2009-2010

2010-2011

2011-2012

Area in square kilometers

Composite average snow covered area, 2000- 2012

319.6875

499.5

672.125

409

971.6875

600

504.625

834.1875

714.5

769

824.875

532.875

947.75

593.25

698.8333333333

622.5

781.75

491.9166666667

799.5

716.6666666667

812.8333333333

1009.5833333333

796.4166666667

361.4166666667

906.875

857.875

746.5625

852.3125

753.5

627.25

1037.5625

874.0625

903.875

809.625

757.75

504.1875

921.9375

1061.8125

666.375

812.0625

1054.5833333333

977.9375

862.0625

804.5

947.0625

1038.625

1027.125

925.4375

893.5

1100.6875

1054.375

1102.75

898

1075.125

1027.375

1072.3125

1007.8125

1024.9375

983.3125

1003.1875

1084.25

1117.8333333333

1078.4375

965.5625

1092.75

1069.0625

1093.125

1026.4375

1009.0625

1018.1875

1083.25

1051.6875

970.1875

1033.625

1001.8125

866.5

988.75

969.8125

894.6875

993.8125

996.4375

954.25

1012.8125

982.5625

There are more than 12000 glaciers in the Indian Himalaya including Nepalese Himalayas and more than 3000 of them contain glacial lakes. These lakes are quietly growing because of rising temperatures, but nobody knows how many are close to bursting, and there are no early warning systems for the villages downstream.

A glacier lake catastrophe happened once in a decade 50 years ago,' said UK geologist John Reynolds, whose company advises Nepal.

There is also the risk of sudden flash floods as rapidly expanding glacial lakes burst through their natural dams.

Now a nearby lake, below the Thorthormi glacier, is in imminent danger of bursting. That could release 50 million cubic meters of water, a flood reaching to northern India 150 miles downstream.

Terminal retreat of Samudra Tapu and Gepang Gath glaciers from 1971-2014 (last 43 years)

•Samudra Tapu : lower terminal retreat 31.3 ma-1, : area loss 0.1 km2a-1, : volumetric wastage 32.7 x106 m3 a-1

•Gepang Gath :lower terminal retreat 20.1 ma-1, : area loss 0.03 km2a-1, : volumetric wastage 4.1 x106 m3 a-1

Catastrophic impact of GLOG (Chorabari glacier) An example of the catastrophic impact by breaching the Chorabari lakes, Uttrakhand in India , which claimed more than 5000 loss of life during 2013

One more example sweeping Luggye Tsho, in Bhutan, which burst its banks in 1994, sweeping 10 million cubic meters of water down the mountain. It struck Panukha, 50 miles away, killing 21 people.

These disruptions are quite likely to trigger major conflicts locally and internationally, as people clash over water and land.

People will be forced to migrate away from their dry or flooded land. Governments will bicker over who will get the

remaining water and who will pay the economic costs.

But in a few decades the glaciers will be largely gone and the water level in rivers will drastically decline, meaning massive eco and environmental problems for people in China, Nepal, India, and Bangladesh. Without the glaciers, drinking and

irrigation water will be drastically reduced, especially during the dry season. Crops will die. Hundreds of millions of people

will be affected.

Snowmelt and glacier melt increases linearly with temperature rise

Snowmelt runoff, glacier melt runoff, and total streamflow increased 4-18%, 33-38%, and 6-12% respectively in +2°C scenario

Higher streamflow in the spring Discharge increases by 150-170% before dropping by

33% and 4-18% in the Western and Eastern catchment respectively

ANDY CHAN - THE IMPACT OF CLIMATE WARMING ON THE HYDROLOGY OF GLACIER AND SNOW MELTING IN THE HIMALAYAS - PHYSICAL CLIMATOLOGY - GEO387H

Short term Temperature rise causes streamflow to increase in

spring and decrease in summer Water demand is highest during summer Reduced contribution of snowmelt from lower

elevation is counteracted by increased glacier melt from higher elevation

Long term Streamflow initially increases as glaciers melt, then

decreases when glaciers are depleted

ANDY CHAN - THE IMPACT OF CLIMATE WARMING ON THE HYDROLOGY OF GLACIER AND SNOW MELTING IN THE HIMALAYAS - PHYSICAL CLIMATOLOGY - GEO387H

Source: UNEP- GRID 2012

Snow/ice melt contribution to discharge

Shortage of Fresh Water :The rapid melting of Himalayan glaciers will first increase the volume of water in rivers by 20-30 percent, causing widespread flooding. Hundreds of millions live on flood plains that are highly vulnerable to raised water levels.

ISSUES

ACTION FOR CLIMATE SERVICES(Specific in high mountain region)

An operational User Interface Platform can promote engaging all stakeholders and ensure effective decision-making where it involves climate considerations.

Government-led organizational arrangements for implementation

Coordination:

Interaction:

Technical: Multi-sector integrated information sharing and interactive platform which provides climate service products to users including media

Full coverage: Appropriate cooperation mechanisms of working and technical levels put in place at provincial, prefectural and county levels

Mechanism building in progress at rural and urban community levels

At local and community levels:

At national level:

Recommendations

1. Governments at all levels should play a leading role in promoting the implementation of GFCS, it’s essential to ensure the operational and functional services in place, particularly for grass-root service delivery. 2. Communication and outreach is a key component of the partnership construction process. Dialogue on practical action at regional and national levels should be encouraged.

3. Improved services and benefits is the necessary condition for wide and effective engagement by various stakeholders. Climate service must be driven by user needs and sound science, and supported by practical participatory mechanism including that of investment.

Practice in priority areas Case 1. Agriculture sectors: Information service system to farming community -- Real-time service delivery system (grass-roots level service stations -- Straight services to end users including farmer associations, village leaders etc.) -- Experts alliance to enhance partnership and capacity development Case 2. Disaster Risk Reduction: Climate prediction based flood/drought preparedness -- Task teams dispatched to prone areas -- Structural measures taken in selected points -- Materials reserved in selected zones, Shelters consolidated…… Disaster risk warning based multi-sector coordinated response -- Risk census and Determine hazard thresholds in cooperation with partners -- Combat disasters by governments and grassroots organizations with early warning, relief and resettlement measures including evacuation Others cases: water, human health, urban planning……

a) Research Station at Sutri Dhaka, Chandra basin

b) Automatic Weather Station at altitude of 5000m amsl (Sutri Dhaka glacier)

c) Hydrological Station at altitude of 4200m (down stream of Sutri Dhaka glacier )

Slide Number 1Slide Number 2Slide Number 3Slide Number 4Slide Number 5Shares of national populations living in HKH region water basinsSlide Number 7Slide Number 8Temperature and precipitation trends Himalaya during 1987-2011Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15Slide Number 16Slide Number 17Slide Number 18Slide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Slide Number 28Slide Number 29Slide Number 30