Remote Sensing of SWE in CanadaRemote Sensing of SWE in Canada

Anne WalkerClimate Research Division, Environment Canada

Polar Snowfall Hydrology Mission Workshop, June 26-28, 2007

Satellite Remote Sensing Satellite Remote Sensing –– Snow CoverSnow Cover

Reflection of visible light< 1 km spatial res.Impeded by cloud cover and lack of sunlight40+ year record of satellite sensors (AVHRR, Landsat, MODIS)

Optical -- Snow extent Passive Microwave – Depth/SWE

Microwave emission from earth’s surface10-25 km spatial res.“All-weather”, independent of light conditions~ 30 year record of satellite sensors (SMMR, SSM/I, AMSR-E)

Passive Microwave Remote Sensing of Snow Passive Microwave Remote Sensing of Snow Cover PropertiesCover Properties

Volume scattering of emitted earth radiation by snow cover provides basis for retrieval of snow cover properties from passive microwave data Data available in near real time and as historical archive in griddedformat (1978 – SMMR, 1987 – SSM/I, 2002 – AMSR-E)Canadian focus on development of regional-based retrieval methods (algorithms) for dominant landscapes – prairies, boreal forest, tundra

Snow cover ⇒ snow grains and air

Microwave energy emitted by underlying ground (TBg) is scattered by grains

TB snow surface < TBg

Amount of scattering is a function of snow depth and density ⇒ SWE

TB = SWE

Environment Canada SWE AlgorithmsEnvironment Canada SWE Algorithms

Prairie (open) algorithmSWE algorithm developed using airborne microwave radiometer data set (1982 experiment)weekly SWE maps produced using SSM/I data since 1989

SWE = a + b (TB37V - TB19V)18

Boreal forest algorithms3 forest SWE algorithms derived using BOREAS airborne microwave radiometer data, ground SWE data

coniferous, deciduous, sparse forest

4 algorithms applied to gridded SSM/I data with addition of land cover classification data to yield an overall SWE value that takes into account effects of land cover variations

SWE = FDSWED + FCSWEC + FSSWES + FOSWEO

D - deciduous; C - conifer, S - sparse forest, O - openSWEi = A + B (37V - 19V)Fi = Land cover fraction per grid point (i = D, C, S or O)

Regional SWE Products for Research and Regional SWE Products for Research and Operational ApplicationsOperational Applications

Manitoba – Red River watershed- specialized maps sent to provincial water resource agencies focussed on priority river basins for forecasting spring runoff and flood risk

Mackenzie Basin- MAGS research on snow cover variations, RCM evaluation

Snare River Basin – NWT- maps for hydro companies (e.g. NWT Power Corp.) in support of planning hydroelectric power operations

CCanadian Prairies-weekly maps produced and sent to users (federal, provincial agencies, private industry) who have a requirement for regular monitoring of snow cover in western Canada- available to public on www.socc.ca (State of Canadian Cryosphere)

Regional SWE for Weather Forecasting Regional SWE for Weather Forecasting ––NWT/NunavutNWT/Nunavut

Request from Arctic Weather Centre in Edmonton

Investigation into potential contribution of SSM/I SWE maps for prediction of blowing snow events (severe weather forecasting)

SWE maps provided 3X per week for use/evaluation by forecasters

Change in SWE map – areas of change over a week (Friday to Friday)

Change in SWE over a week

Validation of Satellite Derived SWE InformationValidation of Satellite Derived SWE Information

6.9 GHz

1.4 GHz

19.35 GHz85.5 GHz37 GHz

MSC microwave radiometers on NRC Twin Otter

In-situ measurements

1) Airborne/field validation campaigns1) Airborne/field validation campaignsAcquisition of airborne microwave radiometer data and ground-based measurements to support:

- validation of satellite retrievals- algorithm refinement/new development

2) Regional snow 2) Regional snow surveyssurveys

Targetted to specific landscape environments

ground-based measurement transects over extensive areas

3) Comparison with snow depth/SWE 3) Comparison with snow depth/SWE available from EC monitoring networks + available from EC monitoring networks + other agenciesother agencies

Current MSC Snow Depth/SWE Network

Improving Passive Microwave SWE Retrievals for Northern Improving Passive Microwave SWE Retrievals for Northern Canada (Canada (EC and EC and WilfridWilfrid Laurier U.)Laurier U.)

Derksen et al., Remote Sensing of Environment, 2005

Tundra Ecosystem Research Station (TERS) located at Daring Lake NWT

Study Objectives:Conduct in-situ snow survey to assess variability

and physical properties of snow cover.

Integrate ground based and airborne radiometer data to investigate snow cover relationships for representative terrain units at a variety of scales (aircraft/field campaigns).

Investigate the influence of lake ice on passive microwave brightness temperatures - existing algorithms do not consider lake covered area and cause SWE underestimations.

Field sampling and aircraft remote sensing data collection during 2004-2007

Northern Boreal ForestNorthern Boreal Forest

Primary challenge: existing algorithms tend to underestimate SWE in the typically deep (up to 1 metre) snowpacks of the northern boreal forest.

Stakeholders – NWT Power, Manitoba Hydro have supported this research through in-kind support of field activities. .

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in situ SWE (mm)

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Surface measurement sites, 2004-2007

Use of AMSR-E 10 GHz

Tundra Landscape Effects on SWE RetrievalTundra Landscape Effects on SWE RetrievalTerrain

Lakes

Lakes present a major problem for SWE retrieval in tundra regions high fractional lake surface area

Ongoing Challenges for SWE RetrievalOngoing Challenges for SWE Retrieval

Areas of dense forest cover, deep snowpacksComplex landscapes (heteorogeneous land cover, terrain), esp. mountainsSeasonal and interannual variations in snowpackproperties (e.g. melt/re-freeze)Lack of available ground measurements for evaluating algorithm performance (esp. in northern regions of Canada)Coarse spatial resolution of current satellite instruments (10-25km)

No single algorithm will provide representative SWE over Canada due to the wide range of landscapes and snow cover properties

Regional algorithm development an ongoing activity

Variability and Change in the Canadian Cryosphere

Can. contribution to the “State and Fate of the Cryosphere” IPY 105

Photo: Vital Arctic Graphics, UNEP, GRID-Arendal, 2005

Canadian cryospheric data portal for IPYDIS

The human dimension

Simulation of the cryosphere in climate models

Improved representation of Arctic processes in CLASS

Cryosphere-climate variability and feedbacks

Cryospheric information contributing to the IPY snapshot

Activities

Planned IPY snow cover field campaigns in Canadian tundra regions:

April-May 2007 NWTJan-Feb 2008 Northern QuebecApril-June 2008 NWT & Arctic

Islands

IPY Field Activities in Support of Validated IPY Field Activities in Support of Validated Satellite SWE Products (Maps, Data Sets)Satellite SWE Products (Maps, Data Sets)

April 2007 Alaska-Canada Barrens Snowmobile Transect (SnowSTAR2007)

2007 and 2008 Field surveys and aircraft/field campaigns

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20082008

2007

IPY research to address snow cover retrieval in mountainsIPY research to address snow cover retrieval in mountains

• Kelly (U. Waterloo) in collaboration with Hall (NASA) and Cline (NOHRSC) will apply SNODAS snow data assimilation system to the Yukon at ~1 km scale

• Will make use of MODIS, QuikSCAT, AMSR-E, surface observations and snow model (SNTHERM) simulations

Sample SNODAS output for southern Rockies, April 1, 2006