The use of earth observation technology to improve the characterization of ice and snow

Transcription

1 The use of earth observation technology to improve the characterization of ice and snow Swedish Meteorological and Hydrological Institute (SMHI)

2 Outline: EO and ice/snow CryoLand North Hydrology EO in HYPE? 2

3 Satellite input for snow and ice

4 Background Snow, glaciers, lake ice and river ice are land surface parameters characterized by high spatial and temporal variability. Satellite observations are efficient means to deliver accurate(?) and up-to-date information on these key elements of the cryosphere. 4

5 Methods for EO 1/2 Optical and active (radar, SAR) and passive microwave (MW) satellite imagery. Snow and ice products are based on single sensor data inputs, as well as on dual or multi-sensor algorithms. 5

6 Methods for EO 2/2 The data is based on basic principles of optical and radar wave interactions with snow and ice. Automated snow classification methods are applied for the retrieval of snow covered area (SCA). These methods apply multi-spectral algorithms for retrieving binary SCA maps, enabling clear discrimination of snow and clouds and compensation of terrain illumination effects. More advanced algorithms take into account effects of forest cover on the reflectivity of snow covered regions. 6

7 Products Topic Seasonal Snow Glaciers Lake / River Ice Main Parameters snow extent snow mass melt state of snow (wet / dry) snowfall glacier outline (area) snow / ice extent ice velocity glacier dammed lakes ice depth temporal changes of ice extent snow burden surface temperatures fraction of the water surface covered by ice 7

8 CryoLand

9 Background CryoLand is part of the European Earth observation programme GMES (Global Monitoring for Environment and Security). Financing: EU (7th Framework Programme). Duration: 4 years, starting ENVEO EOX SYKE FMI KSAT NR NORUT NMA GAMMA SMHI ENVEO Environmental Earth Observation IT GmbH (Coordinator) EOX IT Services GmbH Suomen Ymparistokeskus Ilmatieteen Laitos Kongsberg Satellite Services AS Norsk Regnesentral Stiftelse Northern Research Institute Tromso AS Administratia Nationala De Meteorologie R.A. Gamma Remote Sensing Research and Consulting AG Sveriges Meteorologiska och Hydrologiska Institut Austria Austria Finland Finland Norway Norway Norway Romania Switzerland Sweden 9

10 Objectives The CryoLand project aims at developing, implementing and validating a service on monitoring snow and land ice. CryoLand will build upon, integrate and widen structural and technical capabilities of the European service providers. The products will be tailored to the user needs. 10

11 Expected scientific and technological results Integration of land cover information from the GMES service to improve the quality of the snow cover products, in particular in forested regions and in areas of complex land use. Automating the processing lines from EO data. Integration of insitu measurements data and remote sensing based snow and ice products. Perform rigorous tests and benchmarking of the snow cover products in cooperation with the users. 11

12 North Hydrology

13 Background Organized by University of Waterloo, Canada Financing: ESA (European Space Agency) Duration: Participants: University of Waterloo (Canada), Environment Canada (Canada), ENVEO IT GmbH (Austria), Finnish Environment Institute (Finland), Finnish Meteorological Institute (Finland), INRS Centre Eau Terre Environnement (Canada), Northern Research Institute Tromsø (Norway), Norsk Regnesentral (Norway), Swedish Meteorological and Hydrology Institute (Sweden) Website: 13

14 Objectives Exploit the use of EO technology, models and in situ data to improve the characterization of river and lake ice processes. Global and regional lake and river ice characterization, and lake surface temperature for NWP, climate models and regional hydrological studies. Basin scale river ice characterization for flood forecasting and early warning systems. 14

15 Expected scientific and technological results Develop and validate a dedicated Portfolio of novel EO-based products (maximizing the use of ESA data). Assess the impact of the developed products to improve: a) regional models and b) flood forecasting. Develop a Scientific Roadmap identifying further research and devolvement activities. Frontiera Pungwe Falls Katiyo Honde Mavonde Pungwe Nhazonia Vunduzi Pungue Sul Tacuraminga Bue Maria 15

16 Use of satellite data in HYPE

17 Snow and ice in HYPE? The snow melt process is important for the spring flood Ice jams cause flooding Ice cover is important for biological processes At present, no representation of ice in lakes or streams. 17

18 Possible improvements within these projects Dynamic lake ice module Accounting for ice jams Better estimations of snow and ice volumes Improve spring flood predictions 18

19 Possible use of EO within HYPE Evaluation of the model performance Calibration Update state variables Temporal fit: Best model performance (S-HYPE) 80 Water discharge (m3/s) 10.4 Lake water level (m) Vattenföring (m 3 /s) Sjövattenstånd (m) Total Nitrogen (µg/l) Total Phosphorus (µg/l) Tot-N 8000 Tot-P

20 Thank you!