Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data
In the course of the 21st century, thawing of permafrost is expected to occur in large areas as a consequence of climate change, which could trigger a number of climatic feedback mechnisms on the local to global scale. As the vast and remote permafrost areas cannot be sufficiently covered by ground-...
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ftawi:oai:epic.awi.de:25721 2023-05-15T15:19:33+02:00 Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data Westermann, Sebastian Gisnas, Kjersti Schuler, T. V. Boike, Julia Langer, Moritz Etzelmüller, B. 2011-12-06 https://epic.awi.de/id/eprint/25721/ http://m.core-apps.com/agu2011/abstract/ed111cea5ff27cc0c5786310e948ab3d https://hdl.handle.net/10013/epic.38705 unknown Westermann, S. , Gisnas, K. , Schuler, T. V. , Boike, J. orcid:0000-0002-5875-2112 , Langer, M. orcid:0000-0002-2704-3655 and Etzelmüller, B. (2011) Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data , American Geophysical Union Fall Meeting, San Francisco, USA, 5 December 2011 - 9 December 2011 . hdl:10013/epic.38705 EPIC3American Geophysical Union Fall Meeting, San Francisco, USA, 2011-12-05-2011-12-09 Conference notRev 2011 ftawi 2021-12-24T15:35:30Z In the course of the 21st century, thawing of permafrost is expected to occur in large areas as a consequence of climate change, which could trigger a number of climatic feedback mechnisms on the local to global scale. As the vast and remote permafrost areas cannot be sufficiently covered by ground-based monitoring of soil temperatures in boreholes alone, it is desirable to exploit the wealth of multi-sensor-multi-source data to assess the thermal ground conditions on large scales. Soil temperatures can be modeled using Fourier’s law of heat conduction, so that the key challenge is to supply accurate time series of three key input variables at suitable spatial and temporal resolutions: 1. land surface temperature, 2. snow water equivalent, and 3. soil and snow thermal properties. In Norway, permafrost conditions range from mountain permafrost over organic-rich wetlands to high-arctic permafrost in Svalbard. Furthermore, the availability of gridded data sets from various sources makes it an well-suited test region to evaluate the performance of soil thermal models run with different input data, which facilitates comparing and benchmarking requirements on data quality. Surface temperatures are available from MODIS LST (1km resolution), interpolations from weather stations (1km resolution) and atmospheric modeling, e.g. reanalysis products (much coarser resolution). When computing weekly to monthly averages as needed for permafrost applications, all tested data sets display shortcomings. The overrepresentations of clear-sky conditions in MODIS LST can lead to a significant negative bias of wintertime snow surface temperatures, as demonstrated for a site on Svalbard. Interpolations from weather stations are problematic in mountain settings during stable atmospheric stratification conditions, with maximum biases found at mountain tops underlain by permafrost. We discuss data fusion approaches to achieve both quality assessment and improvement, with the goal to compile the best possible 50-year data set for permafrost applications. The snow water equilvalent is available from passive microwave remote sensing (25 km), interpolations from weather stations (1km resolution) and atmospheric modeling (much coarser resolution). None of these data sets can account for the considerable snow redistribution by wind on small scales. As a result, the snow depth is generally overestimated in permafrost mountain settings, which results in a warm-bias of modeled soil temperatures. Snow redistribution models based on the output of atmospheric modeling can provide probability density functions of snow depth, which allow a probabilistic assessment of permafrost conditions in larger grid cells. Finally, we highlight the importance of improved data sets on landcover and soil thermal properties, which currently constitute a major source of uncertainty for thermal permafrost modeling. Conference Object Arctic Climate change permafrost Svalbard Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Arctic Svalbard Norway |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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ftawi |
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description |
In the course of the 21st century, thawing of permafrost is expected to occur in large areas as a consequence of climate change, which could trigger a number of climatic feedback mechnisms on the local to global scale. As the vast and remote permafrost areas cannot be sufficiently covered by ground-based monitoring of soil temperatures in boreholes alone, it is desirable to exploit the wealth of multi-sensor-multi-source data to assess the thermal ground conditions on large scales. Soil temperatures can be modeled using Fourier’s law of heat conduction, so that the key challenge is to supply accurate time series of three key input variables at suitable spatial and temporal resolutions: 1. land surface temperature, 2. snow water equivalent, and 3. soil and snow thermal properties. In Norway, permafrost conditions range from mountain permafrost over organic-rich wetlands to high-arctic permafrost in Svalbard. Furthermore, the availability of gridded data sets from various sources makes it an well-suited test region to evaluate the performance of soil thermal models run with different input data, which facilitates comparing and benchmarking requirements on data quality. Surface temperatures are available from MODIS LST (1km resolution), interpolations from weather stations (1km resolution) and atmospheric modeling, e.g. reanalysis products (much coarser resolution). When computing weekly to monthly averages as needed for permafrost applications, all tested data sets display shortcomings. The overrepresentations of clear-sky conditions in MODIS LST can lead to a significant negative bias of wintertime snow surface temperatures, as demonstrated for a site on Svalbard. Interpolations from weather stations are problematic in mountain settings during stable atmospheric stratification conditions, with maximum biases found at mountain tops underlain by permafrost. We discuss data fusion approaches to achieve both quality assessment and improvement, with the goal to compile the best possible 50-year data set for permafrost applications. The snow water equilvalent is available from passive microwave remote sensing (25 km), interpolations from weather stations (1km resolution) and atmospheric modeling (much coarser resolution). None of these data sets can account for the considerable snow redistribution by wind on small scales. As a result, the snow depth is generally overestimated in permafrost mountain settings, which results in a warm-bias of modeled soil temperatures. Snow redistribution models based on the output of atmospheric modeling can provide probability density functions of snow depth, which allow a probabilistic assessment of permafrost conditions in larger grid cells. Finally, we highlight the importance of improved data sets on landcover and soil thermal properties, which currently constitute a major source of uncertainty for thermal permafrost modeling. |
format |
Conference Object |
author |
Westermann, Sebastian Gisnas, Kjersti Schuler, T. V. Boike, Julia Langer, Moritz Etzelmüller, B. |
spellingShingle |
Westermann, Sebastian Gisnas, Kjersti Schuler, T. V. Boike, Julia Langer, Moritz Etzelmüller, B. Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data |
author_facet |
Westermann, Sebastian Gisnas, Kjersti Schuler, T. V. Boike, Julia Langer, Moritz Etzelmüller, B. |
author_sort |
Westermann, Sebastian |
title |
Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data |
title_short |
Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data |
title_full |
Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data |
title_fullStr |
Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data |
title_full_unstemmed |
Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data |
title_sort |
towards operational permafrost monitoring in norway using data fusion of multi-sensor data |
publishDate |
2011 |
url |
https://epic.awi.de/id/eprint/25721/ http://m.core-apps.com/agu2011/abstract/ed111cea5ff27cc0c5786310e948ab3d https://hdl.handle.net/10013/epic.38705 |
geographic |
Arctic Svalbard Norway |
geographic_facet |
Arctic Svalbard Norway |
genre |
Arctic Climate change permafrost Svalbard |
genre_facet |
Arctic Climate change permafrost Svalbard |
op_source |
EPIC3American Geophysical Union Fall Meeting, San Francisco, USA, 2011-12-05-2011-12-09 |
op_relation |
Westermann, S. , Gisnas, K. , Schuler, T. V. , Boike, J. orcid:0000-0002-5875-2112 , Langer, M. orcid:0000-0002-2704-3655 and Etzelmüller, B. (2011) Towards operational permafrost monitoring in Norway using data fusion of multi-sensor data , American Geophysical Union Fall Meeting, San Francisco, USA, 5 December 2011 - 9 December 2011 . hdl:10013/epic.38705 |
_version_ |
1766349749841035264 |