Analysis of the global ESA GlobPermafrost map for Scandinavia
(2018) In Student thesis series INES NGEM01 20181Dept of Physical Geography and Ecosystem Science
- Abstract
- Due to its high vulnerability, permafrost is one of the key features studied in the field of climate change impacts. Permafrost is widespread in the Arctic region. The majority of the area underlain by permafrost is however difficult to access for in-situ monitoring and it is difficult to get an overview of the current state of permafrost in many areas.
Permafrost modeling provides a solution which overcomes this difficulty and allows studies on permafrost distribution as well as some characteristics, i.e. ground temperatures over large remote areas. Temperature at the top of the permafrost (TTOP) is one of several permafrost modeling approaches which conceptually represents a steady-state equilibrium model. In this study, two TTOP-based... (More) - Due to its high vulnerability, permafrost is one of the key features studied in the field of climate change impacts. Permafrost is widespread in the Arctic region. The majority of the area underlain by permafrost is however difficult to access for in-situ monitoring and it is difficult to get an overview of the current state of permafrost in many areas.
Permafrost modeling provides a solution which overcomes this difficulty and allows studies on permafrost distribution as well as some characteristics, i.e. ground temperatures over large remote areas. Temperature at the top of the permafrost (TTOP) is one of several permafrost modeling approaches which conceptually represents a steady-state equilibrium model. In this study, two TTOP-based models were used; the GlobPermafrost model which was used to produce the most recent global permafrost map (Alfred-Wegener-Institut) and a local Scandinavian model.
The aim of this study was twofold; firstly, the performance of the GlobPermafrost model in Scandinavia was analyzed by comparing the model output with the output from the local Scandinavian model. Secondly, the role of land cover data as an input variable in the TTOP model was investigated. The TTOP-based GlobPermafrost model was run with different land cover input data to evaluate this.
In general, the GlobPermafrost model underestimated permafrost occurrence in Scandinavia (overall r2 being 0.39). The lowest underestimation is located in the regions with little or no permafrost. The biggest underestimations are found in peatlands and mountainous areas with more likely permafrost occurrence. Unexpected underestimation of permafrost was observed in the forests. This exposed the weaknesses of regional permafrost model overestimating permafrost occurrence in forests.
The rerun of the GlobPermafrost model with three times more detailed land cover input data did surprisingly not have a great effect on the model performance (r2 only changed by 8%). The small changes detected in the GlobPermafrost output could be explained by the changes in wetland fraction between the two land cover datasets used as input to the GlobPermafrost model.
The overall conclusions from this study are 1) that the GlobPermafrost model underestimates the amount of permafrost in the study area, especially in the mountains and 2) that improved input land cover data was only of minor importance to the TTOP model performance and future research should hence focus on other forcing input data to improve model performance. (Less) - Popular Abstract
- Permafrost is a frozen ground which remains with a temperature of 0 degrees C or lower for 2 or more seasons. Permafrost is widespread in the Arctic region. Due to its high vulnerability to the temperature changes permafrost can degrade and lead to serious consequence such as infrastructure collapses, changes in the relief and vegetation. Nowadays climate change makes permafrost present even bigger interest for the researchers. To make correct projections it is important to explore the distribution of the permafrost and to study its parameters.
Unfortunately, most of the area underlain by permafrost is however difficult to access for the field studies. Permafrost modeling provides a solution which overcomes this difficulty and allows... (More) - Permafrost is a frozen ground which remains with a temperature of 0 degrees C or lower for 2 or more seasons. Permafrost is widespread in the Arctic region. Due to its high vulnerability to the temperature changes permafrost can degrade and lead to serious consequence such as infrastructure collapses, changes in the relief and vegetation. Nowadays climate change makes permafrost present even bigger interest for the researchers. To make correct projections it is important to explore the distribution of the permafrost and to study its parameters.
Unfortunately, most of the area underlain by permafrost is however difficult to access for the field studies. Permafrost modeling provides a solution which overcomes this difficulty and allows studies on permafrost distribution as well as some of its characteristics.
In this study we explored outcomes of two models: global (GlobPermafrost) and regional Scandinavian. The outcome we focused on was the map of permafrost occurrence probabilities. These values present on how likely it is that permafrost can be found in a certain cell of the map. Permafrost occurrence map produced by regional Scandinavian model is more accurate than the global one since the input data were more detailed. In fact, it is the most precise permafrost map for Scandinavia available, thus we assume that regional Scandinavian permafrost map represents the truth.
The aim of this study was twofold. Firstly, we estimated the performance of the GlobPermafrost model in Scandinavia. To achieve it we compared the map of permafrost probabilities in Scandinavia produced by global (GlobPermafrost) model with the regional Scandinavian permafrost probability map which we assumed as truth. Secondly global (GlobPermafrost) model was rerun with a one input replaced. This input was a land cover data (the information about physical material at the surface). We replaced the land cover used with a more detailed one. The new outcome permafrost probability map was compared with the original GlobPermafrost permafrost probability map and with the one produced by the regional model. This gave a chance to estimate the role of this input in this type of the models for Scandinavia.
The results showed that the highest mismatch of the GlobPermafrost model output compared the output of the regional model were found in the mountains and wetlands in the northern Scandinavia. These are the areas where Scandinavian permafrost was found by the researchers. The lowest disagreement was found in the areas with little or no permafrost. These results give some new information about the performance of GlobPermafrost map in Scandinavia and can be used as the insight of where and how it should be improved.
Second results showed a very small role of the land cover as an input to the model. This gave us an understanding on the importance of other inputs in this model in Scandinavia. We can say that other inputs are more influential on the result, and thus are more prior to be enhanced.
In general, this study contributes to the knowledge of the permafrost models. Improvement of the permafrost models is a necessary step to perform the accurate projections on the permafrost distribution and parameters. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8955040
- author
- Nesterova, Nina LU
- supervisor
- organization
- course
- NGEM01 20181
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Physical Geography and Ecosystem analysis, Arctic, permafrost, permafrost modeling, TTOP, CryoGRID 1, GlobPermafrost, Scandinavia
- publication/series
- Student thesis series INES
- report number
- 461
- funder
- Global Education Program
- language
- English
- additional info
- External supervisors: Sebastian Westermann, PhD, University of Oslo, Department of Geosciences
Jaroslav Obu, PhD, University of Oslo, Department of Geosciences - id
- 8955040
- date added to LUP
- 2018-07-17 12:34:30
- date last changed
- 2018-07-17 12:34:30
@misc{8955040,
abstract = {{Due to its high vulnerability, permafrost is one of the key features studied in the field of climate change impacts. Permafrost is widespread in the Arctic region. The majority of the area underlain by permafrost is however difficult to access for in-situ monitoring and it is difficult to get an overview of the current state of permafrost in many areas.
Permafrost modeling provides a solution which overcomes this difficulty and allows studies on permafrost distribution as well as some characteristics, i.e. ground temperatures over large remote areas. Temperature at the top of the permafrost (TTOP) is one of several permafrost modeling approaches which conceptually represents a steady-state equilibrium model. In this study, two TTOP-based models were used; the GlobPermafrost model which was used to produce the most recent global permafrost map (Alfred-Wegener-Institut) and a local Scandinavian model.
The aim of this study was twofold; firstly, the performance of the GlobPermafrost model in Scandinavia was analyzed by comparing the model output with the output from the local Scandinavian model. Secondly, the role of land cover data as an input variable in the TTOP model was investigated. The TTOP-based GlobPermafrost model was run with different land cover input data to evaluate this.
In general, the GlobPermafrost model underestimated permafrost occurrence in Scandinavia (overall r2 being 0.39). The lowest underestimation is located in the regions with little or no permafrost. The biggest underestimations are found in peatlands and mountainous areas with more likely permafrost occurrence. Unexpected underestimation of permafrost was observed in the forests. This exposed the weaknesses of regional permafrost model overestimating permafrost occurrence in forests.
The rerun of the GlobPermafrost model with three times more detailed land cover input data did surprisingly not have a great effect on the model performance (r2 only changed by 8%). The small changes detected in the GlobPermafrost output could be explained by the changes in wetland fraction between the two land cover datasets used as input to the GlobPermafrost model.
The overall conclusions from this study are 1) that the GlobPermafrost model underestimates the amount of permafrost in the study area, especially in the mountains and 2) that improved input land cover data was only of minor importance to the TTOP model performance and future research should hence focus on other forcing input data to improve model performance.}},
author = {{Nesterova, Nina}},
language = {{eng}},
note = {{Student Paper}},
series = {{Student thesis series INES}},
title = {{Analysis of the global ESA GlobPermafrost map for Scandinavia}},
year = {{2018}},
}