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Regional synthesis and mapping of soil organic carbon and nitrogen stocks at the Canadian Beaufort coast

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Abstract. Permafrost soils are particularly vulnerable to climate change. To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region. In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales. Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional synthesis of terrestrial carbon stocks based on spatial modelling has been conducted yet. This study synthesises available field data from the Canadian coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data. We explore local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks. We mapped SOC and N stocks for the entire region and provide separate models for the coastal mainland area and Qikiqtaruk Herschel Island. We assessed performance of different random forest models by using the Area of Applicability (AOA) method. We further applied the quantile regression forest method to the mainland and Qikiqtaruk Herschel Island models for SOC stocks and compared the results with the AOA method. Our results indicate that not only the selection of data is crucial for the resulting maps, but also the chosen covariates, which were picked by the models as most important. The estimated SOC stock for the upper metre is 56.7 ± 5.6 kg m−2 and the N stock 2.19 ± 0.51 kg m−2. The average SOC stocks vary significantly when including or excluding data in the predictive models. Qikiqtaruk Herschel Island is geologically different from the coastal mainland and has lower SOC stocks. Including Qikiqtaruk Herschel Island soil data to predict SOC stocks at the mainland has large impact on the results. Differences in N stocks were not as dependent on the location as SOC stocks and rather differences between individual studies occurred. The results of the separate models show 36.2 ± 5.7 kg C m−2 and 2.66 ± 0.39 kg N m−2 for Qikiqtaruk Herschel Island and 57.2 ± 4.5 kg C m−2 and 2.17 ± 0.50 kg N m−2 for the mainland. Our results diverge from previous studies of lower resolution, showing the added regional-scale accuracy and precision that can be achieved at intermediate resolution and with sufficient field data.
Title: Regional synthesis and mapping of soil organic carbon and nitrogen stocks at the Canadian Beaufort coast
Description:
Abstract.
Permafrost soils are particularly vulnerable to climate change.
To assess and improve estimations of carbon (C) and nitrogen (N) budgets it is necessary to accurately map soil carbon and nitrogen in the permafrost region.
In particular, soil organic carbon (SOC) stocks have been predicted and mapped by many studies from local to pan-Arctic scales.
Several studies have been carried out at the Canadian Beaufort Sea coast, though no regional synthesis of terrestrial carbon stocks based on spatial modelling has been conducted yet.
This study synthesises available field data from the Canadian coastal plain and uses it to map regional SOC and N stocks using the machine learning algorithm random forest and environmental variables based on remote sensing data.
We explore local differences in soil properties and how soil data distribution across the region affects the accuracy of the predictions of SOC and N stocks.
We mapped SOC and N stocks for the entire region and provide separate models for the coastal mainland area and Qikiqtaruk Herschel Island.
We assessed performance of different random forest models by using the Area of Applicability (AOA) method.
We further applied the quantile regression forest method to the mainland and Qikiqtaruk Herschel Island models for SOC stocks and compared the results with the AOA method.
Our results indicate that not only the selection of data is crucial for the resulting maps, but also the chosen covariates, which were picked by the models as most important.
The estimated SOC stock for the upper metre is 56.
7 ± 5.
6 kg m−2 and the N stock 2.
19 ± 0.
51 kg m−2.
The average SOC stocks vary significantly when including or excluding data in the predictive models.
Qikiqtaruk Herschel Island is geologically different from the coastal mainland and has lower SOC stocks.
Including Qikiqtaruk Herschel Island soil data to predict SOC stocks at the mainland has large impact on the results.
Differences in N stocks were not as dependent on the location as SOC stocks and rather differences between individual studies occurred.
The results of the separate models show 36.
2 ± 5.
7 kg C m−2 and 2.
66 ± 0.
39 kg N m−2 for Qikiqtaruk Herschel Island and 57.
2 ± 4.
5 kg C m−2 and 2.
17 ± 0.
50 kg N m−2 for the mainland.
Our results diverge from previous studies of lower resolution, showing the added regional-scale accuracy and precision that can be achieved at intermediate resolution and with sufficient field data.

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