Rapid warming in northern latitudes will lead to permafrost thaw and subsequent carbon remobilisa­tion and release to the atmosphere. To incorporate the permafrost carbon climate feedback in globalEarth System Models, it is of importance to know the carbon stored in the circum­Arctic permafrostregion as accurate and precise as possible. Whereas soil, Yedoma and delta carbon stocks are alreadyquantified, deep carbon stocks for vast areas of the current permafrost region are still unaccountedfor. The aim of this Master thesis project is to estimate the deep carbon stock (>3m) for Quaternarydeposits outside the known reservoirs. Therefore, 363 boreholes and exposures were compiled fromthe scientific literature. 244 sites provide profile descriptions (depositional environment, depth andthickness) and another 119 sites contain data to calculate carbon densities (ground ice content, coarsefraction (>1cm and/or >2mm), bulk density and total organic carbon). Data gaps were filled usinglocal, regional and global average facies values from the compiled dataset. For spatial upscaling,key regions are defined using the permafrost zone, overburden thickness and ice content. The fielddata compiled here shows disagreements with the Circum­Arctic Map of Permafrost and Ground­Iceconditions (Brown et al. 2002), which should be updated especially in thin and ice­poor regions. Atotal C stock of 1698 ±255 PgC is estimated for 3­25m in Quaternary deposits of the circum­Arcitcpermafrost region, next to the Yedoma domain (327 ­ 466 PgC, Strauss et al. 2017) and deltas (41 ­151 PgC, Hugelius et al. 2014). About 70% of the carbon is stored within 3­10m (1200 ±238 PgC).Due to a publication bias towards thick and organic­rich sediments in the literature, C stocks calcualtedhere might be overestimated. Additional to the Yedoma domain, 309 ±99 PgC are stored in ice­richdeposits of the continuous permafrost zone, a regions especially prone to thermokarst and deep carbonremobilisation. Thermokarst, thermo­fluvial erosion along rivers and coasts as well as carbon releasethrough inland water systems presents possible release mechanisms for stored carbon. The permafrostcarbon estimate determined here doubles the known carbon reservoir in the permafrost region and em­phasises the importance for possible deep carbon release with future permafrost thawing.

The permafrost region covers a vast area of land surface on the northern hemisphere,storing large amounts of carbon. Unfortunately, climate warming leads to permafrostthaw altering the hydrothermal state of permafrost soils. Due to the remoteness of thepermafrost region, access to field measurements is restricted. Therefore, remotesensing is an asset to study the permafrost region. Since permafrost is a sub-surfacephenomenon it cannot be directly observed from space. However, by using differenttypes of satellites the soil properties of the top soil layer, down to 10 cm depth, can beaccessed. To establish soil properties for the deeper soil layers modelling is required.The ERA5-Land (ERA5L) soil moisture is modelled based on climate reanalysis. Inthis study in-situ soil moisture data from the International Soil Moisture Network(ISMN) is used to evaluate the performance of the ERA5L soil moisture data withinthe permafrost region. The performance of the ERA5L soil moisture is found toperform best in soil layer 1 (0-7 cm depth) and worst in soil layer 3 (28-100 cm depth).For both soil layer 1 and 2 (0-7 and 7-28 cm depth) a moderate correlation(0.309 < R < 0.335) was found between ERA5L and in-situ soil moisture data, in Julyand August. The performance of the ERA5L soil moisture is best in Europe and worstin North-America. Compared to other evaluations of ERA5L soil moisture, within thepermafrost region, this study found a relatively low correlation. Therefore, this studyconcludes that on a global scale the ERA5L soil moisture is not ideal for directlyinforming permafrost research and decision making. However, integrating multisourcedatasets, resampled to a finer spatial resolution, could improve the performance ofERA5L soil moisture model on a global level. Moreover, on a local scale theapplication of a bias correction could also improve the performance of the ERA5L soilmoisture model.