# Decadal trends in the release of terrigenous organic carbon to the Mackenzie Delta (Canadian Arctic) using satellite ocean color data (1998–2019)

Atsushi Matsuoka, Marcel Babin, Jorien Vonk

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Atsushi Matsuoka, Marcel Babin, Jorien Vonk. Decadal trends in the release of terrigenous organic carbon to the Mackenzie Delta (Canadian Arctic) using satellite ocean color data (1998–2019). Remote Sensing of Environment, 2022, 283, pp.113322. 10.1016/j.rse.2022.113322. hal-04295720

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# Decadal trends in the release of terrigenous organic carbon to the Mackenzie Delta (Canadian Arctic) using satellite ocean color data (1998–2019)

Atsushi Matsuoka<sup>a,b,c,*</sup>, Marcel Babin<sup>b,c</sup>, Jorien E. Vonk<sup>d</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Edited by Dr. Menghua Wang

**Keywords:**

Climate change

Organic carbon

Permafrost thaw

Ocean color remote sensing

## ABSTRACT

Arctic rivers operate as integrators of northern high latitude regions, where large stocks of soil organic carbon (OC) are currently experiencing rapid warming. Here we show that tracking total OC in the Mackenzie Delta whose upstream catchment is underlain by permafrost soils is now possible using polar-orbiting satellite ocean color observations. A non-parametric trend analysis that is valid for hydrological data shows a significant increase in dissolved OC (DOC) as well as particulate OC (POC) concentrations in late summer (0.019 and 0.069 g m⁻³ year⁻¹; *p* < 0.05 for both). Uncertainties of the satellite estimates of DOC and POC do not influence our results. These concentration increases are not related to changes in river discharge. Parallel increases of independent long-term (1979–2018) in situ measurements of thaw depth of the active layer, as well as meteorological and hydrological patterns suggest that these late summer increases can likely be explained by increasing inputs of permafrost OC. This study shows great promise for remote, large-scale detection of catchment-scale thaw impacts from space.

## 1. Introduction

Storage of soil organic carbon (OC) in the northern circumpolar permafrost region is estimated to be 1030 ± 150 Pg in the top 3 m (Hugelius et al., 2014), which accounts for nearly a half of global soil C estimates (2300 Pg C; Jobbágy and Jackson, 2000; McGuire et al., 2009) and is more than currently present in the atmosphere (800 Pg C). Permafrost is particularly vulnerable to increasing thaw and degradation due to climate warming (McGuire et al., 2018; Biskaborn et al., 2019) and therefore may increasingly release OC into inland and coastal waters (McGuire et al., 2009; Wild et al., 2019). Given recent findings that old permafrost-derived dissolved organic carbon from many regions is more bioavailable than previously thought, as explained by high levels of aliphatic compounds that can be rapidly utilized by microbes (Mann et al., 2015; Spencer et al., 2015), an increasing amount of the newly-mobilized permafrost OC in aquatic systems (e.g., Schwab et al., 2020) may contribute to the global water-atmosphere fluxes of carbon dioxide (CO₂) and methane (Koven et al., 2011; IPCC, 2013, 2019).

Complex ecological (e.g., production of plant biomass), physical (e. g., weathering), photochemical (e.g., photo-oxidation), and biogeochemical (e.g., microbial degradation) processes in the Arctic Ocean watershed and tributaries largely control the variations in the concentrations of dissolved (DOC) and particulate organic carbon (POC) in coastal waters. While tracking changes in fluxes of DOC and POC is essential for assessing possible related water-atmosphere CO₂ fluxes (Manizza et al., 2011, 2013, 2019), their large temporal and geographical variability prevents getting a holistic view using in situ data alone (Holmes et al., 2002; Raymond et al., 2007).

Satellite ocean color remote sensing is a complementary tool for overcoming this challenge. In particular, a combination of ocean color data by several polar-orbiting sensors, such as Orbview-2/SeaWiFS, Aqua/MODIS, Envisat/MERIS, Suomi-NPPVIRRS, NOAA-20/VIIRS, Sentinel-3/OLCI, and GCOM-C/SGLI, have been collected globally over more than two decades with good radiometric accuracy based on a series of calibration systems (i.e., solar, lunar, and vicarious calibrations; Franz et al., 2007; Eplee Jr. et al., 2009; Lee and Meister, 2017). While ocean

<footnote><sup>a</sup> Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA</footnote>

<footnote><sup>b</sup> Takuvik International Research Laboratory, CNRS, 1045 avenue de la Médecine, Québec, QC G1V 0A6, Canada</footnote>

<footnote><sup>c</sup> Takuvik International Research Laboratory, Département de Biologie, Université Laval, 1045, avenue de la Médecine, Québec, QC G1V 0A6, Canada</footnote>

<footnote><sup>d</sup> Department of Earth Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081HV, the Netherlands</footnote>

<footnote>* Corresponding author at: Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA.
E-mail address: atsushi.matsuoka@unh.edu (A. Matsuoka).</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Example true color imagery of the Mackenzie river plume acquired on July 7, 2018 using Aqua/MODIS sensor (original file: A2008189214000.L1A_LAC). The area where endmember values of DOC and POC concentrations (at zero salinity) were extracted is shown as the yellow polygon. Stations where river discharge (10LC014) as well as air temperature, thaw depth, and precipitation in Inuvik (C7A) and Norman Wells (C11A) are also indicated. The geographical region of the study area is shown in the black rectangle of a plain global map at the bottom right corner. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

color observations of polar regions inevitably suffer from frequent
clouds and low sun elevation, the large number of satellite images per
day as a result of the polar orbit somewhat compensates for these issues,
making ocean color data valuable for polar seas, as well (IOCCG, 2015).
Ocean color algorithms for deriving DOC (POC) concentrations with
reasonable uncertainties have recently been developed for Arctic waters
based on retrieval of light absorption (backscatter) by organic matter in

water (Matsuoka et al., 2017; Doxaran et al., 2015). Despite the
importance of organic carbon exports to the Arctic coast, optical prop-
erties of Arctic coastal waters have been less studied (Matsuoka et al.,
2011, 2014). This fact prevented us from tracking the carbon using
ocean color data (Fichot et al., 2013; Griffin et al., 2018; Juhls et al.,
2019) until recently. Most importantly, an uncertainty of those esti-
mates has often not been taken into account, so a long-term trend

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a) The number of available scene images for each month for 2003–2019. (b) Summary of the number of scenes used from June to September for 2003–2019. On average each year, 16.4 scenes with standard deviation of 1.5 scenes per month (Jun-Sep) were obtained, demonstrating the availability of reasonable number of scenes in the Mackenzie delta region. This result assures statistical results obtained from the present study.</figcaption></figure>
<table><caption>**Table 1**
Summary of the final data used in the present study.</caption><thead><tr><th>Variable</th><th>Type</th><th>Unit</th><th>Period</th></tr></thead><tbody><tr><td>River discharge</td><td>In situ</td><td>m<sup>3</sup> month<sup>-1</sup></td><td>1979–2019</td></tr><tr><td>DOC flux</td><td>Merged satellite (SeaWiFS + MODIS)</td><td>Tg C summer<sup>-1</sup></td><td>1998–2019</td></tr><tr><td>DOC concentration</td><td>Merged satellite (SeaWiFS + MODIS)</td><td>g m<sup>-3</sup></td><td>1998–2019</td></tr><tr><td>POC flux</td><td>Satellite (MODIS)</td><td>Tg C summer<sup>-1</sup></td><td>2003–2019</td></tr><tr><td>POC concentration</td><td>Satellite (MODIS)</td><td>g m<sup>-3</sup></td><td>2003–2019</td></tr><tr><td>Air temperature</td><td>In situ</td><td>°C</td><td>1979–2018</td></tr><tr><td>Precipitation</td><td>In situ/reanalysis</td><td>mm day<sup>-1</sup></td><td>1979–2018</td></tr><tr><td>Thaw depth</td><td>In situ</td><td>cm</td><td>1992–2018</td></tr></tbody></table>

analysis has been challenging.

To investigate the impact of ongoing climate warming on permafrost thaw and its transport to the Arctic Ocean, we chose the Mackenzie Delta (Fig. 1) as a model study area because 1) discontinuous and continuous permafrost together occupy about 50% of the watershed (Obu et al., 2019), 2) climate warming is already affecting this area (e.g., Tank et al., 2016; Schwab et al., 2020), and 3) long-term hydrological and meteorological records are available. It is worth mentioning that the optical properties in this area are well known thanks to several field campaigns (e.g., Bélanger et al., 2008; Doxaran et al., 2012; Matsuoka et al., 2012, 2013, 2016).

Here we show, using satellite remote sensing data, that both DOC and POC concentrations exhibit significant increasing trends in late summer over the last two decades. These trends are not correlated with variations in river discharge. Our results suggest that these increases are associated with permafrost-derived OC, which might have a notable impact on biogeochemical fluxes in coastal waters.

## 2. Data and methods

### 2.1. Satellite data

For our long-term analysis, we used the atmospheric correction algorithm for Sea-viewing Wide Field-of-View (SeaWiFS) and Moderate resolution Imaging Spectroradiometer (MODIS) specifically designed and validated for our study area (Doxaran et al., 2015; Matsuoka et al., 2016). Level 1A scenes (reconstructed, unprocessed data together with sensor calibration coefficients as well as geolocation information) of the SeaWiFS sensor onboard the Orbview-2 satellite platform from 1998 to 2004 were downloaded from the NASA Ocean Color website (https://oceandata.sci.gsfc.nasa.gov/SeaWiFS/L1/). Similarly, level 1A scenes of the MODIS onboard the Aqua satellite from 2002 to 2019 were downloaded (https://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/L1/). Although we acknowledge the radiometric degradation issue particularly since 2014, the majority of the degradation in the data processing version used in the present study (R2018.1) has been corrected (Lee and Meister, 2017; Franz et al., 2018).

Due to the omnipresence of sea ice and clouds during several months of the year, we restricted our analysis to the period from June to September each year. A reasonable number of scenes (16.4 ± 1.5 scenes per month) were available and used in the present study (Fig. 2a and b).

Reflectance of the water, so-called remote sensing reflectance, $R_{rs}(\lambda)$ (in units of $sr^{-1}$; $\lambda$ represents wavelength in nm), is obtained after applying an atmospheric correction (AC) algorithm to radiometrically calibrated radiance measured from the top of the atmosphere. While radiance in the shortwave infrared spectral domain (SWIR), which is useful for AC particularly in highly turbid coastal waters, is available for MODIS, this was not available for SeaWiFS. To overcome this issue, two different ACs that are both applicable for coastal waters were considered: SWIR AC developed by Doxaran et al. (2015) specifically designed and validated for our study area and another one (so-called MUMM)

developed by Ruddick et al. (2000). SWIR AC was used only for MODIS, whereas MUMM was used for both SeaWiFS and MODIS.

Table 1 summarizes the final data used in the present study. Consistent estimates of DOC concentrations using SWIR-$R_{rs}(\lambda)$ and MUMM-$R_{rs}(\lambda)$ on MODIS data allowed to merge it for both SeaWiFS for 1998–2002 and MODIS for 2003–2019. The merged data were obtained by correcting a slight difference observed for the overlapped period from 2003 to 2004 (Supplementary Material S1; Fig. S1). POC concentrations are restricted to the MODIS era because the algorithm is only valid using SWIR bands. Prior to the analysis, $R_{rs}(\lambda)$ spectra at each pixel were quality-controlled by removing suspicious data (e.g. zigzag-like spectra, negative value at any of the wavelengths considered in the present study) following the procedure proposed by Matsuoka et al. (2016).

DOC and POC concentrations for each scene were computed using a semi-analytical algorithm developed by Matsuoka et al. (2017) and an empirical one proposed by Doxaran et al. (2012), respectively. Regarding DOC, the absorption coefficient of colored dissolved organic matter at 443 nm ($a_{CDOM}$(443), $m^{-1}$) was first estimated using a spectral optimization method (Matsuoka et al., 2013). By applying an empirical relationship between $a_{CDOM}$(443) and DOC concentration established for Arctic coastal waters (Matsuoka et al., 2017), satellite DOC concentration per pixel was calculated. Regarding POC, the concentration of suspended particulate matter (SPM, $g m^{-3}$) was first estimated using an empirical relationship between SPM and the ratio of $R_{rs}(\lambda)$ at two bands in the green and near-infrared spectral domains using in situ data. The SPM concentrations were converted into the POC ones using an empirical relationship valid in the Mackenzie delta (Doxaran et al., 2012). Mean uncertainties of DOC and POC concentrations are 28% (Matsuoka et al., 2017) and 37% (Doxaran et al., 2012), respectively. These uncertainties do not affect our conclusions (see Results and Discussion).

Due to missing data in different pixels within the delta, a robust trend analysis cannot be applied for daily scene images. We instead produced monthly images of DOC and POC concentrations ($g m^{-3}$) by averaging those concentrations within the delta for each month (Matsuoka et al., 2016, 2017). Endmember values of DOC and POC concentrations at zero salinity in the Mackenzie Delta were extracted from an area defined by Doxaran et al. (2015) and averaged (68.7–69.5°N, 133–137°W; Fig. 1). Extracted values were confirmed to be consistent with in situ measurements (Matsuoka et al., 2012, 2016, 2017).

### 2.2. Hydrological and meteorological data

In situ river discharge data at station 10LC014 (Mackenzie River at Arctic Red), the nearest station to the Mackenzie Delta, from 1979 to 2019 were obtained from Environment Canada (https://wateroffice.ec.gc.ca/). Thaw depth data from the Reindeer Depot station (station code: CA7) from 1992 to 2018 and from upstream station Norman Wells (Station code: C11A) from 1992 to 2005 were downloaded from the CALM website (https://www2.gwu.edu/~calm/). Monthly mean air temperature data at Inuvik (station id: mm2202578) and Norman Wells (station id: mm2202810) both from 1979 to 2018 were downloaded from Environment and Climate Change Canada (https://www.canada.ca/en/environment-climate-change/services/climate-change/scie nce-research-data/climate-trends-variability/adjusted-homogenized-ca nadian-data/surface-air-temperature-access.html). Those data included only three missing months over the entire period (October 1995, September 1996 and October 1996 for data in Inuvik; April 2015, October 2016, and April 2017 for data in Norman Wells), which were filled using the climatology of each of the particular months. A sensitivity analysis showed that filling the gaps in data does not affect our results (see Supplementary Material S2). Monthly mean precipitation data at stations from Inuvik (station id: mt2202578) and Norman Wells (station id: mt2202800) both from 1979 to 2017 were also downloaded from the Environment and Climate Change Canada (https://www.canada.ca/en/environment-climate-change/services/climate-change/scie nce-research-data/climate-trends-variability/adjusted-homogenized-ca
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Comparison of satellite-derived (a) summer-total DOC flux (sum of DOC flux from June to September; red in left axis) and (b) summer-mean DOC concentrations to in situ annual data reported by Tank et al. (2016) (black in right axis). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

nadian-data/precipitation-access.html). Those data included several missing data points, which made it difficult to apply reliable trend analysis. To overcome this issue, precipitation data were downloaded from the NOAA website (https://www.esrl.noaa.gov/psd/data/gri dded/data.cmap.html) and compared to the in situ data, from which a correction factor was obtained (Supplementary Material S2). The correction factor was then applied to NOAA's precipitation for having complete data from 1979 to 2018.

Our trend analyses showed that similar results were obtained for stations between Inuvik and Norman Wells (Supplementary Material S3). In this study, results using much longer data for thaw depth in Inuvik (compared to that in Norman Wells) accompanied by hydrological and meteorological data are reported in the main text (see results in Norman Wells in Supplementary Materials S2 and S3).

## 2.3. Estimates of DOC and POC fluxes

To avoid outliers (e.g., maximal value) that might have been influenced by unknown factors even after rigid quality control (section 2.1), we rather used monthly mean values within the delta (yellow rectangle

shown in Fig. 1) for examining the general trend. For the monthly DOC and POC fluxes, satellite-derived monthly mean DOC and POC concentrations in the Mackenzie Delta were multiplied by monthly in situ river discharge data. A careful inspection showed similar estimates of DOC flux using data from SeaWiFS and MODIS with the common MUMM AC, which allowed us to merge DOC fluxes using the two different sensors (see Supplementary Material S1). Given that SWIR AC provides slightly better $R_{rs}(\lambda)$, MUMM-based $R_{rs}(\lambda)$ were corrected to SWIR-equivalent $R_{rs}(\lambda)$ (mean ratio of SWIR to MUMM was 1.01), which was used for the final product of DOC flux for the entire period covering 1998 to 2019 (see Supplementary Material S1). For POC flux, because the algorithm for estimating POC concentration proposed by Doxaran et al. (2012) is only valid using SWIR AC, the final product (i.e., POC flux) is restricted to MODIS period (2003–2019) and was obtained by multiplying monthly concentration by monthly river discharge.

## 2.4. Trend analysis

It is known that hydrological data do not follow a normal distribution (Yue et al., 2002; Ziegler et al., 2003; Déry et al., 2005). Most
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Long-term trends in DOC and POC concentrations and fluxes. (a) Interannual variability of the summer-total fluxes of DOC (red in left axis) and POC (blue in right axis) for 1998–2019 and 2003–2019, respectively. (b) Same as in (a) but for September only. (c) Same as in (b) but for concentrations (September only). The significant increase trends in DOC (*p* < 0.01; y = 4.1324 + *x*^0.0191) and POC concentrations (*p* < 0.05; y = 1.1476 + *x*^0.0692) are highlighted using Kendal-Theil robust lines (KTRL in black solid lines). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
<caption>**Table 2**</caption>

Summary of trends (slopes) for river discharge, during the two satellite periods, DOC concentration and flux estimated using a merged ocean color data (SeaWiFS + MODIS) for 1998–2019 period, and POC concentrations and flux estimated using MODIS for 2003–2009. Trends shown in parentheses are based on the MODIS time period. A significant level is shown as a star symbol. A positive or negative trend is shaded by the light orange or light blue, respectively.

<table><thead><tr><th>Trend in 1998-2019<br>(2003-2019)</th><th>Summer<sup>†</sup></th><th>Jun</th><th>Jul</th><th>Aug</th><th>Sep</th></tr></thead><tbody><tr><td>River discharge x 10<sup>8</sup><br>in m<sup>3</sup> yr<sup>-1</sup></td><td>-7.42<br>(-4.81)</td><td>-4.67<br>(-8.74)</td><td>-1.67<br>(-5.92)</td><td>-1.58<br>(-1.72)</td><td>-0.31<br>(-0.27)</td></tr><tr><td>DOC flux<br>in TgC yr<sup>-1</sup></td><td>0.00<br>(-0.012)</td><td>0.000<br>(-0.004)</td><td>0.000<br>(-0.003)</td><td>0.000<br>(0.000)</td><td>0.001<br>(0.001)</td></tr><tr><td>POC flux<br>in TgC yr<sup>-1</sup></td><td>-<br>(-0.004)</td><td>-<br>(-0.005)</td><td>-<br>(-0.002)</td><td>-<br>(0.001)</td><td>-<br>(0.001)</td></tr><tr><td>DOC concentration<br>in g m<sup>-3</sup> yr<sup>-1</sup></td><td>0.016<sup>**</sup><br>(0.004)</td><td>0.019<br>(-0.004)</td><td>0.017<br>(-0.009)</td><td>0.024<sup>*</sup><br>(0.005)</td><td>0.019<sup>*</sup><br>(0.015)</td></tr><tr><td>POC concentration<br>in g m<sup>-3</sup> yr<sup>-1</sup></td><td>-<br>(0.013)</td><td>-<br>(-0.049)</td><td>-<br>(-0.016)</td><td>-<br>(0.034)</td><td>-<br>(0.069<sup>*</sup>)</td></tr></tbody></table>

importantly, a parametric trend analysis can be significantly influenced by a serial correlation (Yue et al., 2002). Prior to performing a trend analysis, our satellite DOC and POC concentrations and fluxes were first detrended by removing a serial correlation and pre-whitened (white noise removed) following the procedure proposed by Yue et al. (2002). Afterwards, the identified trend was added back to the pre-whitened data. A non-parametric Kendall-Theil Robust Line (KTRL) statistic was then applied to the data (Yue et al., 2002; Déry et al., 2009). To highlight a decadal trend, a 11-year moving average was applied to the standardized meteorological and hydrological data obtained from in situ measurements (Déry et al., 2009; Woo et al., 2018). The standardisation was performed by subtracting the mean from a variable considered and dividing by the standard deviation (Déry et al., 2005, 2009).

### 3. Results and discussion

We first compared our satellite-derived DOC flux and concentration to annual estimates based on in situ measurements (Tank et al., 2016, hereafter referred to as T16) in the delta of the Mackenzie River. The comparison showed that the interannual variability of satellite-derived DOC flux is similar to that from in situ measurements (cross correlation = 0.94, *p* < 0.001), indicating reasonable satellite-derived DOC flux estimates (Fig. 3a). A similar result was obtained for comparison of DOC concentrations (Fig. 3b; cross correlation = 0.77, *p* < 0.01).

The magnitude of the satellite-derived summer total DOC flux (0.718 ± 0.099 TgC summer⁻¹) is on average ~ 60% lower than the annual ones of T16 for 1998–2012 (1.807 ± 0.242 TgC yr⁻¹). In comparison to the annual fluxes reported by T16, the lower estimate of satellite-DOC flux is not surprising because it is the sum of the flux from June to September when ocean color data are available (hereafter referred to as summer months). In addition, the data of T16 were obtained at an upstream gauging station (Mackenzie River upstream of Arctic Red) approximately 250 km from the delta outflow. Lower values downstream may be related to DOC degradation and removal during fluvial and estuarine transport, or due to a comparatively large inflow of DOC at

the upstream site from nearby vegetation and resuspension of floodplain waters (Emmerton et al., 2008a; Tank et al., 2011).

Investigating recent trends in fluxes of terrigenous organic matter is of great interest given the potential impact on the global climate system (Koven et al., 2011; IPCC, 2013, 2019). Our results show no significant trend for DOC (1998–2019) or POC fluxes (2003–2019) when the total flux during the summer months is considered (Fig. 4a). This is explained by the balance between the concentrations and river discharge (Table 2). Indeed, the river discharge tended to decrease in the past 10 years albeit not significantly (*p* > 0.05; Fig. S2). This decrease is in contrast to the significantly increasing trends in DOC concentrations during the summer months (see Table 2).

Given the strong seasonality of organic matter concentration and flux (Neff et al., 2006; Guo et al., 2007; Holmes et al., 2012), as well as the increasing thaw depths and hydrological connectivity throughout the summer, we also examined long-term patterns for individual months. In June and July, DOC and POC concentrations and fluxes showed no significant trends (Table 2). In August, while a significant positive trend in DOC concentration was detected for 1998–2019 (*p* < 0.05), its flux showed no trend, explained by the decreasing river discharge. Decreasing river discharge also led to an insignificant trend in POC flux for August. In September, however, both DOC and POC concentrations exhibited significant positive trends (Table 2; Fig. 4c). This significant trend is consistent with results reported by Tank et al. (2016). Given that variability in monthly river discharge decreases from June to September, the significant increases in those concentrations in late summer (i.e., September) are not likely influenced by the dilution due to the variation in the freshwater (Table 2); the dilution effect rather occurs in spring (i.e., June; Emmerton et al., 2008b; Ahmed et al., 2020).

A similar trend for DOC concentration and flux was also obtained for 2003–2019 covered by MODIS only (see values in parenthesis of Table 2). A change in statistical significance might appear depending on the length of the time-series (Ziegler et al., 2003), so the same result is not necessarily obtained. Nevertheless, our rather consistent results between the two different periods (1998–2019 versus 2003–2019)

<footnote>* *p* < 0.05, ** *p* < 0.01.</footnote>

<footnote>†Summer represents total (mean) of fluxes (concentrations) from June to September.</footnote>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Summary of long-term trends in thaw depth of the active layer, DOC and POC concentrations and fluxes in the Mackenzie delta, as well as meteorological and hydrological data observed in Inuvik. Regarding meteorological and hydrological data, a similar result was obtained in Norman Wells (an upstream station; see section 2.2). Upper panel: Standardized trend of river discharge (blue), air temperature (red), thaw depth (brown), and precipitation (gray). A 11-year moving average was applied to individual variable. The longest year of each moving window is shown as x-axis. Lower panel: Summary of trend analysis for DOC and POC remote sensing period (1998–2019), longer period (1992–2018), and full period (1979–2018). Separation of the latter two periods is constrained by the availability of thaw depth of active layer (1992–2018) but is determined to facilitate comparison to data in the above-mentioned remote sensing period. Note that each year shown in the x-axis of the upper panel represents the last year of the 11-year window (e.g., data at the year 2018 shows moving average for 2008–2018). Two horizontal (thick and thin) arrows show no significant trend. The thick upper-right pointing arrow shows a significant level at *p* < 0.05. The other upper-right pointing arrow shows a significant level at *p* < 0.01. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

support the robustness of the increasing trends in DOC and POC con-
centrations in late summer. Note that the uncertainty of those estimates
(i.e., 28% and 37%, respectively) does not affect our conclusions (see
Supplementary Material S4). This was confirmed for trend analyses by
propagating the uncertainties to the estimates of DOC and POC con-
centrations each month for 2003–2019 (see Supplementary Material
S4).

The significant increases in September concentrations of DOC and
POC but no parallel increase in fluxes of DOC and POC may seem con-
tradictory at first. It should be emphasized that the concentration

estimates may be more robust as those are directly detected by satellites,
while the fluxes may be subject to a number of uncertainties (e.g., due to
the omission of OC and water inflow within the delta or from ground-
water). Therefore, it is likely that - when fluxes could be calculated more
precisely - these would also show an increasing trend.

The observed increasing concentrations in September could either be
caused by a weakening of removal processes, or by increasing source
inputs. For DOC, removal processes can be (i) degradation by hetero-
trophic bacteria (Spencer et al., 2015) or (ii) photo-oxidation (Bélanger
et al., 2006; Granskog et al., 2009). In addition, (iii) flocculation occurs
as a significant removal process of DOC out of the system once the river water enters a saline delta (Asmala et al., 2014). For POC, removal processes are mostly determined by settling. It is unlikely that any of these removal processes in our region of detection became stronger over time, so instead we argue that it is more plausible that OC input sources have increased.

Sources of POC typically are permafrost thaw and river bank erosion (Guo et al., 2007; Wild et al., 2019) while DOC derives mostly from modern vascular plant input. However, during the thawing season, the contribution of permafrost-derived OC increases for both DOC and POC (Neff et al., 2006; Wild et al., 2019) due to deepening thaw layers. Apart from storing lots of sediments, the delta itself also generates significant amounts of sediments that are released into the river from bank erosion (Carson et al., 1998, 1999). Intensification of slump processes on the Peel Plateau (Kokelj et al., 2015) may also contribute to the OC flux, by releasing massive amounts of sediments that cascade through downstream systems eventually ending up in the Mackenzie delta and outflow (Kokelj et al., 2021; Emmerton et al., 2008a and b). We hypothesize that the significant increasing trends in DOC and POC concentrations detected in September result from recent release from permafrost thaw (both deepening thaw as well as increased slumping).

To assess the hypothesis that permafrost thaw is the driver of the detected increases in OC concentrations, we further investigated long-term hydrological and meteorological data, including river discharge, air temperature, precipitation, and permafrost thaw depth (see also supplementary materials S2 and S3 for results in an upstream station). To be comparable to trend analyses performed for DOC and POC fluxes (Table 2), the same trend analysis was performed for a short (1992–2018) and a long period (1979–2018).

For the short period (1992–2018), no trend in river discharge was detected, which is consistent with river discharge data for 1998–2019 and 2003–2019 when satellite DOC and POC data are available, respectively (Fig. 5, lower panel; Fig. S2). However, air temperature and thaw depth both exhibit strong positive trends and were strongly correlated (cross correlation = 0.95, *p* < 0.0001). No significant trend in precipitation was observed. For the long period (1979–2018), while no data of thaw depth are available, strong positive trend in air temperature was detected and tightly correlated with precipitation (maximal cross correlation = 0.95, *p* < 0.0001).

Whether the origin of moisture in the atmosphere (precipitable water) can be attributed to transport from lower latitudes and/or local evaporation-precipitation processes is an ongoing debate (IPCC, 2013, 2019). Using global climate models, Bintanja and Selten (2014) showed a direct link among sea ice decrease and increases in air moisture and precipitation. Kopec et al. (2016) supported this finding using observational isotopic trace element analysis. These findings suggest a relevant and statistically significant link between increases in air temperature, thaw depth, and precipitation on a regional scale, which is consistent with findings in the present study. Given deepened thaw depth in September, the long-term hydrological and meteorological data suggest an increase in release of permafrost-originating OC to the delta.

To derive decadal hydrological and meteorological changes, a 11-year moving average was applied, yielding increasing trends in air temperature and precipitation as a consequence of ongoing warming (Fig. 5, upper panel). A remarkable feature is an abrupt increase in thaw depth since 2006 (Fig. 5, upper panel). The increase in thaw depth coupled with an increase in precipitation suggests that the hydrological connection has been getting more active (Dery et al., 2009; Vonk et al., 2015), which might further accelerate thawing of permafrost through a positive feedback (IPCC, 2013, 2019). We note a recent decrease in river discharge over the last 10 years (Fig. S2). Our analysis is presently limited by the short length of the time-series itself and thus the reason remains unknown. The increase in thaw depth and precipitation but a decrease in river discharge implies that groundwater contribution may be contributing to fluxes of organic matter (Connolly et al., 2020).

# 4. Implications and conclusions

The present study provides, for the first time, reliable satellite-derived DOC (POC) concentrations and fluxes for 1998–2019 (2003–2019) in the Mackenzie Delta. We found significant increasing trends in both DOC and POC concentrations in late summer over the last two decades, which are likely explained by an increasing permafrost-derived OC input. This result suggests that permafrost leakage into Arctic coastal waters can be detected from space when the thaw depth is at its maximum. The increasing trend of DOC flux in September is 0.0005 TgC yr⁻¹, suggesting an 8% increase in DOC flux from 1998 to 2019 (0.142 TgC in 2019 versus 0.132 TgC in 1998; Fig. 4b). In contrast, POC flux in September showed a much higher increase by 57% from 2003 to 2019 (0.058 TgC in 2019 versus 0.037 TgC in 2003; Fig. 4b). The latter estimate is similar with a finding reported by Doxaran et al. (2015) but over a longer period, suggesting that the POC flux increased in recent years (i.e., 57% for 2003–2019 in the present study versus 50% for 2003–2013 reported by Doxaran et al., 2015). While the increasing rate in DOC flux is much lower (8%) than the increase in POC flux (57%), the former is not negligible given the fact that the absolute magnitude in DOC flux is approximately two times higher than the POC flux (see Fig. 4b). Increasing DOC and POC concentrations (and fluxes) may decrease the light availability within the water column in the delta particularly in late summer, and could also lead to coastal browning (Aksnes et al., 2009). It is also likely that increasing OC input strengthens the likelihood of increased ocean acidification (Semiletov et al., 2016; Capelle et al., 2020). How the increasing DOC and POC fluxes influence coastal marine ecosystems from marine microbes to beluga habitats should be investigated further to better understand the impact of permafrost thaw in Arctic coastal waters.

While the present study focuses on the Mackenzie Delta where long-term in situ data are available for comparison, a similar remote sensing method can equally be applied to other regions, particularly Eurasian deltas (e.g., Matsuoka et al., 2017). In parallel to satellite monitoring of changes in organic matter, field work is needed to identify the quality of organic matter (sources, compositions, and bioavailability) at a regional scale (Juhls et al., 2019; Tanski et al., 2019). Knowledge gained from in situ measurements including radiocarbon dating of OC (Schwab et al., 2020) and on-site or laboratory experiments (e.g., release of CO₂ to the atmosphere by heterotrophic bacteria due to consumption of organic matter originating from permafrost; Tanski et al., 2019; Bruhn et al., 2021) can be incorporated into a numerical model for better simulating biogeochemical processes (Le Fouest et al., 2018), which will allow assessing the potential impact of an increasing land-ocean flux of permafrost-originating organic matter on the global air-sea fluxes of greenhouse gases (Koven et al., 2011; Manizza et al., 2013, 2019).

## Credit author statement

Dr. Atsushi Matsuoka prepared and wrote the initial version of the entire scientific story based on the supporting scientific results. He analyzed all the data and prepared all the figures and tables. Drs. Marcel Babin and Jorien E. Vonk contributed to improve the quality of manuscript. All the authors contributed to acquisition of the financial support for the project leading to this publication.

## Declaration of Competing Interest

None

## Data availability

Data will be made available on request.
## Acknowledgements

All data used in the present study are publicly available. Satellite data are available at NASA Ocean Color Website (https://oceandata.sci.gsfc.nasa.gov). Hydrological and meteorological data are available at the Water Office (https://wateroffice.ec.gc.ca/) and Environment Canada (https://www.canada.ca/en/environment-climate-change.html), respectively. Precipitation data are also available at NOAA website (https://psl.noaa.gov/data/). Thaw depth data are available at CALM website (https://www2.gwu.edu/~calm/). Data presented in this study are available upon request.

This publication is part of the Nunataryuk project, which has received funding under the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 773421. Parts of this research was supported by CNRS (Centre national de la recherche scientifique), CNES (Centre national d'études spatiales; contract #131425), ArcticNet (contract #P066), and Sentinel North (contract #113079). Support to complete the analyses conducted in the present study was also provided by a NASA ROSES project (subcontract #1658689 of the prime contract awarded to Jet Propulsion Laboratory, California Institute of Technology, 80NM0018D0004 between Caltech and NASA) and the Japan Aerospace Exploration Agency (JAXA) Global Change Observation Mission-Climate (GCOM-C; contracts #19RT000542 and #20RT000350) to AM.

We thank comments by Eric Rehm and Suzanne Tank, and the five anonymous reviewers, which greatly improved the quality of the manuscript.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.rse.2022.113322.

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