Hang Wu, Binbin Deng, Jinlong Wang, Sheng Zeng, Juan Du, Peng Yu, Qianqian Bi, Jinzhou Du. Sedimentary record of climate change in a high latitude fjord—Kongsfjord[J]. Acta Oceanologica Sinica, 2023, 42(1): 91-102. doi: 10.1007/s13131-022-2098-x
Citation: Hang Wu, Binbin Deng, Jinlong Wang, Sheng Zeng, Juan Du, Peng Yu, Qianqian Bi, Jinzhou Du. Sedimentary record of climate change in a high latitude fjord—Kongsfjord[J]. Acta Oceanologica Sinica, 2023, 42(1): 91-102. doi: 10.1007/s13131-022-2098-x

Sedimentary record of climate change in a high latitude fjord—Kongsfjord

doi: 10.1007/s13131-022-2098-x
Funds:  The National Natural Science Foundation of China under contract Nos 42107251 and 41706089; the Natural Science Foundation of Fujian Province under contract No. 2020J05232.
More Information
  • Corresponding author: E-mail: jlwang@sklec.ecnu.edu.cn
  • Received Date: 2022-05-26
  • Accepted Date: 2022-08-15
  • Available Online: 2022-12-29
  • Publish Date: 2023-01-25
  • The sedimentary record of climate change in the Arctic region is useful for understanding global warming. Kongsfjord is located in the subpolar region of the Arctic and is a suitable site for studying climate change. Glacier retreat is occurring in this region due to climate change, leading to an increase in meltwater outflow with a high debris content. In August 2017, we collected a sediment Core Z3 from the central fjord near the Yellow River Station. Then, we used the widely used chronology method of 210Pb, 137Cs, and other parameters to reflect the climate change record in the sedimentary environment of Kongsfjord. The results showed that after the mid-late 1990s, the mass accumulation rate of this core increased from 0.10 g/(cm2·a) to 0.34 g/(cm2·a), while the flux of 210Pbex increased from 125 Bq/(m2·a) to 316 Bq/(m2·a). The higher sedimentary inventory of 210Pbex in Kongsfjord compared to global fallout might have been caused by sediment focusing, boundary scavenging, and riverine input. Similarities between the inventory of 137Cs and global fallout indicated that terrestrial particulate matter was the main source of 137Cs in fjord sediments. The sedimentation rate increased after 1997, possibly due to the increased influx of glacial meltwater containing debris. In addition, the 137Cs activity, percentage of organic carbon (OC), and OC/total nitrogen concentration ratio showed increasing trends toward the top of the core since 1997, corresponding to a decrease in the mass balance of glaciers in the region. The results of δ13C, δ15N and OC/TN concentration ratio showed both terrestrial and marine sources contributed to the organic matter in Core Z3. The relative contribution of terrestrial organic matter which was calculated by a two-endmember model showed an increased trend since mid-1990s. All these data indicate that global climate change has a significant impact on Arctic glaciers.
  • The neon flying squid, Ommastrephes bartramii, is one of the most important cephalopod with great potential for economic development, widely distributed over the Pacific Ocean (Roper et al., 1984). The life history stages of O. bartramii are affected by the ambient oceanographic regimes and the epipelagic environment (Alabia et al., 2016; Igarashi et al., 2017), and its spatial and temporal distributions are highly related to the variability in various oceanographic variables (Yu et al., 2015). Additionally, based on the relationships between the environmental variables and the distribution of the O. bartramii, the potential fishing ground and the habitat suitability of the O. bartramii can also be detected and assessed (e.g., Cao et al., 2009; Chen et al., 2011; Nishikawa et al., 2014). As such, understanding of the relationship between the oceanographic environmental factors and the spatio-temporal distributions of O. bartramii is essential for predicting its potential habitat pattern in the Pacific Ocean.

    Several oceanographic variables, such as chlorophyll a concentration (Chl a) (e.g., Chen et al., 2010; Yu et al., 2017; Nishikawa et al., 2014) and sea surface temperature (SST) (e.g., Chen et al., 2007; Yatsu et al., 2010; Yu et al., 2020) are demonstrated to affect the habitat variations of O. bartramii in the Northwest Pacific Ocean. In order to deduce the distribution of O. bartramii, previous studies (e.g., Gong et al., 2012; Alabia et al., 2015; Wang et al., 2015, 2016; Yu et al., 2016a, 2016b, 2021) usually build the model between the distribution of O. bartramii and the environmental factors. Generally, traditional models are directly built based on the satellite-based oceanographic environment variables (e.g., Chl a and SST). However, the Chl a and SST data products cannot fully describe the spectrum characteristics of the oceanic surface. On one hand, Chl a and SST are not the only indicators for the ocean water. On the other hand, uncertainties in Chl a and SST remain after making several corrections during the data processing (e.g., Cui et al., 2020; Gentemann and Hilburn, 2015). As a result, the connection between conventional satellite-based oceanographic variables and distribution of O. bartramii may be not able to accurately represent the habitation of O. bartramii under different oceanic conditions.

    In fact, the Chl a and SST measurements are not the raw information of the satellite observations. The Chl a can be estimated based on the ratio (O’Reilly et al., 1998; O’Reilly and Werdell, 2019) or difference (Hu et al., 2012, 2019) of spectral remote sensing reflectance (Rrs) at blue and green bands. In addition, the SST can be retrieved with different algorithms (e.g., Shibata, 2006; Wentz and Meissner, 2007; Meissner and Wentz, 2012; Merchant et al., 2008, 2009) based on the brightness temperature (BT). As such, the Rrs and BT measurements are the more neglected remote sensing information than the Chl a and SST, respectively.

    In this study, the neglected remote sensing Rrs and/or BT data are firstly introduced to simulate and predict the distribution of O. bartramii with the feed-forward back propagation (BP) artificial neural network (ANN) model in the Northwest Pacific Ocean. In order to assess the performance of Rrs and/or BT on representing the distribution of O. bartramii, the ANN- stimulated and -predicted CPUE of O. bartramii are compared with the nominal CPUE from in situ daily fishery logbook data. Moreover, in order to clarify the superiority of the neglected remote sensing data to the conventional oceanographic variables, the performance differences between them on predicting the distribution of O. bartramii are also investigated.

    The O. bartramii daily fishery logbook data were obtained from the Chinese Squid-Jigging Technology Group of Shanghai Ocean University from July to December during 2004–2018. These data include fishing dates, daily catch (tonnes), fishing effort (days fished) and fishing locations (latitude and longitude) for the Chinese commercial squid fishery operating on the traditional fishing ground between 35°–50°N and 145°–175°E in the Northwest Pacific Ocean. The western stock of winter–spring O. bartramii accounted for most of the catch in the western Pacific Ocean with no bycatch. Chinese squid-jigging fishing vessels were equipped with almost identical engine, lamp and fishing power. These data were compiled into monthly data and grouped using 1°×1° grid cells. As a result, a total of 416 grids (26 columns by 16 rows) are generated. The monthly nominal catch per unit effort (CPUE) in one fishing unit of 1°×1° can then be calculated by

    $$ {\mathrm{CPUE}}_{y,m,i}=\frac{{C}_{y,m,i}}{{F}_{y,m,i}}, $$ (1)

    where $ {\mathrm{C}\mathrm{P}\mathrm{U}\mathrm{E}}_{y,m,i} $ is the monthly nominal CPUE, $ {C}_{y,m,i} $ is the total catch for all the fishing vessels within a fishing grid, $ {F}_{y,m,i} $ is the number of fishing vessels within one fishing grid, i is fishing unit at 1°×1° grids, m is month and y is year. In this paper, the derived monthly nominal CPUE was used as a reliable index of squid abundance, as well as the response variable to assess the performance of the prediction model.

    The Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) Chl a and the neglected Rrs monthly data, collected by both Terra and Aqua from 2004 to 2018, were acquired from National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (http://oceancolor.gsfc.nasa.gov). Both Chl a and Rrs data are at a 9 km×9 km (at nadir) spatial resolution. The MODIS Chl a is calculated using an empirical relationship derived from in situ measurements and Rrs in the blue-to-green region of the visible spectrum. Specifically, Rrs at 465 nm, 555 nm and 645 nm spectral regimes are used to estimate the near-surface MODIS Chl a product via merging the standard OC3/OC4 (OCx) band ratio algorithm (O’Reilly et al., 1998) and the color index of Hu et al. (2012). Therefore, only MODIS Rrs data at 465 nm, 555 nm and 645 nm spectral regimes are incorporated for further analysis in this paper. In addition, after averaging the monthly Chl a (and Rrs) data from Terra and Aqua platforms, the averaged Chl a (and Rrs) data were resampled according to the location of the monthly nominal CPUE grids (i.e., at a horizontal resolution of 1°).

    SST and the neglected BT measurements were retrieved from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard Aqua launched in May 2002 and decommissioned in October 2011, as well as the follow-on instrument AMSR2 flown on the Global Change Observing Mission (GCOM-1) launched in May 2012. In this paper, BT measurements at 6 GHz horizontal (H) and vertical (V) polarization, 10H, 10V, 23 V, 37H and 37V are used for comparisons with SST. Both AMSR-E and AMSR2 Level-3 monthly data products, with a spatial resolution of 25 km and 10 km, respectively, were obtained from Japanese Aerospace Exploration Agency (JAXA; https://sharaku.eorc.jaxa.jp/AMSR/index.html). In order to obtain the spatiotemporally synchronized matchups between CPUE and SST (and BT) data, SST and BT data are also resampled at 1°×1° grid cells.

    In this paper, we use the ANN to build the prediction model between the CPUE of O. bartramii and oceanographic information. The ANN is a model that is motivated by the biological neural network of the human brain and is used to simulate the processes that depends on a huge number of unknown inputs (Priddy and Keller, 2005). ANN is the collections of interconnected neurons that interchanges information among each other, and the connections are weighted and adjusted to get appropriate results. ANN contains mainly input layer, hidden layer and output layer. Neurons in the input layer accept the inputs for further processing. Neurons in the hidden layer accept the input from input layer with allotted weights, as well as forward the output to the output layer. In the output layer, neurons are represented with expected attribute values to the external world as output.

    Back propagation (BP) algorithm is utilized in the layered feed-forward ANN in this study to build the prediction model of O. bartramii in the Northwest Pacific Ocean. The feed-forward BP neural network is a supervised learning ANN and based on the learning rule for decreasing error till the ANN becomes skilled at the data training (Wang et al., 2015). In addition, the Levenberg–Marquardt algorithm (trainlm) training algorithms is selected during the implementation of the feed-forward BP ANN (Zhang et al., 2015). Trainlm training function is based on Levenberg–Marquardt optimization and updates the bias to weight values. Trainlm falls in the supervised training algorithm category and serves as the fastest BP algorithm (Sangwan et al., 2020). The disadvantage of trainlm is that it takes more memory than other algorithms.

    In this study, the input layer of the feed-forward BP ANN may include one or more kinds of remote sensing data, and the output layer is the CPUE of O. bartramii. Additionally, we implement the ANN training at each grid cell in each month. In order to assess the performance of conventional oceanographic variables and neglected remote sensing information on building the prediction model of O. bartramii in the Northwest Pacific Ocean, six schemes are designed to build the feed-forward BP ANN model in this paper (see Table 1). The schemes differ from each other by using different data sources as the model input. Schemes Ⅰ, Ⅱ and Ⅲ use the conventional Chl a, SST and their combination as the input, and Schemes Ⅳ, Ⅴ and Ⅵ use the corresponding neglected Rrs, BT and their combination as the input. Additionally, performances of the schemes also provide us an opportunity to examine the potential of neglected remote sensing information on improving our understanding of the distribution of O. bartramii. Moreover, both the O. bartramii fishery data and remote sensing measurements are split into two temporal groups for model training and validating, respectively. The first group in July–December of 2004–2013 is used to build the prediction model between the remote sensing data and the CPUE of O. bartramii, and estimate the internal coincidence precision of the model by comparing the model output with the monthly nominal CPUE of O. bartramii. The other group in July–December of 2014–2018 is used to validate the performance of the built model in terms of external coincidence precision via comparing the model predictions with the nominal CPUE

    Table  1.  The input and response data for the feed-forward back propagation artificial neural network (BP ANN) model between Ommastrephes bartramii and oceanographic information from July to December during 2004–2018 in the Northwest Pacific Ocean
    SchemeInput dataResponse data
    chlorophyll a concentration (Chl a)monthly nominal CPUE of O. bartramii
    sea surface temperature (SST)monthly nominal CPUE of O. bartramii
    Chl a and SSTmonthly nominal CPUE of O. bartramii
    spectral remote sensing reflectance (Rrs)monthly nominal CPUE of O. bartramii
    brightness temperature (BT)monthly nominal CPUE of O. bartramii
    Rrs and BTmonthly nominal CPUE of O. bartramii
     | Show Table
    DownLoad: CSV

    In order to assess the performance of the ANN-derived distribution of CPUE of O. bartramii in the Northwest Pacific Ocean, we analyzed the precision of the prediction model of O. bartramii. Figure 1 displayed the root mean square error (RMSE) of the ANN-simulated CPUE of O. bartramii from July to December during 2004–2013 with the conventional oceanographic variables (Figs 1ac) and the neglected remote sensing data (Figs 1df) as input. Uncertainties in the ANN-simulated CPUE of O. bartramii with Chl a as input (Fig. 1a) generally exhibited similar spatial distribution to those with Rrs as input (Fig. 1d). Additionally, the overall RMSE of the ANN-simulated CPUE in Schemes Ⅰ and Ⅳ was approximately 0.49 t/d and 0.47 t/d, respectively, indicating both Chl a and Rrs were suitable to simulate the CPUE of O. bartramii with the feed-forward BP ANN. Moreover, the overall RMSE of the ANN-simulated CPUE of O. bartramii with both SST (Fig. 1b) and BT (Fig. 1e) as input was about 0.45 t/d. When the conventional Chl a and SST from July to December during 2004–2013 were combined as the input to the feed-forward BP ANN (i.e., Scheme Ⅲ), the ANN-simulated CPUE of O. bartramii agreed well with the nominal CPUE (Fig. 1c). Moreover, the combined Rrs and BT also successfully simulated the CPUE of O. bartramii (i.e., Scheme Ⅵ). The overall RMSE of ANN-simulated CPUE was approximately 0.46 t/d and 0.42 t/d in Schemes Ⅲ and Ⅵ, respectively. In general, when the conventional oceanographic variables were input to build the prediction model (i.e., Schemes Ⅰ, Ⅱ and Ⅲ), the RMS of the ANN-simulated CPUE of O. bartramii was greater than the simulation results with the corresponding neglected remote sensing data as input (i.e., Schemes Ⅳ, Ⅴ and Ⅵ) (Fig. 2).

    Figure  1.  Spatial distribution of root mean square error (RMSE) of the artificial neural network (ANN)-simulated catch per unit effort (CPUE) of Ommastrephes bartramii from July to December during 2004–2013. a. Scheme I with chlorophyll a concentration (Chl a) as input; b. Scheme Ⅱ with SST as input; c. Scheme Ⅲ with Chl a and SST as input; d. Scheme Ⅳ with remote sensing reflectance (Rrs) as input; e. Scheme Ⅴ with brightness temperature (BT) as input; f. Scheme Ⅵ with Rrs and BT as input.
    Figure  2.  Spatial distribution of the differential (diff.) root mean square error (RMSE) of the artificial neural network (ANN)-simulated catch per unit effort (CPUE) of Ommastrephes bartramii from July to December during 2004–2013. a. Scheme I minus Ⅳ; b. Scheme Ⅱ minus Ⅴ; c. Scheme Ⅲ minus Ⅵ.

    When the conventional oceanographic variables are inputted to the ANN, remarkable uncertainties (e.g., RMSE > 7.0 t/d) were observed in the predicted CPUE of O. bartramii (Figs 3ac). After replacing with the neglected remote sensing data as input, the uncertainties of the ANN-predicted CPUE of O. bartramii were obviously mitigated (Figs 3df). The overall RMSE of the ANN-predicted CPUE with conventional oceanographic variables as input was approximately 1.55 t/d, 1.29 t/d and 1.51 t/d in Schemes Ⅰ, Ⅱ and Ⅲ, as well as 1.48 t/d, 1.18 t/d and 1.30 t/d with the neglected remote sensing data as input in Schemes Ⅳ, Ⅴ and Ⅵ, respectively. Although the incorporation of the neglected remote sensing data also worsened the RMS of the ANN-predicted CPUE of O. bartramii at some grids, the RMS improvements were more widely observed in the Northwest Pacific Ocean (Fig. 4).

    Figure  3.  Distribution of root mean square error (RMSE) of artificial neural network (ANN)-predicted catch per unit effort (CPUE) of Ommastrephes bartramii from July to December during 2014–2018. a. Scheme I with chlorophyll a concentration (Chl a) as input; b. Scheme Ⅱ with SST as input; c. Scheme Ⅲ with Chl a and SST as input; d. Scheme Ⅳ with remote sensing reflectance (Rrs) as input; e. Scheme Ⅴ with brightness temperature (BT) as input; f. Scheme Ⅵ with Rrs and BT as input.
    Figure  4.  Spatial distribution of the differential (diff.) root mean square error (RMSE) of the artificial neural network (ANN)-predicted catch per unit effort (CPUE) of Ommastrephes bartramii from July to December during 2014–2018. a. Scheme I minus Ⅳ; b. Scheme Ⅱ minus Ⅴ; c. Scheme Ⅲ minus Ⅵ.

    The distribution of fish species is highly related with the environmental variations (Chen, 2004). Hence, accurately building the relationship between fish species and ambient environment is essential to understand the habitat preferences of fish species and predict the dynamics of the fish population (Dickey, 2003; Ishikawa et al., 2009; Nakada et al., 2014). The ANN model has been used to predict the distributions of capelin (Mallotus villosus) (Huse, 2001), European eel (Anguilla anguilla) (Laffaille et al., 2003, 2004), Eurasian perch (Perca fluviatilis) (Brosse and Lek, 2002), neon flying squid (O. bartramii) (Wang et al., 2015) and skipjack tuna (Katsuwonus pelamis) (Wang et al., 2018). The input parameters of the ANN model in previous studies were mostly the conventional oceanographic variables, such as Chl a, SST, and/or sea surface height (SSH). However, these conventional oceanographic variables are not representative of the oceanic environment. In this study, the neglected Rrs and/or BT data were firstly proposed to simulate and predict the spatio-temporal distributions of O. bartramii in the Northwest Pacific Ocean based on the feed-forward BP ANN model.

    The robust connections between the distributions of O. bartramii and SST have also been demonstrated by previous studies (e.g., Chen and Tian, 2005; Chen et al., 2007, 2008). Wang et al. (2015) also suggested that the favourable range of SST for O. bartramii was 11–18°C in the Northwest Pacific Ocean based on the BP ANN model. This article further demonstrated that the neglected BTs have consistent effects with SST on simulating the CPUE of O. bartramii with the feed-forward BP ANN in the Northwest Pacific Ocean (Figs 1b, e and 2b; Table 2). Moreover, we also found that BT was better than SST in predicting the distribution of O. bartramii, since the RMSE of the CPUE of O. bartramii is decreased by approximately 9% for the former (Figs 3b, e and 4b; Table 2).

    Table  2.  The overall mean root mean square (RMS) of artificial neural network (ANN)-derived CPUE of Ommastrephes bartramii with different schemes (Unit: t/d)
    SchemeOverall mean RMS
    ANN-simulated CPUEANN-predicted CPUE
    0.4921.547
    0.4471.294
    0.4571.514
    0.4731.481
    0.4461.180
    0.4801.304
     | Show Table
    DownLoad: CSV

    Considering that the Chl a is a good indicator of the food availability for squid (Nishikawa et al., 2014), it is an important environmental variables that significantly affects the distribution of O. bartramii (Xu et al., 2004). This study confirmed the importance of Chl a during the simulation and prediction of the CPUE of O. bartramii in the Northwest Pacific Ocean with the BP ANN (Figs 1a and 3a). Additionally, we also found that the Rrs measurements at 465 nm, 555 nm and 645 nm were more suitable than the Chl a to simulate and predict the distribution of O. bartramii (Figs 1d, 2a, 3d and 4a; Table 2). As such, the neglected Rrs (and BT) could be a prefer data source than the conventional Chl a (and SST) in studying the habitat suitability of O. bartramii in the Northwest Pacific Ocean.

    While the RMSEs of the simulated and predicted CPUE of O. bartramii with the combined Chl a and SST (Rrs and BT) as the model input were less than those with Chl a (Rrs) as the model input, they were greater than those with SST (BT) as the model input (Table 2). Moreover, the uncertainties in the simulated and predicted CPUE of O. bartramii with the Chl a (Rrs) as model input were remarkably larger than those with the SST (BT) as model input (Table 2). This indicated that the RMSE improvements with the combined parameters as input were mainly owe to the SST (BT), and that the SST (BT) was better than the Chl a (Rrs) in simulating and predicting the CPUE of O. bartramii in the Northwest Pacific Ocean. The results were also consistent with Wang et al. (2015), who found that the SST was the most important environmental factor in the formation of fishing grounds and it had the greatest influence on the prediction model.

    It is also worth mentioning that despite only the CPUE of O. bartramii in the Northwest Pacific Ocean is simulated and predicted in this paper, the neglected Rrs (and BT) data could be further popularized to build the prediction model of other marine species over other sea areas. Furthermore, the corresponding neglected remote sensing information to other conventional oceanographic variables (e.g., SSH, sea surface salinity and wind stress curl) can be also further explored for studying the habitat suitability of the marine species.

    Acknowledgements: The authors thank two anonymous reviewers for their constructive suggestions and insightful criticisms that substantially improved the quality of our work.
  • Aarkrog A. 2003. Input of anthropogenic radionuclides into the World Ocean. Deep-Sea Research Part II: Topical Studies in Oceanography, 50(17–21): 2597–2606,
    Aliani S, Bartholini G, Degl'innocenti F, et al. 2004. Multidisciplinary investigations in the marine environment of the inner Kongsfiord, Svalbard Islands (September 2000 and 2001). Chemistry and Ecology, 20(S1): S19–S28. doi: 10.1080/02757540410001655396
    Andreassen K, Hubbard A, Winsborrow M, et al. 2017. Massive blow-out craters formed by hydrate-controlled methane expulsion from the Arctic seafloor. Science, 356(6341): 948–953. doi: 10.1126/science.aal4500
    Andrews J E, Greenaway A M, Dennis P F. 1998. Combined carbon isotope and C/N ratios as indicators of source and fate of organic matter in a poorly flushed, tropical estuary: Hunts Bay, Kingston Harbour, Jamaica. Estuarine, Coastal and Shelf Science, 46(5): 743–756,
    Appleby P G. 2004. Environmental change and atmospheric contamination on Svalbard: sediment chronology. Journal of Paleolimnology, 31(4): 433–443. doi: 10.1023/B:JOPL.0000022545.73163.ed
    Barros G V, Martinelli L A, Novais T M O, et al. 2010. Stable isotopes of bulk organic matter to trace carbon and nitrogen dynamics in an estuarine ecosystem in Babitonga Bay (Santa Catarina, Brazil). Science of the Total Environment, 408(10): 2226–2232. doi: 10.1016/j.scitotenv.2010.01.060
    Belicka L L, Harvey H R. 2009. The sequestration of terrestrial organic carbon in Arctic Ocean sediments: A comparison of methods and implications for regional carbon budgets. Geochimica et Cosmochimica Acta, 73(20): 6231–6248. doi: 10.1016/j.gca.2009.07.020
    Bendixen M, Iversen L L, Bjørk A A, et al. 2017. Delta progradation in Greenland driven by increasing glacial mass loss. Nature, 550(7674): 101–104. doi: 10.1038/nature23873
    Berge J, Heggland K, Lønne O J, et al. 2015. First records of Atlantic mackerel (Scomber scombrus) from the Svalbard Archipelago, Norway, with possible explanations for the extension of its distribution. Arctic, 68(1): 54–61. doi: 10.14430/arctic4455
    Bogen J, Bønsnes T E. 2003. Erosion and sediment transport in High Arctic rivers, Svalbard. Polar Research, 22(2): 175–189. doi: 10.3402/polar.v22i2.6454
    Boldt K V, Nittrouer C A, Hallet B, et al. 2013. Modern rates of glacial sediment accumulation along a 15°S-N transect in fjords from the Antarctic Peninsula to southern Chile. Journal of Geophysical Research: Earth Surface, 118(4): 2072–2088. doi: 10.1002/jgrf.20145
    Bourgeois S, Kerhervé P, Calleja M L, et al. 2016. Glacier inputs influence organic matter composition and prokaryotic distribution in a high Arctic fjord (Kongsfjorden, Svalbard). Journal of Marine Systems, 164: 112–127. doi: 10.1016/j.jmarsys.2016.08.009
    Box J E, Colgan W T, Christensen T R, et al. 2019. Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters, 14(4): 045010. doi: 10.1088/1748-9326/aafc1b
    Carreira R S, Wagener A L R, Readman J W, et al. 2002. Changes in the sedimentary organic carbon pool of a fertilized tropical estuary, Guanabara Bay, Brazil: an elemental, isotopic and molecular marker approach. Marine Chemistry, 79(3–4): 207–227,
    Choudhary S, Nayak G N, Khare N. 2020. Source, mobility, and bioavailability of metals in fjord sediments of Krossfjord-Kongsfjord system, Arctic, Svalbard. Environmental Science and Pollution Research, 27(13): 15130–15148. doi: 10.1007/s11356-020-07879-1
    Christiansen H H, Etzelmüller B, Isaksen K, et al. 2010. The thermal state of permafrost in the Nordic area during the international polar year 2007–2009. Permafrost and Periglacial Processes, 21(2): 156–181. doi: 10.1002/ppp.687
    Cowan E A, Seramur K C, Powell R D, et al. 2010. Fjords as temporary sediment traps: history of glacial erosion and deposition in Muir Inlet, Glacier Bay National Park, southeastern Alaska. Geological Society of America Bulletin, 122(7–8): 1067–1080,
    D’Angelo A, Giglio F, Miserocchi S, et al. 2018. Multi-year particle fluxes in Kongsfjorden, Svalbard. Biogeosciences, 15(17): 5343–5363. doi: 10.5194/bg-15-5343-2018
    Dibb J E, Jaffrezo J L. 1993. Beryllium-7 and lead-210 in aerosol and snow in the Dye 3 gas, aerosol and snow sampling program. Atmospheric Environment. Part A. General Topics, 27(17–18): 2751–2760,
    Du Jinzhou, Wu Ying, Huang Dekun, et al. 2010. Use of 7Be, 210Pb and 137Cs tracers to the transport of surface sediments of the Changjiang Estuary, China. Journal of Marine Systems, 82(4): 286–294. doi: 10.1016/j.jmarsys.2010.06.003
    Ferreira P A D L, Ribeiro A P, Nascimento M G D, et al. 2013. 137Cs in marine sediments of Admiralty Bay, King George Island, Antarctica. Science of the Total Environment, 443: 505–510. doi: 10.1016/j.scitotenv.2012.11.032
    Goldberg E D. 1963. Geochronology with 210Pb. In: Radioactive Dating. Vienna: IAEA, 121–131
    Goñi M A, Teixeira M J, Perkey D W. 2003. Sources and distribution of organic matter in a river-dominated estuary (Winyah Bay, SC, USA). Estuarine, Coastal and Shelf Science, 57(5–6): 1023–1048,
    Gordon E S, Goñi M A. 2003. Sources and distribution of terrigenous organic matter delivered by the Atchafalaya River to sediments in the northern Gulf of Mexico. Geochimica et Cosmochimica Acta, 67(13): 2359–2375. doi: 10.1016/s0016-7037(02)01412-6
    Gwynn J P, Dowdall M, Davids C, et al. 2004. The radiological environment of Svalbard. Polar Research, 23(2): 167–180. doi: 10.1111/j.1751-8369.2004.tb00006.x
    He Qing, Walling D E. 1996. Interpreting particle size effects in the adsorption of 137Cs and unsupported 210Pb by mineral soils and sediments. Journal of Environmental Radioactivity, 30(2): 117–137. doi: 10.1016/0265-931x(96)89275-7
    Hedges J I, Clark W A, Come G L. 1988. Organic matter sources to the water column and surficial sediments of a marine bay. Limnology and Oceanography, 33(5): 1116–1136. doi: 10.4319/lo.1988.33.5.1116
    Hinton T G, Kaplan D I, Knox A S, et al. 2006. Use of illite clay for in situ remediation of 137Cs-contaminated water bodies: field demonstration of reduced biological uptake. Environmental Science & Technology, 40(14): 4500–4505. doi: 10.1021/es060124x
    Hodgkins R, Bryant R, Darlington E, et al. 2016. Pre-melt-season sediment plume variability at Jökulsárlón, Iceland, a preliminary evaluation using in-situ spectroradiometry and satellite imagery. Annals of Glaciology, 57(73): 39–46. doi: 10.1017/aog.2016.20
    Husum K, Howe J A, Baltzer A, et al. 2019. The marine sedimentary environments of Kongsfjorden, Svalbard: an archive of polar environmental change. Polar Research, 38: 3380. doi: 10.33265/polar.v38.3380
    Jaworowski Z, Hoff P, Hagen J O, et al. 1997. A highly radioactive Chernobyl deposit in a Scandinavian Glacier. Journal of Environmental Radioactivity, 35(1): 91–108. doi: 10.1016/S0265-931X(96)00004-5
    Kim J H, Peterse F, Willmott V, et al. 2011. Large ancient organic matter contributions to Arctic marine sediments (Svalbard). Limnology and Oceanography, 56(4): 1463–1474. doi: 10.4319/lo.2011.56.4.1463
    Klaminder J, Appleby P, Crook P, et al. 2012. Post-deposition diffusion of 137Cs in lake sediment: implications for radiocaesium dating. Sedimentology, 59(7): 2259–2267. doi: 10.1111/j.1365-3091.2012.01343.x
    Knies J, Martinez P. 2009. Organic matter sedimentation in the western Barents Sea region: terrestrial and marine contribution based on isotopic composition and organic nitrogen content. Norwegian Journal of Geology, 89: 79–89
    Koide M, Soutar A, Goldberg E D. 1972. Marine geochronology with 210Pb. Earth and Planetary Science Letters, 14(3): 442–446. doi: 10.1016/0012-821x(72)90146-x
    Koppes M, Hallet B, Rignot E, et al. 2015. Observed latitudinal variations in erosion as a function of glacier dynamics. Nature, 526(7571): 100–103. doi: 10.1038/nature15385
    Koziorowska K, Kuliński K, Pempkowiak J. 2016. Sedimentary organic matter in two Spitsbergen fjords: terrestrial and marine contributions based on carbon and nitrogen contents and stable isotopes composition. Continental Shelf Research, 113: 38–46. doi: 10.1016/j.csr.2015.11.010
    Koziorowska K, Kuliński K, Pempkowiak J. 2017. Distribution and origin of inorganic and organic carbon in the sediments of Kongsfjorden, Northwest Spitsbergen, European Arctic. Continental Shelf Research, 150: 27–35. doi: 10.1016/j.csr.2017.08.023
    Krishnaswamy S, Lal D, Martin J M, et al. 1971. Geochronology of lake sediments. Earth and Planetary Science Letters, 11(1–5): 407–414,
    Kuliński K, Kędra M, Legeżyńska J, et al. 2014. Particulate organic matter sinks and sources in high Arctic fjord. Journal of Marine Systems, 139: 27–37. doi: 10.1016/j.jmarsys.2014.04.018
    Kuzyk Z Z A, Gobeil C, Macdonald R W. 2013. 210Pb and 137Cs in margin sediments of the Arctic Ocean: controls on boundary scavenging. Global Biogeochemical Cycles, 27(2): 422–439. doi: 10.1002/gbc.20041
    Lamb A L, Wilson G P, Leng M J. 2006. A review of coastal palaeoclimate and relative sea-level reconstructions using δ13C and C/N ratios in organic material. Earth-Science Reviews, 75(1–4): 29–57,
    Larsen J, Appleby P G, Christensen G N, et al. 2010. Historical and geographical trends in sediment chronology from lakes and marine sites along the Norwegian coast. Water, Air, and Soil Pollution, 206(1–4): 237–250,
    Lefauconnier B, Hagen J O, Rudant J P. 1994. Flow speed and calving rate of Kongsbreen glacier, Svalbard, using SPOT images. Polar Research, 13(1): 59–65. doi: 10.1111/j.1751-8369.1994.tb00437.x
    Luckman A, Benn D I, Cottier F, et al. 2015. Calving rates at tidewater glaciers vary strongly with ocean temperature. Nature Communications, 6(1): 8566. doi: 10.1038/ncomms9566
    Lydersen C, Assmy P, Falk-Petersen S, et al. 2014. The importance of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway. Journal of Marine Systems, 129: 452–471. doi: 10.1016/j.jmarsys.2013.09.006
    Magand O, Ferrari C, Gauchard P A, et al. 2006. Analysis of 7Be and 210Pb air concentrations in Ny-Ålesund, Svalbard: CHIMERPOL II project, preliminary results. Memoirs of National Institute of Polar Research, 59: 96–115
    Maksymowska D, Richard P, Piekarek-Jankowska H, et al. 2000. Chemical and isotopic composition of the organic matter sources in the Gulf of Gdansk (Southern Baltic Sea). Estuarine, Coastal and Shelf Science, 51(5): 585–598,
    Matishov G G, Matishov D G, Usyagina I S, et al. 2011. Assessment of 137Cs and 90Sr fluxes in the Barents Sea. Doklady Earth Sciences, 439(2): 1190–1195. doi: 10.1134/s1028334x11080265
    Meksumpun S, Meksumpun C, Hoshika A, et al. 2005. Stable carbon and nitrogen isotope ratios of sediment in the gulf of Thailand: Evidence for understanding of marine environment. Continental Shelf Research, 25(15): 1905–1915. doi: 10.1016/j.csr.2005.04.009
    Meyers P A. 1994. Preservation of elemental and isotopic source identification of sedimentary organic matter. Chemical Geology, 114(3–4): 289–302.
    Mohan M, Sreelakshmi U, Sagar M K V, et al. 2018. Rate of sediment accumulation and historic metal contamination in a tidewater glacier fjord, Svalbard. Marine Pollution Bulletin, 131: 453–459. doi: 10.1016/j.marpolbul.2018.04.057
    Moore H E, Poet S E, Martell E A. 1973. 222Rn, 210Pb, 210Bi, and 210Po profiles and aerosol residence times versus altitude. Journal of Geophysical Research, 78(30): 7065–7075. doi: 10.1029/JC078i030p07065
    Mottram R, Simonsen S B, Svendsen S H, et al. 2019. An integrated view of Greenland ice sheet mass changes based on models and satellite observations. Remote Sensing, 11(12): 1407. doi: 10.3390/rs11121407
    Naidu A S, Cooper L W, Finney B P, et al. 2000. Organic carbon isotope ratios (δ13C) of Arctic Amerasian continental shelf sediments. International Journal of Earth Sciences, 89(3): 522–532. doi: 10.1007/s005310000121
    Nilsen F, Skogseth R, Vaardal-Lunde J, et al. 2016. A simple shelf circulation model: intrusion of Atlantic water on the West Spitsbergen Shelf. Journal of Physical Oceanography, 46(4): 1209–1230. doi: 10.1175/jpo-d-15-0058.1
    Ogrinc N, Fontolan G, Faganeli J, et al. 2005. Carbon and nitrogen isotope compositions of organic matter in coastal marine sediments (the Gulf of Trieste, N Adriatic Sea): indicators of sources and preservation. Marine Chemistry, 95(3–4): 163–181,
    Paatero J, Hatakka J, Holmén K, et al. 2003. Lead-210 concentration in the air at Mt. Zeppelin, Ny-Ålesund, Svalbard. Physics and Chemistry of the Earth, Parts A/B/C, 28(28–32): 1175–1180,
    Pinglot J F, Hagen J O, Melvold K, et al. 2001. A mean net accumulation pattern derived from radioactive layers and radar soundings on Austfonna, Nordaustlandet, Svalbard. Journal of Glaciology, 47(159): 555–566. doi: 10.3189/172756501781831800
    Pinglot J F, Pourchet M, Lefauconnier B, et al. 1994. Natural and artificial radioactivity in the Svalbard Glaciers. Journal of Environmental Radioactivity, 25(1–2): 161–176,
    Pinglot J F, Vaikmäe R A, Kamiyama K, et al. 2003. Ice cores from Arctic sub-polar glaciers: chronology and post-depositional processes deduced from radioactivity measurements. Journal of Glaciology, 49(164): 149–158. doi: 10.3189/172756503781830944
    Promińska A, Małgorzata C, Waldemar W. 2017. Kongsfjorden and Hornsund hydrography-comparative study based on a multiyear survey in fjords of west Spitsbergen. Oceanologia, 59(4): 397–412. doi: 10.1016/j.oceano.2017.07.003
    Ruttenberg K C, Goñi M A. 1997. Phosphorus distribution, C: N: P ratios, and δ13Coc in Arctic, temperate, and tropical coastal sediments: tools for characterizing bulk sedimentary organic matter. Marine Geology, 139(1–4): 123–145,
    Saiers J E, Hornberger G M. 1996. The role of colloidal kaolinite in the transport of cesium through laboratory sand columns. Water Resources Research, 32(1): 33–41. doi: 10.1029/95wr03096
    Samuelsson C, Hallstadius L, Persson B, et al. 1986. 222Rn and 210Pb in the Arctic summer air. Journal of Environmental Radioactivity, 3(1): 35–54. doi: 10.1016/0265-931x(86)90048-2
    Sanchez-Cabeza J A, Ruiz-Fernández A C. 2012. 210Pb sediment radiochronology: an integrated formulation and classification of dating models. Geochimica et Cosmochimica Acta, 82: 183–200. doi: 10.1016/j.gca.2010.12.024
    Schellenberger T, Dunse T, Kääb A, et al. 2015. Surface speed and frontal ablation of Kronebreen and Kongsbreen, NW Svalbard, from SAR offset tracking. The Cryosphere, 9(6): 2339–2355. doi: 10.5194/tcd-8-6193-2014
    Schubert C J, Calvert S E. 2001. Nitrogen and carbon isotopic composition of marine and terrestrial organic matter in Arctic Ocean sediments: implications for nutrient utilization and organic matter composition. Deep-Sea Research Part I: Oceanographic Research Papers, 48(3): 789–810. doi: 10.1016/s0967-0637(00)00069-8
    Shi Fengdeng, Cheng Zhenbo, Wu Yonghua, et al. 2011. The research on glacial-marine deposit types and sedimentary processes in the Arctic Kongsfjorden. Haiyang Xuebao (in Chinese), 33(2): 115–123
    Svendsen H, Beszczynska-Møller A, Hagen J O, et al. 2002. The physical environment of Kongsfjorden-Krossfjorden, an Arctic fjord system in Svalbard. Polar Research, 21(1): 133–166. doi: 10.3402/polar.v21i1.6479
    Taylor J R, Thompson W. 1998. An introduction to error analysis: the study of uncertainties in physical measurements. Physics Today, 51(1): 57–58. doi: 10.1063/1.882103
    Thornton S F, McManus J. 1994. Application of organic carbon and nitrogen stable isotope and C/N ratios as source indicators of organic matter provenance in estuarine systems: evidence from the Tay Estuary, Scotland. Estuarine, Coastal and Shelf Science, 38(3): 219–233,
    Usui T, Nagao S, Yamamoto M, et al. 2006. Distribution and sources of organic matter in surficial sediments on the shelf and slope off Tokachi, western North Pacific, inferred from C and N stable isotopes and C/N ratios. Marine Chemistry, 98(2–4): 241–259,
    Voss M, Liskow I, Pastuszak M, et al. 2005. Riverine discharge into a coastal bay: A stable isotope study in the Gulf of Gdańsk, Baltic Sea. Journal of Marine Systems, 57(1–2): 127–145,
    Walkusz W, Kwasniewski S, Falk-Petersen S, et al. 2009. Seasonal and spatial changes in the zooplankton community of Kongsfjorden, Svalbard. Polar Research, 28(2): 254–281. doi: 10.1111/j.1751-8369.2009.00107.x
    Wang Jinlong, Baskaran M, Niedermiller J. 2017. Mobility of 137Cs in freshwater lakes: a mass balance and diffusion study of Lake St. Clair, Southeast Michigan, USA. Geochimica et Cosmochimica Acta, 218: 323–342. doi: 10.1016/j.gca.2017.09.017
    Wickström S, Jonassen M O, Cassano J J, et al. 2020. Present temperature, precipitation, and rain-on-snow climate in Svalbard. Journal of Geophysical Research: Atmospheres, 125(14): e2019JD032155. doi: 10.1029/2019JD032155
    Winkelmann D, Knies J. 2005. Recent distribution and accumulation of organic carbon on the continental margin west off Spitsbergen. Geochemistry, Geophysics, Geosystems, 6(9): Q09012,
    Zaborska A, Pempkowiak J, Papucci C. 2006. Some sediment characteristics and sedimentation rates in an Arctic Fjord (Kongsfjorden, Svalbard). Środkowo-Pomorskie Towarzystwo Naukowe Ochrony Środowiska, 8: 79–96
    Zajaczkowski M. 2008. Sediment supply and fluxes in glacial and outwash fjords, Kongsfjorden and Adventfjorden, Svalbard. Polish Polar Research, 29(1): 59–72
    Zeng Sheng, Deng Binbin, Wang Jinlong, et al. 2022. Distribution of gamma-ray radionuclides in surface sediments of the Kongsfjorden, Arctic: implications for sediment provenance. Acta Oceanologica Sinica, 41(1): 21–29. doi: 10.1007/s13131-021-1916-x
    Zhang Fule, Wang Jinlong, Baskaran Mark, et al. 2021. A global dataset of atmospheric 7Be and 210Pb measurements: annual air concentration and depositional flux. Earth System Science Data, 13(6): 2963–2994. doi: 10.5194/essd-13-2963-2021
    Zhu Zhuoyi, Wu Ying, Liu Sumei, et al. 2016. Organic carbon flux and particulate organic matter composition in Arctic valley glaciers: examples from the Bayelva River and adjacent Kongsfjorden. Biogeosciences, 13(4): 975–987. doi: 10.5194/bg-13-975-2016
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(3)

    Article Metrics

    Article views (504) PDF downloads(16) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return