Preprints
https://doi.org/10.5194/essd-2024-290
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2024-290
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 Jul 2024
| 30 Jul 2024
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.
Lena River biogeochemistry captured by a 4.5-year high-frequency sampling program
Abstract. The Siberian Arctic is warming rapidly, causing permafrost to thaw and altering the biogeochemistry of aquatic environments, with cascading effects on the coastal and shelf ecosystems of the Arctic Ocean. The Lena River, one of the largest Arctic rivers, drains a catchment dominated by permafrost. Baseline discharge biogeochemistry data is necessary to understand present and future changes in land-to-ocean fluxes. Here, we present a high-frequency, 4.5-year-long dataset from a sampling program of the Lena River’s biogeochemistry, spanning April 2018 to August 2022. The dataset comprises 587 sampling events and measurements of various parameters, including water temperature, electrical conductivity, stable oxygen and hydrogen isotopes, dissolved organic carbon concentration and 14C, coloured and fluorescent dissolved organic matter, dissolved inorganic and total nutrients, and dissolved elemental and ion concentrations. Sampling consistency and continuity and data quality were ensured through simple sampling protocols, real-time communication, and collaboration with local and international partners. The data is available as a collection of datasets separated by parameter groups and periods at https://doi.org/10.1594/PANGAEA.913197 (Juhls et al., 2020b). To our knowledge, this dataset provides an unprecedented temporal resolution of an Arctic river’s biogeochemistry. This makes it a unique baseline on which future environmental changes, including changes in river hydrology, at temporal scales from precipitation event to seasonal to interannual, can be detected.
How to cite. Juhls, B., Morgenstern, A., Hölemann, J., Eulenburg, A., Heim, B., Miesner, F., Grotheer, H., Mollenhauer, G., Meyer, H., Erkens, E., Gehde, F. Y., Antonova, S., Chalov, S., Tereshina, M., Erina, O., Fingert, E., Abramova, E., Sanders, T., Lebedeva, L., Torgovkin, N., Maksimov, G., Povazhnyi, V., Gonçalves-Araujo, R., Wünsch, U., Chetverova, A., Opfergelt, S., and Overduin, P. P.: Lena River biogeochemistry captured by a 4.5-year high-frequency sampling program, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-290, in review, 2024.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on essd-2024-290', Anonymous Referee #1, 16 Aug 2024
The work is within the scope of the journal; however, the authors have to invest a bit more to provide it a clear added value. The authors used adequate procedure for sampling, handling and analyses
The manuscript reports highly impressive number of sampling points and various hydrochemical parameters. It clearly presents a valuable data set. However, the need for such a dataset should be clearly explained and justified. As a minimal starting point in the Introduction, please explain the difference (and novelty) of this work compared to data available from Partners/Arctic GRO at this river terminal gauging station.
- In the main text and figures, the authors should provide a comparison with data of ARCTIC GRO/Partners obtained at the Kysur gauging station. Appendix D is just great, but it should be in the main text. Other measured parameters (those available from ArcticGRO) should be shown as well
- The authors possess both discharges and concentrations. Export fluxes (via, for example, LOADEST or any other mean) should be calculated and compared with earlier fluxes. It is the duty of the authors to provide the fluxes, the readers cannot do it themselves. Within the concept of this journal, I assume no discussion of concentration dependence on the discharge and comparison with other rivers are needed. However, the export fluxes (mean multi-annual values or yields) should be there.
Specific issues:
L90-91 Please note that annual fluxes of most solutes in Arctic rivers can be reasonably (within 20-30 %, which is lower than annual inter-variations) can be approximated by July-August sampling (see for instance https://doi.org/10.1016/j.chemgeo.2022.121180)
L 190-191 This is certainly a valid explanation
L461-463 I certainly agree with this statement
L473-474 Here I also completely agree with authors’ statement. Great and very timing work, badly needed for world scientific community.
Fig. A1: The dates should be shown on these graphs
Fig. B2: Comparison of two method of sample treatment for analyses is very useful. It is a pity that no “filtered and frozen” method was tested, because this technique is certainly the best for adequate assessment of nutrients
p.34, Fig. B3: The data on P are unclear – what do negative values mean?
It is clear that for analyses of Fe, Ca, Ba, Al, on site filtration is mandatory prior to analyses. Please make sure you let it express in the text, because this is very important finding
Citation: https://doi.org/10.5194/essd-2024-290-RC1 -
RC2: 'Comment on essd-2024-290', Anonymous Referee #2, 03 Sep 2024
General Comments:This is a very valuable contribution. The existence and maintenance of such sustained sampling and measurement programs of physical and biogeochemical parameters of river systems is of paramount importance given the integrative nature of the information rivers carry about the corresponding watersheds, their role in linking terrestrial and marine environments and ecosystems, and their ability to reveal system-wide change. Such initiatives are of particular importance for regions of the planet that are experiencing accelerated change, such as the Arctic, where information can be used to gauge biogeochemical, and ecological responses to changing hydrological and climate conditions. Furthermore, the fate of the vast stores of carbon currently residing in permafrost in the face of on-going warming and hydrological change underlines the significance of this region in terms of global climate. With the unprecedented pace of change underway, there is the urgent need for comprehensive and intensive observation programs that provide context for this change.Fortunately, the biogeochemistry of the major Arctic rivers have been the focus of sustained observations as a consequence of programs such as the pan-Arctic River sampling programs (PARTNERS) and Arctic Great Rivers Observatory (ArcticGRO) which extent back more than 20 years. However, these programs have been characterized by low temporal resolution, with large data gaps, particularly for specific seasons and transitional periods (shoulder seasons of freshet & freeze-up) rendering it hard to investigate different processes and constrain shorter-term variability. In such circumstances, the authors correctly highlight the limitations of models as an approach to bridge data gaps, and argue for the need for high-frequency measurements to better constrain flux estimates, and investigate short-term variability resulting from changes in hydrologic pathways and other phenomena. The articulated need for baseline observations is clear, although it is evident that marked changes are already upon us.This present study describes a diverse suite of data acquired over a 4.5-year period from sampling at a station in the delta of the Lena River, one of the largest Arctic rivers, with a catchment dominated by permafrost. The 4+-year period covers a time interval during which winter discharge that is higher than the long-term average, and captures both record low and record high intervals of summer discharge. High-frequency (daily to weekly) sampling resulting in a total of almost 600 sampling dates, focussing exclusively on dissolved parameters. Acquisition of such detailed and long-term datasets always represents a compromise given logistical constraints associated with ease of sampling, sampling methods and volumes, sample storage and shipment, instrumental techniques, performance and reliability, and range of parameters sought, and of course cost. Clearly, a great deal of thought and care, as well as pragmatism, has gone into the design and execution of this high-resolution sampling program. Despite some apparent limitations and inconsistencies in the dataset the existence of such high-resolution, extended datasets remains rare, and yet is of crucial value. It is not surprising for such a long-term, multi-institution and logistically challenging endeavor focused on a remote location that the datasets are somewhat heterogeneous with respect to sample processing, storage and shipment, as well as where and how the measurements were made, with some resulting patchiness in data quality. However, the manuscript benefits from a detailed description of the methods used, and discussion is provided concerning changes in methodology over the course of the observation period, which are also indicated in the figures. Analytical uncertainties in the measurements are also provided (in Table 1). For parameters where there is significant data scatter associated with measurement on specific instruments and different laboratories, and the authors caution use and interpretation of such data where this is evident (e.g., SUVA and SR in Figure 7; nutrients in Figure 11). In general, I think such discussions of data quality are satisfactory.What was less clear is whether efforts were made to analyze splits of the same samples for the same parameters in different labs in order to address inter-lab data comparability (i.e., beyond measurement of standards). It seems that sample batches were processed in serial fashion by only one lab or another. Clarification of this point for the different parameters would be helpful. I note that in some cases comparisons were made for the same samples that were frozen versus unfrozen (Appendix B1), but what about splits of the same sample treated in the same way, but measured by different methods/research groups? One example is the water (oxygen and hydrogen) isotope data, which were obtained by mass spectrometry and optical spectroscopy. The Lena river can exhibit quite high DOM concentrations (DOC up to 20 mg L-1), which can influence spectroscopic properties. Was there any systematic comparison of water isotope data for splits of Lena water samples (not standards) between MS and CRDS methods? Irrespective, the transition in instrumental methods used in the measurement of specific parameters is indicated in the Figures, which is very helpful (e.g., water isotopes in Figure 5; DOC concentrations and absorbance in Figure 6).The data reveals some interesting contrasts for the same parameter but measured using different measurement methods (e.g., colorometric versus ion chromatographic determination of silicon concentrations; e.g., Fig. 12a) as well as different sampling handing protocols (e.g., electrical conductivity; cf. Appendix B). Such contrasts and systematic biases are to be expected given logistical challenges in operating such a sustained measurement program. Although such offsets/biases are not optimal, the overall density of data holds promise for the potential to anticipate and correct deviations between sample suites processed and analyzed in different ways. I think these data are also highly informative for other researchers who may be applying/developing protocols for sample collection, processing and storage. Overall, I think the manuscript provides an objective assessment of the data quality and highlights key features that emerge over the time series.Specific comments:- For the DOC radiocarbon data, presumably DOC concentration data is also obtained from the elemental analyzer-MICADAS AMS measurement? If so, how did DOC concentrations compare with corresponding measurements using the more conventional DOC method (high-temperature catalytic oxidation)?- Appendix D. I am glad that the authors drew a comparison between their observations and those reported by the ArcticGRO program (albeit at a more upstream location), however, I think that this would be good to include in the main body of the manuscript as I am sure this comparison will be of direct interest to the reader. Moreover, Figure D1 and D2 clearly shows the merit of performing high temporal resolution sampling and measurement in order to constrain (sub-)seasonal variability. It would be helpful to list which measured parameters (beyond DOC concentration and CDOM absorption) are covered by both the ArcticGRO and the present 4.5-year time series.- A key question that could perhaps be addressed by the authors (at the end of the Discussion or in the Conclusions section) is whether, based on their findings, all parameters need to be measured with the same sampling frequency (given observed variability). In other words, can the data presented can be used to develop a recommended protocol for future, more streamlined sampling. For example, are there specific parameters that appear to be most diagnostic of specific (changes in) processes that are not captured in low-resolution datasets? Given the challenges (and costs) associated with sustaining such a sampling/measurement program, it might be helpful to consider things from a strategic point of view. Furthermore, would a repeat intensive phase of high-resolution sampling/measurements spanning a similar time interval be worth undertaking a decade from now? This may be particularly pertinent as I suspect maintaining this program given the current geopolitical situation will be challenging.- Is any of the sampled material archived for future (repeat or new) measurements? If so, this should be mentioned.Citation: https://doi.org/
10.5194/essd-2024-290-RC2 - AC1: 'Authors responses on essd-2024-290', Bennet Juhls, 25 Oct 2024
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on essd-2024-290', Anonymous Referee #1, 16 Aug 2024
The work is within the scope of the journal; however, the authors have to invest a bit more to provide it a clear added value. The authors used adequate procedure for sampling, handling and analyses
The manuscript reports highly impressive number of sampling points and various hydrochemical parameters. It clearly presents a valuable data set. However, the need for such a dataset should be clearly explained and justified. As a minimal starting point in the Introduction, please explain the difference (and novelty) of this work compared to data available from Partners/Arctic GRO at this river terminal gauging station.
- In the main text and figures, the authors should provide a comparison with data of ARCTIC GRO/Partners obtained at the Kysur gauging station. Appendix D is just great, but it should be in the main text. Other measured parameters (those available from ArcticGRO) should be shown as well
- The authors possess both discharges and concentrations. Export fluxes (via, for example, LOADEST or any other mean) should be calculated and compared with earlier fluxes. It is the duty of the authors to provide the fluxes, the readers cannot do it themselves. Within the concept of this journal, I assume no discussion of concentration dependence on the discharge and comparison with other rivers are needed. However, the export fluxes (mean multi-annual values or yields) should be there.
Specific issues:
L90-91 Please note that annual fluxes of most solutes in Arctic rivers can be reasonably (within 20-30 %, which is lower than annual inter-variations) can be approximated by July-August sampling (see for instance https://doi.org/10.1016/j.chemgeo.2022.121180)
L 190-191 This is certainly a valid explanation
L461-463 I certainly agree with this statement
L473-474 Here I also completely agree with authors’ statement. Great and very timing work, badly needed for world scientific community.
Fig. A1: The dates should be shown on these graphs
Fig. B2: Comparison of two method of sample treatment for analyses is very useful. It is a pity that no “filtered and frozen” method was tested, because this technique is certainly the best for adequate assessment of nutrients
p.34, Fig. B3: The data on P are unclear – what do negative values mean?
It is clear that for analyses of Fe, Ca, Ba, Al, on site filtration is mandatory prior to analyses. Please make sure you let it express in the text, because this is very important finding
Citation: https://doi.org/10.5194/essd-2024-290-RC1 -
RC2: 'Comment on essd-2024-290', Anonymous Referee #2, 03 Sep 2024
General Comments:This is a very valuable contribution. The existence and maintenance of such sustained sampling and measurement programs of physical and biogeochemical parameters of river systems is of paramount importance given the integrative nature of the information rivers carry about the corresponding watersheds, their role in linking terrestrial and marine environments and ecosystems, and their ability to reveal system-wide change. Such initiatives are of particular importance for regions of the planet that are experiencing accelerated change, such as the Arctic, where information can be used to gauge biogeochemical, and ecological responses to changing hydrological and climate conditions. Furthermore, the fate of the vast stores of carbon currently residing in permafrost in the face of on-going warming and hydrological change underlines the significance of this region in terms of global climate. With the unprecedented pace of change underway, there is the urgent need for comprehensive and intensive observation programs that provide context for this change.Fortunately, the biogeochemistry of the major Arctic rivers have been the focus of sustained observations as a consequence of programs such as the pan-Arctic River sampling programs (PARTNERS) and Arctic Great Rivers Observatory (ArcticGRO) which extent back more than 20 years. However, these programs have been characterized by low temporal resolution, with large data gaps, particularly for specific seasons and transitional periods (shoulder seasons of freshet & freeze-up) rendering it hard to investigate different processes and constrain shorter-term variability. In such circumstances, the authors correctly highlight the limitations of models as an approach to bridge data gaps, and argue for the need for high-frequency measurements to better constrain flux estimates, and investigate short-term variability resulting from changes in hydrologic pathways and other phenomena. The articulated need for baseline observations is clear, although it is evident that marked changes are already upon us.This present study describes a diverse suite of data acquired over a 4.5-year period from sampling at a station in the delta of the Lena River, one of the largest Arctic rivers, with a catchment dominated by permafrost. The 4+-year period covers a time interval during which winter discharge that is higher than the long-term average, and captures both record low and record high intervals of summer discharge. High-frequency (daily to weekly) sampling resulting in a total of almost 600 sampling dates, focussing exclusively on dissolved parameters. Acquisition of such detailed and long-term datasets always represents a compromise given logistical constraints associated with ease of sampling, sampling methods and volumes, sample storage and shipment, instrumental techniques, performance and reliability, and range of parameters sought, and of course cost. Clearly, a great deal of thought and care, as well as pragmatism, has gone into the design and execution of this high-resolution sampling program. Despite some apparent limitations and inconsistencies in the dataset the existence of such high-resolution, extended datasets remains rare, and yet is of crucial value. It is not surprising for such a long-term, multi-institution and logistically challenging endeavor focused on a remote location that the datasets are somewhat heterogeneous with respect to sample processing, storage and shipment, as well as where and how the measurements were made, with some resulting patchiness in data quality. However, the manuscript benefits from a detailed description of the methods used, and discussion is provided concerning changes in methodology over the course of the observation period, which are also indicated in the figures. Analytical uncertainties in the measurements are also provided (in Table 1). For parameters where there is significant data scatter associated with measurement on specific instruments and different laboratories, and the authors caution use and interpretation of such data where this is evident (e.g., SUVA and SR in Figure 7; nutrients in Figure 11). In general, I think such discussions of data quality are satisfactory.What was less clear is whether efforts were made to analyze splits of the same samples for the same parameters in different labs in order to address inter-lab data comparability (i.e., beyond measurement of standards). It seems that sample batches were processed in serial fashion by only one lab or another. Clarification of this point for the different parameters would be helpful. I note that in some cases comparisons were made for the same samples that were frozen versus unfrozen (Appendix B1), but what about splits of the same sample treated in the same way, but measured by different methods/research groups? One example is the water (oxygen and hydrogen) isotope data, which were obtained by mass spectrometry and optical spectroscopy. The Lena river can exhibit quite high DOM concentrations (DOC up to 20 mg L-1), which can influence spectroscopic properties. Was there any systematic comparison of water isotope data for splits of Lena water samples (not standards) between MS and CRDS methods? Irrespective, the transition in instrumental methods used in the measurement of specific parameters is indicated in the Figures, which is very helpful (e.g., water isotopes in Figure 5; DOC concentrations and absorbance in Figure 6).The data reveals some interesting contrasts for the same parameter but measured using different measurement methods (e.g., colorometric versus ion chromatographic determination of silicon concentrations; e.g., Fig. 12a) as well as different sampling handing protocols (e.g., electrical conductivity; cf. Appendix B). Such contrasts and systematic biases are to be expected given logistical challenges in operating such a sustained measurement program. Although such offsets/biases are not optimal, the overall density of data holds promise for the potential to anticipate and correct deviations between sample suites processed and analyzed in different ways. I think these data are also highly informative for other researchers who may be applying/developing protocols for sample collection, processing and storage. Overall, I think the manuscript provides an objective assessment of the data quality and highlights key features that emerge over the time series.Specific comments:- For the DOC radiocarbon data, presumably DOC concentration data is also obtained from the elemental analyzer-MICADAS AMS measurement? If so, how did DOC concentrations compare with corresponding measurements using the more conventional DOC method (high-temperature catalytic oxidation)?- Appendix D. I am glad that the authors drew a comparison between their observations and those reported by the ArcticGRO program (albeit at a more upstream location), however, I think that this would be good to include in the main body of the manuscript as I am sure this comparison will be of direct interest to the reader. Moreover, Figure D1 and D2 clearly shows the merit of performing high temporal resolution sampling and measurement in order to constrain (sub-)seasonal variability. It would be helpful to list which measured parameters (beyond DOC concentration and CDOM absorption) are covered by both the ArcticGRO and the present 4.5-year time series.- A key question that could perhaps be addressed by the authors (at the end of the Discussion or in the Conclusions section) is whether, based on their findings, all parameters need to be measured with the same sampling frequency (given observed variability). In other words, can the data presented can be used to develop a recommended protocol for future, more streamlined sampling. For example, are there specific parameters that appear to be most diagnostic of specific (changes in) processes that are not captured in low-resolution datasets? Given the challenges (and costs) associated with sustaining such a sampling/measurement program, it might be helpful to consider things from a strategic point of view. Furthermore, would a repeat intensive phase of high-resolution sampling/measurements spanning a similar time interval be worth undertaking a decade from now? This may be particularly pertinent as I suspect maintaining this program given the current geopolitical situation will be challenging.- Is any of the sampled material archived for future (repeat or new) measurements? If so, this should be mentioned.Citation: https://doi.org/
10.5194/essd-2024-290-RC2 - AC1: 'Authors responses on essd-2024-290', Bennet Juhls, 25 Oct 2024
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Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Anne Morgenstern
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Jens Hölemann
Physical Oceanography of Polar Seas, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
Antje Eulenburg
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Birgit Heim
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Frederieke Miesner
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Department of Geosciences, University of Oslo, Oslo, 0316, Norway
Hendrik Grotheer
Marine Geochemistry, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
MARUM - Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Bremen, 28359, Germany
Gesine Mollenhauer
Marine Geochemistry, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, 27570, Germany
MARUM - Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Bremen, 28359, Germany
Hanno Meyer
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Ephraim Erkens
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Institute for Geosciences, University of Potsdam, Potsdam, 14476, Germany
Felica Yara Gehde
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Sofia Antonova
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Sergey Chalov
Faculty of Geography, Department of Hydrology, Lomonosov Moscow State University, Moscow, 129626, Russia
Institute of Ecology and Environment, Kazan Federal University, Kazan 420097, Russia
Maria Tereshina
Faculty of Geography, Department of Hydrology, Lomonosov Moscow State University, Moscow, 129626, Russia
Oxana Erina
Faculty of Geography, Department of Hydrology, Lomonosov Moscow State University, Moscow, 129626, Russia
Evgeniya Fingert
Faculty of Geography, Department of Hydrology, Lomonosov Moscow State University, Moscow, 129626, Russia
Ekaterina Abramova
Lena Delta Nature Reserve, Tiksi, 678400, Sakha Republic, Russia
Tina Sanders
Department of Aquatic Nutrient Cycles, Institute of Carbon Cycles, Helmholtz Centre Hereon, Geesthacht, 21502, Germany
Liudmila Lebedeva
Laboratory of Permafrost Groundwater and Geochemistry, Melnikov Permafrost Institute, Russian Academy of Sciences, Yakutsk, 677010, Russia
Nikolai Torgovkin
Laboratory of Permafrost Groundwater and Geochemistry, Melnikov Permafrost Institute, Russian Academy of Sciences, Yakutsk, 677010, Russia
Georgii Maksimov
Laboratory of General Geocryology, Melnikov Permafrost Institute, Russian Academy of Sciences, Yakutsk, 677010, Russia
Vasily Povazhnyi
Otto Schmidt Laboratory for Polar and Marine Research, Arctic and Antarctic Research Institute, St. Petersburg, 199397, Russia
Rafael Gonçalves-Araujo
Section for Oceans and Arctic, National Institute of Aquatic Resources, Technical University of Denmark, Lyngby, 2800, Denmark
Urban Wünsch
Section for Oceans and Arctic, National Institute of Aquatic Resources, Technical University of Denmark, Lyngby, 2800, Denmark
Antonina Chetverova
Otto Schmidt Laboratory for Polar and Marine Research, Arctic and Antarctic Research Institute, St. Petersburg, 199397, Russia
Institute of Earth Science, St. Petersburg University, St. Petersburg, 199034, Russia
Sophie Opfergelt
Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, 1348, Belgium
Pier Paul Overduin
Permafrost Research, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Potsdam, 14473, Germany
Short summary
The Siberian Arctic is warming fast: permafrost is thawing, river chemistry is changing, and coastal ecosystems are affected. We want to understand changes to the Lena River, a major Arctic river flowing to the Arctic Ocean, by collecting 4.5 years of detailed water data, including temperature and carbon and nutrient contents. This dataset records current conditions and helps us to detect future changes. Explore it at https://doi.org/10.1594/PANGAEA.913197 and https://lena-monitoring.awi.de/.
The Siberian Arctic is warming fast: permafrost is thawing, river chemistry is changing, and...
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