The Multiscale Monitoring of Peatland Ecosystem Carbon Cycling in the Middle Taiga Zone of Western Siberia: The Mukhrino Bog Case Study

The peatlands of the West Siberian Lowlands, comprising the largest pristine peatland area of the world, have not previously been covered by continuous measurement and monitoring programs. The response of peatlands to climate change occurs over several decades. This paper summarizes the results of peatland carbon balance studies collected over ten years at the Mukhrino field station (Mukhrino FS, MFS) operating in the Middle Taiga Zone of Western Siberia. A multiscale approach was applied for the investigations of peatland carbon cycling. Carbon dioxide fluxes at the local scale studied using the chamber method showed net accumulation with rates from 110, to 57.8 gC m−2 at the Sphagnum hollow site. Net CO2 fluxes at the pine-dwarf shrubs-Sphagnum ridge varied from negative (−32.1 gC m−2 in 2019) to positive (13.4 gC m−2 in 2017). The cumulative May-August net ecosystem exchange (NEE) from eddy-covariance (EC) measurements at the ecosystem scale was −202 gC m−2 in 2015, due to the impact of photosynthesis of pine trees which was not registered by the chamber method. The net annual accumulation of carbon in the live part of mosses was estimated at 24–190 gC m−2 depending on the Sphagnum moss species. Long-term carbon accumulation rates obtained by radiocarbon analysis ranged from 28.5 to 57.2 gC m−2 yr−1, with local extremes of up to 176.2 gC m−2 yr−1. The obtained estimates of various carbon fluxes using EC and chamber methods, the accounting for Sphagnum growth and decomposition, and long-term peat accumulation provided information about the functioning of the peatland ecosystems at different spatial and temporal scales. Multiscale carbon flux monitoring reveals useful new information for forecasting the response of northern peatland carbon cycles to climatic changes

Overview: Recent advances in the understanding of the northern Eurasian environments and of the urban air quality in China – a Pan-Eurasian Experiment (PEEX) programme perspective

The Pan-Eurasian Experiment (PEEX) Science Plan, released in 2015, addressed a need for a holistic system understanding and outlined the most urgent research needs for the rapidly changing Arctic-boreal region. Air quality in China, together with the long-range transport of atmospheric pollutants, was also indicated as one of the most crucial topics of the research agenda. These two geographical regions, the northern Eurasian Arctic-boreal region and China, especially the megacities in China, were identified as a "PEEX region". It is also important to recognize that the PEEX geographical region is an area where science-based policy actions would have significant impacts on the global climate. This paper summarizes results obtained during the last 5 years in the northern Eurasian region, together with recent observations of the air quality in the urban environments in China, in the context of the PEEX programme. The main regions of interest are the Russian Arctic, northern Eurasian boreal forests (Siberia) and peatlands, and the megacities in China. We frame our analysis against research themes introduced in the PEEX Science Plan in 2015. We summarize recent progress towards an enhanced holistic understanding of the land-atmosphere-ocean systems feedbacks. We conclude that although the scientific knowledge in these regions has increased, the new results are in many cases insufficient, and there are still gaps in our understanding of large-scale climate-Earth surface interactions and feedbacks. This arises from limitations in research infrastructures, especially the lack of coordinated, continuous and comprehensive in situ observations of the study region as well as integrative data analyses, hindering a comprehensive system analysis. The fast-changing environment and ecosystem changes driven by climate change, socio-economic activities like the China Silk Road Initiative, and the global trends like urbanization further complicate such analyses. We recognize new topics with an increasing importance in the near future, especially "the enhancing biological sequestration capacity of greenhouse gases into forests and soils to mitigate climate change" and the "socio-economic development to tackle air quality issues".Peer reviewe

Comparison of Artificial Neural Network and Regression Models for Filling Temporal Gaps of Meteorological Variables Time Series

Continuous meteorological variable time series are highly demanded for various climate related studies. Five statistical models were tested for application of temporal gaps filling in time series of surface air pressure, air temperature, relative air humidity, incoming solar radiation, net radiation, and soil temperature. A bilayer artificial neural network, linear regression, linear regression with interactions, and the Gaussian process regression models with exponential and rational quadratic kernel were used to fill the gaps. Models were driven by continuous time series of meteorological variables from the ECMWF (European Centre for Medium-range Weather Forecasts) ERA5-Land reanalysis. Raw ECMWF ERA5-Land reanalysis data are not applicable for characterization of specific local weather conditions. The linear correlation coefficients (CC) between ERA5-Land data and in situ observations vary from 0.61 (for wind direction) to 0.99 (for atmospheric pressure). The mean difference is high and estimated at 3.2 °C for air temperature and 3.5 hPa for atmospheric pressure. The normalized root-mean-square error (NRMSE) is 5–13%, except for wind direction (NRMSE = 49%). The linear bias correction of ERA5-Land data improves matching between the local and reanalysis data for all meteorological variables. The Gaussian process regression model with an exponential kernel based or bilayered artificial neural network trained on ERA5-Land data significantly shifts raw ERA5-Land data toward the observed values. The NRMSE values reduce to 2–11% for all variables, except wind direction (NRMSE = 22%). CC for the model is above 0.87, except for wind characteristics. The suggested model calibrated against in situ observations can be applied for gap-filling of time series of meteorological variables

Spatial structure of vegetation cover and top layer of peat of northeastern spurs of the great Vasyugan mire

The use of satellite images for a comprehensive study of natural objects, including the assessment of the current resource potential of wetlands and speed of peat accumulation is relevant in the study of remote areas of wetlands in Western Siberia. Structure of bog complexes is well reflected in the satellite images due to their indicator properties - texture and color of contours of bog microlandscapes. Satellite images allow evaluating the current state of wetland ecosystems and their violations in connection with the anthropogenic impact. The main aim of the study is to map the surface cover using satellite images for South-Taiga key «Bakchar-Iksa»; to carry out geospatial analysis of the structure of vegetation and the top layer of peat on the basis of the map data and ground surveys. The methods used in the study. Thematic mapping of surface cover of forest-bog complexes at a key area based on satellite images Landsat will identify the areas occupied by different types of bog complexes. A complex of geo-information programs will be used for interpretation of satellite imagery, mapping and assessment calculations. Results. Thematic processing of satellite images Landsat in the ERDAS Imagine system and compilation of digital layers in ArcGIS show clearly the spatial structure of vegetation over «Bakchar-Iksa» key area and allow investigating the compliance between modern vegetation and supplies types of nearurface peat layer at Iksinskoye bog. The comparative analysis revealed a good correspondence obtained for mapped bog vegetation and underlying peat types, except ombrotrophic fuscum peat type

Bias-corrected monthly precipitation data over South Siberia for 1979-2019 (CPSS 1.2)

Bias-Corrected Precipitation data over South Siberia (CPSS 1.2) contains monthly precipitation data for the area within the coordinates 50-65 N, 60-120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il'in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format. Copernicus Climate Change Service (C3S), 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate

Spatial structure of vegetation cover and top layer of peat of northeastern spurs of the great Vasyugan mire

The use of satellite images for a comprehensive study of natural objects, including the assessment of the current resource potential of wetlands and speed of peat accumulation is relevant in the study of remote areas of wetlands in Western Siberia. Structure of bog complexes is well reflected in the satellite images due to their indicator properties - texture and color of contours of bog microlandscapes. Satellite images allow evaluating the current state of wetland ecosystems and their violations in connection with the anthropogenic impact. The main aim of the study is to map the surface cover using satellite images for South-Taiga key «Bakchar-Iksa»; to carry out geospatial analysis of the structure of vegetation and the top layer of peat on the basis of the map data and ground surveys. The methods used in the study. Thematic mapping of surface cover of forest-bog complexes at a key area based on satellite images Landsat will identify the areas occupied by different types of bog complexes. A complex of geo-information programs will be used for interpretation of satellite imagery, mapping and assessment calculations. Results. Thematic processing of satellite images Landsat in the ERDAS Imagine system and compilation of digital layers in ArcGIS show clearly the spatial structure of vegetation over «Bakchar-Iksa» key area and allow investigating the compliance between modern vegetation and supplies types of nearurface peat layer at Iksinskoye bog. The comparative analysis revealed a good correspondence obtained for mapped bog vegetation and underlying peat types, except ombrotrophic fuscum peat type

Spatial Structure of Forest-Mire Complexes at the Key Site “Bakcharsky”

Проведено картирование растительности района Бакчарского болотного массива на основе анализа снимка Landsat и полевого обследования ключевого участка. При дешифрировании снимка использовалось разделение территории на крупные области (водораздельные, террасные и незаболоченные области) и последующая их классификация. На исследуемой территории выделено 24 типа растительности.Mapping of vegetation types for mire massif Bakcharsky was done basing on the analysis of Landsat space images and field study of the key area. Area of the key site was divided into watershed, terrace areas and uplands. Classification with training was used for classes recognition. 24 classes of vegetation cover were found at the studied area

Spatial Structure of Forest-Mire Complexes at the Key Site “Bakcharsky”

Проведено картирование растительности района Бакчарского болотного массива на основе анализа снимка Landsat и полевого обследования ключевого участка. При дешифрировании снимка использовалось разделение территории на крупные области (водораздельные, террасные и незаболоченные области) и последующая их классификация. На исследуемой территории выделено 24 типа растительности.Mapping of vegetation types for mire massif Bakcharsky was done basing on the analysis of Landsat space images and field study of the key area. Area of the key site was divided into watershed, terrace areas and uplands. Classification with training was used for classes recognition. 24 classes of vegetation cover were found at the studied area

Net Ecosystem Exchange, Gross Primary Production And Ecosystem Respiration In Ridge-Hollow Complex At Mukhrino Bog

The continuous field measurements  of net ecosystem exchange (NEE) of CO2 were provided at ridge-hollow oligotrophic bog in the Middle Taiga zone of West Siberia, Russia  in 2017-2018. The model of net ecosystem  exchange  of CO2  was suggested  to describe  the influence  of different  environmental  factors  on NEE and to estimate  the total carbon budget of the bog over the growing  season. The model uses air and soil temperature, incoming  photosynthetically  active radiation (PAR) and water table depth, as the key factors influencing gross primary production  (GPP) and ecosystem respiration (ER). The model coefficients were calibrated using the data collected  by automated soil CO2  flux system with two transparent long-term  chambers  placed at large hollow and small ridge sites.Experimental and modeling results showed that the Mukhrino bog acted over the study period as a carbon  sink, with  an average NEE of –87.7 gC m-2 at the hollow site and –50.2 gC m-2  at the ridge  site. GPP was – 344.8 and –228.5 gC m-2  whereas ER was 287.6 and 140.9 gC m-2  at ridge and hollow  sites, respectively.  Despite of a large difference in NEE estimates  between 2017 and 2018 the growing  season variability  of NEE were quite similar

PAN EURASIAN EXPERIMENT (PEEX) - A RESEARCH INITIATIVE MEETING THE GRAND CHALLENGES OF THE CHANGING ENVIRONMENT OF THE NORTHERN PAN-EURASIAN ARCTIC-BOREAL AREAS

The Pan-Eurasian Experiment (PEEX) is a new multidisciplinary, global change research initiative focusing on understanding biosphere-ocean-cryosphere-climate interactions and feedbacks in Arctic and boreal regions in the Northern Eurasian geographical domain. PEEX operates in an integrative way and it aims at solving the major scientific and society relevant questions in many scales using tools from natural and social sciences and economics. The research agenda identifies the most urgent large scale research questions and topics of the land-atmosphere-aquatic-anthropogenic systems and interactions and feedbacks between the systems for the next decades. Furthermore PEEX actively develops and designs a coordinated and coherent ground station network from Europe via Siberia to China and the coastal line of the Arctic Ocean together with a PEEX-modeling platform. PEEX launches a program for educating the next generation of multidisciplinary researcher and technical experts. This expedites the utilization of the new scientific knowledge for producing a more reliable climate change scenarios in regional and global scales, and enables mitigation and adaptation planning of the Northern societies. PEEX gathers together leading European, Russian and Chinese research groups. With a bottom-up approach, over 40 institutes and universities have contributed the PEEX Science Plan from 18 countries. In 2014 the PEEX community prepared Science Plan and initiated conceptual design of the PEEX land-atmosphere observation network and modeling platform. Here we present the PEEX approach as a whole with the specific attention to research agenda and preliminary design of the PEEX research infrastructure