Surface energy exchange in pristine and managed boreal peatlands

Surface-atmosphere energy exchange is strongly ecosystem-specific. At the same time, as the energy balance constitutes responses of an ecosystem to environmental stressors including precipitation, humidity and solar radiation, it results in feedbacks of potential importance for the regional climate. Northern peatlands represent a diverse class of ecosystems that cover nearly 6 x 10(6) km(2) in the Boreal region, which makes the inter-comparison of their energy balances an important objective. With this in mind we studied energy exchange across a broad spectrum of peatlands from pristine fens and bogs to forested and agriculturally managed peatlands, which represent a large fraction of the landscape in Finland and Sweden. The effects of management activities on the energy balance were extensively examined from the micrometeorological point of view, using eddy covariance data from eight sites in these two countries (56 degrees 12'-62 degrees 11' N, 13 degrees 03'-30 degrees 05' E). It appears that the surface energy balance varies widely amongst the different peatland types. Generally, energy exchange features including the Bowen ratio, surface conductance, coupling to the atmosphere, responses to water table fluctuations and vapour pressure deficit could be associated directly with the peatland type. The relative constancy of the Bowen ratio in natural open mires contrasted with its variation in tree-covered and agricultural peatlands. We conclude that the impacts of management and the consequences of land-use change in peatlands for the local and regional climate might be substantial.Peer reviewe

A process-based model of methane consumption by upland soils

This study combines a literature survey and field observation data in an ad initio attempt to construct a process-based model of methane sink in upland soils including both the biological and physical aspects of the process. Comparison is drawn between the predicted sink rates and chamber measurements in several forest and grassland sites in the southern part of West Siberia. CH4 flux, total respiration, air and soil temperature, soil moisture, pH, organic content, bulk density and solid phase density were measured during a field campaign in summer 2014. Two datasets from literature were also used for model validation. The modeled sink rates were found to be in relatively good correspondence with the values obtained in the field. Introduction of the rhizospheric methanotrophy significantly improves the match between the model and the observations. The Q10 values of methane sink observed in the field were 1.2-1.4, which is in good agreement with the experimental results from the other studies. Based on modeling results, we also conclude that soil oxygen concentration is not a limiting factor for methane sink in upland forest and grassland ecosystems.Peer reviewe

Relationship between aerodynamic roughness length and bulk sedge leaf area index in a mixed-species boreal mire complex

Leaf area index (LAI) is an important parameter in natural ecosystems, representing the seasonal development of vegetation and photosynthetic potential. However, direct measurement techniques require labor-intensive field campaigns that are usually limited in time, while remote sensing approaches often do not yield reliable estimates. Here we propose that the bulk LAI of sedges (LAI(s)) can be estimated alternatively from a micrometeorological parameter, the aerodynamic roughness length for momentum (z(0)). z(0) can be readily calculated from high-response turbulence and other meteorological data, typically measured continuously and routinely available at ecosystem research sites. The regressions of LAI versus z(0) were obtained using the data from two Finnish natural sites representative of boreal fen and bog ecosystems. LAI(s) was found to be well correlated with z(0) and sedge canopy height. Superior method performance was demonstrated in the fen ecosystem where the sedges make a bigger contribution to overall surface roughness than in bogs.Peer reviewe

Results of Monitoring over the West Nile Fever Pathogen in the Territory of the Russian Federation in 2017. Forecast of Epidemic Situation Development in Russia in 2018

In Europe, in the epidemic season 2017, the incidence of West Nile fever did not exceed the index of the previous season. In the US and Canada, there was an increase in the incidence of cases. West Nile fever morbidity rates in the Russian Federation and in separate constituent entities were below the average long-term index and had the lowest value for the period since 2008. 41.6 % of WNF cases were imported to Russia from the distant countries. Analysis of the monitoring results indicated the circulation of WNF virus markers in carriers of the pathogen in 6 constituent entities of the Russian Federation, and the presence of IgG antibodies in healthy population cohorts in 24 RF entities. According to molecular-genetic typing of WNF virus samples from mosquito Culex modestus from the Volgograd Region, WNF virus genotype II was established. Forecasting of epidemiological situation development for the year 2018 does not rule out the possibility of local increase in WNF incidence in certain regions of Russia

Warming response of peatland CO2 sink is sensitive to seasonality in warming trends

Peatlands have acted as net CO2 sinks over millennia, exerting a global climate cooling effect. Rapid warming at northern latitudes, where peatlands are abundant, can disturb their CO2 sink function. Here we show that sensitivity of peatland net CO2 exchange to warming changes in sign and magnitude across seasons, resulting in complex net CO2 sink responses. We use multiannual net CO2 exchange observations from 20 northern peatlands to show that warmer early summers are linked to increased net CO2 uptake, while warmer late summers lead to decreased net CO2 uptake. Thus, net CO2 sinks of peatlands in regions experiencing early summer warming, such as central Siberia, are more likely to persist under warmer climate conditions than are those in other regions. Our results will be useful to improve the design of future warming experiments and to better interpret large-scale trends in peatland net CO2 uptake over the coming few decades.Peatlands have historically acted as a carbon sink, but it is unclear how climate warming will affect this. The response of peatland carbon uptake to warming depends on the timing of summer warming; early warming leads to increased CO2 uptake and later warming to decreased uptake

Peculiarities of the Epidemic Situation on West Nile Fever in the Territory of the Russian Federation in 2018 and Forecast of its Development in 2019

The epidemic rise in the incidence of West Nile fever (WNF) in the season of 2018 was observed in the countries of the European Union (EU) and bordering states and exceeded the values of all previously recorded epidemic rises of 2010–2012. An increase in the incidence rate was registered in the USA and Canada, however, it did not exceed the indicators of epidemic rises of 2007–2012. In the territory of the Russian Federation, the WNF epidemiological process became more intense mainly in the territory of the Southern and North Caucasian Federal Districts. In general, in Russia, the incidence rates were 2 times lower than the average annual rates, but significantly exceeded those of 2017. The epidemic process had a number of peculiarities in the seasonality, the structure of morbidity and the clinical manifestation of WNF. Genotyping of the isolated WNV RNA fragments from clinical and biological material showed that I, II and IV West Nile virus genotypes were circulating in the European part of Russia. Forecast of epidemic situation development in 2019 reveals further increase in the incidence and does not exclude the possibility of a significant localincrease of WNF incidence in certain regions of Russia

Pan-Eurasian Experiment (PEEX): Towards a holistic understanding of the feedbacks and interactions in the land-Atmosphere-ocean-society continuum in the northern Eurasian region

The northern Eurasian regions and Arctic Ocean will very likely undergo substantial changes during the next decades. The Arctic-boreal natural environments play a crucial role in the global climate via albedo change, carbon sources and sinks as well as atmospheric aerosol production from biogenic volatile organic compounds. Furthermore, it is expected that global trade activities, demographic movement, and use of natural resources will be increasing in the Arctic regions. There is a need for a novel research approach, which not only identifies and tackles the relevant multi-disciplinary research questions, but also is able to make a holistic system analysis of the expected feedbacks. In this paper, we introduce the research agenda of the Pan-Eurasian Experiment (PEEX), a multi-scale, multi-disciplinary and international program started in 2012 (https://www.atm.helsinki.fi/peex/). PEEX sets a research approach by which large-scale research topics are investigated from a system perspective and which aims to fill the key gaps in our understanding of the feedbacks and interactions between the land-Atmosphere-Aquatic-society continuum in the northern Eurasian region. We introduce here the state of the art for the key topics in the PEEX research agenda and present the future prospects of the research, which we see relevant in this context

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe