Estimating 2010–2015 anthropogenic and natural methane emissions in Canada using ECCC surface and GOSAT satellite observations

Methane emissions in Canada have both anthropogenic and natural sources. Anthropogenic emissions are estimated to be 4.1 Tg a−1 from 2010–2015 in the National Inventory Report submitted to the United Nation's Framework Convention on Climate Change (UNFCCC). Natural emissions, which are mostly due to boreal wetlands, are the largest methane source in Canada and highly uncertain, on the order of ∼ 20 Tg a−1 in biosphere process models. Aircraft studies over the last several years have provided “snapshot” emissions that conflict with inventory estimates. Here we use surface data from the Environment and Climate Change Canada (ECCC) in situ network and space-borne data from the Greenhouse Gases Observing Satellite (GOSAT) to determine 2010–2015 anthropogenic and natural methane emissions in Canada in a Bayesian inverse modelling framework. We use GEOS-Chem to simulate anthropogenic emissions comparable to the National Inventory and wetlands emissions using an ensemble of WetCHARTS v1.0 scenarios in addition to other minor natural sources. We conduct a comparative analysis of the monthly natural emissions and yearly anthropogenic emissions optimized by surface and satellite data independently. Mean 2010–2015 posterior emissions using ECCC surface data are 6.0 ± 0.4 Tg a−1 for total anthropogenic and 11.6 ± 1.2 Tg a−1 for total natural emissions. These results agree with our posterior emissions of 6.5 ± 0.7 Tg a−1 for total anthropogenic and 11.7 ± 1.2 Tg a−1 for total natural emissions using GOSAT data. The seasonal pattern of posterior natural emissions using either dataset shows slower to start emissions in the spring and a less intense peak in the summer compared to the mean of WetCHARTS scenarios. We combine ECCC and GOSAT data to characterize limitations towards sectoral and provincial-level inversions. We estimate energy + agriculture emissions to be 5.1 ± 1.0 Tg a−1, which is 59 % higher than the national inventory. We attribute 39 % higher anthropogenic emissions to Western Canada than the prior. Natural emissions are lower across Canada. Inversion results are verified against independent aircraft data and surface data, which show better agreement with posterior emissions. This study shows a readjustment of the Canadian methane budget is necessary to better match atmospheric observations with lower natural emissions partially offset by higher anthropogenic emissions.

Analysis of the state of preservation the historic arcaded houses in Vistula Delta listed in the National Inventory of Historical Monuments

Historic arcaded houses are part of the material heritage of the Vistula Delta. Unfortunately, their number is decreasing year by year. The article is the result of a query of available sources and field research carried out by the author in 2015-2020. The paper presents the current state of preservation the historic arcaded houses in Vistula Delta listed in the National Inventory of Historical Monuments. Criteria for the selection of test objects are described. The author has prepared a tabular list of arcaded houses with information about their location, type according to Kloeppel statistics, date of construction, technical condition and functions. The summary of the analysis are pie charts with a statistical presentation of the data collected by the author during the research.

A Basic Model to Predict Enteric Methane Emission from Dairy Cows and Its Application to Update Operational Models for the National Inventory in Norway

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Development of basic models utilized information that is available only from feeding experiments. Basic models were developed using a database with 63 treatment means from 19 studies and were evaluated against an external database (n = 36, from 10 studies) along with other extant models. In total, the basic model database included 99 treatment means from 29 studies with records for enteric CH4 production (MJ/day), dry matter intake (DMI) and dietary nutrient composition. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4) and thus to be able to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations. A simulated operational database containing CH4 production (predicted by the basic model), feed intake and composition, Ym and gross energy intake (GEI), in addition to the predictor variables energy corrected milk yield and dietary concentrate share were used to develop an operational model. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction accuracy of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.

A Basic Model to Predict Enteric Methane Emission from Dairy Cows and Its Application to Update Operational Models for the National Inventory in Norway

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Basic models were developed using a database with 63 treatment means from 19 studies. The database included records for enteric CH4 production (MJ/day), dry matter intake (DMI), and dietary nutrient composition. The basic models were evaluated against an external database (n=36, from ten studies) along with other extant models. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4) and thus to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations using energy corrected milk and dietary concentrate share as predictor variables. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.

Using Landsat Imagery to Assess Burn Severity of National Forest Inventory Plots

As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots.

Risk and Impact Assessment of Dams in the Contiguous United States Using the 2018 National Inventory of Dams Database

Aging water infrastructure in the United States (U.S.) is a growing concern. In the U.S., over 90,000 dams were registered in the 2018 National Inventory of Dams (NID) database, and their average age was 57 years old. Here, we aim to assess spatiotemporal patterns of the growth of artificial water storage of the existing dams and their hazard potential and potential economic benefit. In this study, we use more than 70,000 NID-registered dams to assess the cumulative hazard potential of dam failure in terms of the total number and the cumulative maximum storage of dams over the 12 National Weather Service River Forecast Center (RFC) regions. In addition, we also estimate potential economic benefits of the existing dams based on their cumulative storage capacity. Results show that the ratios of the cumulative storage capacity to the long-term averaged precipitation range from 8% (Mid-Atlantic) to 50% (Colorado), indicating the significant anthropogenic contribution to the land surface water budget. We also find that the cumulative storage capacity of the dams with high (probable loss of human life is if the dam fails) and significant (potential economic loss and environmental damage with no probable casualty) hazard potential ranges from 50% (North Central) to 98% (Missouri and Colorado) of the total storage capacity within the corresponding region. Surprisingly, 43% of the dams with either high or significant potential hazards have no Emergency Action Plan. Potential economic benefits from the existing dams range from $0.7 billion (Mid Atlantic) to $15.4 billion (West Gulf). Spatiotemporal patterns of hazard potential and economic benefits from the NID-registered dams indicate a need for the development of region-specific preparation, emergency, and recovery plans for dam failure. This study provides an insight about how big data, such as the NID database, can provide actionable information for community resilience toward a safer and more sustainable environment.

A Importância do Inventário Participativo na Preservação do Patrimônio Cultural

O Patrimônio Cultural no Brasil possui o Tombamento e o Registro como instrumentos de salvaguarda para, respectivamente, o Patrimônio Material e o Patrimônio Imaterial. A partir do ano de 2000, é criado o Inventário Nacional de Referências Culturais (INRC) que contempla o Inventário como forma de registro do patrimônio. Com as suas transformações ao passar do tempo, um dos tipos que mais democratizou o acesso do patrimônio cultural à sociedade foi o Inventário Participativo, onde a própria comunidade toma a iniciativa de identificar e registrar suas referências culturais. Portanto, o presente artigo irá abordar como esse novo tipo de instrumento cultural aproxima e quebra barreiras entre a sociedade civil e o Estado, unindo-se no benefício da preservação do patrimônio cultural nacional.Palavras-chave: Inventário Participativo; Patrimônio Cultural; Preservação; Sociedade.Abstract Cultural Heritage in Brazil has listed and registered as safeguard instruments for, respectively, Material Heritage and Intangible Heritage. Established in 2000, the National Inventory of Cultural References (NICR) was created, which contemplates the Inventory as a way of registering the patrimony. With its transformations over time, one of the types that most democratized the access of cultural heritage to society was the Participatory Inventory, in which the community itself takes the initiative to identify and register its cultural references. Therefore, this paper will address how this new type of cultural instrument approaches and breaks down barriers between civil society and the State, uniting in the benefit of the preservation of the national cultural heritage. Keywords: Participatory Inventory; Cultural heritage; Preservation; Society.