Sequestration of carbon in the humus layer of Swedish forests — direct measurements

To determine sequestration rates of carbon dioxide (CO2) we calculated the carbon (C) storage rate in humus layers of Swedish forests with Podsolic soils, which account for 14.2 × 106 ha of the 22.7 × 106 ha of forested land in Sweden. Our data set covered 41 years of humus inventories and mean humus layer thickness in 82 513 plots. We analysed three forest types: (i) all combinations of tree species, (ii) forests dominated (>70%) by Norway spruce ( Picea abies (L.) Karst.), and (iii) forests dominated (>70%) by Scots pine ( Pinus sylvestris L.). To relate changes in humus layer thickness to land area we used the intersections in 25 km × 25 km grids and used kriging interpolation, permitting calculations for each forest type. For each intersection mean humus thickness for each year was calculated and regressed against time to obtain the rate of change. This rate, humus bulk density, and humus C concentration were used to calculate sequestration rates. The mean sequestration rate was 251 kg C·ha–1·year–1, which is higher than theoretical values. The sequestration rate was positively related to temperature sum, albeit including effects of forest management. The pine-dominated forest type had a mean rate of 283 kg C·ha–1·year–1, and the spruce-dominated had a mean rate of 239 kg C·ha–1·year–1. Under similar site conditions, pine sequestered more C than spruce (difference of 71 kg C·ha–1·year–1; p < 0.0001), showing the importance of this type of ecosystem for C sequestration.

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Carbon storage and sequestration potential in aboveground biomass of bamboos in North East India

Scientific Reports ◽

10.1038/s41598-020-80887-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Angom Sarjubala Devi ◽

Kshetrimayum Suresh Singh

Keyword(s):

Aboveground Biomass ◽

C Sequestration ◽

North East India ◽

Age Class ◽

North East ◽

C Storage ◽

Sequestration Potential ◽

Bambusa Tulda ◽

Second Year ◽

Culm Density

AbstractThe Northeastern hilly states of India harbor nearly 90 species of bamboos, 41 of which are endemic to the region. Estimation of C-storage and C-sequestration in aboveground biomass of two common bamboo species namely Bambusa tulda and Dendrocalamus longispathus was carried out in Mizoram-one of the eight states of Northeastern India. Recording of density of culms was done by quadrate method and harvesting of culms was done to estimate the aboveground biomass. C-storage in different components of the culms was found out for three age classes namely 1, 2 and ≥ 3 year old culms. Aboveground biomass ranged from 73.58 to 127 Mg/ha in Bambusa tulda and 115 to 150 Mg/ha in Dendrocalamus longispathus. Culm density and aboveground biomass were maximum in the ≥ 3 year age class in both the species. C-storage ranged from 36.34 to 64.00 Mg/ha in Bambusa tulda and 50.11 to 65.16 Mg/ha in Dendrocalamus longispathus. Although having lower aboveground biomass the rate of C-sequestration was higher in Bambusa tulda with 27.79 Mg/ha/year than Dendrocalamus longispathus which have 15.36 Mg/ha/year. The reason was attributed to higher increment of culm density and DBH of the older age class in the second year study period in Bambusa tulda.

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Higher stand densities can promote soil carbon storage after conversion of temperate mixed natural forests to larch plantations

European Journal of Forest Research ◽

10.1007/s10342-020-01346-9 ◽

2020 ◽

Author(s):

Meng Na ◽

Xiaoyang Sun ◽

Yandong Zhang ◽

Zhihu Sun ◽

Johannes Rousk

Keyword(s):

Forest Management ◽

Soil Carbon ◽

Stand Density ◽

C Sequestration ◽

Tree Density ◽

Natural Forests ◽

Soil C ◽

C Storage ◽

Soil C Storage ◽

Soil C Sequestration

AbstractSoil carbon (C) reservoirs held in forests play a significant role in the global C cycle. However, harvesting natural forests tend to lead to soil C loss, which can be countered by the establishment of plantations after clear cutting. Therefore, there is a need to determine how forest management can affect soil C sequestration. The management of stand density could provide an effective tool to control soil C sequestration, yet how stand density influences soil C remains an open question. To address this question, we investigated soil C storage in 8-year pure hybrid larch (Larix spp.) plantations with three densities (2000 trees ha−1, 3300 trees ha−1 and 4400 trees ha−1), established following the harvesting of secondary mixed natural forest. We found that soil C storage increased with higher tree density, which mainly correlated with increases of dissolved organic C as well as litter and root C input. In addition, soil respiration decreased with higher tree density during the most productive periods of warm and moist conditions. The reduced SOM decomposition suggested by lowered respiration was also corroborated with reduced levels of plant litter decomposition. The stimulated inputs and reduced exports of C from the forest floor resulted in a 40% higher soil C stock in high- compared to low-density forests within 8 years after plantation, providing effective advice for forest management to promote soil C sequestration in ecosystems.

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Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

The Cryosphere ◽

10.5194/tc-13-3045-2019 ◽

2019 ◽

Vol 13 (11) ◽

pp. 3045-3059 ◽

Cited By ~ 3

Author(s):

Nick Rutter ◽

Melody J. Sandells ◽

Chris Derksen ◽

Joshua King ◽

Peter Toose ◽

...

Keyword(s):

Spatial Variability ◽

Layer Thickness ◽

Snow Water Equivalent ◽

Estimation Accuracy ◽

Snow Layer ◽

Data Set ◽

Snow Water ◽

Retrieval Errors ◽

Snow Microstructure ◽

The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

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Evaluation of the CONSUME and FOFEM fuel consumption models in pine and mixed hardwood forests of the eastern United States

Canadian Journal of Forest Research ◽

10.1139/cjfr-2013-0499 ◽

2014 ◽

Vol 44 (7) ◽

pp. 784-795 ◽

Cited By ~ 10

Author(s):

Susan J. Prichard ◽

Eva C. Karau ◽

Roger D. Ottmar ◽

Maureen C. Kennedy ◽

James B. Cronan ◽

...

Keyword(s):

United States ◽

Fuel Consumption ◽

Prescribed Burning ◽

Forest Type ◽

Fire Effects ◽

Eastern United States ◽

Validation Data ◽

Data Set ◽

Predicted Values ◽

Fine Fuels

Reliable predictions of fuel consumption are critical in the eastern United States (US), where prescribed burning is frequently applied to forests and air quality is of increasing concern. CONSUME and the First Order Fire Effects Model (FOFEM), predictive models developed to estimate fuel consumption and emissions from wildland fires, have not been systematically evaluated for application in the eastern US using the same validation data set. In this study, we compiled a fuel consumption data set from 54 operational prescribed fires (43 pine and 11 mixed hardwood sites) to assess each model’s uncertainties and application limits. Regions of indifference between measured and predicted values by fuel category and forest type represent the potential error that modelers could incur in estimating fuel consumption by category. Overall, FOFEM predictions have narrower regions of indifference than CONSUME and suggest better correspondence between measured and predicted consumption. However, both models offer reliable predictions of live fuel (shrubs and herbaceous vegetation) and 1 h fine fuels. Results suggest that CONSUME and FOFEM can be improved in their predictive capability for woody fuel, litter, and duff consumption for eastern US forests. Because of their high biomass and potential smoke management problems, refining estimates of litter and duff consumption is of particular importance.

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Transport and storage of anthropogenic C in the North Atlantic Subpolar Ocean

Biogeosciences ◽

10.5194/bg-15-4661-2018 ◽

2018 ◽

Vol 15 (14) ◽

pp. 4661-4682 ◽

Cited By ~ 1

Author(s):

Virginie Racapé ◽

Patricia Zunino ◽

Herlé Mercier ◽

Pascale Lherminier ◽

Laurent Bopp ◽

...

Keyword(s):

Interannual Variability ◽

North Atlantic ◽

General Circulation ◽

Meridional Overturning Circulation ◽

Intermediate Water ◽

Data Set ◽

The North ◽

Storage Rate ◽

The North Atlantic ◽

And Storage

Abstract. The North Atlantic Ocean is a major sink region for atmospheric CO2 and contributes to the storage of anthropogenic carbon (Cant). While there is general agreement that the intensity of the meridional overturning circulation (MOC) modulates uptake, transport and storage of Cant in the North Atlantic Subpolar Ocean, processes controlling their recent variability and evolution over the 21st century remain uncertain. This study investigates the relationship between transport, air–sea flux and storage rate of Cant in the North Atlantic Subpolar Ocean over the past 53 years. Its relies on the combined analysis of a multiannual in situ data set and outputs from a global biogeochemical ocean general circulation model (NEMO–PISCES) at 1∕2∘ spatial resolution forced by an atmospheric reanalysis. Despite an underestimation of Cant transport and an overestimation of anthropogenic air–sea CO2 flux in the model, the interannual variability of the regional Cant storage rate and its driving processes were well simulated by the model. Analysis of the multi-decadal simulation revealed that the MOC intensity variability was the major driver of the Cant transport variability at 25 and 36∘ N, but not at OVIDE. At the subpolar OVIDE section, the interannual variability of Cant transport was controlled by the accumulation of Cant in the MOC upper limb. At multi-decadal timescales, long-term changes in the North Atlantic storage rate of Cant were driven by the increase in air–sea fluxes of anthropogenic CO2. North Atlantic Central Water played a key role for storing Cant in the upper layer of the subtropical region and for supplying Cant to Intermediate Water and North Atlantic Deep Water. The transfer of Cant from surface to deep waters occurred mainly north of the OVIDE section. Most of the Cant transferred to the deep ocean was stored in the subpolar region, while the remainder was exported to the subtropical gyre within the lower MOC.

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High-resolution glacial and deglacial record of atmospheric methane by continuous-flow and laser spectrometer analysis along the NEEM ice core

Climate of the Past ◽

10.5194/cp-9-2579-2013 ◽

2013 ◽

Vol 9 (6) ◽

pp. 2579-2593 ◽

Cited By ~ 37

Author(s):

J. Chappellaz ◽

C. Stowasser ◽

T. Blunier ◽

D. Baslev-Clausen ◽

E. J. Brook ◽

...

Keyword(s):

High Resolution ◽

Continuous Flow ◽

Ice Core ◽

Rate Of Change ◽

Atmospheric Methane ◽

Data Set ◽

Rate Of Increase ◽

Concentration Changes ◽

Cross Dating ◽

Laser Spectroscopic

Abstract. The Greenland NEEM (North Greenland Eemian Ice Drilling) operation in 2010 provided the first opportunity to combine trace-gas measurements by laser spectroscopic instruments and continuous-flow analysis along a freshly drilled ice core in a field-based setting. We present the resulting atmospheric methane (CH4) record covering the time period from 107.7 to 9.5 ka b2k (thousand years before 2000 AD). Companion discrete CH4 measurements are required to transfer the laser spectroscopic data from a relative to an absolute scale. However, even on a relative scale, the high-resolution CH4 data set significantly improves our knowledge of past atmospheric methane concentration changes. New significant sub-millennial-scale features appear during interstadials and stadials, generally associated with similar changes in water isotopic ratios of the ice, a proxy for local temperature. In addition to the midpoint of Dansgaard–Oeschger (D/O) CH4 transitions usually used for cross-dating, sharp definition of the start and end of these events brings precise depth markers (with ±20 cm uncertainty) for further cross-dating with other palaeo- or ice core records, e.g. speleothems. The method also provides an estimate of CH4 rates of change. The onsets of D/O events in the methane signal show a more rapid rate of change than their endings. The rate of CH4 increase associated with the onsets of D/O events progressively declines from 1.7 to 0.6 ppbv yr−1 in the course of marine isotope stage 3. The largest observed rate of increase takes place at the onset of D/O event #21 and reaches 2.5 ppbv yr−1.

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Reforestation can sequester two petagrams of carbon in US topsoils in a century

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1719685115 ◽

2018 ◽

Vol 115 (11) ◽

pp. 2776-2781 ◽

Cited By ~ 33

Author(s):

Lucas E. Nave ◽

Grant M. Domke ◽

Kathryn L. Hofmeister ◽

Umakant Mishra ◽

Charles H. Perry ◽

...

Keyword(s):

National Level ◽

Mineral Soil ◽

C Sequestration ◽

The United States ◽

Forest Sector ◽

Soil C ◽

C Storage ◽

C Stocks ◽

The Us ◽

Land Use And Management

Soils are Earth’s largest terrestrial carbon (C) pool, and their responsiveness to land use and management make them appealing targets for strategies to enhance C sequestration. Numerous studies have identified practices that increase soil C, but their inferences are often based on limited data extrapolated over large areas. Here, we combine 15,000 observations from two national-level databases with remote sensing information to address the impacts of reforestation on the sequestration of C in topsoils (uppermost mineral soil horizons). We quantify C stocks in cultivated, reforesting, and natural forest topsoils; rates of C accumulation in reforesting topsoils; and their contribution to the US forest C sink. Our results indicate that reforestation increases topsoil C storage, and that reforesting lands, currently occupying >500,000 km2 in the United States, will sequester a cumulative 1.3–2.1 Pg C within a century (13–21 Tg C·y−1). Annually, these C gains constitute 10% of the US forest sector C sink and offset 1% of all US greenhouse gas emissions.

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An improvement to the ANN-enhanced flow rating method

10.5194/egusphere-egu21-221 ◽

2021 ◽

Author(s):

Bernhard Schmid

Keyword(s):

Neural Network ◽

Electrical Conductivity ◽

Water Resources ◽

Flow Rate ◽

Major Ions ◽

Rate Of Change ◽

Data Set ◽

Input Variables ◽

Relationship Of ◽

The Relationship

The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain &#8216;better&#8217;, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author&#8217;s experience.The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the M&#246;dling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the M&#246;dling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only. &#160;&#160;&#160;&#160;ReferencesMoatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.Tunqui Neira, J.M., Andr&#233;assian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.

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Real-Time Prediction of Choke Health Using Production Data Integrated with AI

10.4043/30981-ms ◽

2021 ◽

Author(s):

Ahmed Alghamdi ◽

Olakunle Ayoola ◽

Khalid Mulhem ◽

Mutlaq Otaibi ◽

Abdulazeez Abdulraheem

Keyword(s):

High Pressure ◽

Real Time ◽

Large Scale ◽

Production Systems ◽

Rate Of Change ◽

Production Data ◽

Ann Model ◽

Sand Production ◽

Data Set ◽

Pressure Drops

Abstract Chokes are an integral part of production systems and are crucial surface equipment that faces rough conditions such as high-pressure drops and erosion due to solids. Predicting choke health is usually achieved by analyzing the relationship of choke size, pressure, and flow rate. In large-scale fields, this process requires extensive-time and effort using the conventional techniques. This paper presents a real-time proactive approach to detect choke wear utilizing production data integrated with AI analytics. Flowing parameters data were collected for more than 30 gas wells. These wells are producing gas with slight solids production from a high-pressure high-temperature field. In addition, these wells are equipped with a multi-stage choke system. The approach of determining choke wear relies on training the AI model on a dataset constructed by comparison of the choke valve rate of change with respect to a smoother slope of the production rate. If the rate of change is not within a tolerated range of divergence, an abnormal choke behavior is detected. The data set was divided into 70% for training and 30% for testing. Artificial Neural Network (ANN) was trained on data that has the following inputs: gas specific gravity, upstream & downstream pressure and temperature, and choke size. This ANN model achieved a correlation coefficient above 0.9 with an excellent prediction on the data points exhibiting normal or abnormal choke behaviors. Piloting this application on large fields, where manual analysis is often impractical, saves a substantial man-hour and generates significant cost-avoidance. Areas for improvement in such an application depends on equipping the ANN network with long-term production profile prediction abilities, such as water production, and this analysis relies on having an accurate reading from the venturi meters, which is often the case in single-phase flow. The application of this AI-driven analytics provides tremendous improvement for remote offshore production operations surveillance. The novel approach presented in this paper capitalizes on the AI analytics for estimating proactively detecting choke health conditions. The advantages of such a model are that it harnesses AI analytics to help operators improve asset integrity and production monitoring compliance. In addition, this approach can be expanded to estimate sand production as choke wear is a strong function of sand production.

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A Method to Characterize the Quality of a Polymer Laser Sintering Process

Advances in Mechanical Engineering ◽

10.1155/2014/185374 ◽

2014 ◽

Vol 6 ◽

pp. 185374 ◽

Cited By ~ 8

Author(s):

Stefan Rüsenberg ◽

Stefan Josupeit ◽

Hans-Joachim Schmid

Keyword(s):

Layer Thickness ◽

Laser Sintering ◽

Thermal Ageing ◽

Process Chain ◽

Sintering Process ◽

Data Set ◽

Whole Process ◽

Quality Aspects ◽

Important Challenge

The reproducibility and reliability of quality aspects are an important challenge of the polymer laser sintering process. However, existing quality concepts and standardization activities considering influencing factors along the whole process chain have not been validated experimentally yet. In this work, these factors are analyzed and kept constant to obtain a reliable material data set for different layer thicknesses and testing temperatures. In addition, material qualities regarding powder ageing effects are analyzed using different build heights and layer thicknesses: while an increase of the layer thickness reduces mechanical part strength and density, it also results in a less intense thermal ageing of unmolten powder due to shorter build times.

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