Quantification of nitrate leaching from German forest ecosystems by use of a process oriented biogeochemical model

Simulations with the process oriented Forest-DNDC model showed reasonable to good agreement with observations of soil water contents of different soil layers, annual amounts of seepage water and approximated rates of nitrate leaching at 79 sites across Germany. Following site evaluation, Forest-DNDC was coupled to a GIS to assess nitrate leaching from German forest ecosystems for the year 2000. At national scale leaching rates varied in a range of 0–>80 kg NO₃–N ha⁻¹ yr⁻¹ (mean 5.5 kg NO₃–N ha⁻¹ yr⁻¹). A comparison of regional simulations with the results of a nitrate inventory study for Bavaria showed that measured and simulated percentages for different nitrate leaching classes (0–5 kg N ha⁻¹ yr⁻¹:66% vs. 74%, 5–15 kg N ha⁻¹ yr⁻¹:20% vs. 20%, >15 kg N ha⁻¹ yr⁻¹:14% vs. 6%) were in good agreement. Mean nitrate concentrations in seepage water ranged between 0 and 23 mg NO₃–N l⁻¹.     

Modelling soil carbon development in Swedish coniferous forest soils—An uncertainty analysis of parameters and model estimates using the GLUE method

Boreal forest soils such as those in Sweden contain a large active carbon stock. Hence, a relatively small change in this stock can have a major impact on the Swedish national CO₂ balance. Understanding of the uncertainties in the estimations of soil carbon pools is critical for accurately assessing changes in carbon stocks in the national reports to UNFCCC and the Kyoto Protocol. Our objective was to analyse the parameter uncertainties of simulated estimates of the soil organic carbon (SOC) development between 1994 and 2002 in Swedish coniferous forests with the Q model. Both the sensitivity of model parameters and the uncertainties in simulations were assessed. Data of forests with Norway spruce, Scots pine and Lodgepole pine, from the Swedish Forest Soil Inventory (SFSI) were used. Data of 12 Swedish counties were used to calibrate parameter settings; and data from another 11 counties to validate. The “limits of acceptability” within GLUE were set at the 95% confidence interval for the annual, mean measured SOC at county scale. The calibration procedure reduced the parameter uncertainties and reshaped the distributions of the parameters county-specific. The average measured and simulated SOC amounts varied from 60 t C ha⁻¹ in northern to 140 t C ha⁻¹ in the southern Sweden. The calibrated model simulated the soil carbon pool within the limits of acceptability for all calibration counties except for one county during one year. The efficiency of the calibrated model varied strongly; for five out of 12 counties the model estimates agreed well with measurements, for two counties agreement was moderate and for five counties the agreement was poor. The lack of agreement can be explained with the high inter-annual variability of the down-scaled measured SOC estimates and changes in forest areas over time. We conclude that, although we succeed in reducing the uncertainty in the model estimates, calibrating of a regional scale process-oriented model using a national scale dataset is a sensitive balance between introducing and reducing uncertainties. Parameter distributions showed to be scale sensitive and county specific. Further analysis of uncertainties in the methods used for reporting SOC changes to the UNFCCC and Kyoto protocol is recommended.     

An engineer-based methodology to perform Explosion Risk Analyses

Explosion Risk Analyses (ERA) are usually performed as part of the Quantitative Risk Assessment (QRA). The combination of frequencies and associated consequences allow to get a risk picture of the facility and provides decision support to the risk owner. The outcomes of this study allow also to provide, after adequate interpretation, Design Explosion Loads (DEL) to engineering disciplines (e.g. structures, piping and equipment) according to a given Risk Acceptance Criterion (RAC). For most of the offshore applications, the consequence part of the ERA is done with Computational Fluids Dynamics (CFD) to properly handle congestion and confinement effects as simple models cannot. With the increase of computational power, thanks to Moore's Law, there is an increasing trend to perform more and more CFD simulations with the expectation to improve confidence in results while taking more and more probabilistic variables into account. In the early 2000s, it was 10's of simulations, in the 2010s, it was 100's and now it is common to reach 1000's. However, one should remark that there is still a lot of uncertainties behind these studies since the geometry maturity is generally not enough especially at the early stage of detailed engineering when the preliminary Design Explosion Loads (pDEL) should be provided to disciplines. Anticipated congestion is normally put in the model, but it usually put a bias at the beginning of the consequence modelling part. In the risk-based approach, the frequency part is also of major importance. One need to keep in mind that consequence refinement should be done in close relation with the frequency refinement to ensure consistency in the approach. The practical methodology presented in this paper was developed to provide reliable inputs to engineering disciplines, taking into consideration uncertainties and potential spread of results while using a reasonable number of CFD scenarios. Finally, the safety engineers are still the key contributor in the performance of the ERA, and hence brain-based design is kept in the loop while minimizing computer-based design.     

Uncertainties in integrated assessment modelling of abatement strategies: illustrations with the ASAM model

Following the signing of the Second Sulphur Protocol in 1994 under the 1979 Convention on Long-range Transboundary Air Pollution, preparations are now underway for a new multi-pollutant multi-effects protocol, under the auspices of the United Nations Economic Commission for Europe (UN ECE). A number of scientific models have been used to provide policy makers involved in these preparations with sound scientific information. These include the Abatement Strategies Assessment Model (ASAM). ASAM has recently been extended to cover abatement strategies for NH₃ and NOx as well as SO₂, in order to address the amelioration of acidification and eutrophication in the ECE region. It is important to be able to demonstrate that the scientific information provided to policy makers is robust to uncertainties, and hence there is a need for a thorough sensitivity analysis. In this study ASAM is used to demonstrate a large degree of robustness of derived abatement strategies to uncertainties in critical loads, meteorological data and cost information. This is based on a comparison of strategies at the same overall abatement cost. Systematic changes in data are shown to influence model results more profoundly than random changes.     

Robust prediction of chatter stability in milling based on the analytical chatter stability

A major obstacle that limits the productivity in machining operations is the presence of machine tool chatter. Machining is a dynamic process and chatter behavior depends upon a number of different aspects including spindle speeds, material properties, tool geometry, and even the location of tool respect to the rest of machine. Many of the traditional models used to predict chatter stability lobes assume that parameters such as natural frequency, stiffness, and cutting coefficients remain constant. In reality, these parameters vary and they affect the chatter stability. The uncertainty in these parameters can be taken into consideration by employing the robust stability theory into a two degree of freedom milling model. Utilizing the Edge theorem and the Zero Exclusion condition, a robust chatter stability model, based on the analytical chatter stability milling model, is developed. This improves the reliability compared to the projected pseudo single degree of freedom model. The method is verified experimentally for milling operations while considering a changing natural frequency and cutting coefficient.