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Forests are important components of the greenhouse gas balance of Europe. There is considerable uncertainty about how predicted changes to climate and nitrogen deposition will perturb the carbon and nitrogen cycles of European forests and thereby alter forest growth, carbon sequestration and N2O emission. The present study aimed to quantify the carbon and nitrogen balance, including the exchange of greenhouse gases, of European forests over the period 2010-2030, with a particular emphasis on the spatial variability of change. The analysis was carried out for two tree species: European beech and Scots pine. For this purpose, four different dynamic models were used: BASFOR, Daily Day Cent, INTEGRATOR and Landscape-DNDC. These models span a range from semi-empirical to complex mechanistic. Comparison of these models allowed assessment of the extent to which model predictions depended on differences in model inputs and structure. We found a European average carbon sink of 0.160±0.020 kgCm-2 yr-1 (pine) and 0.138 ±0.062 kgCm-2 yr-1 (beech) and N2O source of 0.285 ±0.125 kgNha-1 yr-1 (pine) and 0.575 ±0.105 kgNha-1 yr-1 (beech). The European average greenhouse gas potential of the carbon source was 18 (pine) and 8 (beech) times that of the N2O source. Carbon sequestration was larger in the trees than in the soil. Carbon sequestration and forest growth were largest in central Europe and lowest in northern Sweden and Finland, N. Poland and S. Spain. No single driver was found to dominate change across Europe. Forests were found to be most sensitive to change in environmental drivers where the drivers were limiting growth, where changes were particularly large or where changes acted in concert. The models disagreed as to which environmental changes were most significant for the geographical variation in forest growth and as to which tree species showed the largest rate of carbon sequestration. Pine and beech forests were found to have differing sensitivities to environmental change, in particular the response to changes in nitrogen and precipitation, with beech forest more vulnerable to drought. There was considerable uncertainty about the geographical location of N2O emissions. Two of the models BASFOR and Landscape DNDC had largest emissions in central Europe where nitrogen deposition and soil nitrogen were largest whereas the two other models identified different regions with large N2O emission. N2O emissions were found to be larger from beech than pine forests and were found to be particularly sensitive to forest growth. [ABSTRACT FROM AUTHOR]

The article presents a study which aims to quantify the uncertainties in pesticide and soil properties and to examine their contribution to the uncertainty in potential usage (SP90). A regular grid sample of points which cover the entire of the agricultural area in the Netherlands was chosen randomly. The analysis revealed that the uncertainty regarding the SP90 is 10 times smaller compared to uncertainty on the leaching concentration at individual point areas. The significant implication for the regulation of pesticide is that the leaching concentration is underestimated systematically when the uncertainties are rejected.

The article discusses an alternative method to the universal kriging-based approach in the identification of heterogeneities between monitoring networks. European gamma dose rate measurements from May 2008 obtained from the European Radiological Data Exchange Platform database were used. The method is based on the assumptions of stationarity of the process within a region and that no preferential sampling is employed. Results show that the method can provide reliable identification of biases and can accommodate network specific measurement uncertainty.

DEM error propagation methodology is extended to the derivation of vector-based objects (stream networks) using geostatistical simulations. First, point sampled elevations are used to fit a variogram model. Next 100 DEM realizations are generated using conditional sequential Gaussian simulation; the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The method is illustrated using two small data sets: Baranja hill (30m grid cell size; 16 512 pixels; 6367 sampled elevations), and Zlatibor (30m grid cell size; 15 000 pixels; 2051 sampled elevations). All computations are run in the open source software for statistical computing R: package geoR is used to fit variogram; package gstat is used to run sequential Gaussian simulation; streams are extracted using the open source GIS SAGA via the RSAGA library. The resulting stream error map (Information entropy of a Bernoulli trial) clearly depicts areas where the extracted stream network is least precise - usually areas of low local relief, slightly concave. In both cases, significant parts of the study area (17.3% for Baranja Hill; 6.2% for Zlatibor) show high error (H > 0.5) of locating streams. By correlating the propagated uncertainty of the derived stream network with various land surface parameters sampling of height measurements can be optimized so that delin eated streams satisfy a required accuracy level. Remaining issue to be tackled is the computational burden of geostatistical simulations: this framework is at the moment limited to small to moderate data sets with several hundreds of points. Scripts and data sets used in this article are available on-line via the www.geomorphometry.org website and can be easily adopted/adjusted to any similar case study. [ABSTRACT FROM AUTHOR]

Most countries use a 1-year-resolution emission factor approach (Tier 1 or 2) to estimate terrestrial N2O emissions as part of their national greenhouse gas inventory. Little attention has so far been paid to the effect of the temporal resolution of the approach (e.g., day, season, and year) on N2O emission estimates. The effect of lumping temporal variation can be very large because of daily or seasonal variations of processes causing N2O emissions. Therefore, we compared atmual N2O emissions from a model with daily time steps (DNDC) with those of a model with annual time steps (INITIATOR). Emissions were simulated for two intensively managed grassland plots in the Dutch fen meadow landscape. Annual N2O emissions from the investigated grasslands were sensitive to rainfall distribution within the year, especially to summer rainfall. We recommend that Tier 2 N2O emission estimates for intensively managed grasslands on peat soils in the temperate climate zone are adjusted for relative summer rainfall. [ABSTRACT FROM AUTHOR]

Understanding the limitations of environmental data is essential both for managing environmental systems effectively and for encouraging the responsible use of scientific research when knowledge is limited and priorities are varied. Using a combination of quantitative and qualitative techniques for assessing probabilities, and acknowledging the importance of scenarios where probabilities cannot be determined, an integrated methodology is presented for handling uncertainties about environmental data. The methodology is based on a fourfold distinction between the empirical quality of data (and the ancillary information, such as 'scale', required to interpret this), the sources of uncertainty in data, the 'fitness for use' of the data, and the quality or 'goodness' of an uncertainty model. [ABSTRACT FROM AUTHOR]

Many research and scientific organizations emphasize the importance of science for society in their strategic plans. This is certainly true for land use studies being discussed in this chapter as new environmental policies are introduced at European and national level. Such policies reflect concerns of society so that a structural link between science and policymaking would appear to be logical and desirable. Rather than following traditional top-down and disciplinary research approaches, emphasis is increasingly being placed on interactive, interdisciplinary work in Communities of Practice (CoPs) in which scientists work together with various stakeholders and policymakers in a joint learning mode. But this requires new research approaches including long-term engagement during the entire policy cycle asking for a new attitude of scientists. Few experiences have been reported so far. Three Dutch case studies are therefore discussed to illustrate the functioning of CoPs by focusing on up- and downscaling (called multiscaling hereafter), a key element of land use research. Five types of multiscaling were used in the three case studies. Three were technical: (1) use of model- or design-based (geo)statistical techniques, (2) extrapolation of data obtained from experimental plots to larger areas, and (3) use of quasi-3D process models to upscale grid data in a Geographic Information System (GIS) to regions. Two were policy oriented: (1) nutrient balances for farms to allow upscaling from fields to farm and (2) a research framework for regions, based on the DPSIR approach, which sequentially covers aspects mentioned in environmental laws as being important for sustainable development. Quite diverse and unrelated questions about land use issues by different members of the CoP cannot be a fruitful basis for research programs. Scientists have therefore an important role to play within a CoP in orchestrating a demand analysis that puts questions into context and defines existing knowledge as well as knowledge gaps. Defining research on the basis of a demand analysis in a CoP creates innovative ideas, creates commitment of participants, and allows definition of needed research that is functional. This includes cutting edge research publishable in literature and requires for land use studies updating of valuable existing soil survey information to a level that can be used in modern modeling techniques including functional characterization of soil series, development of pedotransfer functions, and definition of phenoforms. Particular attention is needed for introducing modern monitoring techniques for soil and water because the high cost of traditional methods implies that little monitoring is done now with detrimental effects for the calibration and validation of simulation models that increasingly secure a live of their own. The scientific community needs to take a fresh look at its paradigms. Next to the establishment of CoPs, we therefore advocate development of Communities of Scientific Practice (CSP) within the research community that define different functions for members of the scientific community in terms of (1) communication within CoPs by shaping the demand analysis and to the outside world and (2) defining research needs and its execution, using knowledge chains including basic research. [ABSTRACT FROM AUTHOR]