MEUSIS in SoilTrEC Project

The Multi-scale Soil Information System (MEUSIS)

The Multi-scale Soil Information System (MEUSIS) is a suitable framework for building a nested system of soil data that could facilitate interoperability through a common coordinate reference system. In the context of INSPIRE Directive, MEUSIS may be implemented as a system facilitating the update of existing soil information and accelerating the harmonization of various soil information systems (Panagos et al. 2011).

Upscaling

Upscaling of environmental indicators applied in regional analyses is sensitive to scale issues of the input Data (Bechini et al., 2011). Environmental assessments are frequently carried out with indicators (Viglizzo et al., 2006) and simulation models (Saffih- Hdadi and Mary, 2008). The environmental indicators have an increasing importance and are easily understandable by the general public. Those quantitative expressions measure the condition of a particular environmental attribute in relation to thresholds set by scientific community. However, decision makers use the environmental indicators to communicate with the general public.

The main components in terms of changing or matching scales are upscaling and downscaling. Upscaling is the process of aggregating information collected at a large scale towards a small scale (Bodegom et al., 2002). In cartography, upscaling or aggregation are the terms used to describe the process of reducing a set of components or values in an area down to a single value representing such an area as a whole. Aggregation implies simplification, the degree of variation in the considered area is reduced and thus there is an obvious loss of information (Kokkonen et al, 2006).

When dealing with areas of different sizes and with information available at different scales, policy makers and decision makers need to either upscale their evaluations and simulations from small to large scale or downscale from large to small scale (Stein et al., 2001). Environmental indicators are dependent upon data availability and also upon the scale for which policy statements are required. As these may not match, changes in scales may be necessary. Moreover, change is scale may requested in research and modeling where the indicator is used as input parameter in a model. It has been recognised that the quality of indicators relies on the scale which they represent. The quality of the state of the environment at a local scale, for example, requires different information compared to the state of the environment at national scale.

The SoilTrEC WP4 team has integrated soil organic carbon modelling platform with Multi-Scale European Soil Information System (MEUSIS). The study consists of two main components which are European Upscaling and Regional Modelling. Both of the studies are using point observations (LUCAS Soil, CZO Measurements, Biosoil) and environmental predictors (terrain, land cover, climate) as inputs for modelling and upscaling soil data.


MEUSIS Structure

Figure 1. Multi-Scale European Soil Information System

 

The SoilTrEC project outputs are integrated in the MEUSIS environment at two levels;

  • Regional Modelling with an objective to transfer the processes for Soil Organic Carbon (SOC) from CZOs to larger areas around the CZOs.
  • European upscaling with an objective to upscale the CZOs data values in combination with European larger datasets (LUCAS) and develop pan-European maps of SOC.

Regional Modelling

The SoilTrEC WP4 team has been studying on upscaling and modeling soil organic carbon to test how and to what extent the natural processes can be extended to larger areas and how the local (CZO) knowledge is transferable to larger areas (Regions, Catchments). For the purpose two critical zone observatories were selected for the spatial extrapolation exercise.

The regional modelling has 2 components: a) one Regression-kriging sub-model which develops relationships between SOC and a set of predictors and this is applied in CZO level (Lysina and Fuchsenbigl CZOs); b) one transfer sub-model which extrapolates the identified relationships of regression-kriging to a much extended area using as a constant the same land cover/use (as the CZOs) and predictors at larger scale. In practice, the regression-kriging sub-model is based on a set of dense SOC measurements at CZO level and then the transfer sub-model is able to upscale the main process of SOC to larger areas (Slavkovsky Forest and North East Austria) where the SOC measured data is scarce.

structure in MEUSIS Czech forest

Figure 2. SOC Modelling Platform for Regional Modelling

 

The principle of the method is transferring the statistical model from smaller reference area (Lysina and Fuchsenbigl) to larger area (Slavkovsky Forest and North East Austria).

The output maps of Soil organic carbon (SOC) content in Lysina and Fuchsenbigl CZOs and the upscaled regions (Slavkovsky Forest and North East Austria) are demonstrated in figures. The basic statistics are shown in the Table. The SOC values in the Slavkovsky Forest are ranging between 0 % - 35.11% and in the Northern East Austria 0.66% – 1.76%.

Layers/Model R2 Maximum Minimum Mean Standard Deviation 
Fuchsenbigl SOC Map  0.42 2.41 0.66 1.738 0.31
Lysina SOC Map 0.31 32.77 0 11.26 5.87
Fuchsenbigl > North East Austria - 1.76 0.94 1.26 0.45
Lysina > Slavkovsky Forest - 35.11 0 17.91 6.54


2 application of MEUSIS

Figure 3. Regional SOC Modelling: Prediction Maps

European upscaling

The Regression-Kriging method was applied for assessing organic carbon distribution and producing a continuous map for EU scale. The dataset used in this study is made up of totally 20,247 soil samples collected from two different studies. 19,860 points from the Land Use/Cover Area frame Statistical Survey (LUCAS) (Montanarella et al., 2011) of European Commission, JRC and 387 samples from the SoilTrEC Project (Aksoy et al., 2013).

The Regression-Kriging method was applied for assessing organic carbon distribution and producing a continuous map for EU scale. The dataset used in this study is made up of totally 20,247 soil samples collected from two different studies. 19,860 points from the Land Use/Cover Area frame Statistical Survey (LUCAS) (Montanarella et al., 2011) of European Commission, JRC and 387 samples from the SoilTrEC Project (Aksoy et al., 2013).


European upscale

Figure 4. Soil organic carbon prediction map of Europe

 

According to results, Ireland, Sweden and Finland has the highest SOC and Portugal, Poland, Hungary, Spain, Italy have the lowest values with the average 3% . Northern Countries with high precipitation and low temperature averages seem that having higher organic carbon amount than warmer southern Countries.

References

Panagos, P., Liedekerke, M.V., Montanarella, L. Multi-scale European Soil Information System (MEUSIS): a multi-scale method to derive soil indicators. Computational Geosciences: Volume 15, Issue 3 (2011), Page 463-475

Montanarella, L., Toth, G. and Jones, A., 2011. Land quality and Land Use Information, In the European Union. Pages; 209-219. European Commission, Joint Research Centre, Institute for Environment and Sustainability. EUR 24590EN. ISBN: 978-92-79-17601-2. Luxemburg.

Aksoy E, Panagos P, Montanarella L. & Dunbar, M. (2013). Assessment of soil organic carbon distribution in two different watersheds. Soil Science and Biogeochemistry: A Journey Through Space and Time. Proceedings of the Annual Meeting of the British Society of Soil Science, Lancaster, UK. September 3-5, 2013 by BSSS, Page 45.

Saffih-Hdadi, K., Mary, B., 2008. Modeling consequences of straw residues export on soil organic carbon. Soil Biol. Biochem. 40, 594–607.

Viglizzo, E.F., Frank, F., Bernardos, J., Buschiazzo, D.E., Cabo, S., 2006. A rapid method for assessing the environmental performance of commercial farms in the Pampas of Argentina. Environ. Monit. Assess. 117, 109–134.

Bechini L., Castoldi N., Stein A. Sensitivity to information upscaling of agro-ecological assessments: Application to soil organic carbon management (2011) Agricultural Systems, 104 (6), pp. 480-490.

Van Bodegom, P.M., Verburg, P.H., Stein, A., Adiningsih,S., Denier van der Gon, H.A.C.: Effects of interpolation anddata resolution on methane emission estimates from rice paddies.Environ. Ecol. Stat. 9(1), 5–26 (2002)

Kokkonen, T., Koivusalo, H., Laurén, A., Penttinen, S.,Starr, M., Kellomäki, S., Finér, L.: Implications of processingspatial data from a forested catchment for a hillslope hydrological model. Ecol. Model. 199(4), 393–408(2006)

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