relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "erosion", "water erosion", "RUSLE", "The Netherlands". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema has been created following the QGIS schema in .docx format, and it is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links (at the moment I included a link to the sharepoint, but when the information to add to Zenodo is ready this will be used)

- History/version

- Authors and contact

### 1.35.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the EJP Soil Serena community and is Open Access (https://doi.org/10.5281/zenodo.13951679). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the SERENA project (see previous section).

The dataset uses an open-source, platform-independent format (geopackage) that can be open in a number of GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS).

### 1.35.2.3 Making data interoperable:

As stated, the dataset is provided as a geopackage file with associated metadata. The format is interoperable across software and platforms. The terms follow common usage in soil erosion by water modelling.

### 1.35.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The data comes from applying the SERENA Erosion and Erosion Control cookbook (Buttafuoco et al., 2024) applying the most updated and accurate datasets available for the Netherlands. No further QA has been applied to the dataset.

### 1.35.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in Zenodo the data are not expected to be curated in the long-term.

<figure><img src="image_85.png" /></figure>
#### 1.35.4 Data security

Data security is provided by Zenodo.

#### 1.35.5 Ethical aspects

The data is not restricted by legal and ethical issues. All data that have been used are openly available (see Hessel et al., 2024) and are of aggregated nature.

#### 1.35.6 References

Buttafuoco et al. 2024. Cookbook to evaluate indicators of soil threats, soil-based ES and their associated bundles over scenarios of change. SERENA deliverable 2.3.2.
https://doi.org/10.5281/zenodo.13993620

Hessel R, G Buttafuoco, E Medina-Roldán, D Smiraglia, J Coblinski, L Kukk, B Lemercier, S Pindral, J Reyes Rojas, R Lorenzetti, C Pesch, K Amalevičiūtė-Volungė, S Asins, F Assennato, A Astover, Z Bakacsi, A Benő, G Bondi, T Brunner, S Callewaert, K Cockx, J Cruijsen, P Csontos, E Doñate, A Fahy, V Feiza, C B Foldal, A Gaillot, L Gardin, C Gedeon, D Josipovic, P Kassai, T Kikas, M Kocsis, A Laborczi, D Luts, J Makovníková, D Montagne, K Oorts, L O'Sullivan, B Pálka, L Pásztor, E Putku, N Riitano, N Saby, E Schmaltz, V Shahrokh, B Szabó, G Szatmári, M Trip, F Ungaro, J Volungevičius, T Weninger, R Žydelis. 2024. Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3, 224 pp. https://doi.org/10.5281/zenodo.13991087

## 1.36 SERENA T3.2 - Map of Net Ecosystem Productivity of the Netherlands

### 1.36.1 Data summary

The present dataset corresponds to a map of Net Ecosystem Productivity (NEP) for areas in the Netherlands classified as wheat by the Eurocrop 2028 spatial product (d'Andrimont et al. 2021). The map is the result of applying the NEP cookbook developed (Buttafuoco et al., 2024) in SERENA/EJP-Soil to the area of interest (AOI) input data. NEP is expressed for a single 8-day period in early summer as it is the date with the highest value. The NEP value is a 2010-2014 average for the particular 8-day period.

SERENA had, among others, the objective of developing methods to calculate and map soil-based ecosystem services and soil threats. NEP was used as an indicator of greenhouse gases and climate regulation supported by soils.

For this purpose, NEP was calculated from (data origin) 4 spatially-explicit datasets to which the GHG cookbook was applied within the AOI: (1) maximum monthly air temperature from WorldClimate, (2) minimum monthly air temperature from WorldClimate, (3) MODIS MOD17A2HGF collection, and (4) daily soil moisture data derived from Zhang et al. (2023). (5) The Eurocrop 2018 database was used to filter the spatialisation of the indicator to wheat areas, and (6) the AOI border.

The NEP map for the Netherlands is 62 Mb and has pixel size of 500 m.

<figure><img src="image_86.png" /></figure>
Although the dataset needs further validation, it is useful as a research tool to investigate CO2 fluxes in the region for the SERENA consortium and other researchers.

## 1.36.2 FAIR data

### 1.36.2.1 Making data findable, including provisions for metadata:

The dataset (geoTIFF file: "SERENA_EJPSOIL_NL_GHG_NEP.tif") is available in the general science repository Zenodo which provides a DOI identifier (https://doi.org/10.5281/zenodo.13960816). The name of the dataset follows the convention agreed upon in SERENA's WP3 (project_country_soilecosystemservice Professional Development). The keywords used to make the dataset findable were agreed upon in SERENA. They include specific tags of the dataset: "greenhouse gases and climate regulation indicators", "net ecosystem productivity", "Netherlands". The dataset consists in a single version since there are no upgradable versions to the dataset in mind.

The project information will be also indicated in the metadata of the dataset: SERENA project, grant number, deliverable number.

A metadata schema has been created following the QGIS schema, and is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links (at the moment I included a link to the sharepoint, but when the information to add to Zenodo is ready this will be used)

- History/version

### 1.36.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the EJP Soil Serena community (https://doi.org/10.5281/zenodo.13960816). It is Open Access.

The dataset uses an open-source, platform-independent format (geopackage) that can be opened in many GIS applications both open (e.g., QGIS) and proprietary (ArcGIS).

### 1.36.2.3 Making data interoperable:

As stated, the dataset is provided as a geopackage file with associated metadata. The format is interoperable across GIS software and platforms. The terms follow common usage in carbon fluxes research.

<figure><img src="image_87.png" /></figure>
#### 1.36.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY 4.0 https://creativecommons.org/licenses/by/4.0/). The data comes from applying the SERENA GHG cookbook which applied different filters to eliminate NEP values inconsistencies. No further QA has been applied to the dataset.

#### 1.36.3 Allocation of resources

The costs associated to make the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in Zenodo the data are not expected to be curated at the long-term.

#### 1.36.4 Data security

Data security is provided by Zenodo.

#### 1.36.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research and is spatially aggregated.

## 1.37 SERENA T3.2 - Map of soil sealing of the Netherlands

### 1.37.1 Data summary

The data was prepared according to the methodology of SERENA Soil Sealing cookbook. For the Netherlands, the application was carried out at national scale.

The map of soil sealing (soil threat) was based on classification of multitemporal satellite images and photointerpretation. The methodology uses vegetation and backscatter indices calculated over time series of images, and decision rules. The semi-automatic classification supports the subsequent photointerpretation phase based on national orthophotos and other free VHR satellite images (e.g. through Google Earth or Bing services). The final maps are the product of a binary classification (sealed/not-sealed) having 10 m spatial resolution

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was degree of soil sealing. By evaluating this degree at two moments in time, the change in soil sealing can be determined.

To create the soil sealing map, we used Sentinel-1 C-band synthetic aperture radar imaging and Sentinel-2 Multispectral Instrument (MSI) (https://dataspace.copernicus.eu/) for the semi-automatic classification, and Google Earth images for the photointerpretation.

Delivered map was prepared in the GeoTIFF raster format at the resolution of 10 m. File is 6828 MB.

<figure><img src="image_88.png" /></figure>
<figure><img src="image_89.png" /></figure>

## 1.37.2 FAIR data

### 1.37.2.1 Making data findable, including provisions for metadata:

The dataset (GeoTIFF file: "SERENA_EJPSOIL_NL.SoILSEALING_SEALINGDEGREE.TIF") is available in the general science repository of Zenodo; and it is provided with a DOI identifier (https://doi.org/10.5281/zenodo.13960887). The name of the dataset follows the conventions agreed upon in SERENA's WP3 (project_country_soilthreatzm indicator). The keywords used to make the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "soil sealing", "remote sensing", "satellite images, "photointerpretation", "The Netherlands". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema has been created following the QGIS schema in .docx format, and it is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links (at the moment I included a link to the sharepoint, but when the information to add to Zenodo is ready this will be used)

- History/version

- Authors and contact

### 1.37.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP Soil community (https://doi.org/10.5281/zenodo.13960887). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the project (see previous section). It will be Open Access.

The dataset uses an open-source, platform-independent format (GeoTIFF) that can be opened in a number of GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS).

### 1.37.2.3 Making data interoperable:

As stated, the dataset is provided as a GeoTIFF file with associated metadata. The format is interoperable across software and platforms. The terms follow common usage in soil sealing research.

### 1.37.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The data comes from applying the SERENA Soil Sealing cookbook applying the most updated and accurate datasets available for the Netherlands.

<figure><img src="image_90.png" /></figure>
### 1.37.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in Zenodo the data are not expected to be curated at the long-term.

### 1.37.4 Data security

Data security is provided by Zenodo.

### 1.37.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

## 1.38 SERENA T3.2 - Map of Soil loss by water erosion of Poland

### 1.38.1 Data summary

The present data was prepared according to the methodology of SERENA Soil erosion and soil erosion control cookbook. For Poland we selected 60% of the Polish area, including agricultural land (arable land, grassland, permanent crops). In the IUNG-PIB we store and manage the data only for agricultural land, hence preparing map for other land uses, would be less accurate due to the lack of data for example for soil texture and SOC content.

The map of soil loss by water erosion (soil threat) was based on the RUSLE model.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil loss was used as an indicator for soil erosion (ST).

To create the soil loss map we used data on monthly precipitation from WorldClim for 2018 (R-factor), Corine Land Cover raster data for 2018 (C-factor), SOC content, sand, silt, clay content (previously modelled in IUNG-PIB using DSM approach and QRF algorithm) (K-factor), LS-factor already available to download, as suggested in the cookbook (https://esdac.jrc.ec.europa.eu/content/ls-factor-slope-length-and-steepness-factor-eu), and P-factor also downloaded from the different resource (https://esdac.jrc.ec.europa.eu/content/support-practices-factor-p-factor-eu).

Both delivered maps were prepared in the GeoTIFF format in the resolution of 100m.

Delivered data will be validated by stakeholders from Poland (scientists) in October, 2024.

### 1.38.2 FAIR data

#### 1.38.2.1 Making data findable, including provisions for metadata:

The dataset (single band raster file: "SERENA_EJPSOIL_PL_EROSION.SoILLOSS.tif") is available in the general science repository of Zenodo, and it is provided with a DOI identifier (https://doi.org/10.5281/zenodo.14017261). The name of the dataset follows the conventions agreed upon in SERENA's WP3 (project_country_soilecosystemservice_indicator). The keywords used to make

<figure><img src="image_91.png" /></figure>
the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "erosion", "water erosion", "RUSLE", "Poland". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema has been created following the QGIS schema in .docx format, and it is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links (at the moment I included a link to the sharepoint, but when the information to add to Zenodo is ready this will be used)

- History/version

- Authors and contact

### 1.38.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP-Soil communities (https://doi.org/10.5281/zenodo.14017261). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset uses an open-source, platform-independent format (Geotiff) that can be open in a number of GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS).

### 1.38.2.3 Making data interoperable:

As stated, the dataset is provided as a single band raster file with associated metadata in .docx format. The format is interoperable across software and platforms. The terms follow common usage in soil erosion by water modelling.

### 1.38.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The data comes from applying the SERENA Erosion and Erosion Control cookbook applying the most updated and accurate datasets available for Poland. No further QA has been applied to the dataset.

<figure><img src="image_92.png" /></figure>
### 1.38.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in Zenodo the data are not expected to be curated at the long-term.

### 1.38.4 Data security

Data security in terms of storage depends on the repository (Zenodo).

### 1.38.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

## 1.39 SERENA T3.4 - Map of Soil mass not eroded of Poland

### 1.39.1 Data summary

The present data was prepared according to the methodology of SERENA Soil erosion and soil erosion control cookbook. For Poland we selected 60% of the Polish area, including agricultural land (arable land, grassland, permanent crops). In the IUNG-PIB we store and manage the data only for agricultural land, hence preparing map for other land uses, would be less accurate due to the lack of data for example for soil texture and SOC content.

The map of soil mass not eroded (soil-based ecosystem service (SES)) was based on the RUSLE model.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil mass not eroded was used as an indicator for soil erosion control (SES).

To create the map we used data on monthly precipitation from WorldClim for 2018 (R-factor), Corine Land Cover raster data for 2018 (C-factor), SOC content, sand, silt, clay content (previously modelled in IUNG-PIB using DSM approach and QRF algorithm) (K-factor), LS-factor already available to download, as suggested in the cookbook (https://esdac.jrc.ec.europa.eu/content/ls-factor-slope-length-and-steepness-factor-eu), and P-factor also downloaded from the different resource (https://esdac.jrc.ec.europa.eu/content/support-practices-factor-p-factor-eu).

Both delivered maps were prepared in the GeoTIFF format in the resolution of 100m.

Delivered data will be validated by stakeholders from Poland (scientists) in October, 2024.

### 1.39.2 FAIR data

#### 1.39.2.1 Making data findable, including provisions for metadata:

The dataset (single band raster file: "SERENA_EJPSOIL_PL_EROSIONCONTROL.SoILMASSNOTERODED.tif") is available in the general science repository of Zenodo, and it is provided with a DOI identifier (https://doi.org/10.5281/zenodo.14018111). The name of the data follows the conventions agreed upon in SERENA's WP3 (project_country_soilecosystemservice Professional). The keywords used to make the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the

<figure><img src="image_93.png" /></figure>
dataset: "SERENA" "EJP-SOIL", "erosion control", "water erosion", "RUSLE", "Poland". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema has been created following the QGIS schema in .docx format, and it is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links (at the moment I included a link to the sharepoint, but when the information to add to Zenodo is ready this will be used)

- History/version

- Authors and contact

### 1.39.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP-Soil communities (https://doi.org/10.5281/zenodo.14018111). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset uses an open-source, platform-independent format (GeoTIFF) that can be open in a number of GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS).

### 1.39.2.3 Making data interoperable:

As stated, the dataset is provided as a raster file with associated metadata. The format is interoperable across software and platforms. The terms follow common usage in soil erosion control modelling.

### 1.39.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The data comes from applying the SERENA Erosion and Erosion Control cookbook applying the most updated and accurate datasets available for Poland. No further QA has been applied to the dataset.

### 1.39.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

<figure><img src="image_94.png" /></figure>
### 1.39.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

### 1.39.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical
ecological indicators research.

## 1.40 SERENA T3.2 - Map of soil organic carbon of Portugal

### 1.40.1 Data summary

The data was prepared according to the methodology of SERENA SOC Loss Cookbook (https://doi.org/10.5281/zenodo.13993621). For Portugal, the application was carried out at national scale.

The dataset was mainly produced to test the methodology of the SERENA SOC loss cookbook, and it
served as a proof of concept for the cookbook.

The map of soil organic carbon (as an indicator of SOC loss threats) was based on ordinary kriging. The map shows SOC concentration (g kg⁻¹) at national scale.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil organic carbon concentration was used as an indicator for soil organic carbon loss (ST).

To create the soil organic carbon map, we used soil organic carbon (0-0.20 m) data from LUCAS 2018 TOPSOIL data - ESDAC - European Commission (europa.eu) database, which are freely available (LUCAS 2018 TOPSOIL data - ESDAC - European Commission (europa.eu))

Delivered map was prepared in the GeoTIFF raster format at the resolution of 50 m.

Delivered data was validated by science stakeholders from Portugal (considered experts) in September 2024.

### 1.40.2 FAIR data

#### 1.40.2.1 Making data findable, including provisions for metadata:

The dataset (geoTIFF file: "SERENA_EJPSOIL_PT_SOCLOSS_SOC.tif") is available in the general science repository of Zenodo; and it is provided with a DOI identifier (https://doi.org/10.5281/zenodo.14006537). The name of the dataset follows the conventions agreed upon in SERENA's WP3 (project_country_soilthreat_indicator). The keywords used to make the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "SOCLOSS", "SOC", "Geostatistics", "PORTUGAL". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema has been created and it is part of the dataset file properties. The Metadata
includes the following info:

<figure><img src="image_95.png" /></figure>
- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links (https://zenodo.org/communities/ejp_soil_research/SERENA)

- History/version

- Authors and contact

### 1.40.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the EJP-Soil SERENA community (https://doi.org/10.5281/zenodo.14006537). It complies with the Zenodo's platform and follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset uses an open-source, platform-independent format (geoTIFF) that can be opened in many GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS).

The data are open accessible in the Zenodo repository after upload, yet a 6-months period of embargo will apply in view of potential publications.

### 1.40.2.3 Making data interoperable:

As stated, the dataset is provided as a geoTIFF file with associated metadata. The format is interoperable across GIS software and platforms. The terms follow common usage in soil organic carbon modelling.

### 1.40.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The data comes from applying the SERENA SOC loss cookbook applying the most updated and freely available for Portugal at national scale. No further QA has been applied to the dataset.

### 1.40.3 Allocation of resources

The costs associated with making the dataset FAIR, as well as for long-term preservation, are unknown, and the dataset creator does not know how to estimate them. SERENA has a data project manager, but once the data is in ZENODO, it is not expected to be curated in the long-term.

### 1.40.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

<figure><img src="image_96.png" /></figure>
### 1.40.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

## 1.41 SERENA T3.2 - Erosion Control map of Slovakia

### 1.41.1 Data summary

The present data was prepared according to the methodology of SERENA Soil erosion and soil erosion control cookbook for the territory of Slovakia. The map of soil loss by water erosion (soil threat) was based on the RUSLE model or the soil erosion control, the difference between the erosion map without vegetation (C-factor = 1) and the erosion map with vegetation was calculated.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil loss was used as an indicator for soil erosion (ST).

To create the erosion control map, we used these data:

- **R factor** - we used data from 100 automatic rain stations on minute rainfall for about 10-year period (national dataset)

- **K factor** - we used the source proposed in the cookbook from ESDAC dataset: Soil Erodibility (K- Factor) High Resolution dataset for Europe

- **LS factor** - we used the source proposed in the cookbook from ESDAC dataset: LS-factor (Slope Length and Steepness factor) for Slovakia

- **C factor** - we used LPIS database-this has information about crops on agricultural soil. We have values of C factor for all crops.

- **P factor** - we used the source proposed in the cookbook from ESDAC dataset: P factor for Slovakia. This map has values about 0.99 for Slovakia, so P-factor does not have much effect on the resulting erosion.

The delivered map was prepared in GeoTIFF format in the resolution of 500 * 500 m.

### 1.41.2 FAIR data

#### 1.41.2.1 Making data findable, including provisions for metadata:

The dataset (file: "SERENA_EJPSOIL_SK_EROSION_ErosionControl.tif") is available in the general science repository of Zenodo and it is provided with a DOI identifier (https://doi.org/10.5281/zenodo.13993746). The name of the dataset follows the conventions agreed upon in SERENA's WP3 (project_country_soilthreat_indicator). The keywords used to make the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "Grant n 862695", "soil erosion control", "soil-based ecosystem service", "RUSLE", "Slovakia". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema will be created and includes the following info:

- Title

- Object type

- Language

<figure><img src="image_97.png" /></figure>
- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links

- History/version

- Authors and contact

### 1.41.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP-Soil communities (https://doi.org/10.5281/zenodo.13993746). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset uses an open-source, platform-independent format (geoTIFF) that can be open in a number of GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS Pro).

### 1.41.2.3 Making data interoperable:

As stated, the dataset is provided as a geoTIFF file with associated metadata. The format is interoperable across software and platforms. The terms follow common usage in soil erosion by water modelling.

### 1.41.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The data comes from applying the SERENA Erosion and Erosion Control cookbook applying the most updated and accurate datasets available for Slovakia. No further QA has been applied to the dataset.

### 1.41.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

### 1.41.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

<figure><img src="image_98.png" /></figure>
### 1.41.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

## 1.42 SERENA T3.2 - Map of Soil loss by water erosion of Slovakia

### 1.42.1 Data summary

The present data was prepared according to the methodology of SERENA Soil erosion and soil erosion control cookbook for the territory of Slovakia. The map of soil loss by water erosion (soil threat) was based on the RUSLE model.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil loss was used as an indicator for soil erosion (ST).

To create the soil loss map we used these data:

- **R factor** - we used data from 100 automatic rain stations on minute rainfall for about 10-year period (national dataset)

- **K factor** - we used the source proposed in the cookbook from ESDAC dataset: Soil Erodibility (K- Factor) High Resolution dataset for Europe

- **LS factor** - we used the source proposed in the cookbook from ESDAC dataset: LS-factor (Slope Length and Steepness factor) for Slovakia

- **C factor** - we used LPIS database-this has information about crops on agricultural soil. We have values of C factor for all crops.

- **P factor** - we used the source proposed in the cookbook from ESDAC dataset: P factor for Slovakia. This map has values about 0.99 for Slovakia, so P-factor does not have much effect on the resulting erosion.

The delivered map was prepared in GeoTIFF format in the resolution of 500 * 500 m.

### 1.42.2 FAIR data

#### 1.42.2.1 Making data findable, including provisions for metadata:

The dataset (file:"SERENA_EJPSOIL_SK_EROSION_soilerosion.tif") is available in the general science repository of Zenodo; and it is provided with a DOI identifier: https://doi.org/10.5281/zenodo.13993556. The name of the dataset follows the conventions agreed upon in SERENA's WP3 (project_country_soilthreat Professional). The keywords used to make the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "Grant n 862695", "soil erosion", "RUSLE", "Slovakia", "erosion cookbook". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema will be created and includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

<figure><img src="image_99.png" /></figure>
- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links

- History/version

- Authors and contact

### 1.42.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP-Soil communities (https://doi.org/10.5281/zenodo.13993556). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset uses an open-source, platform-independent format (geoTIFF) that can be open in a number of GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS Pro).

### 1.42.2.3 Making data interoperable:

As stated, the dataset is provided as a single band raster file with associated metadata in .docx format. The format is interoperable across software and platforms. The terms follow common usage in soil erosion by water modelling.

### 1.42.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The data comes from applying the SERENA Erosion and Erosion Control cookbook applying the most updated and accurate datasets available for Slovakia. No further QA has been applied to the dataset.

### 1.42.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

### 1.42.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

### 1.42.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

<figure><img src="image_100.png" /></figure>
## 1.43 SERENA T3.2 - Evaluation of soil threats and ecosystem service evolution under climate, land use or management changes. Deliverable report 3.4 and literature review.

### 1.43.1 Data summary

Based on an intensive literature review (doi.org/10.5281/zenodo.13969537) and results from previous experiences in member states a scenario framework was developed (climate, land use, and management changes) and common methodologies (statistical methods, simple and/or more sophisticated models) were identified, used or validated to forecast how selected soil ecosystem services (SES) and soil threats (ST) will change according to climate, land-use and management changes. In contrast to WP5 we focus in WP3/Task 3, Deliverable 3.4. on forecasts of changes of various soil indicators on site, regional or national scale, and could rely on soil maps with high resolution that are maintained by several member states. Due to the lack of available methods/models, missing experience by member states or the availability of input data no forecasts could be done for the SES environmental pollution control, habitat for biodiversity, pest and disease control, soil contamination, loss of diversity and salinization. 6 member states (Austria, Czech Republic, Estonia, France, Ireland and Poland) were able to run scenario analysis with different methods.

### 1.43.2 FAIR data

#### 1.43.2.1 Making data findable, including provisions for metadata:

The dataset (document, excel tables and maps) is available in the general science repository of Zenodo; and it is provided with a DOI identifier (https://doi.org/10.5281/zenodo.13945383 for the deliverable 3.4 and https://doi.org/10.5281/zenodo.13969536 for the literature review for milestone 3.4). The name of the datasets follows the conventions agreed upon in SERENA's WP3 (National SoilHUBS: Assessment of (bundles of) Soil functions/threats/ecosystem services at different scales). The keywords used to make the dataset findable were agreed upon in the SERENA community. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "scenario analysis", "climate change", "management" or land use change. The dataset consists of a single version since there are no upgradable versions to the dataset.

#### 1.43.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP-Soil communities (https://doi.org/10.5281/zenodo.13945383 for the deliverable 3.4 and https://doi.org/10.5281/zenodo.13969536 for the literature review for milestone 3.4). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset are results of the scenario analysis in form of the deliverable report, excel tables and maps and the table with all used papers used for the literature review.

<figure><img src="image_101.png" /></figure>
#### 1.43.2.3 Making data interoperable:

As stated, the dataset is provided as document, excel files and as maps. The format is interoperable across software and platforms.

#### 1.43.2.4 Increase data re-use (through clarifying licences):

The dataset licence is the Creative Commons Attribution licence (CC-BY). The maps that were produced for the deliverable report D3.4 is coming from the 6 member states (Austria, Czech Republic, Estonia, France, Latvia, Poland) and will be uploaded to ZENODO by each responsible person using the link to the deliverable report Doi.

#### 1.43.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

#### 1.43.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

#### 1.43.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

### 1.44 SERENA T4.2 - Extending LUCAS with Corine Land Cover

#### 1.44.1 Data summary

In order to assess Soil Organic Carbon (SOC) response to Land Use Change (LUC), T4.2 of SERENA leverages topsoil samples from LUCAS-soil, a 10% subset of the LUCAS survey. To recover LUC, it uses data from five sequential waves of the LUCAS survey in 2006, 2009, 2012, 2015, and 2018, but soil samples were taken only in 2009 (2012 in Bulgaria and Romania), 2015, and 2018. Based on models, the literature tends to consider that it takes decades for soil carbon sequestration to reach equilibrium, but there is little evidence to quantify how long it actually takes. To augment the temporal and spatial scope of our carbon response analysis, we adopt a machine learning model to predict ground-truthed land-use changes - as observed in LUCAS - with Corine Land Cover (CLC) data based on aerial photographs. The trained model is then used to predict land use based at LUCAS-soil points since 1990, and detect probable land-use changes having occurred between 1990 and 2006.

The model is utilized to estimate the probability of land use being cropland, grassland, or forest for the years 1990, 2000, 2006, 2012, and 2018, in cases where land use information is missing for LUCAS survey points. Specifically, we assign the land cover category with the highest probability to each point. If the highest probability among cropland, grassland, or forest does not exceed 50%, we label the

<figure><img src="image_102.png" /></figure>
points as “Other”. Moreover, we use the machine learning model to assess the uncertainty of these LUC predictions. In a bootstrap-like sensitivity analysis, we draw 100 times a land use on each LUCAS point according to the probabilities given by the machine learning model.

We use this novel dataset to produce upscaled estimates of SOC response over time to LUCS with high precision across EU. We then use the estimates to publish country specific emission factors, compliant with the IPCC guidelines and the LULUCF Regulation, and ready for use in national GHG inventories. These data could be useful for analysing the effects of LUC on Lucas Soil before it is implemented.

### 1.44.2 FAIR data

#### 1.44.2.1 Making data findable, including provisions for metadata:

These data are actually used to perform a statistical analysis, they will be put on the dataverse
[https://entrepot.recherche.data.gouv.fr/dataverse/inrae](https://entrepot.recherche.data.gouv.fr/dataverse/inrae) once the paper is published.

The provision of metadata will comply with the platform which uses metadata standards (DataCite), required keywords with link to standard vocabularies, enables versioning. The platform uses the DOI as persistent identifier.

#### 1.44.2.2 Making data openly accessible:

These data will have the same copyright and licence than initial CLC and LUCAS Soil data. They will be downloadable in the Serena dataverse in a csv format. A Readme file will be also available in the Serena dataverse describing the different files using the template proposed by the data repository. The data could be accessed using any spreadsheet software.

#### 1.44.2.3 Making data interoperable:

We will use a JSON metadata file using as much as possible standard vocabularies or ontologies for describing the data of the csv files.

#### 1.44.2.4 Increase data re-use (through clarifying licenses):

The data will follow the rules from JRC regarding the LUCAS Soil data for re-usability. An authorization will be asked to the JRC to know if these data can be made openly accessible.

If data can be made openly accessible, they will be available for re-use at the end of the project with embargo until the paper is published.

The Serena dataverse is part of the French research repository and thus will follow the rules of this repository which guaranties an access to the files for a minimum of 5 years, renewable after publication of the dataset.

### 1.44.3 Allocation of resources

Making these data available is part of the cost of the Serena project for the data producer team.

The administration of the Serena dataverse is taken in charge by INRAE.

### 1.44.4 Data security

Data security is provided by the dataverse.

<figure><img src="image_103.png" /></figure>
### 1.44.5 Ethical aspects

As the data are compiled from the LUCAS Soil data without aggregation, they are faced with personal data issues linked to the coordinates of the sampling sites.

## 1.45 SERENA T3.3 – Maps for Soil loss by water from climate change scenarios for Austria

### 1.45.1 Data summary

The present data was prepared according to the methodology of SERENA Soil erosion and soil erosion control cookbook (Buttafuoco et al., 2024). The maps of soil loss by water erosion (soil threat) were based on the RUSLE model.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. Soil loss was used as an indicator for the soil threat soil erosion.

The methods and data used to create the soil loss map are described in Buttafuoco et al. (2024) and Hessel et al. (2024).

The maps of relative soil loss are provided as .tiff files with a resolution of 25 m.

This dataset contains the change of modelled annual soil loss rates for changing R-factor according to RCP4.5 and RPC8.5 climate scenarios, relative to modelled soil loss in the base scenario, using R-factor calculated for the 1990-2021 period for the country of Austria. For each climate scenario, four periods were considered: 1991-2020, 2021-2040, 2041-2060 and 2061-2080. The RUSLE-based soil loss calculations were done according to the SERENA/EJP-Soil soil erosion control cookbook and are described in the respective project deliverables D3.3 and D3.4.

### 1.45.2 FAIR data

#### 1.45.2.1 Making data findable, including provisions for metadata:

The dataset was published open access under https://doi.org/10.5281/zenodo.13991805 via the Zenodo repository. The published zip-folder contains the raw data as .tiff files with auxiliary files.

As the dataset is described by SERENA/EJP-Soil Deliverable D3.4, the DOI of this Deliverable was linked for better findability: https://doi.org/10.5281/zenodo.13945384.

Keywords for search in zenodo.org and in the metadata file: SERENA project, EJPSOIL, Grant no 862695, D3.4

A metadata schema has been created following the QGIS schema in .txt format, and is part of the dataset. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

<figure><img src="image_104.png" /></figure>
- Links

- History/version

- Authors and contact

### 1.45.2.2 Making data openly accessible:

All data are provided in the open access repository as it was put into the survey. There are .tiff files which are packed in a .zip-file, hence accessible with standard GIS software.

### 1.45.2.3 Making data interoperable:

See explanations above – the raster data can be included into any GIS workflow and easily operable without any special requirements. The terms follow common usage in soil erosion by water modelling.

### 1.45.2.4 Increase data re-use (through clarifying licenses):

Data is open for re-use und CC BY 4.0 as long as the repository zenodo.org exists.

### 1.45.3 Allocation of resources

Making the dataset accessible costed around 4 hours of working time, funded by the grant mentioned in the dataset keywords.

### 1.45.4 Data security

For data security, the highly secure repository zenodo.org is used. There is a copy stored and available at the internal server system of the main authors. No sensitive data is included.

### 1.45.5 Ethical aspects

No ethical or legal issues impacting the data sharing.

## References

Buttafuoco et al., 2024. Cookbook to evaluate indicators of soil threats, soil-based ES and their associated bundles over scenarios of change. SERENA deliverable 2.3.2 https://doi.org/10.5281/zenodo.13993620

Hessel R, G Buttafuoco, E Medina-Roldán, D Smiraglia, J Coblinski, L Kukk, B Lemercier, S Pindral, J Reyes Rojas, R Lorenzetti, C Pesch, K Amalevičiūtė-Volungė, S Asins, F Assennato, A Astover, Z Bakacsi, A Benő, G Bondi, T Brunner, S Callewaert, K Cockx, J Cruijsen, P Csontos, E Doñate, A Fahy, V Feiza, C B Foldal, A Gaillot, L Gardin, C Gedeon, D Josipovic, P Kassai, T Kikas, M Kocsis, A Laborczi, D Luts, J Makovníková, D Montagne, K Oorts, L O'Sullivan, B Pálka, L Pásztor, E Putku, N Riitano, N Saby, E Schmaltz, V Shahrokh, B Szabó, G Szatmári, M Trip, F Ungaro, J Volungevičius, T Weninger, R Žydelis. 2024. Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3, 224 pp. https://doi.org/10.5281/zenodo.13991087

<figure><img src="image_105.png" /></figure>
# 1.46 Map of soil organic carbon for France

## 1.46.1 Data summary

The data was prepared according to the methodology of SERENA SOC loss cookbook.

Random forest was used for the prediction of the SOC concentration using the general DSM workflow implemented in the R package *Seedling* (Genova, G., Poggio, L., Kempen, B., & Colman, B. (2024). DSM Workflow Seedling. ISRIC - World Soil Information. https://doi.org/10.17027/ISRIC-FSX2-2691.)

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats (ST). Soil organic carbon concentration was used as an indicator for soil organic carbon loss (ST).

To compile the soil organic carbon map, we used soil organic carbon (0-30 cm) data of the French Soil Information and Monitoring System (SIMS), which are not publicly available. The data are described in the following paper: Mulder, V.L., Martin, M., Lacoste, M., Saby, N., Richer-De-Forges, A., Arrouays, D., 2014. Controlling factors explaining soil carbon in relation to soil depth for French soils, in: ELS 2014, The Earth Living Skin: Soil, Life and Climate Change.

The selection of environmental covariates, which are then used to model and map SOC content, was based on the 'scorpan' model: (s) soil properties, (c) climate, (o) organisms, (r) relief, (p) parent material, (a) age and (n) spatial coordinates. These covariates are described in the following paper: Dobarco, M.R., Bourennane, H., Arrouays, D., Saby, N.P., Cousin, I., Martin, M.P., 2019. Uncertainty assessment of GlobalSoilMap soil available water capacity products: A French case study. Geoderma, 344, 14-30.

Delivered map was prepared in GeoTIFF raster format at the resolution of 100 x 100 m.

## 1.46.2 FAIR data

### 1.46.2.1 Making data findable, including provisions for metadata:

The dataset (geoTIFF file: "SERENA_EJPSOIL_FR_SOCLOSS_SOC.tif") will be available in the SERENA dataverse https://entrepot.recherche.data.gouv.fr/dataverse/ejp_soil_serena.

The name of the dataset follows the conventions agreed upon in SERENA's WP3 (project_country_soilthreatindicator). The keywords used to make the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJPSOIL", "SOCLOSS", "SOC", "DSM", "France". The dataset consists of a single version since there are no upgradable versions to the dataset.

A metadata schema has been created and it is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

<figure><img src="image_106.png" /></figure>
- Links (at the moment I included a link to the sharepoint, but when the information to add to Zenodo is ready this will be used)

- History/version

- Authors and contact

### 1.46.2.2 Making data openly accessible:

The dataset will be uploaded in the Serena dataverse and will be provided a DOI.

The dataset uses an open-source, platform-independent format (geoTIFF) that can be opened in many GIS applications both open-source (e.g., QGIS) and proprietary (ArcGIS).

### 1.46.2.3 Making data interoperable:

As stated, the dataset is provided as a geoTIFF file with associated metadata. The format is interoperable across GIS software and platforms. The terms follow common usage in soil organic carbon modelling.

### 1.46.2.4 Increase data re-use (through clarifying licenses):

The dataset licence will be the etalab 2.0 which is compatible with a CC-BY licence.

### 1.46.3 Allocation of resources

The costs associated to make the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in the dataverse the data are not expected to be curated at the long-term.

### 1.46.4 Data security

Data security in terms of storage depends on the repository (https://entrepot.recherche.data.gouv.fr/).

### 1.46.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

## 1.47 Literature review Task 3.3

### 1.47.1 Data summary

We reviewed existing modelling studies from all over the world focusing on scenario analyses of SES and ST including climate change, land use change and management scenarios. A publication has been submitted to "Science of the total environment" and is currently being reviewed. The title of the manuscript is: Assessing and mapping soil ecosystem services and soil threats changes in agroecosystems through scenario-based approaches – a systematic review.

Work was split between various authors. All Co-authors that are mentioned in the first page were working on either one or more SES or one ST. Excel sheets were prepared by INRAE and BFW to ensure the comparability of results that members extracted from the papers found. Literature search was done in Scopus and Web of Science. The aim of the literature review was mainly to:

<figure><img src="image_107.png" /></figure>
- understand how different drivers of global change are most commonly studied in scientific literature.

- identify which SES and ST are most commonly studied and how they are assessed and mapped in scenario-based approaches.

- Finally, provide a preliminary discussion on how soil properties are represented in these scenario-based approaches.

After screening the Web and sorting out unusable papers, we found 230 published articles related to 7 SES and 10 ST.

## 1.47.2 FAIR data

### 1.47.2.1 Making data findable, including provisions for metadata:

The dataset (Excel table with all used papers and a Zotero list with all papers that were used for the literature review and for the above-mentioned publication) will be available in the general science repository of Zenodo; and it will be provided with a DOI identifier. The name of the dataset follows the conventions agreed upon in SERENA's WP3 (National SoilHUBS: Assessment of (bundles of) Soil functions/threats/ecosystem services at different scales). The keywords used to make the dataset findable were agreed upon in the SERENA community. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "SERENA" "EJP-SOIL", "review", "global", "literature". The dataset consists of a single version since there are no upgradable versions to the dataset.

### 1.47.2.2 Making data openly accessible:

The dataset will be uploaded to Zenodo in the SERENA/EJP-Soil communities. It complies with the Zenodo's platform and it will follow the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset is an excel table and a Zotero file.

The paper will be uploaded as soon as it is accepted for publication.

### 1.47.2.3 Making data interoperable:

As stated, the dataset is provided as an Excel file and as a Zotero literature list. The format is interoperable across software and platforms.

### 1.47.2.4 Increase data re-use (through clarifying licenses):

The dataset licence will be the Creative Commons Attribution license (CC-BY). The extracted data comes from the papers that were used for the literature review. Data were later on harmonized to make it comparable and to be sure that the same wording is used in each column.

## 1.47.3 Allocation of resources

The costs associated to make the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

<figure><img src="image_108.png" /></figure>
### 1.47.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

### 1.47.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

## 1.48 Soil based ecosystem services individual assessments and bundles results

### 1.48.1 Data summary

The data was prepared according to the methodology described in detail in the deliverable D5.2 (Coblinski et al., 2024, https://doi.org/10.5281/zenodo.13950477)

The dataset contains the evaluation of 4 soil ecosystem services and associated bundles for current period and in 2050 for two climate change scenarios corresponding to two contrasted Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5).

These maps were produced using GIS software and digital soil mapping approaches to map selected indicators of the different soil threats.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats.

Delivered map was prepared in the GeoTIFF raster format at the resolution of 1000 m.

<table><thead><tr><th></th><th>geodataset name</th><th>Description</th></tr></thead><tbody><tr><td rowspan="3">Individual assessment of soil ecosystem services expressed in original scale<br>Evaluation of the 5 SES for 3 scenarios (current, 2050_ssp126 or 2050_ssp585)<br>The SES are expressed in original scale</td><td>ErosCont_2050_SSP126.tif<br>ErosCont_2050_SSP585.tif<br>ErosCont_current.tif</td><td>ErosCont means Erosion control, Amount of soil not eroded by water (t ha<sup>-1</sup> yr<sup>-1</sup>)</td></tr><tr><td>FloodCont_current.tif<br>FloodCont_2050_SSP126.tif<br>FloodCont_2050_SSP585.tif</td><td>FloodCont means Flood control, Soil water retention capacity (mm)</td></tr><tr><td>WatProd_2050_SSP126.tif<br>WatProd_2050_SSP585.tif<br>WatProd_current.tif</td><td>WaterProd means Water production, Water yield (mm yr<sup>-1</sup>)</td></tr><tr><td rowspan="3">Individual assessment of soil ecosystem services expressed in classes</td><td>PrimProd_2050_SSP126.tif<br>PrimProd_2050_SSP585.tif<br>PrimProd_current.tif</td><td>PrimProd means Primary biomass production, Net primary production (gC m<sup>-2</sup> yr<sup>-1</sup>)</td></tr><tr><td>ClimReg_current.tif<br>ClimReg_2050_SSP126.tif<br>ClimReg_2050_SSP585.tif</td><td>ClimReg, Climate regulation, Net ecosystem productivity (gC m<sup>-2</sup> yr<sup>-1</sup>)</td></tr><tr><td>ErosCont_2050_SSP126_classes.tif<br>ErosCont_2050_SSP585_classes.tif<br>ErosCont_current_classes.tif<br>FloodCont_2050_SSP126_classes.tif<br>FloodCont_2050_SSP585_classes.tif<br>FloodCont_current_classes.tif<br>WatProd_2050_SSP126_classes.tif<br>WatProd_2050_SSP585_classes.tif<br>WatProd_current_classes.tif<br>PrimProd_2050_SSP126_classes.tif<br>PrimProd_2050_SSP585_classes.tif</td><td>We have considered the SES values corresponding to the 40% and 60% quantiles of the current situation to define the thresholds for low and high intensity levels of SES, respectively. These threshold values were then used for the two 2050 scenarios</td></tr></tbody></table>

<figure><img src="image_109.png" /></figure>
<table><tr><td></td><td>PrimProd_current_classes.tif<br/>ClimReg_2050_SSP126_classes.tif<br/>ClimReg_2050_SSP585_classes.tif<br/>ClimReg_current_classes.tif</td><td></td></tr><tr><td>Bundles of soil ecosystem services</td><td>SES_bundles_current_raw.tif<br/>SES_bundles_SSP126.tif<br/>SES_bundles_SSP585.tif</td><td>Id of the SES bundles for the 3 scenarios<br/>We obtained 21 bundles</td></tr></table>

To compute the above raster, we used the following data available online

<table><thead><tr><th>Parameters</th><th>Variables</th><th>Unit</th><th>Raw resolution</th><th>Source</th><th>SES</th></tr></thead><tbody><tr><td rowspan="12">Climate</td><td>Monthly precipitation</td><td>mm</td><td>1 km</td><td>worldclim.org/</td><td>WatProd; ErosCont</td></tr><tr><td>Annual precipitation</td><td>mm yr<sup>-1</sup></td><td></td><td></td><td></td></tr><tr><td>Minimum temperature</td><td>°C</td><td rowspan="2">1 km</td><td rowspan="2">worldclim.org/</td><td rowspan="2">WatProd</td></tr><tr><td>Maximum temperature</td><td>°C</td></tr><tr><td>Annual mean temperature</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Mean diurnal range</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Isothermality</td><td>No unit</td><td></td><td></td><td></td></tr><tr><td>Temperature seasonality</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Max temp of warm month</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Temp annual range</td><td>°C</td><td rowspan="7">1 km</td><td rowspan="7">worldclim.org/</td><td rowspan="7">FloodCont</td></tr><tr><td>Mean temp of wet quarter</td><td>°C</td></tr><tr><td>Mean temp of driest quarter</td><td>°C</td></tr><tr><td>Precipitation of driest month</td><td>mm</td></tr><tr><td>Precipitation seasonality</td><td>Fraction</td></tr><tr><td>Precip of warmest quarter</td><td>mm</td></tr><tr><td>Precip of coldest quarter</td><td>mm</td></tr><tr><td rowspan="7">Soil</td><td>Clay</td><td>g 100g<sup>-1</sup></td><td>250 m</td><td></td><td></td></tr><tr><td>Sand</td><td>g 100g<sup>-1</sup></td><td>250 m</td><td rowspan="2">https://data.isric.org<br/>/geonetwork/srv/en<br/>g/catalog.search#</td><td rowspan="7">FloodCont; ErosCont</td></tr><tr><td>Silt</td><td>g 100g<sup>-1</sup></td><td>250 m</td></tr><tr><td>Coarse fragments</td><td>g 100g<sup>-1</sup></td><td>250 m</td><td></td><td></td></tr><tr><td>SOC content</td><td>g kg<sup>-1</sup></td><td>250 m</td><td></td><td></td></tr><tr><td>Soil depth</td><td>m</td><td>1 km</td><td>esdac.jrc.ec.europa.eu/content/europea<br/>n-soil-database-v2-</td><td></td></tr></tbody></table>

<figure><img src="image_110.png" /></figure>
<table><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td></td><td></td><td>raster-library-1kmx1km</td><td></td><td></td><td></td></tr><tr><td rowspan="3">Topography</td><td>Aspect</td><td>No unit</td><td>25 m</td><td>land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1/view</td><td>FloodCont; ErosCont</td></tr><tr><td>Elevation</td><td>m</td><td>25 m</td><td></td><td></td></tr><tr><td>Slope</td><td>%</td><td>25 m²</td><td></td><td></td></tr><tr><td>Land use</td><td>Land use classes</td><td>No unit</td><td>100 m</td><td>joint-research-centre.ec.europa.eu/luisa_en</td><td>FloodCont; ErosCont</td></tr><tr><td rowspan="3">Support Practices</td><td>Contour farming</td><td>No unit</td><td>100 m</td><td>esdac.jrc.ec.europa.eu/themes/support-practices-factor</td><td>ErosCont</td></tr><tr><td>Stone walls</td><td>No unit</td><td>100 m</td><td></td><td></td></tr><tr><td>Grass margins</td><td>No unit</td><td>100 m</td><td></td><td></td></tr><tr><td rowspan="2">Vegetation</td><td>NPP</td><td>gC m² yr⁻¹</td><td>55 km</td><td>https://data.isimip.org/search/simulation_round/ISIMIP3b/</td><td>BiomProd; ClimReg</td></tr><tr><td>RH</td><td>gC m² yr⁻¹</td><td>55 km</td><td></td><td></td></tr></tbody></table>

## 1.48.2 FAIR data

### 1.48.2.1 Making data findable, including provisions for metadata:

The spatial datasets will be available in the general science repository of entrepot.recherche.data.gouv.fr in the Serena dataverse (https://entrepot.recherche.data.gouv.fr/dataverse/ejpsoil serena) with a DOI identifier.

A metadata schema has been created and it is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- History/version

- Authors and contact

### 1.48.2.2 Making data openly accessible:

The dataset contains several rasters.

The dataset uses an open-source, platform-independent format (geoTIFF) that can be opened in many GIS applications both open-source (e.g., QGIS, R terra package) and proprietary (ArcGIS).

The data are open accessible in the ERDG repository after upload, yet a 6-months period of embargo will be applied in view of potential publications.

<figure><img src="image_111.png" /></figure>
### 1.48.2.3 Making data interoperable:

As stated, the dataset is provided as a geoTIFF file with associated metadata. The format is interoperable across GIS software and platforms.

### 1.48.2.4 Increase data re-use (through clarifying licenses):

The dataset licence will be the etalab 2.0 which is compatible with a CC-BY licence.

### 1.48.2.5 Allocation of resources

The costs associated to make the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in entrepot.recherche.data.gouv.fr the data are not expected to be curated at the long-term.

### 1.48.3 Data security

Data security in terms of storage depends on the repository (entrepot.recherche.data.gouv).

### 1.48.4 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

## 1.49 Soil threats individual assessments and bundles results for 2050 in Europe

### 1.49.1 Data summary

The data was prepared according to the methodology described in details in the deliverable D5.1 (Coblinski et al., 2023, https://doi.org/10.5281/zenodo.13891492)

The dataset contains the evaluation of 5 soil threats in 2050 and associated bundles for two climate change scenarios corresponding to two contrasted Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5).

These maps were produced using GIS software and digital soil mapping approaches to map selected indicators of the different soil threats.

The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats.

Delivered map was prepared in the GeoTIFF raster format at the resolution of 1000 m.

<table><caption>Table 1. List of the raster files and description provided in the dataset.</caption><thead><tr><td>geodataset name</td><td>Description</td></tr></thead><tbody><tr><td>Individual assessment of soil parameters:<br>BulkDensity_2050_SSP126.tif<br>BulkDensity_2050_SSP585.tif<br>BulkDensity_current.tif<br>SOCstock_2050_SSP126.tif<br>SOCstock_2050_SSP585.tif<br>SOCstock_current.tif<br>Erosion_2050_SSP126.tif</td><td>Maps of individual assessment of soil threat indicator for current scenario and 2 future scenarios (2050 SSP1-2.6 and SSP5-8.5).<br>Bulk density evaluation of 0-30 cm (BulkDensity)<br>Soil Carbon stock up to 1m (SOCstock)</td></tr></tbody></table>

<figure><img src="image_112.png" /></figure>
<table><tr><td>Erosion_2050_SSP585.tif<br/>Sealing_2050.tif</td><td>Sheet and rill erosion by water assessed by the revised universal soil loss equation (Erosion)<br/>Degree of soil sealing (Sealing)</td></tr><tr><td>Evaluation of soil threats, classified:<br/>Compaction_2050_SSP126_classes.tif<br/>Compaction_2050_SSP585_classes.tif<br/>Erosion_2050_SSP126_classes.tif<br/>Erosion_2050_SSP585_classes.tif<br/>Sealing_2050_class.tif<br/>SOCloss_2050_SSP126_classes.tif<br/>SOCloss_2050_SSP585_classes.tif</td><td>Compaction: changes in bulk density in topsoil (0-30 cm), expressed in classes of relative change (<-40%, -40% -10%, -10% - 10%, > 10%)<br/>SOC loss (up to 1 m deep) expressed in classes of relative change ( -40% -10%, -10% - 10%, > 10%)<br/>Erosion: sheet and rill erosion by water by 2050 using RUSLE equation: soil loss intensity classes<br/>0-1 t ha<sup>-1</sup> yr<sup>-1</sup>: negligible soil loss;<br/>1-2 t ha<sup>-1</sup> yr<sup>-1</sup>: very slight soil loss;<br/>2-5 t ha<sup>-1</sup> yr<sup>-1</sup>: slight soil loss;<br/>5-10 t ha<sup>-1</sup> yr<sup>-1</sup>: moderate soil loss;<br/>> 10 t ha<sup>-1</sup> yr<sup>-1</sup>: severe soil loss.<br/>Changes in soil sealing by 2050: Soil sealing by 2050: classes of soil sealing change between 2012 and 2050 (-80%-30%, 30% 100%)</td></tr><tr><td>Bundles of soil threats<br/>ST_bundles_2050_SSP126.tif<br/>ST_bundles_2050_SSP585.tif</td><td>Soil Threats bundles map by 2050 under land use and under SSP5-8.5 and SSP1-2.6</td></tr></table>

Since several ST indicators need soil parameters (change in SOC or bulk density and erosion), climate data (same indicators) or land use (change in SOC or bulk density and soil artificialization), the use of different data bases as input data could induce discrepancies that might arise from the inherent variations in the different sources. To avoid this drawback, we chose a unique soil database per data type, that is SoilGrids for soil data, WorldClim for climate, and LUISA for land use. Data sources used to assess SOC stock change, soil bulk density change and erosion by 2050 are synthesized in Table 2 to 4.

<figure><img src="image_113.png" /></figure>
<table><caption>Table 2. Environmental-based covariates used in the predictive model using DSM approach for the assessment of soil compaction and SOC loss.</caption><thead><tr><th>SCORPAN variables</th><th>Covariates</th><th>Unit</th><th>Spatial raw resolution</th><th>Source</th></tr></thead><tbody><tr><td rowspan="10">Soil</td><td>Bulk density</td><td>kg/dm³</td><td>250 m</td><td rowspan="10">SoilGrids</td><td rowspan="10">data.isric.org/ geonetwork/srv/eng/ catalog.search#/home</td></tr><tr><td>Clay</td><td>%</td><td>250 m</td></tr><tr><td>Sand</td><td>%</td><td>250 m</td></tr><tr><td>Silt</td><td>%</td><td>250 m</td></tr><tr><td>Cation exchange capacity</td><td>cmol(c)/kg</td><td>250 m</td></tr><tr><td>Coarse fragments</td><td>%</td><td>250 m</td></tr><tr><td>Organic carbon density</td><td>kg/m³</td><td>250 m</td></tr><tr><td>pH</td><td>pH</td><td>250 m</td></tr><tr><td>Soil depth</td><td>m</td><td>1 km</td></tr><tr><td>Soil water content 33 kPa</td><td>kPa</td><td>250 m</td></tr><tr><td rowspan="3">Topography</td><td>Aspect</td><td>no unit</td><td>25 m</td><td rowspan="3">ESDAC</td><td rowspan="3">esdac.jrc.ec.europa.eu/content/european-soil-database-v2-raster-library-1kmx1km</td></tr><tr><td>Elevation</td><td>m</td><td>25 m</td></tr><tr><td>Slope</td><td>%</td><td>25 m</td></tr><tr><td>Climate</td><td>Bioclimatic variables*</td><td>-</td><td>1 km</td><td>Copernicus</td><td>zenodo.org/record/ 2784001#.YxCkbXZByCh</td></tr><tr><td>Land use</td><td>Land use classes</td><td>no unit</td><td></td><td>See table 4</td><td></td></tr></tbody></table>

<figure><img src="image_114.png" /></figure>
<table><caption>Table 3. Input data for the assessment of the individual RUSLE factors</caption><thead><tr><th>RUSLE factors</th><th>Input data</th><th>Unit</th><th>Spatial raw resolution</th><th>Source</th></tr></thead><tbody><tr><td>P factor<br>Support practices</td><td>P factor</td><td>-</td><td>100 m</td><td>ESDAC<br><a href="">esdac.jrc.ec.europa.eu/<br>themes/support-practices-factor</a></td></tr><tr><td>LS factor<br>Slop length and steepness</td><td>LS factor</td><td>-</td><td>100 m</td><td>ESDAC<br><a href="">esdac.jrc.ec.europa.eu/content/<br>ls-factor-slope-length-and-steepness-factor-eu</a></td></tr><tr><td>C factor<br>Cover management</td><td>Land use</td><td>No unit</td><td>100 m</td><td>LUISA<br><a href="">joint-research-centre.ec.europa.eu/<br>luisa_en</a></td></tr><tr><td rowspan="2">R factor<br>Rainfall erosivity</td><td>Annual precipitation</td><td>mm</td><td>1 km</td><td rowspan="2">WorldClim<br><a href="">www.worldclim.org/</a></td></tr><tr><td>Monthly precipitation</td><td>mm</td><td>1 km</td></tr><tr><td rowspan="3">K factor<br>Soil erodibility</td><td>Clay</td><td>%</td><td>250 m</td><td rowspan="3">SoilGrids<br><a href="">data.isric.org/geonetwork/srv/eng/catalog.search#/home</a></td></tr><tr><td>Sand</td><td>%</td><td>250 m</td></tr><tr><td>Silt</td><td>%</td><td>250 m</td></tr><tr><td></td><td>Soil organic carbon content</td><td>g/kg</td><td>250 m</td><td></td></tr></tbody></table>

<figure><img src="image_115.png" /></figure>
<table><caption>Table 4. List of variables used to represent climate and land use changes scenarios used to assess soil compaction, SOC loss and soil erosion, and the respective source. Climate data used for 2050 were the mean of the results produced by the two ACCESS-CM2 and HadGEM3-GC31-LL global climate models for the two selected SSPs</caption><thead><tr><th>Parameters</th><th>Variables</th><th>Unit</th><th>Raw resolution</th><th>Database</th><th>Period</th></tr></thead><tbody><tr><td rowspan="10">Precipitation</td><td>Monthly total precipitation</td><td>mm</td><td></td><td></td><td></td></tr><tr><td>Annual mean temperature</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Mean Diurnal Range</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Isothermality</td><td>No unit</td><td></td><td></td><td></td></tr><tr><td>Temperature Seasonality</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Max Temperature of Warmest Month</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Min Temperature of Coldest Month</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Temperature Annual Range</td><td>°C</td><td></td><td></td><td></td></tr><tr><td>Mean Temperature of Wettest Quarter</td><td>°C</td><td></td><td></td><td></td></tr><tr><td rowspan="8">Bioclimatic variables</td><td>Mean Temperature of Driest Quarter</td><td>°C</td><td rowspan="8">1 km</td><td rowspan="8">worldclim.org<br>(Dix et al., 2019; Good, 2019)</td><td rowspan="8">Historical<br>(1970-2000)<br>to<br>2050<br>(SSP 1.2-6<br>and SSP 5.8-5)</td></tr><tr><td>Mean Temperature of Warmest Quarter</td><td>°C</td></tr><tr><td>Mean Temperature of Coldest Quarter</td><td>°C</td></tr><tr><td>Annual Precipitation</td><td>mm</td></tr><tr><td>Precipitation of Wettest Month</td><td>mm</td></tr><tr><td>Precipitation of Driest Month</td><td>mm</td></tr><tr><td>Precipitation Seasonality</td><td>Fraction</td></tr><tr><td>Precipitation of Wettest Quarter</td><td>mm</td></tr><tr><td rowspan="4">Land use</td><td>Land use classes</td><td>No unit</td><td>100 m</td><td>LUISA - joint-research-centre.ec.europa.eu/luisa_en<br>(Joint Research Centre, 2017)</td><td>2012<br>to 2050</td></tr></tbody></table>

<figure><img src="image_116.png" /></figure>
## 1.49.2 FAIR data

### 1.49.2.1 Making data findable, including provisions for metadata:

The spatial datasets will be available in the general science repository of entrepot.recherche.data.gouv.fr in the Serena dataverse (https://entrepot.recherche.data.gouv.fr/dataverse/ejp_soil_serena) with a DOI identifier.

A metadata schema has been created and it is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- History/version

- Authors and contact

### 1.49.2.2 Making data openly accessible:

The dataset contains several rasters.

The dataset uses an open-source, platform-independent format (geoTIFF) that can be opened in many GIS applications both open-source (e.g., QGIS, R terra package) and proprietary (ArcGIS).

The data are open accessible in the ERDG repository after upload, yet a 6-months period of embargo will be applied in view of potential publications.

### 1.49.2.3 Making data interoperable:

As stated, the dataset is provided as a geoTIFF file with associated metadata. The format is interoperable across GIS software and platforms.

### 1.49.2.4 Increase data re-use (through clarifying licenses):

The dataset licence will be the etalab 2.0 which is compatible with a CC-BY licence.

## 1.49.3 Allocation of resources

The costs associated to make the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in entrepot.recherche.data.gouv the data are not expected to be curated at the long-term.

## 1.49.4 Data security

Data security in terms of storage depends on the repository (entrepot.recherche.data.gouv).

<figure><img src="image_117.png" /></figure>
### 1.49.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical
ecological indicators research.

### 1.49.6 Reference

Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah, C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M., Bodman, R., Dias, F.B., Domingues, C.M., Hannah, N., Heerdegen, A., Savita, A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan, S.M., Roberts, D., Smillie, J., Snow, K., Ward, M., Yang, R., 2019. CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP. https://doi.org/10.22033/ESGF/CMIP6.2281

Good, P., 2019. MOHC HadGEM3-GC31-LL model output prepared for CMIP6 ScenarioMIP. https://doi.org/10.22033/ESGF/CMIP6.10845

Joint Research Centre, 2017. The LUISA territorial reference scenario 2017: a technical description.
Publications Office of the European Union, Luxembourg.

## 1.50 Net Ecosystem Productivity Cookbook

### 1.50.1 Data summary

The present dataset corresponds to a derived dataset (SERENA NEP dataset hereafter) to analyse the
relationship between Net Ecosystem Productivity (NEP) components and a number of spatially-
exhaustive environmental variables in wheat croplands/rotations, with a focus on the EU. The SERENA
NEP dataset is derived from eddy-covariance data for those stations in the Fluxnet network with data
for wheat, and:

- MODIS MOD17A2HGF Collection

- Daily soil moisture data (derived from Zhang et al. 2023)

- World Climate maximum monthly temperature

- World Climate minimum monthly temperature

SERENA had, among others, the objective of developing methods to calculate and map soil-based
ecosystem services and soil threats. NEP was used as an indicator of greenhouse gases and climate
regulation supported by soils.

For this purpose, the relationship between Fluxnet NEP (the negative of net ecosystem exchange), its
components; and those variables above was investigated through a number of statistical approaches.
The dataset was compiled using a data collection and management approach implemented in R.

The SERENA NEP dataset in comma separated values format has a size of 551.6 Kb.

The data is useful as a research tool to investigate CO₂ fluxes in the region for the SERENA consortium
and other researchers.

<figure><img src="image_118.png" /></figure>
### 1.50.2 FAIR data

#### 1.50.2.1 Making data findable, including provisions for metadata:

The dataset (csv file: "SERENA_EJPSOIL_GHG_NEP_cookbook.csv") is available in the general science repository of Zenodo; and it is provided with a DOI identifier (https://doi.org/10.5281/zenodo.14002496). The name of the dataset follows the fact that it was created in a part of the project aimed at developing "cookbooks". The keywords used to make the dataset findable were agreed upon in SERENA. They include a minimum number of identifier words relating the dataset to the SERENA project, grant number, deliverable number and specific tags of the dataset: "greenhouse gases and climate regulation indicators", "net ecosystem productivity", "ecosystem respiration", "gross primary productivity", "GPP MODIS", "monthly temperature", "soil moisture". The dataset consists of a single version since there are no upgradable versions to the dataset in mind.

A metadata schema file (SERENA_EJPSOIL_GHG_NEP_cookbook_metadata.txt) has been created explaining the structure of the file and it provides details on the variables present.

#### 1.50.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP-Soil communities (https://doi.org/10.5281/zenodo.14002496). It complies with the Zenodo's platform and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset uses an open-source, platform-independent format (.csv) that can be open in a number of applications both open (e.g., LibreOffice Calc) and proprietary (MS Excel).

#### 1.50.2.3 Making data interoperable:

As stated, the dataset is provided as a comma separated values file with associated metadata. The format is interoperable across software and platforms. The terms follow common usage in carbon fluxes research.

#### 1.50.2.4 Increase data re-use (through clarifying licenses):

The dataset licence is the Creative Commons Attribution license (CC-BY). The data comes from applying the SERENA GHG cookbook which applied different filters to eliminate NEP values inconsistencies. No further QA has been applied to the dataset.

### 1.50.3 Allocation of resources

The costs associated to make it the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

### 1.50.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

### 1.50.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

<figure><img src="image_119.png" /></figure>
### 1.50.6 References

Zhang, Y., Liang, S., Ma, H., He, T., Wang, Q., Li, B., Xu, J., Zhang, G., Liu, X., Xiong, C., 2023. Generation of global 1-km daily soil moisture product from 2000 to 2020 using ensemble learning. Earth Syst. Sci. Data Discuss. 2023, 1–37.

## 1.51 Deliverable T2.2 soil threats and soil ecosystem services of interest in SERENA

### 1.51.1 Data summary

In Task 2.2, we discussed definitions of soil threats (ST) and soil-based ecosystem services (SES) to be further analysed in SERENA. In this process, we held information from literature search and the results from the national prioritization of the ST and SES. This dataset is an Excel with sheets of literature study and the national prioritization from participation countries.

### 1.51.2 FAIR data

#### 1.51.2.1 Making data findable, including provisions for metadata:

The final report (https://doi.org/10.5281/zenodo.10389896) and the Excel file (https://doi.org/10.5281/zenodo.13940435) were uploaded in the open-source repository ZENODO. The name of the dataset follows the conventions agreed upon in SERENA. The keywords used to make the dataset findable were agreed upon in SERENA: "SERENA", "Horizon 2020 grant number 862695", deliverable number "D2.2" and specific tags of the dataset: "description and definition", "soil threats and ecosystem services", "prioritization".

#### 1.51.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the SERENA/EJP-Soil communities. It complies with the Zenodo's platform, and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

#### 1.51.2.3 Making data interoperable:

As stated, the dataset is provided as an Excel file with associated metadata. The format is interoperable across software and platforms. The terms follow common usage.

#### 1.51.2.4 Increase data re-use (through clarifying licenses):

The dataset licence is the Creative Commons Attribution license (CC-BY).

#### 1.51.3 Allocation of resources

The costs associated to make the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

#### 1.51.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

#### 1.51.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

<figure><img src="image_120.png" /></figure>
# 1.52 Map of SESs/STs Bundles of Emilia-Romagna, Italy

## 1.52.1 Data summary

The present dataset corresponds to 2 maps of soil-based ecosystem services for the area of Emilia-Romagna, Italy. The maps are the result of applying the bundles cookbook developed in SERENA/EJP-Soil to the area of interest (AOI) input data ("SERENA_EJPSOIL_IT_bundles.gpkg"); and a variation of the cookbook to the other geodata (SERENA_EJPSOIL_IT_bundles_previous.gpkg). The resulting bundles are clusters of SESs/STs where the intra-cluster variability is lower than the inter-cluster variability in the mean values of the selected SESs/STs to identify the bundles.

SERENA had, among others, the objective of developing methods to calculate and map soil-based ecosystem services and soil threats and their bundles.

For this purpose, the bundles in the present geodatasets were determined from spatially-explicit datasets (more details below)

The 2 geodatasets of bundles are in vector format and are:

<table><thead><tr><td>Bundles' geodataset name</td><td>Description</td><td>Size</td></tr></thead><tbody><tr><td>SERENA_EJPSOIL_IT_bundles.gpkg</td><td>Soil-based ecosystem services map, resulting from applying the bundles cookbook developed in SERENA/EJP-Soil to the area of interest (AOI) input data of Emilia-Romagna, Italy. The resulting bundles are clusters of SESs/STs where the intra-cluster variability is lower than the inter-cluster variability in the mean values of the selected SESs/STs to identify the bundles. The bundles in the present geodatasets were determined from 3 spatially-explicit datasets to which the bundles cookbook was applied within the AOI: (1) Emilia-Romagna soil C stock, (2) Emilia-Romagna soil erosion control, and (3) Net ecosystem productivity in early summer for Emilia-Romagna developed in SERENA. The nominal spatial resolution of the dataset is 500m. Because the indicator is rendered spatial in association only with wheat fields (either common or durum), several "gaps" are present. Other big gaps appear as a result of a data gaps in the layers used to calculate NEP.</td><td>455.4 Mb</td></tr></tbody></table>

<figure><img src="image_121.png" /></figure>
<table><tr><td>SERENA_EJPSOIL_IT_bundles_prevous.gpkg</td><td>Bundle map in which the input geodata are 7 soil-based ecosystem services. Details are found in Medina-Roldán et al. 2024 Geoderma, 448, 116962</td><td>17.8 Mb</td></tr></table>

Although the dataset needs further validation, it is useful as a research tool to investigate co-occurrence of SESs and STs for the SERENA consortium and other researchers.

## 1.52.2 FAIR data

### 1.52.2.1 Making data findable, including provisions for metadata:

The geodatasets (geopackage files: see names above) are available in the general science repository Zenodo which provides a DOI identifier (https://doi.org/10.5281/zenodo.14001697). The name of the datasets follows the convention agreed upon in SERENA's WP3 (project_country_soilecosystemservice_indicator). The keywords used to make the dataset findable were agreed upon in SERENA. They include specific tags of the dataset: "soil-based ecosystem services co-occurrence", "Emilia-Romagna". The dataset correspond to single versions since there are no upgradable versions to the datasets in mind.

The project information will be also indicated in the metadata of the dataset: SERENA project, grant number, deliverable number.

A metadata schema has been created following the QGIS schema, and is part of the dataset file properties. The Metadata includes the following info:

- Title

- Object type

- Language

- Abstract/description of the dataset

- Dataset keyword categories

- Dataset keywords

- Licence type

- Geographic info: layer CRS, extent, etc.

- Contact

- Links (at the moment I included a link to the sharepoint, but when the information to add to Zenodo is ready this will be used)

- History/version

### 1.52.2.2 Making data openly accessible:

The dataset was uploaded to Zenodo in the EJP-Soil Serena community (https://doi.org/10.5281/zenodo.14001697). It complies with the Zenodo's platform, and it follows the information that was agreed upon in the Final Meeting of the project (see previous section).

The dataset uses an open-source, platform-independent format (geopackage) that can be opened in many GIS applications both open (e.g., QGIS) and proprietary (ArcGIS).

The data are open accessible in the Zenodo repository after upload, yet a 6-months period of embargo will apply in view of potential publications.

<figure><img src="image_122.png" /></figure>
#### 1.52.2.3 Making data interoperable:

As stated, the dataset is provided as a geopackage file with associated metadata. The format is interoperable across GIS software and platforms. The terms follow common usage in ecosystem services bundles research.

#### 1.52.2.4 Increase data re-use (through clarifying licenses):

The dataset licence is the Creative Commons Attribution license (CC-BY 4.0 https://creativecommons.org/licenses/by/4.0/). The data comes from applying the SERENA bundles cookbook. No further QA has been applied to the dataset.

### 1.52.3 Allocation of resources

The costs associated to make the dataset FAIR as well as for long-term preservation are unknown, and the creator of the dataset does not know how to estimate this. SERENA has a data project manager, but once in ZENODO the data are not expected to be curated at the long-term.

### 1.52.4 Data security

Data security in terms of storage depends on the repository (ZENODO).

### 1.52.5 Ethical aspects

The data is not restricted by legal and ethical issues as far as it is generated in the context of biophysical ecological indicators research.

### 1.52.6 References

Medina-Roldán, E., Lorenzetti, R., Calzolari, C., & Ungaro, F. (2024). Disentangling soil-based ecosystem services synergies, trade-offs, multifunctionality, and bundles: A case study at regional scale (NE Italy) to support environmental planning. Geoderma, 448, 116962.

<figure><img src="image_123.png" /></figure>