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The gCube System - Accounting Aggregator -------------------------------------------------- Accounting Aggregator Smart Executor Plugin This software is part of the gCube Framework (https://www.gcube-system.org/): an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments by favouring the realisation of reuse oriented policies. The projects leading to this software have received funding from a series of European Union programmes including: * the Sixth Framework Programme for Research and Technological Development - DILIGENT (grant no. 004260); * the Seventh Framework Programme for research, technological development and demonstration - D4Science (grant no. 212488), D4Science-II (grant no. 239019),ENVRI (grant no. 283465), EUBrazilOpenBio (grant no. 288754), iMarine (grant no. 283644); * the H2020 research and innovation programme - BlueBRIDGE (grant no. 675680), EGIEngage (grant no. 654142), ENVRIplus (grant no. 654182), Parthenos (grant no. 654119), SoBigData (grant no. 654024); Version -------------------------------------------------- 1.2.0-4.8.0-154717 (2017-12-01) Please see the file named "changelog.xml" in this directory for the release notes. Authors -------------------------------------------------- * Alessandro Pieve (alessandro.pieve-AT-isti.cnr.it), Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). * Luca Frosini (luca.frosini-AT-isti.cnr.it), Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). Maintainers ----------- * Luca Frosini (luca.frosini-AT-isti.cnr.it), Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" - CNR, Pisa (Italy). Download information -------------------------------------------------- Source code is available from SVN: https://svn.research-infrastructures.eu/public/d4science/gcube/trunk/accounting/accounting-aggregator-se-plugin Binaries can be downloaded from the gCube website: https://www.gcube-system.org/ Installation -------------------------------------------------- Installation documentation is available on-line in the gCube Wiki: https://wiki.gcube-system.org/gcube/index.php/SmartExecutor Documentation -------------------------------------------------- Documentation is available on-line in the gCube Wiki: https://wiki.gcube-system.org/gcube/index.php/SmartExecutor Support -------------------------------------------------- Bugs and support requests can be reported in the gCube issue tracking tool: https://support.d4science.org/projects/gcube/ Licensing -------------------------------------------------- This software is licensed under the terms you may find in the file named "LICENSE" in this directory.

ISIMIP-2a climate data cutout provided for Sardinia in the framework of SIM4NEXUS

The five scenarios describe plausible changes in 15 external drivers of the socioecosystem of the Mar Menor and surrounding Campo de Cartagena between 1964 and 2070. These scenarios were developed for evaluation of their impacts on Key Performance Indicators of sustainability using a simulation model based on System Dynamics. This model, developed by Martínez-López et al (2022) can be consulted here. Historic data combined with the Shared Socioeconomic Pathways (SSPs) from the IPCC report ‘Global warming of 1.5°C’ and the Representative Concentration Pathways (RCPs), were used as starting point to develop the model-specific scenarios. The five scenarios are based on SSP 1, SSP2, SSP4 and SSP5 in combination with emission scenarios that will keep global temperature rise below 1.5ºC. The BAU scenario represents a combination of SSP2 without any climate change. The detailed documentation of SSPs from O’Neil et al (http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004) and subsequent expert interviews and input received during stakeholder workshops organised in the framework of the COASTAL project were used to prepare the region-specific time-series of the 15 variables for the Mar Menor and surrounding Campo de Cartagena. The 15 external drivers are: Agricultural revenue per hectare Growth rate of agriculture Percentage of nutrients that are metabolized by the native lagoon ecosystem Average excess of fertilizer use Yearly effectiveness in nutrients reduction of nutrients, soil and water retention measures Electricity Price Mean number of hours per day of photovoltaic electricity production Photovoltaic energy facilities growth rate in Megawatts installed Growth rate of tourism Average percentage of groundwater desalinated Agricultural water demand per hectare Catchment water sources Urban wastewater treatment plant effluents Yearly average of sea water desalination Amount of water transferred from the Tagus river (RCP15ATS)

Parcel soil scan with electric conductivity measurement from Netherlands

The scenarios are developed based on projected climate and socio-economic changes, following the representative concentration pathways (RCPs) and the shared socioeconomic pathways (SSPs) for the region. The Norrström-Baltic SD model analyzes possible future shifts in the annual average conditions of sectoral and natural water system interactions. Such shifts are evaluated based on recent annual averages reflecting the condition of system components. Parameters taken into account are, amongst others, sectoral water availability, water fluxes between sectors and the corresponding nutrient (nitrogen and phosphorus) exchanges, coastal runoff and nitrogen and phosphorous loads ending up in the Baltic Sea. An overview of the model input variables and the parameters that are identified as system external uncertainties that may affect the behavior of the model: - Precipitation: climate change - Agricultural land: Development policies and market forces, food security and trade regulations, population growth and corresponding food demand/diet changes - Built-up land: Development policies and market forces, population growth, regional urbanization level, tourism expansion level - Forest land: Mitigation policies on climate change (i.e. afforestation and/or reforestation to maintain/enhance carbon capture and storage capacity), socio-economic developments leading to sectoral land competition (i.e. deforestation) - Open lands and wetlands: Policies and market forces supporting social and economic development in the region A total of 5 scenarios were developed for the Norrström/Baltic Sea case. One of them represents the ‘Base case’ conditions, while the rest are rooted in the combination of a certain SSP with a climate scenario linked to a certain RCP. The following overview shows the combinations used during the scenario building process: - Scenario 1: SSP1 + RCP 4.5 - Scenario 2: SSP2 + RCP 4.5 - Scenario 3: SSP4 + RCP 4.5 - Scenario 4: SSP5 + RCP 4.5 - Base Case scenario: Continuation into the future of the past-recent long-term average conditions in relation to hydro-climate and land use variables in the SD model. All the scenarios developed for the Norrström-Baltic region are linked to a climate scenario corresponding with RCP4.5, because projected patterns and changes for climate variables under this climate scenario were found to be more consistent with the observed changes in the region than other RCPs. The period 2010-2100 is compared with the normal mean for the period 1961-1990. Each year is compared separately with the long-term annual average precipitation. The xsls file is organized as follows. It comprises three sheets: Precipitation RCP with annual data of changes in annual precipitation (in percentage), precipitation (in million of m3/year and in mm/year); Land cover RCPs and SSPs with scenario data on land cover, annual change in land cover (in percentage), annual land cover areas for the Norrström water management district area, land dover area average for teh Norrström water management district area and average change in land cover compared to the long-term average (in percentage); Input data model with the four input variables (precipitation change rate in hydro-climate scenarios, urban growth rate in socioeconomic scenarios, forest land change rate in socioeconomic scenarios and agricultural land change rate in socioeconomic scenarios) and their change for each scenario (expressed in percentage).

EOMORES is a European innovation project aiming to develop commercial services for monitoring the quality of inland and coastal water bodies, using data from Earth Observation (EO) satellites and in situ sensors to measure, model and forecast water quality parameters. The current data set is a sample of the data generated within the second and third project years (2018, 2019), and consists of Earth Observation (EO) data and in situ data from lakes and coastal areas. For full data sets, please contact the respective contact point listed for each area. Data sets of the first (2017) year of EOMORES have also be submitted. The following is included: - Estonia lakes and coast: in situ data 2019 - Finland: links to repositories of EO data - Italy CNR: sample of EO data 2018 and 2019 - Italy Trasimeno: sample of WISPstation in situ spectral data and water quality parameters data - Lithuania Curonian Lagoon: sample of WISPstation in situ spectral data and satellite based water quality maps - Netherlands Lake Markermeer: sample of EO data 2018 and 2019 (Chl, Secchi, SPM) - Netherlands Lake Paterswoldsemeer: EO data 2015, 2016, 2017 - UK Scotland: links to relevant repositories of in situ data http://eomores-h2020.eu The dataset of the first year can be found at DOI: 10.5281/zenodo.1462850

Changes from version 1.0 Improved iterative procedure for reducing the Priestley-Taylor coefficient in TSEB-PT and DTD Use consistent temperature units (Kelvin) in both input and output Option to use differential temperatures in the One Source Energy Balance model, similarly as in DTD Improved code readability

Parcel soil scan with electric conductivity measurement from Netherlands

ISIMIP-2a climate data cutout provided for Sardinia in the framework of SIM4NEXUS

Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food.  Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector