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Dataset of major hydropower plants in Austria. Provides location, capacity, turbine technology, head, flow, and further data.

# Reproductive data Fagus sylvatica: Widespread masting breakdown in beech [https://doi.org/10.5061/dryad.qz612jmps](https://doi.org/10.5061/dryad.qz612jmps) This dataset, used in the Global Change Biology article "Widespread breakdown in masting in European beech due to rising summer temperatures", contains 50 time series of population-level annual reproductive data by European beech (*Fagus sylvatica*, L) across Europe. The dataset builds on the open-access dataset [MASTREE+](https://doi.org/10.1111/gcb.16130), and expands it for European beech. ## Description of the data The dataset column names follow that of MASTREE+. A description of MASTREE+ column names (Modified from Table 1 in the [MASTREE+ article)](https://doi.org/10.1111/gcb.16130): | *Columns* | *Description* | *Contains NA?* | | :-------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------- | | Alpha\_Number | Unique code associated with each original source of data, that is, the publication, report or thesis containing extracted data, or the previously unpublished data set included in MASTREE+. | No | | Segment | Temporal segment of a time-series containing gaps (note that years with no observations are not recorded). Individual timeseries can consist of multiple segments. | No | | Site\_number | Code to differentiate multiple sites from the same original source (Alpha\_Number/Study\_ID). | No | | Variable\_number | Code to differentiate multiple measures of reproductive output from the same species-site combination (e.g. where seeds and cones were recorded separately). | No | | Year | Year of observation. | No | | Species | Species identifier, standardised to The Plant List nomenclature. ‘spp.’ is used to indicate a record identified to the genus level only. ‘MIXED’ indicates a non-species-specific community-level estimate of annual reproductive effort. | No | | Species\_code | Six-character species identifier. | No | | Mono\_Poly | Monocarpic (semelparous) or Polycarpic (iteroparous) species. | No | | Value | The measured value of annual reproductive output. | No | | VarType | Continuous or ordinal data. Continuous time-series are recorded on a continuous scale. Ordinal series are recorded on an ordered categorical scale. All ordinal series are rescaled to start at 1 (lowest reproductive effort) and to contain only integer values. | No | | Max\_value | The unit of measurement, where VarType is continuous (otherwise: NA). | No | | Unit | The maximum value in a time-series. | No | | Variable | Categorical classification of the measured variable. Options limited to: cone, flower, fruit, seed, pollen, total reproduction organs. | No | | Collection\_method | Classification of the method used to measure reproductive effort. Options are limited to: cone count, cone scar count, flower count, fruit count, fruit scar sound, seed count, seed trap, pollen count, lake sediment pollen count, harvest record, visual crop assessment, other quantification, dendrochronological reconstruction. | No | | Latitude | Latitude of the record, in decimal degrees. | No | | Longitude | Longitude of the record, in decimal degrees. | No | | Coordinate\_flag | A flag to indicate the precision of the latitude and longitude. A = coordinates provided in the original source B = coordinates estimated by the compiler based on a map or other location information provided in the original source C = coordinates estimated by the compiler as the approximate centre point of the smallest clearly defined geographical unit provided in the original source (e.g. county, state, island), and potentially of low precision. | No | | Site | A site name or description, based on information in the original source. | No | | Country | The country where the observation was recorded. | No | | Elevation | The elevation of the sample site in metres above sea level, where provided in the original source (otherwise: NA). | Yes | | Spatial\_unit | Categorical classification of spatial scale represented by the record, estimated by the compiler based on information provided in the original source. stand = <100 ha, patch = 100–10,000 ha, region = 10,000–1,000,000 ha, super-region = >1,000,000 ha. | No | | No\_indivs | Either the number of monitored individual plants, or the number of litter traps. NA indicates no information in the original source, and 9999 indicates that while the number of monitored individuals was not specified, the source indicated to the compiler that the sample size was likely ≥10 individuals or litter traps. | No | | Start | The first year of observations for the complete time-series, including all segments. | No | | End | The final year of observations for the complete time-series, including all segments. | No | | Length | The number of years of observations. Note that may not be equal to the number of years between the Start and End of the time-series, due to gaps in the time-series. | No | | Reference | Identification for the original source of the data. | No | | Record\_type | Categorisation of the original source. Peer-reviewed = extracted from peer reviewed literature Grey = extracted from grey literature Unpublished = unpublished data. | No | | ID\_enterer | Identification of the original compiler of the data. AHP, Andrew Hacket-Pain; ES, Eliane Schermer; JVM, Jose Moris; XTT, Tingting Xue; TC, Thomas Caignard; DV, Davide Vecchio; DA, Davide Ascoli; IP, Ian Pearse; JL, Jalene LaMontagne; JVD, Joep van Dormolen. | No | | Date\_entry | Date of data entry into MASTREE+ in the format yyyy-mm-dd. | No | | Note on data location | Notes on the location of the data within the original source, such as page or figure number. If not provided, NA. | Yes | | Comments | Additional comments. If not provided, NA. | Yes | | Study\_ID | Unique code associated with each source of data. M\_ = series extracted from published literature; A\_ = series incorporated from Ascoli et al. (2020), Ascoli, Maringer, et al. (2017) and Ascoli, Vacchiano, et al. (2017); PLK\_ = series incorporated from Pearse et al. (2017); D\_ = unpublished data sets. NA is attributed if no study ID has been previously associated with this time-series in MASTREE+ v.1. | Yes | Note that the new beech reproductive data has been assigned an arbitrary Alpha_Number for the purpose of this study. Future MASTREE+ updates which incorporate this new data may alter the time series ID columns (e.g. Alpha_Number, Site_number, Variable_number). MASTREE+ updates can be found on [GITHUB](https://github.com/JJFoest/MASTREEplus). Climate change effects on tree reproduction are poorly understood even though the resilience of populations relies on sufficient regeneration to balance increasing rates of mortality. Forest-forming tree species often mast, i.e. reproduce through synchronised year-to-year variation in seed production, which improves pollination and reduces seed predation. Recent observations in European beech show, however, that current climate change can dampen interannual variation and synchrony of seed production, and that this masting breakdown drastically reduces the viability of seed crops. Importantly, it is unclear under which conditions masting breakdown occurs, and how widespread breakdown is in this pan-European species. Here, we analysed 50 long-term datasets of population-level seed production, sampled across the distribution of European beech, and identified increasing summer temperatures as the general driver of masting breakdown. Specifically, increases in site-specific mean maximum temperatures during June and July were observed across most of the species range, while the interannual variability of population-level seed production (CVp) decreased. The declines in CVp were greatest where temperatures increased most rapidly. Additionally, the occurrence of crop failures and low-seed years has decreased during the last four decades, signalling altered starvation effects of masting on seed predators. Notably, CVp did not vary among sites according to site mean summer temperature. Instead, masting breakdown occurs in response to warming local temperatures (i.e. increasing relative temperatures), such that the risk is not restricted to populations growing in warm average conditions. As lowered CVp can reduce viable seed production despite the overall increase in seed count, our results warn that a covert mechanism is underway that may hinder the regeneration potential of European beech under climate change, with great potential to alter forest functioning and community dynamics.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DWD.MPI-ESM1-2-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutscher Wetterdienst, Offenbach am Main 63067, Germany (DWD) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

# Data and code for: Modelling the carbon balance in bryophytes and lichens: presentation of PoiCarb 1.0, a new model for explaining distribution patterns and predicting climate-change effects ## Description of the data and file structure ### **File list** · Nikolic_et_al_2023_CO2_curve_data_Lange_2002.csv · Nikolic_et_al_2023_Light_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Tempetarure_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Tempetarure_dark_respiration_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Water_curve_data_Lange_2004.csv · Nikolic_et_al_2023_Water_dark_respiration_curves_data_Lange_2002.csv · Nikolic_et_al_2023_Microclimatic_input_data_17-Sept-01-Oct-1993_Lange_2004.csv · Nikolic_et_al_2023_Parameters_for_the_model_P_aurata_and_L_muralis.csv · Nikolic_et_al_2023_Microclimatic_input_data_17-24-Sep-93.csv · Nikolic_et_al_2023\_ Microclimatic_input_data_24-Sep-1-Oct-93.csv · Nikolic_et_al_2023_Getting_parameters_from_response_curves.R · Nikolic_et_al_2023_PoiCarb_model.R ### **File descriptions** **Nikolic_et_al_2023_CO2_curve_data_Lange_2002.csv** Data of measured responses of CO2-exchange rates to different CO2 levels. Gas-exchange measurements were made on the lichen *Protoparmeliopsis muralis (Lange, 2002).* We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: CO2abs – CO2 concentration in ppm A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Light_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates (net photosynthesis and dark respiration) to different light (PAR) levels. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: PAR - Photosynthetic Active Radiation expressed in µmol m-2 s-1 A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Tempetarure_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates (net photosynthesis and dark respiration) to different temperature levels. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: Tcuv - Temperature in Celsius degrees measured A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Tempetarure_dark_respiration_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates (dark respiration) to different temperature levels. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: Tcuv - Temperature in Celsius degrees measured A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Water_curve_data_Lange_2004.csv** Data of measured responses of CO2-exchange rates to changes in lichen water content. Gas-exchange measurements were made on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004). We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: WC - Relative Water content expressed in % of the dry mass A – The instantaneous gas-exchange rate in nmolg-1s-1 **Nikolic_et_al_2023_Water_dark_respiration_curves_data_Lange_2002.csv** Data of measured responses of CO2-exchange rates (dark respiration) to changes in lichen water content. Gas-exchange measurements were made on the lichen *Protoparmeliopsis muralis (Lange, 2002).* We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: WC - Relative Water content expressed in % of the dry mass A – The instantaneous gas-exchange rate in µmol m-2 s-1 **Nikolic_et_al_2023_Microclimatic_input_data_17-Sept-01-Oct-1993_Lange_2004.csv** Microclimatic data together with gas-exchange measurements data which we used for model validation and also to run the climate change experiments examples. There are data for 15 days of in situ gas-exchange measurements on the broad-lobed lichen *Crocodia aurata *from a montane rainforest (at ca 1200 m a.s.l) in Panama (Lange et al., 2004) together with the following climatic factors: air temperature, PAR, and lichen water content, determined at the same time as the CO2-exchange measurements. We did not have access to the original data, so we used WebPlotDigitizer to extract data points from the published data visualizations. Explanation for each column in the file: Datum – date of each record in the form: 17-Sep-93 time – date and time of each record PAR - Photosynthetic Active Radiation expressed in µmol m-2 s-1 T - Temperature in Celsius degrees measured WC - Relative Water content expressed in % of the dry mass CO2 - CO2 levels expressed in ppm Ameasured – Measured gas-exchange rate in nmolg-1s-1 dWC – Difference in water content between two measurements (this we used to determine coefficient k, would not be needed if you have the water loss curve measured on different VPDs) coef_k – drying speed coefficient start – contains the date and time for the beginning of the daylight for each day, the rest of the column is filled with NAs (NA stands for not available, this is how the missing values are represented in R). This column is added to the original data to be able to plot the periods of daylight and night in different colors end – contains the date and time for the end of the daylight for each day, the rest of the column is filled with NAs (NA stands for not available, this is how the missing values are represented in R). This column is added to the original data to be able to plot the periods of daylight and night in different colors day_night - contains the string value either day, night or NA (NA stands for not available, this is how the missing values are represented in R), this column is added to the original data to be able to plot the periods of daylight and night in different colors **Nikolic_et_al_2023_Parameters_for_the_model_P_aurata_and_L_muralis.csv** Table with parameters we used for validation. To use the PoiCarb 1.0 model, you will need a table like this with parameters for your species. You can obtain the same table by running the **Nikolic_et_al_2023_Getting_parameters_from_response_curves.R** Explanation for each column in the file: LC_par_a, LC_par_b, LC_par_c are the columns containing parameters from the light-response curve; WC_par_a, WC_par_b, WC_par_c are the columns containing parameters from the water-response curve; WC_Rd_par_a, WC_Rd_par_b, WC_Rd_par_c are the columns containing parameters from the dark respiration water-response curve; CO2_par_a, CO2_par_b, CO2_par_c are the columns containing parameters from the CO2-response curve; T_par_a, T_par_b, T_par_c are the columns containing parameters from the temperature-response curve; T_Rd_par_a, T_Rd_par_b are the columns containing parameters from the dark respiration temperature-response curve. **Nikolic_et_al_2023_Microclimatic_input_data_17-24-Sep-93.csv** **Nikolic_et_al_2023\_ Microclimatic_input_data_24-Sep-1-Oct-93.csv** These two files contain microclimatic data, the same columns and data as in Nikolic_et_al_2023_Microclimatic_input_data_17-Sept-01-Oct-1993_Lange_2004.csv, just separated into two different files, it was better for plotting. **Nikolic_et_al_2023_Getting_parameters_from_response_curves.R** R script to be used to get the parameters from the environmental gas exchange response curves and drying speed curves. **Nikolic_et_al_2023_PoiCarb_model.R** PoiCarb model R script. The script is commented, in case something is not clear enough or you have questions write to the author (). ## Sharing/Access information Data was derived from the following sources: * Lange, O. L. 2002. Photosynthetic productivity of the epilithic lichen *Lecanora muralis*: Long-term field monitoring of CO2 exchange and its physiological interpretation. I. Dependence of photosynthesis on water content, light, temperature, and CO2 concentration from laboratory measurements. *Flora *197: 233–249. * Lange, O. L., B. Büdel, H. Zellner, G. Zotz, and A. Meyer. 1994. Field measurements of water relations and CO2 exchange of the tropical, cyanobacterial basidiolichen *Dictyonema glabratum* in a Panamanian rainforest*. *Botanica Acta* 107: 279–290. ## Code/Software There are two R scripts that can be downloaded together with the data. Nikolic_et_al_2023_Getting_parameters_from_response_curves.R and Nikolic_et_al_2023_PoiCarb_model.R. Both scripts are commented (have explanations and notes how to use them). Premise Bryophytes and lichens have important functional roles in many ecosystems. Insight into how their CO2 exchange responds to climatic conditions is essential for understanding current and predicting future productivity and biomass patterns, but responses are hard to quantify at time-scales beyond instantaneous measurements. We present PoiCarb 1.0, a model to study how CO2 exchange rates of these poikilohydric organisms change through time as a function of weather conditions. Methods PoiCarb simulates diel fluctuations of CO2 exchange and estimates long-term carbon balances, identifying optimal and limiting climatic patterns. Modelled processes are net photosynthesis, dark respiration, evaporation and water uptake. Measured CO2-exchange responses to light, temperature, atmospheric CO2 concentration, and thallus water content (calculated in a separate module) are used to parameterise the model's carbon module. We validated the model by comparing modelled diel courses of net CO2 exchange to such courses from field measurements on the tropical lichen Crocodia aurata. To demonstrate the model's usefulness, we simulated potential climate-change effects. Results Diel patterns were reproduced well and modelled and observed diel carbon balances were strongly positively correlated. Simulated warming effects via changes in metabolic rates were consistently negative, while effects via faster drying were variable, depending on the timing of hydration. Conclusions Being able to reproduce the weather-dependent variation in diel carbon balances is a clear improvement compared to simple extrapolations of short-term measurements or potential photosynthetic rates. Apart from predicting climate-change effects, future uses of PoiCarb include testing hypotheses about distribution patterns of poikilohydric organisms and guiding species' conservation. Usage Notes We here present the data and code used in this paper. The list of data files together with their detailed explanations can be found in the README.PDF

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

SECURES-Energy Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided. The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation. Further information: Project website SECURES: https://www.secures.at/ All project-related publications: https://www.secures.at/publications Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf The SECURES-Energy dataset provides variables visible in the table. Hourly profiles ERA5-Land 1981-2010 Hourly profiles RCP 4.5/RCP 8.5 2011-2100 Production profiles: Variable Short name Unit Temporal resolution Photovoltaics pv - hourly Wind onshore wind - hourly Wind offshore wind_offshore - hourly Hydro run-of-river hydro_ror - hourly Demand profiles: Variable Short name Unit Explanation Temperature temperature °C Population-weighted mean temperature (2 m) Rounded temperature rounded_temperature °C Temperature values rounded to zero decimal places Daytype day type - weekdays = typeday 0; Saturday or day before a holiday = typeday 1; Sunday or holiday = typeday 2 Month month - The column “month” refers to the month of the year. 1 = January, 2 = February etc. Season season - 0 = Summer (15/05 - 14/09) 1 = Winter (1/11 - 20/3) 2 = Transition (21/3 - 14/5 & 15/9 - 31/10) Load e-mobilty load_emobility - E-mobility electricity demand profile, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Non-metallic minerals non_metallic_minerals - Electricity demand profile of the industrial sector non-metallic minerals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Paper paper - Electricity demand profile of the industrial sector paper, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Iron and steel iron_and_steel - Electricity demand profile of the industrial sector iron and steel, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Chemicals and petrochemicals chemicals_and_petrochemicals - Electricity demand profile of the industrial sector chemicals and petrochemicals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) Food and tobacco food_and_tobacco - Electricity demand profile of the industrial sector food and tobacco, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent) SHW residential shw_residential - Electricity demand profile for sanitary hot water in the residential sector, normalized to an annual demand of 1,000,000 (non-weather-dependent) SHW tertiary shw_tertiary Electricity demand profile for sanitary hot water in the tertiary sector, normalized to an annual demand of 1,000,000 (non-weather-dependent) Cooling residential cooling_residential - Electricity demand profile for cooling in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Heating residential heating_residential - Electricity demand profile for heating in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Cooling tertiary cooling_tertiary - Electricity demand profile for cooling in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Heating tertiary heating_tertiary - Electricity demand profile for heating in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent) Rest rest - Rest electricity demand profile, normalized to an annual demand of 1,000,000 (non-weather-dependent) Exogenous H2 exogenous_H2 - Electricity demand profile for electrolysis (flat profile), normalized to an annual demand of 1,000,000 (non-weather-dependent) Total total - Total electricity demand profile containing all components above (e-mobility, industry, residential heating, residential sanitary hot water, residential cooling, tertiary heating, tertiary sanitary hot water, tertiary cooling, rest, and exogenous H2 electricity demand), normalized to an annual demand of 10,000,000 in the reference year 2010 Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO. Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2]. SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (RCP 4.5 and RCP 8.5, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the spatial aggregation level NUTS0 (country-wide). The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shapefiles of the different NUTS levels. Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883. The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532. [1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories. [2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report – Open Data Set for the EU28. 2019.

TECHNICAL INFO No data quality control has been carried out. No gap-filling has been applied. Detailed information about the site and deployment can be found in the Technical documentation of the urbisphere-Paris campaign. ACKNOWLEDGEMENTS Authors thank SIRTA/LMD staff for providing support and facilities; ATMO-TNA-3—0000000125 funding; Meteo France for hosting the instrumentation at Meteo France stations. COPYRIGHT NOTICE Copyright Jörn Birkmann, Andreas Christen, Nektarios Chrysoulakis, and Sue Grimmond. Some rights reserved. CREATOR NOTICE This work is owned by the Principal Investigators (PIs) of the Urbisphere project. ATTRIBUTION NOTICE The [creation and] curation of this work has been funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 855005). DISCLAIMER NOTICE The use of the work is at the user's own risk. The authors, the involved institutions, and/or the European Research Council accept no liability for material or non-material damage arising from the use or non-use or from the use of incorrect or incomplete information in this work. The authors, the involved institutions, and/or the European Research Council are not responsible for any use that may be made of the information in this work. The legal provisions remain unaffected. MATERIAL NOTICE The notices cover data in databases, text and images contained in the work. MATERIAL URI Urbisphere project Original logger data files from radiometer measurements of shortwave irradiance and longwave irradiance at Nangis (Départment 77) in the rural area to the SE of Greater Paris. Measurements were taken at the MétéoFrance weather station at Nangis on the airfield at Nangis-les-Loges (ID 77211001)

Lachgas (N2O) ist ein klimarelevantes Spurengas, welches auch zur Ozonzerstörung in der Stratosphäre beiträgt. Es herrscht Konsens darüber, dass eine Reduktion der N2O Emissionen anzustreben ist. Hauptquelle der N2O Freisetzung in Deutschland sind landwirtschaftlich genutzte Böden. Aufgrund des hohen N-Inputs über die Düngung wird die N2O-Emission stimuliert, da der Stickstoff als Substrat für die wesentlichen Prozesse der N2O-Bildung in Böden wie die Nitrifikation und Denitrifikation dient. Neben den hohen N2O-Emissionen während der Vegetationsperiode kann auch im Winter eine hohe N2O-Freisetzung in Zusammenhang mit Frost-Tau Zyklen auftreten. Der Anteil dieser Winteremissionen an der Jahresemission beträgt in Deutschland etwa 50%. Deshalb sind annuelle Datensätze eine unerlässliche Voraussetzung für die zuverlässige Bewertung von N2O-Reduktionsstrategien in Gegenden mit Winterfrost. Für landwirtschaftlich genutzte Böden liegt bereits eine Vielzahl an Untersuchungen zur Minderung der N2O-Freisetzung vor. Jedoch wurde die N2O-Freisetzung aus gemüsebaulich genutzten Böden nur selten untersucht. Keine der bisher durchgeführten Spurengasmessungen im intensiven Gemüsebau ist repräsentativ für die klimatischen Bedingungen Süddeutschlands. Durch den hohen N-Düngerinput (der zu hohen Gehalten an mineralischem Stickstoff im Boden führt) und stickstoffreiche Ernterückstände im Spätherbst sind hohe N2O-Jahresemissionen aus diesen Flächen zu erwarten. Im Rahmen dieser Studie wurden die N2O-Flussraten zwei Jahre lang in mindestens wöchentlicher Auflösung auf einer Gemüsebaufläche in Süddeutschland mit der geschlossenen Kammermethode ermittelt. Während der beiden Versuchsjahre wurde jeweils ein Satz Kopfsalat und darauffolgend ein Satz Blumenkohl angebaut. Um Aufschluss über die N2O-Quellen (Dünger, Ernterückstände, bodeninterne Mineralisation) zu erhalten wurde zusätzlich eine Studie mit 15N markiertem Ammonsulfatsalpeter (ASS) und Austausch markierter und unmarkierter Erntereste durchgeführt. Ferner wurden verschiedene Strategien zur Reduktion der N2O-Emissionen wie Düngerreduktion, Zusatz eines Nitrifikationshemmstoffes (3,4-Dimethylpyrazolphosphat, DMPP) und eine Depotdüngung hinsichtlich ihres Potentials zur Reduktion der N2O-Emissionen auf Jahresbasis getestet. Die Reduktion der N2O Emissionen sollte bei diesen Strategien wie folgt erreicht werden: Bei einer Reduktion des Dünger N-Inputs wurde eine Absenkung der Menge an mineralischem N im Boden erwartet und dadurch niedrigere Substratkonzentrationen für N2O produzierende Mikroorganismen. DMPP ist ein chemischer Hemmstoff, der die Nitrifikation auf enzymatischer Ebene inhibiert. Bei der Depotdüngung wird ammoniumreicher Dünger hoch konzentriert in Form eines Bandes im Boden abgelegt. Die hohen Ammoniumkonzentrationen sollen durch Ihre Toxizität die Nitrifikanten ebenfalls hemmen. Aufgrund der gehemmten Nitrifikation sollte einerseits die N2O-Bildung während der Nitrifikation direkt vermindert und andererseits die Denitrifikation über das geringere Nitratangebot limitiert werden. Es wurde eine sehr hohe zeitliche Variabilität der N2O-Flussraten beobachtet. Ausgeprägte Emissionsmaxima traten vor allem nach N-Düngungsmaßnahmen, nach der Einarbeitung von Ernterückständen (besonders in Kombination mit der N-Düngung), nach Wiederbefeuchtung von trockenem Boden im Hochsommer sowie nach dem Auftauen von gefrorenem Boden im Winterhalbjahr auf. Die kumulativen Jahresemissionen in der konventionell (breitflächig) gedüngten Variante beliefen sich im ersten und zweiten Versuchsjahr auf 8.8 und 4.7 kg N2O-N ha-1 a-1. Die N-Düngung erfolgte hier nach dem kulturbegleitenden Nmin Sollwertsystem. Die N2O-Emissionsfaktoren lagen mit 1.6% und 0.8% innerhalb des Unsicherheitsbereiches von 0.3 - 3%, den der Weltklimarat (IPCC; 2006) in seinen Richtlinien zur Berechnung Nationaler Treibhausgasinventare angibt. Es konnte ein positiver Zusammenhang zwischen den mittleren Nitratgehalten des Oberbodens und den kumulativen N2O-Emissionen in den beiden Versuchsjahren (r2=0.44 und 0.68) sowie zwischen den N-Überschüssen und den kumulativen N2O Emissionen der Düngersteigerungsreihe (r2=0.95) im ersten Versuchsjahr nachgewiesen werden. Eine Reduktion der N-Düngermenge von praxisüblicher Düngung auf Düngung nach dem kulturbegleitenden Nmin Sollwertsystem führte im ersten Versuchsjahr zu einer Minderung der N2O-Jahresemissionen um 17%, die Gemüseerträge wurden durch die verminderte N-Gabe nicht beeinträchtigt. Im zweiten Versuchsjahr wurde die mittlere N2O-Emission bei reduzierter N-Gabe um 10% gesenkt, dieser Effekt war jedoch statistisch nicht abgesichert. Eine weitere Absenkung der Düngermenge um 20% führte zwar zu einer weiteren Minderung der N2O-Emission, allerdings waren im ersten Versuchsjahr dadurch auch die Kopfsalaterträge geringer. Eine weitere Absenkung der Düngermenge ist somit nicht empfehlenswert. Für die DMPP-Anwendung liegen durch diese Arbeit erstmals Jahresdaten zur N2O-Freisetzung vor. Die Anwendung von DMPP verringerte die N2O-Emissionen in den beiden Versuchsjahren signifikant um mehr als 40%. Dieser Effekt trat sowohl während der Vegetationsperiode als auch im Winter auf. Der Grund für die Emissionsminderung im Winter konnte nicht geklärt werden: Der Abbau des Wirkstoffs DMPP ist temperaturabhängig und wird unter den gegebenen Temperaturen im Sommer mit ca. 6 bis 8 Wochen veranschlagt. Die von uns beobachteten Minderungseffekte traten jedoch auch im Winter auf, also noch 3 Monate nach Applikation des Wirkstoffes. Ferner wurde eine ebenfalls verminderte CO2-Freisetzung gemessen, die ein Hinweis auf einen Effekt des DMPP auf heterotrophe Mikroorganismen oder zumindest deren C-Umsatz sein könnte. Aufgrund des hohen N2O-Minderungspotentials scheinen weiterführende Untersuchungen zu funktionellen und strukturellen Veränderungen der mikrobiellen Biomasse nach DMPP-Anwendung sinnvoll. Eine Depotdüngung mit ASS führte nicht zur erhofften Reduktion der N2O Freisetzung auf Jahresbasis. Selbst der Ersatz von ASS durch (nitratfreies) Ammoniumsulfat führte nicht zu einer Reduktion der Emissionen. Vermutlich gehen die relativ hohen Flussraten auf die mikrobiell intakten Bereiche um die Düngerdepots zurück, in denen die Nitrifikation abläuft und in denen durch die hohen Nitratgehalte ideale Bedingungen für denitrifizierende Mikroorganismen herrschten. Nach einem Jahr fand sich ein Großteil des mit dem Dünger ausgebrachten 15N im Boden wieder. Nur 13 - 15% wurden über die marktfähige Ware aufgenommen. 1.4% des 15N gingen in Form von N2O-N verloren. Die Wiederfindungsrate nach einem Jahr betrug 70%. Die Verluste an 15N sind vermutlich auf Nitratauswaschung oder gasförmige Verluste in Form von N2 oder NOx zurückzuführen. Verglichen mit dem Getreideanbau ist die N-Ausnutzung im Gemüsebau also selbst bei optimierter Düngung wesentlich niedriger. Die Messung der 15N Häufigkeit im N2O zeigte, dass der Hauptteil der N2O-Emissionen (38%) aus den Ernteresten des Blumenkohls stammte (genauergesagt Dünger-N, der über die Pflanzen in die Ernteresten eingelagert wurde). 26% und 20% stammten jeweils direkt aus dem Dünger zu Kopfsalat und Blumenkohl. Bodeninterne Quellen waren für 15% der Gesamtemission verantwortlich, während der Beitrag der Erntereste des Kopfsalats aufgrund der geringen C- und N-Mengen vernachlässigbar gering war. Der beträchtliche Anteil der N2O-Emissionen aus den Ernteresten des Blumenkohls wurde darauf zurückgeführt, dass das System zeitweise C-limitiert war und so durch das organische Material Elektronendonatoren zur Verfügung gestellt wurden. Zudem wird beim Abbau von organischer Substanz in Böden O2 verbraucht, was bei hohen Wassergehalten zur Bildung anaerober Kompartimente und so zu idealen Bedingungen für Denitrifikanten führt. Besonders der kombinierte Eintrag von organischer Substanz und mineralischem N-Dünger erhöhte die N2O-Emissionen. Daher wurde in einem Zusatzversuch zu Mangold getestet, inwiefern eine Desynchronisation der Einarbeitung von Ernteresten und der mineralischen N-Düngung durch Wartezeiten (bis zu 3 Wochen) zu einer Emissionsminderung beiträgt. Je länger die Einarbeitung der Erntereste von der N-Düngerapplikation entfernt lag, desto geringer waren auch die N2O-Emissionen, allerdings war dieser Effekt auf Jahresbasis nicht statistisch gesichert. In einem Inkubationsversuch mit Mikrokosmen wurde der Effekt von verschiedenen C/N-Verhältnissen von Blumenkohlernteresten sowie die Einarbeitung reduzierter und erhöhter Mengen modellhaft untersucht. Es zeigte sich, dass aufgrund des generell hohen Nitratangebots in den Kosmen lediglich die verschiedenen Ernterestmengen einen Effekt auf die N2O-Freisetzung zeigten. Die N2O-Emission stieg mit der Menge an Ernteresten an. Insgesamt konnte in dieser Arbeit gezeigt werden, dass im Gemüsebau relativ hohe absolute N2O-Emissionen erwartet werden können, auch wenn der relative Anteil (Emissionsfaktoren) im Rahmen des IPCC-Unsicherheitsbereichs lag. Weitere Untersuchungen sind nötig, um die genauen Wirkungsmechanismen von DMPP auf die Bildung von N2O im Feld zu verstehen. Die vorliegende Studie belegt, dass der Vermeidung von N-Überschüssen und der Entwicklung von Strategien zum Ernterestmanagement im Gemüsebau große Bedeutung zur Reduktion der N2O-Emissionen zukommt. Nitrous oxide (N2O) is a potent greenhouse gas which is also involved in stratospheric ozone depletion. There is consensus that a reduction in N2O emissions is ecologically worthwhile. Agricultural soils are the major source of N2O emissions in Germany. It is known that high N-fertilization stimulates N2O emissions by providing substrate for the microbial production of N2O by nitrification and denitrification in soils. However, outside the vegetation period, winter freeze/thaw events can also lead to high N2O emissions. Winter emissions constitute about 50% of total emissions in Germany. Therefore, annual datasets are a prerequisite for the development of N2O mitigation strategies in regions with winter frost. Many studies have investigated mitigation strategies for N2O emissions from agricultural soils. However, N2O release from vegetable production has seldom been studied. None of the existing trace gas measurements on intensive vegetable production is representative for the climatic conditions of Southern Germany. Due to the high fertilizer N-input (resulting in high levels of mineral N in the soil) and N-rich residues in late autumn, high annual N2O emissions are to be expected. N2O fluxes were measured from a soilcropped with lettuce and cauliflower in Southern Germany by means of the closed chamber method, at least weekly, for two years. An additional study was conducted using 15 N labeled ammonium sulfate nitrate (ASN) fertilizer and exchange of labeled and unlabeled residues to obtain information about the sources (fertilizer, residues, soil internal mineralization) of N2O emissions. Different mitigation strategies such as fertilizer reduction, addition of the nitrification inhibitor 3,4-dimethylpyrazole phosphate (DMPP) and banded fertilization were evaluated with respect to their reduction potential on an annual base. Fertilizer reduction is supposed to decrease the soil mineral N level, reducing the available substrate for N2O producing microorganisms. DMPP is a chemical compound which inhibits nitrification enzymatically. In banded fertilization, ammonium rich fertilizer is applied in a depot. This high concentration is also supposed to inhibit nitrification as it is toxic to microorganisms. N2O emissions should be firstly reduced directly by this inhibition of nitrification and secondly, by a lower nitrate content in soil resulting in less N2O release due to denitrification. A high temporal variability in N2O fluxes was observed with emission peaks after N-fertilization, after the incorporation of crop residues (especially in combination with N-fertilization), after rewetting of dry soil and after thawing of frozen soil in winter. Total cumulative annual emissions were 8.8 and 4.7 kg N2O-N ha-1 a-1 for the first and second experimental year in the conventionally (broadcast) fertilized treatment. This treatment was fertilized according to the German Target Value System. N2O emission factors were 1.6 and 0.8%. This is within the range of 0.3 - 3% which is cited in the Guidelines for the Calculation of National Greenhouse Gas Inventories proposed by the Intergovernmental Panel of Climate Change (IPCC). A positive correlation was found in both years between the mean nitrate content of the top soil and the cumulative N2O emissions of all treatments (r2=0.44 and 0.68) as well as between the N-surpluses and the cumulative N2O emissions of the different fertilizer levels during the first year (r2=0.95). Fertilizer reduction from fertilization according to good agricultural practice following the recommendations of the German Target Value System reduced annual N2O emissions by 17% in the first experimental year without yield reduction. For the second year, the reducing effect was 10%, but statistically not significant. Another fertilizer reduction of a further 20% reduced N2O emissions, but also resulted in lower lettuce yields in the first year. Therefore, an additional fertilizer reduction is not recommendable. This work provides, for the first time, annual datasets on the effect of DMPP-application on N2O emissions. Addition of DMPP significantly reduced annual N2O emissions by > 40% during both years, there was also a pronounced effect, both during the vegetation period and winter. The reason for the reducing effect in winter is not yet clear because the degradation of the active agent DMPP is temperature dependent and should take about 6 to 8 weeks under summer climatic conditions. However, we still observed significant reductions in N2O emissions in winter, about 3 months after the application. Furthermore, a reduction in CO2 release was observed indicating a possible influence on heterotrophic activities or at least on their C-turnover. Due to its high N2O mitigation potential, further investigations concerning the functional and structural changes in microbial biomass after DMPP application are needed. Banded fertilization with ASN did not result in the expected reduction in N2O emissions on an annual base. Even when exchanging the ASN fertilizer by nitrate-free ammonium sulfate, N2O emissions were not diminished. We assume that the high emissions were derived from the microbially intact surroundings of the depots, where nitrification was not inhibited and nitrate concentrations were probably very high, creating ideal conditions for denitrification. After one year, the major part of the fertilizer-15N was found in the soil. Only between 13 -15% of the fertilizer was taken up by the marketable plant parts. 1.4% of the 15N was lost as N2O-N. Total 15N recovery was 70% after one year. The losses of non-recovered N were probably caused by nitrate leaching or as gaseous compounds such as N2 or NOx. Compared to cereal production systems, the N use efficiency of this vegetable production system is much lower, even with an optimized fertilization strategy. The measurement of 15N abundances in the N2O revealed that the most significant part of the emissions (38%) was derived from the fertilizer-N which had been taken up by cauliflower residues. N2O emissions directly derived from lettuce and cauliflower fertilizer contributed 26% and 20% respectively while N2O emissions from soil internal N pools accounted for 15%. The contribution of lettuce residues was negligible due to their low amount of C and N. The reason for the high importance of the cauliflower residues was ascribed to the temporarily C-limitation of the system and the provision of electron donators by organic material. Furthermore, O2 is consumed during their degradation leading to the formation of anaerobic microsites when soil moisture is high. These sites offer ideal conditions for denitrification. Especially the combination of mineral N-fertilization and input of organic substance was found to increase N2O emissions. Therefore, the influence of a de-synchronization of the incorporation of crop residues and the mineral N-fertilization by waiting periods of up to 3 weeks was tested in an additional field trial during the cultivation of chard. The longer the waiting time between incorporation of crop residues and N-fertilizer application was, the lower were the N2O emissions. However, the effect was not statistically significant on an annual base. In an additional microcosm incubation model study, the effect of reduced and increased input as well as of different C/N-ratios of cauliflower residues was analyzed. It was shown that due to the high nitrate level in the microcosms only the amount of residue input has an effect on the N2O emissions. The N2O emissions increased with increased amount of cauliflower residues. Although the emission factors were within the range given by the IPCC, the absolute annual N2O emission was high in intensive vegetable production due to the high N-input. Further research is required in order to fully understand the effect of DMPP on the processes of N2O production in the field. Our study underlines the importance of avoiding N-surpluses and of strategies for residue management to reduce N2O emissions in intensive vegetable production.