The SOS-Water D3.1 Data inventory was created as part of the SOS-Water project (sos-water.eu, 10.3030/101059264). The inventory provides an overview of all available water-related remote-sensing datasets relevant for use in the project and their key dataset attributes. It includes relevant services from Copernicus (CGLS, CLMS, C3S), the ESA CCI (lakes, land cover, snow, soil moisture), EUMETSAT (H-SAF), and other national and international organizations (e.g., NASA, NOAA, USGS, FAO). The accompanying report "D3.1 - Data inventory and EO data needs for water resources monitoring" is accessible over the EU Funding & Tenders portal), highlighting mismatches between water resource challenges and the availability of EO/in-situ data.

The island species–area relationship (ISAR) describes how larger islands support more species. Studies on oceanic archipelagos have shown that ISARs assembled over millions of years have predictable shapes. However, it remains unclear how rapidly “classic” ISARs develop, and how they are formed on much younger systems. Here, we compile a dataset for the fish communities of 79 postglacial peri-Alpine lakes, and report that an ISAR with a classical shape has formed de novo in less than 15,000 years. Despite their very young age, these lakes exhibit an ISAR mirroring older systems, with a characteristic asymptotic shape. Immigration responds primarily to area and saturates, whereas speciation is primarily driven by lake depth. This young ISAR has been reshaped by anthropogenic activities, with species introductions erasing its upper limit. We demonstrate that ISARs can develop rapidly after habitat formation in semi-isolated systems, offering insights into the assembly of ecological patterns.

Supplementary Table 1 - Overview of sampling metadata and sampling sites. (Suppl_Table_1.xlsx)Supplementary Table 2 – Raw assigned ASV Table. Assignment was done using Decipher trained on the MIDORI2 database. Numbers are read numbers for this ASV within this sample. (Suppl_Table_2.xlsx)Supplementary Table 3 – Raw assigned ASV Table after correcting for contamination by removing read numbers found in negative controls from ASVs, as wella s removal of NUMTs. Assignment was done using Decipher trained on the Midori2 database. Numbers are read numbers for this ASV within this sample. (Suppl_Table_3.xlsx)Supplementary Table 4 - Species and ASV richness along the Adriatic-Ligurian MDD. The corrected ASVs are corrected for the number of sites within the basin.(Suppl_Table_4.xlsx)Supplementary Table 5 – Summary of GDM outputs for different models. (Suppl_table_5.xlsx)Supplementary Table 6 - List of Haplotypes, corresponding ASV_IDs, slope of occurrence of the asv and frequency per slope. (Suppl_Table_6.xlsx)Supplementary Figures 4 - 21 - Geographic distribution of different species based on ASV (Amplicon Sequence Variant) detections. The figure presents spatial maps displaying the occurrence of all ASVs across different locations in Northern Italy. The plotted regions include coastal areas and the Po River basin, distinguished by different colors. Longitude (°E) and latitude (°N) coordinates are provided to indicate the spatial extent of the study area. The underlying base map includes hydrological features such as rivers. The species shown did undergo filters, but not all of them were included in intraspecific diversity analyses. (Suppl_Fig_4_21.pdf)

Supplementary Table 1 - Overview of sampling metadata and sampling sites. (Suppl_Table_1.xlsx)Supplementary Table 2 – Raw assigned ASV Table. Assignment was done using Decipher trained on the MIDORI2 database. Numbers are read numbers for this ASV within this sample. (Suppl_Table_2.xlsx)Supplementary Table 3 – Raw assigned ASV Table after correcting for contamination by removing read numbers found in negative controls from ASVs, as wella s removal of NUMTs. Assignment was done using Decipher trained on the Midori2 database. Numbers are read numbers for this ASV within this sample. (Suppl_Table_3.xlsx)Supplementary Table 4 - Species and ASV richness along the Adriatic-Ligurian MDD. The corrected ASVs are corrected for the number of sites within the basin.(Suppl_Table_4.xlsx)Supplementary Table 5 – Summary of GDM outputs for different models. (Suppl_table_5.xlsx)Supplementary Table 6 - List of Haplotypes, corresponding ASV_IDs, slope of occurrence of the asv and frequency per slope. (Suppl_Table_6.xlsx)Supplementary Figures 4 - 21 - Geographic distribution of different species based on ASV (Amplicon Sequence Variant) detections. The figure presents spatial maps displaying the occurrence of all ASVs across different locations in Northern Italy. The plotted regions include coastal areas and the Po River basin, distinguished by different colors. Longitude (°E) and latitude (°N) coordinates are provided to indicate the spatial extent of the study area. The underlying base map includes hydrological features such as rivers. The species shown did undergo filters, but not all of them were included in intraspecific diversity analyses. (Suppl_Fig_4_21.pdf)

Materials of the "Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions"ContentThis dataset contains the following data from the "Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions":Combat_description_final.pdf (description of the provided materials, the implementation and competition rules, and the performance evaluation and ranking for the combat)Case_study.inp (calibrated SWMM file of the case study)rain2018.dat (rain data over 1 year in 1min resolution)temp2018.dat (temperature data over 1 year in 10min resolution)implementation_details.xlsx (details to the connectable imperviousness area and installable NBS area per NBS type and subcatchment)solutions.xlsx (empty spreadsheet to fill out the connected imperviousness area and implemented NBS area per NBS type and subcatchment)PerformanceIndicator_Teams.xlsx (anonymised performance indicators of the participating teams)ReferenceIf you use any material of the combat, please cite it as:Reference

Materials of the "Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions"ContentThis dataset contains the following data from the "Combat of Retrofitting Urban Drainage Networks with Nature-Based Solutions":Combat_description_final.pdf (description of the provided materials, the implementation and competition rules, and the performance evaluation and ranking for the combat)Case_study.inp (calibrated SWMM file of the case study)rain2018.dat (rain data over 1 year in 1min resolution)temp2018.dat (temperature data over 1 year in 10min resolution)implementation_details.xlsx (details to the connectable imperviousness area and installable NBS area per NBS type and subcatchment)solutions.xlsx (empty spreadsheet to fill out the connected imperviousness area and implemented NBS area per NBS type and subcatchment)PerformanceIndicator_Teams.xlsx (anonymised performance indicators of the participating teams)ReferenceIf you use any material of the combat, please cite it as:Reference

We present a 25-meter resolution dataset for Switzerland, encompassing three key biodiversity indicators—Complementarity, Extinction Risk, and Ecological Connectivity—developed across 17 major taxonomic groups, using habitat suitability maps for approximately 7,500 individual species. These three indicators capture (1) the contribution of landscapes to taxonomic, functional, and phylogenetic diversity, (2) species vulnerability to extinction and (3) ecological connectivity. Outputs are provided for both terrestrial and aquatic realms, including versions adjusted for species richness, and integrated into composite indices.The preprint documenting this dataset can be found at: https://www.biorxiv.org/content/10.1101/2025.06.10.657334v1File: CH3Div_all_aqua_terr.zipThis archive provides 25-m resolution raster objects (.tif files) for the three key biodiversity indicators: Complementarity (CI), Extinction Risk (ERI), and Ecological Connectivity (ECI), as well as their composite versions (adjusted and unadjusted for species richness) at the aggregate level for all taxonomic group combined, terrestrial and aquatic. See the companion manuscript for technical details and the aggregation scheme.File: CH3Div_individual_taxa.zipThis archive provides 25-m resolution raster objects (.tif files) for the three key biodiversity indicators: Complementarity (CI), Extinction Risk (ERI), and Ecological Connectivity (ECI), as well as their composite versions (adjusted and unadjusted for species richness) at the taxonomic group level. See the companion manuscript for technical details and the aggregation scheme.File: CH3Div_pa_simulations.zipThis archive provides 100-m resolution raster objects (.tif files) for simulated protected area networks under Current (13%) and Target (30%) scenarios. The simulation produced a classification in which each pixel was assigned to one of three categories: (1) unprotected areas, which remained unprotected under the scenario; (2) protected areas, which were already part of the network; and (3) extended protected areas, which were newly added to meet the scenario target. This classification was produced for both the “current” and “target” scenarios, using the composite index in both its species richness-adjusted and unadjusted versions. See Appendix S3 of the companion manuscript for additional technical details and the R code used for these simulations.File: appendices.zipThis archive provides supplementary information on (1) the 7,461 species, (2) the computation of indicators, and (3) the protected area extension scenarios.1.    Detailed species listo    File: Appendix S1_species list.csvo    Description: List of the 7,461 species considered in this study, along with information on the grouping schemes (main taxonomic groups, and aquatic vs terrestrial realms).2.    Supplementary information on indicator computationo    File: Appendix S2_indicator computation.docxo    Description: Supplementary information on the weighting schemes for species occurrence and IUCN conservation status used for the ERI indicator (Text S2.1) and on the methods used for computing ecological proximity and distance for the ECI indicator (Text S2.2).3.    Protected areas extension scenarioso    File: Appendix S3_protected areas scenarios.docxo    Description: Supplementary information on the simulations of protected areas extension scenarios in Switzerland, exemplifying a potential use of the dataset. Includes Text S3.1. Simulating protected areas extension scenarios in Switzerland, Figure S3.1. Simulated protected areas figures, and Code S3.1. R script.

The SwissFuRiTe dataset consist of daily water temperature simulations from 82 catchments in Switzerland, from 1990 to 2099, made with the climate change scenarios for Switzerland (CH2018) and the hydrologic scenarios (Hydro-CH2018). Temperature simulations were conducted with the air2stream and air2water models. The SwissFuRiTe.zip file contains water temperature simulations as well as model input data of air temperature and river discharge in three folders Water_Temp, Air_Temp and Flow. Note that the input data have been adjusted by the authors in this study. For original air temperature and flow data as well as additional information the reader is pointed towards the sources given below. Each individual file composes all available climate simulations (GCM-RCMs) used at that station. The filename gives the following information separated by “_”:MeteoSwiss meteorological station (ex. AIG)FOEN Hydrological station (ex. 2009)Emission scenario (ex. RCP8.5)Water temperature model (ex. air2stream)Hydrological model (ex. PREVAH-WSL)Physical parameter (ex. Watertemperatur “Wassertemperatur”) Dataset can be freely used under the License; https://creativecommons.org/licenses/by/4.0/ A paper and a project report describing the dataset and the underlying methods has been published:Multi-fidelity model assessment of climate change impacts on river water temperatures, thermal extremes and potential effects on cold water fish in Switzerland. L Råman Vinnå, V Bigler, OS Schilling, J Epting., EGUsphere 2025, 1-44 (2025), https://doi.org/10.5194/egusphere-2024-3957Swiss-wide future river temperature under climate change «SwissFuRiTe», Applied and Environmental Geology, Hydrogeology Research Group, Department of Environmental Sciences, University Basel, Bernoullistrasse 32, 4056 Basel, Report number: BGA-CH-78 Original model input data sources. Atmospheric temperature climate data from the CH2018 project was obtained from the Swiss National Centre for Climate Services (nccs.admin.ch) data portal. On the same portal, discharge datasets from the Hydro-CH2018 project are available but at a temporally limited scale (monthly, seasonally and yearly means). We required daily resolved discharge data which was obtained directly from Massimiliano Zappa (model M1), Daphné Freudiger (M3), and Adrien Michel (M4). Data from model M2 (Muelchi et al., 2021) is available at http://doi. org/10.5281/zenodo.3937485.

The SwissFuRiTe dataset consist of daily water temperature simulations from 82 catchments in Switzerland, from 1990 to 2099, made with the climate change scenarios for Switzerland (CH2018) and the hydrologic scenarios (Hydro-CH2018). Temperature simulations were conducted with the air2stream and air2water models. The SwissFuRiTe.zip file contains water temperature simulations as well as model input data of air temperature and river discharge in three folders Water_Temp, Air_Temp and Flow. Note that the input data have been adjusted by the authors in this study. For original air temperature and flow data as well as additional information the reader is pointed towards the sources given below. Each individual file composes all available climate simulations (GCM-RCMs) used at that station. The filename gives the following information separated by “_”:MeteoSwiss meteorological station (ex. AIG)FOEN Hydrological station (ex. 2009)Emission scenario (ex. RCP8.5)Water temperature model (ex. air2stream)Hydrological model (ex. PREVAH-WSL)Physical parameter (ex. Watertemperatur “Wassertemperatur”) Dataset can be freely used under the License; https://creativecommons.org/licenses/by/4.0/ A paper and a project report describing the dataset and the underlying methods has been published:Multi-fidelity model assessment of climate change impacts on river water temperatures, thermal extremes and potential effects on cold water fish in Switzerland. L Råman Vinnå, V Bigler, OS Schilling, J Epting., EGUsphere 2025, 1-44 (2025), https://doi.org/10.5194/egusphere-2024-3957Swiss-wide future river temperature under climate change «SwissFuRiTe», Applied and Environmental Geology, Hydrogeology Research Group, Department of Environmental Sciences, University Basel, Bernoullistrasse 32, 4056 Basel, Report number: BGA-CH-78 Original model input data sources. Atmospheric temperature climate data from the CH2018 project was obtained from the Swiss National Centre for Climate Services (nccs.admin.ch) data portal. On the same portal, discharge datasets from the Hydro-CH2018 project are available but at a temporally limited scale (monthly, seasonally and yearly means). We required daily resolved discharge data which was obtained directly from Massimiliano Zappa (model M1), Daphné Freudiger (M3), and Adrien Michel (M4). Data from model M2 (Muelchi et al., 2021) is available at http://doi. org/10.5281/zenodo.3937485.

We present a 25-meter resolution dataset for Switzerland, encompassing three key biodiversity indicators—Complementarity, Extinction Risk, and Ecological Connectivity—developed across 17 major taxonomic groups, using habitat suitability maps for approximately 7,500 individual species. These three indicators capture (1) the contribution of landscapes to taxonomic, functional, and phylogenetic diversity, (2) species vulnerability to extinction and (3) ecological connectivity. Outputs are provided for both terrestrial and aquatic realms, including versions adjusted for species richness, and integrated into composite indices.The preprint documenting this dataset can be found at: https://www.biorxiv.org/content/10.1101/2025.06.10.657334v1File: CH3Div_all_aqua_terr.zipThis archive provides 25-m resolution raster objects (.tif files) for the three key biodiversity indicators: Complementarity (CI), Extinction Risk (ERI), and Ecological Connectivity (ECI), as well as their composite versions (adjusted and unadjusted for species richness) at the aggregate level for all taxonomic group combined, terrestrial and aquatic. See the companion manuscript for technical details and the aggregation scheme.File: CH3Div_individual_taxa.zipThis archive provides 25-m resolution raster objects (.tif files) for the three key biodiversity indicators: Complementarity (CI), Extinction Risk (ERI), and Ecological Connectivity (ECI), as well as their composite versions (adjusted and unadjusted for species richness) at the taxonomic group level. See the companion manuscript for technical details and the aggregation scheme.File: CH3Div_pa_simulations.zipThis archive provides 100-m resolution raster objects (.tif files) for simulated protected area networks under Current (13%) and Target (30%) scenarios. The simulation produced a classification in which each pixel was assigned to one of three categories: (1) unprotected areas, which remained unprotected under the scenario; (2) protected areas, which were already part of the network; and (3) extended protected areas, which were newly added to meet the scenario target. This classification was produced for both the “current” and “target” scenarios, using the composite index in both its species richness-adjusted and unadjusted versions. See Appendix S3 of the companion manuscript for additional technical details and the R code used for these simulations.File: appendices.zipThis archive provides supplementary information on (1) the 7,461 species, (2) the computation of indicators, and (3) the protected area extension scenarios.1.    Detailed species listo    File: Appendix S1_species list.csvo    Description: List of the 7,461 species considered in this study, along with information on the grouping schemes (main taxonomic groups, and aquatic vs terrestrial realms).2.    Supplementary information on indicator computationo    File: Appendix S2_indicator computation.docxo    Description: Supplementary information on the weighting schemes for species occurrence and IUCN conservation status used for the ERI indicator (Text S2.1) and on the methods used for computing ecological proximity and distance for the ECI indicator (Text S2.2).3.    Protected areas extension scenarioso    File: Appendix S3_protected areas scenarios.docxo    Description: Supplementary information on the simulations of protected areas extension scenarios in Switzerland, exemplifying a potential use of the dataset. Includes Text S3.1. Simulating protected areas extension scenarios in Switzerland, Figure S3.1. Simulated protected areas figures, and Code S3.1. R script.