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Data on multi-year drought impacts on European beech in northern Switzerland
This study investigated multi-year drought impacts on beech forests through a unique large-scale monitoring of 963 individual beech trees, which showed either premature leaf discoloration during the drought in summer 2018 or no visible damage. We conducted the study in two highly drought-affected regions in northern Switzerland and one less drought-affected region located further south. We quantified the development of crown dieback and tree mortality as well as secondary drought damage, i.e. the presence of bleeding cankers and bark beetle infestations, in these trees for three consecutive years. We also determined the impact of several potential climate- and stand-related (predisposing) factors on mortality and drought legacy processes.
AFF agricultural land change scenarios in Europe 2015-2050
This dataset contains the spatial layers showing future land change pathways in Europe between 2015 and 2050, as simulated by the CLUMondo model and according to the Agricultural Futures Framework (AFF) scenarios. Three scenarios are included: - Land for Food and Land for Nature (LFLN) - Land as Culture (LaC) - Land for Society (LfS)
LUSzoning: Land-use simulations integrating zoning regulations in Spanish functional urban areas
Table of Content: 1. General context of the data set "LUSzoning”; 2. Background and aims of the study using the data set LUSzoning; 3. The data set LUSzoning. 1. General context of the data set "LUSzoning". The data set "LUSzoning" stands for Land-use simulations integrating zoning regulations in Spanish functional urban areas. The data set has been generated as part of the CONCUR research project (https://www.wsl.ch/en/projects/concur.html) led by Dr. Anna M. Hersperger and funded by the Swiss National Science Foundation (ERC TBS Consolidator Grant (ID: BSCGIO 157789) for the period 2016-2021. The CONCUR research project is interdisciplinary and aims to develop a scientific basis for adequately integrating spatial policies (in this case, digital zoning plans) into quantitative land-change modelling approaches at the urban regional level. 2. Background and aims of the study using the data set “LUSzoning”. As part of the CONCUR project, a specific task was to integrate planning spatial policies in land-change modelling. Planning can be implemented in modelling using either hard or gradual restrictions. Different studies have addressed the inclusion of spatial planning policies in land-use change modelling. However, the integration of zoning constraints is generally established as hard or Boolean-based restrictions (e.g., whether urban development is allowed or not), while not accounting for the spatial heterogeneity or gradual characteristics within planning zones (e.g., whether planning regulations allow low, medium or high urban density), though these could improve real patterns simulations in urban areas. We assume Spanish General Zoning plans were suitable to explore the integration of planning into land-change modelling as soft constrains because they define land-use intensities in the buildable zoning areas. In light of the above considerations, the overall aim of the study was to model urban land-use changes using a multi-scenario approach that integrates digitized zoning plans for the Functional Urban Areas (FUAs) of Madrid, Barcelona, Valencia, and Zaragoza. The following specific objectives were addressed: i) to analyse the role of planning by defining three future scenarios that integrate digitized zoning plans and one scenario that assumes almost no planning intervention; ii) to introduce zoning constraints that reflect different degrees of urban densities; iii) to generate a transferable spatially-explicit modelling framework to integrate planning into land-use change simulations. Four future land-use demands scenarios were defined for the FUAs. Storylines were created considering probable development scenarios related to zoning plans, current Spanish legislation and sustainability goals defined along two axes: a high market-oriented vs. high planning-intervention axis, and an axis of short-term economic growth vs. long-term sustainable growth. The sustainable development scenario (S1) is characterized by low gross floor area (GFA) growth that is limited to areas that are currently under development according to zoning plans. The business-as-usual scenario (S2) is characterized by medium GFA growth in the range of on-going trends. The strong development scenario (S3) is characterized by high GFA growth rates. Growth is restricted to buildable areas without urbanization project designated in zoning plans. The unrestricted development scenario (S4) prioritizes a high degree of market liberalization characterized by high GFA growth that surpasses population demands. S4 follows a rapid economic growth pattern with almost no planning intervention. 3. The data set “LUSzoning”. The dataset includes 16 .asc raster layers providing the simulated land-uses under four defined scenarios for Barcelona, Madrid, Valencia and Zaragoza Functional Urban Areas (FUAs) for 2030. The simulated raster layers were created using CLUMondo simulation framework and have a spatial resolution of 30m. The .asc layers name include the name of the FUA and scenario number. For example, the output from simulating the urban growth for the city of Zaragoza under Scenario 2 is named “Zaragoza_S2.tif”. Furthermore, a .txt file named “Legend.txt” includes the numeric value of the land-use and the category of land-use that represents to interpret the .asc raster layers. The name of the land-use classes is a reclassification of the Urban Atlas 2012 land-use classes within the four Spanish FUAs analyzed.
Dataset for the publication New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale
This repository contains PMCMx-UF model outputs used for the paper: D. Patoulias K. Florou S. N. Pandis and A. Nenes: New particle formation events can reduce cloud droplets in boundary layer clouds at the continental scale, Geophysical Research Letters, in review, 2023.
Weather Station Klosters
A weather station (Lufft WS600) measured meteorological parameters at Klosters (LON: 9.880413, LAT: 46.869019). Detailed information on the specifications can be found [here](https://www.lufft.com/products/compact-weather-sensors-293/ws600-umb-smart-weather-sensor-1832/productAction/outputAsPdf/).
Modeling snow failure with DEM
This data set includes the modeling results described in the research article by Bobiller et al. (2020). All the figures in the article can be reproduced with the data provided.
Gao_Drought resistance and resilience of rhizosphere communities_13C-PLFA
Data of 13C-pulselabeling experiments in a young beech model ecosystem and a mature pine forest exposed to drought and rewetting. Data include microclimate during the experiment, total PLFA contents, d13C signatures in PLFA , microbial biomass, soil-respired CO2 measure during 30 days following 3-4 hour long pulse labeling with highly 13C-enriched CO2.
Escalating effects of multiple perturbations on soil functionality
Soil chemical and biological properties of soils affected by 10 different perturbations related with global change, applied individually or in combination. Greenhouse experiment. Perturbations applied to intact soil cores (15 cm diameter, 15 cm deep) collected in an extensively managed grassland on the WSL grounds in June 2023. This dataset contains all data on which the publication below was based: "Fioratti Junod M, Gombeer S, Holmes J, Zimmerman S, Rillig M, Risch AC and Cordero I. Soil functionality declines under multiple superimposed global change perturbations. XXXX " Please, cite this publication together with the citation of the datafile. Database includes: Pot_ID: pot or soil core identifier. Batch: numeric, 1 or 2. Enzyme_plate: numeric, from 1 to 6. Number_of_perturbations: numeric, from 0 to 10. N_addition: binary. Yes = 1, No = 0. P_addition: binary. Yes = 1, No = 0. Defoliation: binary. Yes = 1, No = 0. Trampling: binary. Yes = 1, No = 0. Insecticide: binary. Yes = 1, No = 0. Fungicide: binary. Yes = 1, No = 0. Herbicide: binary. Yes = 1, No = 0. Antibiotic: binary. Yes = 1, No = 0. Drought: binary. Yes = 1, No = 0. Heat_wave: binary. Yes = 1, No = 0. Treatment: character. Description of the treatment, 15 levels. SWC: soil water content. Numeric, unitless. Green_biomass: green plant biomass. Numeric, g. Brown_biomass: brown or dead plant biomass. Numeric, g. Biomass_cut: plant biomass cut during defoliation treatments. Numeric, g. CO2_flux_light: net CO2 flux under ambient light. Numeric, mg CO2 m-2 h-1. CO2_flux_dark: ecosystem respiration. Numeric, mg CO2 m-2 h-1. S: organic matter stabilisation factor (tea bag index). Numeric, unitless. k: organic matter decomposition rate (tea bag index). Numeric, unitless. DOC: dissolved organic carbon, mg Kg-1 dry soil. IC: inorganic carbon, mg Kg-1 dry soil. Ammonium_KCl: plant available ammonium, mg Kg-1 dry soil. Nitrate_KCl: plant available nitrate, mg Kg-1 dry soil. Phosphate: phosphate, mg Kg-1 dry soil. MBC: microbial biomass carbon, mg Kg-1 dry soil. MBN: microbial biomass nitrogen, mg Kg-1 dry soil. PHO: phosphatase activity, nmol h-1 g-1 dry soil. BG: β-glucosidase, nmol h-1 g-1 dry soil. XYL: xylosidase, nmol h-1 g-1 dry soil. CBH: cellobiohydrolase, nmol h-1 g-1 dry soil. NAG: N-acetylglucosaminidase, nmol h-1 g-1 dry soil. LAP: leucine aminopeptidase, nmol h-1 g-1 dry soil. POX: phenoloxidase, nmol h-1 g-1 dry soil. PER: peroxidase, nmol h-1 g-1 dry soil. pH: soil pH, unitless Water_stable_aggregates: water stable aggregates, % Surface_infiltration_time: water surface infiltration time, s.
Photogrammetric Drone Data Latschuelfurgga
To map and assess snow depth on different dates, 9 flights were conducted in the winter season of 2020/21 at the Latschüelfurgga in Davos. The data was captured with a Sony RX1R II mounted on a Wingtra drone and was processed with the Agisoft Metashape software. High-resolution DSMs, orthomosaics and snow height rasters, as well as the original RGB images from each flight are available.
Energy Cooperatives in Switzerland: Survey Results // Energiegenossenschaften in der Schweiz: Befragungsergebnisse
Topic of Survey The data at hand on energy cooperatives in Switzerland were collected in 2016 as part of the project "Collective financing of renewable energy projects in Switzerland and Germany" of the National Research Programme 71 "Managing Energy Consumption". The cooperatives were surveyed on their organizational structure, their activities in electricity and heat generation, their finances, the political context and their assessments of the future. Survey Method The survey was targeted at all energy cooperatives in Switzerland (this is the basic population). The Swiss Commercial Register was searched for cooperatives and specific keywords in order to determine this basic population and collect addresses. This search in May 2016 resulted in a total of 304 energy cooperatives, to which a questionnaire was sent in July 2016. A pre-test with 8 persons had been carried out before the questionnaire was sent out. The questionnaire was provided in German and French. It was sent by mail and an attached letter referred to a link for the digital version if preferred. The online version was designed with the software "Sawtooth". After three weeks, a first, and after six weeks a second reminder letter was sent to those cooperatives that had not yet completed the questionnaire. The returned hardcopy questionnaires were manually entered into the database and then combined with the electronic data from the online survey. In the course of the survey, the total population was reduced from 304 to 289: in 4 cases the survey was not deliverable, 4 cooperatives had dissolved, 6 were not actually energy cooperatives, 1 case had recently changed its legal form. With a response rate of 47%, the final data set comprises 136 responses (from 77 digital and 59 hardcopy questionnaires). However, not all 136 of the returned questionnaires were filled out completely. We checked for answers that seemed contradictory or incomprehensible. If an error could be clearly identified and the correct answer derived, the answer was adjusted, otherwise the answer was replaced by "missing data". Anonymization Participating cooperatives have been assured that their information will be kept confidential and will only be made public anonymously. For this reason, the data have been anonymized in in order to prevent any identification of individual cooperatives. How to Use the Data * The data are available in CSV and SPSS (sav.) format. * A codebook and a modified version of the used questionnaire are provided to illustrate the data and variable structure. In the questionnaire, the variable names are assigned to the corresponding questions. In the codebook, further information on these variables (valid n, answer categories) can be found. This information (of the codebook) is already integrated in the SPSS file. Current Embargo on Data These data are currently under embargo and will only be released when the project is completed (not before 2020). #Additional Information * The used questionnaire is provided in German and French. * Descriptive results of the survey were published in a WSL report: https://www.dora.lib4ri.ch/wsl/islandora/object/wsl:18943