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Processed permafrost borehole data (2394 m asl), Fluelapass A, Switzerland
Processed ground temperature measurements at the Fluelapass permafrost borehole A (FLU_0102) in canton Graubunden, Switzerland. The borehole is located at 2394 m asl on a moderate (26°) North-east slope (45°). The surface material is talus and borehole depth is 23 m. Thermistors used YSI 44006. Year of drilling 2002. This borehole is part of the Swiss Permafrost network, PERMOS (www.permos.ch). Contact phillips@slf.ch for details of processing applied.
Sample plot inventory data form the Parc Naturel du Jorat
The Jorat is one of the largest continously forested areas in the Swiss Plateau. On a forested area of 778 ha, the Parc Naturel du Jorat (PNJ), a periurban parc, has been established in 2022. To document the initial state of the forest within the perimeter of the PNJ, a sample plot inventory (SPI) was carried out on 132 sample plots (SP) in winter 2021/22. This dataset contains results from this sample plot inventory. It consists of the following files: - results_trees.csv: Results for or living and dead trees. - results_regeneration.csv: Results for trees with DBH < 7.0 cm, assessed in three height classes. - results_lying_deadwood.csv: Results for lying deadwood, assessed no three line transects - results_trems.csv: Results for occurence of tree related microhabitats (TreMs) - results_habitat_trees.csv: Results for occurence / densities of trees carrying at least one TreM or with a DBH >= 80 for living trees or >= 36 cm for dead trees respectively. - lookup.csv: Contains lookup tables which describe the respective results in-depth. - data_description.pdf: Briefly describes the datasets mentioned above.
Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7–9 June 2021, Birmensdorf, Switzerland
Forest monitoring to assess forest functioning under air pollution and climate change. Proceedings. FORECOMON 2021 - the 9th forest ecosystem monitoring conference. 7-9 June 2021, WSL, Birmensdorf, Switzerland The goal of FORECOMON 2021 is to highlight the extensive ICP Forests data series on forest growth, phenology and leaf area index, biodiversity and ground vegetation, foliage and litter fall, ambient air quality, deposition, meteorology, soil and crown condition. We combine novel modeling and assessment approaches and integrate long-term trends to assess air pollution and climate effects on European forests and related ecosystem services. Latest results and conclusions from local scale to European scale studies will be presented and discussed. Copyright © 2021 by WSL, Birmensdorf The authors are responsible for the content of their contribution.
Tree species profiles from Sentinel-2 time series
Tree species profiles from Sentinel-2 time series We obtained tree species-specific Sentinel-2 time series for the years 2019 and 2020. The Sentinel-2 time series originated from [here](https://www.envidat.ch/#/metadata/sentinel-2-time-series-of-switzerland), and the tree species information was derived from pure plots of the Swiss National Forest Inventory, spread all over Switzerland. In addition to the 10 and 20 m bands from Sentinel-2 (Blue, Green, Red, Red-Edge-1, Red-Edge-2, Red-Edge-3, NIR, SWIR-1, SWIR-2), we calculated indices (CCI, CIre, EVI, NDMI/NDWI, NDVI). In total, tree species profiles of 763 pixels for 2020 and 562 pixels for 2019 are available for seven tree species (*Abies alba*, *Castanea sativa*, *Fagus sylvatica*, *Fraxinus excelsior*, *Larix* spp., *Picea abies*, *Pinus sylvestris*).
Multi-resolution CLM5 simulations across Switzerland
This dataset contains Community Land Model 5 (CLM5) simulation output over the spatial extent of Switzerland at different resolutions and based on a range of input datasets. It further contains land-use surface data used for the CLM5-simulations. **Detailed description of the CLM5 simulation setup and the various input datasets can be found in the accompanying publication: https://doi.org/10.5194/egusphere-2023-1832.** CLM5 simulation output This dataset includes gridded CLM5 simulations of snow depth, gross primary productivity (GPP) and evapotranspiration at different resolutions ( 1km, 0.25° and 0.5°) and based on a range of input datasets over the spatial extent of Switzerland (see folder *gridded_CLM5_simulations*). Additionally, point-scale CLM5 simulations of snow depth and snow-water-equivalent at 36 snow-station locations (see folder *point_scale_CLM5_simulations*) are included. Latitude, longitude and elevation for these station locations can be found in table A1 of the above-mentioned publication. All simulation output spans from 01/01/2015 - 31/12/2019. Included CLM5 simulation results are based on 3 different meteorological forcing datasets: * Clim_CRU: standard global dataset, we used the recent state-of-the-art standrd global dataset CRU-JRA (https://catalogue.ceda.ac.uk/uuid/aed8e269513f446fb1b5d2512bb387ad) * Clim_CRU*: ClimCRU upraded by downscaling temperature data using a temperature lapse rate of -6.5K/1000m and a high-resolution DEM * Clim_OSHD: highest level of detail, meteorological forcing generated according to methods developed by the Operational Snow Hydrological Service (OSHD), at 1km spatial and 1hour temporal resolution</li> Land-use surface data This dataset further includes forcing land surface datasets used for the CLM5 simulations at 1km, 0.25° and 0.5° resolution (see folder *surface_landuse_datasets*). For the 1km resolution both the standard global (LU_Gl) and the high-resolution dataset (LU_HR), which includes a higher level of detail and is based on a more up-to-date land use datase, are provided. More details on these two datasets can be found in the above-mentioned publication.
Distributed Acoustic Sensing Brienz
This dataset contains the Distributed Acoustic Sensing (DAS), radar detection data used for training and result analysis in the GRL paper titled `Automatic Monitoring of Rock-Slope Failures Using Distributed Acoustic Sensing and Semi-Supervised Learning`. The DAS dataset (both waveform and cross-spectral density matrices), extracted features, labeled dataset, two trained models (feature extraction model and xgboost classification model), scripts to reproduce the whole training and classification processes, and a notebook to replicate the result analysis part are provided under the MIT license. To provide a reasonable data size, we chunked the raw data to a few hundred channels which we used in our project. Abstract: Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi-supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million cubic meters on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground-truth labeling. The proposed algorithm is capable of distinguishing between rock-slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal-to-noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.
ALS-based snow depth and canopy height maps from flights in 2017 (Grisons, CH and Grand Mesa, CO)
This dataset includes snow depth, canopy height and terrain elevation maps of forest stands in the Grisons (CH) and at Grand Mesa (CO,USA) derived from airborne lidar. Data were acquired i) within a pilot mission of NASA's Airborne Snow Observatory in the Swiss Alps in March 2017 and ii) during NASA’s SnowEx campaign at Grand Mesa in February 2017. Snow depth maps are available for two dates separated by approx.1 week, and include an area of ca. 0.5km2 for each of the three sites Davos, Engadine and Grand Mesa. All data were presented and analyzed in the publication 'Revisiting Snow Cover Variability and Canopy Structure within Forest Stands: Insights from Airborne Lidar Data' (Mazzotti et al., 2019, WRR, doi: 10.1029/2019WR024898). This publication must be cited when using this dataset. Paper Citation: > _Giulia Mazzotti; William Ryan Currier; Jeffrey S. Deems; Justin M. Pflug; Jessica D. Lundquist; Tobias Jonas (2019). Revisiting Snow Cover Variability and Canopy Structure Within Forest Stands: Insights From Airborne Lidar Data. Water Resources Research, 55, 6198– 6216, [doi: 10.1029/2019WR024898](https://doi.org/10.1029/2019WR024898)._
Causal effect of LUP
Title: Does zoning contain built-up land expansion? Causal evidence from Zhangzhou City, China. Research objective: Built-up land zoning is an imporatant policy measure of land use planning (LUP) to contain built-up land expansion in China. We used a difference-indifference model with propensity score matching to estimate the average and annual effect of built-up land zoning on built-up land expansion in Zhangzhou City, China between 2010 and 2020. Data: Data.dbf contains the varibles of 1662 villages in Zhangzhou Cities in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020. XZQDM2 is villages' unique administrative ID; Area is the land area of village i; Dis2water is the Euclidean distance from village i to the nearest waterbody; Dis2coastl is the Euclidean distance from village i to the nearest coastline; Dis2city is the the Euclidean distance from village i to the city center; Dis2county is the the Euclidean distance from village i to the nearest county center; Elevation is the the average elevation within village i; Dis2road is the the Euclidean distance from village i to the nearest road; Nei_Built_ is the the area of built-up land (Nei Built.upit) in the neighboring villages of village i in year t; Treated is a binary variable, Treated = 1 to the villages that were partially or entirely located inside the development-permitted zones, and Treated = 0 to the villages that were entirely located outside the development-permitted zones; Intensity is the percentage of land that was assigned to the development-permitted zones in village i; Year represent the year in 1995, 2000, 2005, 2010, 2013, 2015, 2018, and 2020; BuLE is the dependent variable, representing built-up land expansion in village i in year t; Town is town' unique administrative ID. Method: First, we employed propensity score matching to overcome the selection bias and satisfy the parallel trend assumption. Second, we built four Difference-in-Difference models to estimate the average and annual effect.
Playground biodiversity and preschool children well-being
This repository contains data related to an interdisciplinary study on playgrounds, looking at both playground vegetation and the well-being of pre-school children using those playgrounds. This project was funded by and initiated at the Institute for Landscape Planning and Ecology (ILPÖ) of the University of Stuttgart, Germany. Data was collected by ILPÖ. The project was finalised in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). The aim of the study was to (1) describe playground vegetation (species richness and diversity at the ground, shrub and tree layers; habitat structure); (2) describe preschool children’s well-being and nature-connectedness; and (3) investigate whether there is a relationship between plant diversity and well-being while exploring potential other predictors of well-being, including children’s and parents’ or carer’s nature-connectedness, greenspace exposure, seasonality, and socio-demographic background. We sampled ground (<1m height), shrub (1-2m height) and tree (>2m hieght) vegetation of 29 playgrounds of Stuttgart's inner city. Sampling took place between May 25 and July 01, 2021. Ground layer vegetation was sampled in ten one-by-one m2 quadrats per playground, within which we recorded the cover of each species based on the Braun-Blanquet scale. Woody vegetation was exhaustively sampled within the playgrounds and in a surrounding two meters buffer, recording each individual plant. Vegetation cover was visually estimated as a percentage for each vegetation layer, namely the ground, shrub and tree (canopy) layers. In parallel, we distributed questionnaires to parents and carers to assess the well-being and connectedness-to-nature of preschool children (aged 6 or younger) across the seasons. The questionnaire covered children's (1) use of greenspaces, (2) nature-connectedness, (3) well-being, and (4) socio-demographic variables. Questionnaires were distributed accross all seasons: sampling seasons included autumn (Oct-Nov 2021), winter (Jan-Feb 2022), spring (April-May 2022) and summer (June-July 2022). Ethical approval was granted by the University of Stuttgart Commission on Responsibility in Research (Az. 21-033).
Meteorological Time Series Aligned with Projected Temperature Changes
This dataset provides 50 years of seasonal synthetic hourly time series for five meteorological variables: incoming shortwave radiation, total precipitation, relative humidity, 2-meter air temperature and wind speed. The data are generated from observational records (1990–present) from nine MeteoSwiss stations (ABO, BAS, BER, LUG, PUY, SCU, SIO, STG, WFJ). We use a sampling approach that preserves both the temporal structure and inter-variable dependencies. Specifically, the time series are constructed by drawing 7-day blocks from historical observations, ensuring realistic short-term variability and maintaining the physical coherence between variables. The synthetic sequences are constrained to follow daily temperature change trajectories corresponding to three future climate scenarios for the period 2040–2070: RCP2.6, RCP8.5 and RCP0: a counterfactual scenario with no long-term warming trend, but with temperature variability consistent with the RCP2.6 one. The temperature constraints are derived from projections downscaled at the staiton level, available at: https://www.envidat.ch/#/metadata/climate-change-scenarios-at-hourly-resolution