AndreaBozzo/ceres-open-data-index huggingface dataset:
Hail hazard on a reference area of 100m2: hail size with 50-year return period

The hail hazard maps show statistically estimated return values of the hailstone size LEHA-100 for the 50-year return period. LEHA-100 describes the largest hailstone size to be expected on a reference area of 100 m2. It is derived from the radar algorithm MESHS, which is the size of the maximum expected hailstone per square kilometre (radar data from MeteoSwiss for 2002 to 2020). The new hail hazard maps represent the hailstone size as a function of the return period T. They describe the hailstone size that is exceeded under current climate conditions, per reference area, with a probability of 1/T per year. Thus, the map for 50 years shows which hail size is to be exceeded on the long term on average once in 50 years or with a probability of 2% per year. These maps provide an updated basis for assessing the local hail hazard. The map of hail zones in Annex G1 of the standard SIA 261/1 (2020) remains valid. The SIA 261/1 standard (Hail chapter) is currently (2026) being revised, and the map of hail zones is being updated to reflect the new meteorological data. MESHS: Maximum Expected Severe Hail Size is defined as the maximum expected hail size per square kilometre. This means that most of the hail stones in this area of one square kilometre are smaller. LEHA: Largest Expected Hail on a reference Area is a mathematical derivation from the hail size MESHS, which refers to an area of one square kilometre. LEHA describes the largest expected hail size that can be expected on a smaller reference area. In extreme cases, a hail stone of size MESHS can occur on the smaller reference area, but this will rarely happen. DOI: https://doi.org/10.18751/Climate/Griddata/CHHC/1.0 Download (NetCDF, GeoTIFF): https://data.geo.admin.ch/browser/index.html#/collections/ch.meteoschweiz.ogd-radar-derived-grid/items/archive-ch
Hail hazard on a reference area of 100m2: hail size with 20-year return period

The hail hazard maps show statistically estimated return values of the hailstone size LEHA-100 for the 20-year return period. LEHA-100 describes the largest hailstone size to be expected on a reference area of 100 m2. It is derived from the radar algorithm MESHS, which is the size of the maximum expected hailstone per square kilometre (radar data from MeteoSwiss for 2002 to 2020). The new hail hazard maps represent the hailstone size as a function of the return period T. They describe the hailstone size that is exceeded under current climate conditions, per reference area, with a probability of 1/T per year. Thus, the map for 50 years shows which hail size is to be exceeded on the long term on average once in 50 years or with a probability of 2% per year. These maps provide an updated basis for assessing the local hail hazard. The map of hail zones in Annex G1 of the standard SIA 261/1 (2020) remains valid. The SIA 261/1 standard (Hail chapter) is currently (2026) being revised, and the map of hail zones is being updated to reflect the new meteorological data. MESHS: Maximum Expected Severe Hail Size is defined as the maximum expected hail size per square kilometre. This means that most of the hail stones in this area of one square kilometre are smaller. LEHA: Largest Expected Hail on a reference Area is a mathematical derivation from the hail size MESHS, which refers to an area of one square kilometre. LEHA describes the largest expected hail size that can be expected on a smaller reference area. In extreme cases, a hail stone of size MESHS can occur on the smaller reference area, but this will rarely happen. DOI: https://doi.org/10.18751/Climate/Griddata/CHHC/1.0 Download (NetCDF, GeoTIFF): https://data.geo.admin.ch/browser/index.html#/collections/ch.meteoschweiz.ogd-radar-derived-grid/items/archive-ch
Hail hazard on a reference area of 100m2: hail size with 10-year return period

The hail hazard maps show statistically estimated return values of the hailstone size LEHA-100 for the 10-year return period. LEHA-100 describes the largest hailstone size to be expected on a reference area of 100 m2. It is derived from the radar algorithm MESHS, which is the size of the maximum expected hailstone per square kilometre (radar data from MeteoSwiss for 2002 to 2020). The new hail hazard maps represent the hailstone size as a function of the return period T. They describe the hailstone size that is exceeded under current climate conditions, per reference area, with a probability of 1/T per year. Thus, the map for 50 years shows which hail size is to be exceeded on the long term on average once in 50 years or with a probability of 2% per year. These maps provide an updated basis for assessing the local hail hazard. The map of hail zones in Annex G1 of the standard SIA 261/1 (2020) remains valid. The SIA 261/1 standard (Hail chapter) is currently (2026) being revised, and the map of hail zones is being updated to reflect the new meteorological data. MESHS: Maximum Expected Severe Hail Size is defined as the maximum expected hail size per square kilometre. This means that most of the hail stones in this area of one square kilometre are smaller. LEHA: Largest Expected Hail on a reference Area is a mathematical derivation from the hail size MESHS, which refers to an area of one square kilometre. LEHA describes the largest expected hail size that can be expected on a smaller reference area. In extreme cases, a hail stone of size MESHS can occur on the smaller reference area, but this will rarely happen. DOI: https://doi.org/10.18751/Climate/Griddata/CHHC/1.0 Download (NetCDF, GeoTIFF): https://data.geo.admin.ch/browser/index.html#/collections/ch.meteoschweiz.ogd-radar-derived-grid/items/archive-ch
Hail hazard on a reference area of 100m2: hail size with 100-year return period

The hail hazard maps show statistically estimated return values of the hailstone size LEHA-100 for the 100-year return period. LEHA-100 describes the largest hailstone size to be expected on a reference area of 100 m2. It is derived from the radar algorithm MESHS, which is the size of the maximum expected hailstone per square kilometre (radar data from MeteoSwiss for 2002 to 2020). The new hail hazard maps represent the hailstone size as a function of the return period T. They describe the hailstone size that is exceeded under current climate conditions, per reference area, with a probability of 1/T per year. Thus, the map for 50 years shows which hail size is to be exceeded on the long term on average once in 50 years or with a probability of 2% per year. These maps provide an updated basis for assessing the local hail hazard. The map of hail zones in Annex G1 of the standard SIA 261/1 (2020) remains valid. The SIA 261/1 standard (Hail chapter) is currently (2026) being revised, and the map of hail zones is being updated to reflect the new meteorological data. MESHS: Maximum Expected Severe Hail Size is defined as the maximum expected hail size per square kilometre. This means that most of the hail stones in this area of one square kilometre are smaller. LEHA: Largest Expected Hail on a reference Area is a mathematical derivation from the hail size MESHS, which refers to an area of one square kilometre. LEHA describes the largest expected hail size that can be expected on a smaller reference area. In extreme cases, a hail stone of size MESHS can occur on the smaller reference area, but this will rarely happen. DOI: https://doi.org/10.18751/Climate/Griddata/CHHC/1.0 Download (NetCDF, GeoTIFF): https://data.geo.admin.ch/browser/index.html#/collections/ch.meteoschweiz.ogd-radar-derived-grid/items/archive-ch
Hillsides and slopes

In order to take the difficult production and living conditions in agriculture adequately into consideration, slope subsidies have been established for areas eligible for direct payments. A slopes dataset has been created for general direct payments (GeoIV 152.1; DSO Art. 43) and a dataset for vineyards on slopes for viticulture payments (GeoIV 152.2; DSO Art. 45). There are different categories of slope gradient. The slopes dataset is part of the data used to calculate the agricultural direct payments on the basis of a geographic information system (GIS). Standardised calculations have been produced for the whole of Switzerland. The production is based on the "SwissAlti3D" terrain model from swisstopo. A distinction is made between the following slope gradient categories: &lt;18; 18-35; 35-50; &gt;50 percent. Sloping areas less than 100 square metres in size were not taken into consideration.
Hanglagen Abschwemmung

Der Datensatz Hanglagen Abschwemmung dient dazu, die Flächen zu bestimmen, auf welchen Massnahmen gegen Abschwemmung zu ergreifen sind. Die Vorgaben zu den Massnahmen gegen Abschwemmung richten sich nach dem ÖLN gemäss DZV Anhang 1 Ziffer 6.1a.4 und nach den Weisungen der Zulassungsstelle Pflanzenschutzmittel des Bundesamtes für Lebensmittelsicherheit und Veterinärwese betreffend die Massnahmen zur Reduktion der Risiken bei der Anwendung von Pflanzenschutzmitteln. Der Datensatz ist Teil der Grundlagen zur Berechnung der landwirtschaftlichen Direktzahlungen unter Verwendung eines geografischen Informationssystems GIS. Er wurde gesamtschweizerisch einheitlich berechnet. Die Produktion beruht auf dem Terrainmodell „SwissAlti3D“ von swisstopo. Folgende Hangneigungsklassen wurden unterschieden: <=2 Prozent; >2 Prozent. Hangneigungsflächen von weniger als 1 Are wurden nicht berücksichtigt.
Erosion risk map for arable land, with average soil erosion in tonnes /(ha*year)

Erosion risk map for arable land, with average soil erosion in tonnes /(ha*year) (Federal Office for Agriculture)
Erosion risk map for arable land in Switzerland in a 2x2-metre grid based on SwissALTI3D and cantonal surface-area data on arable land (status 2021). The map shows the long-term average potential soil erosion in tonnes per hectare and year, calculated on the basis of bare fallow (erosion which theoretically could occur if the soil were permanently bare). Shades of green, yellow and red mean a slight, average and high risk of erosion, respectively. The values calculated in the model are consistently much higher than in actual fact (by around a factor of 10), since soil tillage and management (C-factor) were excluded and incorporated in the calculation as a constant with the value of 1. Calculating the erosion risk with multiple-flow algorithms allows us to illustrate the structure of the terrain in great detail – e.g. the shape of basins or of steep transverse slopes – and hence e.g. also to identify the risk of thalweg erosion. This is shown in detail in the flow-path map (which see). Field blocks (see field-block map) constitute the basis of the spatial calculation.
National Map 1:10'000 (color)

The 1:10,000 national map is swisstopo’s largest scale topographic map. It stands out for its detailed representation of traffic, settlements, terrain and vegetation as well as its thorough labelling. The contents are supplemented by rock hachures and relief. The perimeter of the map covers the national territory of both Switzerland and the Principality of Liechtenstein. Updates are made every year based on the latest version of swisstopo’s topographic landscape model (TLM). The 1:10,000 national map is produced in digital form in colour and greyscale. Both of these versions of the map form the basis for Switzerland’s geo data viewer (map.geo.admin.ch) in the first four zoom levels. The 1:10,000 map is also available as “Swiss Map Raster 10” in the form of geo-referenced TIF data for individual sections of the perimeter.
Potential use of lakes and rivers for heat extraction and heat discharge

Lakes and rivers provide a great yet largely untapped source of thermal energy. In Switzerland, this renewable source of energy source could be used for heating (heat extraction) and cooling (heat discharge), since many cities are located close to lakes and rivers and the technology is well established.

The potential of Switzerland’s largest lakes and rivers for heat extraction and heat discharge was estimated using simple assumptions. Water body-specific characteristics were only partially taken into account. The potentials are to be understood as a reference point, and should therefore not be used as a definitive basis for planning. The assessment was carried out by Eawag, the Swiss Federal Institute of Aquatic Science and Technology of the ETH Domain, and financed by the Federal Office for the Environment.

This dataset indicates the potential of Switzerland’s larger lakes and rivers for heat extraction and heat discharge.
Radon map

The radon map indicates the probability (in %) of exceeding the reference value of the radon concentration of 300 Bq/m3 in a building. However, only a measurement can determine the radon concentration in a specific, existing building. The list of approved measuring services is available at www.ch-radon.ch. Recommendations regarding protective measures against radon in new constructions are currently being drawn up. The confidence index (high, medium, low, very low) indicates the reliability of the estimated probability.
Microwave links

Directional radio is used in telecommunications to transmit information (telephony, data, images) between two points. High frequency radio waves (between 4 and 90 GHz) can be tightly collimated into a beam which is transmitted to the receiver using highly directional antenna (parabolic reflector). Directional radio systems are mounted at convenient locations to transmit over quasi-line of sight (no obstacles such as trees, houses, etc.).The illustration provides information though does not represent legally binding information regarding the licensed parameters. The displayed accuracy of the sites is limited to approx. 30 m.
Tick bite model

The Federal Office of Public Health presents the updated “Tick Hazard Model” of Switzerland and the Principality of Liechtenstein. It shows the static geographical distribution of areas where ticks can establish themselves and survive in nature. The representation of the different levels of tick hazard is based on a colour scale with red zones of high tick hazard and blue zones of no tick hazard. The conditions on which the model is based correspond to early summer weather: mild and humid weather conditions with temperatures of 15 to 20 degrees Celsius and a relative humidity of 80 %. 
Tick bite risk is the product of tick hazard potential and exposure – where and how often people spend time outside. To get an idea of the current tick risk, the present "Tick Hazard Model" has to be combined with outdoor activity and current weather conditions. Furthermore, tick activity is subject to seasonality; it starts in early spring with temperatures starting at seven degrees Celsius, increases steadily in spring and early summer until the first summer heat. After several days with temperatures well above 30 degrees Celsius and bright sunshine, tick activity and thus the risk of tick bites collapses. In autumn, when temperatures drop, and relative humidity rises again, the chance of ticks bites increases again. If the temperature is below seven degrees Celsius, the risk of tick bites decreases because the ticks reduce their bodily functions in order to survive the winter in a dormant state.
swissEO VHI: state of forest health

Map of forest condition based on the VHI (Vegetation Health Index), which reflects the current state of forest in relation to historical data 1991-2020. The index is based on satellite data (Meteosat, Landsat 5,7,8, and Sentinel-2) of different spatial resolutions, aggregated of ten and thirty metres respectively. The VHI consists of two components: one part (VCI - Vegetation Condition Index) represents the local biophysical conditions of the vegetation, and the other part (TCI - Thermal Condition Index) characterizes the climatic conditions (temperature). Both components depict the current situation in relation to a reference period. The combined VHI is used to assess drought and other disturbances or stress factors— the lower the value, the greater the stress and its impact on vegetation. Values from 100 down to 60 indicate excellent vegetation condition, values up to 50 are good, and values up to 40 are normal. Below 40, the vegetation shows signs of stress, up to 30 it is lightly stressed, up to 20 stressed, up to 10 severely stressed and values below 10 indicate an extremely stressed vegetation condition. The forest area is shown on the map, providing a robust indicator of the regional vegetation conditions. The dataset was developed in collaboration with MeteoSwiss for the federal drought portal.Contains modified Copernicus Sentinel data.
Vegetation height model NFI

Countrywide vegetation height models (VHM) were generated for Switzerland based on stereo aerial images, acquired by the Federal Office of Topography swisstopo.From the ADS-80/100 sensor data, first a digital surface model (DSM) with a spatial resolution of 1 × 1 m is processed. This DSM is then normalized using the digital terrain model (DTM) swissALTI3D (swisstopo). Buildings and other artificial objects are masked out from the normalized DSM (nDSM) using information from the topographic landscape model - TLM (swisstopo) to obtain the actual vegetation height model (VHM). These VHMs are produced in the framework of the Swiss National Forest Inventory (NFI). Each year about 1/6 of Switzerland's surface is updated using the leaf-on aerial images acquired by swisstopo (May - October). Further information on the creation of the VHM NFI can be found in the paper Ginzler and Hobi (2015, https://doi.org/10.3390/rs70404343).
Switzerland-wide maps showing light-induced regeneration potential (as of NFI VHM 2024)

The map shows where light conditions are sufficient for sufficient height growth of regeneration (<= 300 cm) for each tree species group. The map is based on expert field surveys in 39 forest districts spread across Switzerland and at various altitudes, as well as on the degree of canopy opening derived from LiDAR data. The tree species considered were divided into the classes listed below based on their light requirements. The map can also be used as a guide for other tree species in the respective class:<br><br> <ul> <li>Larch, birch, English oak, sessile oak, Scots pine, and cherry: light-demanding tree species (class 1),</li> <li>Sycamore maple and Norway maple: semi-light-demanding tree species (class 2),</li> <li>Spruce, small-leaved and large-leaved lime: semi-shade tree species (class 3),</li> <li>Beech and fir: shade tree species (class 4),</li> <li>Too dark for all tree species to grow (class 5).</li> </ul> <ul>Purpose: The map shows whether the local light conditions are sufficient for young trees to grow vigorously into pole wood without additional intervention, thus identifying potentially necessary silvicultural measures at an early stage.</ul> <ul>Notes on use: Support for foresters and forest managers in planning regeneration measures, young forest maintenance, tree species selection, and temporal and spatial monitoring, as the map is regularly updated with new LiDAR flights.</ul> <ul>Comments: The regeneration potential was calculated solely on the basis of the degree of canopy opening, which takes into account the shading caused by the surrounding tree crowns and the terrain. Direct sunlight has not been taken into account so far, as this leads to statistically less reliable results. This means that slope exposure or the orientation of gaps cannot be taken into account at this stage. Similarly, no altitude-specific subdivisions can be made at present, as the national model used performs best in statistical tests. In stands with trees and shrubs of approx. 3-7 m in height, the light conditions in the understorey are overestimated, i.e. in such cases the map shows an excessively high regeneration potential. The map has been reduced to the forest area according to the NFI, and forest roads have been masked out as much as possible.</ul> <ul>The map shows the regeneration potential at the time of the LiDAR survey, which was used to derive the canopy openness. A map showing the years for which the LiDAR data were collected is available under ‘Data sources'.</ul>
Estimation of direct CO₂ emissions based on heat demand according to SIA standard 380/1 (2016)

The Swiss building stock is responsible for around a quarter of CO2 emissions. Reductions in this area are key to achieving the climate targets. </p><p> The map "CO2 Emissions Buildings (SIA 380/1)" can be used to estimate how much CO2 is emitted for each residential building. These CO2 emissions are calculated for standard conditions for heating and hot water requirements. For the calculation, the publicly accessible input data from the Federal Register of Buildings and Dwellings (GWR) of the Federal Statistical Office are used. This data is not up-to-date everywhere and does not include sufficient information on building renovations. An interactive CO₂ calculator can be used to simulate how the CO2 value changes when energy-efficient renovations are taken into account or a change of energy source is made. The data entered is not saved. You can use this tool for each building under "Object information". </p><p> The information is to be understood as a rough estimate and does not represent official or binding data.</p><p> The CO2 visualisation with the CO2 calculator creates transparency and shows that it is particularly important to keep the ‘GWR' data up to date. Building owners can help themselves to keep the climate and energy-relevant building data in the ‘GWR' up to date. A link under "Object information" for each building contains a description of how to report any outdated data. </p><p> You can find further information at https://www.bafu.admin.ch/en/calculator-co2-buildings.
Large-scale photovoltaic systems in Switzerland

Depending on their location and orientation, high-alpine photovoltaic systems can generate more electricity per installed kilowatt of power in the winter months than comparable systems in the lowlands. This is because the radiation conditions are better (thinner atmosphere, less fog) and because the sunlight is reflected by the snow (known as the albedo effect).
This dataset contains all information on planned or realised large-scale photovoltaic installations that have been reported to the SFOE.
Aerial Images swisstopo color

The aerial imagery catalogue of swisstopo comprises around 360’000 analogue aerial photographs (black and white, colour or infrared colour). About 280’00 aerial images are represented by the centre-image coordinates, the image footprint as well as the attributed metadata and 265‘000 images of the collection have already been digitalised and can be viewed in their full resolution (as of October 2018). The remaining scanned images will be continuously integrated over the coming years. The aerial images can be ordered as digital files. If available, the orientation elements of the aerial images can be delivered as well.
Database of wastewater treatment plants (ARA-DB)

Since the 1980s, the FOEN (formerly the Swiss Agency for the Environment, Forests and Landscape) has kept a database with information about the central ARA. The underlying original maps were drawn up by the operators of wastewater treatment plants each time a plant was opened but later, when technical improvements were made, they were not updated frequently or systematically. Information about key figures on wastewater disposal comes from a 2011 survey by the Swiss Water Association and is based on the SWA's 2006 recommendations. The percentages of wastewater in receiving watercourses at low water were estimated as part of the FOEN's “Strategy Micropoll” project. In future, the updated SWA recommendation will form the basis for definitions of the key figures. The survey will be carried out periodically. In addition, the FOEN surveys the condition of Switzerland's communal wastewater disposal in cantons approximately every five years. This was last done on 01/01/2011.