name: Amt für Statistik und Daten, Data Management

de: Bei der Geburt wird der Vorname amtlich registriert. Der Datensatz enthält die im Kanton Zürich registrierten Babynamen ab dem Jahr 1987. In diesen Daten finden Sie eine Auflistung der vergebenen Vornamen, unterteilt nach Geburtsjahr und Geschlecht. Zweit- und Drittnamen werden nicht aufgeführt. Die Daten enthalten lediglich Vornamen von Kindern, die im Kanton Zürich geboren wurden, er macht keine Aussage darüber, wie viele Personen eines Namens im Kanton Zürich leben. Der Datensatz wird jährlich komplett aktualisiert, um mögliche rückwirkende Korrekturen zu berücksichtigen. Die Daten stammen aus der Erhebung BEVNAT des Bundesamts für Statistik (BFS).

de: Vornamen der Neugeborenen des Kantons Zürich

de: kinder bevoelkerung geburten vornamen

statistisches-amt-kanton-zuerich

de: Das Amt für Statistik und Daten umfasst das Kompetenzzentrum Daten des Kantons Zürich, organisiert, koordiniert und steuert die politische Teilhabe inkl. der Wahlen und Abstimmungen im Kanton und beobachtet die Entwicklung des Kantons mit statistischen Methoden. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Information und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.

de: Amt für Statistik und Daten Kanton Zürich

soci

de: Bevölkerung und Gesellschaft

en: Population and society

fr: Population et société

de: CSV-File mit den Spalten: VORNAME (String), GEBURTSJAHR (Number), GESCHLECHT (String; Codierung m = männlich, f=weiblich), ANZAHL_NEUGEBORENE (Number)

de: Vornamen der Neugeborenen (tabellarisch)

de: Daten als Json-File für die Webapp unter:  .... Das Json-File enthält folgende Knoten: year= Geburtsjahr (Number), n = Vorname (String), s = Geschlecht (String; Codierung m = männlich, f=weiblich), v =Anzahl der Verwendungen des Vornamens pro Geburtsjahr (Number; nach Geburtsjahr absteigend sortiert).

de: Vornamen der Neugeborenen

bevoelkerung

display_name: bevoelkerung

geburten

display_name: geburten

kinder

display_name: kinder

vornamen

display_name: vornamen


$oid: OBSOLETE

name: Dienststelle Landwirtschaft und Wald (lawa)

de: Der Waldrand angrenzend an Bauzonen ist als statische Waldgrenze grundeigentümerverbindlich festgelegt und im Zonenplan eingetragen.

de: Statische Waldgrenzen

de: baulinie nutzungsplanung zonenplan wald eigentumsbeschrankungoreb

buwd-lawa

approval_status: approved

de:
Die Dienststelle Landwirtschaft und Wald fördert die Land- und Forstwirtschaftsbetriebe und schafft bessere Rahmenbedingungen für die primäre Produktion. Sie strebt artenreiche, naturnahe ländliche Räume an und stellt die nachhaltige Nutzung des staatlichen Fischerei- und Jagdregals sicher.

Die Dienststelle umfasst die Abteilungen Landwirtschaft, Wald, Natur, Jagd und Fischerei sowie Zentrale Dienste.

en:
The Department of Agriculture and Forestry supports agricultural and forestry operations and creates better conditions for primary production. It strives to foster biodiverse, near-natural rural areas and ensures the sustainable use of the state’s fishing and hunting rights.

The department comprises the divisions of Agriculture, Forestry, Nature, Hunting, and Fisheries, as well as Central Services.

fr:
L'office de l'agriculture et des forêts soutient les exploitations agricoles et forestières et crée des conditions-cadres plus favorables à la production primaire. Il œuvre en faveur d'espaces ruraux riches en biodiversité et proches de la nature, et veille à l'exploitation durable des droits de pêche et de chasse appartenant à l'État.

Le Service comprend les départements de l'agriculture, des forêts, de la nature, de la chasse et de la pêche, ainsi que les services centraux.

en: Department of Agriculture and Forestry

de: Dienststelle Landwirtschaft und Wald

fr: Office de l'agriculture et des forêts

political_level: canton

state: active

type: organization

capacity: public

de: Kanton Luzern, Abteilung Geoinformation

de: geocat.ch Permalink

fr: geocat.ch permalien

it: geocat.ch link permanente

en: geocat.ch permalink

spatial: Kanton Luzern

type: dataset

agri

de: Landwirtschaft, Fischerei, Forstwirtschaft und Nahrungsmittel

en: Agriculture, fisheries, forestry and food

fr: Agriculture, pêche, sylviculture et alimentation

it: Agricoltura, pesca, silvicoltura e prodotti alimentari

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

de: Produktansicht Geodatenshop

de: Datenshop

value: Kt. LU LAWA - geocat harvester


erdwarmenutzung

de: Zeigt an welchen Orten der Einsatz von Erdwärmesonden zulässig ist und wo bereits Erdwärmenutzungsanlagen in Betrieb sind.

de: Erdwärmenutzung

de: energie erdwarme geologie gewasserschutz grundwasser bewilligungen ver--und-entsorgung

buwd-uwe

de:
Zum Leistungsauftrag der Dienststelle Umwelt und Energie gehören die Umweltbeobachtung, Information und Beratung, die Prüfung von umweltrelevanten Vorhaben, der Schutz von Umweltgütern (z.B. Grundwasser), die Kontrolle u.a. von Abfallanlagen und Feuerungen sowie die Förderung von erneuerbaren Energien und Energieeffizienz.

Die Dienststelle umfasst die Abteilungen Gewässer und Boden, Entsorgung und Risiko, Energie, Luft, Lärm & Strahlen sowie die Zentralen Dienste.

en:
The mandate of the Department of Environment and Energy includes environmental monitoring, providing information and advice, reviewing environmentally relevant projects, protecting environmental resources (e.g., groundwater), inspecting waste facilities and combustion plants, among other things, and promoting renewable energy and energy efficiency.

The department comprises the divisions of Water and Soil, Waste Management and Risk, Energy, Air, Noise & Radiation, and Central Services.

fr:
Le mandat de prestations de l'Office de l'environnement et de l'énergie comprend la surveillance de l'environnement, l'information et le conseil, l'examen des projets ayant une incidence sur l'environnement, la protection des ressources environnementales (par exemple les eaux souterraines), le contrôle, entre autres, des installations de traitement des déchets et des installations de combustion, ainsi que la promotion des énergies renouvelables et de l'efficacité énergétique.

L'Office comprend les divisions Eaux et sols, Gestion des déchets et risques, Énergie,  Air, bruit et rayonnements, ainsi que les services centraux.

en: Department of Environment and Energy

de: Dienststelle Umwelt und Energie

title_for_slug: erdwarmenutzung

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

ener

de: Energie

en: Energy

fr: Énergie

it: Energia

value: Kt. LU UWE - geocat harvester


erdwarmenutzung-anlagen

de: Übersicht der Standorte von Erdwärmenutzungsanlagen, welche Erdwärmesonden, Energiepfähle, Erdregister und Erdwärmekörbe zur Wärmegewinnung nutzen.

de: Erdwärmenutzung: Anlagen

de: energie erdwarme geologie gewasserschutz grundwasser ver--und-entsorgung

buwd-uwe

fr: Office de l'environnement et de l'énergie

title_for_slug: erdwarmenutzung-anlagen


raumliche-energieplanungen

de: Die Kollektion zeigt regionale und kommunale räumliche Energieplanungen im Kanton Luzern. Dies beinhaltet eine Übersicht über den Planungsstand der Energieplanungen, die Energieträger für die thermische Netze und die individuelle Wärmeversorgung sowie Informationen zur Gasstillegung wie auch zur Gebieten wo der Einsatz von Luft-Wasser-Wärmepumpen oder Erdsonden aufgrund zu erwartender Lärmemissionen oder thermischer Beeinflussung koordiniert werden soll.

de: Räumliche Energieplanungen

de: richtplanung energie erdwarme klima raumplanung umweltschutz gebaude-und-anlagen

title_for_slug: raumliche-energieplanungen


name: Direction de l'information du territoire

fr: Depuis décembre 2003, toutes les parcelles avant modification par une mutation juridique, soit par division ou réunion, sont transférées par duplication en couche historique des parcelles de la mensuration. Entre juin 2006 et février 2008 en plus de la règle ci-dessus, une partie des biens-fonds modifiés par numérisation (amélioration de sa précision planimétrique) furent intégrés dans cette classe d'entités. Depuis mars 2008, tout bien-fonds subissant une modification géométrique ou attributaire est intégré dans cette classe d'entités. Voir couche en vigueur : CAD_PARCELLE_MENSU

fr: Historique des parcelles de la mensuration

canton-geneve

fr: Le canton de Genève s'est engagé dans une démarche d’ouverture des données publiques depuis plusieurs années. Sur ce portail, vous trouvez toutes les données provenant de l'administration cantonale genevoise qui répondent aux principes de l'Open Government Data, ainsi que les géodonnées OpenData du Système d’Information du Territoire à Genève (SITG).

de: Der Kanton Genf verfolgt seit einigen Jahren eine Open Data Strategie. Auf diesem Portal finden Sie Daten aus der kantonalen Verwaltung die die Prinzipien des Open Government Data erfüllen, sowie sämtliche "Open-Geodaten" des Genfer Geoinformationssystems (SITG). ***Die Datensätze finden Sie unter der betreffenden Organisation***

en: The canton of Geneva has been committed to making public data open for a few years now. On this portal, you will find all the data from the Geneva cantonal administration that meet the principles of Open Government Data, as well as geodata from the geographic information system of Geneva (SITG) that fall under the OpenData policy.

it: Il cantone di Ginevra si è impegnato in un processo di apertura dei dati pubblici per diversi anni. Su questo portale, è possibile trovare tutti i dati dell'amministrazione del cantone di Ginevra che soddisfano i principi del Open Government Data, inoltre ai geodati OpenData del Sistema Informativo Territoriale di Ginevra (SITG).

de: Kanton Genf

en: Canton of Geneva

fr: Canton de Genève

it: Cantone di Ginevra

de: geocat.ch Permalink

spatial: GE

regi

de: Regionen und Städte

en: Regions and cities

fr: Régions et villes

it: Regioni e città

format: API

format: WFS

format: WMS

fr: Serveur de fichiers Opendata

fr: CSV

format: CSV

media_type: text/csv

fr: GDB

format: ZIP

fr: GML

format: GML

fr: KML

format: KML

fr: SHP

format: SHP

value: GEOCAT canton GE


name: Centre de compétences et de coordination OGD / Kompetenz- und Koordinationszentrum für OGD

fr: Ce jeu de données présente les prénoms attribués aux nouveau-né·e·s dans le canton de Fribourg, ventilés par genre (garçon, fille), région linguistique (francophone, germanophone) et année, dès 1987. Les effectifs correspondent au nombre d'enfants nés vivants enregistrés au cours de l'année considérée. Seul le premier prénom attribué à l'enfant est pris en compte. Les variantes orthographiques liées aux accents sont regroupées. Les données proviennent de la Statistique du mouvement naturel de la population (BEVNAT) établie par l'Office fédéral de la statistique. Pour plus d'informations: [https://www.bfs.admin.ch/bfs/fr/home/statistiques/population/enquetes/bevnat.assetdetail.35996254.html](https://www.bfs.admin.ch/bfs/fr/home/statistiques/population/enquetes/bevnat.assetdetail.35996254.html)

fr: Prénoms, dès 1987

fr: demographie nouveau-ne-e prenom

ssd

fr: Le Service de la statistique et de la donnée est le centre de compétence cantonal pour la statistique publique. Il collecte, analyse et diffuse des données statistiques sur la situation et l’évolution du canton, soutient les services de l’État dans la gestion des données et coordonne la diffusion des données administratives en libre accès.

en: The Statistics and Data Service is the cantonal competence center for public statistics. It collects, analyzes, and disseminates statistical data on the situation and development of the canton, supports state services in data management, and coordinates the dissemination of administrative data in open access.

de: Das Amt für Statistik und Daten ist das kantonale Kompetenzzentrum für die öffentliche Statistik. Es erhebt, analysiert und verbreitet statistische Daten über die Lage und die Entwicklung des Kantons, unterstützt die Dienststellen des Staats bei der Verwaltung ihrer Daten und koordiniert die Veröffentlichung der offenen Verwaltungsdaten.

it: Il Servizio Statistiche e Dati è il centro di competenza cantonale per la statistica pubblica. Raccoglie, analizza e diffonde dati statistici sulla situazione e l'evoluzione del cantone, supporta i servizi statali nella gestione dei dati e coordina la diffusione dei dati amministrativi ad accesso aperto.

de: Amt für Statistik und Daten Freiburg

en: Statistics and Data Service Fribourg

fr: Service de la statistique et de la donnée Fribourg

it: Servizio Statistiche e Dati Friburgo

de: ssd

key: guid

key: uri

value: DCAT Harvester Kt. FR

soci

de: Bevölkerung und Gesellschaft

fr: Population et société

it: Popolazione e società

fr: Prénoms, dès 1987 (csv)

fr: csv

fr: Prénoms, dès 1987 (json)

fr: json

format: JSON

media_type: application/json

fr: Prénoms, dès 1987 (jsonl)

fr: jsonl

fr: Prénoms, dès 1987 (parquet)

fr: parquet

format: PARQUET

fr: Prénoms, dès 1987 (rdfxml)

fr: rdfxml

format: RDF XML

media_type: application/rdf+xml

fr: Prénoms, dès 1987 (jsonld)

fr: jsonld

format: JSON-LD

fr: Prénoms, dès 1987 (turtle)

fr: turtle

format: RDF Turtle

media_type: text/turtle

fr: Prénoms, dès 1987 (n3)

fr: Prénoms, dès 1987 (xlsx)

fr: xlsx

format: XLS

demographie

display_name: demographie

nouveau-ne-e

display_name: nouveau-ne-e

prenom

display_name: prenom


fr:
Ce jeu de données présente le parc annuel des bâtiments à usage d'habitation de 2010 à 2019, par commune, selon la catégorie de bâtiment, le nombre de niveaux, le nombre de logements, l'époque de construction, le générateur de chaleur et la source de chaleur pour le chauffage et l'eau chaude sanitaire.Données selon la dernière version du [catalogue des caractères](https://www.housing-stat.ch/fr/docs/catalog/index.html) du Registre fédéral des bâtiments et des logements (RegBL).
Si la valeur indiquée pour le nombre de logements est nulle, c'est qu'elle ne peut être indiquée (ex. hôtels, homes pour personnes âgées, etc.). Liens utiles : [Page OFS](https://stats.swiss/?pg=0&fc=Th%C3%A8me&fs[0]=Th%C3%A8me%2C1%7CConstruction%20et%20logement%2309%23%7CB%C3%A2timents%2309-02%23&bp=true&snb=6), [Page d'accueil du domaine SSD](https://www.fr.ch/deef/ssd/statistiques-par-themes/batiments).

fr: batiments logements chauffage energie commune

ssd

fr: Le Service de la statistique et de la donnée est le centre de compétence cantonal pour la statistique publique. Il collecte, analyse et diffuse des données statistiques sur la situation et l’évolution du canton, soutient les services de l’État dans la gestion des données et coordonne la diffusion des données administratives en libre accès.

fr: Service de la statistique et de la donnée Fribourg

econ

de: Wirtschaft und Finanzen

en: Economy and finance

fr: Économie et finances

it: Economia e finanze

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (csv)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (json)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (jsonl)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (parquet)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (rdfxml)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (jsonld)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (turtle)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (n3)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, de 2010 à 2019 (xlsx)

batiments

display_name: batiments

chauffage

display_name: chauffage

commune

display_name: commune

energie

display_name: energie

logements

display_name: logements


fr: Ce jeu de données présente le parc annuel des bâtiments à usage d'habitation par commune, selon la catégorie de bâtiment, le nombre de niveaux, le nombre de logements, l'époque de construction, le générateur de chaleur et la source de chaleur pour le chauffage et l'eau chaude sanitaire dès 2020.Données selon la dernière version du [catalogue des caractères](https://www.housing-stat.ch/fr/docs/catalog/index.html) du Registre fédéral des bâtiments et des logements (RegBL).Si la valeur indiquée pour le nombre de logements est nulle, c'est qu'elle ne peut être indiquée (ex. hôtels, homes pour personnes âgées, etc.). Liens utiles : [Page OFS](https://stats.swiss/?pg=0&fc=Th%C3%A8me&fs[0]=Th%C3%A8me%2C1%7CConstruction%20et%20logement%2309%23%7CB%C3%A2timents%2309-02%23&bp=true&snb=6), [Page d'accueil du domaine SSD](https://www.fr.ch/deef/ssd/statistiques-par-themes/batiments).

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (csv)

fr: csv

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (json)

fr: json

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (jsonl)

fr: jsonl

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (parquet)

fr: parquet

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (rdfxml)

fr: rdfxml

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (jsonld)

fr: jsonld

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (turtle)

fr: turtle

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (n3)

fr: Bâtiments à usage d'habitation selon différentes caractéristiques, dès 2020 (xlsx)


en:
This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.
These data were analyzed and results published by Schweizer et al. (2021). They characterized the avalanche danger levels based on signs of instability (whumpfs, shooting cracks, recent avalanches), snow stability class and new snow height.
The data are provided in a csv file (589 records); the variables are described in the corresponding read-me file.
These data are the basis of the following publication: Schweizer, J., Mitterer, C., Reuter, B., and Techel, F.: Avalanche danger level characteristics from field observations of snow instability, Cryosphere, 15, 3293-3315, https://doi.org/10.5194/tc-15-3293-2021, 2021.
 Acknowlegements
Many of the data were recorded by SLF observers and staff members, among those Roland Meister, Stephan Harvey, Lukas Dürr, Marcia Phillips, Christine Pielmeier and Thomas Stucki. Their contribution is gratefully acknowledged.

en: avalanche-forecasting avalanche-danger danger-level snow-instability snow avalanche

envidat

fr: EnviDat est le portail de données du WSL; il offre un accès uniformisé et réglementé aux données de surveillance et de recherche en matière d’environnement. Ce portail est capable d’héberger et de publier des ensembles de données précieuses et uniques, qui sont partagées aux quatre coins de la planète. Ce faisant, EnviDat favorise la recherche et contribue à l’avancement des sciences environnementales.

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

de: EnviDat ist das Datenportal der WSL für einen einheitlichen und verwalteten Zugang zu Umweltdaten aus Monitoring- und Forschungsprojekten. Auf dem Portal können Datensätze gehostet und veröffentlicht werden. Durch die weltweite Bereitstellung einzigartiger und wertvoller Datensätze leistet EnviDat einen Beitrag zur Forschung im Bereich der Umweltwissenschaften.

it: EnviDat è il portale dati del WSL che fornisce un accesso unificato e gestito ai dati di monitoraggio ambientale e di ricerca. Il portale ha la capacità di ospitare e pubblicare serie di dati preziosi e unici che vengono condivisi in tutto il mondo. Così facendo, EnviDat promuove la ricerca e contribuisce al progresso della scienza ambientale.

de: EnviDat: das Umwelt-Datenportal

en: EnviDat: the Environmental Data Portal

fr: EnviDat: le portail de données environnementales

it: EnviDat: il portale dei dati ambientali

political_level: other

de: EnviDat

value: Envidat Harvester

envi

de: Umwelt

fr: Environnement

en:
Resource Title: Read-me file
    Size: 133885 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/321f2050-93be-4772-b5d8-562f46658256/resource/b3aeef4d-1b54-467c-88b4-4d2da8076885/download/read-me.pdf
    Description: This read-me file contains the descriptions of the variables.

format: PDF

media_type: application/pdf

en:
Resource Title: Field observations of snow instabilities
    Size: 107300 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/321f2050-93be-4772-b5d8-562f46658256/resource/5927f50c-3a33-4ceb-91bf-d583efd14cfe/download/snow_instability_field_data.csv
    Description: This data set includes 589 snow profile observations including a rutschblock test, observations of signs of instability and an assessment of the local avalanche danger level, mainly recorded in the region of Davos (eastern Swiss Alps) during the winter seasons 2001-2002 to 2018-2019.

avalanche

display_name: avalanche

avalanche-danger

display_name: avalanche-danger

avalanche-forecasting

display_name: avalanche-forecasting

danger-level

display_name: danger-level

snow

display_name: snow

snow-instability

display_name: snow-instability


en:
Atmospheric drought (high vapor pressure deficit, VPD) and soil drought frequently co-occur, yet plants respond to these stressors through partially distinct physiological pathways, potentially altering source (photosynthesis) and sink (growth and respiration) activities. Despite significant advances in understanding drought impacts on photosynthesis and plant hydraulics, the independent and interactive effects of soil vs. atmospheric drought on whole-tree carbon balance, including non-structural carbohydrate (NSC) dynamics, allocation priorities, and growth, remain insufficiently understood.
This project aims to disentangle the effects of soil and atmospheric droughts on tree sink and source dynamics in European forests, by studying leaf-to-whole-tree ecophysiological processes that drive growth, carbon sequestration and tree mortality. Research will primarily be conducted in Pfynwald (Valais, Switzerland), as part of the VPDrought experiment, where mature Scots pines (Pinus sylvestris) are exposed to increased soil drought and reduced VPD.
Our study addresses the research question: how do soil and atmospheric droughts differentially affect tree carbon uptake and allocation to growth? We hypothesize that atmospheric drought primarily limits carbon assimilation via stomatal closure, leading to reduced growth and respiration (sink activity), whereas soil drought suppresses both carbon uptake (source activity) and sink strength (growth and respiration), leading to stronger shifts in carbon allocation and early reductions in sink demand. Soil drought restricts carbon transport to belowground sinks, while atmospheric drought promotes NSC accumulation in source tissues due to stomatal closure and reduced sink demand.
**Methods:**
Four campaigns (~4 days each) will be conducted at VPDrought during the summer of 2026-2028, covering four experimental treatments: 1) soil drought + ambient air, 2) soil drought + reduced VPD, 3) irrigation + ambient air, 4) irrigation + reduced VPD. Each treatment will include 6-9 replicates.
During the campaigns, gas exchange measurements will be performed, including net photosynthesis, stomatal conductance, temperature responses of photosynthesis and respiration, and A/Ci curves, using three LI-COR systems (LI-6800). Twig water potential (pre-dawn and midday) will be measured with a Scholander pressure chamber (PMS, model 1505D). Twig, needles, and root samples will be collected across treatments for non-structural carbohydrate (NSC) analyses. Stem respiration measurements will be taken using a custom-made chamber and analyzed with the LI-COR (LI-8100A). In addition, stem radial growth (measured with dendrometers) and leaf area development (using Lidar measurements) will be utilized as proxies for sink activity and carbon allocation to structural growth.  Measurements will be repeated across all years, with additional assessments in 2027 and 2028, including resin flow and phloem transport efficiency using a 13C pulse labeling approach.

en: Carbon balance under dual droughts: Disentangling the effects of soil and atmospheric moisture on tree sink and source dynamics in European forests.

en: gas-exchange vpd carbon-dynamics pinus-sylvestris soil-drought

envi

en: Environment

format: TXT

carbon-dynamics

display_name: carbon-dynamics

gas-exchange

display_name: gas-exchange

pinus-sylvestris

display_name: pinus-sylvestris

soil-drought

display_name: soil-drought

vpd

display_name: vpd


en:
The dataset includes spatially aggregated canopy temperature measurements (°C) of one to two tree crowns obtained using infrared (IR) point sensors.
The dataset was quality screened using a multi-step outlier detection procedure based on Median Absolute Deviation (MAD) statistics within a defined analysis period (= 1 year).
**Three complementary quality-control checks were applied**:
1\. Global MAD screening – detects observations that strongly deviate from the overall distribution of each individual sensor time series.
2\. Local spike detection – identifies short-term anomalous spikes within each sensor time series using rolling median and rolling MAD statistics.
3\. Cross-sensor consistency screening – compares measurements across all sensors recorded at the same timestamp to identify extreme deviations relative to the network-wide median behaviour.
Each observation received individual QC flags for all three checks, followed by a combined final quality flag. Observations passing all criteria were labelled as `good`, while flagged observations were labelled as `outlier` if considered suspect, or as `bad` if associated with known sensor drifts. Observations labelled as `outlier` may still represent physically plausible values, while observations labelled as `bad` should not be considered for further analysis. All observations were retained in the exported dataset for traceability.

en: Pfynwald VPDrought experiment canopy temperature parameter

en: pinus-sylvestris irrigation ir-sensor transpiration cooling pfynwald vpdrought heat-stress

en:
Resource Title: pfynwald_vpdrought_canopy_temperature_1min_2024.zip
    Size: 51370310 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a585bca2-a5e2-4810-b9a8-e831fdc7aebd/resource/966309dc-3bf5-4bbc-9964-6af79bf294fd/download/pfynwald_vpdrought_canopy_temperature_1min_2024.zip
    Description:

media_type: application/zip

en:
Resource Title: pfynwald_vpdrought_canopy_temperature_1min_2025.zip
    Size: 50905903 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a585bca2-a5e2-4810-b9a8-e831fdc7aebd/resource/3ed284f7-5ca4-4b25-9441-00722b92e58d/download/pfynwald_vpdrought_canopy_temperature_1min_2025.zip
    Description:

en:
Resource Title: pfynwald_vpdrought_canopy_temperature_10min_2024.zip
    Size: 5872908 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a585bca2-a5e2-4810-b9a8-e831fdc7aebd/resource/212221fb-14a0-4157-97e9-1670e221959d/download/pfynwald_vpdrought_canopy_temperature_10min_2024.zip
    Description:

en:
Resource Title: pfynwald_vpdrought_canopy_temperature_10min_2025.zip
    Size: 5833672 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a585bca2-a5e2-4810-b9a8-e831fdc7aebd/resource/66747b91-d345-4e98-97c7-9d7b3ce92266/download/pfynwald_vpdrought_canopy_temperature_10min_2025.zip
    Description:

cooling

display_name: cooling

heat-stress

display_name: heat-stress

ir-sensor

display_name: ir-sensor

irrigation

display_name: irrigation

pfynwald

display_name: pfynwald

transpiration

display_name: transpiration

vpdrought

display_name: vpdrought


en:
The dataset refers to samples collected across 6 farms in the Bega Valley region of Australia. The farms are located in the grassy woodland ecoregion and are extensively managed with sheep and cattle grazing. Livestock shares the landscape with native mammalian and invertebrate herbivores. On each farm, two nested fenced exclosure systems were established. These included an open area, a fence excluding livestock but allowing access to native mammals and invertebrates and a fence excluding all mammals but allowing access to invertebrates. In each farm, one of these systems was located in a zone dominated by the native Kangaroo grass, and another in a zone co-dominated by the Invasive African lovegrass. The sample and treatment numbering, as well as a summary of the aggregated indicators is provided in the file Indicator_summary.
Leaf swab s(phyllosphere) samples were taken and DNA was extracted and amplified for 16s and ITS markers. The resulting metabarcoding derived tables are presented (Bacteria_swabs and Fungi_swabs respectively), as are the inferred functional profiles for bacteria at the pathway level (PICRUSt2_pathways), as derived by the PICRUSt2 pipeline.
Two 20 by 100 cm strips had their vegetation removed and their living and litter biomass quantified by dry weight (Biomass). Sensors measuring temperature 10 cm above the soil surface were installed in each plot and their output is presented in Climate. Invertebrates were collected with both pitfall traps and sweepnets and the counts for both sampling techniques can be found in Invertebrates. Damage scores across different categories were collected on 5 leaves of the two species in each plot and the average scores can be found in Leaf_damage.

en: Herbivory disrupts invasion_associated phyllosphere and arthropod community shifts in grasslands

en: biological-invasions phyllosphere grassland invertebrates herbivore-exclusion

en:
Resource Title: Indicator_summary.csv
    Size: 19067 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/d68e884c-0153-4d0f-95c6-eb5702972cce/download/indicator_summary.csv
    Description: Sample metadata and aggregate indicators

en:
Resource Title: PICRUSt2.csv
    Size: 469821 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/665f8907-396a-4b1e-b564-fa100a2b5968/download/picrust2.csv
    Description: Predicted pathway profiles of sampled phyllosphere bacterial communities

en:
Resource Title: Fungi_swabs.csv
    Size: 1059062 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/02fb3082-a33e-4af4-8ea1-d4c4953eec4e/download/fungi_swabs.csv
    Description: OTU table of fungal phyllosphere communities

en:
Resource Title: Climate.csv
    Size: 5096079 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/787a3947-a316-4ac5-aeda-ffacfd832bed/download/climate.csv
    Description: Sensor collected soil surface temperatures

en:
Resource Title: Invertebrates.csv
    Size: 18842 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/493ae610-9929-4499-8dfb-fdb7c8feafed/download/invertebrates.csv
    Description: Counts of invertebrates collected with pitfall traps and sweepnets

en:
Resource Title: Biomass.csv
    Size: 3031 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/ac6aecef-5a8d-4539-b231-3403691fc3f9/download/biomass.csv
    Description: Dry biomass weight of collected plant material

en:
Resource Title: Bacteria_swabs.csv
    Size: 1153434 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/f97a1423-4749-45fa-a07e-93697dd72676/download/bacteria_swabs.csv
    Description: OTU table of bacterial phyllosphere communities

en:
Resource Title: Leaf_damage.csv
    Size: 2007 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/593901f4-0a08-4dc6-98a4-1decf904dbc1/resource/5c2f7e5f-cc22-4ac6-aa16-f3ef13d27ea7/download/leaf_damage.csv
    Description: Aggregated leaf damage scores for African lovegrass and Kangaroo grass

biological-invasions

display_name: biological-invasions

grassland

display_name: grassland

herbivore-exclusion

display_name: herbivore-exclusion

invertebrates

display_name: invertebrates

phyllosphere

display_name: phyllosphere


en:
Dataset content
Continuous soil measurements (10-min resolution) from the Pfynwald long-term irrigation experiment, covering five treatments: **control, irrigation, irrigation+VPD, roof, roof+VPD**.
Measurements were taken at multiple plots and at three depths (-0.1, -0.8, -1.2 m below soil surface).
Sensors include TEROS-21 (matric potential), TEROS-12 (volumetric water content), TEROS-21 (soil temperature), and TEROS-12 (electrical conductivity).
 Processing steps
**1. Raw retrieval** from the LWF database (`ada.v_messvar`, project `Pfynwald irrigation`), 10-min cadence.
**2. Range filtering** to remove physically implausible values:
   - SWP: drop values ≥ 0 kPa or ≤ −8000 kPa
   - VWC: drop values ≤ 0 or - 0.8 m³/m³
   - TEM: drop values - 40 °C or - −20 °C
**3. Duplicate aggregation** within the same 10-min timestamp by mean (median for SWP).
**4. Temperature correction (SWP_TC)**: matric potential is temperature-corrected using the TEROS-21 manufacturer equation referenced to T_ref = 20 °C:
   - Convert SWP (kPa) to head h = |SWP|·10.197, then pF = log10(h)
   - ΔpF = (919.890694·ΔT − 6.945352·ΔT²)·exp(−150.754657 / (pF + 9.153084))
   - SWP_TC = −10^(pF + ΔpF) / 10.197
**5. No gap filling applied.** Missing values remain absent from the delivered CSV.
 Methods
- Database query and aggregation: R, `data.table`, `rLWFpg`
- Temperature correction: in-house implementation of TEROS-21 manufacturer formula
- See repository / script reference below
 Limitations
- Values flagged out of range during step 2 are absent (not interpolated)
- Winter periods may include frozen-soil readings — these pass the range filter as physical sensor outputs but should be interpreted with caution by downstream users
- SWP_TC rows carry the messvar_id of the underlying SWP sensor; the corresponding TEM sensor used for correction is paired via site/plot/depth/datetime but not encoded in the messvar_id
- Sensor calibration uses manufacturer defaults; site-specific calibration was not performed
- Roofs were installed in spring 2024. Readings under "roof" / "roof_vpd" treatments before that reflect ambient conditions, not the experimental treatment.
- In 2025, three additional TEROS-21 sensors were added per roofed treatment at 10 cm depth to obtain a balanced sample relative to roof position (under panels, at panel edges, and in inter-panel gaps).
  This expansion means roofed treatments have six positions at 10 cm from 2025 onward, compared to three before.

en: Pfynwald VPDrought experiment soil parameters

en: teros-21 irrigation pfynwald soil-moisture vpdrought teros-12 irrigation-manipulation water-potential electrical-conductivity soil-temperature pinus-sylvestris drought-experiment

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: pfynwald_vpdrought_soil_10Minutes_2025.zip
    Size: 66201780 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/12bc2274-e48f-4ff1-8961-b86699aaa450/resource/edaa3e3a-1045-4d2f-952f-e23689c20bb9/download/pfynwald_vpdrought_soil_10minutes_2025.zip
    Description:

en:
Resource Title: pfynwald_vpdrought_soil_10Minutes_2024.zip
    Size: 59025329 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/12bc2274-e48f-4ff1-8961-b86699aaa450/resource/3606ad5b-f308-4bfc-8aa4-db5b017ee47a/download/pfynwald_vpdrought_soil_10minutes_2024.zip
    Description:

drought-experiment

display_name: drought-experiment

electrical-conductivity

display_name: electrical-conductivity

irrigation-manipulation

display_name: irrigation-manipulation

soil-moisture

display_name: soil-moisture

soil-temperature

display_name: soil-temperature

water-potential

display_name: water-potential


en:
Description
This dataset contains continuous sap-flow measurements and derived whole-tree transpiration estimates for 45 Pinus sylvestris trees at the Pfynwald experimental platform in southwestern Switzerland during 2024–2025. Trees were distributed across five treatment combinations established within the VPDrought experiment: Control, Irrigation, Irrigation + VPD reduction, Roof, and Roof + VPD reduction. Sap flux density was measured every 10 min using heat-ratio sap-flow sensors (IX-SF30, Implexx, Melbourne, Australia) installed at breast height on the north-facing side of each tree.
Raw temperature signals were converted to sap flux density following the heat ratio method (Marshall 1958; Burgess et al. 2001; Forster 2019). Thermal diffusivity and related wood properties were parameterized using increment cores collected near the site and tree-specific allometric relationships. Probe misalignment was corrected by estimating a zero-flow offset from nighttime measurements during well-watered, low-VPD conditions in February 2024 and subtracting this offset from the full time series.
Sap flux density measurements at 0.5 and 1.5 cm sapwood depth were combined to total sap flow for the depth where the sensors were installed. Whole-tree transpiration can be calculated by integrating sap flux density across concentric sapwood depth of 2 cm.
The dataset includes processed sap flux density values together with metadata on treatment, tree identity, sensor depth, and quality-control corrections. Limitations include possible residual uncertainty due to sensor drift, assumptions in the sapwood depth, and the replacement of two trees that died during summer 2024 and lost parts of their crown in 2025.
 Additional Information
Data processing was performed in an R environment following the heat ratio method workflow described by Forster (2019). Raw heat-pulse measurements were converted to sap flux density and subsequently corrected for thermal diffusivity, and probe misalignment. Outliers and implausible values were identified and removed using the R package datacleanr (Hurley et al. 2022). Sensor needle misalignment was corrected by defining a zero-flow offset during periods of low atmospheric demand and assumed zero sap flow (i.e. rainy periods), and subtracting this offset from the time series. The processing script used to derive the dataset is available from the authors upon request.
Relevant literature:
- Burgess S.S.O., Adams M.A., Turner N.C., Beverly C.R., Ong C.K., Khan A.A.H. & Bleby T.M. (2001) An improved heat pulse method to measure low and reverse rates of sap flow in woody plants†. Tree Physiology 21, 589–598.
- Forster M.A. (2019) The Dual Method Approach (DMA) Resolves Measurement Range Limitations of Heat Pulse Velocity Sap Flow Sensors. Forests 10.
- Hurley A.G., Peters R.L., Pappas C., Steger D.N. & Heinrich I. (2022) Addressing the need for interactive, efficient, and reproducible data processing in ecology with the datacleanr R package. PLOS ONE 17, e0268426-.
- Marshall D.C. (1958) Measurement of Sap Flow in Conifers by Heat Transport. 1. Plant Physiology 33, 385–396.

en: Pfynwald VPDrought  experiment sap flow parameter

en: transpiration vpd xylem-flux pinus-sylvestris drought-experiment sap-flow irrigation continuous-monitoring tree-transpiration reduction forest-ecophysiology rhone-valley pfynwald rain-exclusion vpdrought

en:
Resource Title: pfynwald_vpdrought_sapflow_10Minutes_2024.zip
    Size: 53546175 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a9e439e5-827a-40a7-956c-1a05699e6adc/resource/6a0cd3b8-8e29-4b85-9296-86196808fd32/download/pfynwald_vpdrought_sapflow_10minutes_2024.zip
    Description:

en:
Resource Title: pfynwald_vpdrought_sapflow_10Minutes_2025.zip
    Size: 53424631 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/a9e439e5-827a-40a7-956c-1a05699e6adc/resource/455662d6-f0b7-4ea8-8cb6-d31eb714952e/download/pfynwald_vpdrought_sapflow_10minutes_2025.zip
    Description:

continuous-monitoring

display_name: continuous-monitoring

forest-ecophysiology

display_name: forest-ecophysiology

pinus-sylvestris

display_name: pinus-sylvestris

rain-exclusion

display_name: rain-exclusion

reduction

display_name: reduction

rhone-valley

display_name: rhone-valley

sap-flow

display_name: sap-flow

tree-transpiration

display_name: tree-transpiration

vpd

display_name: vpd

xylem-flux

display_name: xylem-flux


en:
Project description:
Forest soils are among the largest terrestrial carbon pools. How soil carbon (C) responds to changing water availability is still poorly understood, particularly in already water-limited systems. This project investigates the composition and persistence of soil organic matter (SOM) and associated microbial communities in a dry inner-Alpine Scots pine forest exposed to long-term changes in precipitation and atmospheric demand. Earlier work showed that 19 years of irrigation redistributed bulk soil C from the organic layer into the mineral soil. Although total soil C stocks remained unchanged, a higher incorporation into mineral soils may increase long-term C storage (Guidi et al., 2022; Minich et al., 2026). We now ask, how persistent this redistributed C is, in which soil C fraction (particulate or mineral-associated) it resides, and which microbial communities are associated with it.
The work is based at Pfynwald, where a long-term irrigation experiment (since 2003) and the recently installed [VPDrought platform](https://vpdrought.wsl.ch/de/) together span three precipitation regimes (≈300, 600, and 1200 mm per year) crossed with manipulation of atmospheric vapour-pressure deficit. This setup allows us to disentangle the effects of soil and atmospheric drought, which typically co-vary in nature but may shape soil carbon dynamics through distinct pathways. SOM is separated by physical fractionation into particulate (POM) and mineral-associated (MAOM) fractions, characterized through elemental and isotopic analyses (C, N, δ¹³C, δ¹⁵N, ¹⁴C) and fraction-specific microbial community profiling. At the bulk soil level, we measure nutrient pools and extracellular enzyme activities, continue year-round soil respiration, and complement them by CH4 fluxes. Together, these data will reveal how soil carbon stocks and microbial processing respond to changing precipitation in a water-limited pine forest.

en: Soil organic matter dynamics under altered precipitation in a dry Scots pine forest

en: som microbial-communities drought pfynwald radiocarbon

drought

display_name: drought

microbial-communities

display_name: microbial-communities

pfynwald

display_name: pfynwald

radiocarbon

display_name: radiocarbon

som

display_name: som


historical-vegetation-height-model-nfi

en:
The main datasets available are 4 nationwide **Digital Surface Models (DSMs)** and corresponding **Vegetation Height Models (VHMs)** with a spatial resolution of 1 m.
The Vegetation Height Models are calculated by subtraction of a Digital Terrain Model (swissAlti3d version 2017, swisstopo) from the generated DSMs. DSMs were derived by image matching of **scanned historical images** (panchromatic 8-bit) acquired with a RC10 frame camera, in the context of the swisswide image acquisition campaigns of swisstopo.
Nationwide products could be generated for the **four epochs**
 1979-1985 / 1985-1991 / 1990-1998 / 1998-2006.
Additional information about successful image matching vs. interpolation of pixels and about the year of image acquisition are available in the corresponding files (**matched_area_xxxx_xxxx_binay.tif** and **meta_acquisition_year_xxxx_xxxx.json**, respectively).
To separate vegetation from non-vegetation areas a mask derived from a combination of information of the Topographic landscape model - TLM (swisstopo) and a NDVI layer was used and is supplied as **"TLM_mask.tif"**
Due to the occurrence of unrealistic vegetation height values caused by the failure of the image matching process (mainly overexposed images and clouds or very steep rocky terrain), pixel values - 60 m were set to NoData and - 0 m to 0.
All datasets are available in the Reference System **LV95 (EPSG: 2056) LN02 (EPSG: 5728)** and share the common **extent definition** (xmin ymin xmax ymax): 2480000 1070000 2840000 1300000

en: Historical Vegetation Height Model NFI

en: historical dsm nfi forest vhm photogrammetry

title_for_slug: historical-vegetation-height-model-nfi

en:
Resource Title: 1990-1998 Vegetation Height Model
    Size: 219902325555 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.528%2F1990-1998_vegetation_height_model_nfi%2F
    Description:

en:
Resource Title: readme.txt
    Size: 2045 bytes
    Format: text/plain
    URL: https://www.envidat.ch/dataset/d16d27e5-b239-4909-b914-1b0b5337dab3/resource/f6cabeaa-bf7c-4141-bea9-af99d3224487/download/readme.txt
    Description: Description of available data

media_type: text/plain

en:
Resource Title: 1979-1985 Vegetation Height Model
    Size: 219902325555 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.528%2F1979-1985_vegetation_height_model_nfi%2F
    Description:

en:
Resource Title: 1985-1991 Vegetation Height Model
    Size: 219902325555 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.528%2F1985-1991_vegetation_height_model_nfi%2F
    Description:

en:
Resource Title: 1998-2006 Vegetation Height Model
    Size: 219902325555 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.528%2F1998-2006_vegetation_height_model_nfi%2F
    Description:

dsm

display_name: dsm

forest

display_name: forest

historical

display_name: historical

nfi

display_name: nfi

photogrammetry

display_name: photogrammetry

vhm

display_name: vhm


vegetation-height-model-lidar-nfi

en:
Countrywide **vegetation height models (VHM)** were generated for Switzerland based on
LiDAR data acquired in the framework of the **swissSURFACE3D** campaign (swisstopo).
To derive vegetation height, the classified point cloud got normalized by calculating height above ground for all points not belonging to the class ground (2). Vegetation point (class 3,4,5) got then rasterized to a grid size of 0.5 m using an interpolation distance of one grid cell. Raster cells not belonging to a vegetation class got set to 0 m above ground.
These VHM's are produced in the framework of the Swiss National forest Inventory (NFI).
Each year about 1/6 of switzerland surface is updated using swissSURFACE3D data in leaf-off state.

en: Vegetation Height Model LiDAR NFI

en: vegetation three-dimensional forest vegetation-classification vegetation-height point-cloud nfi

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

title_for_slug: vegetation-height-model-lidar-nfi

envi

en: Environment

en:
Resource Title: 2024 Vegetation Height Model LiDAR NFI
    Size: 149786984448 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.664%2F2024%2F
    Description: Vegetation Height Model of Switzerland for the year 2024 (cloud optimized geotiff)
and Metadata file with information about acquisition date and point densities of used LiDAR data for tiles of 1 x 1 km (geopackage).

en:
Resource Title: 2025 Vegetation Height Model LiDAR NFI (current)
    Size: 160202280141 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2Fenvicloud%2F&prefix=doi%2Fenvidat.664%2F2025%2F
    Description: Vegetation Height Model of Switzerland for the year 2025 (cloud optimized geotiff)
and Metadata file with information about acquisition date and point densities of used LiDAR data for tiles of 1 x 1 km (geopackage).

point-cloud

display_name: point-cloud

three-dimensional

display_name: three-dimensional

vegetation

display_name: vegetation

vegetation-classification

display_name: vegetation-classification

vegetation-height

display_name: vegetation-height


en:
The data show locations of the most recent outdoor activity (referred to as restorative location, RL) of Swiss residents, and descriptive characteristics of these places. The data was collected via an online participatory mapping survey targeting residents of urban, peri-urban, and rural areas across Switzerland. Respondents were selected using a stratified random sampling method, considering the greenness and noise exposure of their home locations, and demographic factors such as age, gender, and language region.
The survey was conducted using Maptionnaire software (https://maptionnaire.com). Participants were asked to map the location of their most recent outdoor activity (referred to as restorative location, RL) and provide additional information on their experience in these places. The responses were integrated with the quantitative spatial information (biophysical landscape characteristics, referred to as metrics) on the visited locations, obtained from spatial and remote sensing data.

en: Restorative locations from participatory mapping by Swiss residents

en: soundscape restorative-environments gis-analysis traffic-noise sensory-perception quiet-areas

gis-analysis

display_name: gis-analysis

quiet-areas

display_name: quiet-areas

restorative-environments

display_name: restorative-environments

sensory-perception

display_name: sensory-perception

soundscape

display_name: soundscape

traffic-noise

display_name: traffic-noise


en:
This study examines the siting scenarios for renewable energy infrastructure (REI) in Switzerland, incorporating the external costs of ecosystem services and, innovatively, social preferences. This approach challenges the prevalent techno-economic siting paradigm, which often overlooks these externalities. To minimize the external costs of the scenarios while maximizing energy yield, Marxan, an optimization software, was employed.
MARXAN was run for 2 versions: a) without ground-mounted open space PV infrastructure (excl. OS) and b) with ground-mounted open space PV infrastructure (incl. OS). In each version optimization was done using ecological costs (ECUess) or social costs (ECUsoc) in a regular grid of 4x4km (planning unit).
File:
PU_data.shp: compressed Shapefile (ESRI) from the MARXAN optimization with 2216 rows (objects) and 18 columns (variables) with sf (simple feature) and data frame classes.
Headers are described below:
X01 = Planning_units (PUs)
X02 = incl.OS. number of times pu was selected in MARXAN  when optimized for ecological costs
X03 = incl.OS. number of times pu was selected in MARXAN when optimized for social  costs
X04 = incl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X05 = incl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X06 = incl.OS. total energy (MWh/a)
X07 = incl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X08 = incl.OS. energy from wind turbines (MWh/a)
X09 = incl.OS. energy from ground-mounted PV infrastructure (MWh/a)

X10 = excl.OS. number of times pu was selected in MARXAN  when optimized for ecological costs
X11 = excl.OS. number of times pu was selected in MARXAN when optimized for social  costs
X12 = excl.OS. ecological costs (ECUess) of pu when realizing the total energy (normalized)
X13 = excl.OS. social costs (ECUsoc) of pu when realizing the total energy (normalized)
X14 = excl.OS. total energy (MWh/a)
X15 = excl.OS. energy from roof-mounted PV infrastructure (MWh/a)
X16 = excl.OS. energy from wind turbines (MWh/a)
X17 = excl.OS. energy from ground-mounted PV infrastructure (MWh/a)
geometry = Simple feature XY geometry (SFC_POINT) each representing the center of a 4x4km planning unit (PU) in EPSG 21781 (CRS CH1903 / LV03).
For MARXAN optimizations (for meaning of standard files in MARXAN see https://scholar.google.ch/scholar_url?url=https://courses.washington.edu/cfr590/software/Marxan1810/marxan_manual_1_8_2.pdf&hl=de&sa=X&ei=NAWHZI6JJ8PFmAGMo47oDQ&scisig=AGlGAw-DBrsV4kUzkR6GkN1jtG66&oi=scholarr), pu_data.shp was used to generate MARXAN files input.dat, pu.dat, puvfeat.dat, spec.dat.
Coordinate Reference System: CH1903 / LV03. EPSG: 21781.

en: Optimizing renewable energy siting in the Swiss landscape

en: social-acceptance spatial-planning optimization photovoltaic landscape wind-power ecosystem-services energy marxan

ecosystem-services

display_name: ecosystem-services

energy

display_name: energy

landscape

display_name: landscape

marxan

display_name: marxan

optimization

display_name: optimization

photovoltaic

display_name: photovoltaic

social-acceptance

display_name: social-acceptance

spatial-planning

display_name: spatial-planning

wind-power

display_name: wind-power


alan-spongy-moth-laboratory-experiment

en:
Data and Code for ALAN Spongy Moth lab experiment
In this repository you will find all the raw datasets used for the data analysis performed in our paper about the impacts of artificial light at night (ALAN) on the development of Spongy Moth caterpillars. The files are attached as a csv file.
Also attached is all the R-code later used for the statistical analysis and for generating the graphics displayed in the paper. This files are attached as an R.file.

en: ALAN Spongy Moth Laboratory Experiment

en: lab-experiment spongy-moth alan light-pollution insect-development

title_for_slug: alan-spongy-moth-laboratory-experiment

en:
Resource Title: SM_R_NvK.csv
    Size: 56547 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/c93124fd-448c-403b-bc42-70f4b6722636/download/sm_r_nvk.csv
    Description: Raw data used for the Pupation and Cox Hazard/Kaplan Meier Analysis.

en:
Resource Title: SM_R_long_NvK.csv
    Size: 567035 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/401242a6-4430-4725-8c41-263a4f769041/download/sm_r_long_nvk.csv
    Description: Raw data modified and used for the Bodymass Analysis

en:
Resource Title: CoxHazard_KaplanMeier_Mort_Pup.R
    Size: 3933 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/0a205353-8812-4f39-af3f-8d67d2d1495c/download/coxhazard_kaplanmeier_mort_pup.r
    Description: R code for the Cox hazard analysis and producing the Kaplan Meier survival curves for both Mortality and Pupation.

format: OCTET

en:
Resource Title: BodyMass_analysis.R
    Size: 4254 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/0ff15832-2eb3-4420-9afd-869e36ce92ef/download/bodymass_analysis.r
    Description: R code for the bodymass analysis and producing the graphics.

en:
Resource Title: Pupation_analysis.R
    Size: 7606 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/7f7beb40-642a-4a45-a99f-e631c90e1042/resource/f5ad6fb7-b540-4742-8b77-e5fa86855b1d/download/pupation_analysis.r
    Description: R code for the pupation analysis and producing the graphics.

alan

display_name: alan

insect-development

display_name: insect-development

lab-experiment

display_name: lab-experiment

light-pollution

display_name: light-pollution

spongy-moth

display_name: spongy-moth


forest-canopy-layer

en: This dataset provides a LiDAR-based classification of the forest canopy layer for selected Swiss cantons. Combining an area-based and tree-centred approach, forest areas are categorised into four canopy layering classes that differentiate between single-layered and multi-layered canopies.

en: Forest Canopy Layer

en: forest-structure forest-management mountain-forest forest-canopy nfi lidar

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en: EnviDat: the Environmental Data Portal

title_for_slug: forest-canopy-layer

envi

en: Environment

en:
Resource Title: IFA_Data.csv
    Size: 3837 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/01aaf289-b61f-43ab-be61-39d8e4705c30/resource/a15b3bdb-c45e-4a8a-b526-8d43b265862c/download/ifa_data.csv
    Description: Validation data from the Independent Forester Assessment (IFA). x- /y-coordinate (CH1903+ LV03) and layer (1: single-layered; 2: multi-layered; -999: NA).

en:
Resource Title: ForestCanopyLayer.qml
    Size: 8139 bytes
    Format: application/octet-stream
    URL: https://www.envidat.ch/dataset/01aaf289-b61f-43ab-be61-39d8e4705c30/resource/378dbed2-33a3-4398-8a41-5c341d27557d/download/forestcanopylayer.qml
    Description: QGIS QML file with stored layer style definitions for the ForestCanopyLayer.tif.

en:
Resource Title: ForestCanopyLayer.tif
    Size: 558505541 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/01aaf289-b61f-43ab-be61-39d8e4705c30/resource/90bcde13-c71d-4618-a198-362ebccd0516/download/forestcanopylayer.tif
    Description: Forest Canopy Layer (class 1: single-layered; class 2: probably single-layered; class 3: probably multi-layered; class 4: multi-layered).

format: TIFF

media_type: image/tiff

forest-canopy

display_name: forest-canopy

forest-management

display_name: forest-management

forest-structure

display_name: forest-structure

lidar

display_name: lidar

mountain-forest

display_name: mountain-forest

nfi

display_name: nfi


en:
This dataset contains the input data generated for the pseudo-global warming (PGW) experiments used in the study of the March 2022 Antarctic heatwave with the CRYOWRF model. It includes:
- PGW delta fields derived from CMIP6 simulations, used to perturb ERA5 reanalysis data for past and  future storyline experiments.
- Model configuration files required to reproduce the CRYOWRF simulations.
These resources allow researchers to reproduce the experimental setup and generate model outputs consistent with the results presented in the study.

en: Storylines of the March 2022 heatwave event at Concordia - East Antarctica

en: era5 extreme-events event-attribution atmospheric-rivers climate-change antarctica cmip6 heatwave

en:
Resource Title: PGW_deltas.tar
    Size: 3701853962 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/79b7c457-e689-49bd-8330-29b0508d2b2c/resource/9936c23c-dc86-4eaa-8a6f-5a49cffb338c/download/pgw_deltas.tar
    Description: PGW delta fields derived from CMIP6 simulations, used to perturb ERA5 reanalysis data for future storyline experiments.

en:
Resource Title: CRYOWRF_ANT_20220315_ConfigFiles.tar
    Size: 912259096 bytes
    Format: application/x-tar
    URL: https://www.envidat.ch/dataset/79b7c457-e689-49bd-8330-29b0508d2b2c/resource/7f77d116-cd3b-4eec-a6d6-061b0d7b43f6/download/cryowrf_ant_20220315_configfiles.tar
    Description: Model configuration files required to reproduce the CRYOWRF simulations over Antarctica.

antarctica

display_name: antarctica

atmospheric-rivers

display_name: atmospheric-rivers

climate-change

display_name: climate-change

event-attribution

display_name: event-attribution

extreme-events

display_name: extreme-events

heatwave

display_name: heatwave


en:
This dataset contains gridded snow depth projections for Switzerland at 1km X 1km resolution. This study was supported by Speed2Zero. The dataset contains the following structure:
- data/data_reference/hs_reference_1991_2020.nc
- data/data_projection_flat/hs_projection_flat.nc
- data/data_projection_aspect/hs_projection_aspect.nc
The data is based on climatological SPASS dataset (Marty et al., 2015) using the reference period 1991-2020. For this reference data, median snow depth evolution, as well as 5th and 95th percentile were computed for each grid point and can be found in this dataset (data/data_reference/*). Future snow depth evolutions for the flat field (data/data_projection_flat/*) were projected for the temperature scenarios +1°C, +2°C, +3°C and +4°C (deviation from 1991-2020 annual mean temperature in Switzerland) following the literature-based approach of Richter and Marty, 2026. Furthermore, this dataset contains gridded projections for north- and south-facing ascpects with a slope anle of 15° (data/data_projection_aspect/*). Detailed can be found in the related publication Richter et al., in preparation.
The files in this data set have a similar structure. Each file is a NetCDF-file containing snow depths (median, 5th, 95th percentile) for each day of the year, coordinate, temperature scenario (data_projection_flat and data_projection_aspect) and aspect (data_projection_aspect).
References:
Marty, C., Michel, A., Jonas, T., Steijn, C., Muelchi, R., and Kotlarski, S.: SPASS – new gridded climatological snow datasets for Switzerland: potential and limitations, The Cryosphere, 19, 4391–4407, https://doi.org/10.5194/tc-19-4391-2025, 2025.
Richter, B. and Marty, C.: Technical note: Literature based approach to estimate future snow, Hydrol. Earth Syst. Sci., 30, 659–670, https://doi.org/10.5194/hess-30-659-2026, 2026.
Richter et al., Gridded snow depth projections, in prepration.

en: Gridded projections of snow depth for Switzerland

en: snow-depth climate-projections swiss-alps gridded-data scenario future switzerland climate-change snow

climate-projections

display_name: climate-projections

future

display_name: future

gridded-data

display_name: gridded-data

scenario

display_name: scenario

snow

display_name: snow

snow-depth

display_name: snow-depth

swiss-alps

display_name: swiss-alps

switzerland

display_name: switzerland


individual-detected-trees

en: This dataset provides individual detected trees for selected Swiss Cantons (Appenzell Outer-Rhodes - AR, Grisons - GR, Nidwald - NW, Obwald - OW and St. Gall - SG), including the derived diameter at breast height and the derived NFI development stage. Data show the Individual detected trees (ITD) with the x- and y-positions of each detected tree based on a spike-free vegetation height model based on nationwide LiDAR data. The shapefile contains the following attributes: tree height [m] (Height), estimated diameter at breast height [cm] (BHD), and the NFI development stage (Ent; 1: young growth/thicket (- 12 cm); 2: pole timber (12-30 cm); 3: young timber (31-40 cm); 4: medium timber (41-50 cm); 5: old timber (- 50 cm).

en: Individual Detected Trees

en: itd forest-structure forest-management mountain-forest nfi development-stage

title_for_slug: individual-detected-trees

development-stage

display_name: development-stage

forest-management

display_name: forest-management

forest-structure

display_name: forest-structure

itd

display_name: itd


en:
This dataset contains all necessary data to reproduce the validation simulations performed in the linked manuscript.
The validation data includes:
- 2 cm DEM of the study site Weissfluhjoch. For the 3 cm DEM of the study site Chant Sura, see [this dataset](https://www.doi.org/10.16904/envidat.174).
- roughness index rasters for both study sites
- rock point clouds of EOTA and Real Equant shapes
- release points used in the validation simulations of both study sites
- a sample XML input file for the rockfall simulator

en: Validation Data for Rockfall Simulations with Geometric Roughness Model

en: surface-roughness numerical-simulation validation rockfall natural-hazards

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en:
Resource Title: Release Points
    Size: 4649 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/b6c60fc1-448d-445b-98bf-57806bbade9e/resource/636d34b5-5031-4bb6-aa28-b3d1cb11b80e/download/release.zip
    Description: Release points used in the validation simulations of both study sites. Use the *.txt files as input for the simulator.

en:
Resource Title: Roughness Index Rasters
    Size: 36884 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/b6c60fc1-448d-445b-98bf-57806bbade9e/resource/2c863503-c73d-4b97-a660-d2998b717093/download/roughness.zip
    Description: Roughness index rasters used for the validation simulations. They have a resolution of 3 m.

en:
Resource Title: Sample Simulation
    Size: 126372 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/b6c60fc1-448d-445b-98bf-57806bbade9e/resource/230c5218-e7a2-4e4f-8633-0c00695cff71/download/sample_sim.zip
    Description: This dataset contains a small sample simulation to showcase the recommended folder/file structure to run rockfall simulations. The input parameters for the simulation can be changed in the XML file, which is then also used as input for the rockfall simulator.

en:
Resource Title: Rock Pointclouds
    Size: 3779 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/b6c60fc1-448d-445b-98bf-57806bbade9e/resource/c99baa33-50ba-480a-8e4b-82450130900b/download/rocks.zip
    Description: Rock point clouds of EOTA and Real Equant shapes

en:
Resource Title: DEM Weissfluhjoch
    Size: 1005521469 bytes
    Format: image/tiff
    URL: https://www.envidat.ch/dataset/b6c60fc1-448d-445b-98bf-57806bbade9e/resource/9598c524-85b4-4dad-981c-59a126314973/download/dem_weissfluhjoch_2cm.tif
    Description: Digital Elevation Model (DEM) of validation test site Weissfluhjoch with a grid resolution of 2 cm. The DEM for test site Chant Sura can be found in the linked dataset.

natural-hazards

display_name: natural-hazards

numerical-simulation

display_name: numerical-simulation

rockfall

display_name: rockfall

surface-roughness

display_name: surface-roughness

validation

display_name: validation


iwet-insect-weight-estimation-tool

en:
Because of some corrections, a new version of the whole Dataset will be published soon.
We present a novel tool called IWET (Insect Weight Estimation Tool) for estimating morphometrics of arthropod samples using image analysis. IWET is a Java application built around ImageJ libraries and is designed to estimate length, width, area and body mass of individual arthropods from images of specimens sorted into pre-defined taxonomic groups. Using allometric relationships from Sohlströhm et al. (2018), we developed specific regression models to calculate both the individual body mass and the total biomass of arthropod taxonomic groups. IWET provides several advantages: it is non-destructive, cost-effective, and capable of processing large numbers of samples, making it particularly well-suited for biodiversity monitoring projects investigating changes in abundance and biomass, as well as for automating measurements such as specimen length, width and area.

en: IWET – Insect Weight Estimation Tool

en: arthropods biomass image-analysis body-size temperate allometric automatic-detection

envidat

en: EnviDat: the Environmental Data Portal

title_for_slug: iwet-insect-weight-estimation-tool

envi

en: Environment

en:
Resource Title: IWET_EnviDat.zip
    Size: 10255073 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/3d7e7b44-af7f-4824-84b8-fc2abd1f04b3/resource/7f5681bf-cf49-47b0-a8cc-a1e8dde61354/download/iwet_envidat.zip
    Description: IWET-Software with prorpriety and model files (inkl. materials)

en:
Resource Title: R-Script_and_suppl.zip
    Size: 2243953 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/3d7e7b44-af7f-4824-84b8-fc2abd1f04b3/resource/6ca41ee8-bda9-4e65-b859-1dd9c0184c50/download/r-script_and_suppl.zip
    Description: R Script for Regression models

allometric

display_name: allometric

arthropods

display_name: arthropods

automatic-detection

display_name: automatic-detection

biomass

display_name: biomass

body-size

display_name: body-size

image-analysis

display_name: image-analysis

temperate

display_name: temperate


sliding-deadwood-geometry-generator

en: This dataset contains a python code to create deadwood log geometries as well as sample data used for simulations of sliding deadwood with RAMMS::Rockfall.

en: Sliding Deadwood Geometry Generator

en: deadwood natural-hazards forest-management mass-movements protection-forest

title_for_slug: sliding-deadwood-geometry-generator

en:
Resource Title: Sample Data
    Size: 108780 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/a6325cb3-bdb3-4b7c-90f6-1ca41c159918/resource/5ba5fd5d-1b1e-485f-ab24-422df5a509ac/download/sample_data.zip
    Description: Contains all necessary data to do a sample simulation of sliding deadwood:
- Deadwood log geometries (*.pts)
- Release points of the logs (*.shp)
- Treefiles (*.txt), containing the locations, heights, BHD and energy absorption capacities of the standing trees

The data is from the two case studies Gruobenwald and Rhäzüns presented in the linked publication. The DEM for these case studies can be downloaded from swisstopo (swissALTI3D dataset)

en:
Resource Title: Sliding deadwood geometry generator
    Size: 6062 bytes
    Format: application/x-zip-compressed
    URL: https://www.envidat.ch/dataset/a6325cb3-bdb3-4b7c-90f6-1ca41c159918/resource/6fc08e26-7bbe-45c9-8ea1-ab85c36c1793/download/sliding_deadwood_geometry-main.zip
    Description: Python code to create deadwood logs as input for RAMMS::Rockfall.

This version was used for the linked publication. The most recent version and the full documentation can be found in the [GitHub Repository](https://github.com/joelborner/sliding_deadwood_geometry)

deadwood

display_name: deadwood

mass-movements

display_name: mass-movements

protection-forest

display_name: protection-forest


en:
The dataset contains the time series of: overland flow (OF) and topsoil interflow (TIF) measured at two large runoff plots (8 m large by 10 m long), streamflow from two sub-catchments of the Studibach catchment, and streamflow from the outlet of the Studibach and Erlenbach catchments. The time series cover 12 rainfall events in the summer 2022.
OF, TIF and the streamflow of the sub-catchments and the Studibach catchment outlet were measured at a 5-min resolution. The streamflow at the Erlenbach catchment outlet was measured at 10-min resolution.
The precipitation was measured with a tipping bucket at the Erlenhöhe meteorological station and has a 10-min resolution.

en: Time serie of overland flow, topsoil interflow and stream flow in the Studibach Catchment and streamflow in the Erlenbach catchment - summer 2022

en: alptal erlenbach upscaling runoff-plot streamflow studibach topsoil-interflow overland-flow

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

alptal

display_name: alptal

erlenbach

display_name: erlenbach

overland-flow

display_name: overland-flow

runoff-plot

display_name: runoff-plot

streamflow

display_name: streamflow

studibach

display_name: studibach

topsoil-interflow

display_name: topsoil-interflow

upscaling

display_name: upscaling


en:
ERRA is a data-driven, nonparametric, model-independent method for quantifying rainfall-runoff relationships across a spectrum of time lags, in systems that may be nonlinear, nonstationary, and spatially heterogeneous.
Researchers using ERRA in published work should cite J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-28-4427-2024, 2024 (for ERRA itself) and J.W. Kirchner, "Impulse response functions for nonlinear, nonstationary, and heterogeneous systems, estimated by deconvolution and de-mixing of noisy time series", Sensors, 22(9), 3291, https://doi.org/10.3390/s22093291, 2022 (for the underlying mathematics).
This data set includes two versions of the ERRA script written in the open-source programming language R, a detailed user's guide, and sample scripts and source data for all of the results in Kirchner (2024).
These scripts are made publicly available under GNU General Public License 3; for details see https://www.gnu.org/licenses/.  The data and documentation are made available under Creative Commons Attribution Share-Alike CC-BY-SA.
ETH Zurich, WSL, and James Kirchner make ABSOLUTELY NO WARRANTIES OF ANY KIND, including NO WARRANTIES, expressed or implied, that this software is free of errors or is suitable for any particular purpose. Users are solely responsible for determining the suitability and reliability of this software for their own purposes.

en: ERRA -- an R script for Ensemble Rainfall-Runoff Analysis

en: rainfall-runoff-modelling time-series hydrology ecohydrology streamflow precipitation nonstationarity nonlinear-deconvolution

en:
Resource Title: {{deprecated version}} An introduction to ERRA v1.06.pdf
    Size: 3166700 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/0c048816-816a-4c1f-bedf-32be20e4d4c2/download/an-introduction-to-erra-v1.06.pdf
    Description: "An introduction to ERRA" presents a series of vignettes that introduce some of the capabilities of the Ensemble Rainfall-Runoff Analysis (ERRA) script, and that show how to invoke them. This introduction also includes documentation for ERRA function calls, largely reproducing the documentation embedded in the ERRA script itself.

en:
Resource Title: Demonstration scripts.zip
    Size: 65399685 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/ca1f4818-aff6-4b13-a83d-82376dde2834/download/demonstration-scripts.zip
    Description: Zip-compressed folder containing demonstration scripts, source data, and output files for the ERRA results shown in "Introduction to ERRA" and in J.W. Kirchner, "Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using Ensemble Rainfall-Runoff Analysis (ERRA): proof of concept", Hydrology and Earth System Sciences, 2024.

en:
Resource Title: {{deprecated version}} ERRA_scripts.zip
    Size: 114811 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/9e833898-3a78-4e79-83bd-d23c5ba76765/download/erra_scripts.zip
    Description: Zip-compressed folder containing two versions of the ERRA script.  ERRA_v1.0.R uses Rcpp to speed up several key calculations, but requires a C++ compiler.  ERRA_v1.0_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp.  Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en:
Resource Title: An introduction to ERRA v1.12.pdf
    Size: 3072328 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/927de1da-fef7-4b05-8534-ae8330bbb48c/download/an-introduction-to-erra-v1.12.pdf
    Description: "An introduction to ERRA" presents a series of vignettes that introduce some of the capabilities of the Ensemble Rainfall-Runoff Analysis (ERRA) script, and that show how to invoke them. This introduction also includes documentation for ERRA function calls, largely reproducing the documentation embedded in the ERRA script itself.

en:
Resource Title: ERRA_scripts_v1.12.zip
    Size: 2621440 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/11af276e-c779-468c-9504-ef56bd490c7d/download/erra_scripts_v1.12.zip
    Description: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.12.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.12_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en:
Resource Title: {{deprecated version}} ERRA_scripts_v1.06.zip
    Size: 114452 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/64819efe-d661-41c8-8410-f61665564c05/download/erra_scripts_v1.06.zip
    Description: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.06.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.06_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en:
Resource Title: {{deprecated version}} ERRA_scripts_v1.11.zip
    Size: 2621440 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/6e05e468-ccd2-4d42-be7b-b7578a68061e/download/erra_scripts_v1.11.zip
    Description: Zip-compressed folder containing two versions of the ERRA script. ERRA_v1.11.R uses Rcpp to speed up several key calculations, but requires a C++ compiler. ERRA_v1.11_no_Rcpp.R is a bit slower but does not require a C++ compiler or Rcpp. Public use of these scripts is permitted under GNU General Public License 3 (GPL3); for details see https://www.gnu.org/licenses/

en:
Resource Title: {{deprecated version}} An introduction to ERRA v1.11.pdf
    Size: 3072328 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/eee3eb76-74b7-4d84-bd23-fa2a97544b58/download/an-introduction-to-erra-v1.11.pdf
    Description: "An introduction to ERRA" presents a series of vignettes that introduce some of the capabilities of the Ensemble Rainfall-Runoff Analysis (ERRA) script, and that show how to invoke them. This introduction also includes documentation for ERRA function calls, largely reproducing the documentation embedded in the ERRA script itself.

en:
Resource Title: {{deprecated version}} An introduction to ERRA.pdf
    Size: 3156214 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/84277dcd-ccde-4eaf-a65b-d1ecf6a0385c/resource/51f419cd-f933-4cad-88b4-a20ac7dcf545/download/an-introduction-to-erra.pdf
    Description: "An introduction to ERRA" presents a series of vignettes that introduce some of the capabilities of the Ensemble Rainfall-Runoff Analysis (ERRA) script, and that show how to invoke them.  This introduction also includes documentation for ERRA function calls, largely reproducing the documentation embedded in the ERRA script itself.

ecohydrology

display_name: ecohydrology

hydrology

display_name: hydrology

nonlinear-deconvolution

display_name: nonlinear-deconvolution

nonstationarity

display_name: nonstationarity

precipitation

display_name: precipitation

rainfall-runoff-modelling

display_name: rainfall-runoff-modelling

time-series

display_name: time-series


en:
The dataset consists of the standardized precipitation (SPI) and the standardized precipitation evapotranspiration index (SPEI) index with a 30 arcsec (~1km) horizontal resolution.
Data for precipitation rates (pr) and potential evapotranspiration (pet) are taken from CHELSA V2.1 (https://doi.org/10.16904/envidat.228.v2.1). Both data sets are monthly time series from 1980 to 2018 at 30 arcsec. and have the unit of kg m-2 month-1. SPEI is a more reliable measure of drought than SPI as it additionally considers the effect of potential evapotranspiration. Potential evapotranspiration (pet) is the amount of water per area per time that can evaporate at the soil surface or be transpired by plants in absence of water supply limitations. SPEI effectively estimates for a given location how the climatic water balance, i.e., the difference between precipitation rate (pr) and potential evapotranspiration (pet), relates to the long-term mean. Here, pet was calculated with the Penman-Montheith approach, assuming surface conductance of a reference crop of 12 cm height following the definition of the Food and Agriculture Organization of the United Nations (FAO), originating from CHELSA-BIOCLIM+ database (https://www.doi.org/10.16904/envidat.332). To obtain global time series of SPEI, in each pixel the frequency distribution of water balance estimates (pr-pet) is approximated with a log-logistic probability distribution function. We calculate monthly SPEI using a 12-month memory and approximate the parameters of the log-logistic probability distribution based on the time series from 1980-2018. Such a 12-month time window is suitable to capture long-term drought events that are long enough to impose substantial impacts on agriculture, hydrology, and ecosystems while ones at shorter time scales would be more appropriate to detect short-term drought. For each month, SPEI is calculated considering the respective water-balance value and the values of the eleven preceding months. Here we use SPEI-12 in December to represent the overall water-balance condition of the respective year. The resulting SPEI data has a horizontal resolution of 130 arcsec (~1km). The calculation of SPI and SPEI is done in R with the package SPEI (https://www.doi.org/10.32614/CRAN.package.SPEI).

en: High resolution global standardized drought indices

en: chelsa multi-year-drought megadrought precipitation potential-evapotranspiration extreme-events drought

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: Multiyear meteorological droughts (MYMD10)
    Size: 107930 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fannual%2Fmymd10%2F
    Description: Global annual layers of the multiyear meteorological droughts (MYMD) at a 1/12° (~10km) spatial resolution

en:
Resource Title: CHELSA-drought-indices Technical Documentation
    Size: 335096 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/a4ec699c-1e4a-45ab-8b7c-ccdf09c6e828/download/chelsa_file_specification_drought_indicies.pdf
    Description: Technical documentation document including the variable names, units, scaling, offset, and background.

en:
Resource Title: Standardised Precipitation Evapotranspiration Index (SPEI)
    Size: 209715200 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fmonthly%2Fspei12%2F
    Description: Global monthly layers of the Standardized Precipitation Evapotranspiration Index (SPEI) a 30 arcsec (~1km) spatial resolution

en:
Resource Title: Multiyear meteorological droughts (MYMD) statistics
    Size: 785961 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/21046cb1-0a1c-4890-8025-aefb5d9f3f1d/download/mymd_stats.csv
    Description: Statistics of the multiyear meteorological droughts, containing the ID, duration, area, and empirical ranking of the droughts.

en:
Resource Title: Multiyear meteorological droughts (MYMD) statistics - Description
    Size: 1114 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/a32ef5b4-fab9-4d15-b6da-c8244029027d/download/mymd_stats_description.csv
    Description: Description of column names, and units of the fields contained in the dataset "Multiyear meteorological droughts (MYMD) statistics".

en:
Resource Title: Multiyear meteorological droughts (MYMD10) statistics
    Size: 928956 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/fa3d92e1-1eb0-478c-900d-dca818e3b4ff/download/mymd10_stats.csv
    Description: Multiyear meteorological droughts (MYMD10) statistics

Statistics of the multiyear meteorological droughts, containing the ID, duration, area, and empirical ranking of the droughts, and the anomalies in kNDVI

en:
Resource Title: Multiyear meteorological droughts (MYMD10) statistics - Description
    Size: 1384 bytes
    Format: application/vnd.ms-excel
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/c1c0dbc9-ec96-462e-bf62-bc90b6aad3ab/download/mymd10_stats_description.csv
    Description: Description of column names, and units of the fields contained in the dataset "Multiyear meteorological droughts (MYMD10) statistics".

en:
Resource Title: Multiyear meteorological droughts (MYMD)
    Size: 368640 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fannual%2Fmymd%2F
    Description: Global annual layers of the multiyear meteorological droughts (MYMD) at a 1/24° (~5km) spatial resolution

en:
Resource Title: Kernel Normalized Difference Vegetation Index Anomaly (QKNDVI)
    Size: 6710886 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fannual%2Fqkndvi%2F
    Description: Global annual layers of the the kernel normalized difference vegetation index anomaly (qkndvi) at a 1/12° (~10km) spatial resolution.

en:
Resource Title: CHELSA-drought-indices Technical Documentation
    Size: 257611 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/8670a317-ca6b-4bce-9191-428e2827184e/resource/3149a573-b55c-499c-9efa-2cbe920bc483/download/chelsa_file_specification_drought_indicies.pdf
    Description: Technical documentation document including the variable names, units, scaling, offset, and background.

en:
Resource Title: Standardized Precipitation Index (SPI)
    Size: 209715200 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.unil.cloud.switch.ch%2Fchelsa02%2F&prefix=chelsa%2Fglobal%2Fmonthly%2Fspi12%2F
    Description: Global monthly layers of the Standardized Precipitation Index (SPI) a 30 arcsec (~1km) spatial resolution

chelsa

display_name: chelsa

drought

display_name: drought

extreme-events

display_name: extreme-events

megadrought

display_name: megadrought

multi-year-drought

display_name: multi-year-drought

potential-evapotranspiration

display_name: potential-evapotranspiration


en:
**CH-POA300** is a dataset of hourly solar irradiance maps projected onto tilted surfaces (analogous to monofacial solar panels).
- **Spatial resolution:** 300 m
- **Coverage:** Switzerland
Two temporal extents are available, depending on the number of surface orientations (azimuth–tilt combinations):
 1. Long-term
- **Period:** 2004–2020
- **Orientations:**
  - Azimuth: 180° (South)
  - Tilts: 0°, 35°, 60°, 90°
 2. Typical year
- **Period:** 2016
- **Orientations:**
  - Azimuths: 0° (North), 90° (East), 135°, 180° (South), 225°, 270° (West)
  - Tilts: 0°, 3°, 8°, 15°, 25°, 35°, 45°, 60°, 80°, 90°
 Notes
- Azimuth angles: 0° = North, 90° = East, 180° = South, 270° = West
- Tilt angles: 0° = horizontal, 90° = vertical
 Additional data
- Seasonal and annual statistics derived from the maps are included.
- Methodology and quality assessment are documented in *CH-POA300-v-specs.pdf*.

en: CH-POA300 -- Hourly Plane-of-Array Solar Irradiance at High Resolution for Switzerland

en: speed2zero plane-of-array solar-irradiance agri-pv sweet-edge rooftop-pv alpine-pv

envidat

en: EnviDat is the WSL data portal providing unified and managed access to environmental monitoring and research data. The portal has the capability to host and publish data sets. Through the sharing of unique and high-value data sets worldwide, EnviDat fosters research and contributes to advancing environmental science.

en:
Resource Title: CH-POA300_v1_statistics
    Size: 1445061901 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0ec532fd-a319-4569-a181-ab391104b47f/resource/ea36025c-c855-488d-857d-f658bace53e2/download/chpoa300_statistics.zip
    Description: Contains seasonal and annual statistics (mean, sum) of the CH-POA300 dataset.

- "yyyy_agg_period_hourly_stat.nc": statistic <stat> for the period <agg_period> of the year <yyyy>
- "all_agg_period_stat_mean.nc": statistic <stat> for the period <agg_period> reduced on the entire dataset (all years).
\
yyyy: [2004, 2020]
agg_period: [winter, summer, annual]
stat: [mean, sum]

en:
Resource Title: Data 2016
    Size: 751619276800 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2FCHPOA300%2F&prefix=2016
    Description:

en:
Resource Title: CH-POA300-v2-specs
    Size: 11473219 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/0ec532fd-a319-4569-a181-ab391104b47f/resource/c184fb33-6751-47cd-a936-2f04de24c760/download/ch-poa300-v2-specs.pdf
    Description: This document provides an in-depth description of the CH-POA300 dataset. It includes methodology and validation details.

en:
Resource Title: Data 2004-2020
    Size: 858993459200 bytes
    Format: No Info
    URL: https://envicloud.wsl.ch/#/?bucket=https%3A%2F%2Fos.zhdk.cloud.switch.ch%2FCHPOA300/&prefix=2004-2020
    Description:

en:
Resource Title: CH-POA300_v1_specs
    Size: 11472383 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/0ec532fd-a319-4569-a181-ab391104b47f/resource/7146fc4a-df12-44a8-923f-69476b147a55/download/chpoa300_specs_v1.pdf
    Description: This document provides an in-depth description of the CH-POA300 dataset. It includes methodology and validation details.

en:
Resource Title: CH-POA300-v2-statistics
    Size: 1889349528 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/0ec532fd-a319-4569-a181-ab391104b47f/resource/ed1238d2-b8c0-42f1-9b65-b837ac2dd259/download/stats.zip
    Description: Contains seasonal and annual statistics (mean and sum) derived from the CH-POA300 dataset.
File naming conventions:
yyyy_agg_period_hourly_stat.nc : statistic <stat> for aggregation period <agg_period> in year <yyyy>
all_agg_period_stat_mean.nc : statistic <stat> for aggregation period <agg_period> computed over the entire dataset (all years)
Definitions:
yyyy = year (2004–2020)
agg_period = winter, summer, annual
stat = mean, sum

agri-pv

display_name: agri-pv

alpine-pv

display_name: alpine-pv

plane-of-array

display_name: plane-of-array

rooftop-pv

display_name: rooftop-pv

solar-irradiance

display_name: solar-irradiance

sweet-edge

display_name: sweet-edge


en:
Lebensraumkarte der Schweiz v1.2 2025/La carte des milieux naturels de Suisse v1.2 2025
The FOEN funded project ‘The Habitat Map of Switzerland:continual improvement’ conducted at the WSL, has improved on versions 1.0 and 1.1 of a map of Swiss habitats according to the TypoCH classification (Delarze et al. 2015). The datasets is now separated into two data sets:  The Habitat Map of Switzerland v1.2, ground cover layer,  and The Habitat Map of Switzerland v1.2, individual trees and shrubs outside forest v1.0 (above ground) layer.
The Habitat Map of Switzerland maps the TypoCH habitat types wall-to-wall across the whole of Switzerland, to at least the classification’s 2nd level of detail (where possible to the 3rd level of detail).
Habitats are mapped through a variety of approaches that can be grouped as either:
1: Derived from the existing Swiss-wide datasets a) the high quality landcover mapping from Swisstopo’s Topographical Landscape Model (TLM), b) the harmonised mapping of agricultural fields area from the ‘Landwirtschaftliche Nutzfläche’ (LWN) mapping and, in settlements areas, c) the harmonised cadastral data (‘Amitliche Vermessung’ AV).
2: Modelled within the project using Random Forest or Ensemble Modelling techniques to model the spatial distribution of individual habitat types (further detail below),
3: Combining existing species distribution models to determine habitat types, or
4: Classification with relatively simple rule-sets based on auxiliary spatial datasets, i.e. vegetation height models (based on digital aerial photogrammetry and/or SwissSurface3D aerial laser scanning (ALS) data), the digital terrain model, the normalised difference vegetation index (NDVI) derived from aerial imagery and/or time-series of growing season Sentinel-2 or Planet satellite imagery.
Further detail on the methodology can be found within the README document.

en: The Habitat Map of Switzerland v1_2 2025

en: tof sentinel-2 remote-sensing grasslands milieux-naturels random-forest urban ensemble-modelling lebensraumkarte typoch habitat-modelling wetland lidar forest shrub-forest dwarf-shrubs

envidat

en: EnviDat: the Environmental Data Portal

envi

en: Environment

en:
Resource Title: symbology_v1_2_2025.zip
    Size: 61778 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d6182107-2086-49c0-97dd-f3a77056157c/resource/47c0e914-986f-4ad3-9bcf-49160023c030/download/symbology_v1_2_2025.zip
    Description: Symbology for the Habitat Map, ground layer and tree and shrub layer, and in ArcGIS and QGIS layer formats. German, French and Italian versions

en:
Resource Title: LUT_einzelbaum_gebuesch_v_1_0.csv
    Size: 216 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/d6182107-2086-49c0-97dd-f3a77056157c/resource/0035dd77-ea6d-4d3a-89bd-3ad6ee36302e/download/lut_einzelbaum_gebuesch_v_1_0.csv
    Description: Look up table for the data N2025_einzelbaum_gebuesche_v1_0_20251216
giving the full text names for each habitat type in German, French and Italian

en:
Resource Title: Data_Description_Lebensraumkarte_v1_2_Dec2025.pdf
    Size: 645376 bytes
    Format: application/pdf
    URL: https://www.envidat.ch/dataset/d6182107-2086-49c0-97dd-f3a77056157c/resource/564b16e5-0523-4431-94bc-3e6d1415c5a1/download/data_description_lebensraumkarte_v1_2_dec2025.pdf
    Description: Data description and documentation of methods

en:
Resource Title: N2025_einzelbaum_gebuesche_v1_0_20251216.gdb.zip
    Size: 2469606195 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d6182107-2086-49c0-97dd-f3a77056157c/resource/cd4e36db-926c-4e2b-bb89-27dbe606dac3/download/n2025_einzelbaum_gebuesche_v1_0_20251216.gdb.zip
    Description: The Habitat Map of Switzerland v1.2, non-forest trees and shrubs layer, v1.0. Vector dataset in file geodatabase format. Complements the ground cover layer dataset

en:
Resource Title: N2025_HabitatMap_CH_v1_2_20251211.gdb.zip
    Size: 7247757312 bytes
    Format: application/zip
    URL: https://www.envidat.ch/dataset/d6182107-2086-49c0-97dd-f3a77056157c/resource/a90d17bf-221e-4819-a8b1-94b807a33720/download/n2025_habitatmap_ch_v1_2_20251211.gdb.zip
    Description: The Habitat Map of Switzerland version 1.2 2025. Ground cover layer, All of Switzerland. Vector data in file geodatabase format. Is complemented by the trees and shrubs layer.

en:
Resource Title: TypoCH_NUM_LookupTable_26112025.csv
    Size: 46705 bytes
    Format: text/csv
    URL: https://www.envidat.ch/dataset/d6182107-2086-49c0-97dd-f3a77056157c/resource/28aace15-eb00-4ec2-98ef-b55acc18286f/download/typoch_num_lookuptable_26112025.csv
    Description: Look up table for the data N2025_HabitatMap_CH_v1_2_20251211
giving the full text names for habitat types in German, French and Italian for each habitat type as well as the key for the type of methodology used

dwarf-shrubs

display_name: dwarf-shrubs

ensemble-modelling

display_name: ensemble-modelling

forest

display_name: forest

grasslands

display_name: grasslands

habitat-modelling

display_name: habitat-modelling

lebensraumkarte

display_name: lebensraumkarte

lidar

display_name: lidar

milieux-naturels

display_name: milieux-naturels

random-forest

display_name: random-forest

remote-sensing

display_name: remote-sensing

shrub-forest

display_name: shrub-forest

tof

display_name: tof

typoch

display_name: typoch

urban

display_name: urban

wetland

display_name: wetland