Source: The Emergency Events Database (EM-DAT) , Centre for Research on the Epidemiology of Disasters (CRED) / Université catholique de Louvain (UCLouvain), Brussels, Belgium – www.emdat.be.Category: Climate and WeatherData series: Climate related disasters frequency, Number of Disasters: TOTAL  Climate related disasters frequency, Number of Disasters: Drought  Climate related disasters frequency, Number of Disasters: Extreme temperature  Climate related disasters frequency, Number of Disasters: Flood  Climate related disasters frequency, Number of Disasters: Landslide  Climate related disasters frequency, Number of Disasters: Storm  Climate related disasters frequency, Number of Disasters: Wildfire Climate related disasters frequency, People Affected: Drought  Climate related disasters frequency, People Affected: Extreme temperature  Climate related disasters frequency, People Affected: Flood  Climate related disasters frequency, People Affected: Landslide  Climate related disasters frequency, People Affected: Storm  Climate related disasters frequency, People Affected: Wildfire Climate related disasters frequency, People Affected: TOTAL  Disaster IntensityMetadata:EM-DAT: The International Disasters Database - Centre for Research on the Epidemiology of Disasters (CRED), part of the University of Louvain (UCLouvain) www.emdat.be, Brussels, Belgium. Only climate related disasters (Wildfire, Storm, Landslide, Flood, Extreme Temperature, and Drought) are covered. See the CID Glossary for the definitions. EM-DAT records country level human and economic losses for disasters with at least one of the following criteria: i.          Killed ten (10) or more people  ii.         Affected hundred (100) or more people  iii.        Led to declaration of a state of emergency iv.        Led to call for international assistance   The reported total number of deaths “Total Deaths” includes confirmed fatalities directly imputed to the disaster plus missing people whose whereabouts since the disaster are unknown and so they are presumed dead based on official figures. “People Affected” is the total of injured, affected, and homeless people. Injured includes the number of people with physical injuries, trauma, or illness requiring immediate medical assistance due to the disaster. Affected includes the number of people requiring immediate assistance due to the disaster. Homeless includes the number of people requiring shelter due to their house being destroyed or heavily damaged during the disaster. Disaster intensity is calculated by summing “Total Deaths” and 30% of the “People Affected”, and then dividing the result by the total population. For each disaster and its corresponding sources, the population referred to in these statistics and the apportionment between injured, affected, homeless, and the total is checked by CRED staff members. Nonetheless, it is important to note that these are estimates based on certain assumptions, which have their limitations. For details on the criteria and underlying assumptions, please visit https://doc.emdat.be/docs/data-structure-and-content/impact-variables/human/. Methodology:Global climate related disasters are stacked to show the trends in climate related physical risk factors.

In 2023, the most common natural disaster type in the world were floods, with 164 events reported that year. Storms were the type of natural disasters with the second highest occurrence, with 139 events.

Floods are part of the natural hydrological cycle (the seasonal fluctuation of water levels) and occur along rivers and streams somewhere in Canada every year. Flooding is a common natural hazard that has caused 260 known disasters since 1900, resulting in the loss of 235 lives and 8.7 billion dollars in damage. This map depicts 260 flood disaster events from 1902 - 2005.

Using data from the U.S. Geological Survey, this map plots major flood events in the U.S. from 1978 - March 2018. Symbology is scaled to the total cost of the event. Symbol locations are generalized since many floods, especially hurricanes, nor’easters, and major river events occur over many thousands of square miles. The map includes information on the event name, year, and total cost as pop-ups.

In 2024, the economic losses due to natural disasters worldwide amounted to about 368 billion U.S. dollars. Natural disasters occur as a result of natural processes on Earth. Many different types of natural disasters can occur, including floods, hurricanes, earthquakes, and tsunamis. Natural disasters in 2024 Tropical cyclones generated the highest amount of economic losses in 2024 with 145 billion U.S. dollars worldwide. Hurricanes Helene and Milton were the most destructive events worldwide that year with over a hundred billion U.S. dollars in economic losses. Flooding events ranked second in the costliest events in 2024, with flooding in Valencia, Spain, and South and Central China being the worst examples. Asia hardest hit by natural disasters A highly destructive force, Asia is one of the most susceptible regions to natural disasters. The repercussions of natural disasters are not only physical, but also economic. Costs may be high – depending on the severity – as areas affected by natural disasters might need to be rebuilt. Lower income countries are more likely to be affected by natural disasters for a multitude of reasons, including a lack of developed infrastructure, inadequate housing, and lack of back-resources.

This Map shows natural climatological drought disasters occurrence from 1900 to 2015. The data source is from the Centre for Research on the Epidemiology of Disasters, EM-DAT database.

EM-DAT is a global database on natural and technological disasters that contains essential core data on the occurrence and effects climatological disasters in the world from 1900 to present. EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université catholique de Louvain located in Brussels, Belgium. The database is compiled from various sources, including UN agencies, non-governmental organisations, insurance companies, research institutes and press agencies. The main objective of the database is to serve the purposes of humanitarian action at national and international levels in order to rationalise decision making for disaster preparedness, as well as providing an objective base for vulnerability assessment and priority setting. In EM-DAT data are considered at the country level for two reasons: first, it is at this level that they are usually reported; and second, it allows the aggregation and disaggregation of data. In order to facilitate the comparison over time, the event start date has been used as the disaster reference date.

Affected people are the number of people requiring immediate assistance during a period of emergency; this may include displaced or evacuated people. Total affected are the sum of injured, homeless and affected. Total Deaths are the number of people who lost their life because the event happened (it includes also the missing people based on official figures). Homeless are the number of people whose house is destroyed or heavily damaged and therefore need shelter after an event.

Analyzing historical impact from these types of events and then factoring them into models for the future enables companies to build data-driven response strategies.

This data set has live coverage of breaking events such as severe weather and terrorism and the API updates minute to minute to ensure accuracy.

Location: Texas Visibility Window: 1-month historical Categories: Severe weather, Disasters, Airport Delays, Terror, Health Warnings.

Fields Included: - Title - Category - Labels - Description - Start date and time - End date and time - Predicted end time - Country - Lat / Lon - Venue Name - Venue Address - Rank (PHQ Rank, Local Rank, and Aviation Rank) - PHQ Attendance - Event status - Place Hierarchy - Created/updated timestamps

Polygon information: PredictHQ's polygons enable you to see the full area impacted by an event represented as a shape, because many types of events don't occur neatly at a point on a map. That means you will get a much more accurate picture of impact. Data samples including polygons are available upon request.

Data quality: PredictHQ's data quality is one of its key strengths: 1) We have developed a set of Quality Standards for Processing Demand Causal Factors (QSPD), which are used to define the criteria for high-quality event data. By following these standards, PredictHQ ensures that their data meets the highest levels of quality. 2) We use more than 450 data sources to collect event data, including public records, social media, and ticketing websites. 3) We have built thousands of machine learning models that standardize, verify, enrich, and rank every single event. 4) On average we process 28 million events and 422,000 entities every day 5) We track the quality of our data over time and make improvements as needed.

About PredictHQ: PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events. PredictHQ’s intelligent data of verified global events enables businesses to forecast shifts in demand from events to be able to adjust their inventory, make changes to labor, dynamically price and operate more efficiently. Think conferences, sports games, college graduations, floods, and more. PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way. We meet customers exactly where they are, ensuring they can access our data the way that suits them best.

Learn more about PredictHQ's real-world event data by visiting our Developer and Data Science Documentation: https://docs.predicthq.com/categoryinfo/unscheduled-events

Keywords: attended events, attendance, sports, festivals, expos, conferences, concerts, performing arts, community, polygon, consumer spending, predicted spend, location information, demand intelligence, financial data, venue location, accommodation, transportation, restaurant, demand intelligence, event intelligence, event categorisation, business insights, event tracking, historical event data, even impact analysis, event-driven decisions, predictive analytics, school holiday, observances, public holidays, election, campaign, holiday, delays, hospitality, travel, tourism, aviation, flight, ride-sharing, transportation, mobility, weather, severe weather, historical weather,

Coastal hazards occur when a natural process has adverse effects on human safety, property or on objects or areas that are valued by humans, or when human activities generate anomalies in natural processes, causing those processes to act in unforeseen ways. Human responses to such hazards may, in turn, have other adverse effects on the environment and on the economic, social, and cultural well-being and health and safety of people. The Coastal Environment of the Gisborne District is defined in Part E of the Tairāwhiti Plan and shown on the planning maps. This Coastal Environment is particularly susceptible to a number of coastal hazards because the majority of people in the Gisborne District live close to, or use, the coast. These include: Tsunami, Storm surge inundation, Erosion, River mouth movement and Dune and Coastal Sediment movement. All of these events have occurred in the past within the Gisborne District. The entire Gisborne District coastline is subject to, and is likely to continue to be subject to, adverse effects from one or a combination of the natural hazards of sea and wind erosion, landslip and flooding from the sea and coastal rivers. Natural coastal hazards are an example of an issue which straddles the administrative boundary between the land and sea set up in the RMA. The majority of the coast has undergone an initial assessment of sensitivity to coastal hazards (ASCH). This represents a rapid and less rigorous assessment of the extent to which an area is subject to coastal hazards over a 100 year planning horizon. For areas where there is an identified coastal hazard problem, a rigorous and complete Coastal Hazard Overlay assessment has been undertaken. The assessments are based on an acceptable level of risk, and strike a balance between the expectations of members of the community to be able to use their land against the need to protect life, adjacent property, and values from coastal hazards on the other.

An often-overlooked question of the biodiversity crisis is how natural hazards contribute to species extinction risk. To address this issue, we explored how four natural hazards: earthquakes, hurricanes, tsunamis, and volcanoes, overlapped with the distribution ranges of amphibians, birds, mammals, and reptiles that have either narrow distributions or populations with few mature individuals. To assess which species are at risk from these natural hazards, we combined the frequency and magnitude of each natural hazard to estimate a probability of impact. We considered species at risk if they overlapped with regions where any of the four natural hazards historically occurred (n = 3,722). Those species with at least a quarter of their range subjected to a high probability of impact were considered at high risk (n = 2,001) of extinction due to natural hazards. In total, 834 reptiles, 617 amphibians, 302 birds, and 248 mammals were at high risk and they were mainly distributed on islands and ..., To address this knowledge gap, we provide an evaluation of the risk posed by natural hazards to terrestrial vertebrate species worldwide, focusing especially on those species that have limited distributions and/or occur at low numbers. First, we selected all amphibian, bird, mammal, and reptile species with a maximum population size of 1,100 mature individuals and/or those with a range size less than or equal to 2,500 km² based on the IUCN Red List of Threatened Species. Second, we constructed an estimate for the likelihood of impact from four natural hazards (earthquakes, hurricanes, tsunamis, and volcanoes) by analysing approximately 50 years of historical data concerning the frequency and magnitude of events. We then identified all species whose ranges overlap with known occurrences of hurricanes, earthquakes, tsunamis, and volcanoes. Finally, we classified the species at ‘high-risk’ as those for which at least a quarter of their range overlapped with areas of high probability of imp..., , # A global map of species at risk of extinction due to natural hazards

https://doi.org/10.5061/dryad.m0cfxpp8s

To account for potential errors of commission and omission in the IUCN range maps, we tested two approaches: (1) estimating the percentage of the ranges intersecting with each cell (Datasets S1-S4). Then we conducted binary pixel-based analyses, using the entire cell area is a species range intersected it (Datasets S5-S20).

Dataset S1: Information about reptile species at risk and high risk due to natural hazards.

Dataset S2: Information about amphibian species at risk and high risk due to natural

hazards.

Dataset S3: Information about bird species at risk and high risk due to natural hazards.

Dataset S4: Information about mammal species at risk and high risk due to natural

hazards.

Dataset S5: Information about reptile species at risk and high risk due to earthquakes

*calculated using six different transformati...

A natural disaster event can stretch emergency personnel to breaking point. However, at the very time that the community is most pre-occupied with response, important but perishable information on the event is available. The information helps us to understand how and why the event impacted on the community, and systematic efforts are needed to collect the data.Risk managers need to base their decisions on accurate and reliable forecasting of the future.Organisations such as Geoscience Australia provide risk assessments to assist this decisionmaking. Post-disaster data collection is essential to test risk assessment models against whathas happened in real events.Data collection technologies can also assist response teams by transmitting near real-timespatial information between field personnel and coordinating centres.

The geographical location of Afghanistan and years of environmental degradation in the country make Afghanistan highly prone to intense and recurring natural hazards such as flooding, earthquakes, snow avalanches, landslides, and droughts. These occur in addition to man-made disasters resulting in the frequent loss of live, livelihoods, and property. The creation, understanding and accessibility of hazard, exposure, vulnerability and risk information is key for effective management of disaster risk. Assuring the resilience of new reconstruction efforts to natural hazards, and maximizing the effectiveness of risk reduction investments to reduce existing risks is important to secure lives and livelihoods. So far, there has been limited disaster risk information produced in Afghanistan, and information that does exist typically lacks standard methodology and does not have uniform geo-spatial coverage. To better understand natural hazard and disaster risk, the World Bank and Global Facility for Disaster Reduction and Recovery (GFDRR) are supporting the development of new fluvial flood, flash flood, drought, landslide, avalanche and seismic risk information in Afghanistan, as well as a first-order analysis of the costs and benefits of resilient reconstruction and risk reduction strategies. For fluvial flood risk a flood modeling framework is being developed that consists of three components: • Hydrological analysis which models how much precipitation comes to runoff. The hydrological analysis is used as a back-bone to compute flow and flooding through the full catchment area during selected events as well as selected return periods. The hydrological simulations also form the backbone of the drought risk assessments (work package 3). • Hydrodynamic analysis, to translate runoff into river flow and inundation and flow over floodplain areas. • Flood impact analysis for calculating the impacts of a flood applied to flood prone areas with high damage potential.

The data set is a 2015 heat wave risk data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of year. Heat wave risk refers to the probability or loss possibility of harmful consequences caused by the interaction between heat wave hazard (possible heat wave events in the future), heat wave exposure (total population, livelihood and assets in the area where heat wave events may occur) and heat wave vulnerability (the tendency of the disaster bearing body to suffer adverse effects when affected by heat wave events) . The risk assessment method of heat wave is "hazard-exposure-vulnerability". The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.

Community resilience describes the capacity of individuals and households within a community to absorb a disaster’s external stressors. The standard Community Resilience Estimates (CRE) measures a community’s social vulnerability to natural disasters. However, the social vulnerabilities to extreme heat exposure differ from other natural disasters. As a result, the CRE Team created a new set of estimates called the Community Resilience Estimates for Heat (CRE for Heat).The CRE for Heat is an experimental data product from the U.S. Census Bureau. Experimental data products are innovative statistical products created using new data sources or methodologies that benefit data users in the absence of other relevant products. The Census Bureau is seeking feedback from data users and stakeholders on the quality and usefulness of these new products.In collaboration with Arizona State University’s Knowledge Exchange for Resilience (KER), the CRE Team produced the 2022 CRE for Heat using data on individuals and households. The data sources include the 2022 American Community Survey (ACS), the Census Bureau’s Population Estimates Program (PEP), and the 2020 Census. Based on feedback from data users, the CRE for Heat contains a new component of social vulnerability, “Households that potentially lack air conditioning”. This component of social vulnerability was created using data from the 2021 American Housing Survey, machine learning techniques, and auxiliary data. More information about this is found in the CRE for Heat Quick Guide.Local planners, policymakers, public health officials, and community stakeholders can use the CRE for Heat to assess their community’s vulnerability to extreme heat and plan cooling and intervention strategies. WHAT’S NEWComponents of Social Vulnerability (SV)The CRE adjusted terminology from “risk factors” to “components of social vulnerability” after discussions with stakeholders such as emergency managers and urban planners. In these fields, “risk” refers to the likelihood a disaster or event will occur. “Vulnerabilities” refer to the conditions people experience which may compound the impact of a disaster.The CRE Program is committed to providing a data product that is understandable and meets the needs of its users. To better explain the purpose of the estimates and how they were developed, the language was adjusted.“Components” highlights the combination of factors that define social vulnerability. “Social vulnerability” refers to the characteristics that could impede a community’s ability to deal with disasters and external stressors. The results of this assessment form the basis of a community’s Community Resilience Estimate.Extreme Heat ExposureThe CRE for Heat 2022 estimates contain an additional measure of exposure to extreme heat (PRED3EXP). Not all socially vulnerable communities are equally exposed to extreme heat. Pairing the CRE for Heat estimates with heat exposure data provides a more comprehensive look at social vulnerability to heat. In the 2022 CRE for Heat dataset, an area is considered exposed to extreme heat if it meets one of two criteria. The two heat exposure criteria are:Areas where the maximum air temperature has reached or exceeded 90 degrees Fahrenheit for two or more days in a row during 2022.Areas where estimated wet bulb temperature has reached or exceeded 80 degrees at any time during 2022.On the county and tract level files, these exposure variables are available as LONG_90_DAY and MAX_WBT.On the state and national file, the exposure variable, PRED3EXP_E, measures the estimated number of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. Similarly, PREDEXP_PE, measures the rate of individuals with three plus components of social vulnerability who also live in a county exposed to an extreme heat event in 2022. These variables, and their accompanying margins of error, are available on the national and state files.Components of Social VulnerabilityComponents of Social Vulnerability (SV) for Households (HH) and Individuals (I)SV 1: Financial hardship defined as: Income-to-Poverty Ratio (IPR) < 130 percent (HH) or50% < for housing/rental costs (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH).SV 3: Housing quality described as:Unit-level crowding with > 0.75 persons per room (HH) orLive in mobile home, boat, RV, Van, or other (HH). SV 4: Communication barrier defined as either:Limited English-speaking households (HH) or No one in the household has a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH).SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: Transportation exposure described as:No vehicle access (HH) orWork commuting methods with increased exposure to heat (e.g., public transportation, bicycle, walking) (I). SV 10: Households without broadband Internet access (HH). SV 11: Households that potentially lack air conditioning (HH).

Gisborne areas sensitive to coastal hazards (ASCH) as identified in the Tairawhiti Resource Management Plan.

Coastal hazards occur when a natural process has adverse effects on human safety, property or on objects or areas that are valued by humans, or when human activities generate anomalies in natural processes, causing those processes to act in unforeseen ways. Human responses to such hazards may, in turn, have other adverse effects on the environment and on the economic, social, and cultural well-being and health and safety of people.

The Coastal Environment of the Gisborne District is defined in Part E of the Tairāwhiti Plan and shown on the planning maps. This Coastal Environment is particularly susceptible to a number of coastal hazards because the majority of people in the Gisborne District live close to, or use, the coast. These include: • Tsunami • Storm surge inundation • Erosion • River mouth movement • Dune and Coastal Sediment movement.

All of these events have occurred in the past within the Gisborne District. The entire Gisborne District coastline is subject to, and is likely to continue to be subject to, adverse effects from one or a combination of the natural hazards of sea and wind erosion, landslip and flooding from the sea and coastal rivers. Natural coastal hazards are an example of an issue which straddles the administrative boundary between the land and sea set up in the RMA.

The majority of the coast has undergone an initial assessment of sensitivity to coastal hazards (ASCH). This represents a rapid and less rigorous assessment of the extent to which an area is subject to coastal hazards over a 100 year planning horizon. For areas where there is an identified coastal hazard problem, a rigorous and complete Coastal Hazard Overlay assessment has been undertaken. The assessments are based on an acceptable level of risk, and strike a balance between the expectations of members of the community to be able to use their land against the need to protect life, adjacent property, and values from coastal hazards on the other.

VERSION HISTORY:-On October 18, 2018 we republished all simulation data for all water (global) sector impact models to get the data sets into the new ESGF search facet structure. There were no changes to the simulation data.- On November 27, 2018 we republished simulation data for monthly variables swe, soilmoist and rootmoist for impact model PCR-GLOBWB due to an error in the units. Instead of reporting mass per area (kg/m2), values corresponded to mass flux rate (kg/m2/s). Values were thus multiplied by 86400 in order to obtain the correct values in kg/m2. This data caveat was documented in the ISIMIP website (ISIMIP2a: PCR-GLOBWB reported three variables in wrong unit). ----------------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/. For accessing the data set as in http://doi.org/10.5880/PIK.2017.010 before November 27, 2018 please write to the ISIMIP Data Management Team: isimip-data[at]pik-potsdam.de.---------------------------------------------------------------------------- DATA DESCRIPTION: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically-relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate change impacts across sectors. ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010 approx.) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This may serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. The focus topic for ISIMIP2a is model evaluation and validation, in particular with respect to the representation of impacts of extreme weather events and climate variability. During this phase, four common global observational climate data sets were provided across all impact models and sectors. In addition, appropriate observational data sets of impacts for each sector were collected, against which the models can be benchmarked. Access to the input data for the impact models is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/#input-data-bias-correction). This entry refers to the ISIMIP2a simulation data from global hydrology models: CLM4, DBH, H08, JULES_W1, JULES_B1, LPJmL, MATSIRO, MPI-HM, ORCHIDEE, PCR-GLOBWB, SWBM, VIC, WaterGAP2

This dataset contains the .wav sound files, .trs Transcriber files, .txt Toolbox-compatible Notepad files and .pdf files with the completely transcribed, glossed, parsed and translated examples of the following recordings that belong to the following publication:

Bodt, Timotheus Adrianus. 2020. Grammar of Duhumbi. Leiden: Brill. ISBN 978-90-04-40947-7. https://brill.com/view/title/55767

CHUK221212D2A Bonpo prediction text.

The explanation of all the grammatical features that occur in these sound files can be found in the Grammar of Duhumbi.

The main Toolbox files can be found in the zip file “Settings”, this includes the IPA keys for Duhumbi, the entire setup of the Toolbox database, and the Duhumbi dictionary and Parsing dictionary.

The .wav, .txt and .trs files combined in the same folder will enable to open Toolbox and work with the recordings, e.g. play them sentence for sentence and see the transcriptions and translations.

Transcriber version 1.5.1: http://trans.sourceforge.net/en/presentation.php or https://osdn.net/projects/sfnet_trans/downloads/transcriber/1.5.1/Transcriber-1.5.1-Windows.exe/

Toolbox version 1.6.1: https://software.sil.org/toolbox/download/

For the metadata of the sound files in this data set, I refer to Chapter 13 Texts in the Grammar of Duhumbi. This Chapter has a complete listing of the texts, their topics, the speakers and their background etc.

Kesang was one of the last bon-po, practitioners of the traditional religious system in the Chug valley. This religious system has no ‘name’, but comes under what is often called ‘Bon’. The locus of propitiation is on the opposition between the high, in winter snow-clad mountain peaks phu (Tib. phu) that represent purity, cleanliness, goodness and beneficial powers versus the low-lying marshy, swampy areas da (tib. mdaḥ) that represent pollution, disease, evil and malevolent forces. In between these two there is a plethora of other local deities, many of which are local representations of the lha-srin bde-brgyad ‘eight classes of deities and demons’ also found in Tibetan Buddhism, but many of whom also represent deified human beings who have taken on some negative or positive force. It is the role of the bon-po to maintain the balance between the phu, the ‘good’ and the da, the ‘evil’ and hence prevent damage to humans and their livelihoods in the form of diseases, natural disasters, death etc. The phu-da religious system is not limited to the Chug valley, but, in various forms, can also be found among the related Khispi, Sartang and Sherdukpen people, as well as among the Tshangla speakers of West Kameng and eastern Bhutan.

The main ritual conducted by the bon-po is called zhiwa (Tib. źi-ba ‘peace’) or jomo soykha (Tib. jo-mo gsol-kha ‘propitiation of the Jomo). It is conducted once before the 20th day of every Tibetan month. Jomo is the main female deity in the area (see also the files concerning the on-site Jomo propitiation). During this ritual, the bon-po first invites the Jomo and all other deities and spirits to attend the offering. He then offers nyingba (Tib. sñiṅ-ba ‘old’), also called lemchang, a mixture of rice, maize, finger millet (traditionally also wheat, barley, broomcorn millet, foxtail millet, buckwheat and amaranth) that has been kept fermenting for a long time. After that, he offers tochang, freshly cooked rice (Tib. lto-chaṅ ‘food-liquor’), and after that darcok (Tib. dar-lcog ‘prayer flags’), small twigs with triangular-shaped flags made of traditional paper. He then conducts a prediction for the coming month, by making three heaps of a mixture of grains, and interprets the way in which these grains pattern. He then sends off the assembled deities.

Bon-po Kesang died in early 2016. His son Tow Tsering has taken over his role, but does not seem to know the ritual as well as his father. He may well be the last of the bon-po in Chug valley, given that no one has come forward to learn from him.

This material is made freely available to everyone for informative or scientific purposes as long as the source (this DOI) / the collectors are properly credited. Please note that use of the material for commercial purposes of any kind, which includes conversion into commercial audio-visual media (documentaries etc.), storage and dissemination through sites that require registration & payment for access, or sites that rely on advertisement (including YouTube) is not permitted without specific written consent from the speakers and their community, obtained through the collectors of the material. By downloading our material, you agree to these restrictions.

This data set falls under the Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license. This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit us and license your new creations under the identical terms. License Deed on https://creativecommons.org/licenses/by-nc-sa/4.0/. Legal Code on https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.

Tim Bodt: monpasang (at) gmail (dot) com

This metadata corresponds to the EUNIS forest and other wooded land habitat types, predicted distribution of habitat suitability dataset.

The forest and other wooded land habitat type is the land where the dominant vegetation is, or was until very recently, trees with a canopy cover of at least 10%. It includes temporarily unstocked areas due to clear-cutting as part of a forest management practice or natural disasters which are expected to be regenerated within 5 years but does not include land that is predominantly under agricultural or urban land use. Trees are defined as woody plants, typically single-stemmed, that can reach a height of at least 5 m at maturity unless stunted by poor climate or soil. Includes Alnus and Populus swamp forest and riverine Salix forest. Excludes Corylus avellana scrub and Salix and Frangula carrs. Excludes lines of trees, coppices, regularly tilled tree nurseries. Excludes stands of climatically-limited dwarf trees (krummholz) < 3m high, such as occur at the arctic or alpine tree limit which are considered scrub (section S) . Excludes tree stands in agricultural production systems, such as fruit tree plantations, olive orchards and agroforestry systems (dehesa and montado) where crops are grown under tree cover - canopy less than 10%, which are listed under sparsely wooded grasslands. Old plantations which have many of the characteristics of natural or semi-natural forests are included, more intensively managed, and less natural, forests are included in vegetated man-made habitats.

The modelled suitability for EUNIS forest and other wooded land habitat types is an indication of where conditions are favourable for the habitat type based on sample plot data (Braun-Blanquet database) and the Maxent software package. The modelled suitability map may be used as a proxy for the geographical distribution of the habitat type. Note however that it is not representing the actual distribution of the habitat type. As predictors for the suitability modelling not only climate and soil parameters have been taken into account, but also so-called RS-EVB's, Remote Sensing-enabled Essential Biodiversity Variables, like land use, vegetation height, phenology, and LAI (Leaf Area Index). Because the EBV's are restricted by the extent of the remote sensing data (EEA38 countries and the United Kingdom) the modelling result does also not go beyond this boundary. The dataset is provided both in Geodatabase and Geopackage formats.

This layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP, NOAA-20, and NOAA-21 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1) or NOAA-21 satellite (2). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireHours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.Additional information can be found on the NASA FIRMS site FAQ.Note about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.RevisionsMarch 7, 2024: Updated to include source data from NOAA-21 Satellite.September 15, 2022: Updated to include 'Hours_Old' field. Time series has been disabled by default, but still available.July 5, 2022: Terms of Use updated to Esri Master License Agreement, no longer stating that a subscription is required!This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

This service displays post-fire debris flow data for fires that have occurred in 2018. Post-fire debris-flow likelihood, volume, and combined hazards are estimated at both the drainage-basin scale and in a spatially distributed manner along the drainage network within each basin. For more information about these data please visit the scientific backgound information page. Estimates of the probability and volume of debris flows that may be produced by a storm in a recently burned area, using a model with characteristics related to basin shape, burn severity, soil properties, and rainfall.

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can produce dangerous flash floods and debris flows. The USGS conducts post-fire debris-flow hazard assessments for select fires in the Western U.S. We use geospatial data related to basin morphometry, burn severity, soil properties, and rainfall characteristics to estimate the probability and volume of debris flows that may occur in response to a design storm.

More USGS information and FAQs here.