The cloud data desensitization market is experiencing robust growth, driven by increasing regulatory compliance needs (like GDPR and CCPA), the rising volume of sensitive data stored in the cloud, and the expanding adoption of cloud computing across diverse sectors. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. Key growth drivers include the escalating need to protect sensitive data from breaches and unauthorized access, particularly within healthcare (medical research data), finance (financial risk assessment), and government (government statistics). The cloud-based delivery model offers scalability and cost-effectiveness, further fueling market expansion. While strong security measures are integral to the success of this technology, challenges remain regarding the balance between data usability and robust security protocols. Integration complexities with existing infrastructure and the potential for unforeseen vulnerabilities represent key restraints. Market segmentation reveals a strong preference for cloud-based solutions, given their inherent flexibility and scalability. The application segments, medical research data, financial risk assessment, and government statistics, are currently leading the market, primarily due to the highly sensitive nature of the data involved. Leading vendors like Micro Focus, IBM, Thales, Google Cloud, and others are actively shaping the market landscape through continuous innovation and the introduction of advanced data masking and tokenization techniques. Regional analysis indicates strong growth in North America and Europe, driven by stringent data privacy regulations and a high concentration of organizations handling sensitive data. However, increasing adoption in the Asia-Pacific region, fueled by rapid digital transformation, is expected to significantly boost market growth in the coming years. The forecast period of 2025-2033 presents a significant opportunity for market expansion, driven by increased data security awareness and evolving technological advancements.

The size and share of the market is categorized based on Application (Compliance Management, Risk Management, Identity and Access Management (IAM), Incident Response Management, Consent Management, Data Analytics) and Product (Data Discovery and Classification Tools, Consent Management Tools, Data Masking and Anonymization Tools, Data Loss Prevention (DLP) Tools, Data Governance Tools) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Market Summary of Database Security Market:

• The Global Database Security market size in 2023 was XX Million. The Database Security Industry's compound annual growth rate (CAGR) will be XX% from 2024 to 2031. • The database security industry is growing faster and is expected to expand at a faster rate due to these strict regulatory frameworks. Also, the increase in advanced technology for better protection of data is driving the growth of the Database security market. • The dominating segment is the software. It includes encryption, auditing, tokenization, data masking, and access control management. • Due to the increase in internet users, remote working demand, and risk of data breaches, the COVID-19 pandemic has had a beneficial effect on the market for data security solutions. • The database security market is dominated by North America in terms of both revenue and market share. This can be attributed to the region's concentration of significant industry participants and increasing technical advancements in their product line.

Market Dynamics of Database Security Market:

Key Drivers of Database Security Market:

An increase in advanced technology for better protection of data is driving the growth of the Database security market

Retail, banking, healthcare, and government are just a few of the industries where a strong data security plan could help companies stay compliant and lower their exposure to threats. When data is used by the principles of availability, confidentiality, and integrity, it becomes the most precious resource that aids in decision-making, strategic endeavor execution, and the development of closer relationships between companies and their clients. For Instance, Records from thousands of people assembled and reindexed leaks, breaches, and privately sold databases are part of a supermassive Mother of all Breaches or MOAB. The huge release includes information from multiple earlier breaches, totaling an incredible 12 gigabytes of data covering an incredible 26 billion records. The leak is most likely the biggest to be found to date and includes user data from Tencent, Weibo, LinkedIn, Twitter, and other networks.(Source: https://cybernews.com/security/billions-passwords-credentials-leaked-mother-of-all-breaches/) Hence, the protection of data is of utmost importance in almost all sectors. Hardware-based security, data backup and resilience, data erasure, data masking, encryption, firewalls, and authentication and authorization are examples of data security technologies. It is essential to corporate development, operations, and financing. Companies can better comply with regulatory standards and avoid data breaches and reputational harm by securing their data. Data is locked up by modern encryption methods with a single key, making it only accessible to the key holder. AES-compliant standards are used by many databases to encrypt data. These remedies are the most robust against hardware loss, possibly due to theft. The data is protected even if the encryption key is incorrect. For Instance, An innovative method for protecting personal information for use with generative artificial intelligence has been released, according to security company Baffle. Assuring that their regulated data is compliant and cryptographically safe, Baffle Data Protection for AI interacts with current data pipelines to help businesses expedite generative AI initiatives. According to Baffle, the method encrypts sensitive data using the advanced encryption standard (AES) algorithm so that outside parties cannot view private information in plaintext. (Source: https://baffle.io/news/baffle-releases-encryption-solution-to-secure-data-for-generative-ai/) Hence, technology is playing an important role in reducing data breaches and protecting data, which is eventually increasing the market for database security as many companies require data protection.

The Database Security Market is driven by the strict regulatory framework to address information security

Regulatory frameworks can establish standards that developers and users must follow to guarantee a secure database. The market is growing as a result of increasingly stringent regulations enforced globally to protect sensitive data by governments and other relevant authorities in numerous nations. ...

The size and share of the market is categorized based on Type (Data encryption tools, Data masking solutions, Privacy compliance software, Anonymization tools, Data protection platforms) and Application (Data protection, Compliance management, Risk mitigation, Privacy management, Regulatory adherence) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Flood and Coastal Risk Management - Incident Management.

The Urban/rural mask dataset categories areas as either urban or rural.

When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic’s onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economic burden of mas..., Dataset collected from code., , ===================================================================

CONSOLE APPLICATION : Project Overview

This file contains a summary of what you will find in each of the files that make up your application.

We uploaded two data files:Â

1. The code file involves performing numerical simulations using C++ in Visual Studio.
2. Two tabular data files.

Requirements

• Visual Studio 2019 or later
• C++14 standard or higher

Installation

1. Clone the repository:
2. bash
3. Copy code
4. Open the project in Visual Studio:
   • Launch Visual Studio.
   • Select "Open a project or solution".
5. Build the project:
   • In the Solution Explorer, right-click on the solution and select "Build Solution" or press Ctrl+Shift+B.

Output File

Two sample Excel files, which were used to create the 2D graphs, have been uploaded. Using the C++ code provided in the Dryad system, 2D CSV files can be generated from the data....

Snapshot img

Laryngeal Airway Mask Market Size 2024-2028

The laryngeal airway mask market size is forecast to increase by USD 52.4 million at a CAGR of 5.3% between 2023 and 2028.

The market is experiencing significant growth due to several key factors. The rising prevalence of chronic disorders and an aging population are driving demand for this medical device. In surgical theaters, laryngeal airway masks are increasingly being used in minimally invasive surgery as they cause less tissue damage compared to endotracheal tubes. The importance of maintaining patient safety and comfort during surgical procedures is leading healthcare professionals to prefer these masks, especially for fasting patients. Reusable medical devices, such as reusable LMAs, offer cost savings but require rigorous cleaning and maintenance procedures to ensure safety. Moreover, the design of laryngeal airway masks has evolved to include multiple cuffs, which provide better sealing and reduce the risk of aspiration. The materials used in these masks are also being improved to enhance their durability and resistance to inspiratory forces.

What will be the Size of the Market During the Forecast Period?

Request Free Sample

The market is a significant segment of the anesthesia equipment industry, playing a crucial role in airway management during surgical procedures. LMAs are essential tools for securing a patient's airway during anesthesia, enabling the delivery of oxygen and removal of carbon dioxide. The design of LMAs is a critical factor in their effectiveness and safety. Anatomical shape designs aim to mimic the natural shape of the human pharynx, ensuring a snug fit and minimal trauma to the patient. Airway device selection depends on various factors, including the patient's age, size, and medical history.
Moreover, innovations in LMA design continue to emerge, focusing on improving patient comfort, ease of use, and safety. Patient safety is a top priority in the LMA market. Stringent safety standards ensure that these devices are effective, reliable, and minimize the risk of complications. Training programs for healthcare professionals are essential for proper use and maintenance of LMAs. Infection control is another critical area of focus, with disposable LMAs being a popular choice due to their single-use nature and reduced risk of infection transmission. Cost-effective healthcare solutions are increasingly important in the LMA market.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Disposal
  Resuable


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  Asia

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Product Insights

The disposal segment is estimated to witness significant growth during the forecast period.

In the market, disposable masks held the largest market share in 2023. This is due to several advantages, including patient comfort and elimination of disease transmission since they are discarded after a single use. Additionally, laryngeal masks maintain a consistent intracuff pressure during nitrous oxide anesthesia, enhancing segment growth. Furthermore, the increasing adoption of disposable masks is driving market expansion. Market participants are also concentrating on innovations to improve patient care. Minimally invasive surgery have gained popularity in surgical theatres, leading to an increased demand for laryngeal airway masks. These masks are essential during surgical procedures for fasting patients and emergencies, especially for those with chronic conditions.

Get a glance at the market report of share of various segments Request Free Sample

The disposal segment was valued at USD 107.10 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 42% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

In the United States, the market holds a substantial share due to the advanced healthcare infrastructure and the increasing prevalence of chronic diseases. According to the Centers for Disease Control and Prevention (CDC), chronic diseases account for approximately 7 out of 10 deaths in the US. Consequently, the high incidence of chronic diseases, such as heart disease and diabetes, necessitates an increased demand for airway ventilation devices during outpatient surgery and anesthesia procedure

Additional file 1: Table S1. Top-ten commuting areas disclosing the highest mean multiannual Health Risk Increase during the day and the night scenarios. When the same city is reported multiple times, a number is assigned next to the name, representing the districts of the major cities. As general criterion, the numbers are assigned with increasing order, starting from the city centre and moving radially and clockwise.

This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales. A QWRA considers several different components, each resolved spatially across the region, including:likelihood of a fire burning, the intensity of a fire if one should occur,the exposure of assets and resources based on their locations, and the susceptibility of those assets and resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. WildEST results were modified for risk calculations in the PNW QWRA 2023 using an irrigated agriculture mask to assign FLPs to pixels that are likely to be irrigated during fire season. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”) and data was collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels and pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). FSim is a comprehensive fire occurrence, growth, behavior, and suppression simulation system that uses locally relevant fuel, weather, topography, and historical fire occurrence information to generate spatially resolved estimates of the contemporary likelihood and intensity of wildfire events. FSim generates stochastic simulation data based on many thousands of iterations and then integrates those into a probabilistic result. These FSim model results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE). which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. This simulation is calibrated to the 2022 trend in wildfire occurrence. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified period (1 year).The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Timber eNVC represents risk integrated across all Timber sub-HVRAs. The timber HVRA is intended to evaluate wildfire risk to commercial timber resources. We grouped sub-HVRAs based on three criteria: land manager, assumed management priority, and timber size class. Land managers included private, state, U.S. Forest Service, Bureau of Land Management, and Tribal entities. Sub-HVRAs include:Private, Non-industrial, QMD < 10"Private, Non-industrial, QMD 10" - 20"Private, Non-industrial, QMD > 20"Private, Industrial, QMD < 10"Private, Industrial, QMD 10" - 20"Private, Industrial, QMD > 20"Tribal, Active Management, QMD < 10"Tribal, Active Management, QMD 10" - 20"Tribal, Active Management, QMD > 20"Tribal, Other Management, QMD < 10"Tribal, Other Management, QMD 10" - 20"Tribal, Other Management, QMD > 20"U.S. Forest Service, Active Management, QMD < 10"U.S. Forest Service, Active Management, QMD 10" - 20"U.S. Forest Service, Active Management, QMD > 20"U.S. Forest Service, Other Management, QMD < 10"U.S. Forest Service, Other Management, QMD 10" - 20"U.S. Forest Service, Other Management, QMD > 20"BLM, Active Management, QMD < 10"BLM, Active Management, QMD 10" - 20"BLM, Active Management, QMD > 20"BLM, Other Management, QMD < 10"BLM, Other Management, QMD 10" - 20"BLM, Other Management, QMD > 20"State, QMD < 10"State, QMD 10" - 20"State, QMD > 20"Methods for mapping the extent of each land manager’s timberlands are described in detail in the following sections. We used assumed management priority criteria to distinguish between lands where commercial timber management is the primary objective from those lands where commercial timber management is part of a multiple-use strategy. Tribal Active Management, U.S. Forest Service Active Management, BLM Active Management, and Private Industrial sub-HVRAs all represent timberlands where commercial timber management is assumed to be the primary management objective. Within all other timber sub-HVRAs, commercial timber management is presumed to be one of several equally important management objectives. State and federal agencies made these designations on public land and used available data for Tribally-managed lands. We mapped timber size class data using Quadratic Mean Diameter (QMD) from the most recent forest structure data available which approximates forest structure in 2021 (LEMMA, 2023a). We included the fire regime group (FRG), along with the timber size class, as a covariate to explain the response to fire. We gave all land managers equal relative importance, but within a land manager type, about twice as much importance was placed on active management timberlands compared to timberlands with multiple, equally important management objectives. Additionally, within any sub-HVRA the most relative importance was assigned to the largest size class and the least was assigned to the smallest size class. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise it may be possible to experience beneficial fire in areas with negative NVC values. Citations:Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-zLEMMA, 2023a. Greatest Nearest Neighbor (GNN): QMD_DOM, unpublished.Primary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov

This layer uses data from the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales.A QWRA considers several different components, each resolved spatially across the region, including:likelihood of a fire burning,the intensity of a fire if one should occur,the exposure of assets and resources based on their locations, andthe susceptibility of those assets and resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. WildEST results were modified for risk calculations in the PNW QWRA 2023 using an irrigated agriculture mask to assign FLPs to pixels that are likely to be irrigated during fire season. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”) and data was collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels and pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315.The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer represents the cNVC integrated across the people and property, infrastructure, and drinking water HVRAs. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Finally, HVRA-level NVC risk can be summed across several or all HVRAs to calculate integrated NVC, representing a risk to multiple HVRAs. Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and, likewise, it may be possible to experience a beneficial fire in areas with negative NVC values.Citations:Ketchum, D., Jencso, K., Maneta, M.P., Melton, F., Jones, M.O., Huntington, J., 2020. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing 12, 2328. https://doi.org/10.3390/rs12142328Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-zPrimary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov

Risk of infection over pandemic phases with minima and maxima risk per federal state/county according to regional agency data.

This dataset is a product of the 2023 Pacific Northwest Quantitative Wildfire Risk Assessment (PNW QWRA 2023). The purpose of the PNW QWRA 2023 is to provide foundational information about wildfire risk across the Pacific Northwest Region (which encompasses the states of Oregon and Washington). Analytics from the QWRA are used to guide vegetation management, fire response, and community planning at multiple scales. A QWRA considers several different components, each resolved spatially across the region, including:likelihood of a fire burning, the intensity of a fire if one should occur,the exposure of assets and resources based on their locations, and the susceptibility of those assets and resourcesData users are encouraged to refer to the PNW QWRA 2023 Methods Report for full details: https://oe.oregonexplorer.info/externalcontent/wildfire/PNW_QWRA_2023Methods.pdfPyrologix LLC modeled wildfire intensity and likelihood for the PNW QWRA 2023. Wildfire intensity was modeled using the WildEST model. These WildEST results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE v2.2.0), which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. WildEST results were modified for risk calculations in the PNW QWRA 2023 using an irrigated agriculture mask to assign FLPs to pixels that are likely to be irrigated during fire season. An irrigated agriculture mask was created from LANDFIRE 2.2.0 Fire Behavior Fuel Models (where the model = “NB3”) and data was collected from IrrMapper (Ketchum et al., 2020). All NB3 pixels and pixels that were classified as irrigated in three of the most recent five years in IrrMapper were included in the irrigated agriculture mask. Pixels in the irrigated agriculture mask were assigned an FLP of 0.75 for flame lengths between 0 – 2 feet, 0.25 for flame lengths 2 – 4 feet, and an FLP of 0 for all intensity values greater than 4 feet. Fire-effects flame-length probability rasters generated in WildEST were used for effects analysis in a landscape wildfire risk assessment, as described in USFS GTR-315. Wildfire likelihood was modeled using the large fire simulator, FSim (Finney et a., 2011). FSim is a comprehensive fire occurrence, growth, behavior, and suppression simulation system that uses locally relevant fuel, weather, topography, and historical fire occurrence information to generate spatially resolved estimates of the contemporary likelihood and intensity of wildfire events. FSim generates stochastic simulation data based on many thousands of iterations and then integrates those into a probabilistic result. These FSim model results were completed on the 2022 current-condition fuelscape (derived from LANDFIRE). which reflects fuelscape conditions for the year 2022 and includes all historical fuel disturbances through 2021. This simulation is calibrated to the 2022 trend in wildfire occurrence. Wildfire likelihood is represented as burn probability (BP), which is the probability that a specific geographic location (30-m pixel) will experience a wildland fire during a specified period (1 year).The PNW QWRA 2023 evaluated risk to eight highly-valued resources and assets (HVRAs): People and Property, Infrastructure, Drinking Water, Timber, Ecological Integrity, Wildlife Habitat, Agriculture, and Recreation. This data layer, Integrated Expected Net Value Change (ieNVC) represents risk integrated across eight HVRAs. Risk is estimated within the QWRA framework by integrating wildfire hazard with HVRA susceptibility (Scott et al., 2013). Risk is calculated for each pixel separately based on the fire hazard data for that pixel and based on which HVRAs are present. Fire impacts to each HVRA are characterized by the estimated change in value, a unitless approximation of whether the HVRA is beneficially or adversely affected by fire and to what magnitude. Accordingly, risk is expressed as net value change (NVC). Net value change is first calculated for all pixels across a sub-HVRA. The NVC for each HVRA is then calculated by summing the NVC of all its constituent sub-HVRAs. Finally, HVRA-level NVC risk can be summed across several or all HVRAs to calculate integrated NVC, representing a risk to multiple HVRAs.Positive values indicate that wildfire is likely to have beneficial impacts on the HVRA while negative values indicate that the net outcomes are likely to be adverse. Risk is calculated based on a very wide range of plausible weather conditions, much wider than the range under which we have typically experienced large fires in the past. The specific conditions under which a wildfire occurs will determine the outcomes. When interpreting QWRA risk results bear in mind that fire will not always be beneficial in areas with positive NVC values and likewise, it may be possible to experience beneficial fire in areas with negative NVC values. Citations:Ketchum, D., Jencso, K., Maneta, M.P., Melton, F., Jones, M.O., Huntington, J., 2020. IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S. Remote Sensing 12, 2328. https://doi.org/10.3390/rs12142328Scott, J.H., Thompson, M.P., Calkin, D.E., 2013. A wildfire risk assessment framework for land and resource management (No. RMRS-GTR-315). U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ft. Collins, CO. https://doi.org/10.2737/RMRS-GTR-315Finney, M.A., McHugh, C.W., Grenfell, I.C., Riley, K.L., Short, K.C., 2011. A simulation of probabilistic wildfire risk components for the continental United States. Stoch Environ Res Risk Assess 25, 973–1000. https://doi.org/10.1007/s00477-011-0462-zPrimary Data Contact: Ian Rickert, Regional Fire Planner, Forest Service R6/R10, ian.rickert@usda.gov

The National Hurricane Center and Central Pacific Center Tropical Weather Summary Web Service is a web service that contains the tropical cyclone data for possible storms throughout the United States and its territories. Specific storms can be identified on this summary service by the storm’s wallet. Wallet information is found in the "idp_source" with a field alias GIS Source attribute field of the data as the leading three characters.This web service visually displays potential impact of tropical cyclones on coastal communities. The provided wind, probability of flooding, surge inundation layers, watch warnings and tidal masks offers critical information for emergency preparedness and response efforts. This includes helping residents, emergency managers, and policymakers understand the potential severity of coastal flooding and take appropriate precautions. This web service covers a wide range of coastal areas prone to tropical cyclones, ensuring that stakeholders across different regions have access to essential tropical storm information. This comprehensive coverage enhances the service's utility and relevance for a diverse audience.However, understanding the full extent of risk requires a comprehensive view of the affected areas. Therefore, it's highly recommended to complement the National Hurricane Center and Central Pacific Center Tropical Weather Web Service is complimented with the use of additional resources including NHC Peak Storm Surge Web Service that provide information about major roads, railways, landmarks, and areas likely to be flooded. Incorporating data on past flood levels can further enrich the analysis and aid in predicting future impacts.One such valuable asset is the NWS National Viewer’s Tropical Site, which offers a wealth of supplementary information to enhance situational awareness and risk assessment. By integrating these complementary resources, stakeholders can gain a holistic understanding of the potential impacts of tropical cyclones and make more effective decisions to safeguard lives, property, and critical.Layer Descriptions:2 Day Outlook depicts the 2-day Graphic Tropical Weather Outlook from the NHC.7 Day Outlook depicts the 7-day Graphic Tropical Weather Outlook from the NHC.Forecast Points depicts the and current position and forecast positions of the storm out to 120 hours.Forecast Track is a line connecting the forecast points.Forecast Cone depicts the forecast "Cone of uncertainty".Watch-Warning depicts a "watch/warning" line indicating which sections of the coastline are in a watch/warning state due to the storm.Past Points depicts the "best" track of the storm to the current time.Past Track is a line connecting the past points.Best Wind Radii shows how the size of the storm has changed and the areas potentially affected so far by sustained winds.Surface Wind Field is intended to show the areas potentially being affected by sustained winds of tropical storm force (34 knot), (50 knot) and hurricane force (64 knot).Forecast Wind Radii are intended to show the expected size of the storm and the areas potentially affected by sustained winds of tropical storm force (34 Knot), (50 knot) and hurricane force (64 knot).Arrival Time of TS Winds depicts the earliest reasonable or the most likely arrival time of tropical storm force winds.Inundation depicts the total water level that occurs on normally dry ground as a result of the storm tide.Tidal Mask depicts the total water level that occurs on normally dry ground as a result of the storm tide, plus intertidal zones/estuarine wetlands.Probabilistic Winds depicts the probability of 34, 50 and 64 knot winds.Update Frequency: Every 6 hours and every 3 hours if the storm is approaching the shore.Link to graphical web page: https://www.nhc.noaa.govLink to data download (shapefile): https://www.nhc.noaa.gov/gisLink to metadataQuestions/Concerns about the service, please contact the DISS GIS team.Time Information: This service is not time enabled.