@incollection{weiseAssessingLiveFuel1998,
  title = {Assessing Live Fuel Moisture for Fire Management Applications},
  booktitle = {Fire in Ecosystem Management: Shifting the Paradigm from Suppression to Prescription},
  author = {Weise, David R. and Hartford, Roberta A. and Mahaffey, Larry},
  editor = {Pruden, Teresa L. and Brennan, Leonard A.},
  year = {1998},
  volume = {20},
  pages = {49--55},
  publisher = {{Tall Timbers Research Station}},
  address = {{Tallahassee, Florida}},
  abstract = {The variation associated with sampling live fuel moisture was examined for several shrub and canopy fuels in southern California, Arizona, and Colorado. Ninety-five \% confidence intervals ranged from 5 to \% . Estimated sample sizes varied greatly. The value of knowing the live fuel moisture content in fire decision making is unknown. If the fuel moisture is highly variable, then it is possible for the confidence intervals to span one or more fire behavior or danger classes. Errors in live fuel moisture data may directly affect the costs in safety and resources associated with prescribed fire and wildfire suppression.

[Excerpt: Fire management implications]

As pointed out in the Live Fuel Moisture Task Force Report (Cohen et al. 1995), live fuel moisture information is currently best used in strategic decisions instead of tactical decisions because of the current limitations in our understanding of live fuels and fire behavior. Since the 1940's,live fuel moisture information has been used in fire danger calculations, a strategic level use of the data. In the first version of the National Fire-Danger Rating System (NFDRS), live fuel moisture was sampled along transects at each fire danger station. This approach was replaced by live fuel moisture models for herbaceous and shrub fuels in the 1978 NFDRS (Bradshaw et al. 1983). The 1978 NFDRS live fuel moisture model transfers herbaceous fuels into the 1-hour timelag fuel class; however, live woody fuels are not transferred into the corresponding dead fuel classes. Live fuels can still contribute heat to the combustion process (Richards 1940, Bradshaw et al. 1983).

[] Some users of live fuel moisture information have used the data to develop general guidelines related to fire behavior and danger (Cohen et al. 1995). Monitoring live fuel moisture data over several years has enabled others to use the data to estimate what the current fire danger is relative to previous years' fire danger. By developing an '' average'' annual live fuel moisture profile, a fire management agency can make these fire danger assessments. [...] However, error associated with live fuel moisture samples should be considered when using live fuel moisture data in this fashion.

[] Consider the following example. The confidence intervals for chamise at Boquet Canyon 1 were roughly {$\pm$}40\,\% and at San Marcos were roughly {$\pm$}10\,\%. 

[] Assume guidelines such as: [::1)] live fuel moisture {$>$} 120\,\%-low fire danger; [::2)] 80\,\% {$<$} live fuel moisture {$<$} 120\,\%-moderate fire danger; [::3)] 60\,\% {$<$} live fuel moisture 80\,\%-high fire danger; and [::4)] live fuel moisture {$<$} 60\,\%-extreme fire danger; [] have been developed.

[] If live fuel moisture is estimated to be 90\,\%, fire danger would be rated anywhere from low to extreme at Boquet and moderate at San Marcos because of the width of the confidence intervals. This may have serious implications for fire management applications. [...]

[Summary] The variation associated with sampling live fuel moisture was examined for several shrub and canopy fuels in southern California, Arizona, and Colorado. Ninety-five \% confidence intervals ranged from {$\pm$}5\,\% to {$\pm$} 100\,\%. Estimated sample sizes also varied greatly. At allowable error of 5\,\%, maximum mean estimated sample size was as high as 630. Increasing allowable error to 25\,\% reduced estimated sample sizes to less than 30.

[] The value of live fuel moisture in fire decision making is unknown. If the fuel moisture is highly variable, then it is possible for the confidence intervals to span one or more fire behavior or dangers classes. Errors in live fuel moisture data may directly affect the costs in safety and resources associated with prescribed fire and wildfire suppression. If live fuel moisture content is to be sampled to provide information for fire management decisions, care should be taken to collect an adequate sample to insure that the precision of the estimate is within acceptable bounds. [...]},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14148981,arizona,california,classification,colorado,field-measurements,fire-fuel,forest-resources,live-fuel-moisture-content,management,shrubs,united-states},
  lccn = {INRMM-MiD:c-14148981},
  series = {Tall {{Timbers Fire Ecology Conference Proceedings}}}
}

{"_id":"2dP3gu3n6kEP5A9gc","bibbaseid":"weise-hartford-mahaffey-assessinglivefuelmoistureforfiremanagementapplications-1998","downloads":0,"creationDate":"2017-06-22T15:25:07.881Z","title":"Assessing Live Fuel Moisture for Fire Management Applications","author_short":["Weise, D. R.","Hartford, R. A.","Mahaffey, L."],"year":1998,"bibtype":"incollection","biburl":"https://sharefast.me/php/download.php?id=zOUKvA&token=29","bibdata":{"bibtype":"incollection","type":"incollection","title":"Assessing Live Fuel Moisture for Fire Management Applications","booktitle":"Fire in Ecosystem Management: Shifting the Paradigm from Suppression to Prescription","author":[{"propositions":[],"lastnames":["Weise"],"firstnames":["David","R."],"suffixes":[]},{"propositions":[],"lastnames":["Hartford"],"firstnames":["Roberta","A."],"suffixes":[]},{"propositions":[],"lastnames":["Mahaffey"],"firstnames":["Larry"],"suffixes":[]}],"editor":[{"propositions":[],"lastnames":["Pruden"],"firstnames":["Teresa","L."],"suffixes":[]},{"propositions":[],"lastnames":["Brennan"],"firstnames":["Leonard","A."],"suffixes":[]}],"year":"1998","volume":"20","pages":"49–55","publisher":"Tall Timbers Research Station","address":"Tallahassee, Florida","abstract":"The variation associated with sampling live fuel moisture was examined for several shrub and canopy fuels in southern California, Arizona, and Colorado. Ninety-five % confidence intervals ranged from 5 to % . Estimated sample sizes varied greatly. The value of knowing the live fuel moisture content in fire decision making is unknown. If the fuel moisture is highly variable, then it is possible for the confidence intervals to span one or more fire behavior or danger classes. Errors in live fuel moisture data may directly affect the costs in safety and resources associated with prescribed fire and wildfire suppression. [Excerpt: Fire management implications] As pointed out in the Live Fuel Moisture Task Force Report (Cohen et al. 1995), live fuel moisture information is currently best used in strategic decisions instead of tactical decisions because of the current limitations in our understanding of live fuels and fire behavior. Since the 1940's,live fuel moisture information has been used in fire danger calculations, a strategic level use of the data. In the first version of the National Fire-Danger Rating System (NFDRS), live fuel moisture was sampled along transects at each fire danger station. This approach was replaced by live fuel moisture models for herbaceous and shrub fuels in the 1978 NFDRS (Bradshaw et al. 1983). The 1978 NFDRS live fuel moisture model transfers herbaceous fuels into the 1-hour timelag fuel class; however, live woody fuels are not transferred into the corresponding dead fuel classes. Live fuels can still contribute heat to the combustion process (Richards 1940, Bradshaw et al. 1983). [] Some users of live fuel moisture information have used the data to develop general guidelines related to fire behavior and danger (Cohen et al. 1995). Monitoring live fuel moisture data over several years has enabled others to use the data to estimate what the current fire danger is relative to previous years' fire danger. By developing an '' average'' annual live fuel moisture profile, a fire management agency can make these fire danger assessments. [...] However, error associated with live fuel moisture samples should be considered when using live fuel moisture data in this fashion. [] Consider the following example. The confidence intervals for chamise at Boquet Canyon 1 were roughly $\\pm$40\\,% and at San Marcos were roughly $\\pm$10\\,%. [] Assume guidelines such as: [::1)] live fuel moisture $>$ 120\\,%-low fire danger; [::2)] 80\\,% $<$ live fuel moisture $<$ 120\\,%-moderate fire danger; [::3)] 60\\,% $<$ live fuel moisture 80\\,%-high fire danger; and [::4)] live fuel moisture $<$ 60\\,%-extreme fire danger; [] have been developed. [] If live fuel moisture is estimated to be 90\\,%, fire danger would be rated anywhere from low to extreme at Boquet and moderate at San Marcos because of the width of the confidence intervals. This may have serious implications for fire management applications. [...] [Summary] The variation associated with sampling live fuel moisture was examined for several shrub and canopy fuels in southern California, Arizona, and Colorado. Ninety-five % confidence intervals ranged from $\\pm$5\\,% to $\\pm$ 100\\,%. Estimated sample sizes also varied greatly. At allowable error of 5\\,%, maximum mean estimated sample size was as high as 630. Increasing allowable error to 25\\,% reduced estimated sample sizes to less than 30. [] The value of live fuel moisture in fire decision making is unknown. If the fuel moisture is highly variable, then it is possible for the confidence intervals to span one or more fire behavior or dangers classes. Errors in live fuel moisture data may directly affect the costs in safety and resources associated with prescribed fire and wildfire suppression. If live fuel moisture content is to be sampled to provide information for fire management decisions, care should be taken to collect an adequate sample to insure that the precision of the estimate is within acceptable bounds. [...]","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-14148981,arizona,california,classification,colorado,field-measurements,fire-fuel,forest-resources,live-fuel-moisture-content,management,shrubs,united-states","lccn":"INRMM-MiD:c-14148981","series":"Tall Timbers Fire Ecology Conference Proceedings","bibtex":"@incollection{weiseAssessingLiveFuel1998,\n title = {Assessing Live Fuel Moisture for Fire Management Applications},\n booktitle = {Fire in Ecosystem Management: Shifting the Paradigm from Suppression to Prescription},\n author = {Weise, David R. and Hartford, Roberta A. and Mahaffey, Larry},\n editor = {Pruden, Teresa L. and Brennan, Leonard A.},\n year = {1998},\n volume = {20},\n pages = {49--55},\n publisher = {{Tall Timbers Research Station}},\n address = {{Tallahassee, Florida}},\n abstract = {The variation associated with sampling live fuel moisture was examined for several shrub and canopy fuels in southern California, Arizona, and Colorado. Ninety-five \\% confidence intervals ranged from 5 to \\% . Estimated sample sizes varied greatly. The value of knowing the live fuel moisture content in fire decision making is unknown. If the fuel moisture is highly variable, then it is possible for the confidence intervals to span one or more fire behavior or danger classes. Errors in live fuel moisture data may directly affect the costs in safety and resources associated with prescribed fire and wildfire suppression.\n\n[Excerpt: Fire management implications]\n\nAs pointed out in the Live Fuel Moisture Task Force Report (Cohen et al. 1995), live fuel moisture information is currently best used in strategic decisions instead of tactical decisions because of the current limitations in our understanding of live fuels and fire behavior. Since the 1940's,live fuel moisture information has been used in fire danger calculations, a strategic level use of the data. In the first version of the National Fire-Danger Rating System (NFDRS), live fuel moisture was sampled along transects at each fire danger station. This approach was replaced by live fuel moisture models for herbaceous and shrub fuels in the 1978 NFDRS (Bradshaw et al. 1983). The 1978 NFDRS live fuel moisture model transfers herbaceous fuels into the 1-hour timelag fuel class; however, live woody fuels are not transferred into the corresponding dead fuel classes. Live fuels can still contribute heat to the combustion process (Richards 1940, Bradshaw et al. 1983).\n\n[] Some users of live fuel moisture information have used the data to develop general guidelines related to fire behavior and danger (Cohen et al. 1995). Monitoring live fuel moisture data over several years has enabled others to use the data to estimate what the current fire danger is relative to previous years' fire danger. By developing an '' average'' annual live fuel moisture profile, a fire management agency can make these fire danger assessments. [...] However, error associated with live fuel moisture samples should be considered when using live fuel moisture data in this fashion.\n\n[] Consider the following example. The confidence intervals for chamise at Boquet Canyon 1 were roughly {$\\pm$}40\\,\\% and at San Marcos were roughly {$\\pm$}10\\,\\%. \n\n[] Assume guidelines such as: [::1)] live fuel moisture {$>$} 120\\,\\%-low fire danger; [::2)] 80\\,\\% {$<$} live fuel moisture {$<$} 120\\,\\%-moderate fire danger; [::3)] 60\\,\\% {$<$} live fuel moisture 80\\,\\%-high fire danger; and [::4)] live fuel moisture {$<$} 60\\,\\%-extreme fire danger; [] have been developed.\n\n[] If live fuel moisture is estimated to be 90\\,\\%, fire danger would be rated anywhere from low to extreme at Boquet and moderate at San Marcos because of the width of the confidence intervals. This may have serious implications for fire management applications. [...]\n\n[Summary] The variation associated with sampling live fuel moisture was examined for several shrub and canopy fuels in southern California, Arizona, and Colorado. Ninety-five \\% confidence intervals ranged from {$\\pm$}5\\,\\% to {$\\pm$} 100\\,\\%. Estimated sample sizes also varied greatly. At allowable error of 5\\,\\%, maximum mean estimated sample size was as high as 630. Increasing allowable error to 25\\,\\% reduced estimated sample sizes to less than 30.\n\n[] The value of live fuel moisture in fire decision making is unknown. If the fuel moisture is highly variable, then it is possible for the confidence intervals to span one or more fire behavior or dangers classes. Errors in live fuel moisture data may directly affect the costs in safety and resources associated with prescribed fire and wildfire suppression. If live fuel moisture content is to be sampled to provide information for fire management decisions, care should be taken to collect an adequate sample to insure that the precision of the estimate is within acceptable bounds. [...]},\n keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14148981,arizona,california,classification,colorado,field-measurements,fire-fuel,forest-resources,live-fuel-moisture-content,management,shrubs,united-states},\n lccn = {INRMM-MiD:c-14148981},\n series = {Tall {{Timbers Fire Ecology Conference Proceedings}}}\n}\n\n","author_short":["Weise, D. R.","Hartford, R. A.","Mahaffey, L."],"editor_short":["Pruden, T. L.","Brennan, L. A."],"key":"weiseAssessingLiveFuel1998","id":"weiseAssessingLiveFuel1998","bibbaseid":"weise-hartford-mahaffey-assessinglivefuelmoistureforfiremanagementapplications-1998","role":"author","urls":{},"keyword":["*imported-from-citeulike-INRMM","~INRMM-MiD:c-14148981","arizona","california","classification","colorado","field-measurements","fire-fuel","forest-resources","live-fuel-moisture-content","management","shrubs","united-states"],"downloads":0},"search_terms":["assessing","live","fuel","moisture","fire","management","applications","weise","hartford","mahaffey"],"keywords":["arizona","california","classification","colorado","field-measurements","fire-fuel","forest-resources","live-fuel-moisture-content","management","shrubs","united-states","*imported-from-citeulike-inrmm","~inrmm-mid:c-14148981"],"authorIDs":[],"dataSources":["5S2zj2hKW8TWTkuMq"]}