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Prediction models and the extrapolation effects for water content of surface dead fuels in the typical stand of the Great Xing'an Mountains of China by one-hour time step.

Identifieur interne : 000D26 ( Main/Exploration ); précédent : 000D25; suivant : 000D27

Prediction models and the extrapolation effects for water content of surface dead fuels in the typical stand of the Great Xing'an Mountains of China by one-hour time step.

Auteurs : Hong Zhou Yu [République populaire de Chine] ; Li Fu Shu [République populaire de Chine] ; Ji Feng Deng [République populaire de Chine] ; Guang Yang [République populaire de Chine] ; Qi Liang [République populaire de Chine] ; Jing Hao Li [République populaire de Chine] ; Hang Yong Zhu [République populaire de Chine]

Source :

RBID : pubmed:30584722

Descripteurs français

English descriptors

Abstract

The water content of surface dead fuels is one of the most important indicators for forecasting fire danger and fire behaviors. We employed the timelag equilibrium water content methods (i.e. Nelson and Simard models) and the meteorological variable regression method to continuously measure the water content of surface dead fuels by one-hour time step from September to October in 2010 under Populus davidiana + Betula platyphylla, Picea koraiensis and the cutover lands (Pinus sylvestris var. mongolica + Betula platyphylla) with different canopy densities in Pangu Forestry Bureau, the Great Xing'an Mountains, Heilongjiang Province, China. We established prediction models and obtained prediction errors. The models were also used to extrapolate the water contents of surface dead fuels under other forest stands and the extrapolation accuracy was analyzed. The results showed that the mean absolute error, the mean relative error and the mean square error root of Nelson model (0.0154, 0.104 and 0.0226) were lower than those of Simard model (0.0185, 0.117 and 0.0256). In terms of extrapolation effects, the mean absolute error, the mean relative error and the mean square error root of meteorological variable regression method (0.0410, 0.0300 and 0.0740) were lower than those of Simard model (0.610, 0.492 and 0.846), but they were higher than those of Nelson model (0.034, 0.021 and 0.0660). Such results indicated that the timelag equilibrium moisture content method by one-hour time step, especially Nelson model, was sui-table for the forest stands in the Great Xing'an Mountains. Although extrapolation could not reduce the prediction errors, it could help improve the prediction accuracy and the efficiency of the present models applied to different forest stands or in a larger scale. The modeling and extrapolation errors were closely related to species identity and canopy densities, thus the appropriate timelag equilibrium moisture content methods should be selected according to different forest stands and locations.

DOI: 10.13287/j.1001-9332.201812.008
PubMed: 30584722


Affiliations:


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Le document en format XML

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<div type="abstract" xml:lang="en">The water content of surface dead fuels is one of the most important indicators for forecasting fire danger and fire behaviors. We employed the timelag equilibrium water content methods (i.e. Nelson and Simard models) and the meteorological variable regression method to continuously measure the water content of surface dead fuels by one-hour time step from September to October in 2010 under Populus davidiana + Betula platyphylla, Picea koraiensis and the cutover lands (Pinus sylvestris var. mongolica + Betula platyphylla) with different canopy densities in Pangu Forestry Bureau, the Great Xing'an Mountains, Heilongjiang Province, China. We established prediction models and obtained prediction errors. The models were also used to extrapolate the water contents of surface dead fuels under other forest stands and the extrapolation accuracy was analyzed. The results showed that the mean absolute error, the mean relative error and the mean square error root of Nelson model (0.0154, 0.104 and 0.0226) were lower than those of Simard model (0.0185, 0.117 and 0.0256). In terms of extrapolation effects, the mean absolute error, the mean relative error and the mean square error root of meteorological variable regression method (0.0410, 0.0300 and 0.0740) were lower than those of Simard model (0.610, 0.492 and 0.846), but they were higher than those of Nelson model (0.034, 0.021 and 0.0660). Such results indicated that the timelag equilibrium moisture content method by one-hour time step, especially Nelson model, was sui-table for the forest stands in the Great Xing'an Mountains. Although extrapolation could not reduce the prediction errors, it could help improve the prediction accuracy and the efficiency of the present models applied to different forest stands or in a larger scale. The modeling and extrapolation errors were closely related to species identity and canopy densities, thus the appropriate timelag equilibrium moisture content methods should be selected according to different forest stands and locations.</div>
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<AbstractText>地表死可燃物含水率是火险天气和火行为预报中的重要指标.本研究基于时滞平衡含水率法(Nelson和Simard方法)及气象要素回归方法,于2010年9—10月对黑龙江省大兴安岭地区盘古林场不同郁闭度的山杨-白桦混交林、红皮云杉纯林,以及采伐迹地(原1∶1樟子松-白桦混交林)地表死可燃物含水率进行以小时为步长的连续测定,建立其预测模型,得到预测误差,并使用相应的模型对其他林分地表死可燃物含水率进行外推精度分析.结果表明:采用Nelson平衡含水率法构建的地表死可燃物含水率变化模型的平均绝对误差、平均相对误差和均方误差根(0.0154、0.104和0.0226)低于Simard法(0.0185、0.117和0.0256)和气象要素回归法(0.0222、0.150和0.0331).在外推效果方面,气象要素回归法的平均绝对误差、平均相对误差和均方误差根(0.0410、0.0300和0.0740)低于Simard法(0.610、0.492和0.846),但前两者均高于Nelson法(0.034、0.021和0.0660),说明以小时为步长的时滞平衡含水率法,尤其是Nelson法适用于大兴安岭地区所测林分.外推虽不能降低误差,但有助于提高现有模型应用至不同林分条件或大尺度范围内的地表死可燃物含水率预测精度和利用率.模型建模和外推误差与不同树种和郁闭度条件差异有关,研究时应根据不同林分和地点选择合适的平衡含水率模型.</AbstractText>
</OtherAbstract>
<KeywordList Owner="NOTNLM">
<Keyword MajorTopicYN="N">extrapolation</Keyword>
<Keyword MajorTopicYN="N">moisture content of fuel</Keyword>
<Keyword MajorTopicYN="N">one-hour time step</Keyword>
<Keyword MajorTopicYN="N">prediction model</Keyword>
<Keyword MajorTopicYN="N">the Great Xing'an Mountains</Keyword>
</KeywordList>
</MedlineCitation>
<PubmedData>
<History>
<PubMedPubDate PubStatus="entrez">
<Year>2018</Year>
<Month>12</Month>
<Day>26</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="pubmed">
<Year>2018</Year>
<Month>12</Month>
<Day>26</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
<PubMedPubDate PubStatus="medline">
<Year>2019</Year>
<Month>3</Month>
<Day>21</Day>
<Hour>6</Hour>
<Minute>0</Minute>
</PubMedPubDate>
</History>
<PublicationStatus>ppublish</PublicationStatus>
<ArticleIdList>
<ArticleId IdType="pubmed">30584722</ArticleId>
<ArticleId IdType="doi">10.13287/j.1001-9332.201812.008</ArticleId>
</ArticleIdList>
</PubmedData>
</pubmed>
<affiliations>
<list>
<country>
<li>République populaire de Chine</li>
</country>
<region>
<li>Dongbei</li>
<li>Jilin</li>
</region>
<settlement>
<li>Changchun</li>
<li>Pékin</li>
</settlement>
</list>
<tree>
<country name="République populaire de Chine">
<noRegion>
<name sortKey="Yu, Hong Zhou" sort="Yu, Hong Zhou" uniqKey="Yu H" first="Hong Zhou" last="Yu">Hong Zhou Yu</name>
</noRegion>
<name sortKey="Deng, Ji Feng" sort="Deng, Ji Feng" uniqKey="Deng J" first="Ji Feng" last="Deng">Ji Feng Deng</name>
<name sortKey="Li, Jing Hao" sort="Li, Jing Hao" uniqKey="Li J" first="Jing Hao" last="Li">Jing Hao Li</name>
<name sortKey="Liang, Qi" sort="Liang, Qi" uniqKey="Liang Q" first="Qi" last="Liang">Qi Liang</name>
<name sortKey="Shu, Li Fu" sort="Shu, Li Fu" uniqKey="Shu L" first="Li Fu" last="Shu">Li Fu Shu</name>
<name sortKey="Yang, Guang" sort="Yang, Guang" uniqKey="Yang G" first="Guang" last="Yang">Guang Yang</name>
<name sortKey="Yu, Hong Zhou" sort="Yu, Hong Zhou" uniqKey="Yu H" first="Hong Zhou" last="Yu">Hong Zhou Yu</name>
<name sortKey="Zhu, Hang Yong" sort="Zhu, Hang Yong" uniqKey="Zhu H" first="Hang Yong" last="Zhu">Hang Yong Zhu</name>
</country>
</tree>
</affiliations>
</record>

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