| Forest fires are one of the important factors affecting the forest ecosystem,human life,and infrastructure in northeast of China.Thus,it is very important to improve fire risk forecasting,reduce the possibility and potential fire hazards,and thereby reduce fire losses.Therefore,it is particularly important to strengthen forest fire management and improve forest fire prediction capabilities.The ignition,propagation and development of forest fires are strongly affected by the moisture content of forest fuels,in particular the frequency of forest fires is directly affected by the moisture content of dead fuels on the ground.Therefore,accurately predicting the moisture content of dead fuels on the forest surface is the key for forecasting forest fire risk and fire behaviour,and is the key to strengthening research on the prediction model of dead fine fuel moisture content.The main objective of this thesis was to develop the model of dead fine fuel moisture content(FMC)as it an important tool in forest fire management and to estimate the forest fire risk with weather factors relation with FMC.The specific objectives of this thesis were(1)to develop the water content models of dead surface fuels based on weather variables,and(2)to evaluate the use of the Canadian Forest Fire Danger Rating System in wildfire management in the northeast China.The forest stand of Maoer Mountain located in Heilongjiang Province in northeast China was chosen as the study area.In this area,typical forest ecosystems were chosen from which samples of the dead fine surface fuels were collected to analyse the variation of the water content of fuels.Developed models relying on three-years meteorological data and were computed using linear and non-linear regressions.The performance of the model was evaluated,showing its ability to simulate the prediction of the moisture content of fuels.Widely proved as the best forest fire risk prediction system in several many parts around the world,the Canadian Forest Fire Danger Rating System(CFFDRS)effectiveness was tested in a typical ecosystem in northeast China to ensure its use in the region.An overview across all sites in 2018 puts relative humidity first,followed by rain,temperature,wind speed and finally solar radiation.Developed models with diurnal data show that relative humidity,temperature and rain influenced much the fuel moisture content.In all sites,the R2-adj is greater than 80%.FMC models developed with daily and/or diurnal data show that all sites are characterized by a model with high predictive power,R2-adj>0.80.In 2019,FMC meter data predicted better the FMC than data collected from China Weather Station.Rain and relative humidity influence on FMC were strong than for temperature,wind speed,solar radiation and sunshine time.All models built with both data source presented a good predive power(R-sq.(adj.?70).A general overview on daily developed models showed that relative humidity,temperature,solar radiation and wind velocity influenced the FMC;however,only one of five sampling sites was characterised by a high predictive power model:R-sq.(adj.)=0.70.Daily developed models of FMC in 2020 indicated that relative humidity,rain and temperature were the most important factors influencing the water content of fuels in all sampling sites.Two of six sampling sites were characterised by models with high predictive power:0.73 ? R-sq.(adj.)? 0.83.The other four sites were characterised by models in medium predictive power:0.52 ? R-sq.(adj.)? 0.66.In addition,developed models with diurnal data showed that relative humidity,temperature and rain were the influencing drivers of FMC.Three sites were characterised with models in high predictive power,R-sq.(adj.)>0.70 and three others with medium predictive power:0.61 ? R-sq.(adj.)? 0.66.The variation on FWI in 2018 fire season shows that the period from 3 to 11 October was characterised by an overall increase in FWI,with a maximum in site 3,slightly reaching the high level of forest fire.In overall,the FWI varied from the lower class to the moderate of forest fires.The value of FWI in 2019 was low from October 1st-12th,moderate from October 13th-18th,very high from October 19th-26th,and low from October 26th-31st.From 14 October to 31 October 2020,the FWI varied between 0 and 5.56,thus was very low or low There was no difference between 2018 and 2019 or 2020 FWI values;however,a significant difference was observed between 2019 and 2020 FWI values.Thus,the following conclusions emerge:1.Computed models allowed us to select the most important variables that influence the dead fine fuel moisture content.Temperature,relative humidity,and rain,in overall,affected the FMC significantly and were retained as control variables.Wind speed and solar radiation exerted less influence on FMC.A significant difference was observed among the fuel moisture content when comparing the values observed on the FMC meter,the predicted values with the FMC meter weather data,and the predicted values with the CWS data.The same is true for the different years of study and the sampling sites.A significant difference was also observed between the water content of dead fuels in the 15 sampling sites and among the three years of this study.2.The calculation of fire risk using the CFFDRS in Maoer mountain forest ecosystems presented low,medium or high risk.However,there is a significant difference in FWI variation over the three years of data collection.FMC meter data were more accurate in estimating fire danger than CWS data,suggesting more local meteorological stations in fire-prone regions would be beneficial in fire-risk assessment.3.These results prove that continuous monitoring is needed to control this index in order to prevent forest fires. |
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