The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timel...The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.展开更多
Understanding the influencing factors of ecosystem services(ESs)and their relationships is essential for sustainable ecosystem management in degraded alpine ecosystems.There is a lack of integrated multi-model approac...Understanding the influencing factors of ecosystem services(ESs)and their relationships is essential for sustainable ecosystem management in degraded alpine ecosystems.There is a lack of integrated multi-model approaches to explore the multidimensional influences on ESs and their relationships in alpine ecosystems.Taking the Daxing'anling forest area,Inner Mongolia(DFAIM)as a case study,this study used the integrated valuation of ecosystem services and trade-offs(InVEST)model to quantify four ESs—soil conservation(SC),water yield(WY),carbon storage(CS),and habitat quality(HQ)—from 2013 to 2018.We adopted root mean square deviation(RMSD)and coupling coordination degree models(CCDM)to analyze their relationships,and integrated three complementary approaches—optimal parameter-based geographical detector model(OPGDM),gradient boosting regression tree model(GBRTM),and quantile regression model(QRM)—to reveal multidimensional influencing factors.Key findings include the following:(1)From 2013 to 2018,WY,SC,and HQ declined while CS increased.WY was primarily influenced by mean annual precipitation(MAP),forest ratio(RF),and soil bulk density(SBD);CS and HQ by RF and population density(PD);and SC by slope(S),RF,and MAP.Mean annual temperature(MAT),gross domestic product(GDP),and road network density(RND)showed increasing negative impacts.(2)Low trade-off intensity(TI<0.15)dominated all ES pairs,with RF,MAP,PD,and normalized difference vegetation index(NDVI)being the dominant factors.The factor interactions primarily showed two-factor enhancement patterns.(3)The average coupling coordination degree(CCD)of the four ESs was low and declined over time,with low-CCD areas becoming increasingly prevalent.RF,S,SBD,and NDVI positively influenced CCD,while PD,MAT,GDP,and RND had increasing negative impacts,with over 62%of the factor interactions exceeding the individual factor effects.In summary,ES supply generally decreased.Local relationships showed moderate coordination,while overall relationships indicated primary dysfunction.Land use and natural factors primarily shaped these ES and their relationships,while climate and socioeconomic changes diminished ES supply and intensified competition.We recommend enhancing the resilience of natural systems rather than replacing them,establishing climate adaptation monitoring systems,and promoting conservation tillage and cross-departmental coordination mechanisms for collaborative ES optimization.These results provide valuable insights into the sustainable management of alpine ecosystems.展开更多
Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate qua...Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate quantitative analysis of the impact of these factors.This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer(MODIS)SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree(HGBRT)model.The results indicated that the monthly distribution of SCF exhibited a bimodal pattern.The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions.Overall,the SCF showed a decreasing trend during 2000-2021.The decrease in SCF occurred at higher elevations,while an increase was observed at lower elevations.At the annual scale,the SCF showed a downward trend in the western regions affected by westerly(52.84%of the QLM).However,the opposite trend was observed in the eastern regions affected by monsoon(45.73%of the QLM).The SCF displayed broadly similar spatial patterns in autumn and winter,with a significant decrease in the western regions and a slight increase in the central and eastern regions.The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF.Furthermore,compared with meteorological factors,a variation of 46.53%in spring surface runoff can be attributed to changes in spring SCF.At the annual scale,temperature and relative humidity were the most important drivers of SCF change.An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF,with a maximum decrease of 0.22%/a.An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF(about 0.06%/a).The impacts of slope and aspect were found to be minimal.At the seasonal scale,the primary factors impacting SCF change varied.In spring,precipitation and wind speed emerged as the primary drivers.In autumn,precipitation and temperature were identified as the primary drivers.In winter,relative humidity and precipitation were the most important drivers.In contrast to the other seasons,slope exerted the strongest influence on SCF change in summer.This study facilitates a detailed quantitative description of SCF change in the QLM,enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.展开更多
基金funded by the National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program (2018YFE0207800)the National Natural Science Foundation of China (31971483)。
文摘The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.
基金funded primarily by the Central Public Welfare Research Institutes Basic Research Business Funds to Support the Administration’s Central Work Project(Grant No.CAFYBB2023ZA003-4)the National Natural Science Foundation of China(Grant Nos.31170593 and 31570633)National Forestry and Grassland Administration Forestry Under the Project“Forestry Major Issues Research”(Grant Nos.500102-1776 and 500102-5110).
文摘Understanding the influencing factors of ecosystem services(ESs)and their relationships is essential for sustainable ecosystem management in degraded alpine ecosystems.There is a lack of integrated multi-model approaches to explore the multidimensional influences on ESs and their relationships in alpine ecosystems.Taking the Daxing'anling forest area,Inner Mongolia(DFAIM)as a case study,this study used the integrated valuation of ecosystem services and trade-offs(InVEST)model to quantify four ESs—soil conservation(SC),water yield(WY),carbon storage(CS),and habitat quality(HQ)—from 2013 to 2018.We adopted root mean square deviation(RMSD)and coupling coordination degree models(CCDM)to analyze their relationships,and integrated three complementary approaches—optimal parameter-based geographical detector model(OPGDM),gradient boosting regression tree model(GBRTM),and quantile regression model(QRM)—to reveal multidimensional influencing factors.Key findings include the following:(1)From 2013 to 2018,WY,SC,and HQ declined while CS increased.WY was primarily influenced by mean annual precipitation(MAP),forest ratio(RF),and soil bulk density(SBD);CS and HQ by RF and population density(PD);and SC by slope(S),RF,and MAP.Mean annual temperature(MAT),gross domestic product(GDP),and road network density(RND)showed increasing negative impacts.(2)Low trade-off intensity(TI<0.15)dominated all ES pairs,with RF,MAP,PD,and normalized difference vegetation index(NDVI)being the dominant factors.The factor interactions primarily showed two-factor enhancement patterns.(3)The average coupling coordination degree(CCD)of the four ESs was low and declined over time,with low-CCD areas becoming increasingly prevalent.RF,S,SBD,and NDVI positively influenced CCD,while PD,MAT,GDP,and RND had increasing negative impacts,with over 62%of the factor interactions exceeding the individual factor effects.In summary,ES supply generally decreased.Local relationships showed moderate coordination,while overall relationships indicated primary dysfunction.Land use and natural factors primarily shaped these ES and their relationships,while climate and socioeconomic changes diminished ES supply and intensified competition.We recommend enhancing the resilience of natural systems rather than replacing them,establishing climate adaptation monitoring systems,and promoting conservation tillage and cross-departmental coordination mechanisms for collaborative ES optimization.These results provide valuable insights into the sustainable management of alpine ecosystems.
基金funded by the Key Research and Development Project for Ecological Civilization Construction in Gansu Province(24YFFA010)the Gansu Province Major Science and Technology Project(22ZD6FA005)+2 种基金the Natural Science Foundation of Gansu Province(24JRRA091)the Shanxi Province Basic Research Program(Free Exploration Category)Youth Project(202403021212316)the Science and Technology Innovation Program for Universities in Shanxi Province(2024L327)。
文摘Understanding the impact of meteorological and topographical factors on snow cover fraction(SCF)is crucial for water resource management in the Qilian Mountains(QLM),China.However,there is still a lack of adequate quantitative analysis of the impact of these factors.This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer(MODIS)SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree(HGBRT)model.The results indicated that the monthly distribution of SCF exhibited a bimodal pattern.The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions.Overall,the SCF showed a decreasing trend during 2000-2021.The decrease in SCF occurred at higher elevations,while an increase was observed at lower elevations.At the annual scale,the SCF showed a downward trend in the western regions affected by westerly(52.84%of the QLM).However,the opposite trend was observed in the eastern regions affected by monsoon(45.73%of the QLM).The SCF displayed broadly similar spatial patterns in autumn and winter,with a significant decrease in the western regions and a slight increase in the central and eastern regions.The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF.Furthermore,compared with meteorological factors,a variation of 46.53%in spring surface runoff can be attributed to changes in spring SCF.At the annual scale,temperature and relative humidity were the most important drivers of SCF change.An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF,with a maximum decrease of 0.22%/a.An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF(about 0.06%/a).The impacts of slope and aspect were found to be minimal.At the seasonal scale,the primary factors impacting SCF change varied.In spring,precipitation and wind speed emerged as the primary drivers.In autumn,precipitation and temperature were identified as the primary drivers.In winter,relative humidity and precipitation were the most important drivers.In contrast to the other seasons,slope exerted the strongest influence on SCF change in summer.This study facilitates a detailed quantitative description of SCF change in the QLM,enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.
