Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study propos...Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.展开更多
In recent years,intensified land use change driven by climate change and human activities have markedly impacted the ecological environmental quality of the arid inland river basins.The implementation of forestry proj...In recent years,intensified land use change driven by climate change and human activities have markedly impacted the ecological environmental quality of the arid inland river basins.The implementation of forestry projects,coupled with continuous population growth,has increased the need for systematic assessments of ecological effects to ensure sustainable development in arid inland river basins.This study generated a 22-a(2000-2021)remote sensing ecological index(RSEI)data series for the Manas River Basin,a typical arid inland river basin in China,utilizing Moderate Resolution Imaging Spectroradiometer(MODIS)data and the Google Earth Engine(GEE)platform.We examined the spatiotemporal patterns of ecological environmental quality in the Manas River Basin through the Theil-Sen estimator,Mann-Kendall trend test,coefficient of variation(CV),and Hurst index.Furthermore,we employed the Optimal Parameter-based Geographical Detector(OPGD)method to quantify the influence of seven key drivers:elevation,slope,temperature,precipitation,gross domestic product(GDP),population density,and land use change.The key findings revealed that the basin's ecological environmental quality showed significant improvement(mean RSEI of 0.38,with a range of 0.34-0.41),with areas exhibiting good and excellent grades increasing by 16.71%,particularly in the midstream oasis region and upstream mountainous region,while areas exhibiting poor and relatively poor grades decreased by 11.52%in the downstream desert region.Spatial heterogeneity of ecological environmental quality was pronounced,with 32.23%of the areas showing localized degradation,the midstream oasis region exhibiting sustainable recovery potential(Hurst index>0.50),and only 36.67%of the areas maintaining stable and highly stable conditions(primarily in the upstream mountainous region).The OPGD analysis revealed that temperature(q-value=0.496-0.780),land use change(q-value=0.705-0.782),and elevation(q-value=0.245-0.637)were dominant factors,with the influence of land use change increasing during 2000-2020.Strong interaction effects emerged between land use change and temperature(q-value>0.705)and between land use change and elevation(q-value=0.751 in 2020),highlighting intensified human-nature coupling.These findings provide vital perspectives for ecosystem management in arid inland river basins under both climate and anthropogenic pressures.展开更多
As one of China's most important ecological conservation regions,the source region of the Yellow River(SRYR)has a fragile ecological environment.Investigating land use transformations and their ecological conseque...As one of China's most important ecological conservation regions,the source region of the Yellow River(SRYR)has a fragile ecological environment.Investigating land use transformations and their ecological consequences in this region is of great significance for optimizing territorial spatial structure and promoting regional sustainable development.Based on the dominant functions of production-living-ecological space(PLES),we employed the land use transfer matrix and the standard deviational ellipse method to elucidate the spatiotemporal evolution characteristics of PLES in the SRYR from 2000 to 2020.Furthermore,the mechanism underlying the differentiation of eco-environmental effects in this region was explored using the optimal parameter-based geographical detector(OPGD)model.Results indicated that ecological space predominated within the PLES of the SRYR,accounting for approximately 98.74%of the total area.Living space was sparsely distributed in township areas with a proportion below 1.00%.Production space was mainly distributed in Guinan County and Gonghe County,accounting for about 1.16%of the area.In terms of the temporal scale,during 2000–2020,the overall eco-environmental quality of the SRYR exhibited an improving trend,primarily driven by the conversion of other ecological spaces into grassland ecological space.Interaction detection results revealed that the interaction between normalized difference vegetation index and gross domestic product was the strongest.In addition,the interaction between precipitation and temperature showed a significant bilinear enhancement effect.This finding suggests that the variations in eco-environmental quality in the SRYR during 2000–2020 have been jointly influenced by natural,climatic,and human factors.This study helps to provide a scientific basis for the rational layout of PLES and guiding ecological restoration efforts in the SRYR.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
The Chaobai River Basin,which is a crucial ecological barrier and primary water source area within the Beijing-Tianjin-Hebei region,possesses substantial ecological significance.The gross ecosystem product(GEP)in the ...The Chaobai River Basin,which is a crucial ecological barrier and primary water source area within the Beijing-Tianjin-Hebei region,possesses substantial ecological significance.The gross ecosystem product(GEP)in the Chaobai River Basin is a reflection of ecosystem conditions and quantifies nature’s contributions to humanity,which provides a basis for basin ecosystem service management and decision-making.This study investigated the spatiotemporal evolution of GEP in the upper Chaobai River Basin and explored the driving factors influencing GEP spatial differentiation.Ecosystem patterns from 2005 to 2020 were analyzed,and GEP was calculated for 2005,2010,2015,and 2020.The driving factors influencing GEP spatial differentiation were identified using the optimal parameter-based geographical detector(OPGD)model.The key findings are as follows:(1)From 2005 to 2020,the main ecosystem types were forest,grassland,and agriculture.Urban areas experienced significant changes,and conversions mainly occurred among urban,water,grassland and agricultural ecosystems.(2)Temporally,the GEP in the basin increased from 2005 to 2020,with regulation services dominating.At the county(district)scale,GEP exhibited a north-west-high and south-east-low pattern,showing spatial differences between per-unit-area GEP and county(district)GEP,while the spatial variations in per capita GEP and county(district)GEP were similar.(3)Differences in the spatial distribution of GEP were influenced by regional natural geographical and socioeconomic factors.Among these factors,gross domestic product,population density,and land-use degree density contributed significantly.Interactions among different driving forces noticeably impacted GEP spatial differentiation.These findings underscore the necessity of incorporating factors such as population density and the intensity of land-use development into ecosystem management decision-making processes in the upper reaches of the Chaobai River Basin.Future policies should be devised to regulate human activities,thereby ensuring the stability and enhancement of GEP.展开更多
基金the National Key Research and Development Program of China(Grant No.2022YFF1302405)the Yunnan Province Key Research and Development Program(Grant No.202203AC100005)+1 种基金the National Natural Science Foundation of China(Grant No.42061005,42067033)Applied Basic Research Programs of Yunnan Province(Grant No.202101AT070110,202001BB050073).
文摘Flood disasters pose serious threats to human life and property worldwide.Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts.This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability,while considering spatial heterogeneity.In this framework,the Optimal Parameter-based Geographic Detector(OPGD),Recursive Feature Estimation(RFE),and Light Gradient Boosting Machine(LGBM)models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters.The SHapley Additive ExPlanation(SHAP)interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters.Yunnan Province,a typical mountainous and plateau area in Southwest China,was selected to implement the proposed framework and conduct a case study.For this purpose,a flood disaster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precipitation,surface environment,and human activity were initially selected.Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity,with geomorphic zoning accounting for 66.1%of the spatial variation in historical flood disasters.The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6%average decrease in RMSE and an average increase of 1%in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nevertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in developing macro-strategies for disaster prevention.
基金supported by the National Natural Science Foundation of China(32360084).
文摘In recent years,intensified land use change driven by climate change and human activities have markedly impacted the ecological environmental quality of the arid inland river basins.The implementation of forestry projects,coupled with continuous population growth,has increased the need for systematic assessments of ecological effects to ensure sustainable development in arid inland river basins.This study generated a 22-a(2000-2021)remote sensing ecological index(RSEI)data series for the Manas River Basin,a typical arid inland river basin in China,utilizing Moderate Resolution Imaging Spectroradiometer(MODIS)data and the Google Earth Engine(GEE)platform.We examined the spatiotemporal patterns of ecological environmental quality in the Manas River Basin through the Theil-Sen estimator,Mann-Kendall trend test,coefficient of variation(CV),and Hurst index.Furthermore,we employed the Optimal Parameter-based Geographical Detector(OPGD)method to quantify the influence of seven key drivers:elevation,slope,temperature,precipitation,gross domestic product(GDP),population density,and land use change.The key findings revealed that the basin's ecological environmental quality showed significant improvement(mean RSEI of 0.38,with a range of 0.34-0.41),with areas exhibiting good and excellent grades increasing by 16.71%,particularly in the midstream oasis region and upstream mountainous region,while areas exhibiting poor and relatively poor grades decreased by 11.52%in the downstream desert region.Spatial heterogeneity of ecological environmental quality was pronounced,with 32.23%of the areas showing localized degradation,the midstream oasis region exhibiting sustainable recovery potential(Hurst index>0.50),and only 36.67%of the areas maintaining stable and highly stable conditions(primarily in the upstream mountainous region).The OPGD analysis revealed that temperature(q-value=0.496-0.780),land use change(q-value=0.705-0.782),and elevation(q-value=0.245-0.637)were dominant factors,with the influence of land use change increasing during 2000-2020.Strong interaction effects emerged between land use change and temperature(q-value>0.705)and between land use change and elevation(q-value=0.751 in 2020),highlighting intensified human-nature coupling.These findings provide vital perspectives for ecosystem management in arid inland river basins under both climate and anthropogenic pressures.
基金financially supported by the Qinghai Provincial Applied Basic Research Program,China(2024-ZJ-953)。
文摘As one of China's most important ecological conservation regions,the source region of the Yellow River(SRYR)has a fragile ecological environment.Investigating land use transformations and their ecological consequences in this region is of great significance for optimizing territorial spatial structure and promoting regional sustainable development.Based on the dominant functions of production-living-ecological space(PLES),we employed the land use transfer matrix and the standard deviational ellipse method to elucidate the spatiotemporal evolution characteristics of PLES in the SRYR from 2000 to 2020.Furthermore,the mechanism underlying the differentiation of eco-environmental effects in this region was explored using the optimal parameter-based geographical detector(OPGD)model.Results indicated that ecological space predominated within the PLES of the SRYR,accounting for approximately 98.74%of the total area.Living space was sparsely distributed in township areas with a proportion below 1.00%.Production space was mainly distributed in Guinan County and Gonghe County,accounting for about 1.16%of the area.In terms of the temporal scale,during 2000–2020,the overall eco-environmental quality of the SRYR exhibited an improving trend,primarily driven by the conversion of other ecological spaces into grassland ecological space.Interaction detection results revealed that the interaction between normalized difference vegetation index and gross domestic product was the strongest.In addition,the interaction between precipitation and temperature showed a significant bilinear enhancement effect.This finding suggests that the variations in eco-environmental quality in the SRYR during 2000–2020 have been jointly influenced by natural,climatic,and human factors.This study helps to provide a scientific basis for the rational layout of PLES and guiding ecological restoration efforts in the SRYR.
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the National Key Research and Development Program of China(No.2022YFF1301804)the Beijing Municipal Education Commission through the Innovative Transdisciplinary Program“Ecological Restoration Engineering”(No.GJJXK210102).
文摘The Chaobai River Basin,which is a crucial ecological barrier and primary water source area within the Beijing-Tianjin-Hebei region,possesses substantial ecological significance.The gross ecosystem product(GEP)in the Chaobai River Basin is a reflection of ecosystem conditions and quantifies nature’s contributions to humanity,which provides a basis for basin ecosystem service management and decision-making.This study investigated the spatiotemporal evolution of GEP in the upper Chaobai River Basin and explored the driving factors influencing GEP spatial differentiation.Ecosystem patterns from 2005 to 2020 were analyzed,and GEP was calculated for 2005,2010,2015,and 2020.The driving factors influencing GEP spatial differentiation were identified using the optimal parameter-based geographical detector(OPGD)model.The key findings are as follows:(1)From 2005 to 2020,the main ecosystem types were forest,grassland,and agriculture.Urban areas experienced significant changes,and conversions mainly occurred among urban,water,grassland and agricultural ecosystems.(2)Temporally,the GEP in the basin increased from 2005 to 2020,with regulation services dominating.At the county(district)scale,GEP exhibited a north-west-high and south-east-low pattern,showing spatial differences between per-unit-area GEP and county(district)GEP,while the spatial variations in per capita GEP and county(district)GEP were similar.(3)Differences in the spatial distribution of GEP were influenced by regional natural geographical and socioeconomic factors.Among these factors,gross domestic product,population density,and land-use degree density contributed significantly.Interactions among different driving forces noticeably impacted GEP spatial differentiation.These findings underscore the necessity of incorporating factors such as population density and the intensity of land-use development into ecosystem management decision-making processes in the upper reaches of the Chaobai River Basin.Future policies should be devised to regulate human activities,thereby ensuring the stability and enhancement of GEP.
