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
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.
