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
Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the ...Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the effects of various factors on urban flooding have been extensively evaluated,few studies consider both interregional flood connection and interactions between driving factors.In this study,driving factors of urban flooding were analyzed based on the water tracer method and the optimal parameters-based geographical detector(OPGD).An urban flood simulation model coupled with the water tracer method was constructed to simulate flooding.Furthermore,interregional flood volume connection was analyzed based on simulation results.Subsequently,driving force of urban flooding factors and interactions between them were quantified using the OPGD model.Taking Haidian Island in Hainan Province,China as an example,the coupled model simulation results show that sub-catchment H6 is the region experiencing the most severe flooding and sub-catchment H9 contributes the most to overall flooding in the study area.The results of subsequent driving effect analysis show that elevation is the factor with the maximum single-factor driving force(0.772) and elevation ∩ percentage of building area is the pair of factors with the maximum two-factor driving force(0.968).In addition,the interactions between driving factors have bivariable or nonlinear enhancement effects.The interactions between two factors strengthen the influence of each factor on urban flooding.This study contributes to understanding the cause of urban flooding and provides a reference for urban flood risk mitigation.展开更多
水源涵养服务供需安全是保障区域水资源可持续利用和维护生态平衡的重要支撑。基于土壤和水评估工具(Soil and Water Assessment Tool,SWAT)测算新安江上游水源涵养服务供需指数,采用Theil-Sen Median趋势分析、标准差椭圆、探索性空间...水源涵养服务供需安全是保障区域水资源可持续利用和维护生态平衡的重要支撑。基于土壤和水评估工具(Soil and Water Assessment Tool,SWAT)测算新安江上游水源涵养服务供需指数,采用Theil-Sen Median趋势分析、标准差椭圆、探索性空间数据分析方法揭示其时空演变规律,耦合最优参数地理探测器和时空地理加权回归模型,分析新安江上游水源涵养服务供需指数存在时空差异的原因。结果显示:(1)在2002—2020年,新安江上游水源涵养服务供需指数呈螺旋上升趋势,其中极显著上升区域约占1.36%。(2)新安江上游水源涵养服务供需指数的标准差椭圆分布主要走向为“东北—西南”,全局呈逐渐加强的正相关性。(3)人口密度、坡向、土壤含水量分别是人类活动、下垫面、水文气象维度中对新安江上游水源涵养服务供需指数驱动力最强的因子。(4)人口密度对新安江上游各子流域水源涵养服务供需指数的驱动作用始终为负,坡向的驱动作用主要为负,土壤含水量的驱动作用始终为正。研究可为制定流域尺度的水土保持和水资源管理策略提供理论参考。展开更多
准确估算区域尺度的陆地生态系统碳储量及其驱动因素,对于制定科学合理的土地利用政策具有重要意义。基于土地利用/覆被数据和气象站点数据,运用InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)模型定量估算了1990-2...准确估算区域尺度的陆地生态系统碳储量及其驱动因素,对于制定科学合理的土地利用政策具有重要意义。基于土地利用/覆被数据和气象站点数据,运用InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)模型定量估算了1990-2020年黄河流域碳储量的时空分布。通过土地利用转移矩阵和碳储量贡献率分析土地利用变化对碳储量的影响,并采用最优参数地理探测器(OPGD)识别碳储量空间分异性的主要驱动因素。结果表明,1990-2020年间,黄河流域耕地面积减少,而林地、草地、建设用地面积增加,碳储量值呈现波动上升趋势,增加了0.549×10^(8)t,增幅为0.37%,经历了1990-1995年和2005-2010年两个增加阶段,以及1995-2005年和2010-2020年两个减少阶段。碳储量的空间分布具有明显的异质性,碳储量变化呈现零散分布,增减不一的特点。极显著热点区集中在青海、陕西、内蒙古等森林覆盖较广泛的山区,冷点分布在经济发达地区。草地是主要碳储存类型,未利用地转为草地对碳储量贡献最大(73.3%),耕地转为建设用地对碳储量产生最大负效应(−20.8%)。在5 km最优空间尺度和因子最佳空间离散化参数下,单因子和交互探测分别显示,归一化植被指数(NDVI)是碳储量空间分异性的主要驱动因素(20.7%),坡度、降水和日照等因素也具有显著影响;归一化植被指数与高程的组合解释力最强,达到29.0%。综合考虑自然地理和气候因素,因地制宜地制定土地利用政策,平衡城市扩张、农业发展与生态保护,是实现区域碳储量增加的关键。展开更多
在气候变化背景下,模拟土壤侵蚀的时空演变特征并探讨其与气候因子之间的响应,对于应对气候变化和防灾减灾具有重要意义。现有研究主要聚焦于气候变化、坡度及植被恢复等因素对黄土高原土壤侵蚀的影响,但较少同时考虑各驱动因子之间的...在气候变化背景下,模拟土壤侵蚀的时空演变特征并探讨其与气候因子之间的响应,对于应对气候变化和防灾减灾具有重要意义。现有研究主要聚焦于气候变化、坡度及植被恢复等因素对黄土高原土壤侵蚀的影响,但较少同时考虑各驱动因子之间的相互作用及其对土壤侵蚀的直接与间接影响。基于气象站点、土地利用/土地覆被和土壤质地等数据,采用Theil⁃Sen Median趋势和Mann⁃Kendal检验对气候因子的时空变化特征进行了分析,利用InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)模型模拟了1990年、2000年、2010年和2020年黄土高原土壤侵蚀的时空分布,并通过最优参数地理探测器和偏最小二乘结构方程模型在考虑自然因子和植被因子的基础上,重点对气候因子对土壤侵蚀的影响强度和路径进行分析。结果表明:气候因子时空变化具有阶段性和区域性,降水量在1990—2000年以-55.96 mm/10a的速率下降,而2000—2020年以53.99 mm/10a的速率上升;研究期内年降水量、降水强度指数、大雨日数、强降水量、平均气温和最低气温的增长率分别为26.15 mm/10a、0.26 mm d^(-1)10a^(-1)、0.56 d/10a、15.21 mm/10a、0.32℃/10a和0.40℃/10a。从空间上看,1990—2000年降水量减少区域为86.36%,而2000年以后增加区域达97.42%;2000—2020年,极端降水指标在整个研究区基本为增加;气温上升区域主要分布在东、西部,气候变化呈现明显的暖湿化趋势且降水的极端性增强。1990—2020年,黄土高原土壤侵蚀模数呈现先减少再增加趋势,2020年土壤侵蚀量为2.19亿t。最优参数地理探测器分析显示,坡度、降水和植被覆盖是土壤侵蚀的主要驱动因素,其中降水量对土壤侵蚀的解释力从1990年的0.11在2020年增至0.18。结合偏最小二乘结构方程模型分析结果,温度主要通过影响降水间接影响土壤侵蚀,降水和自然因子对土壤侵蚀有直接正贡献,而植被因子对土壤侵蚀有直接负贡献,但2020年比2010年降低0.02。因此,在气候暖湿化和降水极端化趋势下,其对土壤侵蚀的影响不可忽视,在未来的土壤侵蚀防控和可持续发展中,需将气候适应和区域发展相结合,以应对未来气候变化的挑战。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(Grant No.52379019,42477501)the Key Research and Development Program of Ningxia Hui Autonomous Region(Grant No.2022BEG02020).
文摘Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the effects of various factors on urban flooding have been extensively evaluated,few studies consider both interregional flood connection and interactions between driving factors.In this study,driving factors of urban flooding were analyzed based on the water tracer method and the optimal parameters-based geographical detector(OPGD).An urban flood simulation model coupled with the water tracer method was constructed to simulate flooding.Furthermore,interregional flood volume connection was analyzed based on simulation results.Subsequently,driving force of urban flooding factors and interactions between them were quantified using the OPGD model.Taking Haidian Island in Hainan Province,China as an example,the coupled model simulation results show that sub-catchment H6 is the region experiencing the most severe flooding and sub-catchment H9 contributes the most to overall flooding in the study area.The results of subsequent driving effect analysis show that elevation is the factor with the maximum single-factor driving force(0.772) and elevation ∩ percentage of building area is the pair of factors with the maximum two-factor driving force(0.968).In addition,the interactions between driving factors have bivariable or nonlinear enhancement effects.The interactions between two factors strengthen the influence of each factor on urban flooding.This study contributes to understanding the cause of urban flooding and provides a reference for urban flood risk mitigation.
文摘水源涵养服务供需安全是保障区域水资源可持续利用和维护生态平衡的重要支撑。基于土壤和水评估工具(Soil and Water Assessment Tool,SWAT)测算新安江上游水源涵养服务供需指数,采用Theil-Sen Median趋势分析、标准差椭圆、探索性空间数据分析方法揭示其时空演变规律,耦合最优参数地理探测器和时空地理加权回归模型,分析新安江上游水源涵养服务供需指数存在时空差异的原因。结果显示:(1)在2002—2020年,新安江上游水源涵养服务供需指数呈螺旋上升趋势,其中极显著上升区域约占1.36%。(2)新安江上游水源涵养服务供需指数的标准差椭圆分布主要走向为“东北—西南”,全局呈逐渐加强的正相关性。(3)人口密度、坡向、土壤含水量分别是人类活动、下垫面、水文气象维度中对新安江上游水源涵养服务供需指数驱动力最强的因子。(4)人口密度对新安江上游各子流域水源涵养服务供需指数的驱动作用始终为负,坡向的驱动作用主要为负,土壤含水量的驱动作用始终为正。研究可为制定流域尺度的水土保持和水资源管理策略提供理论参考。
文摘准确估算区域尺度的陆地生态系统碳储量及其驱动因素,对于制定科学合理的土地利用政策具有重要意义。基于土地利用/覆被数据和气象站点数据,运用InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)模型定量估算了1990-2020年黄河流域碳储量的时空分布。通过土地利用转移矩阵和碳储量贡献率分析土地利用变化对碳储量的影响,并采用最优参数地理探测器(OPGD)识别碳储量空间分异性的主要驱动因素。结果表明,1990-2020年间,黄河流域耕地面积减少,而林地、草地、建设用地面积增加,碳储量值呈现波动上升趋势,增加了0.549×10^(8)t,增幅为0.37%,经历了1990-1995年和2005-2010年两个增加阶段,以及1995-2005年和2010-2020年两个减少阶段。碳储量的空间分布具有明显的异质性,碳储量变化呈现零散分布,增减不一的特点。极显著热点区集中在青海、陕西、内蒙古等森林覆盖较广泛的山区,冷点分布在经济发达地区。草地是主要碳储存类型,未利用地转为草地对碳储量贡献最大(73.3%),耕地转为建设用地对碳储量产生最大负效应(−20.8%)。在5 km最优空间尺度和因子最佳空间离散化参数下,单因子和交互探测分别显示,归一化植被指数(NDVI)是碳储量空间分异性的主要驱动因素(20.7%),坡度、降水和日照等因素也具有显著影响;归一化植被指数与高程的组合解释力最强,达到29.0%。综合考虑自然地理和气候因素,因地制宜地制定土地利用政策,平衡城市扩张、农业发展与生态保护,是实现区域碳储量增加的关键。
文摘在气候变化背景下,模拟土壤侵蚀的时空演变特征并探讨其与气候因子之间的响应,对于应对气候变化和防灾减灾具有重要意义。现有研究主要聚焦于气候变化、坡度及植被恢复等因素对黄土高原土壤侵蚀的影响,但较少同时考虑各驱动因子之间的相互作用及其对土壤侵蚀的直接与间接影响。基于气象站点、土地利用/土地覆被和土壤质地等数据,采用Theil⁃Sen Median趋势和Mann⁃Kendal检验对气候因子的时空变化特征进行了分析,利用InVEST(Integrated Valuation of Ecosystem Services and Tradeoffs)模型模拟了1990年、2000年、2010年和2020年黄土高原土壤侵蚀的时空分布,并通过最优参数地理探测器和偏最小二乘结构方程模型在考虑自然因子和植被因子的基础上,重点对气候因子对土壤侵蚀的影响强度和路径进行分析。结果表明:气候因子时空变化具有阶段性和区域性,降水量在1990—2000年以-55.96 mm/10a的速率下降,而2000—2020年以53.99 mm/10a的速率上升;研究期内年降水量、降水强度指数、大雨日数、强降水量、平均气温和最低气温的增长率分别为26.15 mm/10a、0.26 mm d^(-1)10a^(-1)、0.56 d/10a、15.21 mm/10a、0.32℃/10a和0.40℃/10a。从空间上看,1990—2000年降水量减少区域为86.36%,而2000年以后增加区域达97.42%;2000—2020年,极端降水指标在整个研究区基本为增加;气温上升区域主要分布在东、西部,气候变化呈现明显的暖湿化趋势且降水的极端性增强。1990—2020年,黄土高原土壤侵蚀模数呈现先减少再增加趋势,2020年土壤侵蚀量为2.19亿t。最优参数地理探测器分析显示,坡度、降水和植被覆盖是土壤侵蚀的主要驱动因素,其中降水量对土壤侵蚀的解释力从1990年的0.11在2020年增至0.18。结合偏最小二乘结构方程模型分析结果,温度主要通过影响降水间接影响土壤侵蚀,降水和自然因子对土壤侵蚀有直接正贡献,而植被因子对土壤侵蚀有直接负贡献,但2020年比2010年降低0.02。因此,在气候暖湿化和降水极端化趋势下,其对土壤侵蚀的影响不可忽视,在未来的土壤侵蚀防控和可持续发展中,需将气候适应和区域发展相结合,以应对未来气候变化的挑战。
