采用国产水动力学模型(integrated flood model system,IFMS),对“23·7”海河流域永定河系特大洪水中永定河泛区洪水演进及淹没范围变化进行精细化模拟复盘。将模拟结果与卫星遥感监测获取的泛区淹没范围进行对比,最大淹没面积误...采用国产水动力学模型(integrated flood model system,IFMS),对“23·7”海河流域永定河系特大洪水中永定河泛区洪水演进及淹没范围变化进行精细化模拟复盘。将模拟结果与卫星遥感监测获取的泛区淹没范围进行对比,最大淹没面积误差仅为8.8%,验证了该模型在永定河泛区洪水模拟中的可靠度。构建的永定河泛区模型可以准确反映分洪口门启闭、蓄滞洪量变化及泛区进退洪淹没过程。基于该模型,进一步研究雨带北移对该地区未来防洪情势的影响。结果表明:同等重现期的设计洪水受雨带北移影响后,将导致泛区防洪情势愈加严峻,雨带北移影响下的20 a一遇设计洪水最大淹没范围与现状情景下50 a一遇设计洪水最大淹没范围持平。因此,为更好地应对未来防洪情势的发展,需要针对永定河泛区提出更加合理的防洪规划并科学制定工程与非工程相结合的防洪措施。展开更多
Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook se...Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook several steps to explore the relationship.Initially,we partitioned runoff response change(RRC)into multiple components associated with climate and catchment properties,and examined the spatial patterns and smoothness indicated by the Moran's Index of RRC across the contiguous United States(CONUS).Subsequently,we employed a machine learning model to predict RRC using catchment attribute predictors encompassing climate,topography,hydrology,soil,land use/cover,and geology.Additionally,we identified the primary factors influencing RRC and quantified how these key factors control RRC by employing the accumulated local effect,which allows for the representation of not only dominant but also secondary effects.Finally,we explored the relationship between ecoregion patterns,climate gradients,and the distribution of RRC across CONUS.Our findings indicate that:(1)RRC demonstrating significant connections between catchments tends to be well predicted by catchment attributes in space;(2)climate,hydrology,and topography emerge as the top three key attributes nonlinearly influencing the RRC patterns,with their second-order effects determining the heterogeneous patterns of RRC;and(3)local Moran's I signifies a collaborative relationship between the patterns of RRC and their spatial smoothness,climate space,and ecoregions.展开更多
基金National Natural Science Foundation of China,No.U2243203,No.51979069Natural Science Foundation of Jiangsu Province,China,No.BK20211202Research Council of Norway,No.FRINATEK Project 274310。
文摘Understanding the nonlinear relationship between hydrological response and key factors and the cause behind this relationship is vital for water resource management and earth system model.In this study,we undertook several steps to explore the relationship.Initially,we partitioned runoff response change(RRC)into multiple components associated with climate and catchment properties,and examined the spatial patterns and smoothness indicated by the Moran's Index of RRC across the contiguous United States(CONUS).Subsequently,we employed a machine learning model to predict RRC using catchment attribute predictors encompassing climate,topography,hydrology,soil,land use/cover,and geology.Additionally,we identified the primary factors influencing RRC and quantified how these key factors control RRC by employing the accumulated local effect,which allows for the representation of not only dominant but also secondary effects.Finally,we explored the relationship between ecoregion patterns,climate gradients,and the distribution of RRC across CONUS.Our findings indicate that:(1)RRC demonstrating significant connections between catchments tends to be well predicted by catchment attributes in space;(2)climate,hydrology,and topography emerge as the top three key attributes nonlinearly influencing the RRC patterns,with their second-order effects determining the heterogeneous patterns of RRC;and(3)local Moran's I signifies a collaborative relationship between the patterns of RRC and their spatial smoothness,climate space,and ecoregions.
