What control the spatial patterns and predictions of runoff response over the contiguous USA?  

作　　者：JIANG Shanhu DU Shuping REN Liliang GONG Xinglong YAN Denghua YUAN Shanshui LIU Yi YANG Xiaoli XU Chongyu 

机构地区：[1]The National Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing,210098,China [2]College of Hydrology and Water Resources,Hohai University,Nanjing,210098,China [3]School of Water Conservancy&Civil Engineering,Northeast Agricultural University,Harbin,150030,China [4]Department of Water Resources,China Institute of Water Resources and Hydropower Research,Beijing,100038,China [5]Department of Geosciences,University of Oslo,Oslo,Norway

出　　处：《Journal of Geographical Sciences》2024年第7期1297-1322,共26页地理学报（英文版）

基　　金：National Natural Science Foundation of China,No.U2243203,No.51979069;Natural Science Foundation of Jiangsu Province,China,No.BK20211202;Research Council of Norway,No.FRINATEK Project 274310。

摘　　要：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.

关 键 词：hydrological response prediction machine learning accumulated local effect Moran’s Index large-sample study 

分 类 号：P333[天文地球—水文科学] TP181[水利工程—水文学及水资源]