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

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

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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).Sub-sequently,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 repre-sentation of not only dominant but also secondary effects.Finally,we explored the relation-ship 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 pat-terns,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.

hydrological response predictionmachine learningaccumulated local effectMoran's Indexlarge-sample study

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

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The National Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210098,China

College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China

School of Water Conservancy & Civil Engineering,Northeast Agricultural University,Harbin 150030,China

Department of Water Resources,China Institute of Water Resources and Hydropower Research,Beijing 100038,China

Department of Geosciences,University of Oslo,Oslo,Norway

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNatural Science Foundation of Jiangsu Province,ChinaResearch Council of Norway(FRINATEK Project)

U224320351979069BK20211202274310

2024

10.1007/s11442-024-2249-4

地理学报(英文版)
中国地理学会,中国科学院地理科学与资源研究所

地理学报(英文版)

CSTPCD
影响因子：1.307
ISSN：1009-637X
年,卷(期)：2024.34(7)
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