With an increase in population and economic development,water withdrawals are close to or even exceed the amount of wa-ter available in many regions of the world.Modelling water withdrawals could help water planners improve the efficiency of water use,water resources allocation,and management in order to alleviate water crises.However,minimal information has been obtained on how water withdrawals have changed over space and time,especially on a regional or local scale.This research proposes a data-driven framework to help estimate county-level distribution of water withdrawals.Using this framework,spatial statistical methods are used to estimate water withdrawals for agricultural,industrial,and domestic purposes in the Huaihe River watershed in China for the period 1978-2018.Total water withdrawals were found to have more than doubled,from 292.55×108 m3 in 1978 to 642.93×108 m3 in 2009,and decreased to 602.63×108 m3 in 2018.Agricultural water increased from 208.17×108 m3 in 1978 to 43 5.80×108 m3 in 2009 and decreased to 360.84×108 m3 in 2018.Industrial and domestic water usage constantly increased throughout the 1978-2018 period.In 1978,industrial and domestic demands were 20.35×108 m3 and 60.04 x 108 m3,respectively,and up until 2018,the figures were 105.58 x 108 m3 and 136.20×108 m3.From a spatial distribution perspective,Moran's I statistical results show that the total water with-drawal has significant spatial autocorrelation during 1978-2018.The overall trend was a gradual increase in 1978-2010 with withdraw-al beginning to decline in 2010-2018.The results of Getis-Ord Gi* statistical calculations showed spatially contiguous clusters of total water withdrawal in the Huaihe River watershed during1978-2010,and the spatial agglomeration weakened from 2010 to 2018.This study provides a data-driven framework for assessing water withdrawals to enable a deeper understanding of competing water use among economic sectors as well as water withdrawal modelled with proper data resource and method.

LU Yan;WANG Jinxin;LIU Jianzhong;QIN Fen;WANG Jiayao

School of Geoscience and Technology,Zhengzhou University,Zhengzhou 450001,China;Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions,Ministry of Education,Henan University,Zhengzhou 475000,China;Henan Indus-trial Technology Academy of Spatio-Temporal Big Data,Henan University,Zhengzhou 475000,China;College of Geography and Environmental Science,Henan University,Kaifeng 475004,China

中国地理科学（英文版）

[1]LU Yan;WANG Jinxin;LIU Jianzhong;QIN Fen;WANG Jiayao-.Evaluating Water Withdrawals for Regional Water Management Un-der a Data-driven Framework)[J].中国地理科学（英文版）,2022(03):521-536

Withdrawals,during1978

Evaluating,Water,Regional,Management,Un,Data,driven,Framework,population,economic,development,withdrawals,are,close,even,exceed,amount,available,many,regions,world,Modelling,could,help,planners,improve,efficiency,resources,allocation,management,order,alleviate,crises,However,minimal,information,has,been,obtained,have,changed,space,especially,regional,local,scale,This,research,proposes,data,framework,estimate,county,level,distribution,Using,this,statistical,methods,used,agricultural,industrial,domestic,purposes,Huaihe,River,watershed,China,period,Total,were,found,more,than,doubled,from,decreased,Agricultural,increased,Industrial,usage,constantly,throughout,demands,respectively,up,until,figures,From,perspective,Moran,results,that,total,significant,autocorrelation,overall,trend,was,gradual,beginning,decline,Getis,Ord,Gi,calculations,showed,spatially,contiguous,clusters,agglomeration,weakened,study,provides,assessing,enable,deeper,understanding,competing,among,sectors,well,modelled,proper

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