Local and global factors controlling water-energy balances within the Budyko framework

被引:248
作者
Xu, Xianli [1 ,2 ]
Liu, Wen [3 ]
Scanlon, Bridget R. [4 ]
Zhang, Lu [5 ]
Pan, Ming [6 ]
机构
[1] Chinese Acad Sci, Inst Subtrop Agr, Key Lab Agroecol Proc Subtrop Reg, Changsha 410125, Hunan, Peoples R China
[2] Chinese Acad Sci, Huanjiang Observat & Res Stn Karst Ecosyst, Huanjiang, Peoples R China
[3] Hunan Normal Univ, Coll Resources & Environm Sci, Changsha, Hunan, Peoples R China
[4] Univ Texas Austin, Bur Econ Geol, Jackson Sch Geosci, Austin, TX USA
[5] CSIRO Land & Water, Water Hlth Country Flagship, Canberra, ACT, Australia
[6] Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA
关键词
Budyko; water-energy partition; hydrology; HYDROLOGICAL MODEL; EVAPORATION; CLIMATE;
D O I
10.1002/2013GL058324
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Quantifying partitioning of precipitation into evapotranspiration (ET) and runoff is the key to assessing water availability globally. Here we develop a universal model to predict water-energy partitioning ( parameter for the Fu's equation, one form of the Budyko framework) which spans small to large scale basins globally. A neural network (NN) model was developed using a data set of 224 small U.S. basins (100-10,000 km(2)) and 32 large, global basins (230,000-600,000 km(2)) independently and combined based on both local (slope, normalized difference vegetation index) and global (geolocation) factors. The Budyko framework with NN estimated reproduced observed mean annual ET well for the combined 256 basins. The predicted mean annual ET for 36,600 global basins is in good agreement (R-2=0.72) with an independent global satellite-based ET product, inversely validating the NN model. The NN model enhances the capability of the Budyko framework for assessing water availability at global scales using readily available data.
引用
收藏
页码:6123 / 6129
页数:7
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