Factors Influencing the Spatial Differentiation of Water Yield: Statistics and Appraisals with Predictors of Subbasins

被引:0
|
作者
Wu, Lei [1 ,2 ,3 ,4 ]
Xu, Yonghong [5 ]
Yang, Zhi [6 ,7 ]
Ma, Xiaoyi [6 ,7 ]
机构
[1] Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid & Semiarid Area, Minist Educ, Yangling 712100, Shaanxi, Peoples R China
[2] Texas A&M Univ, Blackland Res & Extens Ctr, Texas A&M AgriLife Res, Temple, TX 76502 USA
[3] Northwest A&F Univ, State Key Lab Soil Eros & Dryland Farming Loess Pl, Yangling 712100, Shaanxi, Peoples R China
[4] Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Shaanxi, Peoples R China
[5] Minist Water Resources, Yellow River Conservancy Commiss, Xifeng Hydrol & Water Resources Survey Bur, Qingyang 745000, Gansu, Peoples R China
[6] Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid & Semiarid Area, Minist Educ, Yangling 712100, Shaanxi, Peoples R China
[7] Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling 712100, Shaanxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Water yield; Climate change; Land use type; Influencing factor; Subbasin; Jinghe River watershed; LAND-USE CHANGE; RIVER-BASIN; HYDROLOGICAL MODEL; ECOSYSTEM SERVICE; DRIVING FACTORS; RESOURCES; CLIMATE; SOUTH; SOIL; PRECIPITATION;
D O I
10.1061/JHYEFF.HEENG-6153
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Water yield in a watershed is mainly affected by both climatic and physiographical factors, but the interaction between different factors still cannot be completely understood. The soil and water assessment tool (SWAT), geographic detector, and natural breaks were integrated to address the impact of different factors on water yield and the identification of important hydrometeorological and underlying surface variables. (1) The significance of precipitation accounted for more than 70% of the total statistics, and its q statistic reached 0.261, which was the main driving factor and contributed an important explanatory power to the spatial variation of water yield. Moreover, the two-factor combination of slope and precipitation has the strongest interactive explanatory power on spatial variation of water yield. (2) The interaction between different meteorological factors has bivariate enhancement and nonlinear enhancement effects on the spatial variation of water yield. However, there is only nonlinear enhancement between land use factors and meteorological factors because the explanatory power of different land use types on water yield is not very prominent, while their explanatory power can be enhanced when interacting with other meteorological factors, especially evaporation. (3) Adding factors does not improve the explanatory power of original single or double factors, indicating that using the SWAT model to delineate subbasins and simulate water yield may homogenize factor attributes, especially peak rainfall, and weaken the explanatory power, and the size of subbasin and distribution of rain gauges make the spatial correspondence between different factor attributes and water yield not entirely consistent. This study can provide a scientific reference for the protection of water resources, the optimization of land use strategy, and the tradeoffs of ecological hydrologic services in semiarid and arid regions.
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页数:11
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