Sciences in Cold and Arid Regions ›› 2021, Vol. 13 ›› Issue (4): 314-325.doi: 10.3724/SP.J.1226.2021.20038.

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Application of geodetector in sensitivity analysis of reference crop evapotranspiration spatial changes in Northwest China

WenJu Cheng1,2,3,HaiYang Xi1,2(),Sindikubwabo Celestin1,2,3   

  1. 1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2.Key Laboratory of Eco-Hydrology of Inland River Basin, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    3.University of Chinese Academy of Science, Beijing 100049, China
  • Received:2020-06-26 Accepted:2020-10-22 Online:2021-08-31 Published:2021-08-19
  • Contact: HaiYang Xi
  • Supported by:
    the Inner Mongolia Key Research and Development program(zdzx2018057);the National Key Research and Development Program(2016YFC0400908)


Reference crop evapotranspiration (ET0) is an important parameter in the research of farmland irrigation management, crop water demand estimation and water balance in scarce data areas, therefore, it is very important to study the factors affecting the spatial variation of ET0. In this paper, the Penman-Monteith formula was used to calculate ET0 which is the dependent variable of elevation (Elev), daily maximum temperature (Tmax), daily minimum temperature (Tmin), daily average temperature (Tmean), wind speed (U2), sunshine duration (SD) and relative humidity (RH). The sensitivity analysis of ET0 was performed using a Geodetector method based on spatial stratified heterogeneity. The applicability of Geodetector in sensitivity analysis of ET0 was verified by comparing it with existing research results. Results show that RH, Tmax, SD, and Tmean are the main factors affecting ET0 in Northwest China, and RH has the best explanatory power for the spatial distribution of ET0. Geodetector has a unique advantage in sensitivity analysis, because it can analyze the synergistic effect of two factors on the change of ET0. The interactive detector of Geodetector revealed that the synergistic effect of RH and Tmean on ET0 is very significant, and can explain 89% of the spatial variation of ET0. This research provides a new method for sensitivity analysis of ET0 changes.

Key words: reference crop evapotranspiration, Penman-Monteith, geodetector, sensitivity analysis, northwest China

Figure 1

Spatial distribution of 177 meteorological stations and location of study area"

Table 1

Types of interaction between two covariates"

q(X1∩X2)< Min(q(X1), q(X2))Weakened, nonlinear
Min(q(X1), q(X2))< q(X1∩X2)< Max(q(X1), q(X2))Weakened, single factor nonlinear
q(X1∩X2)> Max(q(X1), q(X2))Enhanced, double factors
q(X1∩X2) = q(X1) + q(X2)Independent
q(X1∩X2)> q(X1) + q(X2)Enhanced, nonlinear

Figure 2

Flow chart of sensitivity research on ET0 spatial change"

Figure 3

Difference in spatial variation of ET0"

Figure 4

Strata information and spatial distribution of natural factors"

Table 2

The explanatory power of each factor on the spatial change of ET0"

q statistic0.2630.3040.2740.3130.3550.4740.324
p value0.0000.0000.0000.0000.0000.0000.999

Table 3

Result of interaction detection"


Table 4

Results of ecological detection"


Table 5

Recent studies on factors affecting spatial and temporal changes of ET0 in Northwest China or some regions in China"

Research regionDependent variableDominant variableReferences
Northwest ChinaET0U2, RH and TmaxThomas, 2000
Northwest ChinaET0U2Yin et al., 2010
ChinaET0Tmax, RH, and U2Zhang et al., 2019
Shiyang River BasinET0vegetation decreasesLiu et al., 2019
Northwest ChinaET0U2 and TmeanLiu and Zhang, 2013
Loess PlateauET0Tmean, U2, RsLi et al., 2016
Loess PlateauET0ea, Rs, TmeanNing et al., 2017
Northwest ChinaET0U2Li et al., 2014
Northern Loess Plateau of ChinaET0U2 and TmeanNing et al., 2016
Yangtze River basinET0RH, Rns, Tmean and U2Gong et al., 2006
Bosten Lake BasinET0U2 and RnZhong et al., 2019
Northwest ChinaET0RH and U2Zhao et al., 2018
ChinaET0NDVI, Tmax, Tmean, TminZhang et al., 2019
Northwest ChinaET0RH, temperature, U2 an SDThis paper

Figure 5

Schematic diagram of interaction detection (Wang and Xu, 2017)Notes: first, calculate the q(X1) and q(X2) respectively, then overlay the X1 and X2 layers to obtain a new layer X1∩X2 and calculate q(X1∩X2), finally, we can judge the interaction type of the two factors according to Table 1"

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