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Sciences in Cold and Arid Regions ›› 2021, Vol. 13 ›› Issue (4): 337-348.doi: 10.3724/SP.J.1226.2021.20093.

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The driving force of water resource stress change based on the STIRPAT model: take Zhangye City as a case study

Xia Tang1,2(),XinYuan Wang3,Lei Feng4   

  1. 1.Key Laboratory of Ecohydrology of Inland River Basin, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    2.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    3.Gansu Monitoring Center for Ecological Resources, Lanzhou, Gansu 730020, China
    4.Gansu Computing Center, Lanzhou, Gansu 730030, China
  • Received:2020-10-22 Accepted:2021-01-25 Online:2021-08-31 Published:2021-08-19
  • Contact: Xia Tang E-mail:tangxia@llas.ac.cn
  • Supported by:
    the Natural Science Foundation of Gansu Province, China(18JR3RA385);the National Natural Science Foundation of China(41801079)

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Abstract:

A prominent contradiction between supply and demand of water resources has restricted local development in social and economic aspects of Zhangye City, located in a typical arid region of China. Our study quantified the Water Resource Stress Index (WRSI) from 2003 to 2017 and examined the factors of population, urbanization level, GDP per capita, Engel coefficient, and water consumption per unit of GDP by using the extended stochastic impact by regression on population, affluence and technology (STIRPAT) model to find the key factors that impact WRSI of Zhangye City to relieve the pressure on water resources. The ridge regression method is applied to improve this model to eliminate multicollinearity problems. The WRSI system was developed from the following three aspects: water resources utilization (WR), regional economic development water use (WU), and water environment stress (WE). Results show that the WRSI index has fallen from 0.81 (2003) to 0.17 (2017), with an average annual decreased rate of 9.8%. Moreover, the absolute values of normalized coefficients demonstrate that the Engel coefficient has the largest positive contribution to increase WRSI with an elastic coefficient of 0.2709, followed by water consumption per unit of GDP and population with elastic coefficients of 0.0971 and 0.0387, respectively. In contrast, the urbanization level and GDP per capita can decrease WRSI by -0.2449 and -0.089, respectively. The decline of WRSI was attributed to water-saving society construction which included the improvement of water saving technology and the adjustment of agricultural planting structures. Furthermore, this study demonstrated the feasibility of evaluating the driving forces affecting WRSI by using the STIRPAT model and ridge regression analysis.

Key words: water resource stress index, STIRPAT model, driving force analysis, water scarcity

Table 1

Widely cited international water resource stress assessment indicators"

Indicator/indexSpatial scaleReference
Falkenmark water stress indexCountryFalkenmark et al. (1989)
Criticality ratioCountry, RegionRaskin (1997)
Baseline water stressCountry, Region

Zhong et al. (2015)

Luo et al. (2015)

Figure 1

Location of Zhangye City in the Heihe River Basin of China"

Table 2

Details of the WRSI components and sub-dimensions"

Indicator componentsSpecific indicators
Water resources development and utilizationWater availability per capita (m3/(person?a))
(WR)Per hm2 water availability (m3/(person?a))
Regional economic development water useIndustrial water consumption per ten thousand yuan of GDP ($/m3)
(WU)Grain yield of per m3 of water (kg/m3)
Water environment pressure(WE)Average annual groundwater table depth (m)
Annual discharge of waste water (t)

Table 3

Variables in the extended STIRPAT model"

Independent variablesDefinition of measuring methodUnit of measurement
P—populationPopulation sizemillion
A—economic levelGDP per capitaUS dollar
U—urbanization levelThe percent of urban population on the total population%
E—living standardEngel coefficient%
T—technical levelWater consumption per unit of GDPm3/$

Figure 2

Dynamics of water resource stress index from 2003 to 2017"

Figure 3

Ridge trace curve"

Table 4

Univariate statistical analysis of the driving factors"

Independent VariablesP (million)A (US dollar)UET (m3/$)
Min119.950939.74030.873%32.170%389.131
Max128.8104,663.41145.758%40.250%1,556.638
Mean124.3052,528.20637.033%35.542%924.528
SD3.3701,306.0054.314%2.256%428.636
CV0.0270.5170.116%0.063%0.464

Table 5

Matrix of correlations between variables"

PAUET
WRSIPearson correlations0.576*-0.773**-0.815**0.872**0.777**
Sig.0.0250.0010.0000.0000.001

Table 6

Matrix of correlation between variables"

VariableslnIlnPlnUlnAlnElnT
lnI1-----
lnP0.491**1----
lnU-0.807**-0.563**1---
lnA-0.735**-0.764**0.932**1--
lnE0.817**0.571**-0.901**-0.915**1-
lnT0.733**0.742**-0.935**-0.989**0.892**1

Table 7

Influencing factors of water resource stress by OLS"

VariablesUnstandardized coefficientsStd. errort statisticSig.VIF
Constant-118.39872.598-1.6310.137-
lnP8.5694.9471.7320.11714.811
lnU-4.0671.991-2.0420.07113.645
lnA1.6571.0561.5690.15192.286
lnE6.7032.9572.2670.0509.198
lnT0.4620.9290.4970.63156.628
R-squared-0.993Adjusted R-squared-0.996
F-statistic-96.902Sig.-0.007

Table 8

Results of the ridge regression (K=0.2)"

VariablesNon-normalized coefficient (β)Std. errort statisticSig. tVIF
lnP0.03870.022714.62090.00000.4535
lnA-0.08900.01418.71940.00000.9533
lnU-0.24490.014414.14180.00000.2317
lnE0.27090.037312.47880.00000.2208
lnT0.09710.005117.13910.00000.6580
Constant0.65110.144727.61260.0000
R-squared0.9290----
F113.4700----
Sig.F0.0000----

Figure 4

Water structure change"

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