Sciences in Cold and Arid Regions ›› 2020, Vol. 12 ›› Issue (4): 217-233.doi: 10.3724/SP.J.1226.2020.00217

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Assessing spatial and temporal variability in water consumption and the maintainability oasis maximum area in an oasis region of Northwestern China

XueXiang Chang1(),WenZhi Zhao1(),XueLi Chang2,Bing Liu1,Jun Du1   

  1. 1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Linze Inland River Basin Research Station, Key Laboratory of Ecohydrology of Inland River Basin, CAS, Lanzhou, Gansu 730000, China
    2.School of Natural Resources and Environmental Engineering, Ludong University, Yantai, Shandong 264025, China
  • Received:2020-02-15 Accepted:2020-05-09 Online:2020-08-31 Published:2020-09-04
  • Contact: XueXiang Chang,WenZhi Zhao E-mail:chxx@lzb.ac.cn;zhaowzh@lzb.ac.cn

Abstract:

Water consumption is a key role in improving the efficiency and sustainability of water management in arid environments. In this study, we explored an approach based on meta-analysis, MODIS NDVI products, land-use spatial distribution, and soil water physical parameters to gain insight into long-term and large scale distribution of land use and water consumption, maintain maximum Zhangye Oasis area according to Heihe River runoff, and suitable water resource management in Zhangye Oasis. This approach was initiated in order to improve the efficiency of irrigation and water resource management in arid regions Results showed that Heihe River runoff can maintain a maximum Zhangye Oasis area of 22.49×104 hm2. During the 2000-2016 growing seasons, actual oasis water consumption ranged from 11.35×108 m3 to 13.73×108 m3, with a mean of (12.89 ± 0.60)×108 m3; if maintaining agricultural production and oasis stability was chosen, oasis water consumption ranged from 10.24×108 m3 to 12.37×108 m3, with a mean of (11.62 ± 0.53)×108 m3. From the perspective of water resources management and ecosystem stability, it is necessary to reduce the area of Zhangye Oasis or choose the minimum water consumption method to manage the oasis, to ease the pressure of water shortage and maintain stable and sustainable development of the Zhangye Oasis. These results can provide future practical guidance for water resource management of coordinated development of the economy and the environment in an arid area.

Key words: meta-analysis, water consumption, land-use, spatial and temporal distribution, runoff, Northwestern China

Figure 1

Map of Zhangye Oasis and its location in the arid and semi-arid region of Northwest China"

Figure 2

Spatial distribution of soil parameters in Zhangye Oasis. (a) Saturation moisture capacity, (b) Field capacity, and (c) Soil bulk density"

Figure 3

Land-use in Zhangye Oasis in Northwestern China from 2000 to 2016. (a) Trends in oasis area, (b) Planting structure"

Figure 4

Spatial and temporal distribution of land-use in Zhangye Oasis from 2000 to 2016"

Table 1

Land use statistics from 2000 to 2016 for Zhangye Oasis"

TypeMaximum (×104 hm2)Minimum (×104 hm2)Mean (×104 hm2)SD (×104 hm2)Variable coefficient
Maize19.6213.1917.052.1712.70
Wheat3.720.551.530.9360.74
Vegetables + fruit5.091.272.551.1846.14
Shelterbelts0.140.090.120.0219.74
Shrub land0.140.120.130.015.90

Figure 5

Spatial distribution and variability in actual water consumption during the 2000-2016 growing seasons in Zhangye Oasis. Spatial distribution in (a) 2000, (b) 2001, (c) 2002, (d) 2003, (e) 2004, (f) 2005, (g) 2006, (h) 2007, (i) 2008, (j) 2009, (k) 2010, (l) 2011, (m) 2012, (n) 2013, (o) 2014, (p) 2015, (q) 2016, (r) the mean maximum water consumption per year from 2000 to 2016 (bold line with filled circles, bars represent standard deviation of the mean) and oasis maximum water consumption (dash line with filled squares)"

Table 2

Proportion of areas in optimum water consumption level to total oasis from 2000 to 2016"

Type

Year

Level (mm)

20002001200220032004200520062007200820092010201120122013201420152016
Minimum water consumption< 2185.477.844.724.734.746.665.995.525.375.305.415.084.143.745.813.614.05
219-35810.0413.9410.2010.719.7810.339.969.069.249.2710.5314.757.907.2910.586.708.35
359-47113.3216.5814.8115.6114.4514.1814.3114.2712.8314.2317.8121.1313.5913.3117.3911.8716.83
472-56917.1019.1320.9719.4720.2518.8920.1819.9318.7020.2223.0821.7221.9824.0422.6122.0225.39
570-65516.5718.1521.1521.2722.9719.8322.7021.2923.8324.1123.1322.7225.8729.9723.3528.8627.21
656-73418.8215.4717.8716.9718.2618.4618.3017.8919.2417.9215.2911.2520.7017.7715.4321.9615.35
735-82015.557.999.089.658.1610.277.639.539.047.894.082.424.943.394.104.262.27
> 8213.130.901.211.571.371.380.922.511.741.080.680.920.870.490.730.720.54
219-82091.4191.2694.0793.6993.8991.9693.0891.9792.8893.6393.9094.0094.9995.7793.4695.6695.41
Optimum water consumption< 2184.787.044.184.184.205.985.334.914.834.784.844.453.713.395.173.273.67
219-3589.1012.549.019.458.519.278.797.958.178.129.0111.946.866.259.075.777.03
359-47111.2514.3212.4613.3412.1512.1412.1411.9010.9511.5814.6119.6210.8610.7514.549.4913.08
472-56915.1716.7717.6016.5116.6515.4516.3816.5114.4116.5820.0718.3517.5918.0419.3316.3521.31
570-65514.4117.0919.0718.6020.6917.6420.2918.8820.4321.3621.6722.6222.9726.9121.8925.4426.12
656-73417.4217.2820.1019.7821.7420.0021.5220.4122.7821.9920.4616.8325.2325.3820.5727.6421.48
735-82019.5012.1814.1814.1212.8215.5513.2714.4415.0413.298.144.9511.378.268.1810.436.33
> 8218.372.783.394.023.253.962.275.013.392.291.191.241.411.031.231.610.98
359-82077.7577.6483.4182.3584.0580.7983.6182.1483.6184.8284.9582.3788.0289.3384.5289.3588.32
Maximum water consumption< 2184.326.293.803.813.825.584.954.504.394.354.454.003.403.134.833.013.37
219-3588.1111.527.928.307.448.227.837.047.327.287.839.846.055.387.855.046.10
359-47110.0412.7510.8811.7010.7610.8210.5810.209.7510.0112.6918.289.229.0512.528.1010.89
472-56913.2215.2415.4514.8214.2613.5513.8514.1712.2514.0417.7016.7014.5414.5617.1312.9417.70
570-65513.3915.6516.8515.9217.8415.3417.5616.9416.2918.0918.9519.5019.3421.9118.8720.7723.11
656-73414.6316.1218.8019.2020.9918.4520.7119.0621.9321.3821.0919.7023.9426.9421.4426.8523.80
735-82019.5715.4218.1517.3317.4818.4618.3318.0119.8718.6414.379.8619.6016.4514.3318.9412.77
> 82116.727.038.158.937.409.596.2110.078.196.202.922.123.902.583.024.352.27
359-82070.8575.1780.1378.9781.3476.6281.0178.3980.1082.1784.7984.0486.6488.9184.3087.6088.26

Table 3

Water consumption statistics during the growing season from 2000 to 2016 in Zhangye Oasis"

TypeMean (×108 m3)SD (×108 m3)Maximum (×108 m3)Minimum (×108 m3)
Minimum water consumption11.620.5312.3710.24
Optimum water consumption12.270.5713.0710.81
Maximum water consumption12.890.6013.7311.35

Figure 6

Spatial distribution and variability in optimum water consumption during the 2000-2016 growing season in Zhangye Oasis. Spatial distribution in (a) 2000, (b) 2001, (c) 2002, (d) 2003, (e) 2004, (f) 2005, (g) 2006, (h) 2007, (i) 2008, (j) 2009, (k) 2010, (l) 2011, (m) 2012, (n) 2013, (o) 2014, (p) 2015, (q) 2016, (r) the mean optimum water consumption per year from 2000 to 2016 (bold line with filled circles, bars represent standard deviation of the mean) and oasis optimum water consumption (dash line with filled squares)"

Figure 7

Spatial distribution and variation of minimum water consumption during the 2000-2016 growing season in Zhangye Oasis. Spatial distribution in (a) 2000, (b) 2001, (c) 2002, (d) 2003, (e) 2004, (f) 2005, (g) 2006, (h) 2007, (i) 2008, (j) 2009, (k) 2010, (l) 2011, (m) 2012, (n) 2013, (o) 2014, (p) 2015, (q) 2016, (r) mean minimum water consumption per year from 2000 to 2016 (bold line with filled circles, bars represent standard deviation of the mean) and oasis minimum water consumption (dash line with filled squares)"

Figure 8

The variation trend of Heihe River runoff and the relationship between runoff of growing season and oasis area and water consumption from 2000 to 2016. (a) Average monthly of Heihe River runoff, (b) The variation trend of runoff and runoff allowed to be consumed (RAC) in Heihe River from 2000 to 2016, (c) the relationship between runoff of growing season and oasis area, (d) the relationship between runoff of growing season and actual water consumption(WA), (e) the relationship between runoff of growing season and optimum water consumption (WOpt), (f) the relationship between runoff of growing season and minimum water consumption (WWin), (g) Difference between WA and RAC, (h) Difference between WOpt and RAC, (i) Difference between WWin and RAC"

Figure 9

The growing season, ≥8 mm and annual precipitation change in of Linze County, Zhangye Oasis from 1990 to 2017"

Allen RG, 2000. Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. Journal of Hydrology, 229: 27-41. DOI: 10.1016/S0022-1694(99)00194-8.
doi: 10.1016/S0022-1694(99)00194-8
Bai E, Li S, Xu W, et al., 2013. A meta-analysis of experimental warming effects on terrestrial nitrogen pools and dynamics. New Phytologist, 199: 441-451. DOI: 10.1111/nph. 12252.
doi: 10.1111/nph. 12252
Campos I, Neale CMU, Suyker AE, et al., 2017. Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties. Agricultural Water Management, 187: 140-153. DOI: 10.1016/j.agwat.2017.03.022.
doi: 10.1016/j.agwat.2017.03.022
Chang X, Liu B, Liu H, et al., 2015a. Water accounting for conjunctive groundwater and surface water irrigation sources: A case study in the middle Heihe River Basin of arid northwestern China. Sciences in Cold and Arid Regions, 7(6): 0687-0701. DOI: 10.3724/SP.J.1226.2015.00687.
doi: 10.3724/SP.J.1226.2015.00687
Chang X, Zhao W, Zeng F, 2015b. Crop evapotranspiration-based irrigation management during the growing season in the arid region of northwestern China. Environmental Monitoring and Assessment, 187: 699.
Chen YN, Ye ZX, Shen YJ, 2011. Desiccation of the Tarim River, Xinjiang, China, and mitigation strategy. Quaternary International, 244 (2): 264-271. DOI: 10.1016/j.quaint.2011. 01.039.
doi: 10.1016/j.quaint.2011. 01.039
Cheng G, Li X, Zhao W, et al., 2014. Integrated study of the water-ecosystem-economy in the Heihe River Basin. National Science Review, 1(3): 413-428. DOI: 10.1093/nsr/nwu017.
doi: 10.1093/nsr/nwu017
Cruz-Blanco M, Gavilán P, Santos C, et al., 2014. Assessment of reference evapotranspiration using remote sensing and forecasting tools under semi-arid conditions. International Journal of Applied Earth Observation and Geoinformation, 33: 280-289. DOI: 10.1016/j.jag.2014.06.008.
doi: 10.1016/j.jag.2014.06.008
Goovaerts P, 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, pp. 259-368.
Gurevitch J, Morrow LL, Wallace A, et al., 1992. A meta-analysis of competition in field experiments. American Naturalist, 140: 539-572. DOI: 10.1086/285428.
doi: 10.1086/285428
Hafeez M, Khan S, 2005. Remote sensing application for estimation of irrigation water consumption in liuyuankou irrigation system in China. In: Proceedings 16th Congress of the Modelling and Simulation Society of Australia and New Zealand, pp. 12-15.
Han DL, 1999. The progress of research on oasis in China. Scientia Geographica Sinica, 19(4): 313-319.
Hedges LV, Gurevitch J, Curtis PS, 1999. The meta-analysis of response ratios in experimental ecology. Ecology, 80: 1150-1156. DOI: 10.1890/0012-9658.
doi: 10.1890/0012-9658
Huang Z, Shen W, 2000. Water Relationship and Drought Tolerance of Plants in Arid Areas. Beijing: China Environment Press.
Jia B, Ren Y, Yang J, 2001. Theoretical thinking on the ecological construction of oasis landscape. Journal of Arid Land Resources and Environment, 15(1): 56-63.
Ko J, Piccinni G, 2009. Corn yield responses under crop evapotranspiration-based irrigation management. Agricultural Water Management, 96: 799-808. DOI: 10.1016/j.agwat.2008.10.010.
doi: 10.1016/j.agwat.2008.10.010
Li X, Yang K, Zhou Y, 2016. Progress in the study of oasis-desert interactions. Agricultural Forest Meteorology, 230-231: 1-7. DOI: 10.1016/j.agrformet.2016.08.022.
doi: 10.1016/j.agrformet.2016.08.022
Li Y, Huang C, Hou J, et al., 2017. Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River Basin, Northwest China. Agricultural Forest Meteorology, 244-245: 82-97. DOI: 10.1016/j.agrformet.2017. 05.023.
doi: 10.1016/j.agrformet.2017. 05.023
Ling HB, Xu HL, Fu JY, 2013. High- and low-flow variations in annual runoff and their response to climate change in the headstreams of the Tarim River, Xinjiang, China. Hydrological Processes, 27: 975-988. DOI: 10.1002/hyp.9274.
doi: 10.1002/hyp.9274
Liu G, 1996. Standard Methods for Observation and Analysis in Chinese Ecosystem Research Network: Soil Physical and Chemical Analysis & Description of Soil Profiles. China Standards Press: Beijing, China.
Oki T, Kanae S, 2006. Global hydrological cycles and world water resources. Science, 313(5790): 1068-1072. DOI: 10.1126/science.1128845.
doi: 10.1126/science.1128845
Payero JO, Tarkalson DD, Irmak S, et al., 2008. Effect of irrigation amounts applied with subsurface drip irrigation on corn evapotranspiration, yield, water use efficiency, and dry matter production in a semiarid climate. Agricultural Water Management, 95: 895-908. DOI: 10.1016/j.agwat. 2008.02.015.
doi: 10.1016/j.agwat. 2008.02.015
Rana G, Katerji N, 2000. Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review. European Journal of Agronomy, 13: 125-153. DOI: 10.1016/S1161-0301(00)00070-8.
doi: 10.1016/S1161-0301(00)00070-8
Su P, Du M, Zhao A, et al., 2002. Study on water requirement law of some crops and different planting mode in oasis. Agricultural Research in the Arid Areas, 20(2): 79-85.
Tang QH, Peterson S, Cuenca RH, et al., 2009. Satellite-based near-real-time estimation of irrigated crop water consumption. Journal of Geophysical Research, 114: D05114. DOI: 10.1029/2008JD010854.
doi: 10.1029/2008JD010854
Toureiro C, Serralheiro R, Shahidian S, et al., 2017. Irrigation management with remote sensing: Evaluating irrigation requirement for maize under Mediterranean climate condition. Agricultural Water Management, 184: 211-220. DOI: 10.1016/j.agwat.2016.02.010.
doi: 10.1016/j.agwat.2016.02.010
Velpuri NM, Senay GB, Singh RK, et al., 2013. A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET. Remote Sensing of Environment, 139: 35-49. DOI: 10.1016/j.rse.2013.07.013.
doi: 10.1016/j.rse.2013.07.013
Wang J, Chang X, 2013. Change trend of the groundwater depth in Linze county in the middle reaches of Heihe River basin in recent 30 years. Arid Zone Research, 30(4): 594-602.
Wang X, Taub DR, 2010. Interactive effects of elevated carbon dioxide and environmental stresses on root mass fraction in plants: a meta-analytical synthesis using pairwise techniques. Oecologia, 163(1): 1-11. DOI: 10.1007/s00442-010-1572-x.
doi: 10.1007/s00442-010-1572-x
Zhang M, Wang S, Fu B, et al., 2018. Ecological effects and potential risks of the water diversion project in the Heihe River Basin. Science of the Total Environment, 619-620: 794-803. DOI: 10.1016/j.scitotenv.2017.11.037.
doi: 10.1016/j.scitotenv.2017.11.037
Zhao W, Chang X, Chang X, et al., 2018. Estimating water consumption based on meta-analysis and MODIS data for an oasis region in northwestern China. Agricultural Water Management, 208: 478-489. DOI: 10.1016/j.agwat.2018. 06.035.
doi: 10.1016/j.agwat.2018. 06.035
Zhao W, Liu B, Zhang Z, 2010. Water requirements of maize in the middle Heihe River basin, China. Agricultural Water Management, 97: 215-223. DOI: 10.1016/j.agwat.2009. 09.011.
doi: 10.1016/j.agwat.2009. 09.011
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