Please wait a minute...
Journal of Arid Land  2021, Vol. 13 Issue (1): 23-39    DOI: 10.1007/s40333-021-0054-9
Research article     
Investigation of crop evapotranspiration and irrigation water requirement in the lower Amu Darya River Basin, Central Asia
Durdiev KHAYDAR1,2,3,4, CHEN Xi1,2,3,5, HUANG Yue1,3,5,*(), Makhmudov ILKHOM4, LIU Tie1,3,5, Ochege FRIDAY1,3, Abdullaev FARKHOD3,4, Gafforov KHUSEN1,3,4, Omarakunova GULKAIYR1,3
1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2Research Center for Ecology and Environment of Central Asia, Chinese Academy of Sciences, Urumqi 830011, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4Ministry of Water Resources of the Republic of Uzbekistan, Scientific Research Institute of Irrigation and Water Problems, Tashkent 100187, Uzbekistan
5Key Laboratory of GIS & RS Application Xinjiang Uygur Autonomous Region, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Download: HTML     PDF(1742KB)
Export: BibTeX | EndNote (RIS)      


High water consumption and inefficient irrigation management in the agriculture sector of the middle and lower reaches of the Amu Darya River Basin (ADRB) have significantly influenced the gradual shrinking of the Aral Sea and its ecosystem. In this study, we investigated the crop water consumption in the growing seasons and the irrigation water requirement for different crop types in the lower ADRB during 2004-2017. We applied the FAO Penman-Monteith method to estimate reference evapotranspiration (ET0) based on daily climatic data collected from four meteorological stations. Crop evapotranspiration (ETc) of specific crop types was calculated by the crop coefficient. Then, we analyzed the net irrigation requirement (NIR) based on the effective precipitation with crop water requirements. The results indicated that the lowest monthly ET0 values in the lower ADRB were found in December (18.2 mm) and January (16.0 mm), and the highest monthly ET0 values were found in June and July, with similar values of 211.6 mm. The annual ETc reached to 887.2, 1002.1, and 492.0 mm for cotton, rice, and wheat, respectively. The average regional NIR ranged from 514.9 to 715.0 mm in the 10 Irrigation System Management Organizations (UISs) in the study area, while the total required irrigation volume for the whole region ranged from 4.2×109 to 11.6×109 m3 during 2004-2017. The percentages of NIR in SIW (surface irrigation water) ranged from 46.4% to 65.2% during the study period, with the exceptions of the drought years of 2008 and 2011, in which there was a significantly less runoff in the Amu Darya River. This study provides an overview for local water authorities to achieve optimal regional water allocation in the study area.

Key wordscrop evapotranspiration      crop water requirement      net irrigation requirement      CROPWAT model      Amu Darya River      Aral Sea     
Received: 10 September 2020      Published: 10 January 2021
Corresponding Authors: HUANG Yue     E-mail:
About author: *HUANG Yue (E-mail:
Cite this article:

Durdiev KHAYDAR, CHEN Xi, HUANG Yue, Makhmudov ILKHOM, LIU Tie, Ochege FRIDAY, Abdullaev FARKHOD, Gafforov KHUSEN, Omarakunova GULKAIYR. Investigation of crop evapotranspiration and irrigation water requirement in the lower Amu Darya River Basin, Central Asia. Journal of Arid Land, 2021, 13(1): 23-39.

URL:     OR

Fig. 1 Location of the Amu Darya River Basin (ADRB; a) and distribution of the 10 UISs (Irrigation System Management Organizations) as well as meteorological stations (b) in the lower Amu Darya BUIS (Basin Management of Irrigation Systems)
Fig. 2 Annual average temperature (a) and annual precipitation (b) changes of the four meteorological stations in the study area from 2004 to 2017. The dotted line represents the trend.
Fig. 3 Planting pattern showing the proportions of cultivated crops by irrigated area in different UISs in the study area during 2004-2017
Crop Vegetation period Kc
Planting date Harvesting date Days (d) Initial stage Development stage Mid-season stage Late-season stage
Cotton 12 Apr 23 Oct 195 0.35 0.77 1.20 0.60
Wheat 16 Sep 13 May 240 0.70 0.92 1.15 0.25
Rice 20 May 16 Sep 150 0.50 0.73 1.03 0.76
Alfalfa 2 Mar 1 Mar 365 0.40 0.67 0.95 0.90
Maize 21 Apr 23 Aug 125 0.30 0.80 1.20 0.35
Vegetables 5 Feb 10 May 95 0.70 0.87 1.05 0.95
Melon 28 Apr 25 Aug 120 0.50 0.77 1.05 0.75
Fruits 21 Apr 20 Apr 365 0.30 0.57 0.85 0.45
Sorghum 1 May 8 Sep 130 0.30 0.65 1.00 0.55
Potato 10 Mar 17 Jul 130 0.50 0.82 1.15 0.86
Table 1 Crop seasonal information in the study area
Fig. 4 Monthly reference evapotranspiration (ET0) changes at the Chimbay (a), Kungrad (b), Nukus (c), and Urgench (d) stations during 2004-2017. The box and whisker plots show the five-number summary of a set of data: the minimum score, first (lower) quartile, median, third (upper) quartile, and the maximum score. The center represents the middle 50%, or 50th percentile of the data set, and is derived using the lower and upper quartile values. The median value is displayed inside the "box." The maximum and minimum values are displayed with vertical lines ("whiskers") connecting the points to the center box.
Fig. 5 Crop water requirement (CWR) for main crop types during 2004-2017. The box and whisker plots show the five-number summary of a set of data: the minimum score, first (lower) quartile, median, third (upper) quartile, and the maximum score. The center represents the middle 50%, or 50th percentile of the data set, and is derived using the lower and upper quartile values. The median value is displayed inside the "box." The maximum and minimum values are displayed with vertical lines ("whiskers") connecting the points to the center box. Different colors represent different crop types.
Year/Period ETc (mm) Reference
Cotton Wheat Rice Sorghum Alfalfa Vegetables Potato
2018 866.5 438.3 950.0 - - - - Liu et al. (2020)
2003 762.2 509.2 758.0 598.8 881.8 866.9 760.4 Schieder (2011)
2006-2007 799.0 383.0 1050.0 704.0 700.0 619.0 625.0 Bobojonov (2008)
2004-2017 887.2 492.0 1002.1 665.0 991.4 619.1 588.5 This study
Table 2 Comparison of the average crop evapotranspiration (ETc) values for main crops in the study area
Fig. 6 Variations of the UIS-based regional crop water requirements (CWRreg) in the lower ADRB during 2004-2017. 1, Suenli; 2, Kattagar-Bozatau; 3, Kizketken-Kegeyli; 4, Kuanishjarma; 5, Mangit-Nazarkhan; 6, Koramazi-Kilichniyozboy; 7, Shovot-Kulovot; 8, Pakhtaarna-Nayman; 9, Polvon-Gazavot; 10, Toshsoka.
Fig. 7 Crop water requirement (CWR; a), effective precipitation (Peff; b), and net irrigation requirement (NIR; c) for different crop types at the BUIS scale during 2004-2017
Fig. 8 Variations of regional net irrigation requirement (NIRreg) and regional irrigation water demand (IWDreg) on average in the 10 UISs during 2004-2017
Fig. 9 Comparison of the surface irrigation water and inflow from the ARD. SIW, surface irrigation water.
[1]   Abdullaev I, de Fraiture C, Giordano M, et al. 2009. Agricultural water use and trade in Uzbekistan: Situation and potential impacts of market liberalization. International Journal of Water Resources Development, 25(1): 47-63.
[2]   Abdullayev I, Nurmetova F, Abdullaeva F, et al. 2008. Socio-technical aspects of water management in Uzbekistan: emerging water governance issues at the grass root level. In: Rahaman M M, Varis O. Central Asian Waters. Helsinki: Water & Development Publications - Helsinki University Technology, 42-48.
[3]   Ahmed Y, Al-Faraj F, Scholz M, et al. 2019. Assessment of upstream human intervention coupled with climate change impact for a transboundary river flow regime: Nile River basin. Water Resources Management, 33(7): 2485-2500.
[4]   Akramkhanov A, Ul Hassan M, Hornidge A K. 2018. Redrawing soil salinity innovation-focused stakeholder interaction for sustainable land management in Khorezm Province, Uzbekistan. Water, 10(2): 208-225.
[5]   Aleksandrova M, Gain A K, Giupponi C. 2016. Assessing agricultural systems vulnerability to climate change to inform adaptation planning: an application in Khorezm, Uzbekistan. Mitigation and Adaptation Strategies for Global Change, 21(8): 1263-1287.
[6]   Ali M H. 2010. Crop Water Requirement and Irrigation Scheduling. In Fundamentals of Irrigation and On-farm Water Management. New York: Springer, 399-452.
[7]   Ali M H, Mubarak S. 2017. Effective rainfall calculation methods for field crops: An overview, analysis and new formulation. Asian Research Journal of Agriculture, 7(1): 1-12.
[8]   Allen R G, Pereira L S, Raes D, et al. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56. Rome: FAO, 300(9): D05109.
[9]   Allen R G, Pereira L S, Smith M, et al. 2005. FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions. Journal of Irrigation and Drainage Engineering, 131(1): 2-13.
[10]   Aminova M, Abdullayev I. 2009. Water management in a state-centered environment: water governance analysis of Uzbekistan. Sustainability, 1(4): 1240-1265.
[11]   Awan U K, Tischbein B, Martius C. 2014. A GIS-based approach for up-scaling capillary rise from field to system level under soil-crop-groundwater mix. Irrigation Science, 32(6): 449-458.
[12]   Berking J, Beckers B, Reimann T, et al. 2017. Modern impacts on an ancient landscape, the piedmont plain in southwest Turkmenistan. Wires Water, 4(2): e1202, doi: 10.1002/wat2.1202.
[13]   Boboev H, Djanibekov U, Bekchanov M, et al. 2018. Feasibility of conservation agriculture in the Amu Darya River Lowlands, Central Asia. International Journal of Agricultural Sustainability, 17(1): 60-77.
doi: 10.1080/14735903.2018.1560123
[14]   Bobojonov I, Lamers J P A, Bekchanov M, et al. 2013. Options and constraints for crop diversification: A case study in sustainable agriculture in Uzbekistan. Agroecology and Sustainable Food Systems, 37(7): 788-811.
[15]   Bobojonov I B. 2008. Modeling Crop and Water Allocation under Uncertainty in Irrigated Agriculture. A Case Study on the Khorezm Region, Uzbekistan. Bonn: ZEF/Rheinische Friedrich-Wilhelms-Universität, 141.
[16]   Brite E B. 2018. The hydrosocial empire: The Karakum River and the Soviet conquest of Central Asia in the 20th century. Journal of Anthropological Archaeology, 52: 123-136.
[17]   Chen D, Gao G, Xu C Y, et al. 2005. Comparison of the Thornthwaite method and pan data with the standard Penman-Monteith estimates of reference evapotranspiration in China. Climate Research, 28(2): 123-132.
[18]   Conrad C, Schorcht G, Tischbein B, et al. 2012. Agro-meteorological trends of recent climate development in Khorezm and implications for crop production. In: Martius C, Rudenko I, Lamers J, et al. Cotton, Water, Salts and Soums. Dordrecht: Springer, 25-36.
[19]   Conrad C, Lamers J, Ibragimov N, et al. 2016. Analysing irrigated crop rotation patterns in arid Uzbekistan by the means of remote sensing: A case study on post-Soviet agricultural land use. Journal of Arid Environments, 124: 150-159.
[20]   Deng H, Chen Y. 2017. Influences of recent climate change and human activities on water storage variations in Central Asia. Journal of Hydrology, 544: 46-57.
[21]   Djanibekov N, van Assche K, Bobojonov I, et al. 2012. Farm restructuring and land consolidation in Uzbekistan: New farms with old barriers. Europe-Asia Studies, 64(6): 1101-1126.
[22]   Döll P, Siebert S. 2002. Global modeling of irrigation water requirements. Water Resources Research, 38(4): 8-1-8-10, doi: 10.1029/2001WR000355.
[23]   Duchemin B, Hadria R, Erraki S, et al. 2006. Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79(1): 1-27.
[24]   Dukhovny V A, Stulina G. 2001. Strategy of transboundary return flow use in the Aral Sea basin. Desalination, 139(1-3): 299-304.
[25]   Evett S, Ibragimov N, Kamilov B, et al. 2007. Neutron moisture meter calibration in six soils of Uzbekistan affected by carbonate accumulation. Vadose Zone Journal, 6(2): 406-412.
[26]   Gafforov K S, Bao A, Rakhimov S, et al. 2020. The assessment of climate change on rainfall-runoff erosivity in the Chirchik-Akhangaran Basin, Uzbekistan. Sustainability, 12(8): 3369.
doi: 10.3390/su12083369
[27]   Gintzburger G, Le Houérou H N, Toderich K N. 2005. The steppes of middle Asia: Post-1991 agricultural and rangeland adjustment. Arid Land Research and Management, 19(3): 215-239.
doi: 10.1080/15324980590951360
[28]   Güçlü Y S, Subyani A M, Şen Z. 2017. Regional fuzzy chain model for evapotranspiration estimation. Journal of Hydrology, 544: 233-241.
doi: 10.1016/j.jhydrol.2016.11.045
[29]   Hamidov A, Helming K, Balla D. 2016. Impact of agricultural land use in Central Asia: a review. Agronomy for Sustainable Development, 36(1): 6, doi: 10.1007/s13593-015-0337-7.
doi: 10.1007/s13593-015-0337-7
[30]   Hannan T A. 2000. Solution to the Aral Sea crisis? Sustainable water use in Central Asia. Water and Environment Journal, 14(3): 213-218.
doi: 10.1111/wej.2000.14.issue-3
[31]   Herath I K, Ye X, Wang J, et al. 2017. Spatial and temporal variability of reference evapotranspiration and influenced meteorological factors in the Jialing River Basin, China. Theoretical and Applied Climatology, 131(3-4): 1417-1428.
[32]   Howard K W, Howard K K. 2016. The new ''Silk Road Economic Belt'' as a threat to the sustainable management of Central Asia’s transboundary water resources. Environmental Earth Sciences, 75(11): 976, doi: 10.1007/s12665-016-5752-9.
[33]   Huang X, Luo G, Ye F, et al. 2018. Effects of grazing on net primary productivity, evapotranspiration and water use efficiency in the grasslands of Xinjiang, China. Journal of Arid Land, 10(4): 588-600.
doi: 10.1007/s40333-018-0093-z
[34]   Ivushkin K, Bartholomeus H, Bregt A K, et al. 2017. Satellite Thermography for soil salinity assessment of cropped areas in Uzbekistan. Land Degradation & Development, 28(3): 870-877.
[35]   Jalilov S M, Keskinen M, Varis O, et al. 2016. Managing the water-energy-food nexus: Gains and losses from new water development in Amu Darya River Basin. Journal of Hydrology, 539: 648-661.
[36]   Jiang L, Wu F, Liu Y, et al. 2014. Modeling the impacts of urbanization and industrial transformation on water resources in China: An integrated hydro-economic CGE Analysis. Sustainability, 6(11): 7586-7600.
doi: 10.3390/su6117586
[37]   Jiang L, Jiapaer G, Bao A, et al. 2017. Vegetation dynamics and responses to climate change and human activities in Central Asia. Science of The Total Environment, 599-600: 967-980.
doi: 10.1016/j.scitotenv.2017.05.012
[38]   Kamali M I, Nazari R. 2018. Determination of maize water requirement using remote sensing data and SEBAL algorithm. Agricultural Water Management, 209: 197-205.
doi: 10.1016/j.agwat.2018.07.035
[39]   Karthe D, Chalov S, Borchardt D. 2015. Water resources and their management in central Asia in the early twenty first century: status, challenges and future prospects. Environmental Earth Sciences, 73(2): 487-499.
doi: 10.1007/s12665-014-3789-1
[40]   Khasankhanova G. 2005. Public participation to improve water resource management in Uzbekistan. Water Science and Technology, 51(3-4): 365-372.
pmid: 15850210
[41]   Koch M, Missimer T M. 2016. Water resources assessment and management in drylands. Water, 8(6): 239, doi: 10.3390/w8060239.
doi: 10.3390/w8060239
[42]   Lee Y, Yoon T, Hong Y. 2019. Is sustainable watershed management feasible under climate change? An economic appraisal of the Nile River. Sustainability, 12(1): 162, doi: 10.3390/su12010162.
doi: 10.3390/su12010162
[43]   Liu Z, Huang Y, Liu T, et al. 2020. Water balance analysis based on a quantitative evapotranspiration inversion in the Nukus irrigation area, Lower Amu River Basin. Remote Sensing, 12(14): 2317.
doi: 10.3390/rs12142317
[44]   Luo M, Meng F, Liu T. 2017. Multi-model ensemble approaches to assessment of effects of local climate change on water resources of the Hotan River Basin in Xinjiang, China. Water, 9(8): 584, doi: 10.3390/w9080584.
doi: 10.3390/w9080584
[45]   Ma Y, Liu S, Song L, et al. 2018. Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sensing of Environment, 216: 715-734.
[46]   Masafu C K, Trigg M A, Carter R. 2016. Water availability and agricultural demand: An assessment framework using global datasets in a data scarce catchment, Rokel-Seli River, Sierra Leone. Journal of Hydrology: Regional Studies, 8: 222-234.
doi: 10.1016/j.ejrh.2016.10.001
[47]   Multsch S, Elshamy M E, Batarseh S, et al. 2017. Improving irrigation efficiency will be insufficient to meet future water demand in the Nile Basin. Journal of Hydrology: Regional Studies, 12: 315-330.
doi: 10.1016/j.ejrh.2017.04.007
[48]   Omurakunova G, Bao A, Xu W, et al. 2020. Expansion of impervious surfaces and their driving forces in Highly Urbanized Cities in Kyrgyzstan. International Journal of Environmental Research and Public Health, 17(1): 362, doi: 10.3390/ijerph17010362.
doi: 10.3390/ijerph17010362
[49]   Oren A, Plotnikov I S, Sokolov S, et al. 2010. The Aral Sea and the Dead Sea: Disparate lakes with similar histories. Lakes & Reservoirs: Research & Management, 15(3): 223-236.
[50]   Rahimzadegan M, Janani A. 2019. Estimating evapotranspiration of pistachio crop based on SEBAL algorithm using Landsat 8 satellite imagery. Agricultural Water Management, 217: 383-390.
[51]   Rakhmatullaev S, Huneau F, Kazbekov J. 2009. Groundwater resources use and management in the Amu Darya River Basin (Central Asia). Environmental Earth Sciences, 59(6): 1183-1193.
doi: 10.1007/s12665-009-0107-4
[52]   Reyer C P, Otto I M, Adams S, et al. 2017. Climate change impacts in Central Asia and their implications for development. Regional Environmental Change, 17(6): 1639-1650.
[53]   Samian M, Mahdei K N, Saadi H, et al. 2014. Identifying factors affecting optimal management of agricultural water. Journal of the Saudi Society of Agricultural Sciences, 14(1): 11-18.
[54]   Schieder T M. 2011. Analysis of water use and crop allocation for the Khorezm region in Uzbekistan using an integrated hydrologic-economic model. Ecology and Development Series No. 79. Bonn: ZEF/University of Bonn.
[55]   Sentelhas P C, Gillespie T J, Santos E A. 2010. Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada. Agricultural Water Management, 97(5): 635-644.
[56]   Sidike A, Chen X, Liu T. 2016. Investigating alternative climate data sources for hydrological simulations in the upstream of the Amu Darya River. Water, 8(10): 441, doi: 10.3390/w8100441.
[57]   Sime G, Aune J B. 2018. Sustainability of improved crop varieties and agricultural practices: A case study in the Central Rift Valley of Ethiopia. Agriculture, 8(11): 177, doi: 10.3390/agriculture8110177.
doi: 10.3390/agriculture8110177
[58]   Srivastava R K, Panda R K, Chakraborty A. 2018. Comparison of actual evapotranspiration of irrigated maize in a sub-humid region using four different canopy resistance based approaches. Agricultural Water Management, 202: 156-165.
[59]   Stancalie G, Marica A, Toulios L. 2010. Using earth observation data and CROPWAT model to estimate the actual crop evapotranspiration. Physics and Chemistry of the Earth, Parts A/B/C, 35(1-2): 25-30.
[60]   Strickman R, Porkka M. 2008. Water and social changes in Central Asia: Problems related to cotton production in Uzbekistan. In: Rahaman M M, Varis O. Central Asian Waters. Helsinki: Water & Development Publications - Helsinki University Technology, 105.
[61]   Sun J, Li Y, Suo C, et al. 2019. Impacts of irrigation efficiency on agricultural water-land nexus system management under multiple uncertainties—A case study in Amu Darya River basin, Central Asia. Agricultural Water Management, 216: 76-88.
[62]   Surendran U, Sushanth C, Mammen G, et al. 2015. Modelling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource management: A case study in Palakkad district of humid tropical Kerala, India. Aquatic Procedia, 4: 1211-1219.
[63]   Tan M, Zheng L. 2019. Increase in economic efficiency of water use caused by crop structure adjustment in arid areas. Journal of Environmental Management, 230: 386-391.
doi: 10.1016/j.jenvman.2018.09.060 pmid: 30296676
[64]   Tao H, Diop L, Bodian A, et al. 2018. Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso. Agricultural Water Management, 208: 140-151.
[65]   Timpane-Padgham B L, Beechie T, Klinger T. 2017. A systematic review of ecological attributes that confer resilience to climate change in environmental restoration. PLoS ONE, 12(3): e0173812, doi: 10.1371/journal.pone.0173812.
doi: 10.1371/journal.pone.0173812 pmid: 28301560
[66]   Tyagi N, Sharma D, Luthra S. 2000. Evapotranspiration and crop coefficients of wheat and sorghum. Journal of Irrigation and Drainage Engineering, 126: 215-222.
[67]   Veeranna J, Mishra A K. 2017. Estimation of evapotranspiration and irrigation scheduling of Lentilusing CROPWAT 8.0 model for Anantapur District, Andhra Pradesh, India. Journal of AgriSearch, 4(04), doi: 10.21921/jas.v4i04.10204.
[68]   Vlek P L, Martius C, Wehrheim P, et al. 2001. Economic restructuring of land and water use in the Region Khorezm (Uzbekistan). Project Proposal for Phase I. ZEF Work Papers for Sustainable Development in Central Asia No. 1. Bonn: Center for Development Research (ZEF), 1-75.
[69]   Waseem M, Ahmad I, Mujtaba A, et al. 2020. Spatiotemporal dynamics of precipitation in southwest arid-agriculture zones of pakistan. Sustainability, 12(6): 2305, doi: 10.3390/su12062305.
doi: 10.3390/su12062305
[70]   Wegerich K. 2002. Natural drought or human made water scarcity in Uzbekistan. Central Asia and the Caucasus, 2(14): 154-162.
[71]   Xue J, Guan H, Huo Z, et al. 2017. Water saving practices enhance regional efficiency of water consumption and water productivity in an arid agricultural area with shallow groundwater. Agricultural Water Management, 194: 78-89.
doi: 10.1016/j.agwat.2017.09.003
[72]   Yu Y, Pi Y, Yu X, et al. 2018. Climate change, water resources and sustainable development in the arid and semi-arid lands of Central Asia in the past 30 years. Journal of Arid Land, 11(1): 1-14.
doi: 10.1007/s40333-018-0073-3
[73]   Zhong X, Jiang X, Li L, et al. 2020. The impact of socio-economic factors on sediment load: A case study of the Yanhe River Watershed. Sustainability, 12(6): 2457, doi: 10.3390/su12062457.
doi: 10.3390/su12062457
[1] Brian COLLINS, Hadi RAMEZANI ETEDALI, Ameneh TAVAKOL, Abbas KAVIANI. Spatiotemporal variations of evapotranspiration and reference crop water requirement over 1957-2016 in Iran based on CRU TS gridded dataset[J]. Journal of Arid Land, 2021, 13(8): 858-878.
[2] Sanim BISSENBAYEVA, Jilili ABUDUWAILI, Assel SAPAROVA, Toqeer AHMED. Long-term variations in runoff of the Syr Darya River Basin under climate change and human activities[J]. Journal of Arid Land, 2021, 13(1): 56-70.
[3] Adilov BEKZOD, Shomurodov HABIBULLO, FAN Lianlian, LI Kaihui, MA Xuexi, LI Yaoming. Transformation of vegetative cover on the Ustyurt Plateau of Central Asia as a consequence of the Aral Sea shrinkage[J]. Journal of Arid Land, 2021, 13(1): 71-87.
[4] Tomina Tatyana KONSTANTINOVNA, Azhikina Natalya ZHEKSEMBAEVNA. Impact of aridization on soil cover transformation of the Aral Sea and the modern Syr-Darya Delta[J]. Journal of Arid Land, 2011, 3(2): 150-154.