Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2023, Vol. 15 Issue (4): 424-438    DOI: 10.1007/s40333-023-0054-z     CSTR: 32276.14.s40333-023-0054-z
Research article     
Investigating the causes of Lake Urmia shrinkage: climate change or anthropogenic factors?
Mehri SHAMS GHAHFAROKHI1, Sogol MORADIAN2,*()
1Department of Social Sciences, University of Isfahan, Isfahan 81746-73441, Iran
2Department of Civil Engineering, University of Galway, Galway H91TK33, Ireland
Download: HTML     PDF(840KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

In the current scenario, Lake Urmia, one of the vastest hyper saline lakes on the Earth, has been affected by serious environmental degradation. Using different satellite images and observational data, this study investigated the changes in the lake for the period 1970-2020 based on the effects of climate change and several human-induced processes on Lake Urmia, such as population growth, excessive dam construction, low irrigation water use efficiency, poor water resources management, increased sediment flow into the lake, and lack of political and legal frameworks. The results indicated that between 1970 and 1997, the process of change in Lake Urmia was slow; however; the shrinkage was faster between 1998 and 2018, with about 30.00% of the lake area disappearing. As per the findings, anthropogenic factors had a much greater impact on Lake Urmia than climate change and prolonged drought; the mismanagement of water consumption in the agricultural sector and surface and underground water withdrawals in the basin have resulted in a sharp decrease in the lake's surface. These challenges have serious implications for water resources management in Lake Urmia Basin. Therefore, we provided a comprehensive overview of anthropogenic factors on the changes in Lake Urmia along with existing opportunities for better water resources management in Lake Urmia Basin. This study serves as a guideline framework for climate scientists and hydrologists in order to assess the effects of different factors on lake water resources and for decision-makers to formulate strategies and plans according to the management task.

Key words： Lake Urmia      lake shrinkage      climate change      population growth      dam construction      water resources management     
Received: 12 August 2022      Published: 30 April 2023
Corresponding Authors: *Sogol MORADIAN (E-mail: sogolmoradian@live.com)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Mehri SHAMS GHAHFAROKHI
Sogol MORADIAN
Cite this article:

Mehri SHAMS GHAHFAROKHI, Sogol MORADIAN. Investigating the causes of Lake Urmia shrinkage: climate change or anthropogenic factors?. Journal of Arid Land, 2023, 15(4): 424-438.

URL:

http://jal.xjegi.com/10.1007/s40333-023-0054-z     OR     http://jal.xjegi.com/Y2023/V15/I4/424

Fig. 1 Overview of Lake Urmia Basin (a) and satellite images showing the variations of the area of Lake Urmia in 1984 (b), 1998 (c), 2007 (d), 2014 (e), and 2018 (f). Note that the images were acquired by Landsat 5, 7, and 8.
Item Description
Geographical location Coordinates of 44°13′-47°54′E and 35°40′-38°29′N in Iran.
Surface area 3500 km2 in 2013 (maximum length of 140 km and maximum width of 55 km).
Depth Average depth: 6 m; maximum depth: 16 m.
Lake level Ecological level of the lake's water level: 1274.1 m a.s.l.
Permanent main water sources (1) Aji Chai; (2) Alamlou River; (3) Barandooz River; (4) Gadar River; (5) Ghaie River; (6) Leilan River; (7) Mahabad River; (8) Nazloo River; (9) Roze River; (10) Shahar River; (11) Simine River; (12) Zarrine River; (13) Zola River.
Table 1 Detailed information of Lake Urmia
Indicator Formula Reference
Automated water extraction index (AWEI) $\text{AWEI}=4({{X}_{\text{Green}}}-{{X}_{\text{MIR}}})-(0.25{{X}_{\text{NIR}}}+2.75{{X}_{\text{SWIR}}})$ Feyisa et al. (2014)
Automated water extraction index (shadow correction; AWEIsh) $\mathrm{DVW}=\frac{X_{\mathrm{NIR}}-X_{\mathrm{Red}}}{X_{\mathrm{NIR}}+X_{\mathrm{Red}}}-\frac{X_{\mathrm{NIR}}-X_{\mathrm{MIR}}}{X_{\mathrm{NIR}}+X_{\mathrm{MIR}}}$ Acharya et al. (2018); Wang et al. (2018)
Difference between vegetation and water (DVW) $\text{DVW}=\frac{{{X}_{\text{NIR}}}-{{X}_{\text{Red}}}}{{{X}_{\text{NIR}}}+{{X}_{\text{Red}}}}-\frac\\
{{{X}_{\text{NIR}}}-{{X}_{\text{MIR}}}}{{{X}_{\text{NIR}}}+{{X}_{\text{MIR}}}}$		 Lyon et al. (1998); Xu et al. (2013); Naji (2018)
Modified normalized difference water index (MNDWI) $\text{MNDWI}=\frac{{{X}_{\text{Green}}}-{{X}_{\text{MIR}}}}{{{X}_{\text{Green}}}+{{X}_{\text{MIR}}}}$ Xu (2005)
Normalized difference vegetation index (NDVI) $\text{NDVI}=\frac{{{X}_{\text{NIR}}}-{{X}_{\text{Red}}}}{{{X}_{\text{NIR}}}+{{X}_{\text{Red}}}}$ Pettorelli (2013)
Normalized difference water index (NDWI) $\text{NDWI}=\frac{{{X}_{\text{Green}}}-{{X}_{\text{NIR}}}}{{{X}_{\text{Green}}}+{{X}_{\text{NIR}}}}$ Gao (1996); McFeeters (2013)
Table 2 Indicators used to estimate the area of Lake Urmia
Fig. 2 Behavior of a system in response to disturbances: recovery from disturbances to normal state
Index Water pixels (%) Area (km2) Absolute error (km2)
AWEI 6.94 2457.93 457.93
AWEIsh 69.54 24,637.89 22,637.89
DVW 16.11 5708.99 3708.99
MNDWI 15.11 5354.68 3354.68
NDVI 37.03 13,120.57 11,120.57
NDWI 6.19 2193.38 193.38
Table 3 Area of Lake Urmia in 2014 calculated from different indicators
Fig. 3 Changes in the total area of Lake Urmia during 1970-2020 based on normalized difference water index (NDWI)
Fig. 4 Changes in the total water volume of Lake Urmia and precipitation of Lake Urmia Basin during 1970-2020
Fig. 5 Chnages of the average standardized precipitation index (SPI) in Lake Urmia Basin during 1970-2020. Negative red SPI indicates the occurrence of drought and positive blue SPI means the end of drought.
Fig. 6 Changes in the total water volume of Lake Urmia and population of Lake Urmia Basin during 1970-2020
Fig. 7 Changes in the total water volume of Lake Urmia and agricultural water use of Lake Urmia Basin during 1970-2020
Fig. 8 Changes in the total water volume of Lake Urmia and surface water withdrawals of Lake Urmia Basin during 1970-2020
Data Water volume (×109 m3)
2011 2012 2013 2014 2015 2016 2017 2018
Observed data 3.16 2.49 1.87 1.48 1.90 1.95 1.54 1.66
WEAP modelling data 3.66 3.20 2.49 2.09 1.84 1.72 1.71 1.65
Table 4 Comparing the observed water volume of Lake Urmia and modelled water volume from the Water Evaluation and Planning System (WEAP) during 2011-2018
Data Water demand (×109 m3)
Domestic sector Agricultural sector Industrial sector
Observed data 175.90 3351.10 97.10
WEAP modelling data 189.90 3236.30 69.95
Table 5 Comparing the observed water demand of Lake Urmia Basin and modelled water demand from the WEAP in different sectors in 2018
Fig. 9 Observed total water volume of Lake Urmia during 2000-2020 and modelled water volume from the WEAP during 2020-2040 based on the different adopted management strategies. S1, the basic strategy; S2, a 25.00% decrease in water demand in the industrial and domestic sectors; S3, a 40.00% decrease in water demand in the agricultural sector; S4, water transferring to Lake Urmia; S5, a 25.00% reduction in all water demands.
[1]   Abbaspour M, Nazaridoust A. 2007. Determination of environmental water requirements of Lake Urmia, Iran: an ecological approach. International Journal of Environmental Studies, 64(2): 161-169.
doi: 10.1080/00207230701238416
[2]   Acharya T D, Lee D H, Yang I T, et al. 2016. Identification of water bodies in a Landsat 8 OLI image using a J48 decision tree. Sensors-Basel, 16(7): 1075, doi: 10.3390/s16071075.
doi: 10.3390/s16071075
[3]   Acharya T D, Subedi A, Lee D H. 2018. Evaluation of water indices for surface water extraction in a Landsat 8 scene of Nepal. Sensors, 18(8): 2580, doi: 10.3390/s18082580.
doi: 10.3390/s18082580
[4]   Adger W N. 2000. Social and ecological resilience: are they related? Progress in Human Geography, 24(3): 347-364.
doi: 10.1191/030913200701540465
[5]   AghaKouchak A, Norouzi H, Madani K, et al. 2015. Aral Sea syndrome desiccates Lake Urmia: Call for action. Journal of Great Lakes Research, 41(1): 307-311.
doi: 10.1016/j.jglr.2014.12.007
[6]   AghaKouchak A, Mirchi A, Madani K, et al. 2021. Anthropogenic drought: definition, challenges, and opportunities. Reviews of Geophysics, 59(2): e2019RG000683, doi: 10.1029/2019RG000683.
doi: 10.1029/2019RG000683
[7]   Ahmadaali J, Barani G A, Qaderi K, et al. 2018. Analysis of the effects of water management strategies and climate change on the environmental and agricultural sustainability of Urmia Lake basin, Iran. Journal of Water, 10(2): 160, doi: 10.3390/w10020160.
doi: 10.3390/w10020160
[8]   Ahmadi A, Abbaspour M, Arjmandi R, et al. 2016. Resilient approach toward urban development in lake catchments, case of Lake Urmia. Scientia Iranica, 23(4): 1627-1632.
doi: 10.24200/sci.2016.2233
[9]   Ahmadzadeh Kokya T, Pejman A, Mahin Abdollahzadeh E, et al. 2011. Evaluation of salt effects on some thermodynamic properties of Urmia Lake water. International Journal of Environmental Research, 5(2): 343-348.
[10]   Alborzi A, Mirchi A, Moftakhari H, et al. 2018. Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts, Environmental Research Letters, 13(8): 084010, doi: 10.1088/1748-9326/ aad246.
doi: 10.1088/1748-9326/ aad246
[11]   Angelidis P, Maris F, Kotsovinos N, et al. 2012. Computation of drought index SPI with alternative distribution functions. Water Resources Management, 26: 2453-2473.
doi: 10.1007/s11269-012-0026-0
[12]   Awange J L, Mpelasoka F, Goncalves R M. 2016. When every drop counts: analysis of droughts in Brazil for the 1901-2013 period Science of The Total Environment, 566-567: 1472-1488.
[13]   Balkanlou K R, Müller B, Cord A F, et al. 2020. Spatiotemporal dynamics of ecosystem services provision in a degraded ecosystem: A systematic assessment in the Lake Urmia basin, Iran, Science of The Total Environment, 716: 137100, doi: 10.1016/j.scitotenv.2020.137100.
doi: 10.1016/j.scitotenv.2020.137100
[14]   Davis C. 2013. SPSS for Applied Sciences:Basic Statistical Testing. Melbourne: CSIRO Publishing, 1-175.
[15]   de Bruijn K M. 2004. Resilience and flood risk management. Water Policy, 6(1): 53-66.
doi: 10.2166/wp.2004.0004
[16]   Dehghanipour A H, Panahi D M, Mousavi H, et al. 2020. Effects of water level decline in Lake Urmia, Iran, on local climate conditions. Water, 12(8): 2153, doi: 10.3390/w12082153.
doi: 10.3390/w12082153
[17]   Delju A H, Ceylan A, Piguet E, et al. 2013. Observed climate variability and change in Urmia Lake Basin, Iran. Theoretical and Applied Climatology, 111(1-2): 285-296.
doi: 10.1007/s00704-012-0651-9
[18]   Department of Environment of Iran. 2019. Urmia Lake Challenges, Actions, and the way forward (1st ed.). [2019-04-28]. https://www.ulrp.ir/wp-content/uploads/2019/04/node_1420.pdf.
[19]   Edwards D C, McKee T B. 1997. Characteristics of 20th century drought in the United States at multiple time scales. Atmospheric Science Paper, 634: 1-30.
[20]   Emami F, Koch M. 2018. Agricultural water productivity-based hydro-economic modeling for optimal crop pattern and water resources planning in the Zarrine River basin, Iran, in the wake of climate change. Sustainability, 10(11): 3953, doi: 10.3390/su10113953.
doi: 10.3390/su10113953
[21]   Fang G H, Yang J, Chen Y N, et al. 2015. Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19(6): 2547-2559.
doi: 10.5194/hess-19-2547-2015
[22]   Farahmand A, AghaKouchak A. 2015. A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76: 140-145.
doi: 10.1016/j.advwatres.2014.11.012
[23]   Feyisa G L, Meilby H, Fensholt R, et al. 2014. Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140: 23-35.
doi: 10.1016/j.rse.2013.08.029
[24]   Gao B C. 1996. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257-266.
doi: 10.1016/S0034-4257(96)00067-3
[25]   Garousi V, Najafi A, Samadi A, et al. 2013. Environmental crisis in Lake Urmia, Iran: a systematic review of causes, negative consequences and possible solutions. In: Proceedings of the 6th International Perspective on Water Resources & the Environment (IPWE). Izmir, Turkey, doi: 10.13140/RG.2.1.4737.0088.
doi: 10.13140/RG.2.1.4737.0088
[26]   Gersonius B. 2008. Can resilience support integrated approaches to urban drainage management? In:11th International Conference on Urban Drainage. Edinburgh, Scotland, UK.
[27]   Ghale Y A G, Altunkaynak A, Unal A. 2018. Investigation anthropogenic impacts and climate factors on drying up of Urmia Lake using water budget and drought analysis. Water Resources Management, 32(1): 325-337.
doi: 10.1007/s11269-017-1812-5
[28]   Ghale Y A G, Baykara M, Unal A. 2019. Investigating the interaction between agricultural lands and Urmia Lake ecosystem using remote sensing techniques and hydro-climatic data analysis. Agricultural Water Management, 221(20): 566-579.
doi: 10.1016/j.agwat.2019.05.028
[29]   Gholampour A, Nabizadeh R, Hassanvand M S, et al. 2015. Characterization of saline dust emission resulted from Urmia Lake drying. Journal of Environmental Health Science and Engineering, 13: 82, doi: 10.1186/s40201-015-0238-3.
doi: 10.1186/s40201-015-0238-3 pmid: 26617986
[30]   Gohari A, Mirchi A, Madani K. 2017. System dynamics evaluation of climate change adaptation strategies for water resources management in Central Iran. Water Resources Management, 31(5): 1413-1434.
doi: 10.1007/s11269-017-1575-z
[31]   Golian S, Mazdiyasni O, AghaKouchak A. 2014. Trends in meteorological and agricultural droughts in Iran. Theoretical and Applied Climatology, 119(3-4): 679-688.
doi: 10.1007/s00704-014-1139-6
[32]   Gringorten I I. 1963. A plotting rule for extreme probability paper. Journal of Geophysical Research, 68(3): 813-814.
doi: 10.1029/JZ068i003p00813
[33]   Hao Z C, AghaKouchak A, Nakhjiri N, et al. 2014. Global integrated drought monitoring and prediction system. Scientific Data, 1: 140001, doi: 10.1038/sdata.2014.1.
doi: 10.1038/sdata.2014.1
[34]   Hassani A, Azapagic A, D'Odorico P, et al. 2020. Desiccation crisis of saline lakes: A new decision-support framework for building resilience to climate change. Science of The Total Environment, 703: 134718, doi: 10.1016/j.scitotenv.2019.134718.
doi: 10.1016/j.scitotenv.2019.134718
[35]   Hayes M, Svoboda M, Wall N, et al. 2011. The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bulletin of the American Meteorological Society, 92(4): 485-488.
doi: 10.1175/2010BAMS3103.1
[36]   Hemmati M, Ahmadi H, Hamidi S A, et al. 2021. Environmental effects of the causeway on water and salinity balance in Lake Urmia. Regional Studies in Marine Science, 44: 101756, doi: 10.1016/j.rsma.2021.101756.
doi: 10.1016/j.rsma.2021.101756
[37]   Hossein Mardi A, Khaghani A, MacDonald A B, et al. 2018. The Lake Urmia environmental disaster in Iran: A look at aerosol pollution. Science of The Total Environment, 633: 42-49.
doi: 10.1016/j.scitotenv.2018.03.148
[38]   Hosseini-Moghari S M, Araghinejad S, Tourian M J, et al. 2018. Quantifying the impacts of human water use and climate variations on recent drying of Lake Urmia basin: the value of different sets of spaceborne and in-situ data for calibrating a hydrological model. Hydrology and Earth System Sciences, 24(4): 1939-1956.
doi: 10.5194/hess-24-1939-2020
[39]   Jang D. 2018. Assessment of meteorological drought indices in Korea using RCP 8.5 scenario. Water, 10(3): 283, doi: 10.3390/w10030283.
doi: 10.3390/w10030283
[40]   Khazaei B, Khatami S, Alemohammad H, et al. 2018. Climatic or regionally induced by humans?. Tracing hydro-climatic and land-use changes to better understand the Lake Urmia tragedy Journal of Hydrology, 569: 203-217.
doi: 10.1016/j.jhydrol.2018.12.004
[41]   Kinnear P R, Gray C D. 2011. IBM SPSS Statistics 18 Made Simple. Hove, East Sussex: Psychology Press, 120-600.
[42]   Kremelberg D. 2010. Practical statistics:A quick and easy guide to IBM® SPSS® Statistics, STATA, and other statistical software. Sauzendoaks, California: SAGE publications, 1-528.
[43]   Lake Urmia Restoration Program. 2014. Lake Urmia, Causes of Drought and Possible Threats. Official Report. Tehran, Iran: Lake Urmia Revival Headquarters. (in Persian)
[44]   Lévite H, Sally H, Cour J. 2003. Testing water demand management scenarios in a water-stressed basin in South Africa: application of the WEAP model. Physics and Chemistry of the Earth, Parts A/B/C, 28(20-27): 779-786.
doi: 10.1016/j.pce.2003.08.025
[45]   Lyon J G, Yuan D, Lunetta R S, et al. 1998. A change detection experiment using vegetation indices. Photogrammetric Engineering and Remote Sensing, 64(2): 143-150.
[46]   Madani K. 2014. Water management in Iran: What is causing the looming crisis? Journal of Environmental Studies and Sciences, 4: 315-328.
doi: 10.1007/s13412-014-0182-z
[47]   Madani K. 2019. The value of extreme events: What doesn't exterminate your water system makes it more resilient. Journal of Hydrology, 575: 269-272.
doi: 10.1016/j.jhydrol.2019.05.049
[48]   McFeeters S K. 2013. Using the Normalized Difference Water Index (NDWI) within a geographic information system to detect swimming pools for mosquito abatement: A practical approach. Remote Sensing, 5(7): 3544-3561.
doi: 10.3390/rs5073544
[49]   McKee T B, Doesken N J, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In: 8th Conference on Applied Climatology. 17-22 January 1993. Anaheim, California, USA, 179-184.
[50]   McKee T B, Doesken N J, Kleist J. 1995. Drought monitoring with multiple time scales. In: 9th Conference on Applied Climatology. 15-22 January 1995. Dallas: American Meteorological Society, 233-236.
[51]   Mishra A K, Singh V P. 2010. A review of drought concepts. Journal of Hydrology, 391(1-2): 204-216.
[52]   Mohammadi Hamidi S, Nazmfar H, Rezayan A, et al. 2020. Futurology of the economic drivers of Urmia Lake water level fluctuations on the spatial unbalanced. The Journal of Spatial Planning, 24(4): 69-97.
[53]   Mohammadi Hamidi S, Nazmfar H, Fürst C, et al. 2021. Water level decline at Iran's Lake Urmia: changing population dynamics. Environmental Hazards, 21(3): 254-273.
doi: 10.1080/17477891.2021.1949958
[54]   Mondejar J P, Tongco A F. 2019. Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines. Sustainable Environment Research, 29(1): 16, doi: 10.1186/s42834-019-0016-5.
doi: 10.1186/s42834-019-0016-5
[55]   Moradian S, Taleai M, Javadi G H. 2019. A decision support system for water allocation in water scarce basins. Remote Sensing & GIS, 11(1): 19-32.
[56]   Moradian S, Yazdandoost F. 2021. Seasonal meteorological drought projections over Iran using the NMME data. Natural Hazards, 108(1): 1089-1107.
doi: 10.1007/s11069-021-04721-w
[57]   Morid S, Smakhtin V, Moghaddasi M. 2006. Comparison of seven meteorological indices for drought monitoring in Iran. International Journal of Climatology, 26(7): 971-985.
doi: 10.1002/(ISSN)1097-0088
[58]   Naji T A H. 2018. Study of vegetation cover distribution using DVI, PVI, WDVI indices with 2D-space plot. International Journal of Physics: Conference Series, 1003(1): 012083, doi: 10.1088/1742-6596/1003/1/012083.
doi: 10.1088/1742-6596/1003/1/012083
[59]   Nazif S, Karamouz M. 2009. Algorithm for assessment of water distribution system's readiness: planning for disasters. Journal of Water Resources Planning and Management, 135(4): 244-252.
doi: 10.1061/(ASCE)0733-9496(2009)135:4(244)
[60]   Nhu V H, Mohammadi A, Shahabi H, et al. 2020. Monitoring and assessment of water level fluctuations of the Lake Urmia and its environmental consequences using multitemporal Landsat 7 ETM+ images. International Journal of Environmental Research and Public Health, 17(12): 4210, doi: 10.3390/ijerph17124210.
doi: 10.3390/ijerph17124210
[61]   Pettorelli N. 2013. The normalized difference vegetation index. Oxford: Oxford University Press, 24-193.
[62]   Purkey D R, Huber-Lee A, Yates D N, et al. 2007. Integrating a climate change assessment tool into stakeholder-driven water management decision-making processes in California. Water Resources Management, 21(1): 315-329.
doi: 10.1007/s11269-006-9055-x
[63]   Rahimi A, Breuste J. 2021. Why is Lake Urmia drying up? Prognostic modeling with land-use data and artificial neural network. Frontiers in Environmental Science, 9: 603916, doi: 10.3389/fenvs.2021.603916.
doi: 10.3389/fenvs.2021.603916
[64]   Richter B D, Brown J D, DiBenedetto R, et al. 2017. Opportunities for saving and reallocating agricultural water to alleviate water scarcity. Water Policy, 19(5): 886-907.
doi: 10.2166/wp.2017.143
[65]   Sachs J, Kroll C, Lafortune G, et al. 2021. Sustainable Development Report 2021. Cambridge: Cambridge University Press, 2-9.
[66]   Salvador C, Nieto R, Linares C, et al. 2019. Effects on daily mortality of droughts in Galicia (NW Spain) from 1983 to 2013. Science of The Total Environment, 662: 121-133.
doi: 10.1016/j.scitotenv.2019.01.217
[67]   Schmidt M, Gonda R, Transiskus S. 2021. Environmental degradation at Lake Urmia (Iran): exploring the causes and their impacts on rural livelihoods. GeoJournal, 86: 2149-2163.
doi: 10.1007/s10708-020-10180-w
[68]   Schneider U, Becker A, Finger P, et al. 2020. GPCC full data monthly product version 2020 at 1.0°: monthly land-surface precipitation from rain-gauges built on GTS-based and historical data. Deutscher Wetterdienst: Global Precipitation Climatology Centre (GPCC), doi: 10.5676/DWD_GPCC/FD_M_V2020_100.
doi: 10.5676/DWD_GPCC/FD_M_V2020_100
[69]   Schulz S, Darehshouri S, Hassanzadeh E, et al. 2020. Climate change or irrigated agriculture - what drives the water level decline of Lake Urmia. Scientific Reports, 10(1): 236, doi: 10.1038/s41598-019-57150-y.
doi: 10.1038/s41598-019-57150-y pmid: 31937881
[70]   Shukla S, Steinemann A C, Lettenmaier D P. 2011. Drought monitoring for Washington State: indicators and applications. Journal of Hydrometeorology, 12(1): 66-83.
doi: 10.1175/2010JHM1307.1
[71]   Sima S, Rosenberg D E, Wurtsbaugh W A, et al. 2021. Managing Lake Urmia, Iran for diverse restoration objectives: Moving beyond a uniform target lake level. Journal of Hydrology: Regional Studies, 35: 100812, doi: 10.1016/j.ejrh.2021.100812.
doi: 10.1016/j.ejrh.2021.100812
[72]   Sobhani B, Zengir V S, Kianian M K. 2019. Drought monitoring in the Lake Urmia basin in Iran. Arabian Journal of Geosciences, 12(15): 448, doi: 10.1007/s12517-019-4571-1.
doi: 10.1007/s12517-019-4571-1
[73]   Stephens M A. 1974. EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69(347): 730-737.
doi: 10.1080/01621459.1974.10480196
[74]   Tabrizi J S, Farahbakhsh M, Sadeghi-Bazargani H, et al. 2020. Health consequences of Lake Urmia in crisis in the disaster area: a pilot study. Disaster Medicine and Public Health Preparedness, 14(4): 442-448.
doi: 10.1017/dmp.2019.61
[75]   Tahmasebi Birgani Y, Yazdandoost F, Moghadam M. 2013. Role of resilience in sustainable urban stromwater management. Scientific Professional Quarterly, 1(1): 42-50.
[76]   Tahmasebi Birgani Y, Yazdandoost F. 2014. A framework for evaluating the persistence of urban drainage risk management systems. Journal of Hydroenvironment Research, 8(4): 330-342.
[77]   Tayia A, Madani K. 2017. Resilient transboundary water management institutions. World Environmental and Water Resources Congress, doi: 10.1061/9780784480595.055.
doi: 10.1061/9780784480595.055
[78]   Teutschbein C, Seibert J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456: 12-29.
[79]   Thom H C S. 1958. A note on the gamma distribution. Monthly Weather Review, 86(4): 117-122.
doi: 10.1175/1520-0493(1958)086<0117:ANOTGD>2.0.CO;2
[80]   Torabi Haghighi A, Fazel N, Hekmatzadeh A A, et al. 2018. Analysis of effective environmental flow release strategies for Lake Urmia restoration. Water Resources Management, 32(11): 3595-3609.
doi: 10.1007/s11269-018-2008-3
[81]   Tourian M J, Elmi O, Chen Q, et al. 2015. A spaceborne multisensor approach to monitor the desiccation of Lake Urmia in Iran. Remote Sensing of Environment, 156: 349-360.
doi: 10.1016/j.rse.2014.10.006
[82]   Urmia Lake Restoration National Committee. 2015. Necessity of Lake Urmia resuscitation, causes of drought and threats. Report Note ULRP-6-4-3-Rep 1. Tehran, Iran.
[83]   Verma J P. 2012. Data Analysis in Management with SPSS Software. New Delhi: Springer Science & Business Media.
[84]   Vogel R M, Sieber J, Archfield S A, et al. 2007. Relations among storage, yield, and instream flow. Water Resources Research, 43(5): W05403, doi:10.1029/2006wr005226.
doi: 10.1029/2006wr005226
[85]   Wang X B, Xie S P, Zhang X L, et al. 2018. A robust multi-band water index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation, 68: 73-91.
doi: 10.1016/j.jag.2018.01.018
[86]   Xu H Q. 2005. A study on information extraction of water body with the modified normalized difference water index (NDWI). Journal of Remote Sensing, 9(5): 589-595. (in Chinese with English Abstract)
[87]   Xu L Y, Xie X D, Li S. 2013. Correlation analysis of the urban heat island effect and the spatial and temporal distribution of atmospheric particulates using TM images in Beijing. Environmental Pollution, 178: 102-114.
doi: 10.1016/j.envpol.2013.03.006 pmid: 23558302
[88]   Yates D, Sieber J, Purkey D, et al. 2005. WEAP21—A demand-, priority-, and preference-driven water planning model Part 1: Model characteristics. Water International, 30(4): 487-500.
doi: 10.1080/02508060508691893
[89]   Yazdandoost F, Moradian S, Izadi A, Aghakouchak A. 2020a. Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research, 250: 105369, doi: 10.1016/ j.atmosres.2020.105369.
doi: 10.1016/ j.atmosres.2020.105369
[90]   Yazdandoost F, Moradian S, Izadi A. 2020b. Evaluation of water sustainability under a changing climate in Zarrineh River Basin, Iran. Water Resources Management, 34(15): 4831-4846.
doi: 10.1007/s11269-020-02693-3
[91]   Yazdandoost F, Moradian S. 2021. Climate change impacts on the streamflow of Zarrineh River, Iran. Journal of Arid Land, 13(9): 891-904.
doi: 10.1007/s40333-021-0091-4
[92]   Zarghami M, Abdi A, Babaeian I, et al. 2011. Impacts of climate change on runoffs in East Azerbaijan, Iran. Global and Planetary Change, 78(3-4): 137-146.
[93]   Zarrineh N, Abad M A N. 2014. Integrated water resources management in Iran: Environmental, socio-economic and political review of drought in Lake Urmia. International Journal of Water Resources and Environmental Engineering, 6(1): 40-48.
doi: 10.5897/IJWREE
[1] CHEN Zhuo, SHAO Minghao, HU Zihao, GAO Xin, LEI Jiaqiang. Potential distribution of Haloxylon ammodendron in Central Asia under climate change[J]. Journal of Arid Land, 2024, 16(9): 1255-1269.
[2] SUN Chao, BAI Xuelian, WANG Xinping, ZHAO Wenzhi, WEI Lemin. Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022[J]. Journal of Arid Land, 2024, 16(8): 1044-1061.
[3] YAN Yujie, CHENG Yiben, XIN Zhiming, ZHOU Junyu, ZHOU Mengyao, WANG Xiaoyu. Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023[J]. Journal of Arid Land, 2024, 16(8): 1062-1079.
[4] YANG Jianhua, LI Yaqian, ZHOU Lei, ZHANG Zhenqing, ZHOU Hongkui, WU Jianjun. Effects of temperature and precipitation on drought trends in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(8): 1098-1117.
[5] HAN Qifei, XU Wei, LI Chaofan. Effects of nitrogen deposition on the carbon budget and water stress in Central Asia under climate change[J]. Journal of Arid Land, 2024, 16(8): 1118-1129.
[6] WANG Tongxia, CHEN Fulong, LONG Aihua, ZHANG Zhengyong, HE Chaofei, LYU Tingbo, LIU Bo, HUANG Yanhao. Glacier area change and its impact on runoff in the Manas River Basin, Northwest China from 2000 to 2020[J]. Journal of Arid Land, 2024, 16(7): 877-894.
[7] DU Lan, TIAN Shengchuan, ZHAO Nan, ZHANG Bin, MU Xiaohan, TANG Lisong, ZHENG Xinjun, LI Yan. Climate and topography regulate the spatial pattern of soil salinization and its effects on shrub community structure in Northwest China[J]. Journal of Arid Land, 2024, 16(7): 925-942.
[8] Haq S MARIFATUL, Darwish MOHAMMED, Waheed MUHAMMAD, Kumar MANOJ, Siddiqui H MANZER, Bussmann W RAINER. Predicting potential invasion risks of Leucaena leucocephala (Lam.) de Wit in the arid area of Saudi Arabia[J]. Journal of Arid Land, 2024, 16(7): 983-999.
[9] Seyed Morteza MOUSAVI, Hossein BABAZADEH, Mahdi SARAI-TABRIZI, Amir KHOSROJERDI. Assessment of rehabilitation strategies for lakes affected by anthropogenic and climatic changes: A case study of the Urmia Lake, Iran[J]. Journal of Arid Land, 2024, 16(6): 752-767.
[10] LI Chuanhua, ZHANG Liang, WANG Hongjie, PENG Lixiao, YIN Peng, MIAO Peidong. Influence of vapor pressure deficit on vegetation growth in China[J]. Journal of Arid Land, 2024, 16(6): 779-797.
[11] LU Haitian, ZHAO Ruifeng, ZHAO Liu, LIU Jiaxin, LYU Binyang, YANG Xinyue. Impact of climate change and human activities on the spatiotemporal dynamics of surface water area in Gansu Province, China[J]. Journal of Arid Land, 2024, 16(6): 798-815.
[12] YANG Zhiwei, CHEN Rensheng, LIU Zhangwen, ZHAO Yanni, LIU Yiwen, WU Wentong. Spatiotemporal variability of rain-on-snow events in the arid region of Northwest China[J]. Journal of Arid Land, 2024, 16(4): 483-499.
[13] ZHANG Mingyu, CAO Yu, ZHANG Zhengyong, ZHANG Xueying, LIU Lin, CHEN Hongjin, GAO Yu, YU Fengchen, LIU Xinyi. Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(3): 373-395.
[14] WANG Baoliang, WANG Hongxiang, JIAO Xuyang, HUANG Lintong, CHEN Hao, GUO Wenxian. Runoff change in the Yellow River Basin of China from 1960 to 2020 and its driving factors[J]. Journal of Arid Land, 2024, 16(2): 168-194.
[15] LIU Xinyu, LI Xuemei, ZHANG Zhengrong, ZHAO Kaixin, LI Lanhai. A CMIP6-based assessment of regional climate change in the Chinese Tianshan Mountains[J]. Journal of Arid Land, 2024, 16(2): 195-219.