Drought analysis in the Lakes of Iran (Case study: Lake Urmia and Gavkhuni Swamp)

Document Type : Original Research Article

Author

Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran.

Abstract

The lakes are vulnerable to the effects of drought. In this study, Lake Urmia and Gavkhuni Swamp were selected to detect the changes in moisture levels and analysis of drought severity using the SPEI index. This article aims to introduce the appropriate spectral indices for detecting the changes in the humidity of lakes and find the role of teleconnection patterns in the drought of the lakes. Based on the results the MNDWI index has better performance to identify humidity anomalies than the NDWI index in the Gavkhuni Swamp. While in Lake Urmia, the NDWI humidity index revealed the humidity anomalies better than MNDWI. The statistical trend analysis of the meteorological drought indicates a significant increasing trend of drought severity in both lakes at the significant level of 1% during the last decades, and the severity of drought in Lake Urmia was more intense than the Gavkhuni Swamp. In contrast, the water equivalent thickness analysis indicated a more intense negative trend in the underground water level of Gavkhuni compare to the Urmia basin which can be because of the significant anthropogenic effects in the basin. To investigate the cause of the dryness in Iranian lakes, the correlation between the selected teleconnection indices and the meteorological drought index was examined. The results indicated that the drought of Lake Urmia located in the northwest of Iran has a significant correlation with teleconnection indices of the AMO and SOI, and this correlation is more robust in the Lake Urmia basin than the central basin.

Keywords

Main Subjects


Akbari Azirani, T., 2022. Detection and trend analysis of drought in the Jazmurian basin of Iran associated with ocean-atmospheric indices, Climate Change Research, 3(11), 1-16. doi: 10.30488/ccr.2022.359615.1091.
Akbari Azirani, T.A., 2021. Environmental Changes in Lakes of Iran in Anthropocene epoch (Case study: Gavkhuni Swamp). In 2nd International Conference on Quaternary Sciences Iran, Gorgan 2021, 32-35. 
Akbari Azirani, T. & Pazhoh, F., 2022. Teleconection patterns and atmospheric-oceanic feedbacks, Jihad Publication of Shahid Beheshti University, v. 1, Tehran, Iran.
Alinya, Y., 2016. The drying of Lake Urmia and politics hydro impacts on the neighboring area (MSc. thesis), Tehran University.
Ataei, H., Houshmand, S., Oroojian, H. & Mohammad, K., 2017. Study of Climate Change in Gavkhuni Basin Using Kendall Method, In First National and First International Conference on Environmental Sciences, Agriculture and Natural Resources.
Azizadeh, M.R. & Javan, Kh., 2018. Temporal and spatial distribution of extreme precipitation indices over the Lake Urmia Basin, Iran, Environmental Resources Research, 6(1), 25-40.
Enfield, D.B., Mestas-Nuñez, A.M. & Trimble, P.J., 2001. The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S. Geophysical Research Letters, 28, 2077-2080.
Fatehi Maraj, A. & Mahdian, M.H., 2008. Prediction of autumn rainfall using Enso index by neural network method in Lake Urmia basin, Watershed Research Journal, 84, 42-52.
Ghorbani, M., Eskandari, H., Cotton, M., Ghoochani, O.M. & Borji, M., 2021. Harnessing indigenous knowledge for climate change-resilient water management – lessons from an ethnographic case study in Iran, Climate and Development, 10(1), 1080.
Hajian, N., 2013. Estimation of Environmental Needs of Zayandehrud River and Gavkhuni Wetland and Comparison with Inflow Water to the Wetland in Different Years. In National Conference on Water Resources and Agriculture Challenges.
Huang, S., Krysanova, V. & Hattermann, F., 2015. Projections of climate change impacts on floods and droughts in Germany using an ensemble of climate change scenarios, Regional Environmental Change, 15(3), 461-473. https://doi.org/10.1007/s10113-014- 0606-z.
IPCC., 2018. Summary for Policymakers. In Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. & Midgley, P.M., (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 3-29.
Jalili, S., Morid, S., Banakar, A. & Namdar Ghanbari, R., 2011. Assessing the Effect of SOI and NAO Indices on Lake Urmia Water Level Variations, Application of Spectral Analysis, Water and Soil, 25(1), doi: 10.22067/jsw.v0i0.8515.
Jani, R., Khatibi, R., Sadeghfam, S., Hosseini, S.M. & Malekian, A., 2023. Climate zoning under climate change scenarios in the basin of Lake Urmia and in vicinity basins, Theoretical and Applied Climatology, 152(1-2), 181-199. https://doi.org/10.1007/s00704- 023-04380-w.
Javan, Kh., Khaledi, Sh., Yahyavi, A. & Akbari Azirani, T.A., 2021. Climate Change and Extreme Events in Lake Urmia, In 2nd International Conference on Quaternary Sciences Iran, Gorgan 2021, 125-132.
Khorshiddust, A.M., Qavidel Rahimi, Y. & Abbaszadeh, K., 2009. Usage of large-scale atmospheric-oceanic models in the analysis of precipitation fluctuations (case study: Ahar station), Journal of Geographical Space, 29, 128-95. 
Kumar, L. & Mutanga, O., 2018. Google Earth Engine Applications since Inception: Usage, Trends, and Potential, Remote Sensing, 10(10), 1509. https://doi.org/10.3390/rs10101509. Marengo, J.A. et al., 2009. Future Change of Temperature and Precipitation Extremes in South America as Derived from the PRECIS Regional Climate Modeling System, International Journal of Climatology, 29, 2241-2255. https://doi.org/10.1002/joc.1863.
Masoodian, S.A., 2019. Variations of LST frequency distribution as an indicator of environmental changes, case study Zayabderood and Urmia basins, Journal of Natural Environmental Hazards, 8(19), Retrieved from. https://civilica.com/doc/872048.
McCabe, G.J., Palecki, M.A. & Betancourt, J.L., 2004. Pacific and Atlantic Ocean influences on multi- decadal drought frequency in the United States, Proceedings of the National Academy of Sciences of the United States of America, 101, 4136-4141. doi:10.1073/pnas.0306738101.
Mirahsani, M.S., Safianian, A.R., Modares, R., Jafari, R. & Mohammadi, J., 2016. Drought monitoring in Zayandeh Rood catchment area based on time series changes of VCI index of MODIS sensor and SPI index, Geography and dangers of Mahi, 6(4), 1-22. DOI: 10.22067/geo.v6i4.62601. 
Mohammadrezaei, M., Soltani, S. & Modarres, R., 2020. Evaluating the effect of ocean-atmospheric indices on drought in Iran, Theoretical and Applied Climatology, 140, 219-230. https://doi.org/10.1007/s00704-019- 03058-6.
Monteiro, F.F., Gonçalves, W.A., Andrade, L.D.M.B., Villavicencio, L.M.M. & Dos Santos Silva, C.M., 2021. Assessment of Urban Heat Islands in Brazil based on MODIS remote sensing data, Urban Climate, 35, 100726. https://doi.org/10.1016/j.uclim.2020.100726.
Ramesht, H., 1998. Geomorphological Developments and Natural History of Isfahan City in the Fourth Millennium. Scientific-Research Journal of the Faculty of Literature and Humanities, 2. Sadeghfam, S., Mirahmadi, R., Khatibi, R. & et al., 2022. Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts, Scientific Reports, 12, 8285. https://doi.org/10.1038/s41598- 022-11768-7.
Soltanian, M. & Halabian, A., 2018. Remote Sensing Applications in Environmental Sciences: Satellite Image Processing Methods in ENVI. Isfahan University Jihad Publications. 
Tapley, B.D., Bettadpur, S., Ries, J.C., Thompson, P.F. & Watkins, M.M., 2004. GRACE measurements of mass variability in the Earth system, Science, 305(5683), 503-505. https://doi:10.1126/science.1099192.
Vicente-Serrano, S.M., Beguería, S. & López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index, Journal of Climate, 23(7), 1696-1718.