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Future climate-driven drought events across Lake Urmia, Iran

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Abstract

Climate change has increased the vulnerability of arid and semi-arid regions to recurrent and prolonged meteorological droughts. In light of this, our study has sought to assess the nature of future meteorological drought in Lake Urmia basin, Iran, within the context of future climate projections. To achieve this, data from 54 general circulation models (GCMs) was calibrated against both in situ and Global Precipitation Climatology Centre datasets. These GCMs were then employed to project drought conditions expected over 2016–2046 under RCP2.6 and RCP8.5 as the most optimistic and pessimistic scenarios, respectively. To provide a comprehensive analysis, these RCPs were combined with two different time scale Standardized Precipitation Index (SPI), leading to eight different scenarios. The SPI was calculated over two temporal scales for the past (1985–2015) and future (2016–2046), including the medium-term (SPI-6) and long-term (SPI-18) index. Results showed that while precipitation is expected to increase by up to 34%, parts of the basin are projected to face severe and prolonged droughts under both RCPs. The most severe drought event is expected to occur around 2045–2046 under the most pessimistic RCP8.5 scenario. Severe droughts with low frequency are also anticipated to increase under other scenarios. By characterizing meteorological drought conditions for Lake Urmia basin under future climate conditions, our findings call for urgent action for adaptation strategies to mitigate the future adverse effects of drought in this region and other regions facing similar challenges. Overall, this study provides valuable insight into the impacts of climate change on future droughts that can adversely influence water resources in arid and semi-arid regions.

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References

  • 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.

    Article  Google Scholar 

  • Adnan Ikram, R. M., Khan, I., Moayedi, H., Ahmadi Dehrashid, A., Elkhrachy, I., & Nguyen Le, B. (2023). Novel evolutionary-optimized neural network for predicting landslide susceptibility. Environment, Development and Sustainability, 1–33. https://doi.org/10.1007/s10668-023-03356-0

  • Agathokleous, E., Kitao, M., Komatsu, M., Tamai, Y., Harayama, H., & Koike, T. (2023). Single and combined effects of fertilization, ectomycorrhizal inoculation, and drought on container-grown Japanese larch seedlings. Journal of Forestry Research, 34(4), 1077–1094.

    Article  CAS  Google Scholar 

  • AghaKouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., ... & Hasanzadeh, E. (2015). Aral Sea syndrome desiccates Lake Urmia: call for action. Journal of Great Lakes Research, 41(1), 307–311.

    Article  Google Scholar 

  • Ahmadebrahimpour, E., Aminnejad, B., & Khalili, K. (2019). Assessing future drought conditions under a changing climate: a case study of the Lake Urmia basin in Iran. Water Supply, 19(6), 1851–1861.

    Article  Google Scholar 

  • Alborzi, A., Mirchi, A., Moftakhari, H., Mallakpour, I., Alian, S., Nazemi, A., ... & AghaKouchak, A. (2018). Climate-informed environmental inflows to revive a drying lake facing meteorological and anthropogenic droughts. Environmental Research Letters, 13(8), 084010.

  • Amnuaylojaroen, T., & Chanvichit, P. (2019). Projection of near-future climate change and agricultural drought in Mainland Southeast Asia under RCP8. 5. Climatic Change, 155(2), 175–193.

    Article  Google Scholar 

  • Chen, S. H., Zhang, H., Zykova, K. I., Gholizadeh Touchaei, H., Yuan, C., Moayedi, H., Le, Binh Nguyen, & B. (2023). Computational intelligence models for predicting the frictional resistance of driven pile foundations in cold regions. Computers and Concrete, 32(2), 217–232.

    CAS  Google Scholar 

  • Fang, Y. K., Wang, H. C., Fang, P. H., Liang, B., Zheng, K., Sun, Q., & Wang, A. J. (2023). Life cycle assessment of integrated bioelectrochemical-constructed wetland system: Environmental sustainability and economic feasibility evaluation. Resources, Conservation and Recycling, 189, 106740.

    Article  CAS  Google Scholar 

  • Fathian, F., Morid, S., & Kahya, E. (2015). Identification of trends in hydrological and climatic variables in Urmia Lake basin. Iran. Theoretical and Applied Climatology, 119, 443–464.

    Article  Google Scholar 

  • Fuhrer, O., Osuna, C., Lapillonne, X., Gysi, T., Cumming, B., Bianco, M., ... & Schulthess, T. C. (2014). Towards a performance portable, architecture agnostic implementation strategy for weather and climate models. Supercomputing Frontiers and Innovations, 1(1), 45–62.

  • Giorgi, F., & Gutowski, W. J., Jr. (2015). Regional dynamical downscaling and the CORDEX initiative. Annual Review of Environment and Resources, 40, 467–490.

    Article  Google Scholar 

  • Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M. B., Bi, X., ... & Brankovic, C. (2012). RegCM4: model description and preliminary tests over multiple CORDEX domains. Climate Research, 52, 7–29.

  • Giot, O., Termonia, P., Degrauwe, D., De Troch, R., Caluwaerts, S., Smet, G., ... & Van Schaeybroeck, B. (2016). Validation of the ALARO-0 model within the EURO-CORDEX framework. Geoscientific Model Development, 9(3), 1143–1152.

  • Ha, T. V., Huth, J., Bachofer, F., & Kuenzer, C. (2022). A review of Earth observation-based drought studies in Southeast Asia. Remote Sensing, 14(15), 3763.

    Article  Google Scholar 

  • Han, X., Hua, E., Engel, B. A., Guan, J., Yin, J., Wu, N., ... & Wang, Y. (2022). Understanding implications of climate change and socio-economic development for the water-energy-food nexus: A meta-regression analysis. Agricultural Water Management, 269, 107693.

  • Hassanzadeh, E., Zarghami, M., & Hassanzadeh, Y. (2012). Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resources Management, 26, 129–145.

    Article  Google Scholar 

  • Hewitson, B. C., & Crane, R. G. (1996). Climate downscaling: Techniques and application. Climate Research, 7(2), 85–95.

    Article  Google Scholar 

  • IPCC Intergovernmental Panel on Climate Change. (2014). Climate change: Impacts, adaptation, and vulnerability. Part B: Regional aspects. contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1017/CBO9781107415324.004

  • IPCC. (2013). IPCC fifth assessment report. Weather, 68(12), 310–310.

    Google Scholar 

  • Jacob, D., Elizalde, A., Haensler, A., Hagemann, S., Kumar, P., Podzun, R., ... & Wilhelm, C. (2012). Assessing the transferability of the regional climate model REMO to different coordinated regional climate downscaling experiment (CORDEX) regions. Atmosphere, 3(1), 181–199.

  • Khazaei, B., Khatami, S., Alemohammad, S. H., Rashidi, L., Wu, C., Madani, K., ... & Aghakouchak, A. (2019). 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.

  • Liu, Z., Xu, J., Liu, M., Yin, Z., Liu, X., Yin, L., & Zheng, W. (2023). Remote sensing and geostatistics in urban water-resource monitoring: A review. Marine and Freshwater Research.

  • Lu, J., Carbone, G. J., & Grego, J. M. (2019). Uncertainty and hotspots in 21st century projections of agricultural drought from CMIP5 models. Scientific Reports, 9(1), 1–12.

    Google Scholar 

  • Luo, J., Niu, F., Lin, Z., Liu, M., Yin, G., & Gao, Z. (2022). Abrupt increase in thermokarst lakes on the central Tibetan Plateau over the last 50 years. CATENA, 217, 106497.

    Article  Google Scholar 

  • Madani, K. (2014). Water management in Iran: What is causing the looming crisis? Journal of Environmental Studies and Sciences, 4, 315–328.

    Article  Google Scholar 

  • McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179–183).

  • Mehrian, M. R., Hernandez, R. P., Yavari, A. R., Faryadi, S., & Salehi, E. (2016). Investigating the causality of changes in the landscape pattern of Lake Urmia basin, Iran using remote sensing and time series analysis. Environmental Monitoring and Assessment, 188, 1–13.

    Article  CAS  Google Scholar 

  • Miyan, M. A. (2015). Droughts in Asian least developed countries: Vulnerability and sustainability. Weather and Climate Extremes, 7, 8–23.

    Article  Google Scholar 

  • Moayedi, H., & Dehrashid, A. A. (2023). A new combined approach of neural-metaheuristic algorithms for predicting and appraisal of landslide susceptibility map**. Environmental Science and Pollution Research, 30, 82964–82989.

    Article  Google Scholar 

  • Moayedi, H., Salari, M., Dehrashid, A. A., & Le, B. N. (2023). Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: Case study of Shiraz plain. Stochastic Environmental Research and Risk Assessment, 37, 2961–2976.

    Article  Google Scholar 

  • Moayedi, H., Varamini, N., Mosallanezhad, M., Foong, L. K., & Le, B. N. (2022). Applicability and comparison of four nature-inspired hybrid techniques in predicting driven piles’ friction capacity. Transportation Geotechnics, 37, 100875.

    Article  Google Scholar 

  • Nie, S., Mo, S., Gao, T., Yan, B., Shen, P., Kashif, M., ... & Jiang, C. (2023). Coupling effects of nitrate reduction and sulfur oxidation in a subtropical marine mangrove ecosystem with Spartina alterniflora invasion. Science of The Total Environment, 862, 160930.

  • Prodhan, F. A., Zhang, J., Sharma, T. P. P., Nanzad, L., Zhang, D., Seka, A. M., ... & Mohana, H. P. (2022). Projection of future drought and its impact on simulated crop yield over South Asia using ensemble machine learning approach. Science of The Total Environment, 807, 151029.

  • Raziei, T., Bordi, I., & Pereira, L. S. (2011). An application of GPCC and NCEP/NCAR datasets for drought variability analysis in Iran. Water Resources Management, 25, 1075–1086.

    Article  Google Scholar 

  • Rockel, B., Will, A., & Hense, A. (2008). the regional climate model COSMO-CLM (CCLM). Meteorologische Zeitschrift, 17, 347–348.

    Article  Google Scholar 

  • Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H. K., Landerer, F. W., & Lo, M. H. (2018). Emerging trends in global freshwater availability. Nature, 557(7707), 651–659.

    Article  CAS  Google Scholar 

  • Samuelsson, P., Gollvik, S., Kupiainen, M., Kourzeneva, E., & van de Berg, W. J. (2015). The Surface Processes of the Rossby Centre Regional Atmospheric Climate Model (RCA4). SMHI. 157: 58 pp.

  • Shadkam, S., Ludwig, F., van Vliet, M. T., Pastor, A., & Kabat, P. (2016). Preserving the world second largest hypersaline lake under future irrigation and climate change. Science of the Total Environment, 559, 317–325.

    Article  CAS  Google Scholar 

  • Sharma, S., & Mujumdar, P. (2017). Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Scientific Reports, 7(1), 1–9.

    Article  Google Scholar 

  • Shirmohammadi, B., Malekian, A., Salajegheh, A., Taheri, B., Azarnivand, H., Malek, Z., & Verburg, P. H. (2020a). Scenario analysis for integrated water resources management under future land use change in the Urmia Lake region. Iran. Land Use Policy, 90, 104299.

    Article  Google Scholar 

  • Shirmohammadi, B., Malekian, A., Salajegheh, A., Taheri, B., Azarnivand, A., Malek, Z., & Verburg, P. (2020b). Impacts of future climate and land use change on water yield in a semi-arid basin in Iran. Land Degradation and Development, 31(10), 1252–1264.

    Article  Google Scholar 

  • Shirmohammadi, B., Moradi, H., Moosavi, V., Semiromi, M. T., & Zeinali, A. (2013). Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: Southeastern part of east Azerbaijan province, Iran). Natural Hazards, 69, 389–402.

    Article  Google Scholar 

  • Spinoni, J., Barbosa, P., Bucchignani, E., Cassano, J., Cavazos, T., Christensen, J. H., ... & Dosio, A. (2020). Future global meteorological drought hot spots: A study based on CORDEX data. Journal of Climate, 33(9), 3635–3661.

  • Sternberg, T. (2018). Moderating climate hazard risk through cooperation in Asian drylands. Land, 7(1), 22.

    Article  Google Scholar 

  • Tabari, H., & Willems, P. (2018). More prolonged droughts by the end of the century in the Middle East. Environmental Research Letters, 13(10), 104005.

    Article  Google Scholar 

  • Teichmann, C., Eggert, B., Elizalde, A., Haensler, A., Jacob, D., Kumar, P., ... & Weber, T. (2013). How does a regional climate model modify the projected climate change signal of the driving GCM: a study over different CORDEX regions using REMO. Atmosphere, 4(2), 214–236.

    Article  CAS  Google Scholar 

  • Top, S., Kotova, L., De Cruz, L., Aniskevich, S., Bobylev, L., De Troch, R., ... & Caluwaerts, S. (2021). Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22 resolution over the CORDEX Central Asia domain. Geoscientific Model Development, 14(3), 1267–1293.

  • Vaghefi, S. A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, H., & Abbaspour, K. C. (2019). The future of extreme climate in Iran. Scientific Reports, 9(1), 1464.

    Article  Google Scholar 

  • Wilhite, D. A., Svoboda, M. D., & Hayes, M. J. (2007). Understanding the complex impacts of drought: A key to enhancing drought mitigation and preparedness. Water Resources Management, 21(5), 763–774.

    Article  Google Scholar 

  • Wu, H., Hayes, M. J., Wilhite, D. A., & Svoboda, M. D. (2005). The effect of the length of record on the standardized precipitation index calculation. International Journal of Climatology: A Journal of the Royal Meteorological Society, 25(4), 505–520.

    Article  Google Scholar 

  • Yang, Y., Liu, L., Zhang, P., Wu, F., Wang, Y., Xu, C., ... & Kuzyakov, Y. (2023). Large-scale ecosystem carbon stocks and their driving factors across Loess Plateau. Carbon Neutrality, 2(1), 5.

  • Yin, Z., Liu, Z., Liu, X., Zheng, W., & Yin, L. (2023). Urban heat islands and their effects on thermal comfort in the US: New York and New Jersey. Ecological Indicators, 154, 110765.

    Article  Google Scholar 

  • Zhao, M., Zhou, Y., Li, X., Cheng, W., Zhou, C., Ma, T., ... & Huang, K. (2020). Map** urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sensing of Environment, 248, 111980.

  • Zhao, Y., Gor, M., Voronkova, D. K., Gholizadeh Touchaei, H., Moayedi, H., & Le Nguyen, B. (2023). An optimized ANFIS model for predicting pile pullout resistance. Computers and Concrete, 48(2), 179–190.

    Google Scholar 

  • Zhao, Z., Xu, G., Zhang, N., & Zhang, Q. (2022). Performance analysis of the hybrid satellite-terrestrial relay network with opportunistic scheduling over generalized fading channels. IEEE Transactions on Vehicular Technology, 71(3), 2914–2924.

    Article  Google Scholar 

  • Zhou, J., Wang, L., Zhong, X., Yao, T., Qi, J., Wang, Y., & Xue, Y. (2022). Quantifying the major drivers for the expanding lakes in the interior Tibetan Plateau. Science Bulletin, 67(5), 474–478.

    Article  Google Scholar 

  • Zhu, X., Xu, Z., Liu, Z., Liu, M., Yin, Z., Yin, L., & Zheng, W. (2022). Impact of dam construction on precipitation: A regional perspective. Marine and Freshwater Research, 74(10), 877–890.

    Article  Google Scholar 

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Acknowledgements

We would like to express our gratitude to the Iran Meteorological Organization (IRIMO) and the Ministry of Energy for providing the historical record of the precipitation data used in this study. We are also grateful to the anonymous reviewer whose constructive comments significantly improved our manuscript.

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Conceptualization: Bagher Shirmohammadi and Maryam Rostami; data acquisition: Bagher Shirmohammadi and Maryam Rostami; methodology: Bagher Shirmohammadi, Maryam Rostami, Saeid Varameshb, Majid Taie Semiromi, and Abolfazl Jaafari; supervision: Bagher Shirmohammadi and Abolfazl Jaafari; Writing—original draft preparation: Bagher Shirmohammadi, Maryam Rostami, Saeid Varamesh, and Majid Taie Semiromi; Writing—review and editing: Abolfazl Jaafari.

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Correspondence to Bagher Shirmohammadi or Abolfazl Jaafari.

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Shirmohammadi, B., Rostami, M., Varamesh, S. et al. Future climate-driven drought events across Lake Urmia, Iran. Environ Monit Assess 196, 24 (2024). https://doi.org/10.1007/s10661-023-12181-x

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