Development of the Simplified Meta-Statistical Extreme Value (SMEV) Model for Analyzing Extreme Wind Events and Particle Transport Hazards in Eastern Lake Urmia

Document Type : Research/Original/Regular Article

Authors

1 Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran

2 Associate Professor, Urmia Lake Research Institute, Urmia University, Urmia, Iran

Abstract

Introduction

The ongoing desiccation of Lake Urmia in northwestern Iran has transformed its former lakebed into a significant source of airborne dust and salt particles, posing escalating environmental and public health risks. These storms pose serious environmental and health risks by elevating particulate matter concentrations (PM₁₀ and PM₂.₅), degrading air quality, and impairing agricultural productivity. Wind events exceeding 5 m/s can start wind storm mobilization and atmospheric dust generation in arid and semi-arid environments. Wind direction is important for the transport of dust to cities. The east of the Urmia Lake is more affected by wind because of the dominant wind direction in the Urmia Lake basin. This part is more important for risk assessment studies. Classical models, such as the Generalized Extreme Value (GEV) distribution, often fall short of capturing the full complexity of wind extremes under nonstationary conditions. To overcome these limitations, the Simplified Meta-Statistical Extreme Value (SMEV) model is developed and used for the first time, in this study, as a method that integrates both ordinary and extreme wind data into a unified distribution framework. This study aims to estimate return period wind speeds with SMEV and benchmarked against GEV, and evaluate wind direction probabilities for storm prediction. Results will inform regional dust storm risk management and advance extreme value modeling in the Lake Urmia basin.

Materials and Methods

Using three-hourly wind speed and direction data from 2005 to 2024 across four synoptic stations (Tabriz, Maragheh, Bonab, and Shabestar) in the eastern Lake Urmia Basin, SMEV was employed to estimate return period wind speeds and assess directional probabilities. In this research, the CEEMDAN method has been used as a method to remove noise and trends from wind speed data. At the stations, wind events were divided into extreme and ordinary events, based on the wind speed threshold, using the peak-over-threshold (POT) approach by applying the 90th percentile. 5, 10, 20, 50, 100, and 200 periods were chosen for the return period. The model combines a two-parameter Weibull distribution for ordinary winds with annual extreme wind counts to generate composite cumulative distribution functions (CDFs) per dominant direction sector. The bootstrap method was used for SMEV model performance evaluation. The GEV model was used as a benchmark and employed to estimate return period wind speeds, and both models were evaluated using AIC, BIC, FSE, WFSE, and leave-one-out cross-validation (LOO). Additionally, a random forest algorithm was trained to predict the likelihood of wind directions associated with dust transport.

Results and Discussion

SMEV predicted critical wind speeds exceeding 7 m/s with high confidence. In all 4 stations, wind speeds predicted more than 7 m/s, and wind direction analysis revealed over 70% probability of wind-driven dust transport from the southwest and south to the east, toward residential areas. The random forest method has predicted the corresponding wind directions for selected stations east of Lake Urmia. The dominant directions for extreme storm events are southwest and south for short to medium return periods. In longer time periods, the dominance of south and west continues at Shabestar and Maragheh stations, and for Tabriz and Maragheh stations, the dominant direction changes to east. GEV extreme values predicted more than 12 m/s for wind speeds. It shows the GEV overestimated. For Urmia Lake Basin, wind speeds of more than 12 happen rarely and are not common. The SMEV model outperformed the GEV model, providing more stable and realistic estimates of return-level wind speeds, particularly for long recurrence intervals. Error metrics confirmed the superiority of SMEV (FSE = 0.014; WFSE = 20.7) compared to GEV (FSE = 0.081; WFSE = 196), highlighting its improved performance in estimating environmental hazards. The advantage of this method over other classical methods is in distinguishing between extreme and normal events, as well as distinguishing extreme events with the corresponding dominant directions of extreme wind speeds. In addition, the use of a wind speed threshold limit, unlike other statistical methods such as GEV, which only focus on maximum wind speeds in the analysis of extreme events, can provide reliable accuracy for this method in estimating extreme events.

Conclusion

This study focused on the analysis of extreme wind speeds in the eastern part of the Urmia Lake watershed, and using a simplified metastatistical limit value model, was able to provide reliable estimates of strong winds in different return periods. The results showed that speeds exceeding 7 m/s occur with high probability in this area, and this amount is sufficient to initiate the transport of suspended particles and the formation of dust storms in the study area. In conclusion, SMEV demonstrates significant potential for use in regional wind hazard assessments, early warning systems, and dust storm risk mitigation in the Urmia Lake Basin. This model relies solely on wind speed and direction and does not consider other environmental drivers such as soil moisture, land cover, vegetation, or surface roughness that can significantly affect the potential for dust emission. This approach can also help universities, along with other tools, to identify high-risk areas susceptible to dust transport from the dry bed of Lake Urmia. Overall, this model can be used as an effective tool in analyzing climate risks associated with wind and dust storms in the region. In addition, the use of the 90th percentile threshold and 24-hour separation criteria raises statistical assumptions that more extreme events may have been identified, which has increased the accuracy of the model and, on the other hand, has made the model more sensitive to extreme phenomena. However, it is suggested that in future studies, the integration of environmental variables such as relative humidity and precipitation should be considered to improve the SMEV model. Also, combining this model with wind datasets based on satellite images can also improve the spatial representation of wind patterns.

Keywords

Main Subjects

References

منابع:
اللهویردی پور، پویا و ستاری، محمدتقی (1403). بررسی سرعت و جهت باد بیشینه در ایستگاه‌های همدیدی شرق دریاچه ارومیه. جغرافیا و مخاطرات طبیعی، 13(4)، 197-221. doi: 10.22067/geoeh.2024.86654.1464
بیابانی، لیلا، نظری سامانی، علی‌اکبر، خسروی، حسن و کاظم‌زاده، مجید (1398). بررسی روند تغییرات سرعت ماهانه باد در حاشیه دریاچه ارومیه طی 30 سال گذشته. خشک بوم، 9(1)، 139-151. dor: 20.1001.1.2008790.1398.9.1.11.5
بیاتی خطیبی، مریم و ساری صراف، بهروز (1403). شناسایی کانون‌های در معرض خطر فرسایش بادی در جنوب شرق دریاچه ارومیه (مطالعه موردی :شهرستان‌های بناب و ملکان). هیدروژئومورفولوژی، 11(39)، 122-143. doi: 10.22034/hyd.2024.60434.1728
راعی، بیژن، احمدی، عباس، نیشابوری، محمدرضا، قربانی، محمدعلی و اسدزاده، فرخ (2020). تعیین فرسایش‌پذیری بادی در بخشی از اراضی شرق دریاچه ارومیه و بررسی ارتباط آن با ویژگی‌های فیزیکی و شیمیایی خاک. تحقیقات کاربردی خاک، 8(2)، 82–92.
مرادی، محمد و رضازاده، پرویز (1399). بررسی توان حمل ماسه ونمک در اطراف دریاچه ارومیه. پژوهش‌های اقلیم‌شناسی، 11(41)، 71-89.ُ
نظری، محمدرضا و عباسی، مهدی (1400). بررسی پتانسیل انرژی باد در استان یزد با استفاده از توزیع ویبول. نشریه انرژی ایران، 24(3)، 17–31.
 
References
Abadi, A. R. S., Hamzeh, N. H., Shukurov, K., Opp, C., & Dumka, U. C. (2022). Long-term investigation of aerosols in the Urmia Lake region in the Middle East by ground-based and satellite data in 2000–2021. Remote Sensing, 14(15), 3827. doi: 10.3390/rs14153827
AghaKouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., Nasrollahi, N., Farahmand, A., Mehran, A., & Hasanzadeh, E. (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
Ahrari, A., Panchanathan, A., & Haghighi, A. T. (2024). Dust over water: Analyzing the impact of lake desiccation on dust storms on the Iranian Plateau. Journal of Hazardous Materials, 480, 136377. doi: 10.1016/j.jhazmat.2024.13637
Allahverdipour, P., & Sattari, M. T. (2024). Investigating the Maximum Wind Speed and Wind Direction of Synoptic Stations in the East of Lake Urmia. Journal of Geography and Environmental Hazards, 13(4), 197–221. doi: 10.22067/geoeh.2024.86654.1464 [In Persian]
Bayati Khatibi, M., & Sari Sarraf, B. (2024). Identifying the centers at risk of wind erosion around Lake Urmia (Case study: Bonab and Malekan cities). Hydrogeomorphology, 11(39), 122-43. doi.org/10.22034/hyd.2024.60434.1728 [In Persian]
Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A. T., Frei, C., Goyette, S., Halsnaes, K., Holt, T., Jylhä, K., & Koffi, B. (2007). Future extreme events in European climate: an exploration of regional climate model projections. Climatic Change, 81, 71–95. doi.org/10.1007/s10584-006-9226-z
Biabani, L., Nazari Samani, A. A., Khosravi, H., & Kazemzadeh, M. (2019). An investigation of the trends of monthly wind speed fluctuation on the edge of Lake Urmia over the last 30 years. Journal of Arid Biome, 9(1), 139–151. dor: 20.1001.1.2008790.1398.9.1.11.5 [In Persian]
Boroughani, M., Hashemi, H., Hosseini, S. H., Pourhashemi, S., & Berndtsson, R. (2019). Desiccating Lake Urmia: a new dust source of regional importance. IEEE Geoscience and Remote Sensing Letters, 17(9), 1483–1487. doi: 10.1109/LGRS.2019.2949132
 
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. doi: 10.1023/A:1010933404324
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304.
 doi.org/10.1177/0049124104268644
Chen, A., Huang, H., Wang, J., Li, Y., Chen, D., & Liu, J. (2023). An analysis of the spatial variation of tropical cyclone rainfall trends in Mainland Southeast Asia. International Journal of Climatology, 43(13), 5912–5926. doi: 10.1002/joc.8180
Chen, Y., Zhao, M., Liu, Z., Ma, J., & Yang, L. (2025). Comparative analysis of offshore wind resources and optimal wind speed distribution models in China and Europe. Energies, 18(5), 1108. doi: 10.3390/en18051108
Chen, Z., Gao, X., & Lei, J. (2022). Dust emission and transport in the Aral Sea region. Geoderma, 428, 116177. doi: 10.1016/j.geoderma.2022.116177
Coles, S., Bawa, J., Trenner, L., & Dorazio, P. (2001). An introduction to statistical modeling of extreme values (Vol. 208). Springer. doi: 10.1007/978-1-4471-3675-0
Delfi, S., Mosaferi, M., Hassanvand, M. S., & Maleki, S. (2019). Investigation of aerosols pollution across the eastern basin of Urmia lake using satellite remote sensing data and HYSPLIT model. Journal of Environmental Health Science and Engineering, 17(2), 1107-1120. doi: 10.1007/s40201-019-00425-3
Derome, D., Razali, H., Fazlizan, A., & Jedi, A. (2023). Distribution cycle of wind speed: A case study in the Southern Part of Malaysia. IOP Conference Series: Materials Science and Engineering, 1278(1), 12010. doi: 10.1088/1757-899X/1278/1/012010
Effati, M., Bahrami, H., Gohardoust, M., Babaeian, E., & Tuller, M. (2019). Application of satellite remote sensing for estimation of dust emission probability in the Urmia Lake Basin in Iran. Soil Science Society of America Journal, 83(4), 993–1002. doi: 10.2136/sssaj2019.01.0018
Esfeh, M. A., Kattan, L., Lam, W. H. K., Esfe, R. A., & Salari, M. (2020). Compound generalized extreme value distribution for modeling the effects of monthly and seasonal variation on the extreme travel delays for vulnerability analysis of road network. Transportation Research Part C: Emerging Technologies, 120, 102808. doi: 10.1016/j.trc.2020.102808
Esmaeili, L., Naserpour, S., & Nadarajah, S. (2023). Wind energy potential modeling in northern Iran. Stochastic Environmental Research and Risk Assessment, 37(8), 3205–3219. doi: 10.1007/s00477-023-02445-w
Firouzeh, M., & Danesh-Yazdi, M. (2024). The environmental and health impact of salt dust aerosols from the dried Lake Urmia. EGU General Assembly Conference Abstracts, 15430. doi: 10.5194/egusphere-egu24-15430
Gholampour, A., Nabizadeh, R., Hassanvand, M. S., Taghipour, H., Nazmara, S., & Mahvi, A. H. (2015). Characterization of saline dust emission resulted from Urmia Lake drying. Journal of Environmental Health Science and Engineering, 13, 1–11. doi: 10.1186/s40201-015-0238-3
Ghomashi, F., & Khalesifard, H. R. (2020). Investigation and characterization of atmospheric aerosols over the Urmia Lake using the satellite data and synoptic recordings. Atmospheric Pollution Research, 11(11), 2076–2086. doi: 10.1016/j.apr.2020.08.020
Habibi, S. (2025). An Explainable Machine Learning Framework for Forecasting Lake Water Equivalent Using Satellite Data : A 20-Year Analysis of the Urmia Lake Basin. 1–27. doi: 10.3390/w17101431
Hadipour, M., Pourebrahim, S., Heidari, H., Nikooy, F., Najah Ahmed, A., & Jit Ern, C. (2024). Evaluation of water resource balance in the Urmia Lake Basin: Integrating carrying capacity and water footprint model for sustainable management. Ecological Indicators, 166(March), 112464. doi: 10.1016/j.ecolind.2024.112464
Hamzeh, N. H., Abadi, A. R. S., Kaskaoutis, D. G., Mirzaei, E., Shukurov, K. A., Sotiropoulou, R.-E. P., & Tagaris, E. (2023). The importance of wind simulations over dried lake beds for dust emissions in the Middle East. Atmosphere, 15(1), 24. doi: 10.3390/atmos15010024
Hamzeh, N. H., Ranjbar Saadat Abadi, A., Ooi, M. C. G., Habibi, M., & Schöner, W. (2022). Analyses of a lake dust source in the Middle East through models performance. Remote Sensing, 14(9), 2145. doi: 10.3390/rs14092145
Hamzehpour, N., Marcolli, C., Klumpp, K., Thöny, D., & Peter, T. (2022). The Urmia playa as a source of airborne dust and ice-nucleating particles–Part 2: Unraveling the relationship between soil dust composition and ice nucleation activity. Atmospheric Chemistry and Physics, 22(22), 14931–14956. doi: 10.5194/acp-22-14931-2022, 2022.
Harati, H., Kiadaliri, M., Tavana, A., Rahnavard, A., & Amirnezhad, R. (2021). Urmia Lake dust storms occurrences: investigating the relationships with changes in water zone and land cover in the eastern part using remote sensing and GIS. Environmental Monitoring and Assessment, 193, 1–16. doi: 10.1007/s10661-021-08851-3
Hossein Hamzeh, N., Ranjbar Saadat Abadi, A., Abdukhakimovich Shukurov, K., Mhawish, A., Alam, K., & Opp, C. (2024). Simulation and synoptic investigation of a severe dust storm originated from the Urmia Lake in the Middle East. Atmósfera, 38. doi: 10.20937/atm.53290 
Hu, L., Nikolopoulos, E. I., Marra, F., & Anagnostou, E. N. (2023). Toward an improved estimation of flood frequency statistics from simulated flows. Journal of Flood Risk Management, 16(2), e12891. doi: 10.1111/jfr3.12891
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903–995. doi: 10.1098/rspa.1998.0193
Jung, C., & Schindler, D. (2019). Wind speed distribution selection–A review of recent development and progress. Renewable and Sustainable Energy Reviews, 114, 109290. doi: 10.1016/j.rser.2019.109290
Kaplan, Y. A. (2022). Calculation of Weibull distribution parameters at low wind speed and performance analysis. Proceedings of the Institution of Civil Engineers-Energy, 175(4), 195–204. doi: 10.1680/jener.21.00010
Marani, M., & Ignaccolo, M. (2015). A metastatistical approach to rainfall extremes. Advances in Water Resources, 79, 121–126. doi: 10.1016/j.advwatres.2015.03.001
Marra, F., & Morin, E. (2015). Use of radar QPE for the derivation of Intensity–Duration–Frequency curves in a range of climatic regimes. Journal of Hydrology, 531, 427–440. doi: 10.1016/j.jhydrol.2015.08.064
Marra, F., Nikolopoulos, E. I., Anagnostou, E. N., & Morin, E. (2018). Metastatistical extreme value analysis of hourly rainfall from short records: Estimation of high quantiles and impact of measurement errors. Advances in Water Resources, 117, 27–39. doi: 10.1016/j.advwatres.2018.05.001
Marra, F., Zoccatelli, D., Armon, M., & Morin, E. (2019). A simplified MEV formulation to model extremes emerging from multiple nonstationary underlying processes. Advances in Water Resources, 127, 280–290. doi: 10.1016/j.advwatres.2019.04.002
Middleton, N. J. (2017). Desert dust hazards: A global review. Aeolian Research, 24, 53–63. doi: 10.1016/j.aeolia.2016.12.001
Miller-Schulze, J. P., Shafer, M., Schauer, J. J., Heo, J., Solomon, P. A., Lantz, J., Artamonova, M., Chen, B., Imashev, S., & Sverdlik, L. (2015). Seasonal contribution of mineral dust and other major components to particulate matter at two remote sites in Central Asia. Atmospheric Environment, 119, 11–20. doi: 10.1016/j.atmosenv.2015.07.011
Miniussi, A., & Marra, F. (2021). Estimation of extreme daily precipitation return levels at-site and in ungauged locations using the simplified MEV approach. Journal of Hydrology, 603, 126946. doi: 10.1016/j.jhydrol.2021.126946
Miniussi, A., Villarini, G., & Marani, M. (2020). Analyses through the metastatistical extreme value distribution identify contributions of tropical cyclones to rainfall extremes in the eastern United States. Geophysical Research Letters, 47(7), e2020GL087238. doi: 10.1029/2020GL087238
Mobarak Hassan, E., Fattahi, E., & Habibi, M. (2023). Application of a regional climate model on autumn dust events over the Urmia Basin. Atmospheric Pollution Research, 14(11), 101904. doi: 10.1016/j.apr.2023.101904
Mobarak Hassan, E., Fattahi, E., & Habibi, M. (2023). Temporal and Spatial Variability of Dust in the Urmia Basin, 1990–2019. Atmosphere, 14(12), 1761. doi: 10.3390/atmos14121761
Mohammadpour, M., & Bevrani, H. (2024). Comparative analysis of two new wind speed TX models using Weibull and log-logistic distributions for wind energy potential estimation in Tabriz, Iran. ArXiv Preprint ArXiv:2402.01897. doi: 10.48550/arXiv.2402.01897
Moradi, M., & Rezazadeh, P. (2020). Investigation the Sand and Salt Drift Potential in Orumia Lake. Journal of Climate Research, 1399(41), 71–89. [In Persian]
Nazari, M. R., & Abbasi, M. (2021). investigation of wind energy potential in Yazd province Using Weibull distribution. Iranian Journal of Energy, 24(3), 17-31. [In Persian]
Nikulin, G., Kjellstro, M, E., Hansson, U. L. F., Strandberg, G., & Ullerstig, A. (2011). Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Tellus A: Dynamic Meteorology and Oceanography, 63(1), 41–55. doi: 10.1111/j.1600-0870.2010.00466.x
Raei, B., Ahmadi, A., Neyshabouri, M. R., Ghorbani, M. A., & Asadzadeh, F. (2020). Determination of Soil Wind Erodibility in Eastern Urmia Lake and its Relationship with Soil Physicochemical Properties. Applied Soil Research, 8(2), 82-92. [In Persian]
Sattari, M. T., & Allahverdipour, P. (2024). Application of tree-based intelligence methods for wind speed estimation at the east of Lake Urmia. International Conference on Intelligent and Fuzzy Systems, 157–164. doi: 10.1007/978-3-031-67192-0_20
Sefian, H., Bahraoui, F., & Bahraoui, Z. (2022). Weibull and Extreme Value Theory Approach to Estimate Wind Energy in the North Region. International Conference on Advanced Intelligent Systems for Sustainable Development, 515–522. doi: 10.1007/978-3-031-35245-4_47
Sharifi, A., Esfahaninejad, M., & Kabiri, K. (2021). Hydroclimate of the Lake Urmia catchment area: A brief overview. Lake Urmia: A Hypersaline Waterbody in a Drying Climate, 169–185. doi: 10.1007/698_2021_809
Song, J. Y., & Chung, E. S. (2024). Temporal and spatial distribution of extreme rainfall from tropical storms in the Gulf of Mexico from 1979 to 2021. Stochastic Environmental Research and Risk Assessment, 38(8), 3239–3255. doi: 10.1007/s00477-024-02742-y
Sotoudeheian, S., Salim, R., & Arhami, M. (2016). Impact of Middle Eastern dust sources on PM10 in Iran: Highlighting the impact of Tigris‐Euphrates basin sources and Lake Urmia desiccation. Journal of Geophysical Research: Atmospheres, 121(23), 14–18. doi: 10.1002/2016JD025119
Stedinger, J. R. (1993). Frequency analysis of extreme events. Handbook of Hydrology. doi: 10024474232
Vehtari, A., Gelman, A., & Gabry, J. (2017). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing, 27, 1413–1432. doi: 10.1007/s11222-016-9696-4
Vidrio-Sahagún, C. T., & He, J. (2022). Hydrological frequency analysis under nonstationarity using the Metastatistical approach and its simplified version. Advances in Water Resources, 166, 104244. doi: 10.1016/j.advwatres.2022.104244
Vieira, F. F., Oliveira, M., Sanfins, M. A., Garção, E., Dasari, H., Dodla, V., Satyanarayana, G. C., Costa, J., & Borges, J. G. (2023). Statistical analysis of extreme temperatures in India in the period 1951–2020. Theoretical and Applied Climatology, 152(1), 473–520. doi: 10.1007/s00704-023-04377-5
Vogel, R. M., & Fennessey, N. M. (1994). Flow-duration curves. I: New interpretation and confidence intervals. Journal of Water Resources Planning and Management, 120(4), 485–504. doi: 10.1061/(ASCE)0733-9496(1994)120:4(485)
Wu, Z., & Huang, N. E. (2009). Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(01), 1–41. doi: 10.1142/S1793536909000047
 
 
Water and Soil Management and Modelling
Volume 5, Issue 3
September 2025
Pages 173-196

Files

History

  • Receive Date: 06 May 2025
  • Revise Date: 07 June 2025
  • Accept Date: 07 June 2025

Share

How to cite

Statistics