پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 162 |
| تعداد شمارهها | 6,693 |
| تعداد مقالات | 72,239 |
| تعداد مشاهده مقاله | 129,233,358 |
| تعداد دریافت فایل اصل مقاله | 102,067,831 |
Risk Assessment of Dust Extremes and Mud Deposition on Human Activity in the Southwest of Iran. | ||
| Desert | ||
| مقاله 3، دوره 29، شماره 2، اسفند 2024، صفحه 132-143 اصل مقاله (1.01 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jdesert.2024.100129 | ||
| نویسندگان | ||
| Mohammad Rahimi1؛ Maede Nasry* 2؛ Saeed Zalzadeh3 | ||
| 1Combat Desertification Department, Faculty of Desert Studies, Semnan University, Semnan, Iran | ||
| 2Combat to Desertification Department, Faculty of Desert Studies, Semnan University, Semnan, Iran | ||
| 3Department of Statistics, Faculty of Mathematics, Statistics and Computer Science, Semnan University, Semnan, Iran | ||
| چکیده | ||
| Climate changes have a significant effect on dust extremes. Dust extremes in humid ambient air can simultaneously or successively form wet mud deposition on the surface of urban areas. The mud deposition on the power network systems and devices causes irreversible damage and significantly influences system performance and efficiency in southwest Iran. This often results in blackouts that cause problems in the operation of urban infrastructure and people's daily activities for up to several days. Khuzestan province was chosen as the case study in this study, and the climatic conditions and risk assessment of mud formation in this area were investigated. Data on a diurnal and monthly timescale of dust and humidity level was used for assessing extreme dust and wet conditions. The data was taken from Khuzestan synoptic station 8 over 11 years (2009-2019). The multivariate copula-based framework is used to calculate univariate and bivariate return periods of mud deposition hazard. The results imply that dust anomalies increase the probability of dust extreme coincidence with wet extreme and occurrence of wet mud hazards in the cold seasons of the year. In addition, limited adaptive capacity, shortage of information, and poor coordination and cooperation by the authorities caused the large-scale impact of the wet mud hazard in Khuzestan. Considering only relative humidity data, the return period of 2017 Khuzestan mud adhesion hazard is approximately 12 to 43 years. If we consider only dust, the return period of 2017 Khuzestan mud adhesion hazard is estimated at 80 to 700 years. However, for both dust and relative humidity extremes, the joint return periods for TDR (Dust and Relative humidity) and T'DR (Dust or Relative humidity) are respectively estimated greater than 200 and lower than 20 years. | ||
| کلیدواژهها | ||
| Bivariate Frequency Analysis؛ Return Period؛ Climate Extremes؛ Critical Infrastructures؛ Adaptation | ||
| مراجع | ||
|
References
Adukwu, JE., Yilbas, BS., Jalilov, AS. (2020). Adhesion characteristics of solution-treated environmental dust. Scientific Reports, 10(13812). https://doi.org/10.1038/s41598-020-70858-6
AghaKouchak, A., Cheng, L., Mazdiyasni, O., Farahmand, A. (2014). Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophysical Research Letters, 41, 8847–8852. https://doi.org/10.1002/2014GL062308
Bialek, JW. (2010). Critical interrelations between ICT and electricity system. in Securing electricity supply in the cyber age: exploring the risks of Information and communication technology in tomorrow's electricity infrastructure. Springer Press.
Birkmann, J., Wenzel, F., Greiving, S., Garschagen, M., Vallée, D. (2017). Extreme Events, Critical Infrastructures, Human Vulnerability and Strategic Planning: Emerging Research Issues. Journal of Extreme Events. 03. 1650017. https://doi.org/10.1142/S2345737616500172
Cardona, OD et al .(2012). In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation.65–108 (IPCC, Cambridge Univ. Press, 2012). Esen V, Oral B, Akinci TC (2015) the determination of short circuits and ground faults in electric power systems using time-frequency analysis. Journal of Energy in Southern Africa, 26(2), 123-132. https://doi.org/10.17159/2413-3051/2015/v26i2a2206
Caron, J.R., Littmann, B. (2013). Direct monitoring of energy lost due to soiling on first PV modules in California. IEEE J. Photovoltaic’s Press. 3, (1), pp. 336–340.
Gimeno-Sotelo, L., Gimeno, L. (2022). Concurrent extreme events of atmospheric moisture transport and continental precipitation: The role of landfalling atmospheric rivers. Atmospheric Research. 278. 106356. https://doi.org/10.1016/j.atmosres.2022.106356
Hassan, G., Yilbas, BS., Said, S.A., Al-Aqeeli, N., Matin, A. (2016). Chemo-mechanical characteristics of mud formed from environmental dust particles in humid ambient air. Scientific Report. 6, 1–14. https://doi.org/10.1038/srep30253.
Hasan, K., Yousuf, S., Tushar M, Das, B., Das, P., Islam, Md. (2021). Effects of different environmental and operational factors on the PV performance: A comprehensive review. Energy Science & Engineering. 2(21). https://doi.org/10. 10.1002/ese3.1043
Isaifan, R., Johnson, D., Ackermann, L., Figgis, B., Ayoub, M. (2018). Evaluation of the adhesion forces between dust particles and photovoltaic module surfaces. Solar Energy Materials and Solar Cells. 191, 413-421.
Kim, J., So, B., Kwon, H., Kim, T., Lee, JH. (2018). Estimation of return period and its uncertainty for the recent 2013–2015 drought in the Han River watershed in South Korea. Hydrology Research. https://doi.org/49. 10.2166/nh.2018.146
Lyu, Y. (2017). Characterization of dust falls in rural and urban sites during three dust storms in northern China. Aeolian Reserch. 28, 29–37. https://doi.org/10.1016/j.aeolia.2017.06.004
Mesbahzadeh, T., Mirakbari, M., Saravi, M., Sardoo, F., Miglietta, M. (2019). Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP). Meteorological Applications. 27 (7). https://doi.org/10.1002/met.1856
Bradke, T., Lüllmann, H., Poetzsch, A., Riehm, M. (2011). What happens during a blackout: Consequences of a prolonged and wide-ranging power outage SE. Technology Assessment Studies Series. 2 (14).
Pabaghi, Z., Bazrafshan, O., Zamani, H., Shekari, M., Singh, V.P. (2023). Bivariate analysis of extreme precipitation using copula functions in arid and semi-arid regions. Atmosphere Journal, 14(2), 275.
Rashki, A., Middleton, N.J., Goudie, A.S. (2021). Dust storms in Iran-Distribution, causes, frequencies and impacts. Aeolian Research Journal. 48(100655). https://doi.org/10.1016/j.aeolia.2020.100655.
Raymond, C., Horton, R.M., Zscheischler, J., Martius, O., AghaKouchak, A., Balch, J., (2020). Understanding and Managing Connected Extreme Events. Nat. Clim. Chang. 10, 611–621. https://doi.org/10.1038/s41558-020-0790-4.
Sabri Ismail, M and Masseran, N. (2024). Risk assessment for extreme air pollution events using vine copula. Stochastic. Environmental Research and Risk Assessment, 38(2331-2358).
Salvadori, G., Durante, F., De Michele, C., Bernardi, M., Petrella, L. (2016) A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities, Water Resource Reserch. 52 (3701–3721). https://doi.org/10.1002/2015WR017225
Sarver, T., Al-Qaraghuli, A., Kazmerski, L.L. (2013). A comprehensive review of the impact of dust on the use of solar energy: history, investigations, results, literature, and mitigation approaches. Renew. Sustainable Energy Reviews. 22(698–733). https://doi.org/10.1016/j.rser.2012.12.065.
Seneviratne, SI. (2012). In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (IPCC, Cambridge Univ. Press, 2012).
Silva, R.d and Lopes HF. (2008). Copula, marginal distributions and model selection: a Bayesian note. Springer Nature, 18, 313–320. https://doi.org/10.1007/s11222-008-9058-y.
Shiau, JT. (2006). Fitting drought duration and severity with two-dimensional copulas. Water Resources Management, 20 (5), 795–815. https://doi.org/10.1007/s11269-005-9008-9
Sklar, M. (1959). Functions de répartition à n dimensions et leurs marges. Université Paris 8, Paris, France.
Tavakol, A., Rahmani, V., John Harrington, J. (2020). Probability of compound climate extremes in a changing climate: A copula-based study of hot, dry, and windy events in the central United States. Environmental Research Letters, 15. https://doi.org/10.1088/1748-9326/abb1ef
Whitney, K.H., Adam, H.M., Francis, W.Z. (2021). Estimating concurrent climate extremes: A conditional approach. Weather and Climate Extremes, 33 (44). https://doi.org/10.1016/j.wace.2021.100332
Yilbas, B., Ali H, Khaled, M. (2015). Influence of dust and mud on the optical, chemical and mechanical properties of a pv protective glass. Scientific Reports. 5 (15833). https://doi.org/10.1038/srep15833
Zhang, W., Murakami, H., Khouakhi, A., Luo, M. (2021). Editorial: Compound Climate Extremes in the Present and Future Climates: Machine Learning, Statistical Methods and Dynamical Modelling. Front. Atmospheric Science. 9:807224. https://doi.org/10.3389/feart.2021.807224.
Zscheischler J, Seneviratne SI (2017) Dependence of drivers affects risks associated with compound events. Science Adviser. 3 (25). https://doi.org/10.1126/sciadv.1700263
Zscheischler, J., Westra, S., Van Den Hurk, BJ., Seneviratne, SI., Ward, PJ., Pitman, A. (2018). Future climate risk from compound events. Nature Climate Change. 8, 469–477. https://doi.org/10.1038/s41558-018-0156-3. | ||
|
آمار تعداد مشاهده مقاله: 68 تعداد دریافت فایل اصل مقاله: 81 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب