Authors:Muhammad Ali,Syed Asif Ali,ImtiazHussain,Faisal Nawaz,
Keywords:Return level,Maximum Temperature,Return Periods,Heat waves,probabilistic model ,
AbstractSince the problem of global warming and heat waves are the burning issues and became challenge for scientists in this era. Current analysis is also an attempt to solve this problem in Karachi Pakistan. This effort is to analyze frequency distribution by using daily maximum temperature data and then to find the best fitted probabilistic model for yearly maximum temperature series to see the possible return levels of maximum temperature in Karachi.After passing through a number of goodness of fit tests the Log-Logistic [3P] distribution is found to be the best fitted model to calculate return levels. Analysis also indicates that there is a chance of getting 44.3 temperature return level in the next coming 5 years, 45.8 in coming 20 yearsand 46.5 return levels in coming 50 years return period. These return levels propose that the Government officials and planners to take notice on plantation, water supply system, to facilitate better public transport to reduce the number of vehicles, to update health system, to increase electricity production etc.The results of this analysis are also useful to agricultural and environmental research.
I Abbas, F., Rehman, I., Adrees, M., Ibrahim, M., Saleem, F., Ali, S., …&Salik, M. R. (2018). Prevailing trends of climatic extremes across Indus-Delta of Sindh-Pakistan. Theoretical and applied climatology, 131(3-4), 1101-1117.
II Arreyndip, N. A., & Joseph, E. (2015). Extreme temperature forecast in Mbonge, Cameroon, through return level analysis of the generalized extreme value (GEV) distribution. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 9(6), 343-348.
III Chaudhry, Q. Z., Rasul, G., Kamal, A., Mangrio, M. A., &Mahmood, S. (2015). Technical report on Karachi heat wave June 2015. Islamabad: Government of Pakistan Ministry of Climate Change.
IV Coles, S., Bawa, J., Trenner, L., &Dorazio, P. (2001). An introduction to statistical modeling of extreme values (Vol. 208, p. 208). London: Springer.
V Field, C. B., Barros, V., Stocker, T. F., &Dahe, Q. (Eds.). (2012). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press.
VI Grant, K., Kreyling, J., Heilmeier, H., Beierkuhnlein, C., &Jentsch, A. (2014). Extreme weather events and plant–plant interactions: shifts between competition and facilitation among grassland species in the face of drought and heavy rainfall. Ecological Research, 29(5), 991-1001.
VII Gumbel, E. J. (1958). Statistics of Extremes, Columbia Univ. Press, New York, 201.
VIII Hatfield, J. L., &Prueger, J. H. (2015). Temperature extremes: Effect on plant growth and development. Weather and climate extremes, 10, 4-10.
IX Imtiaz, S., &Rehman, Z. U. (2015). Death Toll From Heat Wave in Karachi, Pakistan, hits 1000. New York Times, available at: http://www.nytimes.com/2015/06/26/world/asia/karachi-pakistan-heat-wave-deaths.html.
X Iqbal, M. J., & Ali, M. (2013). A probabilistic approach for estimating return period of extreme annual rainfall in different cities of Punjab. Arabian Journal of Geosciences, 6(7), 2599-2606.
XI Katz, R. W., Parlange, M. B., &Naveau, P. (2002). Statistics of extremes in hydrology. Advances in water resources, 25(8-12), 1287-1304.
XII Kayes, I., Shahriar, S. A., Hasan, K., Akhter, M., Kabir, M. M., & Salam, M. A. (2019). The relationships between meteorological parameters and air pollutants in an urban environment. Global Journal of Environmental Science and Management, 5(3), 265-278.
XIII Mayooran, T., &Laheetharan, A. (2014). The statistical distribution of annual maximum rainfall in Colombo district. Sri Lankan Journal of Applied Statistics, 15(2), 1765-1784.
XIV Meehl, G. A., & Tebaldi, C. (2004). More intense, more frequent, and longer lasting heat waves in the 21st century. Science, 305(5686), 994-997.
XV Mothupi, T., Thupeng, W. M., Mashabe, B., &Mokoto, B. (2016). Estimating Extreme Quantiles of the Maximum Surface Air Temperatures for the Sir SeretseKhama International Airport Using the Generalized Extreme Value Distribution. American Journal of Theoretical and Applied Statistics, 5(6), 365-375.
XVI Omer, M. A., Salh, S. M., & Ahmed, S. A. (2019). Statistical Distribution of Rainfall in Kurdistan-Iraq Region. Al-Mustansiriyah Journal of Science, 30(4), 18-28.
XVII Orlowsky, B., &Seneviratne, S. I. (2012). Global changes in extreme events: regional and seasonal dimension. Climatic Change, 110(3-4), 669-696.
XVIII Pal, J. S., &Eltahir, E. A. (2016). Future temperature in southwest Asia projected to exceed a threshold for human adaptability. Nature Climate Change, 6(2), 197.
XIX Parey, S., Hoang, T. T. H., &Dacunha‐Castelle, D. (2010). Different ways to compute temperature return levels in the climate change context. Environmetrics, 21(7‐8), 698-718.
XX Raza, A., Razzaq, A., Mehmood, S. S., Zou, X., Zhang, X., Lv, Y., &Xu, J. (2019). Impact of climate change on crops adaptation and strategies to tackle its outcome: A review. Plants, 8(2), 34.
XXI Rizwan, M., Guo, S., Xiong, F., & Yin, J. (2018). Evaluation of various probability distributions for deriving design flood featuring right-tail events in pakistan. Water, 10(11), 1603.
XXII Rootzén, H., & Katz, R. W. (2013). Design life level: quantifying risk in a changing climate. Water Resources Research, 49(9), 5964-5972.
XXIII Rust, H. W., Kallache, M., Schellnhuber, H. J., &Kropp, J. P. (2011). Confidence intervals for flood return level estimates assuming long-range dependence. In In Extremis (pp. 60-88). Springer, Berlin, Heidelberg.
XXIV Rust, H. W., Kallache, M., Schellnhuber, H. J., &Kropp, J. P. (2010). Confidence intervals for flood return level estimates assuming long-range dependence. Springer-Verlag, Berlin.
XXV Salim, M., &Mahmood-ul-Hassan, M. (2015). Distribution of Indian flying foxpteropusgiganteusbrünnich, 1782 in four districts of khyberpakhtunkhwa. The Journal of Animal & Plant Sciences, 25(3 suppl 2), 446-449.
XXVI Sharma, S., Sharma, P., Khare, M., &Kwatra, S. (2016). Statistical behavior of ozone in urban environment. Sustainable Environment Research, 26(3), 142-148.
XXVII Sheridan, S. C., & Allen, M. J. (2015). Changes in the frequency and intensity of extreme temperature events and human health concerns. Current Climate Change Reports, 1(3), 155-162.
XXVIII Sherwood, S. C., & Huber, M. (2010). An adaptability limit to climate change due to heat stress. Proceedings of the National Academy of Sciences, 107(21), 9552-9555.
XXIX Smith, R. L. (1989). Extreme value analysis of environmental time series: an application to trend detection in ground-level ozone. Statistical Science, 367-377.
XXX Zahid, M., Blender, R., Lucarini, V., &Bramati, M. C. (2017). Return levels of temperature extremes in southern Pakistan. Earth System Dynamics, 8(4), 1263-1278.View Download