Multivariate Drought Frequency Analysis and Risk Assessment: A case study for Kahramanmaras Province

Abstract

Drought is a natural disaster that occurs in nearly all climatic regions as a result of seasonal or longer-term lack of precipitation. Global warming, insufficient precipitation, low precipitation intensity and duration, low relative humidity and other factors all cause the prevalence of drought. In this study, multivariate drought frequency analysis and risk assessment of Kahramanmaras province were analyzed by using copula functions. After the drought parameters (duration and severity) were obtained by the SPI (Standardized Precipitation Index) method, the most appropriate marginal distributions were found for each parameter. Finally, depending on the appropriate marginal distributions, the conditional return probabilities and periods of the drought parameters of Kahramanmaras province were obtained after the most appropriate copula function was calculated. As a result of this study, the highest conditional return period of drought severity was observed in the center of Kahramanmaras, while the lowest return period was found in the district of Elbistan. Considering the highest conditional drought duration period, Afşin district has the highest return period (least risky), while Elbistan district has the shortest return period (risky). It has been observed that the province of Elbistan has a higher drought risk compared to other districts and the center of Kahramanmaras according to both the conditional drought severity and the drought duration return period. This study provides useful information to decision makers in the management of drought risk assessment for Kahramanmaras province.

Keywords

• Drought
• Copula
• SPI
• Return Period
• Kahramanmaras

Çok Değişkenli Kuraklık Frekans Analizi ve Risk Değerlendirmesi: Kahramanmaraş Örneği

Abstract

Kuraklık, mevsimsel veya daha uzun süreli yağış eksikliğinin bir sonucu olarak neredeyse tüm iklim bölgelerinde meydana gelen doğal bir afettir. Küresel ısınma, yağış yetersizliği, düşük yağış miktarı ve süresi, düşük bağıl nem ve diğer faktörlerin tümü, kuraklığın yaygınlaşmasına sebep olmaktadır. Bu çalışmada, Kahramanmaraş ilinin çok değişkenli kuraklık frekans analizi ve risk değerlendirilmesi kopula fonksiyonları kullanılarak yapılmıştır. Kuraklık parametreleri (süre ve şiddet), SPI (Standartlaştırılmış Yağış İndeksi) indeksi yöntemi ile elde edildikten sonra, her bir parametreye en uygun marjinal dağılımlar bulunmuştur. Son olarak, elde edilen en uygun marjinal dağılımlara bağlı olarak, en uygun kopula fonksiyonu hesaplandıktan sonra, Kahramanmaraş ilinin kuraklık parametrelerinin şartlı dönüş olasılıkları ve süreleri elde edilmiştir. Bu çalışma sonucunda, en yüksek şartlı kuraklık şiddeti dönüş periyodu Kahramanmaraş merkezde görülürken, en düşük dönüş periyoduna ise Elbistan ilçesinde saptanmıştır. En yüksek şartlı kuraklık süresi dikkate alındığında, Afşin ilçesi en yüksek dönüş periyoduna sahip iken (en az riskli), Elbistan ilçesi en kısa dönüş periyoduna sahip olduğu (riskli) gözlemlenmiştir. Elbistan ilinin hem şartlı kuraklık şiddeti hem de kuraklık süresi dönüş periyoduna göre diğer ilçelere ve merkeze göre daha fazla kuraklık riski taşıdığı gözlemlenmiştir. Bu çalışma, Kahramanmaraş ili için kuraklık risk değerlendirmesi yönetiminde karar vericilere faydalı bilgiler sağlamaktadır.

Keywords

• Kuraklık
• Kopula
• SPI
• Dönüş Periyodu
• Kahramanmaraş

References

1. Abramowitz M., Stegun I.A., (1965), Handbook of mathematical functions with formulas, graphs, and mathematical tables, US National Bureau of Standards, Applied Mathematic Series 55, 470ss.
2. Adamson P.T., Metcalfe, A.V., Parmentier, B. (1999), Bivariate extreme value distributions: an application of the Gibbs sampler to the analysis of floods, Water Resources Research, 35(9), 2825-2832.
3. Afshar, M. H., Şorman, A. Ü., Tosunoğlu, F., Bulut, B., Yilmaz, M. T., Danandeh Mehr, A. (2020), Climate change impact assessment on mild and extreme drought events using copulas over Ankara, Turkey, Theoretical and Applied Climatology, 141(3), 1045-1055.
4. Aksoy H., Onoz, B., Cetin, M., Yuce, M.I., Eris, E., Selek, B., Aksu, H., Burgan, H.I., Esit, M., Orta, S., Cavus, Y. (2018), SPI-based drought severity-duration-frequency analysis, 13th International Congress on Advances in Civil Engineering, 12 - 14 September, Izmir, Turkey.
5. Below R., Grover-Kopec, E., Dilley, M., (2007), Documenting drought-related disasters: A global reassessment, The Journal of Environment & Development, 16(3), 328-344.
6. Cannon A.J., (2010), A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology, Hydrological Processes: An International Journal, 24(6), 673-685.
7. Chen L., Singh V.P., Guo S., Zhou J., Zhang J., (2015), Copula-based method for multisite monthly and daily streamflow simulation, Journal of Hydrology, 528, 369-384.
8. Das J., Jha, S., Goyal M.K., (2020), Non-stationary and copula-based approach to assess the drought characteristics encompassing climate indices over the Himalayan states in India, Journal of Hydrology, 580, 124356, doi: 10.1016/j.jhydrol.2019.124356.

9. Diwan P.L. (2002), Water, Environment & Drought, All India Seminar on “Water & Environment-Issues and Challenges”, October, IIT, Roorkee, India, ss.21-42.
10. Edwards D.C., (1997), Characteristics of 20th century drought in the United States at multiple time scales, MSc thesis, Colorado State University, Fort Collins, Colorado, USA.
11. El Adlouni S., Ouarda T.B., Zhang X., Roy R., Bobée B., (2007), Generalized maximum likelihood estimators for the nonstationary generalized extreme value model, Water Resources Research, 43(3), W03410, doi:10.1029/2005WR004545.
12. Eris E., Aksoy H., Onoz B., Cetin M., Yuce M.I., Selek B., Aksu H., Burgan H.I., Esit M., Yildirim I., Karakus E.U., (2019), Frequency analysis of low flows in intermittent and non-intermittent rivers from hydrological basins in Turkey, Water Supply, 19(1), 30-39.
13. Esit M., Yuce M.I., (2021), Kopula Yöntemi ile Osmaniye Bölgesinin İki Değişkenli Kuraklık Frekans Analizi, Academic Platform-Journal of Engineering and Science, 9(3), 388-396.
14. Esit M., Kumar S., Pandey A., Lawrence D.M., Rangwala I., Yeager S., (2021), Seasonal to multi-year soil moisture drought forecasting, npj Climate and Atmospheric Science, 4(1), 16, doi: 10.1038/s41612-021-00172-z.
15. Favre A.C., Musy A., Morgenthaler S., (2002), Two‐site modeling of rainfall based on the Neyman‐Scott process, Water Resources Research, 38(12), 1307, doi:10.1029/2002WR001343.
16. Khan M.M.H., Muhammad, N.S., El-Shafie, A., (2018), A review of fundamental drought concepts, impacts and analyses of indices in Asian continent, Journal of Urban and Environmental Engineering, 12(1), 106-119.
17. Li H., Wang D., Singh V.P., Wang Y., Wu J., Wu J., Liu J., Zou Y., He R., Zhang J., (2019), Non-stationary frequency analysis of annual extreme rainfall volume and intensity using Archimedean copulas: A case study in eastern China, Journal of hydrology, 571, 114-131.
18. Liu C.L., Zhang Q., Singh V.P., Cui Y., (2011), Copula-based evaluations of drought variations in Guangdong, South China, Natural Hazards, 59(3), 1533-1546.
19. 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 , 17(22), 179-183.
20. Mirabbasi R., Fakheri-Fard A., Dinpashoh Y., (2012), Bivariate drought frequency analysis using the copula method, Theoretical and applied climatology, 108(1), 191-206.
21. Mishra A.K., Singh V.P., (2010), A review of drought concepts, Journal of hydrology, 391(1-2), 202-216.
22. Mishra V., Aadhar S., Asoka A., Pai S., Kumar R., (2016), On the frequency of the 2015 monsoon season drought in the Indo‐Gangetic Plain, Geophysical Research Letters, 43(23), 12-102.
23. Nabaei S., Sharafati A., Yaseen Z.M., Shahid S., (2019), Copula based assessment of meteorological drought characteristics: regional investigation of Iran, Agricultural and Forest Meteorology, 276-277, 107611, doi: 10.1016/j.agrformet.2019.06.010.
24. Palmer W.C., (1965), Meteorological drought, Research Paper No. 45, US Department of Commerce, Weather Bureau, Washington, D.C., 65ss.
25. Park S., Im J., Park S., Rhee, J., (2017), Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula, Agricultural and Forest Meteorology, 237, 257-269.
26. Qian L., Wang H., DangS., Wang, C., Jiao Z., Zhao Y., (2018), Modelling bivariate extreme precipitation distribution for data‐scarce regions using Gumbel–Hougaard copula with maximum entropy estimation, Hydrological Processes, 32(2), 212-227.
27. Ramezani Y., Nazeri Tahroudi M., Ahmadi F., (2019), Analyzing the droughts in Iran and its eastern neighboring countries using copula functions, Időjárás / Quarterly Journal of The Hungarıan Meteorologıcal Service, 123(4), 435-453.
28. Reddy M.J., Ganguli P., (2013), Spatio-temporal analysis and derivation of copula-based intensity–area–frequency curves for droughts in western Rajasthan (India), Stochastic environmental research and risk assessment, 27(8), 1975-1989.
29. Shiau J.T., (2006), Fitting drought duration and severity with two-dimensional copulas, Water resources management, 20(5), 795-815.
30. Sklar M., (1959), Fonctions de repartition an dimensions et leurs marges, Publications de l’Institut Statistique de l’Université de Paris, 8, 229-231.
31. Thom H.C., (1951), A frequency distribution for precipitation, Bulletin of the American Meteorological Society, 32(10), 397.
32. Thom H.C.S., (1966), Some methods of climatological analysis, World Meteorological Organization (WMO), Technical Note No. 81 (WMO - No. 199.TP.I03), Geneva, Switzerland, 69ss.
33. Topçu E., (2022), Appraisal of seasonal drought characteristics in Turkey during 1925–2016 with the standardized precipitation index and copula approach, Natural Hazards, 112, 697–723.
34. Tosunoglu F., Can I., (2016), Application of copulas for regional bivariate frequency analysis of meteorological droughts in Turkey, Natural Hazards, 82(3), 1457-1477.
35. URL-1 (2021), Kahramanmaraş, https://www.dogaka.gov.tr/en/east-mediterranean/kahramanmaras, [Erişim 9 Ocak 2022].
36. 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.
37. Vo, Q. T., So, J. M., & Bae, D. H. (2020), An integrated framework for extreme drought assessments using the natural drought index, copula and Gi* statistic. Water Resources Management, 34(4), 1353-1368.
38. Wilhite D.A., Hayes, M.J., Knutson C., Smith K.H., (2000), Planning for drought: Moving from crisis to risk management, Journal of the American Water Resources Association, 36(4), 697-710.
39. Won J., Choi J., Lee O., Kim S., (2020), Copula-based Joint Drought Index using SPI and EDDI and its application to climate change, Science of the Total Environment, 744, 140701, doi: 10.1016/j.scitotenv.2020.140701.
40. Yevjevich, V.M., (1967), Objective approach to definitions and investigations of continental hydrologic droughts, Hydrology Papers, No. 23, Colorado State University, Fort Collins, Colorado, 25ss.
41. Yuce M.I., Esit M., (2021), Drought monitoring in Ceyhan Basin, Turkey, Journal of Applied Water Engineering and Research, 9(4), 293-314.
42. Yue S., Ouarda T.B., Bobée B., (2001), A review of bivariate gamma distributions for hydrological application, Journal of Hydrology, 246(1-4), 1-18.
43. Zhang Q., Xiao M., Singh V.P., Chen X., (2013), Copula-based risk evaluation of hydrological droughts in the East River basin, China, Stochastic Environmental Research and Risk Assessment, 27(6), 1397-1406.
44. Zhang X., Chen N., Li J., Chen Z., Niyogi D., (2017), Multi-sensor integrated framework and index for agricultural drought monitoring, Remote Sensing of Environment, 188, 141-163.
45. Zhou T., Liu Z., Jin J., Hu H., (2019), Assessing the impacts of univariate and bivariate flood frequency approaches to flood risk accounting for reservoir operation, Water, 11(3), 475, doi: 10.3390/w11030475.