Drought analysis based on SPI and RDI drought indices in the Burdur Basin

Year 2024, , 127 - 138, 19.01.2024

Nazire Göksu Soydan Oksal , Neslihan Beden

https://doi.org/10.31127/tuje.1326875

Cited By: 3

Abstract

Drought is the most complex of the recurrent extreme weather events and is defined as a natural disaster with severe environmental, economic, and agricultural impacts resulting from a significant decrease in the average rainfall recorded in an area and the average rainfall recorded in the same place. Droughts have become more frequent and severe in many parts of the world, including Türkiye, due to global warming and climate change (increasing temperatures and changing precipitation patterns). Water resources and the agricultural sector are most severely affected by droughts. In this study, drought analyses of the Burdur Basin, located in the Aegean region, one of Türkiye's seven geographical regions, were carried out. For drought analysis, annual average total precipitation, annual maximum temperature, annual minimum temperature, and annual average temperature data of 17238 Burdur and 17892 Tefenni meteorological observation stations were used. Both meteorological and agricultural drought analyzes are included in the analysis of droughts. Standard Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) methods were used to determine meteorological and agricultural drought, respectively. SPI and RDI values were obtained for 1-, 3-, 6- and 12-month time periods, and the severity, size, and distribution of dry and humid periods were determined for both stations separately. When the results of both methods were examined, severe droughts were observed in the study area in 1973, 1978, 1981, and 2017.

Keywords

Drought, Drought Analysis, SPI, RDI, Burdur Basin

References

• Kundzewicz, Z. W., & Robson, A. (2000). Detecting trend and other changes in hydrological data. World Climate Programme Data and Monitoring, WCDMP – 45
• Beşel, C., & Kayikci, E. T. (2020). Investigation of Black Sea mean sea level variability by singular spectrum analysis. International Journal of Engineering and Geosciences, 5(1), 33-41. https://doi.org/10.26833/ijeg.580510
• Ojha, S. S., Singh, V., & Roshni, T. (2021). Comparison of meteorological drought using SPI and SPEI. Civil Engineering Journal, 7(12), 2130-2149. http://dx.doi.org/10.28991/cej-2021-03091783
• Zarch, M. A. A., Sivakumar, B., & Sharma, A. (2015). Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI). Journal of Hydrology, 526, 183-195. https://doi.org/10.1016/j.jhydrol.2014.09.071
• Surendran, U., Kumar, V., Ramasubramoniam, S., & Raja, P. (2017). Development of drought indices for semi-arid region using drought indices calculator (DrinC)–a case study from Madurai District, a semi-arid region in India. Water Resources Management, 31, 3593-3605. https://doi.org/10.1007/s11269-017-1687-5
• An, S., Park, G., Jung, H., & Jang, D. (2022). Assessment of future drought index using SSP scenario in Rep. of Korea. Sustainability, 14(7), 4252. https://doi.org/10.3390/su14074252
• Mishra, A. K., & Singh, V. P. (2010). A review of drought concepts. Journal of Hydrology, 391(1-2), 202-216. https://doi.org/10.1016/j.jhydrol.2010.07.012
• Wilhite, D. A., & Glantz, M. H. (1985). Understanding: The Drought Phenomenon: The Role of Definitions. Water International, 10, 111-120.
• İnan, B., Demir, V., & Sevimli, M. F. (2021). Drought analysis of Black Sea Region by standardized precipitation index (SPI) and percent of normal index (PNI). Advanced Engineering Days (AED), 1, 8-10.
• Citakoglu, H., & Coşkun, Ö. (2022). Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environmental Science and Pollution Research, 29(50), 75487-75511. https://doi.org/10.1007/s11356-022-21083-3
• Coşkun, Ö., & Citakoglu, H. (2023). Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye. Physics and Chemistry of the Earth, Parts A/B/C, 131, 103418. https://doi.org/10.1016/j.pce.2023.103418
• SYGM, (2019). Burdur Havzası Taşkın Yönetim Planı. Ankara: Türkiye Cumhuriyeti Tarım ve Orman Bakanlığı.
• Ünel, F. B., Kuşak, L., Yakar, M., & Doğan, H. (2023). Coğrafi bilgi sistemleri ve analitik hiyerarşi prosesi kullanarak Mersin ilinde otomatik meteoroloji gözlem istasyonu yer seçimi. Geomatik, 8(2), 107-123. https://doi.org/10.29128/geomatik.1136951
• Demir, V., & Keskin, A. Ü. (2022). Yeterince akım ölçümü olmayan nehirlerde taşkın debisinin hesaplanması ve taşkın modellemesi (Samsun, Mert Irmağı örneği). Geomatik, 7(2), 149-162. https://doi.org/10.29128/geomatik.918502
• Mohamed, M. J., Cemek, B., Küçüktopcu, E., Omar, A. A., & Hassan, S. M. (2022). Drought Analysis in Somalia Using GIS-Based on Reconnaissance Drought Index (RDI) and Standardized Precipitation Index (SPI). African Journal of Climate Change and Resource Sustainability, 1(1), 62-75. https://doi.org/10.37284/ajccrs.1.1.981
• Beden, N., Demir, V., & Keskin, A. Ü. (2020). Samsun İlinde SPI ve PNI Kuraklık İndekslerinin Eğilim Analizi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 22(64), 107-116. https://doi.org/10.21205/deufmd.2020226411
• Gümüş, V., Başak, A., & Oruç N. (2016). Standartlaştırılmış yağış indeksi (SYİ) yöntemi ile Şanlıurfa istasyonunun kuraklık analizi. Harran Üniversitesi Mühendislik Dergisi, 1(1), 36-44.
• Tsakiris, G., Pangalou, D., & Vangelis, H. (2007). Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resources Management, 21, 821-833. https://doi.org/10.1007/s11269-006-9105-4