Mersin’in Farklı Kuraklık İndeksleri Aracılığıyla Kuraklık Tehdidinin Araştırılması

Yıl 2024, , 71 - 84, 27.02.2024

Mehmet Özgür Çelik , Murat Yakar

https://doi.org/10.35414/akufemubid.1331753

Öz

Dünya genelinde küresel iklim değişikliğinin etkileri giderek artmaktadır. Akdeniz havzasında bulunan yerler için küresel iklim değişikliğinin temel olumsuz etkileri arasında kuraklık gelmektedir. Çalışma alanı Türkiye’nin güneyinde Akdeniz havzasında yer alan Mersin’dir ve kuraklık tehdidi altındadır. Bu yüzden, çalışma alanın kuraklık analizi farklı kuraklık indeksleri kullanılarak gerçekleştirilmiştir. Bunun için iklimsel verileri kullanan Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Standardized Precipitation Index (SPI) ve Standardized Precipitation-Evapotranspiration Index (SPEI) standart kuraklık indeksleri tercih edilmiştir. Bu indeksler kuraklık analizinde standart kabul edilmektedir. Söz konusu indeksler hesaplanmış ve grafikler oluşturulmuştur. Buna göre, Mersin’in kuraklık analizi gerçekleştirilmiş ve kuraklık riski altında olduğu saptanmıştır. Ayrıca, gelecek yıllardaki olası durumu tahmin edilmiştir.

Anahtar Kelimeler

Kuraklık, PDSI, PHDI, SPI, SPEI, Akdeniz havzası, Mersin

Investigation of Mersin’s Drought-Threat through Different Drought Indexes

Öz

Global climate change is having a growing impact all around the world. Drought is one of the most destructive effects of global climate change in the Mediterranean basin. The study area is Mersin, which is located in southern Türkiye and is threatened by drought. Therefore, a drought analysis of the research area was conducted. Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Standardized Precipitation Index (SPI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were chosen as drought indices that employ climatic data. The indices are considered standard indices in drought analysis. The indices were calculated, and graphs were created. As a result, it was concluded that Mersin is at risk of drought. The prospective condition in the future was also forecasted

Anahtar Kelimeler

Drought, PDSI, PHDI, SPI, SPEI, Mediterranean basin, Mersin

Kaynakça

• Afshar, M.H., Bulut, B., Duzenli, E., Amjad, M. and Yilmaz, M.T., 2022. Global spatiotemporal consistency between meteorological and soil moisture drought indices. Agricultural and Forest Meteorology, 316, 108848. https://doi.org/10.1016/j.agrformet.2022.108848.
• Alahacoon, N. and Edirisinghe, M., 2022. A comprehensive assessment of remote sensing and traditional based drought monitoring indices at global and regional scale. Geomatics, Natural Hazards and Risk, 13, 762-799. https://doi.org/10.1080/19475705.2022.2044394.
• Allen, R.G., Pereira, L.S., Raes, D. and Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
• Alley, W.M., 1984. The Palmer drought severity index: limitations and assumptions. Journal of Applied Meteorology and Climatology, 23, 1100-1109. https://doi.org/10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2.
• Ateşoğlu, A., Arslan, M., Yılmaz, M., Arıkan, T.B. ve Yıldız, S., 2017. Collect Earth Programı kullanılarak Türkiye kurak alanlarının izleme ve değerlendirilmesi. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17, 252-261.
• Bayissa, Y., 2018. Developing an impact-based combined drought index for monitoring crop yield anomalies in the Upper Blue Nile Basin, Ethiopia. CRC Press.https://doi.org/10.1201/9780429399510.
• Beguería, S., Vicente‐Serrano, S.M., Reig, F and Latorre, B., 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International journal of climatology, 34, 3001-3023. https://doi.org/10.1002/joc.3887.
• Bekçi, R.N., 2022. Güneş Potansiyeli Analizi Ve İnternet Tabanlı CBS Uygulaması. Yüksek Lisans Tezi, Mersin Üniversitesi, Fen Bilimleri Enstitüsü, Mersin, 112.
• Çelik, M.Ö. and Yakar, M., 2023. Arazi kullanımı ve Arazi Örtüsü Değişikliklerinin Uzaktan Algılama ve CBS Yöntemi ile İzlenmesi: Mersin, Türkiye Örneği. Türkiye Coğrafi Bilgi Sistemleri Dergisi, 5, 43-51. https://doi.org/10.56130/tucbis.1300704.
• Çoruhlu, Y.E. and Çelik, M.Ö., 2022. Protected area geographical management model from design to implementation for specially protected environment area. Land Use Policy, 122, 106357. https://doi.org/10.1016/j.landusepol.2022.106357.
• Dikici, M. and Aksel, M., 2021. Comparison of drought indices in the case of the Ceyhan Basin. International Journal of Environment and Geoinformatics, 8, 113-125. https://doi.org/10.30897/ijegeo.792379.
• Drisya, J. And Sathish Kumar, D., 2023. Evaluation of the drought management measures in a semi-arid agricultural watershed. Environment, Development and Sustainability, 25, 811-833. https://doi.org/10.1007/s10668-021-02079-4.
• Dubrovsky, M., Svoboda, M.D., Trnka, M., Hayes, M.J., Wilhite, D.A., Zalud, Z. and Hlavinka, P., 2009. Application of relative drought indices in assessing climate-change impacts on drought conditions in Czechia. Theoretical and Applied Climatology, 96, 155-171. https://doi.org/10.1007/s00704-008-0020-x.
• Hadri, A., Saidi, M.E.M. and Boudhar, A., 2021. Multiscale drought monitoring and comparison using remote sensing in a Mediterranean arid region: a case study from west-central Morocco. Arabian Journal of Geosciences, 14, 1-18.https://doi.org/10.1007/s12517-021-06493-w.
• Hargreaves, G.H. and Samani, Z.A., 1985. Reference crop evapotranspiration from temperature. Applied engineering in agriculture, 1, 96-99. https://doi.org/10.13031/2013.26773.
• Hobbins, M.T., Dai, A., Roderick, M.L. and Farquhar, G. D., 2008. Revisiting the parameterization of potential evaporation as a driver of long‐term water balance trends. Geophysical Research Letters, 35. L12403 https://doi.org/10.1029/2008GL033840.
• İban, M.C., 2022. MODIS Verileri ve VHI İndeksi ile Adana ve Mersin’de Kuraklık Şiddetinin İzlenmesi. 11. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği (TUFUAB) Teknik Sempozyumu, 12-14 Mayıs 2022, Mersin, Türkiye, 16-19.
• Jacobi, J., Perrone, D., Duncan, L.L. and Hornberger, G. (2013). A tool for calculating the Palmer drought indices. Water Resources Research, 49, 6086-6089.https://doi.org/10.1002/wrcr.20342.
• Karadirek, I. E., 2022. Drought Management. In Water and Wastewater Management: Global Problems and Measures Cham. Springer International Publishing, 27-34.
• Karl, T.R., 1986. The sensitivity of the Palmer Drought Severity Index and Palmer's Z-index to their calibration coefficients including potential evapotranspiration. Journal of Climate and Applied Meteorology, 77-86.
• Katipoğlu, O.M. (2023). Prediction of streamflow drought index for short-term hydrological drought in the semi-arid Yesilirmak Basin using Wavelet transform and artificial intelligence techniques. Sustainability, 15, 1109. https://doi.org/10.3390/su15021109.
• Kheyruri, Y., Sharafati, A. and Shahid, S., 2023. Evaluation of the impact of large-scale atmospheric indicators and meteorological variables on drought in different regions of Iran. Environmental Earth Sciences, 82, 317. https://doi.org/10.1007/s12665-023-11015-w.
• Kikon, A. and Deka, P.C., 2022. Artificial intelligence application in drought assessment, monitoring and forecasting: a review. Stochastic Environmental Research and Risk Assessment, 36, 1197-1214. https://doi.org/10.1007/s00477-021-02129-3.
• Kim, T.W., Valdés, J. B. and Aparicio, J., 2002. Frequency and spatial characteristics of droughts in the Conchos River Basin, Mexico. Water International, 27, 420-430. https://doi.org/10.1080/02508060208687021.
• Liu, X., Zhu, X., Pan, Y., Li, S., Liu, Y. and Ma, Y., 2016. Agricultural drought monitoring: Progress, challenges, and prospects. Journal of Geographical Sciences, 26, 750-767. https://doi.org/10.1007/s11442-016-1297-9.
• Mishra, A.K. and Desai, V.R., 2005. Spatial and temporal drought analysis in the Kansabati river basin, India. International Journal of River Basin Management, 3, 31-41.https://doi.org/10.1080/15715124.2005.9635243.
• Mishra, A.K. and Singh V.P., 2011. Drought modeling–A review. Journal of Hydrology, 403, 157-175. https://doi.org/10.1016/j.jhydrol.2011.03.049.
• Mishra, A.K. and Singh, V.P., 2011. Drought modeling–A review. Journal of Hydrology, 403(1-2), 157-175. https://doi.org/10.1016/j.jhydrol.2011.03.049.
• Mishra, A.K., and Singh V.P., 2009. Analysis of drought severity‐area‐frequency curves using a general circulation model and scenario uncertainty. Journal of Geophysical Research: Atmospheres, 114. https://doi.org/10.1029/2008JD010986.
• Nie, N., Zhang, W., Chen, H. and Guo, H., 2018. A global hydrological drought index dataset based on gravity recovery and climate experiment (GRACE) data. Water Resources Management, 32, 1275-1290. https://doi.org/10.1007/s11269-017-1869-1.
• Öztürk, T. ve Gürsoy, F., 2022. Küresel İklim Değişikliğinin Arktik Okyanusu’na Jeopolitik Etkisi. Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22, 117–31. https://doi.org/10.25294/auiibfd.1053878.
• Palmer W.C., 1968. Keeping track of crop moisture conditions, nationwide: the new crop moisture index. Weatherwise, 21, 156.161. https://doi.org/10.1080/00431672.1968.9932814.
• Pande, C.B., Costache, R., Sammen, S.S., Noor, R. and Elbeltagi, A., 2023. Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India. Theoretical and Applied Climatology, 152, 535-558. https://doi.org/10.1007/s00704-023-04426-z.
• Peña-Gallardo, M., Vicente-Serrano, S.M., Camarero, J. J., Gazol, A., Sánchez-Salguero, R., Domínguez-Castro, F. and Galván, J.D., 2018. Drought sensitiveness on forest growth in peninsular Spain and the Balearic Islands. Forests, 9, 524. https://doi.org/10.3390/f9090524.
• Ramirez, S.G., 2023. Applied Machine Learning in Development of Geospatial Information Tools for Sustainable Groundwater Management PhD thesis, Brigham Young University, Department of Civil and Construction Engineering, Brigham, 93.
• Savari, M., Damaneh, H.E. and Damaneh, H.E., 2022. Drought vulnerability assessment: Solution for risk alleviation and drought management among Iranian farmers. International Journal of Disaster Risk Reduction, 67, 102654. https://doi.org/10.1016/j.ijdrr.2021.102654.
• Sawadogo, A., Tim, H., Gündoğdu, K.S., Demir, A.O., Ünlü, M. and Zwart, S.J., 2020. Comparative analysis of the pysebal model and lysimeter for estimating actual evapotranspiration of soybean crop in Adana, Turkey. International Journal of Engineering and Geosciences, 5(2), 60-65. https://doi.org/10.26833/ijeg.573503.
• Sertel, E., Algan, I. Y., Alp, G., Musaoğlu, N. and Kaya, Ş., 2017. Yüksek çözünürlüklü uydu verileri kullanılarak 1: 25000 ölçekli ulusal arazi örtüsü/kullanımı sınıflandırma sisteminin geliştirilmesi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17, 232-241.
• Seyam, M.M.H., Haque, M.R. and Rahman, M.M., 2023. Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: A case study at Bhaluka in Mymensingh, Bangladesh. Case Studies in Chemical and Environmental Engineering, 100293,https://doi.org/10.1016/j.cscee.2022.100293.
• Shoumik, B.A.A., Khan, M.Z. and Islam, M.S., 2023. Spatio-temporal characteristics of meteorological and agricultural drought indices and their dynamic relationships during the pre-monsoon season in drought-prone region of Bangladesh. Environmental Challenges, 11, 100695.https://doi.org/10.1016/j.envc.2023.100695.
• Sohrabi, M.M., Ryu, J.H., Abatzoglou, J. and Tracy, J., 2015. Development of soil moisture drought index to characterize droughts. Journal of Hydrologic Engineering, 20(11), 04015025. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001213.
• Tam, B.Y., Cannon, A.J. and Bonsal, B.R., 2023. Standardized precipitation evapotranspiration index (SPEI) for Canada: assessment of probability distributions. Canadian Water Resources Journal/Revue canadienne des ressources hydriques, 1-17.https://doi.org/10.1080/07011784.2023.2183143.
• Tigkas, D., Vangelis, H. and Tsakiris, G., 2015. DrinC: a software for drought analysis based on drought indices. Earth Science Informatics, 8, 697-709. https://doi.org/10.1007/s12145-014-0178-y.
• Tigkas, D., Vangelis, H. and Tsakiris, G., 2017. An enhanced effective reconnaissance drought index for the characterisation of agricultural drought. Environmental Processes, 4, 137-148. https://doi.org/10.1007/s40710-017-0219-x.
• Van Loon, A. F., 2015. Hydrological drought explained. Wiley Interdisciplinary Reviews: Water, 2, 359-392. https://doi.org/10.1002/wat2.1085.
• Varol, T., Atesoglu, A., Ozel, H.B. and Cetin, M., 2023. Copula-based multivariate standardized drought index (MSDI) and length, severity, and frequency of hydrological drought in the Upper Sakarya Basin, Turkey. Natural Hazards, 116, 3669-3683. https://doi.org/10.1007/s11069-023-05830-4.
• Vicente-Serrano, S.M., 2006. Differences in spatial patterns of drought on different time scales: an analysis of the Iberian Peninsula. Water resources management, 20, 37-60.https://doi.org/10.1007/s11269-006-2974-8.
• Vicente-Serrano, S.M., Beguería, S. and López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23, 1696-1718. https://doi.org/10.1175/2009JCLI2909.1.
• Wells, N., Goddard, S. and Hayes, M.J., 2004. A self-calibrating Palmer drought severity index. Journal of climate, 17, 2335-2351.https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2.
• Yilmaz, C. B., Bodu, H., Yüce, E. S., Demir, V. Ve Sevimli, M.F., 2023. Türkiye’nin uzun dönem ortalama sıcaklık (° C) değerlerinin üç farklı enterpolasyon yöntemi ile tahmini. Geomatik, 8(1), 9-17. https://doi.org/10.29128/geomatik.984310.
• Yìldiz, O., Coruhlu, Y. E. and Biyik, C., 2018. Registration of agricultural areas towards the development of a future Turkish cadastral system. Land use policy, 78, 207-218. https://doi.org/10.1016/j.landusepol.2018.06.041.
• Zambrano, F., Lillo-Saavedra, M., Verbist, K. and Lagos, O., 2016. Sixteen years of agricultural drought assessment of the BioBío region in Chile using a 250 m resolution Vegetation Condition Index (VCI). Remote Sensing, 8, 530. https://doi.org/10.3390/rs8060530.
• Zhang, C., Yang, Z., Zhao, H., Sun, Z., Di, L., Bindlish, R. and Yueh, S.H. (2022). Crop-CASMA: A web geoprocessing and map service based architecture and implementation for serving soil moisture and crop vegetation condition data over US Cropland. International Journal of Applied Earth Observation and Geoinformation, 112, 102902. https://doi.org/10.1016/j.jag.2022.102902.
• Zhang, N., Li, Z. and Quiring, S.M., 2023. Developing Impacts-Based Drought Thresholds for Ohio. Journal of Hydrometeorology, 1225–1240.https://doi.org/10.1175/JHM-D-22-0054.1.
• https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi, (14.05.2023).
• https://data.tuik.gov.tr/Bulten/Index?p=Kent-Kir-Nufus-Istatistikleri-2022-49755, (17.05.2023).
• https://cip.tuik.gov.tr/#, (17.05.2023).
• http://www.mersin.gov.tr/tarim, (15.06.2023).
• https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=MERSIN, (20.06.2023).
• https://www.mgm.gov.tr/veridegerlendirme/kuraklik-analizi.aspx?d=aylik&k=spi#sfB, (20.06.2023).
• https://www.mersin.bel.tr/haber/mersin-cok-siddetli-kuraklik-kategorisine-gecti-1677827585, (20.06.2023).
• https://power.larc.nasa.gov/data-access-viewer/, (20.06.2023).
• https://edo.jrc.ec.europa.eu/documents/factsheets/ factsheet_spi_ado.pdf , (05.07.2023).
• https://climatedataguide.ucar.edu/climate-data/ standardized-precipitation-evapotranspiration-index-spei, (05.07.2023).
• https://www.tarimorman.gov.tr/SYGM/Haber/1139/ Gida-Guvenligi-Ve-Su-Yonetimi, (10.07.2023).