Drought Monitoring and Forecasting across Turkey: A Contemporary Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of Drought Indices
2.2. Meteorological Drought Indices
2.3. Agricultural Drought Indices
2.4. Hydrological Drought Indices
2.5. Socioeconomic and Ecological Drought Indices
3. Review of Drought Monitoring Studies
- Which watershed or drainage basin was selected for the study area?
- Which drought indices and types were selected to monitor drought events?
- What methodology and modeling techniques were followed to reach drought monitoring or assessment findings?
- What were the study’s findings, and how did the authors interpret these findings?
- What kind of data (RS or in situ measurements) was used to derive a drought index?
Authors | Drought Type | Utilized Indices | Study Area |
---|---|---|---|
Afshar et al. [14] | MD | SPI | Ankara Province |
Danandeh Mehr et al. [15] | MD | SPI, SPEI | Ankara Province |
Dabanlı et al. [18] | MD | SPI | Turkey |
Dursun and Babalık [34] | MD | SPI, DMGI | Isparta |
Dikici [55] | MD, AD, HD | NDVI, VCI, DI, SPI, SPEI, SRI | Seyhan basin |
Bacanli et al. [66] | MD | PDSI, EDI, DMI | CAR |
Yıldız [67] | MD | SPI | CAR |
Karabulut [68] | MD | SPI | Antakya-Kahramanmaraş |
Topçu and Seçkin [69] | MD | SPI | Seyhan basin |
Tosunoğlu and Kisi [70] | HD | AMS, AMD | Çoruh basin |
Gumus and Algin [71] | MD and HD | SPI, SDI | Seyhan and Ceyhan River basins |
Bacanlı [72] | MD | SPI | Aegean region |
Payab and Turker [73] | MD | SPI | Turkish Republic of Northern Cyprus |
Kumanlıoğlu [74] | MD and HD | SPI, SPEI, SRI | Gediz River basin |
Bacanlı and Akşan [75] | MD | SPEI | Mediterranean region |
Payab and Türker [76] | MD | SPI, RDI, SZS, CZI, SDDI, CZI-SDDI | Northern Cyprus |
Cavus and Aksoy [77] | MD | SPI | Seyhan river basin |
Altın et al. [78] | HD | SDI | Eastern Mediterranean basin |
Katipoğlu et al. [79] | MD | SPI, SPEI, ZSI, RAI, RDI | Euphrates basin |
Danandeh Mehr and Vaheddoost [80] | MD | SPI, SPEI | Ankara Province |
Eris et al. [81] | MD and HD | SPI, SPEI, DPAI | Küçük Menderes River basin |
Gümüş et al. [82] | MD | SPI | Southeastern Anatolia Project region |
Yüce and Eşit [83] | MD | SPI, SPEI, scPDSI, CZI, MCZI, RAI, RDI, DI, PNI, ZI | Ceyhan basin |
Altın and Altın [84] | MD and HD | SPI, SSI | Seyhan and Ceyhan River basins |
Yılmaz et al. [85] | MD and HD | SPI, SSI | Upper Çoruh basin |
Rolbiecki et al. [86] | MD | SPI | Çukurova region (Adana, Mersin, and Osmaniye) |
Khorrami and Gunduz [87] | MD, AD, HD | SPI, SPEI, WSDI, SRI, GDSI | Turkey |
Alkan and Tombul [88] | MD | SPI, SPEI | Seyhan and Ceyhan Basin |
Akşan and Bacanli [89] | MD | CZI, EDI, PNPI, RAI, SPI, WASP, Z-Score | Southeastern Anatolia Region |
Avsaroglu and Gumus [90] | HD | SDI | Tigris Basin |
Molavizadeh et al. [91] | MD and AD | TCI, VCI, VHI | Turkey |
Ozkaya and Zerberg [92] | HD | SDI | Tigris Basin |
4. Review of Drought Forecasting Studies
5. Discussion
6. Conclusions
- Drought has occurred many times in the country, and their adverse consequences are likely to increase in the future. In general, the potential rise in the length of MD and HD was reported. More studies are required to consider climate change impacts on basins’ hydrology.
- The majority of current studies have focused on historical MD events analyzing the spatiotemporal variation of both short- and long-term SPI time series at the catchment scale. Overall, the recurrence interval of MD events was reported to be between two to four years. Some of the studies have indicated the existence of one year lag between MD and HD events. Further studies on AD, HD, and SC at the catchment scale are highly required.
- In the domains of DMF, most of the current works are still based on merely observatory gaging stations. Satellite-driven data and RS technology were rarely implemented so far. There is a need to improve drought literature using emerging technologies.
- ML techniques, particularly hybrid models, have increased the accuracy of drought forecasting tools significantly. The behavioral patterns monitored during past droughts and large-scale climatic patterns are the mostly implemented predictors. Among a variety of ML techniques, ANNs followed by ANFIS and SVM were applied more frequently for drought prediction in Turkey. However, there is still a gap between research and practice in the usage of ML-based drought prediction models. Further studies on developing a robust drought prediction tool are needed for both short- and long-term drought forecasting and mapping potential drought hazards across the country.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AD | Agricultural Drought |
ANFIS | Adaptive Neuro-Fuzzy Neural Network |
ANN | Artificial Neural Network |
ARIMA | Autoregressive Integrated Moving Average |
AWC | Available Water Content |
BAT-ELM | Bat-optimized Extreme Learning Mchine |
CAR | Central Anatolia Region |
CZI | China Z Index |
DAI | De Martonne Aridity Index |
CZI-SDDI | Combined China Z Index and SDDI |
DI | Deciles Index |
DMF | Drought Monitoring and Forecasting |
DPAI | Dimensionless Precipitation Anomaly Index |
DT | Decision Tree |
EDI | Erinç Drought Index |
ELM | Extreme Learning Machine |
EMD-ANFIS | Empirical Mode Decomposition-ANFIS |
FDT | Fuzzy Decision Tree |
FRF | Fuzzy Random Forest |
GARF | Genetic Algorithm optimized Random Forest |
GBT | Gradient Boosting Regression Tree |
GDSI | GRACE drought severity index (GDSI) |
GP | Genetic Programming |
GPR | Gaussian process regression |
HD | Hydrological Drought |
DMGI | De Martonne-Gottman Index |
IDR | Inflow-Demand Reliability Index |
KNN | K-Nearest Neighbor |
LR | Linear Regression |
MCZI | Modified Chinese Z Index |
MD | Meteorological Drought |
ML | Machine Learning |
MSRRI | Multivariate Standardized Reliability and Resilience Index |
NDVI | Normalized Difference Vegetation Index |
NSE | Nash-Sutcliffe Efficiency |
PDSI | Palmer Drought Severity Index |
PET | Potential Evapotranspiration |
PHDI | Palmer Hydrological Drought Index |
PNI | Percent of Normal Index |
R2 | Correlation Coefficient |
RAI | Rainfall Anomaly Index |
RDI | Reconnaissance Drought Index |
RDIn | Normalized RDI |
RDIst | Standardized RDI |
RF | Random Forest |
RMSE | Root Mean Square Error |
RS | Remote Sensing |
SARIMA | Seasonal Autoregressive Integrated Moving Average |
Sc-PDSI | Self-Calibrated Palmer Drought Severity Index |
SD | Socioeconomic Drought |
SDDI | Supply and Demand Drought Index |
SDI | Streamflow Drought Index |
SEDI | Socioeconomic Drought Index |
SPEI | Standardized Precipitation Evapotranspiration Index |
SPI | Standardized Precipitation Index |
SRI | Standardized Runoff Index |
SSDWI | Standardized Supply and Demand Water Index |
SSI | Standard Streamflow Index |
SVM | Support Vector Machine |
SVR | Support Vector Regression |
SZS | Statistical Z-Score |
TCI | Temperature Condition Index |
VCI | Vegetation Condition Index |
VHI | Vegetation Health Index |
W-GEP | Wavelet-Gene Expression Programming |
WMO | World Meteorological Organization |
WPGP | Wavelet Packet-Genetic Programming |
WRSR | Water Resources System Resilience |
WSDI | Water Storage Deficit Index |
WSR | Water Storage Resilience Index |
WT | Wavelet Transform |
ZSI | Z-Score Index |
References
- Sterling, S.M.; Ducharne, A.; Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Chang. 2013, 3, 385–390. [Google Scholar] [CrossRef]
- Rockström, J.; Lannerstad, M.; Falkenmark, M. Assessing the water challenge of a new green revolution in developing countries. Proc. Natl. Acad. Sci. USA 2007, 104, 6253–6260. [Google Scholar] [CrossRef] [PubMed]
- Henderson. Urbanization in developing countries. World Bank Res. Obs. 2002, 17, 89–112. [Google Scholar] [CrossRef]
- Tuel, A.; Eltahir, E.A.B. Why is the Mediterranean a climate change hot spot? J. Clim. 2020, 33, 5829–5843. [Google Scholar] [CrossRef]
- İban, M.C.; Şahin, E. Monitoring burn severity and air pollutants in wildfire events using remote sensing data: The case of Mersin wildfires in summer 2021. Gümüşhane Univ. J. Sci. Technol. 2022, 12, 487–497. [Google Scholar] [CrossRef]
- Ceylan, A. Drought Management Plan for Ankara, Turkey; Turkish State Meteorological Service: Ankara, Turkey, 2009.
- Dervisoglu, A.; Musaoglu, N.; Tanik, A.; Seker, D.Z.; Kaya, S. Satellite-based temporal assessment of a dried lake: Case study of Akgol Wetland. Fresenius Environ. Bull. 2017, 26, 352–359. [Google Scholar]
- Aydin, F.; Erlat, E.; Türkeş, M. Impact of climate variability on the surface of Lake Tuz (Turkey), 1985–2016. Reg. Environ. Chang. 2020, 20, 68. [Google Scholar] [CrossRef]
- Kilic, S.; Evrendilek, F.; Berberoglu, S.; Demirkesen, A. Environmental monitoring of land-use and land-cover changes in a Mediterranean region of Turkey. Environ. Monit. Assess. 2006, 114, 157–168. [Google Scholar] [CrossRef]
- Patel, K. Turkey Experiences Intense Drought. NASA Earth Observatory. Available online: https://earthobservatory.nasa.gov/images/147811/turkey-experiences-intense-drought (accessed on 29 January 2021).
- Türkeş, M.; Tatlı, H. Use of the standardized precipitation index (SPI) and a modified SPI for shaping the drought probabilities over Turkey. Int. J. Climatol. 2009, 29, 2270–2282. [Google Scholar] [CrossRef]
- Kurnaz, L. Drought in Turkey; İstanbul Policy Center Sabancı Üniversitesi: İstanbul, Turkey, 2014. [Google Scholar]
- Sen, B.; Topcu, S.; Türkeș, M.; Sen, B.; Warner, J.F. Projecting climate change, drought conditions and crop productivity in Turkey. Clim. Res. 2012, 52, 175–191. [Google Scholar] [CrossRef]
- Afshar, M.H.; Şorman, A.Ü.; Tosunoğlu, F.; Bulut, B.; Yilmaz, M.T.; Danandeh Mehr, A. Climate change impact assessment on mild and extreme drought events using copulas over Ankara, Turkey. Theor. Appl. Climatol. 2020, 141, 1045–1055. [Google Scholar] [CrossRef]
- Danandeh Mehr, A.; Sorman, A.U.; Kahya, E.; Hesami Afshar, M. Climate Change Impacts on Meteorological Drought using SPI And SPEI: Case Study of Ankara, Turkey. Hydrol. Sci. J. 2020, 65, 254–268. [Google Scholar] [CrossRef]
- Vazifehkhah, S.; Kahya, E. Hydrological drought associations with extreme phases of the North Atlantic and Arctic Oscillations over Turkey and northern Iran. Int. J. Climatol. 2018, 38, 4459–4475. [Google Scholar] [CrossRef]
- Van Huong, N.; Minh Nguyet, B.T.; Van Hung, H.; Minh Duc, H.; Van Chuong, N.; Do Tri, M.; Do, V.H.; Van Hien, P. Economic impact of climate change on agriculture: A case of Vietnam. AgBioForum 2022, 24, 1–12. [Google Scholar]
- Dabanlı, İ.; Mishra, A.K.; Şen, Z. Long-term spatio-temporal drought variability in Turkey. J. Hydrol. 2017, 552, 779–792. [Google Scholar] [CrossRef]
- Şen, Z. Applied Drought Modeling, Prediction and Mitigation; Elsevier: Amsterdam, The Netherlands, 2015. [Google Scholar]
- Mishra, A.K.; Singh, V.P. A review of drought concepts. J. Hydrol. 2010, 391, 202–216. [Google Scholar] [CrossRef]
- McKee, T.B.; Doesken, N.J.; Kleist, J. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA, 17–22 January 1993; pp. 179–184. [Google Scholar]
- Šebenik, U.; Brilly, M.; Šraj, M. Drought Analysis Using the Standardized Precipitation Index (SPI). Acta Geogr. Slov. 2017, 57, 31–49. [Google Scholar] [CrossRef]
- Łabędzki, L. Categorical Forecast of Precipitation Anomaly Using the Standardized Precipitation Index SPI. Water 2017, 9, 8. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; López-Moreno, J.I. Hydrological response to different time scales of climatological drought: An evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin. Hydrol. Earth Syst. Sci. 2005, 9, 523–533. [Google Scholar] [CrossRef]
- Hayes, M.J.; Svoboda, M.D.; Wilhite, D.A.; Vanyarkho, O.V. Monitoring the 1996 Drought Using the Standardized Precipitation Index. Bull. Am. Meteorol. Soc. 1999, 80, 429–438. [Google Scholar] [CrossRef]
- Szalai, S.; Szinell, C.; Zoboki, J. Drought monitoring in Hungary. Early Warn. Syst. Drought Prep. Drought Manag. 2000, 57, 182–199. [Google Scholar]
- Guttman, N.B. Accepting the Standardized Precipitation Index: A Calculation Algorithm. JAWRA J. Am. Water Resour. Assoc. 1999, 35, 311–322. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M. Differences in Spatial Patterns of Drought on Different Time Scales: An Analysis of the Iberian Peninsula. Water Resour. Manag. 2006, 20, 37–60. [Google Scholar] [CrossRef]
- Sankarasubramanian, A.; Srinivasan, K. Investigation and comparison of sampling properties of L-moments and conventional moments. J. Hydrol. 1999, 218, 13–34. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; Beguería, S.; López-Moreno, J.I. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. J. Clim. 2010, 23, 1696–1718. [Google Scholar] [CrossRef]
- Beguería, S.; Vicente-Serrano, S.M.; Reig, F.; Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 2014, 34, 3001–3023. [Google Scholar] [CrossRef]
- Baltas, E. Spatial distribution of climatic indices in northern Greece. Meteorol. Appl. 2007, 14, 69–78. [Google Scholar] [CrossRef]
- Jahangir, M.H.; Danehkar, S. A comparative drought assessment in Gilan, Iran using Pálfai drought index, de Martonne aridity index, and Pinna combinative index. Arab. J. Geosci. 2022, 15, 90. [Google Scholar] [CrossRef]
- Dursun, İ.; Babalık, A.A. De Martonne-Gottman ve Standart Yağış İndeksi yöntemleri kullanılarak kuraklığın belirlenmesi: Isparta ili örneği. Turk. J. For. 2021, 22, 192–201. [Google Scholar] [CrossRef]
- Palmer, W.C. Meteorological Drought; US Department of Commerce, Weather Bureau: Silver Spring, MD, USA, 1965; Volume 30.
- Alley, W.M. The Palmer drought severity index: Limitations and assumptions. J. Appl. Meteorol. Climatol. 1984, 23, 1100–1109. [Google Scholar] [CrossRef]
- Karl, T.R. Some spatial characteristics of drought duration in the United States. J. Appl. Meteorol. Climatol. 1983, 22, 1356–1366. [Google Scholar] [CrossRef]
- Karl, T.R. The sensitivity of the Palmer Drought Severity Index and Palmer’s Z-index to their calibration coefficients including potential evapotranspiration. J. Clim. Appl. Meteorol. 1986, 25, 77–86. [Google Scholar] [CrossRef]
- Wu, H.; Hayes, M.J.; Weiss, A.; Hu, Q. An evaluation of the Standardized Precipitation Index, the China-Z Index and the statistical Z-Score. Int. J. Climatol. A J. R. Meteorol. Soc. 2001, 21, 745–758. [Google Scholar] [CrossRef]
- Moghimi, M.M.; Zarei, A.R. Evaluating Performance and Applicability of Several Drought Indices in Arid Regions. Asia-Pac. J. Atmos. Sci. 2021, 57, 645–661. [Google Scholar] [CrossRef]
- Mahmoudi, P.; Rigi, A.; Kamak, M.M. Evaluating the sensitivity of precipitation-based drought indices to different lengths of record. J. Hydrol. 2019, 579, 124181. [Google Scholar] [CrossRef]
- Vergni, L.; Todisco, F.; Di Lena, B. Evaluation of the similarity between drought indices by correlation analysis and Cohen’s Kappa test in a Mediterranean area. Nat. Hazards 2021, 108, 2187–2209. [Google Scholar] [CrossRef]
- Ganapuram, S.; Nagarajan, R.; Sehkar, G.C.; Balaji, V. Spatio-temporal analysis of droughts in the semi-arid Pedda Vagu and Ookacheti Vagu watersheds, Mahabubnagar District, India. Arab. J. Geosci. 2015, 8, 6911–6929. [Google Scholar] [CrossRef]
- Morid, S.; Smakhtin, V.; Moghaddasi, M. Comparison of seven meteorological indices for drought monitoring in Iran. International J. Climatol. A J. R. Meteorol. Soc. 2006, 26, 971–985. [Google Scholar] [CrossRef]
- Tsakiris, G.; Pangalou, D.; Vangelis, H. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour. Manag. 2007, 21, 821–833. [Google Scholar] [CrossRef]
- Tigkas, D.; Vangelis, H.; Tsakiris, G. An enhanced effective reconnaissance drought index for the characterisation of agricultural drought. Environ. Process. 2017, 4, 137–148. [Google Scholar] [CrossRef]
- Vangelis, H.; Tigkas, D.; Tsakiris, G. The effect of PET method on reconnaissance drought index (RDI) calculation. J. Arid Environ. 2013, 88, 130–140. [Google Scholar] [CrossRef]
- Pai, D.S.; Sridhar, L.; Guhathakurta, P.; Hatwar, H.R. District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Nat. Hazards 2011, 59, 1797–1813. [Google Scholar] [CrossRef]
- Ndlovu, M.S.; Demlie, M. Assessment of Meteorological Drought and Wet Conditions Using Two Drought Indices Across KwaZulu-Natal Province, South Africa. Atmosphere 2020, 11, 623. [Google Scholar] [CrossRef]
- Çelik, M.A.; Gülersoy, A.E. Climate Classification and Drought Analysis of Mersin. MCBÜ Sos. Bilim. Derg. 2018, 16, 1–26. [Google Scholar]
- Li, Y.; Yao, N.; Sahin, S.; Appels, W.M. Spatiotemporal variability of four precipitation-based drought indices in Xinjiang, China. Theor. Appl. Climatol. 2017, 129, 1017–1034. [Google Scholar] [CrossRef]
- Türkeş, M.; Altan, G. Analysis of the year 2008 fires in the forest lands of the Muğla Regional Forest Service by using drought indices. J. Hum. Sci. 2012, 9, 912–931. [Google Scholar]
- Wilson, N.R.; Norman, L.M.; Villarreal, M.; Gass, L.; Tiller, R.; Salywon, A. Comparison of remote sensing indices for monitoring of desert cienegas. Arid Land Res. Manag. 2016, 30, 460–478. [Google Scholar] [CrossRef]
- Dikici, M.; Aksel, M. Evaluation of two vegetation indices (NDVI and VCI) Over Asi Basin in Turkey. Tek. Dergi 2021, 32, 10995–11011. [Google Scholar] [CrossRef]
- Dikici, M. Drought Analysis for the Seyhan Basin with Vegetation Indices and Comparison with Meteorological Different Indices. Sustainability 2022, 14, 4464. [Google Scholar] [CrossRef]
- Alamdarloo, E.H.; Manesh, M.B.; Khosravi, H. Probability assessment of vegetation vulnerability to drought based on remote sensing data. Environ. Monit. Assess. 2018, 190, 702. [Google Scholar] [CrossRef]
- Salimi, H.; Asadi, E.; Darbandi, S. Meteorological and hydrological drought monitoring using several drought indices. Appl. Water Sci. 2021, 11, 11. [Google Scholar] [CrossRef]
- Vicente-Serrano, S.M.; López-Moreno, J.I.; Beguería, S.; Lorenzo-Lacruz, J.; Azorin-Molina, C.; Morán-Tejeda, E. Accurate Computation of a Streamflow Drought Index. J. Hydrol. Eng. 2012, 17, 318–332. [Google Scholar] [CrossRef]
- Nalbantis, I.; Tsakiris, G. Assessment of hydrological drought revisited. Water Resour. Manag. 2009, 23, 881–897. [Google Scholar] [CrossRef]
- Shukla, S.; Wood, A.W. Use of a standardized runoff index for characterizing hydrologic drought. Geophys. Res. Lett. 2008, 35, L02405. [Google Scholar] [CrossRef]
- Vu, M.T.; Vo, N.D.; Gourbesville, P.; Raghavan, S.V.; Liong, S.Y. Hydro-meteorological drought assessment under climate change impact over the Vu Gia-Thu Bon river basin, Vietnam. Hydrol. Sci. J. 2017, 62, 1654–1668. [Google Scholar] [CrossRef]
- Mehran, A.; Mazdiyasni, O.; AghaKouchak, A. A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand. J. Geophys. Res. Atmos. 2015, 120, 7520–7533. [Google Scholar] [CrossRef]
- Shi, H.; Chen, J.; Wang, K.; Niu, J. A new method and a new index for identifying socioeconomic drought events under climate change: A case study of the East River basin in China. Sci. Total Environ. 2018, 616–617, 363–375. [Google Scholar] [CrossRef]
- Liu, S.; Shi, H.; Sivakumar, B. Socioeconomic drought under growing population and changing climate: A new index considering the resilience of a regional water resources system. J. Geophys. Res. Atmos. 2020, 125, e2020JD033005. [Google Scholar] [CrossRef]
- Zhou, J.; Chen, X.; Xu, C.; Wu, P. Assessing socioeconomic drought based on a standardized supply and demand water Index. Water Resour. Manag. 2022, 36, 1937–1953. [Google Scholar] [CrossRef]
- Bacanli, Ü.G.; Dikbaş, F.; Baran, T. Meteorological drought analysis case study: Central Anatolia. Desalination Water Treat. 2011, 26, 14–23. [Google Scholar] [CrossRef]
- Yıldız, O. Spatiotemporal analysis of historical droughts in the Central Anatolia, Turkey. Gazi Univ. J. Sci. 2014, 27, 1177–1184. [Google Scholar]
- Karabulut, M. Drought analysis in Antakya-Kahramanmaraş Graben, Turkey. J. Arid Land 2015, 7, 741–754. [Google Scholar] [CrossRef]
- Topçu, E.; Seçkin, N. Drought Analysis of the Seyhan Basin by Using Standardized Precipitation Index SPI and L-moments. J. Agric. Sci. 2016, 22, 196–215. [Google Scholar] [CrossRef]
- Tosunoglu, F.; Kisi, O. Trend analysis of maximum hydrologic drought variables using Mann-Kendall and Şen’s innovative trend method. River Res. Appl. 2017, 33, 597–610. [Google Scholar] [CrossRef]
- Gumus, V.; Algin, H.M. Meteorological and hydrological drought analysis of the Seyhan-Ceyhan River Basins, Turkey. Meteorol. Appl. 2017, 24, 62–73. [Google Scholar] [CrossRef]
- Güner Bacanli, Ü. Trend analysis of precipitation and drought in the Aegean region, Turkey. Meteorol. Appl. 2017, 24, 239–249. [Google Scholar] [CrossRef]
- Payab, A.H.; Türker, U. Analyzing temporal-spatial characteristics of drought events in the northern part of Cyprus. Environ. Dev. Sustain. 2018, 20, 1553–1574. [Google Scholar] [CrossRef]
- Kumanlioglu, A.A. Characterizing meteorological and hydrological droughts: A case study of the Gediz River Basin, Turkey. Meteorol. Appl. 2019, 27, e1857. [Google Scholar] [CrossRef]
- Bacanlı, Ü.G.; Akşan, G.N. Drought analysis in Mediterranean region. Pamukkale Üniversitesi Mühendislik Bilim. Derg. 2019, 25, 665–671. [Google Scholar] [CrossRef]
- Payab, A.H.; Türker, U. Comparison of standardized meteorological indices for drought monitoring at northern part of Cyprus. Environ. Earth Sci. 2019, 78, 309. [Google Scholar] [CrossRef]
- Cavus, Y.; Aksoy, H. Spatial drought characterization for Seyhan River basin in the Mediterranean region of Turkey. Water 2019, 11, 1331. [Google Scholar] [CrossRef]
- Altin, T.B.; Altin, B.N. Response of hydrological drought to meteorological drought in the eastern Mediterranean Basin of Turkey. J. Arid Land 2021, 13, 470–486. [Google Scholar] [CrossRef]
- Katipoğlu, O.M.; Acar, R.; Şengül, S. Comparison of meteorological indices for drought monitoring and evaluating: A case study from Euphrates basin, Turkey. J. Water Clim. Chang. 2020, 11, 29–43. [Google Scholar] [CrossRef]
- Danandeh Mehr, A.; Vaheddoost, B. Identification of the trends associated with the SPI and SPEI indices across Ankara, Turkey. Theor. Appl. Climatol. 2020, 139, 1531–1542. [Google Scholar] [CrossRef]
- Eris, E.; Cavus, Y.; Aksoy, H.; Burgan, H.I.; Aksu, H.; Boyacioglu, H. Spatiotemporal analysis of meteorological drought over Kucuk Menderes River Basin in the Aegean Region of Turkey. Theor. Appl. Climatol. 2020, 142, 1515–1530. [Google Scholar] [CrossRef]
- Gumus, V.; Simsek, O.; Avsaroglu, Y.; Agun, B. Spatio-temporal trend analysis of drought in the GAP Region, Turkey. Nat. Hazards 2021, 109, 1759–1776. [Google Scholar] [CrossRef]
- Yuce, M.I.; Esit, M. Drought monitoring in Ceyhan basin, Turkey. J. Appl. Water Eng. Res. 2021, 9, 293–314. [Google Scholar] [CrossRef]
- Bayer Altın, T.; Sarış, F.; Altın, B.N. Determination of drought intensity in Seyhan and Ceyhan River Basins, Turkey, by hydrological drought analysis. Theor. Appl. Climatol. 2020, 139, 95–107. [Google Scholar] [CrossRef]
- Yılmaz, M.; Alp, H.; Tosunoğlu, F.; Aşıkoğlu, Ö.L.; Eriş, E. Impact of climate change on meteorological and hydrological droughts for Upper Coruh Basin, Turkey. Nat. Hazards 2022, 112, 1039–1063. [Google Scholar] [CrossRef]
- Rolbiecki, R.; Yücel, A.; Kocięcka, J.; Atilgan, A.; Marković, M.; Liberacki, D. Analysis of SPI as a drought indicator during the maize growing period in the Çukurova Region (Turkey). Sustainability 2022, 14, 3697. [Google Scholar] [CrossRef]
- Khorrami, B.; Gündüz, O. Detection and analysis of drought over Turkey with remote sensing and model-based drought indices. Geocarto Int. 2022, 37, 1–23. [Google Scholar] [CrossRef]
- Alkan, A.; Tombul, M. Temporal drought assessment using various indices of the Seyhan and Ceyhan Basins, Turkey. Appl. Ecol. Environ. Res. 2022, 20, 555–569. [Google Scholar] [CrossRef]
- Akşan, G.N.; Bacanli, Ü.G. Comparison of the meteorological drought indices according to the parameter(s) used in the Southeastern Anatolia Region, Turkey. Environ. Res. Technol. 2021, 4, 230–243. [Google Scholar] [CrossRef]
- Avsaroglu, Y.; Gumus, V. Assessment of hydrological drought return periods with bivariate copulas in the Tigris river basin, Turkey. Meteorol. Atmos. Phys. 2022, 134, 95. [Google Scholar] [CrossRef]
- Molavizadeh, N.; Sertel, E.; Demirel, H. Drought Conditions in Turkey Between 2004 and 2013 Via Drought Indices Derived from Remotely Sensed Data. In Energy, Transportation and Global Warming; Green Energy and Technology; Grammelis, P., Ed.; Springer: Cham, Switzerland, 2016. [Google Scholar] [CrossRef]
- Ozkaya, A.; Zerberg, Y. A 40-Year Analysis of the Hydrological Drought Index for the Tigris Basin, Turkey. Water 2019, 11, 657. [Google Scholar] [CrossRef]
- Başakın, E.E.; Ekmekcioğlu, Ö.; Özger, M. Drought analysis with machine learning methods. Pamukkale Univ. J. Eng. Sci. 2019, 25, 985–991. [Google Scholar] [CrossRef]
- Tufaner, F.; Özbeyaz, A. Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms. Environ. Monit. Assess. 2020, 192, 576. [Google Scholar] [CrossRef]
- Başakın, E.E.; Ekmekcioğlu, Ö.; Özger, M. Drought prediction using hybrid soft-computing methods for semi-arid region. Model. Earth Syst. Environ. 2021, 7, 2363–2371. [Google Scholar] [CrossRef]
- Özger, M.; Başakın, E.E.; Ekmekcioğlu, Ö.; Hacısüleyman, V. Comparison of wavelet and empirical mode decomposition hybrid models in drought prediction. Comput. Electron. Agric. 2020, 179, 105851. [Google Scholar] [CrossRef]
- Danandeh Mehr, A.; Tur, R.; Çalışkan, C.; Tas, E. A novel fuzzy random forest model for meteorological drought classification and prediction in ungauged catchments. Pure Appl. Geophys. 2020, 177, 5993–6006. [Google Scholar] [CrossRef]
- Mehdizadeh, S.; Ahmadi, F.; Danandeh Mehr, A.; Safari, M.J.S. Drought modeling using classic time series and hybrid wavelet-gene expression programming models. J. Hydrol. 2020, 587, 125017. [Google Scholar] [CrossRef]
- Danandeh Mehr, A.; Fathollahzadeh Attar, N. A gradient boosting tree approach for SPEI classification and prediction in Turkey. Hydrol. Sci. J. 2021, 66, 1653–1663. [Google Scholar] [CrossRef]
- Danandeh Mehr, A. Drought classification using gradient boosting decision tree. Acta Geophys. 2021, 69, 909–918. [Google Scholar] [CrossRef]
- Danandeh Mehr, A.; Safari, M.J.S.; Nourani, V. Wavelet packet-genetic programming: A new model for meteorological drought hindcasting. Tek. Dergi 2021, 32, 11029–11050. [Google Scholar] [CrossRef]
- Gholizadeh, R.; Yılmaz, H.; Danandeh Mehr, A. Multitemporal meteorological drought forecasting using Bat-ELM. Acta Geophys. 2022, 70, 917–927. [Google Scholar] [CrossRef]
- Danandeh Mehr, A.; Torabi Haghighi, A.; Jabarnejad, M.; Safari, M.J.S.; Nourani, V. A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting. Water 2022, 14, 755. [Google Scholar] [CrossRef]
- Citakoglu, H.; Coşkun, Ö. Comparison of hybrid machine learning methods for the prediction of short-term meteorological droughts of Sakarya Meteorological Station in Turkey. Environ. Sci. Pollut. Res. 2022, 29, 75487–75511. [Google Scholar] [CrossRef]
- Katipoğlu, O.M. Prediction of streamflow drought index for short-term hydrological drought in the semi-arid Yesilirmak Basin using Wavelet transform and artificial intelligence techniques. Sustainability 2023, 15, 1109. [Google Scholar] [CrossRef]
- Durdu, Ö.F. Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey. Stoch. Environ. Res. Risk Assess. 2010, 24, 1145–1162. [Google Scholar] [CrossRef]
- Sundararajan, K.; Garg, L.; Srinivasan, K.; Bashir, A.K.; Kaliappan, J.; Ganapathy, G.P.; Selvaraj, S.K.; Meena, T. A contemporary review on drought modeling using machine learning approaches. Comput. Model. Eng. Sci. 2021, 128, 447–487. [Google Scholar] [CrossRef]
- Fung, K.F.; Huang, Y.F.; Koo, C.H.; Soh, Y.W. Drought forecasting: A review of modelling approaches 2007–2017. J. Water Clim. Chang. 2020, 11, 771–799. [Google Scholar] [CrossRef]
Index | Input Parameters | Features |
---|---|---|
SPI | P * | Highlighted by the WMO as a starting point for MD monitoring |
SPEI | P, PET | Similar to SPI but with a temperature component. Sensitive to PET calculation method |
DAI | P, T | Can also be used in climate classifications |
IDMG | P, T | Can also be used in climate classifications |
PDSI and ScPDSI | P, T, AWC | Calculation is complex and needs serially complete data |
CZI and MCZI | P | Intended to improve upon SPI data |
DI | P | Easy to calculate |
RDI | P, T | Similar to SPEI |
PNI | P | Suitable for drought assessment in a single region or season. |
ZSI | P | Easy to calculate |
EDI | P, T | A simple drought indicator for duration separation purposes, Suitable for arid/humid areas such as Turkey |
Authors | Drought Type | Method | Study Area |
---|---|---|---|
Başakın et al. [93] | MD | SVM, KNN | Kayseri |
Tufaner and Özbeyaz [94] | MD | LR, ANN, SVM, DT | Adıyaman |
Başakın et al. [95] | MD | ANFIS, EMD-ANFIS | Adana |
Özger et al. [96] | MD | ANFIS, SVM, DT | Antalya and Adana |
Danandeh Mehr et al. [97] | MD | FRF, FDT | Antalya |
Mehdizadeh et al. [98] | MD | AR, BL, W-GEP | Ankara |
Danandeh Mehr and Attar [99] | MD | GBT | Antalya and Ankara |
Danandeh Mehr [100] | MD | DT, GP, GBT | Ankara and Antalya |
Danandeh Mehr et al. [101] | MD | DT, GP, AR, WPGP | Ankara |
Gholizadeh et al. [102] | MD | BAT-ELM | Ankara |
Danandeh Mehr et al. [103] | MD | RF, ELM, Bat-ELM, GARF | Ankara |
Citakoglu and Coşkun [104] | MD | ANN, ANFIS, GPR, SVR, KNN | Sakarya |
Katipoğlu [105] | HD | SVR, GPR, RT, ET | Yesilirmak |
Durdu [106] | MD | ARIMA, SARIMA | Büyük Menderes river basin |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Soylu Pekpostalci, D.; Tur, R.; Danandeh Mehr, A.; Vazifekhah Ghaffari, M.A.; Dąbrowska, D.; Nourani, V. Drought Monitoring and Forecasting across Turkey: A Contemporary Review. Sustainability 2023, 15, 6080. https://doi.org/10.3390/su15076080
Soylu Pekpostalci D, Tur R, Danandeh Mehr A, Vazifekhah Ghaffari MA, Dąbrowska D, Nourani V. Drought Monitoring and Forecasting across Turkey: A Contemporary Review. Sustainability. 2023; 15(7):6080. https://doi.org/10.3390/su15076080
Chicago/Turabian StyleSoylu Pekpostalci, Dilayda, Rifat Tur, Ali Danandeh Mehr, Mohammad Amin Vazifekhah Ghaffari, Dominika Dąbrowska, and Vahid Nourani. 2023. "Drought Monitoring and Forecasting across Turkey: A Contemporary Review" Sustainability 15, no. 7: 6080. https://doi.org/10.3390/su15076080