Advertisement

Standardized Precipitation Index Analyses with Wavelet Techniques at Watershed Basin

  • Funda DökmenEmail author
  • Zafer Aslan
  • Ahmet Tokgözlü
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10405)

Abstract

Climatic changes play an important role on agricultural production. Their impacts on agricultural sciences are significantly different form one region to another. There are many factors on the impact of climate change in farmlands. SPI (Standardized Precipitation Index) is one of the drought indices. SPI values show similar variation in relatively drought periods and different amounts of precipitation. The main aim of this study is to understand different factors and their role at different scales with wavelet analysis of long-term precipitation and the SPI data. All data is being evaluated under the catchment basin area. Data records cover Big Menderes Catchment area at Aegean Region in Turkey between 1960 and 2015. The results of this study underline in general small scale influences have more important role in all selected stations. Spatial variation of entropy at watershed basin has been associated with micro, meso or large scale influences. In general, gradually increasing trends of drought and wet conditions are resulted in small scale factors.

Keywords

Drought index Rainfall Sustainable agriculture Signal analysis 

Notes

Akcknowledgements

The authors gratefully appreciate and acknowledge the International Centre for Theoretical Physics (ICTP)-Associateship Program.

References

  1. Adib, A.: A new approach for determination of peak flood hydrograph. J. Food Agric. Environ. 9(3&4), 1129–1130 (2011)Google Scholar
  2. Aslan, Z., Siddiqi, A.H., Manchanda, P.: Temporal and spatial variation of temperature and precipitation indices. J. Food Agric. Environ. 9(3–4), 912–922 (2011)Google Scholar
  3. Dökmen, F., Aslan, Z.: Evaluation of the parameters of water quality with wavelet techniques. Water Resour. Manag. 27, 4977–4988 (2013). doi: 10.1007/s11269-013-0454-5. SpringerCrossRefGoogle Scholar
  4. Giadrossich, F., Niedda, M., Cohen, D., Pirastru, M.: Evaporation in a Mediterranean environment by energy budget and penman methods, Lake Barats, Sardania, Italy. Hydrol. Earth Syst. Sci. 19, 2451–2468 (2015). doi: 10.5194/hess-19-2451 CrossRefGoogle Scholar
  5. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2006)zbMATHGoogle Scholar
  6. Incerti, G., Feoli, E., Salvati, L., Brunetti, A.: Analysis of bioclimatic time series and their neural network-based classification to characterise drought risk patterns in south italy. Int. J. Biometeorol. 51, 253–263 (2007). doi: 10.1007/s00484-006-0071-6 CrossRefGoogle Scholar
  7. Jha, S.K., Zhao, H., Woldemeskel, F.M., Sivakumar, B.: Network theory and spatial rainfall connections: an interpretation. J. Hydrol. 527(2015), 13–19 (2015)CrossRefGoogle Scholar
  8. Li, M.: Fractal time series. Math. Probl. Eng. 2010, 1–26 (2009). doi: 10.1155/2010/157264. Article ID 157264. Hindawe Publishing CorporationGoogle Scholar
  9. Massel, S.R.: Wavelet analysis for processing of ocean surface wave records. Ocean Eng. 28, 957–987 (2001)CrossRefGoogle Scholar
  10. McKee, T.B., Doesken, N.J., Kleist, J.: The relationship of drought frequency and duration of time scales. In: 8th Conference on Applied Climatology, pp. 179–186. American Meteorological Society, Boston (1993)Google Scholar
  11. McKee, T.B., Doesken, N.J., Kleist, J.: Drought monitoring with multiple time scales. In: 9th 1999 Conference on Applied Climatology, pp. 233–236. American Meteorological Society, Boston (1995)Google Scholar
  12. Moosavi, V., Talebi, A., Hadian, M.R.: Development of a hybrid wavelet packet-group method of data handling (WPGMDH) model for runoff forecasting. Water Resour. Manag. 31, 43–59 (2017). doi: 10.1007/s11269-016-1507-3 CrossRefGoogle Scholar
  13. Moyano, M.C., Tornos, L., Juana, L.: Water balance and flow rate discharge on a receiving water body: application to the B-XII irrigation district in Spain. J. Hydrol. 527(2015), 38–49 (2015)CrossRefGoogle Scholar
  14. Palmer, W.C.: Meteorological drought. Research Paper No. 45, US Department of Commerce, Weather Bureau, Washington, DC (1965)Google Scholar
  15. Paulo, A.A., Pereira, L.S.: Stochastic prediction of drought class transitions. Water Resour. Manag. 22, 1277–1296 (2008)CrossRefGoogle Scholar
  16. Paulo, A.A., Rosa, R.D., Pereira, L.S.: Climate trends and behaviour of drought indices based on precipitation and evapotranspiration in Portugal. Nat. Hazards Earth Syst. Sci. 12, 1481–1491 (2012)CrossRefGoogle Scholar
  17. Paulo, A., Martins, D., Pereira, L.S.: Influence of precipitation changes on the SPI and related drought severity. An analysis using long-term data series. Water Resour. Manag. 30(15), 5737–5757 (2016)CrossRefGoogle Scholar
  18. Pereira, L.S., Rosa, R.D., Paulo, A.A.: Testing a modification of the palmer drought severity index for Mediterranean environments. In: Rossi, G., Vega, T., Bonaccorso, B. (eds.) Methods and Tools for Drought Analysis and Management, pp. 149–167. Springer, Dordrecht (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Food and Agricultural Vocational SchoolKocaeli UniversityKartepeTurkey
  2. 2.Faculty of EngineeringIstanbul Aydın UniversityIstanbulTurkey
  3. 3.International Centre for Theoretical Physics (ICTP)TriesteItaly
  4. 4.Science and Literature FacultySüleyman Demirel UniversityIspartaTurkey

Personalised recommendations