Abstract
Trend analysis has an interdisciplinary context that is shared by many researchers all over the world. The preliminary recommendation in this chapter is about visual trend examination and identification in a given time series to feel what are the possibilities of trend existence either holistically or partially. In this manner the researcher will be able to decide which type of the probabilistic, statistical, and mathematical approach for its objective determination. A brief discussion about trend analysis usage is presented on the basis of a set of disciplines. Additionally, pros and cons about trend analysis approaches are presented briefly and finally future trend research directions are mentioned with the purpose of this book.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Davis, J. C. (1986). Statistics and data analysis in geology. New York: Wiley.
Esterby, S. R. (1996). Review of methods for the detection and estimation of trends with emphasis on water quality applications. Hydrological Processes, 10(2), 127–149.
Fatichi, S., Ivanov, V. Y., Caporali, E. (2013). Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series. Climate Dynamics, 40, 1841–1861.
Hess, A., Iyer, H., & Malm, W. (2001). Linear trend analysis: A comparison of methods. Atmospheric Environment, 35(30), 5211–5222.
Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water-quality data. Water Resources Research, 18, 107–121.
IPCC. (2007). Climate change 2007: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
IPCC. (2013). Climate change 2013: The physical science basis. Contribution of Working Group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
IPCC. (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge, UK: Cambridge University Press.
Kendall, M. G. (1970). Rank correlation methods (4th ed.). London: Griffin.
Maas, A., Hufschmidt, M., Dorfman, R., Thomas, H. A., Jr., Marglin, S., & Fair, G. M. (1962). Design of water resource systems. Cambridge, MA: Harvard University Press.
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259.
Mastrandrea, M. D., Heller, N. E., Root, T. L., & Schneider, S. H. (2010). Bridging the gap: Linking climate-impact research with adaptation planning and management. Climate Change, 100, 87–101.
Milly, P. C. D., Julio, B., Falkenmark, M., Hirsch, R. M., Kundzewicz, Z. W., Lettenmaier, P., et al. (2008). Stationarity is dead: Whither water management? Science, 319, 573–574.
Ross, J. T. (1995). Fuzzy logic with engineering applications. McGraw-Hill, Inc., 600 p.
Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s Tau. Journal of the American Statistical Association, 63(324), 1379–1389.
Şen, Z. (2010). Fuzzy logic and hydrological modeling (p. 340). New York: Taylor and Francis Group, CRC Press.
Zhang, X., Harvey, K. D., Hogg, W. D., & Yuzyk, T. R. (2010). Trends in Canadian streamflow. Water Resources Research, 37, 987–998.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Şen, Z. (2017). Introduction. In: Innovative Trend Methodologies in Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-52338-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-52338-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-52337-8
Online ISBN: 978-3-319-52338-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)