Skip to main content
SpringerLink
Log in
Book cover

Sustainable Water Resources Planning and Management Under Climate Change pp 3–37Cite as

Climate Variability and Changes in Precipitation Extremes and Characteristics

  • Chapter
  • First Online:

Abstract

Climate variability and change are expected to bring several changes to hydrologic cycles and regimes in different parts of the world. Natural climate variability based on large-scale, global inter-year, quasi-decadal and decadal, and multidecadal-coupled oceanic–atmospheric oscillations (e.g., El Niño Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), Madden–Julian Oscillation (MJO), Indian Ocean Dipole (IOD)) contribute to regional variations in extremes and characteristics of essential climatic variables (e.g., temperature, precipitation, etc.) in different parts of the globe. These oscillations defined based on climate anomalies that are related to each other at large distances (referred to as teleconnections) are known to impact regional and global climate. Linkages of these teleconnections to the variability in regional precipitation patterns have been well documented in several research studies. This chapter focuses on evaluation of climate variability influences on precipitation extremes and characteristics. Several indices and metrics are discussed for such evaluation, and a few results from case studies are presented.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Adams BJ, Howard CDD (1986) Pathology of design storms. Canad Water Resour J 11(3):49–55

    Article  Google Scholar 

  • Adams BJ, Papa F (2000) Urban stormwater management planning with analytical probabilistic models. Wiley, New York

    Google Scholar 

  • Alexandersson HA (1986) Homogeneity test applied to precipitation data. J Climatol 6(6):661–675

    Article  Google Scholar 

  • Beek EV (2010) Managing water under current climate variability. In: Ludwig F, Kabat P, Schaik H, Valk M (eds) Climate change adaptation in water sector. Earthscan, London

    Google Scholar 

  • Behera P, Guo Y, Teegavarapu RSV, Branham T (2010) Evaluation of antecedent storm event characteristics for different climatic regions based on inter-event time definition (IETD). In: Proceedings of the world environmental and water resources congress 2010, ASCE Conference Proceedings. doi:10.1061/41114(371)251

  • Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc 26(2):211–252

    Google Scholar 

  • Buishand TA (1982) Some methods for testing the homogeneity of rainfall records. J Hydrol 58(1–2):11–27

    Article  Google Scholar 

  • Chernick MR (2007) Bootstrap methods: a guide for practitioners and researchers. Wiley-Interscience, Hoboken

    Book  Google Scholar 

  • Chiew FHA, Piechota TC, Dracup JA, McMahon TA (1998) El Niño southern oscillation and Australian rainfall, streamflow and drought: links and potential for forecasting. J Hydrol 204:138–149

    Article  Google Scholar 

  • Chiew FHS, Zhaou SL, McMahon TA (2003) Use of seasonal streamflow forecasts in water resources management. J Hydrol 270:135–144

    Article  Google Scholar 

  • Corder WG, Foreman DI (2009) Nonparametric statistics for non-statisticians. Wiley, Hoboken

    Book  Google Scholar 

  • Cronin TM (2009) Paleoclimates: understanding climate change past and present. Columbia University Press, New York

    Google Scholar 

  • Dai A, Fung J, Del Genio A (1997) Surface observed global land precipitation variations during 1900–1988. J Clim 11:2943–2962

    Article  Google Scholar 

  • Davison AC, Hinkley DV (1997) Bootstrap methods and their applications

    Book  Google Scholar 

  • Dawod MAA, El-Rafy MA (2002) Towards long range forecast of Nile Flood, Proceedings of the fourth conference on meteorology and sustainable development. Meteorologist Specialist Association, Cairo

    Google Scholar 

  • Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–26

    Article  Google Scholar 

  • Efron B, Gong G (1983) A leisurely look at the bootstrap, the jackknife, and cross validation. Am Stat 37:36–48

    Google Scholar 

  • Efron B, Tibshirani R (1993) An introduction to the bootstrap. Chapman and Hall, London

    Book  Google Scholar 

  • Enfield DB, Mestas-Nunez AM, Trimble PJ (2001) The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S. Geophys Res Lett 28(10):2077–2080

    Article  Google Scholar 

  • Ghil M (2002) Natural climate variability. In: MacCraken MC, Perry JS (eds) Encyclopedia of global environmental change—Volume 1, The earth system—physical and chemical dimensions of global environmental change. Wiley, Chichester, pp 544–549

    Google Scholar 

  • Goly A, Teegavarapu RSV (2012) Influence of teleconnections on spatial and temporal variability of extreme precipitation events in Florida. World Environ Water Resour Congress 2012:1899–1908. doi:10.1061/9780784412312.190

    Google Scholar 

  • Goly A, Teegavarapu RSV (2014) Individual and coupled influences of AMO and ENSO on regional precipitation characteristics and extremes. Water Resour Res 50. doi: 10.1002/2013WR014540

  • Gurdak JJ, Hanson RT, Green TR (2009) Effects of climate variability and change on groundwater resources of the United States, Fact Sheet 2009–3074., United States Geological Survey

    Google Scholar 

  • Helsel DR, Hirsch RM (2002) Statistical methods in water resources, Book 4, hydrologic analysis and interpretation, United States Geological Survey

    Google Scholar 

  • Hurrell JW, Van Loon H (1995) Decadal Variations in Climate Associated with the North Atlantic Oscillation. National Center for Atmospheric Research, Boulder

    Google Scholar 

  • Jain D, Singh VP (1987) Comparison of some flood frequency distributions using empirical data. In: Singh VP (ed) Hydrologic frequency modeling. Reidel, Dordrecht, pp 467–485

    Chapter  Google Scholar 

  • Jarque CM, Bera AK (1987) A test for normality of observations and regression residuals. Int Stat Rev 55(2):163–172

    Article  Google Scholar 

  • Koch-Rose M, Berry L, Bloetscher F, Hernández Hammer N, Mitsova-Boneva D, Restrepo J, Root T, Teegavarapu R (2011) Florida water management and adaptation in the face of climate change. Florida Climate Change Task Force

    Google Scholar 

  • Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of Köppen-Geiger climate classification updated. Meteorol Z 15(3):259–263

    Article  Google Scholar 

  • Lilliefors HW (1967) On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J Am Stat Assoc 62(318):399–402

    Article  Google Scholar 

  • Ljung G, Box G (1978) On a measure of lack of fit in time series models. Biometrika 65:297–303

    Article  Google Scholar 

  • Ludwig F (2009) Using seasonal climate forecasts for water management. In: Ludwig F, Kabat P, Schaik HV, Van Der Valk M (eds) Climate change adaptation in the water sector. Earthscan Publishers, London

    Google Scholar 

  • Mage DT (1982) An objective graphical method for testing normal distributional assumptions using probability plots. Am Stat 36(2):116–120

    Google Scholar 

  • Mantua NJ, Hare SR, Zhang Y, Wallace J, Francis R (1997) A Pacific interdecadal oscillation with impacts on salmon production. Bull Am Meteorol Soc 78:1069–1079

    Google Scholar 

  • Mantua NJ, Hare SR (2002) The Pacific decadal oscillation. J Oceanogr 58(1):35–44

    Article  Google Scholar 

  • McBean AE, Rovers FA (1998) Statistical procedures for analysis of environmental monitoring data and risk assessment. Prentice Hall, Upper Saddle River

    Google Scholar 

  • McKee TB, Doesken NJ, Kleist J (1995) Drought monitoring with multiple timescales. In: Proceedings of the ninth conference on applied climatology, Dallas, TX, 15–20 January 1995. Boston American Meteorological Society, pp 233–236

    Google Scholar 

  • McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scale. In: Proceedings of the eighth conference on applied climatology, Anaheim, California, 17–22 January 1993. American Meteorological Society, Boston, pp 179–184

    Google Scholar 

  • Newman M, Compo G, Alexander M (2003) ENSO-forced variability of the Pacific decadal oscillation. NOAA-CIRES Climate Diagnostic Center, Boulder. NOAA. 013

    Google Scholar 

  • Pettitt AN (1979) A non-parametric approach to the change-point problem. Appl Stat 28(2):126–135

    Article  Google Scholar 

  • Pierce M (2013) Influences of decadal and multidecadal oscillations on regional precipitation extremes and characteristics, Thesis, Florida Atlantic University

    Google Scholar 

  • Rosenzweig C, Hillel D (2008) Climate variability and the global harvest: impacts of El Nino and other oscillations on agroecosystems. Oxford University Press, New York

    Google Scholar 

  • Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biom Bull 2:110–114

    Article  CAS  Google Scholar 

  • Schneider N, Cornuelle B (2005) The forcing of the Pacific decadal oscillation. J Clim 18(2005):4255–4273

    Google Scholar 

  • Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC, Boca Raton, FL

    Book  Google Scholar 

  • Smirnov NV (1939) On the estimation of the discrepancy between empirical curves of distribution for two independent samples. (Russian). Bull Moscow Univ 2:3–16

    Google Scholar 

  • Souza FA, Lall U (2003) Seasonal to interannual ensemble streamflow forecasts for Ceara, Brazil: application of a multivariate, semiparametric algorithm. Water Resour Res 39

    Google Scholar 

  • Stone RC, Hammer GL, Marcussen T (1996) Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384:252–255

    Article  CAS  Google Scholar 

  • Svensson C, Jones D (2010) Review of rainfall frequency estimation methods. J Flood Risk Manag 3:296–313

    Article  Google Scholar 

  • Teegaravapu RSV, Goly A, Obeysekera J (2013) Influences of Atlantic multidecadal oscillation phases on spatial and temporal variability of regional precipitation extremes. J Hydrol 495:74–93

    Article  Google Scholar 

  • Teegavarapu RSV (2009) Estimation of missing precipitation records integrating surface interpolation techniques and spatio-temporal association rules. J Hydroinf 11(2):133–146

    Article  Google Scholar 

  • Teegavarapu RSV (2010) Modeling climate change uncertainties in water resources management models. Environ Model Softw 25(10):1261–1265

    Article  Google Scholar 

  • Teegavarapu RSV (2012) Spatial interpolation using non-linear mathematical programming models for estimation of missing precipitation records. Hydrol Sci J 57(3):383–406

    Article  Google Scholar 

  • Teegavarapu RSV (2007) Use of universal function approximation in variance-dependent interpolation technique: an application in Hydrology. J Hydrol 332:16–29

    Article  Google Scholar 

  • Teegavarapu RSV (2013) Floods in a changing climate: extreme precipitation. Cambridge University Press, Cambridge, UK

    Google Scholar 

  • Teegavarapu RSV (2014) Statistical corrections of spatially interpolated missing precipitation data estimates. Hydrol Process 28(11):3789–3808

    Article  Google Scholar 

  • Teegavarapu RSV (2016) Evaluation of trends and variability in monthly sea level, temperature and precipitation of Japan: links to climate variability and change. Technical Report, Research Ceneter for Urban Safety and Security (RCUSS). Kobe University, Kobe

    Google Scholar 

  • Teegavarapu RSV, Chandramouli V (2005) Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. J Hydrol 312:191–206

    Article  Google Scholar 

  • Teegavarapu RSV, Nayak A, Pathak C (2011) Assessment of long‐term trends in extreme precipitation: implications of in‐filled historical data and temporal window‐based analysis. In: Proceedings of the 2011 world environmental and water resources congress, doi:10.1061/41173(414)419

  • USGS (2011) Evidence of multidecadal climate variability in the Gulf of Mexico. USGS

    Google Scholar 

  • Von Neumann J (1941) Distribution of the ratio of the mean square successive difference to the variance. Ann Math Stat 12:367–395

    Article  Google Scholar 

  • Wallace JM, Gutzler DS (1980). Teleconnections in the geopotential height field during the northern hemisphere winter. Department of Atmospheric Sciences, University of Washington, Monthly Weather Review

    Google Scholar 

  • Walsh PD, Lawler DM (1981) Rainfall seasonality: description, spatial patterns and change through time. Weather 36:201–208

    Article  Google Scholar 

  • Welch BL (1947) The generalization of student’s problem when several different population variances are involved. Biometrika 34(1–2):28–35

    CAS  Google Scholar 

  • WMO (2009) Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation. WMO, Geneva

    Google Scholar 

  • WMO (2012) Standard precipitation index user guide, World Meteorological Organization (WMO), WMO-No: 1090. WMO, Geneva

    Google Scholar 

  • Zhang Y, Wallace J, Battisti D (1996) ENSO-like interdecadal variability: 1900–93. Department of Atmospheric Sciences, University of Washington, Seattle, Washington

    Google Scholar 

Download references

Acknowledgments

The author would like to acknowledge the assistance provided by Ms. Milla Pierce in the preparation of this chapter.

Author information

Authors and Affiliations

  1. Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL, USA

    Ramesh S. V. Teegavarapu

Authors
  1. Ramesh S. V. Teegavarapu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ramesh S. V. Teegavarapu .

Editor information

Editors and Affiliations

  1. School of Civil Engineering Division of Hydraulics and Environmental Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Elpida Kolokytha

  2. The Research Center for Urban Safety and Security, Kobe University, Kobe, Hyogo, Japan

    Satoru Oishi

  3. Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, Florida, USA

    Ramesh S.V. Teegavarapu

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Singapore

About this chapter

Cite this chapter

Teegavarapu, R.S.V. (2017). Climate Variability and Changes in Precipitation Extremes and Characteristics. In: Kolokytha, E., Oishi, S., Teegavarapu, R. (eds) Sustainable Water Resources Planning and Management Under Climate Change. Springer, Singapore. https://doi.org/10.1007/978-981-10-2051-3_1

Download citation

Publish with us

Policies and ethics

Search

Navigation