Skip to main content

Advertisement

SpringerLink
Log in

Daily weather generator with drought properties by copulas and standardized precipitation indices

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The weather generator is an essential process in water resource assessment. Most weather generators focus on extreme rainfall events and rainfall amounts in a relatively short time scale. However, drought events often last more than several months, which conventional weather generators hardly generate. Conventional weather generators assume that monthly rainfalls are independent, skewing drought event generation. The purpose of this study is to construct a weather generator with improved drought property generation, combining with monthly rainfall data from conventional weather generators and characteristics of standardized precipitation indices. The proposed weather generators employs four drought parameters, namely starting month, duration, average, and minimum standardized precipitation indices, generated using a copula method. Analytical results show that the four parameters generated by the copula method are consistent with historical records. The proposed weather generator overcomes the limitation of conventional weather generators and can generate both rainfall and drought properties. The results also indicate that the assumption of monthly independence in drought generation can cause underestimated occurrence and duration of drought events. The proposed generator is also demonstrated for climate change assessment. The analytical results show that the uncertainties from the selection of weather generators are even higher than those from the selections of global circulation models while applying to water shortage assessment. We therefore suggest that weather generators should consider drought characteristics which can be measured using the standardized precipitation index to reduce the uncertainty.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Bouyé, E., Durrleman, V., Nikeghbali, A., Riboulet, G., & Roncalli, T. (2000). Copulas for finance-a reading guide and some applications. Available at SSRN 1032533.

  • Bussay, A. M., Szinell, C., Hayes, M., & Svoboda, M. (1998). Monitoring drought in Hungary using the standardized precipitation index. Annales Geophysicae, Supplement 11 to Vol 16, the Abstract Book of 23rd EGS General Assembly, C450. April 1998, Nice; France.

  • Chen, C. T., Chu, J. T., Hsu, H. H., Suu, C. H., Chou, C., & Weng, S. P. (2014). Projection of climate change over Taiwan using statistical downscaling scheme. Atmospheric Sciences, 42(3), 207–251 (Abstract in English).

    Google Scholar 

  • Cowpertwait, P. S. P. (1991). Further developments of the Neyman-Scott clustered point process for modelling rainfall. Water Resources Research, 27, 1431–1438.

    Article  Google Scholar 

  • Fatichi, S., Ivanov, V. Y., & Caporali, E. (2011). Simulation of future climate scenarios with a weather generator. Advances in Water Resources, 34(4), 448–467.

    Article  Google Scholar 

  • Glenis, V., Pinamonti, V., Hall, J., & Kilsby, C. (2015). A transient stochastic weather generator incorporating climate model uncertainty. Advances in Water Resources, 85, 14–26.

    Article  Google Scholar 

  • Gregory, J. M., Wigley, T. M. L., & Jones, P. D. (1992). Determining and interpreting the order of a two-state Markov chain: application to models of daily precipitation. Water Resources Research, 28, 1443–1446.

    Article  Google Scholar 

  • Gregory, J. M., Wigley, T. M. L., & Jones, P. D. (1993). Application of Markov models to area-average daily precipitation series and interannual variability in seasonal totals. Climate Dynamics, 8, 299–310.

    Article  Google Scholar 

  • Guttman, N. B. (1998). Comparing the palmer drought index and the standardized precipitation index. Journal of the American Water Resources Association, 34, 113–121.

    Article  Google Scholar 

  • Hay, L. E., Wilby, R. L., & Leavesley, G. H. (2000). A comparison of delta change and downscaled GCM scenarios for three mounfainous basins in THE UNITED STATES1. Journal of the American Water Resources Association, 36(2), 387–397.

    Article  Google Scholar 

  • Hollinger, S. E., Isard, S. A., Welford, M. R. (1993). A new soil moisture drought index for predicting crop yields. In Preprints, 8th Conference on Applied Climatology (pp. 187–190). Anaheim, CA: American Mathematical Society.

  • Hydrologic Engineering Center (HEC) (1975). Hydrologic engineering methods for water resources development, Vol. 8, reservoir yield. US Army Corps of Engineers: Davis, CA.

    Google Scholar 

  • Kilsby, C. G., Jones, P. D., Burton, A., Ford, A. C., Fowler, H. J., & Harpham, C. (2007). A daily weather generator for use in climate change studies. Environmental Modelling & Software, 22(12), 1705–1719.

    Article  Google Scholar 

  • Kim, D. W., Byun, H. R., & Choi, K. S. (2009). Evaluation, modification, and application of the effective drought index to 200-year drought climatology of Seoul, Korea. Journal of Hydrology, 378, 1–12.

    Article  Google Scholar 

  • Liu, J., Williams, J. R., Wang, X., & Yang, H. (2009). Using MODAWEC to generate daily weather data for the EPIC model. Environmental Modelling & Software, 24(5), 655–664.

    Article  Google Scholar 

  • McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, January 17–22, 1993 (pp. 179–184). Boston, Massachusetts: American Meteorological Society.

  • Meyer, S. J., Hubbard, K. G., & Wilhite, D. A. (1993). The development of a crop specific drought index for corn. Part I: model development and validation. Agronomy Journal, 85, 388–395.

    Article  Google Scholar 

  • Palmer, W. C. (1965). Meteorological drought (Vol. 30). Washington, DC, USA: US Department of Commerce, Weather Bureau.

    Google Scholar 

  • Richardson, J. S. (1981). The anatomy and taxonomy of protein structure. Academic Press, 34, 167–339.

    CAS  Google Scholar 

  • Richardson, C. W., & Wright, D. A. (1984). WGEN: a model for generating daily weather variables (p. 83). Washington, DC, USA: US Department of Agriculture, Agricultural Research Service.

    Google Scholar 

  • Semenov, M. A. (2008). Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35(3), 203.

    Article  Google Scholar 

  • Semenov, M. A., & Brooks, R. J. (1999). Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research, 11(2), 137–148.

    Article  Google Scholar 

  • Serinaldi, F., Bonaccorso, B., Cancelliere, A., & Grimaldi, S. (2009). Probabilistic characterization of drought properties through copulas. Physics and Chemistry of the Earth, Parts A/B/C, 34(10), 596–605.

    Article  Google Scholar 

  • Sklar, A. (1996). Random variables, distribution functions, and copulas: a personal look backward and forward. Lecture Notes-Monograph Series, 1–14.

  • Szalai, S., & Szinell, C. S. (2000). Comparison of two drought indices for drought monitoring in Hungary—a case study. In Drought and drought mitigation in Europe (pp. 161–166). Netherlands: Springer.

  • Thompson, C. S. (1984). Homogeneity analysis of rainfall series: an application of the use of a realistic rainfall model. Journal of Climatology, 4(6), 609–619.

    Article  Google Scholar 

  • Tung, C. P., & Haith, D. A. (1995). Global-warming effects on New York streamflows. Journal of Water Resources Planning and Management, 121(2), 216–225.

    Article  Google Scholar 

  • Wilby, R. L., & Dawson, C. W. (2013). The statistical downscaling model: insights from one decade of application. International Journal of Climatology, 33(7), 1707–1719.

    Article  Google Scholar 

  • Williams, J. R., & Singh, V. P. (1995). The EPIC model. Computer models of watershed hydrology, 909–1000.

Download references

Acknowledgment

The authors would like to thank the National Science Council of Taiwan R.O.C. for financially supporting this research under contract no. NSC 99-2221-E-240-001. Thanks are given for the data supported from Taiwan Climate Change Projection and Information Platform.

Author information

Authors and Affiliations

  1. Graduate Institute of Earth Science, Chinese Culture University, Taipei, Taiwan

    Nien-Ming Hong

  2. Department of Geography, National Taiwan Normal University, Taipei, Taiwan

    Tsung-Yu Lee

  3. National Science and Technology Center for Disaster Reduction, Taipei, Taiwan

    Yun-Ju Chen

Authors
  1. Nien-Ming Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tsung-Yu Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yun-Ju Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tsung-Yu Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hong, NM., Lee, TY. & Chen, YJ. Daily weather generator with drought properties by copulas and standardized precipitation indices. Environ Monit Assess 188, 383 (2016). https://doi.org/10.1007/s10661-016-5395-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-016-5395-z

Keywords

Search

Navigation