Advertisement

Evaluation of multiple stochastic rainfall generators in diverse climatic regions

  • Tue M. Vu
  • Ashok K. Mishra
  • Goutam Konapala
  • Di Liu
Original Paper
  • 167 Downloads

Abstract

Long term synthetic precipitation data are useful for water resources planning and management. Commonly stochastic weather generator (SWG) models are useful to produce synthetic time series of unlimited length of weather data based on the statistical characteristics of observed weather at a given location. However, it is difficult to find a single model which works best for all weather (climate) patterns. The objective of this study is to evaluate five different SWG models namely CLIGEN, ClimGen, LARS-WG, RainSim and WeatherMan to generate precipitation at three diverse climatic regions: a Mediterranean climate of western USA, temperate climate of eastern Australia and tropical monsoon region in northern Vietnam. The performance of SWG models to generate precipitation characteristics (i.e., precipitation occurrence; wet and dry spell; and precipitation intensity on wet days) varies between three selected climatic regimes. It was observed that the second order Markov chain (ClimGen and WeatherMan) performed well for all three selected regions in generating precipitation occurrence statistics. All models are able to simulate the ratio of wet/dry spell lengths with respect to observed precipitation. The RainSim performed well in reproducing wet/dry spell lengths in comparison to other models for wetter regions in Australia and Vietnam. ClimGen and WeatherMan are the two best models in simulating precipitation in the western USA, followed by CLIGEN and LARS. Similarly, ClimGen and WMAN are the two best models for synthetic precipitation generation for eastern Australian and northern Vietnam stations, but CLIGEN performs poorly over these regions. All SWG model performed differently with respect to climatic regimes, therefore careful validation is required depending on the weather pattern as well as its application in different water resources sectors. Although our findings are preliminary in nature, however, in order to generalize the performance of SWG’s in a given climate type, it is recommended that more number of stations needs to be evaluated in future studies.

Keywords

Stochastic weather generator CLIGEN ClimGen LARS-WG RainSim WeatherMan 

Notes

Acknowledgements

We appreciate the suggestions provided by associate editor and reviewers that helped us to improve quality of our manuscript. Authors would also like to thank Risk Engineering and Systems Analytics Center, Clemson University and American International Group for providing financial support.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Supplementary material

477_2017_1458_MOESM1_ESM.docx (117 kb)
Supplementary material 1 (DOCX 116 kb)

References

  1. Baffault C, Nearing MA, Nicks AD (1996) Impact of CLIGEN parameters on WEPP-predicted average annual soil loss. Trans ASAE 39(2):447–457CrossRefGoogle Scholar
  2. Birt AG, Valdez-Vivas MR, Feldman RM, Lafon CW, Cairns D, Coulson RN, Tchakerian M, Xi W, Guldin Jim (2010) A simple stochastic weather generator for ecological modeling. Environ Model Softw 25(10):1252–1255CrossRefGoogle Scholar
  3. Bordoy R, Burlando P (2014) Stochastic downscaling of climate model precipitation outputs in orographically complex regions: 2. Downscaling methodology. Water Resour Res 50(1):562–579CrossRefGoogle Scholar
  4. Burton A, Kilsby CG, Fowler HJ, Cowpertwait PSP, O’Connell PE (2008) RainSim: a spatial temporal stochastic rainfall modelling system. Environ Model Softw 23(12):1356–1369. doi: 10.1016/j.envsoft.2008.04.003 CrossRefGoogle Scholar
  5. Burton A, Fowler HJ, Blenkinsop S, Kilsby CG (2010a) Downscaling transient climate change using a Neyman–Scott rectangular pulses stochastic rainfall model. J Hydrol 381(1–2):18–32. doi: 10.1016/j.jhydrol.2009.10.031 CrossRefGoogle Scholar
  6. Burton A, Fowler HJ, Kilsby CG, O’Connell PE (2010b) A stochastic model for the spatial-temporal simulation of non-homogeneous rainfall occurrence and amounts. Water Resour Res 46:W11501. doi: 10.1029/2009WR008884 CrossRefGoogle Scholar
  7. Camera C, Bruggerman A, Hadjinicolaou P, Michaelides S, Lange MA (2017) Evaluation of a spatial rainfall generator for generating high resolution precipitation projections over orographically complex terrain. Stoch Env Res Risk Assess 31(3):757–773CrossRefGoogle Scholar
  8. Campbell GS (1990) CLIMGEN, a program that generates weather data (precipitation, maximum and minimum temperatures). Biological systems engineering department, Washington State University, Pullman, Washington, USAGoogle Scholar
  9. Caron A, Leconte R, Brissette F (2008) A stochastic weather generator applied to hydrological models in climate impact analysis. Can Water Res J 33(3):233–256CrossRefGoogle Scholar
  10. Castellvi F, Stöckle CO (2001) Comparing the performance of WGEN and ClimGen in the generation of temperature and solar radiation. Trans ASAE 44(6):1683–1687CrossRefGoogle Scholar
  11. Chen J, Brissette FP (2014) Comparison of five stochastic weather generators in simulating daily precipitation and temperature for the Loess Plateau of China. Int J Climatol 34:3089–3105CrossRefGoogle Scholar
  12. Chen J, Brissette FP, Leconte R (2012a) Downscaling of weather generator parameters to quantify the hydrological impacts of climate change. Clim Res 51(3):185–200. doi: 10.3354/cr01062 CrossRefGoogle Scholar
  13. Chen J, Brissette FP, Leconte R (2012b) Downscaling of weather generator parameters to quantify the hydrological impacts of climate change. Clim Res 51(3):185–200. doi: 10.3354/cr01062 CrossRefGoogle Scholar
  14. Corder GW, Foreman DI (2009) Nonparametric statistics for non-statisticians: a step-by-step approach. Wiley, LondonCrossRefGoogle Scholar
  15. Cowpertwait PSP (1994) A generalized point process model for rainfall. Proc Royal Soc London 447:23–37CrossRefGoogle Scholar
  16. Cowpertwait PSP (1995) A generalized spatial—temporal model of rainfall based on a clustered point process. Proc Royal Soc London 450:163–175CrossRefGoogle Scholar
  17. Evans MN, Fairbanks RG, Rubenstone JL (1998) A proxy index of ENSO teleconnections. Nature 394:732–733CrossRefGoogle Scholar
  18. Fischer T, Su B, Luo Y, Scholten T (2012) Probability distribution of precipitation extremes for weather index-based insurance in the Zhujiang River Basin, South China. J Hydrometeorol 13:1023–1037CrossRefGoogle Scholar
  19. Grondona MO, Podestá GP, Bidegain M, Marino M, Hordij H (2000) A stochastic precipitation generator conditioned on ENSO phase: a case study in southeastern South America. J Clim 13:2973–2986CrossRefGoogle Scholar
  20. Jones PG, Thornton PK (2000) MarkSim: software to generate daily weather data for Latin America and Africa. Agron J 92:445–453CrossRefGoogle Scholar
  21. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265CrossRefGoogle Scholar
  22. Katz RW (1977) Precipitation as a chain-dependent process. J Appl Meteorol 16(7):671–676CrossRefGoogle Scholar
  23. Katz RW, Parlange MB (1993) Effects of an index of atmospheric circulation on stochastic properties of precipitation. Water Resour Res 29:2335–2344CrossRefGoogle Scholar
  24. Katz RW, Parlange MB, Tebaldi C (2003) Stochastic modeling of the effects of large-scale circulation on daily weather in the Southeastern US. Clim Change 60:189–216CrossRefGoogle Scholar
  25. Kilsby CG, Jones PD, Burton A, Ford AC, Flower HJ, Harpham C, James P, Smith A, Wilby RL (2007) A daily weather generator for use in climate change studies. Environ Model Softw 22:1705–1719CrossRefGoogle Scholar
  26. Krzywinski M, Altman N (2014) Points of significance: nonparametric tests. Nat Methods 11:467–468. doi: 10.1038/nmeth.2937 CrossRefGoogle Scholar
  27. Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling hydrological time series. Water Resour Res 32:679–693CrossRefGoogle Scholar
  28. Manatsa D, Chingombea W, Matarirab CH (2008) The impact of the positive Indian Ocean dipole on Zimbabwe droughts. Int J Climatol 28:2011–2029CrossRefGoogle Scholar
  29. Mehrotra R, Sharma A (2007) A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability. J Hydrol 335:180–193CrossRefGoogle Scholar
  30. Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290. doi: 10.1016/0022-1694(70)90255-6 CrossRefGoogle Scholar
  31. Neyman J, Scott EL (1958) Statistical approach to problems of cosmology. J R Stat Soc B 20(1):1–43Google Scholar
  32. Ngo DT, Kieu C, Thatcher M, Nguyen LD, Phan VT (2014) Climate projections for Vietnam based on regional climate models. Clim Res 60:199–213CrossRefGoogle Scholar
  33. Nicks AD, Gander GA (1994) CLIGEN: a weather generator for climate inputs to water resources and other models. In: Watson DG, Zazueta FS, Harrison TV (eds) Proceedings of fifth international conference on computer in agriculture. ASAE, St. Joseph, MI, pp 903–909Google Scholar
  34. Nicks AD, Lane LJ, Gander GA (1995) Weather generator. In: Flanagan DC, Nearing MA (eds) USDA-water erosion prediction project: hillslope profile and watershed model documentation. NSERL report no. 10. USDA-ARS Nat. Soil Erosion Research Lab, West Lafayette, in (Chapter 2)Google Scholar
  35. Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Koppen–Geiger climate classification. Hydrol Earth Syst Sci 11:1633–1644CrossRefGoogle Scholar
  36. Pickering NB, Hansen JW, Jones JW, Wells CM, Chan VK, Godwin DC (1994) WeatherMan: a utility for managing and generating daily weather data. Agron J 86:332–337CrossRefGoogle Scholar
  37. Racsko P, Szeidl L, Semenov M (1991a) A serial approach to local stochastic weather models. Ecol Model 57:27–41CrossRefGoogle Scholar
  38. Racsko P, Szeidl L, Semenov M (1991b) A serial approach to local stochastic weather models. Ecol Model 57(27):41Google Scholar
  39. Rajagopalan B, Lall U (1999) A k-nearest-neighbor simulator for daily precipitation and other weather variables. Water Resourc Res 35(10):3089–3101CrossRefGoogle Scholar
  40. Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17:182–190CrossRefGoogle Scholar
  41. Richardson CW, Wright DA (1984) WGEN: a model for generating daily weather variables. US Department of Agriculture, Agricultural Research Service, ARS-8, 83Google Scholar
  42. Roldan J, Woolhiser D (1982) Stochastic daily precipitation models 1. A comparison of occurrence processes. Water Resour Res 18(5):1451–1459CrossRefGoogle Scholar
  43. Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Change 35:397–414CrossRefGoogle Scholar
  44. Semenov MA, Porter JR (1995) Climatic variability and the modelling of crop yields. Agr Forest Meteorol 73:265–283CrossRefGoogle Scholar
  45. Semenov MA, Shewry PR (2011) Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe. Sci Rep 1(66):1–5Google Scholar
  46. Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators in diverse climates. Clim Res 10:95–107CrossRefGoogle Scholar
  47. Soltani A, Hoogenboom G (2003) A statistical comparison of the stochastic weather generators WGEN and SIMMETEO. Clim Res 24:215–230CrossRefGoogle Scholar
  48. Stern RD, Coe R (1984) A model fitting analysis of daily rainfall data. J R Stat Soc A147:1–34Google Scholar
  49. Stöckle CO, Campbell GS, Nelson R (1999) ClimGen manual. Biological systems engineering department, Washington State University, Pullman, WAGoogle Scholar
  50. Vallam P, Qin X (2016) Multi-site rainfall simulation at tropical regions: a comparison of three types of generators. Met Appl 23:425–437CrossRefGoogle Scholar
  51. Verdi A, Rajagopalan B, Kleiber W, Katz RW (2015) Coupled stochastic weather generation using spatial and generalized linear models. Stoch Env Res Risk Assess 29(2):347–356CrossRefGoogle Scholar
  52. Westra S, Evans JP, Mehrotra R, Sharma A (2013) A conditional disaggregation algorithm for generating fine time-scale rainfall data in a warmer climate. J Hydrol 479:86–99CrossRefGoogle Scholar
  53. Whitt W (1981) Approximating a point process by a renewal process, I: two basic methods. Oper Res 30:125–147CrossRefGoogle Scholar
  54. Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34(2995):3008Google Scholar
  55. Wilks DS (1989) Conditioning stochastic daily precipitation models on total monthly precipitation. Water Resour Res 25:1429–1439CrossRefGoogle Scholar
  56. Wilks DS (1999) Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agric For Meteorol 93:153–169CrossRefGoogle Scholar
  57. Wilks DS (2010) Use of stochastic weather generators for precipitation downscaling. WIRE Clim Change 1:898–907. doi: 10.1002/wcc.85 CrossRefGoogle Scholar
  58. Xia J (1996) A stochastic weather generator applied to hydrological models in climate impact analysis. Theor Appl Climatol 55(1):177–183CrossRefGoogle Scholar
  59. Zhang L, Zhou T (2015) Drought over Asia: a review. J Clim 28:3375–3399CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Tue M. Vu
    • 1
  • Ashok K. Mishra
    • 1
  • Goutam Konapala
    • 1
  • Di Liu
    • 1
  1. 1.Glenn Department of Civil EngineeringClemson UniversityClemsonUSA

Personalised recommendations