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Coupling annual, monthly and daily weather generators to simulate multisite and multivariate climate variables with low-frequency variability for hydrological modelling

  • Jie ChenEmail author
  • Richard Arsenault
  • François P. Brissette
  • Pascal Côté
  • Tianhua Su
Article
  • 75 Downloads

Abstract

Weather generators are usually used to produce an ensemble of climate time series for vulnerability assessments and impact studies in hydrological and agricultural communities. Multisite and multivariate weather generators (MMWGs) have many advantages over single-site counterparts in terms of coupling with distributed models for the assessment of spatial variability in various impact sectors. However, the existing MMWGs usually suffer from limitations in preserving multisite and multivariate dependencies at multiple time scales, as well as preserving the low-frequency variability of climate variables. This study proposes a new MMWG which preserves low-frequency climate variability by coupling annual, monthly and daily weather generators into a single model. Specifically, the daily precipitation and temperature time series generated by a widely used multisite daily weather generator is adjusted using monthly and annual climate time series generated by a first-order linear autoregressive model. This combination preserves the multisite and multivariate attributes, as well as low-frequency variability at monthly and annual scales. The performance of the proposed MMWG was evaluated by comparing the baseline model against that of its variants with monthly or annual adjustments for climate generation. The performance was also assessed using a hydrological model over two watersheds with different hydroclimatic characteristics. The results show that the proposed weather generator performs well with respect to reproducing the marginal distributional attributes, multisite and multivariate dependencies, and climate variability at the daily, monthly and annual scales. Weather generators with either monthly or annual adjustments (and not both) only improve the simulations in multisite and multivariate dependencies and low-frequency variability at the corresponding time scales. In terms of hydrological modeling, the proposed model consistently performs better than the baseline model and its variants with only monthly or annual adjustments in representing the mean and variance of monthly and annual streamflows. It also performs better in representing the frequency distribution of mean and extreme streamflow events. Overall, the proposed MMWG can effectively produce multisite and multivariate climate time series with low-frequency variability and has a strong potential for use in climate change impact studies.

Keywords

Weather generator Multisite and multivariate dependencies Low-frequency variability Hydrological modeling 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant nos. 51779176, 41811520121, 51539009), the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) funded by Ministry of Education and State Administration of Foreign Experts Affairs P.R. China (Grant no. B18037) and the Thousand Youth Talents Plan from the Organization Department of CCP Central Committee (Wuhan University, China). The authors wish to thank the China Meteorological Data Sharing Service System and the Hydrographic office of Hunan Province for respectively providing meteorological and streamflow data for the Xiangjiang watershed, and the Rio Tinto Alcan Company (Montreal, Quebec, Canada) for providing datasets for the Lac–Saint–Jean watershed.

Supplementary material

382_2019_4750_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 19 kb)

References

  1. Abbaspour KC, Johnson CA, van Genuchtenb MTh (2004) Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J 3:1340–1352CrossRefGoogle Scholar
  2. Apipattanavis S, Podesta G, Rajagopalan B, Katz RW (2007) A semiparametric multivariate and multisite weather generator. Water Resour Res 43(11):W11401CrossRefGoogle Scholar
  3. Arnold JG, Srinivasan R, Muttiah RS et al (1998) Large area hydrologic modeling and assessment part I: Model development. Jawra J Am Water Resour Assoc 34(1):73–89CrossRefGoogle Scholar
  4. Arsenault R, Malo JS, Brissette F, Minville M, Leconte R (2013) Structural and non-structural climate change adaptation strategies for the Péribonka water resource system. Water Resour Manag 27(7):2075–2087CrossRefGoogle Scholar
  5. Arsenault R, Poulin A, Côté P, Brissette F (2014) A comparison of stochastic optimization algorithms in hydrological model calibration. J Hydrol Eng 19(7):1374–1384CrossRefGoogle Scholar
  6. Arsenault R, Latraverse M, Duchesne T (2016) An efficient method to correct under-dispersion in ensemble streamflow prediction for seasonal volumetric forecasting. Water Resour Manage 30(12):4363–4380CrossRefGoogle Scholar
  7. Breinl K, Turkington T, Stowasser M (2013) Stochastic generation of multi-site daily precipitation for applications in risk management. J Hydrol 498:23–35CrossRefGoogle Scholar
  8. Brissette FP, Khalili M, Leconte R (2007) Efficient stochastic generation of multi-site synthetic precipitation data. J Hydrol 345(3–4):121–133CrossRefGoogle Scholar
  9. Buishand TA, Brandsma T (2001) Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbor resampling. Water Resour Res 37(11):2761–2776CrossRefGoogle Scholar
  10. Burton A, Kilsby CG, Fowler HJ, Cowpertwait PSP, O’Connell PE (2008a) RainSim: a spatial-temporal stochastic rainfall modelling system. Environ Model Software 23(12):1356–1369CrossRefGoogle Scholar
  11. Burton A, Kilsby CG, Fowler HJ, Cowpertwait PSP, O’Connell PE (2008b) RainSim: a spatial–temporal stochastic rainfall modelling system. Environ Model Software 23(12):1356–1369CrossRefGoogle Scholar
  12. Cannon A (2008) Probabilistic multisite precipitation downscaling by an expanded Bernoulli–Gamma density network. J Hydrometeorol 9(6):1284–1300CrossRefGoogle Scholar
  13. Charbonneau R, Fortin J-P, Morin G (1977) The CEQUEAU model: description and examples of its use in problems related to water resource management/Le modèle CEQUEAU: Description et exemples d’utilisation dans le cadre de problèmes reliés à l’aménagement. Hydrol Sci Bull 22(1):193–202CrossRefGoogle Scholar
  14. Chen J, Brissette FP (2015) Combining stochastic weather generation and ensemble weather forecast for short term streamflow prediction. Water Resour Manag 29:3329–3342CrossRefGoogle Scholar
  15. Chen J, Zhang X, Liu W, Li Z (2009) Evaluating and extending CLIGEN precipitation generation for the Loess Plateau of China. J Am Water Resour Assoc 45(2):378–396CrossRefGoogle Scholar
  16. Chen J, Brissette FP, Leconte R (2010) A daily stochastic weather generator for preserving low-frequency of climate variability. J Hydrol 388(3):480–490CrossRefGoogle Scholar
  17. Chen J, Brissette FP, Leconte R (2011) Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. J Hydrol 401:190–202CrossRefGoogle Scholar
  18. Chen J, Brissette FP, Leconte R (2012a) Downscaling of weather generator parameters to quantify the hydrological impacts of climate change. Clim Res 51(3):185–200CrossRefGoogle Scholar
  19. Chen J, Brissette FP, Leconte R, Caron A (2012b) A versatile weather generator for daily precipitation and temperature. Tran ASABE 55(3):895–906CrossRefGoogle Scholar
  20. Chen J, Brissette FP, Li Z (2014a) Post-processing of ensemble weather forecasts using a stochastic weather generator. Mon Weather Rev 142:1106–1124CrossRefGoogle Scholar
  21. Chen J, Brissette FP, Zhang X (2014b) A multi-site stochastic weather generator for daily precipitation and temperature. Tran ASABE 57(5):1375–1391Google Scholar
  22. Chen J, Chen H, Guo S (2018a) Multi-site precipitation downscaling using a stochastic weather generator. Clim Dyn 50(5–6):1975–1992CrossRefGoogle Scholar
  23. Chen J, Li C, Brissette FP, Chen H, Wang M, Essou GRC (2018b) Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. J Hydrol 560:326–341CrossRefGoogle Scholar
  24. Du J, Rui H, Zuo T, Li Q, Zheng D, Chen A, Xu Y, Xu C-Y (2013) Hydrological simulation by SWAT model with fixed and varied parameterization approaches under land use change. Water Resour Manag 27(8):2823–2838CrossRefGoogle Scholar
  25. Dubrovsky M, Buchteke J, Zalud Z (2004) High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modeling. Clim Change 63:145–179CrossRefGoogle Scholar
  26. Evin G, Favre AC, Hingray B (2018) Stochastic generation of multi-site daily precipitation focusing on extreme events. Hydrol Earth Syst Sci 22:655–672CrossRefGoogle Scholar
  27. Fowler HJ, Kilsby CG, O’Connell PE, Burton A (2005) A weather-type conditioned multi-site stochastic rainfall model for the generation of scenarios of climatic variability and change. J Hydrol 308(1–4):50–66CrossRefGoogle Scholar
  28. François B, Hingray B, Hendrickx F, Creutin JD (2014) Seasonal patterns of water storage as signatures of the climatological equilibrium between resource and demand. Hydrol Earth Syst Sci 18(9):3787–3800CrossRefGoogle Scholar
  29. Hansen JW, Mavromatis T (2001) Correcting low-frequency variability bias in stochastic weather generators. Agr Forest Meteorol 109:297–310CrossRefGoogle Scholar
  30. Hanson CL, Cumming KA, Woolhiser DA, Richardson CW (1994) Microcomputer program for daily weather simulations in the contiguous United States. Publ. ARS-114. USDA-ARS, Washington D.C.Google Scholar
  31. Harris CNP, Quinn AD, Bridgeman J (2014) The use of probabilistic weather generator information for climate change adaptation in the UK water sector. Meteorol Appl 21(2):129–140CrossRefGoogle Scholar
  32. Higham NJ (1988) Computing a nearest symmetric positive semidefinite matrix. Linear Algebra Appl 103:103–118CrossRefGoogle Scholar
  33. Iman RL, Conover WJ (1982) A distribution-free approach to inducing rank correlation among input variables. Commun Stat Simul Comput 11(3):311–334CrossRefGoogle Scholar
  34. Johnson GL, Daly C, Taylor GH, Hanson CL (1999) Spatial variability and interpolation of stochastic weather simulation model parameters. J Appl Meteor 39(6):778–796CrossRefGoogle Scholar
  35. Khalili M, Leconte R, Brissette F (2007) Stochastic multisite generation of daily precipitation data using spatial autocorrelation. J Hydrometeorol 8(3):396–412CrossRefGoogle Scholar
  36. King LM, McLeod AI, Simonovic SP (2014) Simulation of historical temperatures using a multi-site, multivariate block resampling algorithm with perturbation. Hydrol Process 28(3):905–912CrossRefGoogle Scholar
  37. King LM, Mcleod AI, Simonovic SP (2015) Improved weather generator algorithm for multisite simulation of precipitation and temperature. J Am Water Resour Assoc 51(5):1305–1320CrossRefGoogle Scholar
  38. Lall U, Sharma A (1996) A nearest neighbour bootstrap for resampling hydrologic time series. Water Resour Res 32(3):679–693CrossRefGoogle Scholar
  39. Leander R, Buishand TA (2009) A daily weather generator based on a two-stage resampling algorithm. J Hydrol 374(3–4):185–195CrossRefGoogle Scholar
  40. Li Z (2014) A new framework for multi-site weather generator: a two-stage model combining a parametric method with a distribution-free shuffle procedure. Clim Dyn 43(3–4):657–669CrossRefGoogle Scholar
  41. Li X, Babovic V (2018) A new scheme for multivariate, multisite weather generator with inter-variable, inter-site dependence and inter-annual variability based on empirical copula approach. Clim Dyn 5:5.  https://doi.org/10.1007/s00382-018-4249-5 CrossRefGoogle Scholar
  42. Li Z, Jin J (2017) Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China. Hydrol Earth Syst Sci 21:5531–5546CrossRefGoogle Scholar
  43. Li C, Sinha E, Horton DE, Diffenbaugh NS, Michalak AM (2014) Joint bias correction of temperature and precipitation in climate model simulations. J Geophys Res Atmos 119(23):13153–13162CrossRefGoogle Scholar
  44. Li Z, Lü Z, Li J, Shi X (2017a) Links between the spatial structure of weather generator and hydrological modeling. Theor Appl Climatol 128(1):103–111CrossRefGoogle Scholar
  45. Li Z, Shi X, Li J (2017b) Multisite and multivariate GCM downscaling using a distribution-free shuffle procedure for correlation reconstruction. Clim Res 72:141–151CrossRefGoogle Scholar
  46. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Theme M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3):3CrossRefGoogle Scholar
  47. Mason SJ (2004) Simulating climate over Western North America using stochastic weather generators. Clim Change 62(1–3):155–187CrossRefGoogle Scholar
  48. Matalas NC (1967) Mathematical assessment of synthetic hydrology. Water Resour Res 3:937–945CrossRefGoogle Scholar
  49. Mehrotra R, Sharma A (2007) A semi-parametric model for stochastic generation of multi-site daily rainfall exhibiting low-frequency variability. J Hydrol 335(1–2):180–193CrossRefGoogle Scholar
  50. Morin G, Fortin JP, Charbonneau R (1975) Utilisation du modèle hydrophysiographique CEQUEAU pour l’exploitation des reservoirs artificiels.” IAHS Publication No. 115, IAHS Press, Wallingford, U.K., pp 176–184 (in French) Google Scholar
  51. Nicks AD, Lane LJ (1989) Weather generator. In: Lane LJ, Nearing MA (eds) USDA water erosion prediction project. NSERL Report No. 2. West Lafayette, Ind.: USDA-ARS National Soil Erosion Research LaboratoryGoogle Scholar
  52. Nicks AD, Lane LJ, Gander GA (1995) Chapter 2: Weather generator. In: Flanagan DC, Nearing MA (eds) USDA water erosion prediction project: hillslope profile and watershed model documentation. NSERL Report No. 10. West Lafayette, Ind.: USDA-ARS National Soil Erosion Research LaboratoryGoogle Scholar
  53. Palma A, González F, Cruickshank C (2015) Managed aquifer recharge as a key element in Sonora River basin management, Mexico. J Hydrol Eng  https://doi.org/10.1061/(asce)he.1943-5584.0001114, B4014004
  54. Palutikof JP, Goodess CM, Watkins SJ, Holt T (2002) Generating rainfall and temperature scenarios at multiple sites: examples from the Mediterranean. J Clim 15(24):3529–3548CrossRefGoogle Scholar
  55. Qian B, Corte-Real J, Xu H (2002) Multisite stochastic weather models for impact studies. Intl J Climatol 22(11):1377–1397CrossRefGoogle Scholar
  56. Rajagopalan B, Lall U (1999) A K-nearest neighbor simulator for daily precipitation and other variables. Water Resour Res 35(10):3089–3101CrossRefGoogle Scholar
  57. Rayner D, Achberger C, Chen D (2016) A multi-state weather generator for daily precipitation for the Torne River basin, northern Sweden/western Finland. Adv Clim Change Res 7:70–81CrossRefGoogle Scholar
  58. Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17(1):182–190CrossRefGoogle Scholar
  59. Richardson CW, Wright DA (1984) WGEN: A model for generating daily weather variables. Publ. ARS-8. USDA Agricultural Research Service, Washington, D.C.Google Scholar
  60. Semenov MA, Barrow EM (2002) LARS-WG: A stochastic weather generator for use in climate impact studies. User manual. Harpenden, Rothamsted Research, UKGoogle Scholar
  61. Singh VP, Frevert DK (2001) Mathematical models of large watershed hydrology. Water Resour Publ, MIGoogle Scholar
  62. Sparks NJ, Hardwick SR, Schmid M, Toumi R (2018) IMAGE: a multivariate multi-site stochastic weather generator for European weather and climate. Stoch Environ Res Risk Assess 32:771–784CrossRefGoogle Scholar
  63. Srikanthan R, McMahon TA (2001) Stochastic generation of annual, monthly and daily climate data: a review. Hydrol Earth Syst Sci 5(4):653–670CrossRefGoogle Scholar
  64. Srikanthan R, Pegram GGS (2009) A nested multisite daily rainfall stochastic generation model. J Hydrol 371(1–4):142–153CrossRefGoogle Scholar
  65. Steinschneider S, Brown C (2013a) A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments. Water Resour Res 49(11):7205–7220CrossRefGoogle Scholar
  66. Steinschneider S, Brown C (2013b) A semiparametric multivariate, multisite weather generator with low-frequency variability for use in climate risk assessments. Water Resour Res 49(11):7205–7220CrossRefGoogle Scholar
  67. Stockle CO, Campbell GS, Nelson R (1999) ClimGen manual. Washington State University, Department of Biological Systems Engineering, PullmanGoogle Scholar
  68. Tarpanelli A, Franchini M, Brocca L, Camici S, Melone F, Moramarco T (2012) A simple approach for stochastic generation of spatial rainfall patterns. J Hydrol 472–473:63–76CrossRefGoogle Scholar
  69. Themeßl MJ, Gobiet A, Leuprecht A (2011) Empirical statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31:1530–1544CrossRefGoogle Scholar
  70. Wang Q, Nathan RJ (2007) A method for coupling daily and monthly time scales in stochastic generation of rainfall series. J Hydrol 346:122–130CrossRefGoogle Scholar
  71. Wilby RW, Tomlinson OJ, Dawson CW (2003) Multisite simulation of precipitation by conditional resampling. Clim Res 23(3):183–194CrossRefGoogle Scholar
  72. Wilks DS (1992) Adapting stochastic weather generation algorithms for climate change studies. Clim Change 22:67–84CrossRefGoogle Scholar
  73. Wilks DS (1998) Multisite generalization of a daily stochastic precipitation generation model. J Hydrol 210(1–4):178–191CrossRefGoogle Scholar
  74. Wilks DS (1999a) Multisite downscaling of daily precipitation with a stochastic weather generator. Clim Res 11:125–136CrossRefGoogle Scholar
  75. Wilks DS (1999b) Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agr Forest Meteorol 93:153–169CrossRefGoogle Scholar
  76. Wilks DS (2008) High-resolution spatial interpolation of weather generator parameters using local weighted regressions. Agric For Meteor 148(1):111–120CrossRefGoogle Scholar
  77. Xu X, Xu C-Y, Sælthun NR, Xu Y, Zhou B, Chen H (2015) Entropy theory based multi-criteria resampling of rain gauge networks for hydrological modelling—a case study of humid area in southern China. J Hydrol 525:138–151CrossRefGoogle Scholar
  78. Zhang XC (2005) Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agric For Meteor 135:215–229CrossRefGoogle Scholar
  79. Zhang X, Garbrecht JD (2003) Evaluation of CLIGEN precipitation parameters and their implication on WEPP runoff and erosion prediction. T ASAE 46(2):311–320CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Water Resources and Hydropower Engineering ScienceWuhan UniversityWuhanChina
  2. 2.École de technologie supérieure, Université du QuébecMontrealCanada
  3. 3.Quebec Power OperationJonquièreCanada

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