Skip to main content
SpringerLink
Log in

Rainfall Generators for Application in Flood Studies

  • Chapter
  • First Online:
Flood Risk Assessment and Management

Abstract

This chapter discusses various approaches for stochastic rainfall synthesis focusing on methods for generation of short time step precipitation as required for flood studies. A brief introduction motivates the utilisation of rainfall generators for flood modelling. Then special characteristics of rainfall as stochastic process are discussed. The rainfall models presented in the following are classified in alternating renewal models, time series models, point process models, disaggregation and resampling approaches. They are usually applied for continuous unconditional simulation of rainfall series in time and/or in space. Two case studies at the end of the chapter illustrate the application of daily and hourly space-time precipitation models for flood studies.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Institutional subscriptions

Similar content being viewed by others

References

  • Acreman MC (1990) Simple stochastic model of hourly rainfall for Farnborough, England. Hydrol Sci J 35(2):119–148

    Article  Google Scholar 

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automatic Control AC-19:716–723

    Article  Google Scholar 

  • Aronica GT, Candela A (2007) Derivation of flood frequency curves in poorly gauged Mediterranean catchments using a simple stochastic hydrological rainfall-runoff model. J Hydrol 347(1–2):132–142

    Article  Google Scholar 

  • Bárdossy A (1998) Generating precipitation time series using simulated annealing. Water Resour Res 34(7):1737–1744

    Article  Google Scholar 

  • Bárdossy A, Plate EJ (1992) Space-time model for daily rainfall using atmospheric circulation patterns. Water Resour Res 28(5):1247–1259

    Article  Google Scholar 

  • Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. J Hydrol 249(1–4):11–29

    Article  Google Scholar 

  • Blazkova S, Beven K (2004) Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the Czech Republic. J Hydrol 292(1–4):153–172

    Article  Google Scholar 

  • Brandsma T, Buishand TA (1998) Simulation of extreme precipitation in the Rhine basin by nearest neighbour resampling. Hydrol Earth Syst Sci 2:195–209

    Article  Google Scholar 

  • Bras RL, Rodriguez-Iturbe I (1994) Random functions and hydrology. Dover, New York, NY, 559 pp

    Google Scholar 

  • 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):2761–2776

    Article  Google Scholar 

  • Cameron DS, Beven KJ, Tawn J, Blazkova S, Naden P (1999) Flood frequency estimation by continuous simulation for a gauged upland catchment (with uncertainty). J Hydrol 219(3–4):169–187

    Article  Google Scholar 

  • Chang TJ, Kavvas ML, Delleur JW (1984a) Daily precipitation modelling by discrete autoregressive moving average processes. Water Resour Res 20:565–580

    Article  Google Scholar 

  • Chang TJ, Kavvas ML, Delleur JW (1984b) Modelling of sequences of wet and dry days by binary discrete autoregressive moving average processes. J Climate Appl Meteorol 23:1367–1378

    Article  Google Scholar 

  • Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London, 208 pp

    Google Scholar 

  • Cowpertwait PSP (2006) A spatial-temporal point process model of rainfall for the Thames catchment, UK. J Hydrol 330(3–4):586–595

    Article  Google Scholar 

  • Delleur JW, Kavvas ML (1978) Stochastic models for monthly rainfall forecasting and synthetic generation. J Appl Meteorol 17:1528–1536

    Article  Google Scholar 

  • De Michele C, Salvadori G (2003) A Generalized Pareto intensity-duration model of storm rainfall exploiting 2-Copulas. J Geophys Res 108 (D2):4067. doi:10.1029/2002JD002534

    Google Scholar 

  • Efron B, Tibshirani RJ (1998) An introduction to the bootstrap. Chapman and Hall, New York, NY, 436 pp

    Google Scholar 

  • Foufoula-Georgiou E, Georgakakos KP (1991) Hydrologic advances in space-time precipitation modeling and forcasting. In: Bowles DS, Connel O (eds) Recent advances in the modeling of hydrologic systems. Kluwer, Dordrecht, 47–65 pp

    Google Scholar 

  • Frigessi A, Haug O, Rue H (2003) A dynamic mixture model for unsupervised tail estimation without threshold selection. Extremes 5:219–235

    Article  Google Scholar 

  • Gaume E, Mouhous N, Andrieu H (2007) Rainfall stochastic disaggregation models: calibration and validation of a multiplicative cascade model. Adv Water Resour 30(5):1301–1319

    Article  Google Scholar 

  • Gilks W, Richardson S, Spiegelhalter D (1996) Markov chain Monte Carlo methods in practice. CRC Press, Boca Raton, FL, 486 pp

    Google Scholar 

  • Grace RA, Eagleson PS (1966) The synthesis of short-time-increment rainfall sequences. Hydrodynamics Laboratory, Massachusetts Institute of Technology, Cambridge, MA

    Google Scholar 

  • Güntner A, Olsson J, Calver A, Gannon B (2001) Cascade-based disaggregation of continuous rainfall time series: the influence of climate. Hydrol Earth Syst Sci 5:145–164

    Article  Google Scholar 

  • Haan CT, Allen DM, Street JO (1976) A Markov Chain model of daily rainfall. Water Resour Res 12(3):443–449

    Article  Google Scholar 

  • Haberlandt U (1998) Stochastic rainfall synthesis using regionalized model parameters. J Hydrol Eng 3(3):160–168

    Article  Google Scholar 

  • Haberlandt U, Ebner von Eschenbach A-D, Buchwald I (2008) A space-time hybrid hourly rainfall model for derived flood frequency analysis. Hydrol Earth Syst Sci 12:1353–1367

    Article  Google Scholar 

  • Hastings W (1970) Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 57:97–109

    Article  Google Scholar 

  • Hipel KW, McLeod AI (1994) Time series modelling of water resources and environmental systems. Elsevier, Amsterdam, 1013 pp.

    Google Scholar 

  • Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, New York, NY, 240 pp.

    Google Scholar 

  • Hundecha Y, Pahlow M, Schumann A (2009) Modeling of daily precipitation at multiple locations using a mixture of distributions to characterize the extremes. Water Resour Res 45:W12412. doi:10.1029/2008WR007453

    Article  Google Scholar 

  • Hutchinson MF (1995) Stochastic space-time weather models from ground-based data. Agric Forest Meteorol 73:237–264

    Article  Google Scholar 

  • Jacobs PA, Lewis PAW (1978a) A discrete time series generated by mixture I: correlation and run properties. J R Stat Soc B 40(1):94–105

    Google Scholar 

  • Jacobs PA, Lewis PAW (1978b) A discrete time series generated by mixture II: asymptotic properties. J R Stat Soc B 40(2):222–228

    Google Scholar 

  • Katz RW (1977) Precipitation as a chain-dependent process. J Appl Meteorol 16:671–676

    Article  Google Scholar 

  • Kavvas ML, Delleur JW (1975) Removal of periodicities by differencing and month subtraction. J Hydrol 26:335–353

    Article  Google Scholar 

  • Koutsoyiannis D, Onof C, Wheater HS (2003) Multivariate rainfall disaggregation at a fine timescale. Water Resour Res 39(7): 1173. doi:10.1029/2002WR001600

    Article  Google Scholar 

  • Lall U, Sharma A (1996) A nearest neighbor bootstrap for resampling hydrological time series. Water Resour Res 32:679–693

    Article  Google Scholar 

  • Lovejoy S, Schertzer D (2006) Multifractals, cloud radiances and rain. J Hydrol 322(1–4):59–88

    Article  Google Scholar 

  • Lu M, Yamamoto T (2008) Application of a random cascade model to estimation of design flood from rainfall data. J Hydrol Eng 13(5):385–391

    Article  Google Scholar 

  • Mielke PW, Jr (1973) Another family of distributions for describing and analyzing precipitation data. J Appl Meteorol 10(2):275–280

    Article  Google Scholar 

  • Moretti G, Montanari A (2008) Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model. Hydrol Earth Syst Sci 12:1141–1152

    Article  Google Scholar 

  • Nelsen RB (2006) An introduction to Copulas. Springer, New York, NY, 270 pp

    Google Scholar 

  • Olsson J (1998) Evaluation of a scaling cascade model for temporal rainfall disaggregation. Hydrol Earth Syst Sci 2:19–30

    Article  Google Scholar 

  • Onof C, Chandler RE, Kakou A, Northrop P, Wheater HS, Isham V (2000) Rainfall modelling using Poisson-cluster processes: a review of developments. Stoch Environ Res Risk Assess 14:384–411

    Article  Google Scholar 

  • Pegram GGS, Clothier AN (2001) High resolution space–time modelling of rainfall: the "string of beads" model. J Hydrol 241(1–2):26–41

    Article  Google Scholar 

  • Rajagopalan B, Lall U (1999) A k-nearest-neighbor simulator for daily precipitation and other weather variables. Water Resour Res 35:3089–3101

    Article  Google Scholar 

  • Richardson CW (1982) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17:182–190

    Article  Google Scholar 

  • Rodríguez-Iturbe I, Cox DR, Isham V (1987a) Some models for rainfall based on stochastic point processes. Proc R Soc, London A 410:269–288

    Article  Google Scholar 

  • Rodríguez-Iturbe I, Febres de Power B, Valdés JB (1987b) Rectangular pulses point process models for rainfall: analysis of empirical data. J Geophys Res 92(D8):9645–9656

    Article  Google Scholar 

  • Roldan J, Woolhiser DA (1982) Stochastic daily precipitation models 1. A comparison of occurrence processes. Water Resour Res 18(5):1451–1459

    Article  Google Scholar 

  • Scharffenberg WA, Fleming MJ (2005) Hydrologic modelling system, HEC-HMS. User's Manual, 248 pp

    Google Scholar 

  • Stedinger JR, Vogel RM, Foufoula-Georgiou E (1993) Frequency analysis of extreme events. In: Maidment DR (ed) Handbook of hydrology. MacGraw-Hill, New York, NY, pp 18.1–18.66

    Google Scholar 

  • Stehlík J, Bárdossy A (2002) Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation. J Hydrol 256(1–2):120–141

    Article  Google Scholar 

  • Stern RD, Coe R (1984) A model fitting analysis of daily rainfall data (with discussion). J R Stat Soc A 147:1–34

    Article  Google Scholar 

  • Vrac M, Naveau P (2007) Stochastic downscaling of precipitation: from dry events to heavy rainfalls. Water Resour Res 43:W07402. doi:10.1029/2006WR005308

    Article  Google Scholar 

  • Wendling U, Schellin H-G, Thomä M (1991) Bereitstellung von täglichen Informationen zum Wasserhaushalt des Bodens für die Zwecke der agrarmeteorologischen Beratung. Meteorologische Zeitschrift 41:468–474

    Google Scholar 

  • Wheater H, Chandler R, Onof C, Isham V, Bellone E, Yang C, Lekkas D, Lourmas G, Segond ML (2005) Spatial-temporal rainfall modelling for flood risk estimation. Stoch Environ Res Risk Assess (SERRA) 19(6):403–416

    Article  Google Scholar 

  • Wilks DS (1998) Multisite generalization of a daily stochastic precipitation generation model. J Hydrol 210(1–4):178–191

    Article  Google Scholar 

  • Woolhiser DA, Roldan J (1982) Stochastic daily precipitation models. 2. A comparison of distribution amounts. Water Resour Res 18:1461–1468

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz Universität Hannover, Hannover, Germany

    Uwe Haberlandt

  2. GFZ German Research Centre for Geosciences, Potsdam, Germany

    Yeshewatesfa Hundecha

  3. Institute of Hydrology, Water Resources Management and Environmental Engineering, Ruhr-University Bochum, Bochum, Germany

    Markus Pahlow

  4. Ruhr-University Bochum, Chair of Hydrology and Water Management, Bochum, Germany

    Andreas H. Schumann

Authors
  1. Uwe Haberlandt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yeshewatesfa Hundecha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Markus Pahlow
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andreas H. Schumann
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Uwe Haberlandt .

Editor information

Editors and Affiliations

  1. LS Hydrologie, Wasserwirtsch., u. Umwelttechnik, Univ. Bochum, Bochum, Germany

    Andreas H. Schumann

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Haberlandt, U., Hundecha, Y., Pahlow, M., Schumann, A.H. (2011). Rainfall Generators for Application in Flood Studies. In: Schumann, A. (eds) Flood Risk Assessment and Management. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9917-4_7

Download citation

Publish with us

Policies and ethics

Search

Navigation