Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

SpringerLink
Log in

The adequacy of stochastically generated climate time series for water resources systems risk and performance assessment

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Stochastic weather generators are designed to produce synthetic sequences that are commonly used for risk discovery, as they would contain rare events that can lead to potentially catastrophic impacts on the environment, or even human lives. These time series are sometimes used as inputs to rainfall-runoff models to simulate the hydrological impacts of these rare events. This paper puts forward a method that evaluates the usefulness of weather generators by assessing how the statistical properties of simulated precipitation, temperatures, and streamflow deviate from those of observations. This is achieved by plotting a large ensemble of (1) synthetic precipitation and temperature time series in a Climate Statistics Space, and (2) hydrological indices using simulated streamflow data in a Risk and Performance Indicators Space. Assessment of weather generator’s performance is based on visual inspection and the Mahalanobis distance between statistics derived from observations and simulations. A case study was carried out on the South Nations watershed in Ontario, Canada, using five different weather generators: two versions of a single-site Weather Generator, two versions of a multi-site Weather Generator (MulGETS) and the K-Nearest Neighbour weather generator (k-nn). Results show that the MulGETS model often outperformed the other weather generators for that particular study area because: (a) the observations were well centered within a point cloud of the synthetically-generated time series in both spaces, and (b) the points generated using MulGETS had a smaller Mahalanobis distance to the observations than those generated with the other weather generators. The \(k\)-nn weather generator performed particularly well in simulating temperature variables, but was poor at modelling precipitation and streamflow statistics.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  • Abbaspour KC, Vejdani M, Haghighat S, Yang J (2007) SWAT-CUP calibration and uncertainty programs for SWAT. In: MODSIM 2007 international congress on modelling and simulation, modelling and simulation society of Australia and New Zealand, pp 1596–1602

  • Abramowitz M, Stegun IA (1972) Handbook of mathematical functions with formulas, graphs, and mathematical tables, vol 9. Chicago, Dover

    Google Scholar 

  • Ailliot P, Allard D, Monbet V, Naveau P (2015) Stochastic weather generators: an overview of weather type models. Journal de la Société Française de Statistique 156(1):101–113

    Google Scholar 

  • Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modeling and assessment part I: model development 1. J Am Water Resour Assoc 34(1):73–89

    Article  CAS  Google Scholar 

  • Arnold JG, Kiniry JR, Sirinivasan R, Williams JR, Haney EB, Neitsh SL (2012) SWAT input–output documentation, version 2012. Texas Water Resource Institute. TR-439

  • Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel R, Van Griensven A, Van Liew MW et al (2012b) SWAT: model use, calibration, and validation. Trans ASABE 55:1491–1508

    Article  Google Scholar 

  • Baigorria GA, Jones JW (2010) GiST: a stochastic model for generating spatially and temporally correlated daily rainfall data. J Clim 23(22):5990–6008

    Article  Google Scholar 

  • Bastola S, Murphy C, Fealy R (2012) Generating probabilistic estimates of hydrological response for Irish catchments using a weather generator and probabilistic climate change scenarios. Hydrol Process 26(15):2307–2321

    Article  Google Scholar 

  • Benestad RE, Nychka D, Mearns LO (2012) Specification of wet-day daily rainfall quantiles from the mean value. Tellus A: Dyn Meteorol Oceanogr 64(1):14981

    Article  Google Scholar 

  • Brissette FP, Khalili M, Leconte R (2007) Efficient stochastic generation of multi-site synthetic precipitation data. J Hydrol 345(3–4):121–133

    Article  Google Scholar 

  • Brocca L, Liersch S, Melone F, Moramarco T, Volk M (2013) Application of a model-based rainfall-runoff database as efficient tool for flood risk management. Hydrol Earth Syst Sci 17(8):3159

    Article  Google Scholar 

  • Brown C, Ghile Y, Laverty M, Li K (2012) Decision scaling: linking bottom up vulnerability analysis with climate projections in the water sector. Water Resour Res 48(9):9537

    Article  Google Scholar 

  • Camera C, Bruggeman A, Hadjinicolaou P, Michaelides S, Lange MA (2016) Evaluation of a spatial rainfall generator for generating high resolution precipitation projections over orographically complex terrain. Stoch Environ Res Risk Assess 31:757

    Article  Google Scholar 

  • Chen J, Brissette F (2014) Comparison of five stochastic weather generators in simulating daily precipitation and temperature for the Loess Plateau of China. Int J Climatol 34(10):3089–3105

    Article  Google Scholar 

  • Chen J, Brissette FP, Leconte R, Caron A (2012) A versatile weather generator for daily precipitation and temperature. Trans ASABE 55(3):895–906

    Article  Google Scholar 

  • Chen JF, Brissette X, Zhang J (2014) A multi-site stochastic weather generator for daily precipitation and temperature. Trans ASABE 2014:1375–1391. https://doi.org/10.13031/trans.57.10685

    Article  Google Scholar 

  • Cunnane C (1989) Statistical distributions for flood frequency analysis. Operational hydrology report (WMO)

  • Environment Canada (2012) National climate data and information archive: climate normals from 1971–2000 environment Canada

  • Forsythe N, Fowler HJ, Blenkinsop S, Burton A, Kilsby CG, Archer DR, Harpham C, Hashmi MZ (2014) Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: the Upper Indus Basin. J Hydrol 517:1019–1034

    Article  Google Scholar 

  • Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578

    Article  Google Scholar 

  • Frich P, Alexander LV, Della-Marta PM, Gleason B, Haylock M, Tank AK, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19(3):193–212

    Article  Google Scholar 

  • Fritsch V, Varoquaux G, Thyreau B, Poline J, Thirion B (2012) DETECTING outliers in high-dimensional neuroimaging datasets with robust covariance estimators. Med Image Anal 16:1359–1370

    Article  Google Scholar 

  • Govindaraju RS, Kavvas ML (1991) Stochastic overland flows. Stoch Hydrol Hydraul 5(2):105–124

    Article  Google Scholar 

  • Goyal MK, Burn DH, Ojha CSP (2013) Precipitation simulation based on k-nearest neighbor approach using gamma kernel. J Hydrol Eng 18:481–487

    Article  Google Scholar 

  • Guo T, Mehan S, Gitau MW, Wang Q, Kuczek T, Flanagan DC (2017) Impact of number of realizations on the suitability of simulated weather data for hydrologic and environmental applications. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-017-1498-5

    Article  Google Scholar 

  • Gupta H, Sorooshian S, Yapo P (1999) Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143

    Article  Google Scholar 

  • Hansen JW, Ines AV (2005) Stochastic disaggregation of monthly rainfall data for crop simulation studies. Agric For Meteorol 131:233–246

    Article  Google Scholar 

  • Hardin J, Rocke DM (2005) The distribution of robust distances. J Comput Graph Stat 14:928–946

    Article  Google Scholar 

  • Hashmi MZ, Shamseldin AY, Melville BW (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Environ Res Risk Assess 25:475–484

    Article  Google Scholar 

  • Helsel DR, Hirsch RM (1992) Statistical methods in water resources, studies in environmental science, vol 49. Elsevier, Amsterdam

    Google Scholar 

  • Huber PJ, Ronchetti EM (2009) Robust tests, in robust statistics, 2nd edn. Wiley, Hoboken, NJ. https://doi.org/10.1002/9780470434697.ch13

    Book  Google Scholar 

  • Iman RL, Conover WJ (1982) A distribution-free approach to inducing rank correlation among input variables. Commun Stat Simul Comput 11(3):311–334

    Article  Google Scholar 

  • Kavvas ML, Herd KR (1985) A radar-based stochastic model for short-time-increment rainfall. Water Resour Res 21(9):1437–1455

    Article  Google Scholar 

  • Kim BS, Kim HS, Seoh BH, Kim NW (2007) Impact of climate change on water resources in Yongdam Dam Basin, Korea. Stoch Environ Res Risk Assess 21:355

    Article  Google Scholar 

  • Kim D, Olivera F, Cho H (2013) Effect of the inter-annual variability of rainfall statistics on stochastically generated rainfall time series: part 1. Impact on peak and extreme rainfall values. Stoch Env Res Risk Assess 27(7):1601–1610

    Article  Google Scholar 

  • Kim D, Cho H, Onof C, Choi M (2017) Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling. Stoch Env Res Risk Assess 31(4):1023–1043

    Article  Google Scholar 

  • Klein Tank AMG, Zwiers FW, Zhang X (2009) Guidelines on Analysis of extremes in a changing climate in support of informed decisions for adaptation. World Meteorological Organization. 72 and WMO Tech. Doc. 1500

  • Kovalchuk SV, Krikunov AV, Knyazkov KV, Boukhanovsky AV (2017) Classification issues within ensemble-based simulation: application to surge floods forecasting. Stoch Env Res Risk Assess 31(5):1183–1197

    Article  Google Scholar 

  • Lennartsson J, Baxevani A, Chen D (2008) Modelling precipitation in Sweden using multiple step Markov chains and a composite model. J Hydrol 363(1):42–59

    Article  Google Scholar 

  • Liew MW, Veith TL, Bosch DD, Arnold JG (2007) Suitability of SWAT for the conservation effects assessment project: a comparison on USDA-ARS experimental watersheds. J Hydrol Eng 12(2):173–189

    Article  Google Scholar 

  • Loucks D, Stedinger J, Haith D (1981) Water resource systems planning and analysis. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  • Mahalanobis PC (1936) On the generalised distance in statistics. Proc Natl Inst Sci India 12(1936):49–55

    Google Scholar 

  • Markov AA (1906) Rasprostranenie zakona bol’shih chisel na velichiny, zavisyaschie drug ot druga. Izvestiya Fiziko-matematicheskogo obschestva pri Kazanskom universitete 15(135–156):18

    Google Scholar 

  • Mehrotra R, Srikanthan R, Sharma A (2006) A comparison of three stochastic multi-site precipitation occurrence generators. J Hydrol 331(1–2):280–292

    Article  Google Scholar 

  • Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900

    Article  Google Scholar 

  • Nash JE, Sutcliffe WH (1970) River flow forecasting through conceptual models: part 1. A discussion of principles. J Hydrol 10(3):282–290

    Article  Google Scholar 

  • Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2011) Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute

  • 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):3529–3548

    Article  Google Scholar 

  • Pilon PJ (1990) The Weibull distribution applied to regional low flow frequency analysis. Water resources branch, inland waters directorate, environment, Canada

  • Polade SD, Pierce DW, Cayan DR, Gershunov A, Dettinger MD (2014) The key role of dry days in changing regional climate and precipitation regimes. Sci Rep 4:4364

    Article  CAS  Google Scholar 

  • Rajagopalan B, Lall U, Tarboton DG, Bowles DS (1997) Multivariate nonparametric resampling scheme for generation of daily weather variables. Stoch Hydrol Hydraul 11(1):65–93

    Article  Google Scholar 

  • Ramesh NI, Garthwaite AP, Onof C (2018) A doubly stochastic rainfall model with exponentially decaying pulses. Stoch Environ Res Risk Assess 32:1645

    Article  Google Scholar 

  • Richardson CW (1981) Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour Res 17(1):182–190

    Article  Google Scholar 

  • Richardson CW, Wright DA (1984) WGEN: a model for generating daily weather variables. US Department of Agriculture, Agricultural Research Service, ARS-8

  • Salas JD, Lee TS (2010) Nonparametric simulation of single-site seasonal streamflows. J Hydrol Eng 15(4):284–296

    Article  Google Scholar 

  • Santhi C, Arnold J, Williams J, Dugas W, Srinivasan R, Hauck L (2001) Validation of the SWAT model on a large river basin with point and nonpoint sources 1. J Am Water Resour Assoc 37(5):1169–1188

    Article  CAS  Google Scholar 

  • Semenov MA, Barrow EM (2002) LARS-WG, a stochastic weather generator for use in climate impact studies, user manual. http://www.rothamsted.ac.uk/mas-models/download/LARS-WGManual.pdf

  • Shao Q, Zhang L, Wang QJ (2016) A hybrid stochastic-weather-generation method for temporal disaggregation of precipitation with consideration of seasonality and within-month variations. Stoch Environ Res Risk Assess 30(6):1705–1724

    Article  Google Scholar 

  • Sharif M, Burn DH (2007) Improved K-nearest neighbor weather generating model. J Hydrol Eng 12(1):42–51

    Article  Google Scholar 

  • Singh J, Knapp HV, Arnold JG, Demissie M (2005) Hydrological modeling of the Iroquois River watershed using HSPF and SWAT. JAWRA J Am Water Resour Assoc 41(2):343–360

    Article  Google Scholar 

  • Srinivasan R, Arnold JG (1994) Integration of a basin-scale water quality model with GIS. Water Resour Bull 30(3):453–462

    Article  Google Scholar 

  • Sun Y, Solomon S, Dai A, Portmann RW (2006) How often does it rain? J Clim 19(6):916–934

    Article  Google Scholar 

  • Sveinsson OGB, Salas JD, Lane WL, Frevert DK (2007) Stochastic Analysis, Modeling, and Simulation (SAMS) version 2007 user’s manual. Technical report no. 11. Computing Hydrology Laboratory, Department of Civil and Environmental Engineering. Colorado State University, Fort Collins, CO

  • Tuppad P, Douglas-Mankin KR, Lee T, Srinivasan R, Arnold JG (2011) Soil and Water Assessment Tool (SWAT) hydrologic/water quality model: extended capability and wider adoption. Trans ASABE 54(5):1677–1684

    Article  Google Scholar 

  • Wang MY, Zwilling CE (2015) Multivariate computing and robust estimating for outlier and novelty in data and imaging sciences. In: Advances in bioengineering. InTech

  • Warner T (2010) Climate modeling and downscaling. In: Warner T (ed) Numerical weather and climate prediction. Cambridge University Press, Cambridge, pp 407–455. https://doi.org/10.1017/CBO9780511763243.017

    Chapter  Google Scholar 

  • White KL, Chaubey I (2005) Sensitivity analysis, calibration, and validations for a multisite and multivariable SWAT model. J Am Water Resour Assoc 41(5):1077–1089

    Article  CAS  Google Scholar 

  • Wilby RL, Fowler HJ (2011) Regional climate downscaling: modelling the impact of climate change on water resources. In: Fai Fung C, Lopez A, New M (eds) Modelling the impact of climate change on water resources. Wiley, Hoboken. ISBN 978-1-405-19671-0

    Google Scholar 

  • Wilby RW, Tomlinson OJ, Dawson CW (2003) Multisite simulation of precipitation by conditional resampling. Clim Res 23(3):183–194

    Article  Google Scholar 

  • Wilks DS (1998) Multi-site generalization of a daily stochastic precipitation model. J Hydrol 210:178–191

    Article  Google Scholar 

  • Yates D, Gangopadhyay S, Rajagopalan B, Strzepek K (2003) A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resour Res 39(7):1199

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Qassim University, Buraidah, Qassim, Saudi Arabia

    Abdullah Alodah

  2. Department of Civil Engineering, University of Ottawa, Ottawa, ON, Canada

    Abdullah Alodah & Ousmane Seidou

Authors
  1. Abdullah Alodah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ousmane Seidou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdullah Alodah.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alodah, A., Seidou, O. The adequacy of stochastically generated climate time series for water resources systems risk and performance assessment. Stoch Environ Res Risk Assess 33, 253–269 (2019). https://doi.org/10.1007/s00477-018-1613-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-018-1613-2

Keywords

Search

Navigation