Technical Papers
Jul 5, 2017

Wavelet and Hidden Markov-Based Stochastic Simulation Methods Comparison on Colorado River Streamflow

Publication: Journal of Hydrologic Engineering
Volume 22, Issue 9

Abstract

Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot. Climate-based conditional streamflow simulation techniques recently have been shown to perform even better in capturing the spectral characteristics of streamflow coupled with block bootstrap simulation. This paper presents a comparison of three recently developed time series models in these frameworks: the climate wavelet autoregressive model (CWARM), the climate hidden Markov model (CHMM), and the climate wavelet-based k-nearest neighbor (K-NN) time series bootstrap (CWKNN) model. The purpose is to determine their applicability in water resources planning and management. These three methods incorporate two large-scale climate forcings, Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO)—recognized as the drivers of underlying nonstationarity—to condition the streamflow simulation. Comparisons are made of performance in both simulation and projection modes using the Lees Ferry (Arizona) flow in the Colorado River basin (CRB). The three methods are generally very good at capturing the distributional statistics and nonstationary features of the historical data in simulation mode. For short-term projections (1–8 years), important for midterm reservoir operations and planning, the CHMM appears to perform slightly better than the other two models. For longer-term projections (20  years), useful for decadal and multidecadal water resources planning, the CWKNN performs much better.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

The authors thankfully acknowledge the funding for this research by the Bureau of Reclamation (R12AC30023). The data used in this research paper can be obtained following the links and descriptions in the Data section. Thanks are due to the three anonymous reviewers and the editor for their insightful comments, which helped to enhance the manuscript significantly.

References

Bracken, C., Rajagopalan, B., and Zagona, E. (2014). “A hidden Markov model combined with climate indices for multi-decadal streamflow simulation.” Water Resour. Res., 50(10), 7836–7846.
Caraway, N. M., McCreight, J. L., and Rajagopalan, B. (2014). “Multisite stochastic weather generation using cluster analysis and k-nearest neighbor time series resampling.” J. Hydrol., 508, 197–213.
Chen, X. Y., Chau, K. W., and Busari, A. O. (2015). “A comparative study of population-based optimization algorithms for downstream river flow forecasting by a hybrid neural network model.” Eng. Appl. Artif. Intell., 46(A), 258–268.
Efron, B., and Tibishirani, R. (1993). An introduction to the bootstrap, Chapman and Hall, New York.
Enfield, D. B., Mestas-Nuñez, A. M., and Trimble, P. J. (2001). “The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US.” Geophys. Res. Lett., 28(10), 2077–2080.
Erkyihun, S. T. (2015). “Multi-decadal stochastic streamflow projection: An application to water resources decision making in the Colorado River Basin, civil, environmental, and architectural engineering.” Ph.D. thesis, Univ. of Colorado, Boulder, CO.
Erkyihun, S. T., Rajagopalan, B., Zagona, E., Lall, U., and Nowak, K. (2016). “Wavelet-based time series bootstrap model for multidecadal streamflow simulation using climate indicators.” Water Resour. Res., 52(5), 4061–4077.
Fernandez, B., and Salas, J. D. (1990). “Gamma-autoregressive models for streamflow simulation.” J. Hydraul. Eng., 1403–1414.
Fortin, V., Perreault, L., Ondo, J. C., and Evra, R. C. (2002). “Bayesian long-term forecasting of annual flows with a shifting-level model.” Proc., Symp. on Managing the Extremes—Floods and Droughts, Environmental and Water Resources Institute, ASCE, Reston, VA.
Grantz, K., Rajagopalan, B., Clark, M., and Zagona, E. (2005). “A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts.” Water Resour. Res., 41(10), W10410.
Grantz, K., Rajagopalan, B., Zagona, E., and Clark, M. (2007). “Water management applications of climate-based hydrologic forecasts: Case study of the Truckee-Carson River Basin, Nevada.” J. Water Resour. Plann. Manage., 339–350.
Gray, S. T., Graumlich, L. J., Betancourt, J. L., and Pederson, G. T. (2004). “A tree-ring based reconstruction of the Atlantic multidecadal oscillation since 1567 A.D.” Geophys. Res. Lett., 31(12), L12205.
Hidalgo, H. G. (2004). “Climate precursors of multidecadal drought variability in the western United States.” Water Resour. Res., 40(12), W12504.
Hipel, K. W., and McLeod, I. A. (1994). Time series modelling of water resources and environmental systems, Vol. 45, 1st Ed., Elsevier, Amsterdam, Netherlands.
Ibe, O. C. (2009). “Markov processes for stochastic modeling.” Univ. of Massachusetts, Academic Press, Amherst, MA.
Kaplan, A., Cane, M. A., Kushnir, Y., Clement, A. C., Blumenthal, M. B., and Rajagopalan, B. (1998). “Analyses of global sea surface temperature 1856–1991.” J. Geophys. Res., 103(C9), 18567–18589.
Koutsoyiannis, D. (2011). “Hurst-Kolmogorov dynamics and uncertainty.” J. Am. Water Res. Assoc., 47(3), 481–495.
Kumar, P., and Foufoula-Georgiou, E. (1994). “Introduction to wavelet transforms.” Wavelets in geophysics, E. Foufoula-Georgiou and P. Kumar, eds., Academic Press, Cambridge, MA.
Kumar, P., and Foufoula-Georgiou, E. (1997). “Wavelet analysis for geophysical applications.” Rev. Geophys., 35(4), 385–412.
Kwon, H. H., Lall, U., and Khalil, A. F. (2007). “Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature.” Water Resour. Res., 43(5), W05407.
Lall, U. (1995). “Recent advances in nonparametric function estimation: Hydrologic applications.” Rev. Geophys., 33(S2), 1093–1102.
Lall, U., and Sharma, A. (1996). “A nearest neighbor bootstrap for resampling hydrologic time series.” Water Resour. Res., 32(3), 679–693.
Lee, T. S., Salas, J. D., Keedy, J., Frevert, D., and Fulp, T. (2007). “Stochastic modeling and simulation of the Colorado River flows.” Proc., World Environmental and Water Resources Congress 2007: Restoring Our Natural Habitat, ASCE, Reston, VA, 1–10.
Lee, T. S., Salas, J. D., and Prairie, J. (2010). “An enhanced nonparametric streamflow disaggregation modeling and genetic algorithm.” Water Resour. Res., 46(8), W08545.
Loucks, D. P., and Van Beek, E. (2005). “Concepts in probability, statistics and stochastic modelling in water resources systems planning and management.” UNESCO Publishing, Paris.
MacDonald, G. M., and Case, R. A. (2005). “Variations in the Pacific decadal oscillation over the past millennium.” Geophys. Res. Lett., 32(8), L08703.
Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M., and Francis, R. C. (1997). “A Pacific interdecadal climate oscillation with impacts on salmon production.” Bull. Am. Meteor. Soc., 78(6), 1069–1079.
McCabe, G. J., Betancourt, J. L., and Hidalgo, H. G. (2007). “Associations of decadal to multidecadal sea surface temperature variability with Upper Colorado River flow.” J. Am. Water Resour. Assoc., 43(1), 183–192.
McCabe, G. J., and Dettinger, M. D. (1999). “Decadal variations in the strength of ENSO teleconnections with precipitation in the western United States.” Int. J. Climatol., 19(13), 1399–1410.
Nowak, K., Hoerling, M., Rajagopalan, B., and Zagona, E. (2012). “Colorado River Basin hydroclimatic variability.” J. Clim., 25(12), 4389–4403.
Nowak, K., Rajagopalan, B., and Zagona, E. (2011). “Wavelet auto-regressive method (WARM) for multi-site streamflow simulation of data with non-stationary spectra.” J. Hydrol., 410(1–2), 1–12.
Prairie, J., and Callejo, R. (2005). “Natural flow and salt computation methods.” U.S. Bureau the Interior, Salt Lake City.
Prairie, J. R., Rajagopalan, B., Fulp, T. J., and Zagona, E. A. (2006). “Modified K-NN model for stochastic streamflow simulation.” J. Hydrol. Eng., 371–378.
Rajagopalan, B., and Lall, U. (1998). “Low frequency variability in western U.S. precipitation.” J. Hydrol., 210(1–4), 51–67.
Rajagopalan, B., and Lall, U. (1999). “A k-nearest-neighbor simulator for daily precipitation and other weather variables.” Water Resour. Res., 35(10), 3089–3101.
Rajagopalan, B., Salas, J. D., and Lall, U. (2010). “Stochastic methods for modeling precipitation and streamflow.” Advances in data-based approaches for hydrologic modeling and forecasting, R. Berndtsson and B. Sivakumar, eds., World Scientific, Singapore.
Regonda, S., Zagona, E., and Rajagopalan, B. (2011). “Prototype decision support system for operations on the Gunnison Basin with improved forecasts.” J. Water Resour. Plann. Manage., 428–438.
Salas, J. D. (1993). “Analysis and modeling of hydrologic time series, Chapter 19.” Handbook of hydrology, D. R. Maidment, ed., McGraw-Hill, New York, 1424.
Salas, J. D., Delleur, J. W., Yevjevich, V., and Lane, W. L. (1980). Applied modeling of hydrological time series, Water Resources Publications, Littleton, CO.
Salas, J. D., and Lee, T. S. (2010). “Nonparametric simulation of single site seasonal streamflows.” J. Hydrol. Eng., 284–296.
Sharma, A., and Mehrotra, R. (2014). “An information theoretic alternative to model a natural system using observational information alone.” Water Resour. Res., 50(1), 650–660.
Sharma, A., Mehrotra, R., Li, J., and Jha, S. (2016). “Programming tool for nonparametric system prediction using partial informational correlation and partial weights.” Environ. Modell. Software, 83, 271–275.
Sharma, A., Tarboton, D. G., and Lall, U. (1997). “Streamflow simulation: A nonparametric approach.” Water Resour. Res., 33(2), 291–308.
Sveinsson, O. (2014). “Time series analysis of hydrologic data.” Handbook of engineering hydrology, Vol. 2, modeling, climate changes and variability, S. Eslamian, ed., Taylor & Francis, CRC Press, Boca Raton, FL.
Sveinsson, O. G., Salas, J. D., Boes, D. C., and Pielke, R. A. (2003). “Modeling the dynamics of long term variability of hydroclimatic processes.” J. Hydrometeorol., 4(3), 489–505.
Switanek, M. B., and Troch, P. A. (2011). “Decadal prediction of Colorado River streamflow anomalies using ocean-atmosphere teleconnections.” Geophys. Res. Lett., 38(23), L23404.
Thyer, M., and Kuczera, G. (2003). “A hidden Markov model for modelling long-term persistence in multi-site rainfall time series. I: Model calibration using a Bayesian approach.” J. Hydrol., 275(1–2), 12–26.
Timilsena, J., Piechota, T., Tootle, G., and Singh, A. (2009). “Associations of interdecadal/interannual climate variability and long-term Colorado River basin streamflow.” J. Hydrol., 365(3–4), 289–301.
Tootle, G. A., Piechota, T. C., and Singh, A. (2005). “Coupled oceanic-atmospheric variability and U.S. streamflow.” Water Resour. Res., 41(12), W12408.
Torrence, C., and Compo, G. P. (1998). “A practical guide to wavelet analysis.” Bull. Am. Meteorol. Soc., 79(1), 61–78.
U.S. Department of the Interior. (2001). “Record of decision Colorado River interim surplus guidelines final environmental impact statement bureau of reclamation.” Boulder, CO.
Wang, W. C., Chau, K. W., Xu, D. M., and Chen, X. Y. (2015). “Improving forecasting accuracy of annual runoff time series using ARIMA based on EEMD decomposition.” Water Resour. Manage., 29(8), 2655–2675.
Wasko, C., Pui, A., Sharma, A., Mehrotra, R., and Jeremiah, E. (2015). “Representing low-frequency variability in continuous rainfall simulations: A hierarchical random Bartlett Lewis continuous rainfall generation model.” Water Resour. Res., 51(12), 9995–10007.
Wei, W. W. S. (2006). Time series analysis, Addison-Wesley, Boston.
Woodhouse, C. A., Gray, S. T., and Meko, D. M. (2006). “Updated streamflow reconstructions for the Upper Colorado River Basin.” Water Resour. Res., 42(5), W05415.
Wu, C., Chau, K., and Li, Y. (2009). “Methods to improve neural network performance in daily flows prediction.” J. Hydrol., 372(1–4), 80–93.
Yates, D., Gangopadhyay, S., Rajagopalan, B., and Strzepek, K. (2003). “A technique for generating regional climate scenarios using a nearest-neighbor algorithm.” Water Resour. Res., 39(7), 1199.
Zhang, Y., Wallace, J. M., and Battisti, D. S. (1997). “ENSO-like interdecadal variability: 1900–93.” J. Clim., 10(5), 1004–1020.
Zucchini, W., and Guttorp, P. (1991). “A hidden Markov model for space-time precipitation.” Water Resour. Res., 27(8), 1917–1923.
Zucchini, W., and MacDonald, I. L. (2009). Hidden Markov models for time series: An introduction using R, CRC Press, Boca Raton, FL.

Information & Authors

Information

Published In

Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 22Issue 9September 2017

History

Received: Apr 5, 2016
Accepted: Mar 2, 2017
Published online: Jul 5, 2017
Published in print: Sep 1, 2017
Discussion open until: Dec 5, 2017

Permissions

Request permissions for this article.

Authors

Affiliations

Dept. of Civil, Environmental and Architectural Engineering, CADSWES, Univ. of Colorado, Boulder, CO 80309 (corresponding author). ORCID: https://orcid.org/0000-0002-0148-1200. E-mail: [email protected]
Edith Zagona, Ph.D. [email protected]
Dept. of Civil, Environmental and Architectural Engineering, CADSWES, Univ. of Colorado, Boulder, CO 80309. E-mail: [email protected]
Balaji Rajagopalan, Ph.D. [email protected]
Dept. of Civil, Environmental and Architectural Engineering, CIRES, Univ. of Colorado, Boulder, CO 80309. E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share