Simple Method for Streamflow Disaggregation
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 3
Abstract
Streamflow disaggregation from monthly to daily was performed using a relatively simple, flexible, and adaptive method. Only streamflow acts as a decision variable in this disaggregation process. To disaggregate monthly to daily flow at the target station (TS), monthly counterparts at the source station (SS) were selected on the basis of minimum error criteria, which are calculated with respect to streamflow volume within a three-month time window. Daily streamflow indexes at SS were then calculated to disaggregate monthly to daily streamflow at TS during the disaggregation process. The effectiveness of the proposed method has been demonstrated through its application at both regulated and unregulated waterways located in the northwest states, including Idaho and Wyoming. For both regulated and unregulated monthly streamflow, the proposed method well represents daily streamflow realizations similar to historical flows and preserves both mass balance and a series of statistical characteristics. However, the results also indicate that the quality of disaggregated daily streamflow varies for individual applications depending on the selection of stations, their geographic information, and data availability. The disaggregation model used in this research is transparent, user friendly, less intensive, and less time-consuming so that it can be used at any watershed without difficulty or much effort. Consequently, because development and availability of daily streamflow is important for water resources planning and management, including reservoir operation, water quality study, and environmental/ecological modeling, this research will help bridge the gap among interdisciplinary water research activities, especially for studies of the impacts of hydrologic events possibly driven by extreme weather variability and climate change.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research is supported by the National Science Foundation (NSF) under award number EPS-0814387. The authors thank collaborators at U.S. Bureau of Reclamation (USBR), including Jennifer Johnson, Karl Tarbet, and Jonathan Rocha at the Pacific Northwest Region Office, Boise, Idaho. Their assistance and comments during the course of tool development are greatly helpful to implement the proposed method in Pisces system at http://www.usbr.gov/pn/hydromet/pisces. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NSF or USBR. Any queries should be directed to the corresponding author for the article. The software package used in this research is available at http://water.cals.uidaho.edu/products/UIDisagg/, as of March 1, 2013.
References
Acharya, A., Piechota, T. C., Stephen, H., and Tootle, G. (2011). “Modeled streamflow response under cloud seeding in the North Platte River watershed.” J. Hydrol., 409(1–2), 305–314.
Acharya, A., Piechota, T. C., and Tootle, G. (2012). “Quantitative assessment of climate change impacts on the hydrology of the North Platte River watershed, Wyoming.” J. Hydrol. Eng., 1071–1083.
Allison, P. D. (2001). Missing data, Sage Publications, Thousand Oaks, CA.
Bras, R. L., and Rodriguez-Iturbe, I. (1985). Random functions and hydrology, Dover, Mineola, NY.
Cohen, J., Cohen, P., West, S. G., and Alken, L. S. (2003). Applied multiple regression/ correlation analysis for the behavioral sciences, 3rd Ed., Lawrence Erlbaum, Mahwah, NJ.
Costelloe, J. F., Grayson, R. B., and McMahon, T. A. (2005). “Modeling stream flow for use in ecological studies in a large, arid zone river, central Australia.” Hydrol. Process., 19(6), 1165–1183.
Ganju, N. K., Knowles, N., and Schoellhamer, D. H. (2008). “Temporal downscaling of decadal sediment load estimates to a daily interval for use in hindcast simulations.” J. Hydrol., 349(3–4), 512–523.
Gippel, C. J. (2001). “Australia’s environmental flow initiative: Filling some knowledge gaps and exposing others.” Water Sci. Technol., 43(9), 73–88.
Green, N. M. D. (1973). “A synthetic model for daily streamflow.” J. Hydrol., 20(4), 351–364.
Grygier, J. C., and Stedinger, J. R. (1988). “Condensed disaggregation procedureS and conservation corrections for stochastic hydrology.” Water Resour. Res., 24(10), 1574–1584.
Hollander, M., and Wolfe, D. A. (1999). Nonparametric statistical methods, Wiley, Hoboken, NJ.
Howell, D. C. (2008). “The treatment of missing data.” Handbook of social science methodology, W. Outhwaite and S. Turner, eds., Sage, London, 208–224.
Krause, P., Boyle, D. P., and Base, F. (2005). “Comparison of different efficiency criteria for hydrological model assessment.” Adv. Geosci., 5, 89–97.
Lee, T., Sales, J. D., and Prairie, J. (2010). “An enhanced nonparametric streamflow disaggregation model with genetic algorithm.” Water Resour. Res., 46(8), W08545.
Little, R. J. A. (1992). “Regression with missing X’s: A review.” J. Am. Stat. Assoc., 87(420), 1227–1237.
Mâsse, B. R., and Truong, Y. K. (1999). “Conditional logspline density estimation.” Can. J. Stat., 27(4), 819–832.
Nagesh Kumar, D., Lal, U., and Petersen, M. R. (2000). “Multisite disaggregation of monthly to daily streamflow.” Water Resour. Res., 36(7), 1823–1833.
Nowak, K., Prairie, J., Rajagopalan, B., and Lall, U. (2010). “A nonparametric stochastic approach for multisite disaggregation of annual to daily streamlfow.” Water Resour. Res., 46(8), W08529.
Pegram, G. G., Salas, J. D., Boes, D. C., and Yevjevich, V. (1980). Stochastic properties of water storage, Colorado State Univ., Fort Collins, CO.
Prairie, J., Rajagopalan, B., Lall, U., and Fulp, T. (2007). “A stochastic nonparametric technique for space time disaggregation of streamflows.” Water Resour. Res., 43(3), W03432.
Salas, J. D., Delleur, J. W., Yevjevich, V., and Lane, W. L. (1980). Applied modeling of hydrologic time series, Water Resources Publications, Littleton, CO.
Santos, E. G., and Salas, J. D. (1992). “Stepwise disaggregation scheme for synthetic hydrology.” J. Hydraul. Eng., 765–784.
Sharma, A., and O’Neill, R. (2002). “A nonparametric approach for representing interannual dependence in monthly streamflow sequences.” Water Resour. Res., 38(7), 5-1–5-10.
Sharma, A., Tarboton, D. G., and Lall, U. (1997). “Streamflow simulation—A nonparametric approach.” Water Resour. Res., 33(2), 291–308.
Sivakumar, B., Wallender, W. W., Puente, C. E., and Islam, M. N. (2004). “Streamflow disaggregation: A nonlinear deterministic approach.” Nonlinear Process. Geophys., 11(3), 383–392.
Smith, M. B., et al. (2004). “The distributed model intercomparison project (DMIP): Motivation and experimental design.” J. Hydrol., 298(1–4), 4–26.
Stedinger, J. R., and Vogel, R. M. (1984). “Disaggregation procedures for generating serially correlated flow vectors.” Water Resour. Res., 20(1), 47–56.
Stephens, M. A. (1970). “Use of the Kolmogorov–Smirnov, Cramer– von-Mises and related statistics without extensive tables.” J. R. Stat. Soc., 32(1), 115–122.
Tarboton, D. G., Sharma, A., and Lall, U. (1998). “Disaggregation procedures for stochastic hydrology based on non-parametric density estimation.” Water Resour. Res., 34(1), 107–119.
Valencia, D., and Schaake, J. C. (1973). “Disaggregation processes in stochastic hydrology.” Water Resour. Res., 9(3), 580–585.
Wood, A. W., Leung, L. R., Sridhar, V., and Lettenmaier, D. P. (2004). “Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs.” Clim. Change, 62(1–3), 189–216.
Xu, Z., Schumann, A., and Brass, C. (2001). “Markov autocorrelation pulse model for two sites daily streamflow.” J. Hydrol. Eng., 189–195.
Xu, Z., Schumann, A., and Li, J. (2003). “Markov cross-correlation pulse model for daily streamflow generation at multiple sites.” Adv. Water Resour., 26(3), 325–335.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 19 • Issue 3 • March 2014
Pages: 509 - 519
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jun 4, 2012
Accepted: Mar 13, 2013
Published online: Mar 15, 2013
Discussion open until: Aug 15, 2013
Published in print: Mar 1, 2014
Authors
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.