Abstract

Deriving unit hydrographs from rainfall-runoff data using optimization techniques circumvents the cumbersome requirements of the conventional manual method. In one of the two main ways of formulating the optimization problem, the decision variables are the individual ordinates of the unit hydrograph (UH). Because of their high number of degrees of freedom, though, such formulations can easily lead to overfitting the input hydrographs in the training set, resulting in highly irregular shapes for the derived UHs. These may be optimal for back-constructing the hydrographs in the training set, but as they do not resemble a typical hydrograph, they might not be capable of reproducing other unseen events adequately. In this work, we propose a set of linear constraints that can be included in existing optimization formulations to force the UH to feature a monotonically ascending limb, a single peak, and a monotonically receding limb. The ordinate of the peak, where the shift in monotonicity occurs, is not established in advance, but is rather determined within the optimization process. If required, the constraints can also be applied piecewise to obtain multiple peaks and troughs with monotonic limbs between them. Application to two case studies reveals that the reduction in the degrees of freedom does help in preventing overfitting of the input hydrographs, resulting in better performance when reproducing unseen events.

Get full access to this article

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

Data Availability Statement

All data and code generated and used during the study are available in Mathworks File Exchange at the link https://www.mathworks.com/matlabcentral/fileexchange/112760-monotonic-linear-constraints-for-optimal-unit-hydrographs.

References

Beven, K. J. 2012. Rainfall–runoff modelling: The primer. 2nd ed. New York: Wiley.
Bhattacharjya, R. K. 2004. “Optimal design of unit hydrographs using probability distribution and genetic algorithms.” Sadhana 29 (5): 499–508. https://doi.org/10.1007/BF02703257.
Bree, T. 1978. “The stability of parameter estimation in the general linear model.” J. Hydrol. 37 (1–2): 47–66. https://doi.org/10.1016/0022-1694(78)90095-1.
Che, D., M. Nangare, and L. W. Mays. 2014. “Determination of optimal unit hydrographs and green-Ampt parameters for watersheds.” J. Hydrol. Eng. 19 (2): 375–383. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000798.
Deinger, R. A. 1969. “Linear programming for hydrologic analyses.” Water Resour. Res. 5 (12): 1105–1109. https://doi.org/10.1029/WR005i005p01105.
Dell’Aira, F., T. J. Chy, T. H. W. Goebel, and C. I. Meier. 2022. “Inferring hydrological properties of the rainfall-runoff conversion process through artificial neural network modeling.” In Proc., World Environmental and Water Resources Congress 2022, 1264–1278. Reston, VA: ASCE. https://doi.org/10.1061/9780784484258.117.
Eagleson, P. S., R. Mejia-R, and F. March. 1966. “Computation of optimum realizable unit hydrographs.” Water Resour. Res. 2 (4): 755–764. https://doi.org/10.1029/WR002i004p00755.
Ghorbani, M. A., V. P. Singh, B. Sivakumar, M. H. Kashani, A. A. Atre, and H. Asadi. 2017. “Probability distribution functions for unit hydrographs with optimization using genetic algorithm.” Appl. Water Sci. 7 (2): 663–676. https://doi.org/10.1007/s13201-015-0278-y.
Gupta, H. V., H. Kling, K. K. Yilmaz, and G. F. Martinez. 2009. “Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modeling.” J. Hydrol. 377 (Oct): 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003.
Lansey, K. E. 2006. “The evolution of optimizing water distribution system applications.” In Proc., Water Distribution Systems Analysis Symp. 2006, 1–20. Reston, VA: ASCE. https://doi.org/10.1061/40941(247)5.
Lasdon, L. 1981. “A survey of nonlinear programming algorithms and software.” Vol. 1 of Foundations computer-aided Chemical Process Design, 185–218. New York: Engineering Foundation.
Lin, Y. 2011. “GCIP/EOP surface: Precipitation NCEP/EMC 4KM gridded data (GRIB) stage IV data, version 1.0.” UCAR/NCAR Earth Observing Laboratory. Accessed October 20, 2019. https://doi.org/10.5065/D6PG1QDD.
Linsley, R. K., M. A. Kohler, and J. L. H. Paulhus. 1982. Hydrology for engineers. 3rd ed. New York: McGraw–Hill.
Mays, L., and L. Coles. 1980. “Optimization of unit hydrograph determination.” J. Hydraul. Div. 106 (1): 85–97.
Mays, L. W., and C.-K. Taur. 1982. “Unit hydrographs via nonlinear programming.” Water Resour. Res. 18 (4): 744–752. https://doi.org/10.1029/WR018i004p00744.
Nash, J., and K. M. O’Connor. 1968. “Comment on ‘Computation of optimum realizable unit hydrographs,’ by Peter S. Eagleson, Ricardo Mejia-R, and Frederick March.” Water Resour. Res. 4 (1): 212–214. https://doi.org/10.1029/WR004i001p00212.
Ponce, V. M. 1989. Engineering hydrology: Principles and practices. Englewood Cliffs, NJ: Prentice Hall.
Rader, D. J. 2010. Deterministic operations research: Models and methods in linear optimization. New York: Wiley.
Rai, R., S. Sarkar, A. Upadhyay, and V. Singh. 2010. “Efficacy of Nakagami-m distribution function for deriving unit hydrograph.” Water Resour. Manage. 24 (3): 563–575. https://doi.org/10.1007/s11269-009-9459-5.
Sapkota, A. 2021. “A variable unit hydrograph incorporating nonlinearity in rainfall-runoff response due to antecedent watershed conditions.” Doctoral dissertation, Dept. of Engineering, Univ. of Memphis.
Sapkota, A., and C. I. Meier. 2020. “A parsimonious rainfall-runoff model for flood forecasting: Incorporating spatially varied rainfall and soil moisture.” In Proc., Watershed Management 2020, 183–196. Reston, VA: ASCE. https://doi.org/10.1061/9780784483060.017.
Sherman, L. K. 1932. “Streamflow from rainfall by the unit-graph method.” Eng. News Record 108 (12): 501–505.
Singh, K. P. 1976. “Unit hydrographs—A comparative study.” J. Am. Water Resour. Assoc. 12 (2): 381–392. https://doi.org/10.1111/j.1752-1688.1976.tb02686.x.
Ünver, O., and L. W. Mays. 1984. “Optimal determination of loss rate functions and unit hydrographs.” Water Resour. Res. 20 (2): 203–214. https://doi.org/10.1029/WR020i002p00203.
USGS. 2021. “USGS water data for the nation.” Accessed January 28, 2021. https://waterdata.usgs.gov/nwis.
Zhao, B., and Y.-K. Tung. 1994. “Determination of optimal unit hydrographs by linear programming.” Water Resour. Manage. 8 (2): 101–119. https://doi.org/10.1007/BF00872431.

Information & Authors

Information

Published In

Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 27Issue 11November 2022

History

Received: Nov 5, 2021
Accepted: Jun 13, 2022
Published online: Sep 8, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 8, 2023

Permissions

Request permissions for this article.

Authors

Affiliations

Ph.D. Student, Herff College of Engineering, Univ. of Memphis, 3817 Central Ave., Memphis, TN 38111 (corresponding author). ORCID: https://orcid.org/0000-0002-8940-5287. Email: [email protected]
Ph.D. Student, Herff College of Engineering, Univ. of Memphis, 3817 Central Ave., Memphis, TN 38111. ORCID: https://orcid.org/0000-0003-0030-0268. Email: [email protected]
Claudio I. Meier, A.M.ASCE [email protected]
Associate Professor, Herff College of Engineering, Univ. of Memphis, 3817 Central Ave., Memphis, TN 38111. Email: [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.

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