Technical Papers
Oct 4, 2013

Threshold of Basin Discretization Levels for HSPF Simulations with NEXRAD Inputs

Publication: Journal of Hydrologic Engineering
Volume 19, Issue 7

Abstract

Basin discretization effects in HSPF simulations were investigated to provide useful insights for hydrologists to determine the proper catchment size for basin scale modeling. The next generation radar (NEXRAD) rainfall estimates were incorporated into the HSPF modeling environment to generate streamflows at various catchments sizes ranging from 37 to 2,484km2. This research aims to identify how HSPF model performance can be improved by a marginal level of spatial discretization in rainfall-runoff modeling. Parameter estimation software was used for model calibration using data periods from 1998 to 2000. All simulations at different discretization levels above approximately 23% of the basin size resulted in good statistical values, with correlation coefficients of 0.82–0.87 and Nash-Sutcliffe efficiency coefficients of 0.61–0.73. However, the modeling performances of HSPF are limited when the catchment size reaches below 8.18% of the basin size, regardless of automatic calibration efforts. The result indicates that basin discretization at finer scales does not necessarily improve HSPF simulation results with NEXRAD inputs.

Get full access to this article

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

Acknowledgments

The authors would like to acknowledge Idaho Water Resources Research Institute (IWRRI) providing USGS 104B grant money for this study. Partial funding support for Jungjin Kim and Jae H. Ryu is also made from the NSF Idaho EPSCoR program (Award Number: EPS-0814387) and NASA (Award Number: NNX08AL94G), respectively.

References

Ajami, N., Gupta, H., Wagener, T., and Sorooshian, S. (2004). “Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system.” J. Hydrol., 298(1–4), 112–135.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). “Crop evapotranspiration (guidelines for computing crop water requirements).”, Food and Agricultural Organization of the United Nations, Rome, 300.
Bell, V. A., and Moore, R. J. (1998). “A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies.” Hydrol. Earth Syst. Sci., 2(3), 283–298.
Boyle, D. P., Gupta, H. V., and Sorooshian, S. (2001). “Toward improved streamflow forecasts: Value of semidistributed modeling.” Water Resour. Res., 37(11), 2749–2759.
Carpenter, T. M., and Georgakakos, K. P. (2004). “Continuous streamflow simulation with the HRCDHM distributed hydrologic model.” J. Hydrol., 298(1–4), 61–79.
Chiew, F. H. S., Kamaladasa, N. N., Malano, H. M., and McMahon, T. A. (1995). “Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia.” Agric. Water Manage., 28(1), 9–21.
Doherty, J., and Johnston, J. M. (2003). “Methodologies for calibration and predictive analysis of a watershed model.” J. Am. Water Resour. Assoc., 39(2), 251–265.
Doherty, J., and Skahill, B. E. (2006). “An advanced regularization methodology for use in watershed model calibration.” J. Hydrol., 327(3–4), 564–577.
EPA. (2000). “BASINS technical note 6: Estimating hydrology and hydraulic parameters for HSPF.”, Office of Water, Washington, DC.
Fienen, M. N., Muffels, C. T., and Hunt, R. J. (2009). “On constraining pilot point calibration with regularization in PEST.” Ground Water, 47(6), 835–844.
Fulton, R. A., Breidenbach, J. P., Seo, D.-J., and Miller, D. A. (1998). “The WSR-88D rainfall algorithm.” Weather Forecast., 13(2), 377–395.
Goegebeur, M., and Pauwels, V. R. N. (2007). “Improvement of the PEST parameter estimation algorithm through extended Kalman filtering.” J. Hydrol., 337(3–4), 436–451.
Greene, D. R., and Hudlow, M. D. (1982). “Hydrometeorologic grid mapping procedures.” AWRA Int. Symp. on Hydrometeorology, American Water Resources Association, Denver, CO.
Grid Analysis and Display System (GrADS) [Computer software]. Center for Ocean-Land-Atmosphere Studies (COLA), George Mason University, Fairfax, VA.
Im, S., Brannan, K. M., and Mostaghimi, S. (2003). “Simulating hydrologic and water quality impacts in an urbanizing watershed.” J. Am. Water Resour. Assoc., 39(6), 1465–1479.
Jensen, M. E., Burman, R. D., and Allen, R. G. (1990). “Evapotranspiration and irrigation water requirements.”, ASCE, Reston, VA.
Jensen, M. E., and Haise, H. R. (1963). “Estimating evapotranspiration from solar radiation.” J. Irrig. Drain. Eng., 89(IR4), 15–41.
Kim, S. M., Benham, B. L., Brannan, K. M., Zeckoski, R. W., and Doherty, J. (2007). “Comparison of hydrologic calibration of HSPF using automatic and manual methods.” Water Resour. Res., 43(1), W01402.
Koren, V. I., Finnerty, B. D., Schaake, J. C., Smith, M. B., Seo, D.-J., and Duan, Q. Y. (1999). “Scale dependencies of hydrology models to spatial variability of precipitation.” J. Hydrol., 217(3–4), 285–302.
Lerat, J., Perrin, C., Andreassian, V., Loumagne, C., and Ribstein, P. (2012). “Towards robust methods to couple lumped rainfall-runoff models and hydraulic models: A sensitivity analysis on the Illinois River.” J. Hydrol., 418–419, 123–125.
Marquardt, D. W. (1963). “An algorithm for least-squares estimation of nonlinear parameters.” J. Soc. Ind. Appl. Math., 11(2), 431–441.
McCuen, R. H., Knight, Z., and Cutter, A. G. (2006). “Evaluation of the Nash-Sutcliffe efficiency index.” J. Hydrol. Eng., 597–602.
Michaud, J., and Sorooshian, S. (1994). “Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed.” Water Resour. Res., 30(3), 593–605.
Mitchell, K. E., et al. (2004). “The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system.” J. Geophys. Res., 109(D7), D07S90.
Monteith, J. L. (1965). “Evaporation and the environment.” State and Movement of Water in Living Organisms, XIX Symp. Society for Experimental Biology, Cambridge University Press, Cambridge, U.K.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models: Part I—A discussion of principles.” J. Hydrol., 10(3), 282–290.
Parameter Estimation (PEST) [Computer software]. Watermark Numerical Computing, Brisbane, Australia.
Raupach, M. R., and Finnigan, J. J. (1988). “Single-layer models of evaporation from plant canopies are incorrect but useful, whereas multilayer models are correct but useless: Discuss.” Aus. J. Plant Physiol., 15(6), 705–716.
Reed, S. M., and Maidment, D. R. (1999). “Coordinate transformations for using NEXRAD data in GIS-based hydrologic modeling.” J. Hydrol. Eng., 174–182.
Refsgaard, J. C., and Knudsen, J. (1996). “Operational validation and intercomparison of different types of hydrological models.” Water Resour. Res., 32(7), 2189–2202.
Ryu, J. H. (2009). “Application of HSPF to the distributed model intercomparison project: Case study.” J. Hydrol. Eng., 847–857.
Seo, D.-J., and Breidenbach, J. P. (2002). “Real-time correction of spatially nonunifrom bias in radar rainfall data using rain gauge measurements.” J. Hydrometeorol., 3(2), 93–111.
Smith, M. B., et al. (2004). “The distributed model intercomparison project (DMIP): Motivation and experiment design.” J. Hydrol., 298(1–4), 4–26.
Smith, M. B., et al. (2012). “Result of the DMIP 2 Oklahoma experiments.” J. Hydrol., 418–419, 17–48.
Smith, R. E., Goodrich, D. C., Woolhiser, D. A., and Unkrich, C. L. (1995). KINEROS: A kinematic runoff and erosion model in computer models of watershed hydrology, Water Resources Pubications, Littleton, CO, 697–732.
Wang, D., Smith, M. B., Zhang, Z., Reed, S. M., and Koren, V. I. (2000). “Statistical comparision of mean real precipitation estimates from WSR-88d, operational and historical gage networks.” Proc., 15th Conf. on Hydrology, American Meteorological Society, Boston.
Wood, E. F. (1995). “Heterogeneity and scaling land-atmospheric water and energy fluxes in climate systems.” Space and time scale variability and interdependencies in hydrological processes, R. A. Feddes, ed., Cambridge University Press, West Nyack, NY, 3–19.
Zhang, Y., Reed, S., and Kitzmiller, D. (2011). “Effects of retrospective gauge-based readjustment of multisensor precipitation estimates on hydrologic simulations.” J. Hydrometeorol., 12(3), 429–433.

Information & Authors

Information

Published In

Go to Journal of Hydrologic Engineering
Journal of Hydrologic Engineering
Volume 19Issue 7July 2014
Pages: 1401 - 1412

History

Received: Apr 17, 2013
Accepted: Oct 2, 2013
Published online: Oct 4, 2013
Discussion open until: Mar 4, 2014
Published in print: Jul 1, 2014

Permissions

Request permissions for this article.

Authors

Affiliations

JungJin Kim [email protected]
Graduate Research Assistant, Dept. of Biological and Agricultural Engineering, Univ. of Idaho, 322 E Front St., Boise, ID 83702. E-mail: [email protected]
Jae Hyeon Ryu, M.ASCE [email protected]
Assistant Professor, Dept. of Biological and Agricultural Engineering, Univ. of Idaho, 322 E Front St., Boise, ID 83702 (corresponding author). 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