Editorial Type:
Article
Article Type:
Research Article

Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs

J. Vaze CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by J. Vaze in
Current site
Google Scholar
PubMed Close
,
D. A. Post CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by D. A. Post in
Current site
Google Scholar
PubMed Close
,
F. H. S. Chiew CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by F. H. S. Chiew in
Current site
Google Scholar
PubMed Close
,
J.-M. Perraud CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by J.-M. Perraud in
Current site
Google Scholar
PubMed Close
,
J. Teng CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by J. Teng in
Current site
Google Scholar
PubMed Close
, and
N. R. Viney CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, Canberra, Australian Capital Territory, Australia

Search for other papers by N. R. Viney in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Oct 2011
DOI:
https://doi.org/10.1175/2011JHM1340.1
Page(s):
1100–1112
Restricted access

Abstract

Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

Corresponding author address: Dr. Jai Vaze, Principal Research Scientist, CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia. E-mail: jai.vaze@csiro.au

Abstract

Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

Corresponding author address: Dr. Jai Vaze, Principal Research Scientist, CSIRO Water for a Healthy Country National Research Flagship, CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia. E-mail: jai.vaze@csiro.au
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Anctil, F., Lauzon N. , Andréassian V. , Oudin L. , and Perrin C. , 2006: Improvement of rainfall-runoff forecasts through mean areal rainfall optimization. J. Hydrol., 328, 717–725.

    • Search Google Scholar
    • Export Citation

  • Andréassian, V., Perrin C. , Michel C. , Usart-Sanchez I. , and Lavabre J. , 2001: Impact of imperfect rainfall knowledge on the efficiency and the parameters of watershed models. J. Hydrol., 250, 206–223.

    • Search Google Scholar
    • Export Citation

  • Bras, R. L., and Rodríguez-Iturbe I. , 1976a: Rainfall network design for runoff prediction. Water Resour. Res., 12, 1197–1208.

  • Bras, R. L., and Rodríguez-Iturbe I. , 1976b: Network design for the estimation of areal mean of rainfall events. Water Resour. Res., 12, 1185–1195, doi:10.1029/WR012i006p01185.

    • Search Google Scholar
    • Export Citation

  • Burnash, R. J. C., Ferral R. L. , and McGuire R. A. , 1973: A generalized streamflow simulation system—Conceptual modelling for digital computers. National Weather Service and State of California, Department of Water Resources, 204 pp.

    • Search Google Scholar
    • Export Citation

  • Chaubey, I., Haan C. T. , Grunwald S. , and Salisbury J. M. , 1999: Uncertainty in the model parameters due to spatial variability of rainfall. J. Hydrol., 220, 48–61.

    • Search Google Scholar
    • Export Citation

  • Chiew, F. H. S., and McMahon T. A. , 1991: The applicability of Morton’s and Penman’s evapotranspiration estimates in rainfall-runoff modelling. Water Resour. Bull., 27, 611–620.

    • Search Google Scholar
    • Export Citation

  • Chiew, F. H. S., and McMahon T. A. , 2002: Modelling the impacts of climate change on Australian streamflow. Hydrol. Processes, 16, 1235–1245.

    • Search Google Scholar
    • Export Citation

  • Chiew, F. H. S., Peel M. C. , and Western A. W. , 2002: Application and testing of the simple rainfall-runoff model SIMHYD. Mathematical Models of Small Watershed Hydrology and Applications, V. P. Singh and D. K. Frevert, Eds., Water Resources Publication, 335–367.

    • Search Google Scholar
    • Export Citation

  • Chiew, F. H. S., Teng J. , Vaze J. , Post D. A. , Perraud J.-M. , Kirono D. G. C. , and Viney N. R. , 2009: Estimating climate change impact on runoff across southeast Australia: Method, results, and implications of the modeling method. Water Resour. Res., 45, W10414, doi:10.1029/2008WR007338.

    • Search Google Scholar
    • Export Citation

  • Duan, Q. Y., Gupta V. K. , and Sorooshian S. , 1993: Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl., 76, 501–521.

    • Search Google Scholar
    • Export Citation

  • Faures, J.-M., Goodrich D. C. , Woolhiser D. A. , and Sorooshian S. , 1995: Impact of small-scale spatial rainfall variability on runoff modeling. J. Hydrol., 173 (1–4), 309–326.

    • Search Google Scholar
    • Export Citation

  • Gan, T. Y., and Burges S. J. , 2006: Assessment of soil-based and calibrated parameters of the Sacramento model and parameter transferability. J. Hydrol., 320, 117–131.

    • Search Google Scholar
    • Export Citation

  • Goswami, M., O’Connor K. M. , and Shamseldin A. Y. , 2002: Structures and performances of five rainfall–runoff models for continuous river-flow simulation. Proc. First Biennial Meeting of the International Environmental Modeling and Software Society Lugano, Switzerland, IEMSS, Vol. 1, 476–481.

    • Search Google Scholar
    • Export Citation

  • Jeffrey, S. J., Carter J. O. , Moodie K. B. , and Beswick A. R. , 2001: Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environ. Model. Software, 16, 309–330.

    • Search Google Scholar
    • Export Citation

  • Morton, F. I., 1983: Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology. J. Hydrol., 66, 1–76.

    • Search Google Scholar
    • Export Citation

  • Nash, J. E., and Sutcliffe J. V. , 1970: River flow forecasting through conceptual models. Part I—A discussion of principles. J. Hydrol., 10, 282–290.

    • Search Google Scholar
    • Export Citation

  • O’Connell, P. E., Nash J. E. , and Farrel J. P. , 1970: River flow forecasting through conceptual models. Part II—The Brosna catchment at Ferbane. J. Hydrol., 10, 317–329.

    • Search Google Scholar
    • Export Citation

  • Oudin, L., Perrin C. , Mathevet T. , Andréassian V. , and Michel C. , 2006: Impact of biased and randomly corrupted inputs on the efficiency and the parameters of watershed models. J. Hydrol., 320, 62–83.

    • Search Google Scholar
    • Export Citation

  • Peel, M. C., Chiew F. H. S. , Western A. W. , and McMahon T. A. , cited 2000: Extension of unimpaired monthly streamflow data and regionalisation of parameter values to estimate streamflow in ungauged catchments. National Land and Water Resources Audit. [Available online at http://www.anra.gov.au/topics/water/pubs/national/water_preface.html.]

    • Search Google Scholar
    • Export Citation

  • Post, D. A., and Jakeman A. J. , 1999: Predicting the daily streamflow of ungauged catchments in S. E. Australia by regionalising the parameters of a lumped conceptual rainfall-runoff model. Ecol. Modell., 123, 91–104.

    • Search Google Scholar
    • Export Citation

  • Rosenbrock, H. H., 1960: An automatic method for finding the greatest or least value of a function. Comput. J., 3, 175–184.

  • Segond, M.-L., Wheater H. S. , and Onof C. , 2007: The significance of spatial rainfall representation for flood runoff estimation: A numerical evaluation based on the Lee catchment, UK. J. Hydrol., 347 (1–2), 116–131.

    • Search Google Scholar
    • Export Citation

  • Simons, M., Podger G. , and Cooke R. , 1996: IQQM—A hydrologic modelling tool for water resource and salinity management. Environ. Software, 11 (1–3), 185–192, doi:10.1016/S0266-9838(96)00019-6.

    • Search Google Scholar
    • Export Citation

  • Tuteja, N. K., and Coauthors, 2003: Predicting the effects of land use change on water and salt balance—A case study of a catchment affected by dry land salinity in NSW, Australia. J. Hydrol., 283 (1–4), 67–90.

    • Search Google Scholar
    • Export Citation

  • Tuteja, N. K., Vaze J. , Teng J. , and Mutendeudzi M. , 2007: Partitioning the effects of pine plantations and climate variability on runoff from a large catchment in southeastern Australia. Water Resour. Res., 43, W08415, doi:10.1029/2006WR005016.

    • Search Google Scholar
    • Export Citation

  • Vaze, J., and Teng J. , 2011: Future climate and runoff projections across New South Wales, Australia—Results and practical applications. Hydrol. Processes, 25, 18–35, doi:10.1002/hyp.7812.

    • Search Google Scholar
    • Export Citation

  • Vaze, J., and Coauthors, 2004: Modelling the effects of land-use change on water and salt delivery from a catchment affected by dry land salinity in south-east Australia. Hydrol. Processes, 18, 1613–1637.

    • Search Google Scholar
    • Export Citation

  • Vaze, J., Davidson A. , Teng J. , and Podger G. , 2011a. Impact of climate change on water availability in the Macquarie–Castlereagh river basin in Australia. Hydrol. Processes, doi: 10.1002/hyp.8030, in press.

    • Search Google Scholar
    • Export Citation

  • Vaze, J., and Coauthors, 2011b: Rainfall-runoff modelling across southeast Australia: Datasets, models and results. Australian J. Water Resour., 14, 101–116.

    • Search Google Scholar
    • Export Citation

  • Viney, N. R., Perraud J. , Vaze J. , Chiew F. H. S. , Post D. A. , and Yang A. , 2009: The usefulness of bias constraints in model calibration for regionalisation to ungauged catchments. Proc. 18th World IMACS/MODSIM Congress, Cairns, Australia, International Environmental Modelling and Software Society, 3421–3427. [Available online at http://mssanz.org.au/modsim09.]

    • Search Google Scholar
    • Export Citation

  • Wilson, C. B., Valdes J. B. , and Rodriguez-Iturbe I. , 1979: On the influence of the spatial distribution of rainfall on storm runoff. Water Resour. Res., 15, 321–328.

    • Search Google Scholar
    • Export Citation

  • Zhang, Y. Q., and Chiew F. H. S. , 2009: Relative merits of different methods for runoff predictions in ungauged catchments. Water Resour. Res., 45, W07412, doi:10.1029/2008WR007504.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 915 224 28
PDF Downloads 810 155 13