Environmental Monitoring and Assessment

, Volume 127, Issue 1–3, pp 147–153 | Cite as

Uncertainty in watershed response predictions induced by spatial variability of precipitation

  • Chia-Ling Chang
  • Shang-Lien Lo
  • Ming-Ying Chen
Original Article


Negligence to consider the spatial variability of rainfall could result in serious errors in model outputs. The objective of this study was to examine the uncertainty of both runoff and pollutant transport predictions due to the input errors of rainfall. This study used synthetic data to represent the “true” rainfall pattern, instead of interpolated precipitation. It was conducted on a synthetic case area having a total area of 20 km2 with ten subbasins. Each subbasin has one rainfall gauge with synthetic precipitation records. Six rainfall storms with varied spatial distribution were generated. The average rainfall was obtained from all of the ten gauges by the arithmetic average method. The input errors of rainfall were induced by the difference between the actual rainfall pattern and estimated average rainfall. The results show that spatial variability of rainfall can cause uncertainty in modeling outputs of hydrologic, which would be transport to pollutant export predictions, when uniformity of rainfall is assumed. Since rainfall is essential information for predicting watershed responses, it is important to consider the properties of rainfall, particularly spatial rainfall variability, in the application of hydrologic and water quality models.


Precipitation Rainfall Spatial variability Uncertainty Watershed response 


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  1. Chaubey, I., Haan, C.T., Salisbury, J.M., & Grunwald, S. (1999). Quantifying model output uncertainty due to spatial variability of rainfall. Journal of American Water Resource Association, 35(5), 1113–1123.Google Scholar
  2. Dawdy, D.R., & Bergman, J.M. (1969). Effect of rainfall variability on strearnflow simulation. Water Resource Research, 5, 958–966.Google Scholar
  3. Goodrich, D.C., Faures, J., Woolhiser, D.A., Lane, L.J., & Sorooshian, S. (1995). Measurement and analysis of small-scale convective storm rainfall variability. Journal of Hydrology, 173, 283–308.CrossRefGoogle Scholar
  4. Hromadka, T.V. (1996). A rainfall-runoff probabilistic simulation program: 1. Synthetic data generation. Environmental Systems, 11(4), 235–242.Google Scholar
  5. Hromadka, T.V. (1996). A rainfall-runoff probabilistic simulation program: 2. Synthetic data analysis. Environmental Systems, 11(4), 243–249.Google Scholar
  6. Osborn, H.B., & Reynolds, W.N. (1963). Convective Storm Patterns in the Southwestern United States. Bull IASH, 8(3), 81–83.Google Scholar
  7. Rodda, J.C. (1967). The Systematic Errors in Rainfall Measurement. Journal of the Institution of Water Engeneering, London, 21, 173–177.Google Scholar
  8. Tisdale, T.S., Kaighn, R.J., & Yu, S.L. (1996). The Virginia storm (VAST) model for stormwater management—User's Guide version 6.0. Virginia, USA: University of Virginia, Charlottesville.Google Scholar
  9. Yu, S.L., Stanford, R.L., & Zhai, Y.Y. (2003). Virginia stormwater model for windows—User's Manual version 1.0. Virginia, USA: University of Virginia, Charlottesville.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2006

Authors and Affiliations

  • Chia-Ling Chang
    • 1
  • Shang-Lien Lo
    • 2
  • Ming-Ying Chen
    • 2
  1. 1.Department of Environmental resources managementChia Nan University of Pharmacy & ScienceTainanChinese Taiwan
  2. 2.Graduate Institute of Environmental EngineeringNational Taiwan UniversityTaipeiChinese Taiwan

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