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Uncertainty in rainfall-runoff simulations An introduction and review of different techniques

Uncertainty in rainfall-runoff simulations An introduction and review of different techniques. M. Shafii, Dept. Of Hydrology, Feb. 2009. Overview. 1. Introduction Different sources of uncertainty Non-stationarity Calibration and uncertainty 2. Methods Probabilistic method

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Uncertainty in rainfall-runoff simulations An introduction and review of different techniques

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  1. Uncertainty in rainfall-runoff simulationsAn introduction and review of different techniques M. Shafii, Dept. Of Hydrology, Feb. 2009

  2. Overview • 1. Introduction • Different sources of uncertainty • Non-stationarity • Calibration and uncertainty • 2. Methods • Probabilistic method • Monte Carlo simulations (GLUE) • Fuzzy Logic based method • Multi-objective calibration • Bayesian inference • 3. Summary and conclusions...

  3. Introduction • Different uncertainty sources • Natural randomness • Data • Model parameters • Model structure • Note 1. Non-Stationarity • Methods to deal with uncertainty • Probability rainfall-runoff model • Monte Carlo Simulations • Dealing with error series • Possibilistic approaches • Hybrid methods

  4. Introduction • Note 2. Data uncertainty and calibration • Data errors and uncertainties are transformed to the model parameters in terms of bias in the parameters (e.g. deviations from their true value). • Melching (1990) says, data uncertainties need not be explicitly considered in reliability analysis, and instead, they may be assumed to be included in parameter uncertainties.

  5. Methods • 1. Early methods • Probabilistic methods • Probability density function of model output • Potential information: • Sharpness of PDF • Rule-of-thumb to assess the quality of modeling would be to investigate whether or not the measured values fall within 95% confidence interval of the predictions.

  6. Methods • 2. GLUE (Monte Carlo Simulations) • Process: • (a) Taking a large number of samples • (b) Calculation of likelihood • (c) Dividing the samples into “behavioral” and “non-behavioral” • (d) Rescale the likelihood and produce PDF of output • (e) Determination of Confidene Intervals (CI) • Keith Beven, “equifinality”

  7. Methods • 2. GLUE (Monte Carlo Simulations)

  8. Methods • 3. Input uncertainty and Fuzzy Logic • Maskey et al. (2004): Treatment of precipitation uncertainty in rainfall-runoff modeling for flood forecasting. • Fuzzy Logic, Prof Zadeh (1965) • Crisp and Fuzzy Sets • Crisp Set • Fuzzy Set

  9. Methods • 3. Input uncertainty and Fuzzy Logic • Conclusion: using time-averaged precipitation over the catchment may lead to erroneous forecasts

  10. Methods • 4. Structural uncertainty • Imperfect representation of catchment processes: structural uncertainty. • Multi-objective calibration: Pareto front • Drawbacks of this method!!!

  11. Methods • 5. Parameter uncertainty, Bayesian Inf. • Bayesian inference: aimingat deriving the posterior distribution of a future hydrological response allowing for both natural and parameter uncertainty. • Bayes’ theorem: allowing us to update the “prior” PDF of parameters by observing “data”, resultingin so-called “posterior” PDF.

  12. Methods • 5. Parameter uncertainty, Bayesian Inf.

  13. Summary • Summary and conclusions • Uncertainty assessment is an essential part of modeling process and should not be neglected at all. • We have to be aware of which kind of uncertainty we are estimating. • We, as modelers, should be aware of all possible methods, their peculiarities, and underlying hypotheses. • An uncertainty assessment method must be able to take into account any type of useful information (Hybrid methods). • To be blunt, there is currently no unifying framework that has been proven to properly address uncertainty in hydrological modeling.

  14. The End • Thank you for your attention… • Any question? • And then, discussion…

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