Uncertainty in rainfall runoff simulations an introduction and review of different techniques
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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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Uncertainty in rainfall runoff simulations an introduction and review of different techniques

Uncertainty in rainfall-runoff simulationsAn introduction and review of different techniques

M. Shafii, Dept. Of Hydrology, Feb. 2009


Overview

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...


Introduction

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


Introduction1

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.


Methods

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.


Methods1

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”


Methods2

Methods

  • 2. GLUE (Monte Carlo Simulations)


Methods3

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


Methods4

Methods

  • 3. Input uncertainty and Fuzzy Logic

  • Conclusion: using time-averaged precipitation over the catchment may lead to erroneous forecasts


Methods5

Methods

  • 4. Structural uncertainty

    • Imperfect representation of catchment processes: structural uncertainty.

    • Multi-objective calibration: Pareto front

    • Drawbacks of this method!!!


Methods6

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.


Methods7

Methods

  • 5. Parameter uncertainty, Bayesian Inf.


Summary

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.


The end

The End

  • Thank you for your attention…

  • Any question?

  • And then, discussion…


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