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Quantifying Sensitivity in a Global Flood Model

Explore the impact of different parameter values on a global flood model using remotely sensed data sets for elevation, vegetation, climate, landsat imagery, and urbanization. Discover how sensitivity analysis measures interaction and direct influence of parameters, providing a deeper understanding of the model's sensitivity. Assess sensitivity at a global scale with multiple test sites to determine the significance of parameters linked to a domain's physical attributes.

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Quantifying Sensitivity in a Global Flood Model

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  1. Quantifying sensitivity in a global flood model Cain Moylan, Jeffrey Neal, Jim Freer, SSBN

  2. We can create globally consistent modelling domains using remotely sensed data sets • Data Layers: • Elevation • Vegetation • Climate • Landsat • Urbanisation • etc

  3. Is data all the model depends on?

  4. We know different parameter values change model outputs…. Parameter set 3 Parameter set 1 Parameter set 2 … but how?

  5. Preliminary results of sensitivity analysis Measure of interaction between parameters Measure of direct influence of parameters

  6. So you understand the “sensitivity” of the model now?

  7. A parameter’s significance is linked to the domain’s physical attributes P3 P1 P2 P4 Urbanisation Climate P7 P5 Pn P6 Topography Vegetation

  8. Hence we need to assess sensitivity at the global scale, with many test sites

  9. Thanks!

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