Module 4: Assessing Error & Sensitivity

  1. Calculate the mean absolute error in the simulation using the observed temperature data (ObservedTempData.xlsx from the data folder in Kinneret97) and the simulated temperature data (the simulated temperature data is from the surface layer in lake.csv).
\[\begin{equation} MAE = \frac{\sum_{i}\left | T_{i}^{sim}-T_{i}^{obs} \right |}{n} \tag{1} \end{equation}\]
    1. Calculate the sensitivity of the modelled temperature to changes in water clarity (the light extinction coefficient, \(K_w\)) and wind speed (wind_factor). These can be found in glm3.nml. Try increasing and decreasing the default parameter value by 0.2 and see how much the output changes.
      2. \[\begin{equation} SI = \frac{(Output_{new}-Output_{original})/Output_{original}}{(Parameter_{new}-Parameter_{original})/Parameter_{original}} \tag{2} \end{equation}\]
    2. Assess how sensitive the temperature and phytoplankton biomass is to water clarity:

      3. GLM results Water clarity Decrease 0.37 Water clarity Original 0.57 Water clarity Increase 0.77
      Average WQ_35 temperature
      Average WQ_35 phytoplankton (green, crypto, diatom) biomass

    3. Assess how sensitive the temperature and phytoplankton biomass is to wind speed:

      4. GLM results Wind Speed Decrease 0.6 Wind Speed Original 0.9 Wind Speed Increase 1.2
      Average WQ_35 temperature
      Average WQ_35 phytoplankton (green, crypto, diatom) biomass