Modeling

• concentration of stable isotopes is affected by

  • precipitation

  • evaporation

  • condensation

• constrain model results and avoid overfitting

• calculate water ages

• extrapolation (time and space)

• can predict effect of land use changes, climate change, …

• increase process-based understanding (which parameter are dominant for certain fluxes)

• calculate water ages in different stores

• determine fluxes that are difficult to measure

  • root water uptake (from which depth?)

  • percolation/ groundwater recharge

distribution of land cover and imperviousness varies greatly depending on resolution (Ichiba et al. 2018)

-> expectation: scalin impacts many different types of modelling results (runoff, transpiration, infiltration)

Nash-Sutcliffe efficiency

Kling-Gupta efficieny:

• takes linear correlation, standard deviation and mean values into account

• 1 is perfect fit and larger than -0.41 is better than using mean

1. conceptual

2. physical model

3. mathematical/numerical model

• process-based, tacer-aided

• integrates energy balance, water balance and vegetation dynamics

• forcing parameters:

  • solar and longwave radiation

  • temperature (min, average, max)

  • wind speed

  • humidity

  • precipitation

  • optionally stable isotopes in precipitation

• have not been used in urban setting

• goal: describe quantitatively water partioning processes and water ages in different types of urban greens