Sentence examples for model parameterisation and from inspiring English sources

The differences between model predictions appear to be mainly related to the hydraulic datasets used for model parameterisation and, to a much lesser extent, to the conceptual modelling approach (equivalent porous medium vs. discrete fracture networks), the model dimensionality (two-dimensional vs. three-dimensional), and/or the parameterisation approach (forward vs. inverse).

The challenges of model parameterisation and validation are covered, as is the application of the model in the analysis of dynamically expanding full scale three dimensional heaps.

A novel aspect of our approach was in using simplified model parameterisation and including parameters from all model domains (surface, subsurface, evapotranspiration) in automated model calibration.

The study provides a sensitivity analysis of both model parameterisation and differing rainfall scenarios on long-term water quality as well as a method for assessing the impact of rainfall variability on catchment scale hydrology.

SP refined the model parameterisation and did the analysis of the data and adapted the methods used.

When performing a validation analysis of a population model, uncertainty should be addressed as discussed above in more detail to address the question to which extent methodological bias, sample size effects or other factors may cause uncertainty of the parameters used for model parameterisation (and consequently the model predictions) or validation.

Hence, STI-GMaS has the potential to facilitate model simulation, parameterisation and inference within the same software package.

Insufficient temporal monitoring of water quality in streams or engineered drains alters the apparent shape of storm chemographs, resulting in shifted model parameterisations and changed interpretations of solute sources that have produced episodes of poor water quality.

These uncertainties can be substantial in the case of environmental processes, where data are sparse, model parameterisation only approximate and non-linearity may rule.

Rius et al. [38] investigated the scatterometric and specular delay observables and considered various sources of uncertainty including observation statistics and model parameterisation or calibration error.

The LSMs biases are caused by errors in forcing data, model structure and model parameterisation.