Fen Management Handbook - Scottish Natural Heritage

environmental variables, and therefore a refined understanding of the functioning
of the site. For example, the independent variables of soil water level or nutrient
status might be used in an attempt to explain plant species distribution quantified
through geo-statistical analysis. Modelling can also be used to predict the impact
of management regimes, and to check for deviations which might represent impacts
caused by one or more sources. There are two main types of modelling.
Empirical modelling is a relatively straightforward form of modelling which is used
to assess the degree of dependence of one variable on another by experimental
transformation of the independent variable(s) through application of mathematical
functions in an attempt to synthesise the behaviour of the dependent variable. In
simple terms, it can be seen that there is a great deal of similarity between the lines,
but there are also some periods where the lines diverge. It can be concluded that
the behaviour of the soil water level is strongly dependent on rainfall, but that there
are also some other factors which have an influence.
Modelling and output of graphs such as this is also useful in highlighting the periods
when the soil water level behaviour is not explained by variations in rainfall.
The illustration below depicts a time-series graph showing hourly soil water level
measurements for a dipwell (BD2a) at Cors Bodeilio on Anglesey, and whether
there is more or less than average rainfall (cumulative difference from average
rainfall (independent variable) over a three month period.
Process modelling is a more exacting form of modelling which involves:
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i. development of a detailed understanding of the important physical processes
within a system;
ii. development of mathematical equations to represent these processes
directly;
iii. use of these equations with values for the independent values to simulate the
dependent variable(s).
Process models can be one-, two- or three-dimensional, and can simulate either
average conditions at an instant (termed ‘steady-state’) or the progression of
conditions through time (termed ‘time-variant’).
A good process model will be more robust than an empirical model in terms of its
ability to simulate conditions beyond the range experienced during the monitoring