2.1.1.3 Gaps

2.1.1.3.1 Density of meteorological observations

The density of stations for meteorological observations impacts the accuracy of smoothed surfaces depicting the spatial variation of a climate variable (e.g. P, Tmax). On a continental scale, overall errors (e.g. RMSE) can be large due to the low density of gauging stations in remote areas. Locally however, errors may be low due to higher station density within a region which captures the spatial variability better. Note that the error is also dependent on how much a given climate variable varies spatially.

The characterisation of input data errors suggests that having a denser network of Bureau of Meteorology stations recording climate data has the potential for reducing the uncertainty in input climate variables, which can lead to improved water-related modelling in the Namoi subregion.

Finding an optimum density of gauging stations is a non-trivial exercise. Therefore it is impractical to suggest if the present density of climate data are sufficient for modelling purposes. It is also possible that any systematic errors in model parameterisation may be compensated through calibration. Furthermore, as the BA Programme reports on the relative difference of hydrological response variables between the baseline and coal resource development pathway (CRDP), any error introduced by the lack of optimum station density would cancel out.