Experiences in assessing deposition model uncertainty and the consequences for policy application Rognvald I Smith Centre for Ecology and Hydrology, Edinburgh

Concentration (measured) >MODEL >Deposition estimate MODEL – field programme of flux measurements Substantial degree of confidence – but not quantified Ammonia concentrations provided by a combination of model and measurement – local variability FLUX measurements Comparison of national model output against measurements would help provide uncertainty measure DRY – 2 sites WET – lack of co-located rainfall amount and precipitation concentration collection

Sensitivity and Uncertainty Analyses on dry and wet deposition Dry – concentration always important component, but also some model parameters were very influential Wet (seeder-feeder model) used for more extensive study Demonstrated usefulness of techniques but also raised questions What is the important output? each 5km square (>10000 for UK) some groups of squares to make regions a smaller area like a hectare Many sensitivity analyses assume there is one, or possibly a few, summary statistics as important output – need to look for 2D area-based approaches.

It appeared that biased output was probably the norm from the deposition models. - even simple models are non-linear - current preferred parameter choices may not be optimal. Bias is not a problem if - it can be estimated with reasonable accuracy, or - the flux estimate is so far away from a ‘test level’ that it can be ignored. but it is a bigger issue when it can be cumulated: regional/national budgets inside transport models (bias may be applied at each time step) It proved to be extremely difficult to get good estimates of uncertainty for the inputs or the parameters. SA/UA identified ‘important’ sources of uncertainty a number of important interactions within the model – these should be used to identify where further work is required on the inputs and parameters

Spatial interpolation PROBLEM: models require values of parameters and input variables everywhere. NH 3 concentration driven by local sources: background 1  g NH 3 m -3 (blue and green) near source 50  g NH 3 m -3 (purple and black) With a wide distribution of farm animals, impossible to interpolate ammonia only from measurements. approx 3km image Smoothly varying fields, e.g. SO 4 in rain 30 site networks: kriged interpolation CV about 30% for most areas [magnitude confirmed by other studies] 2 x standard error approximation => concentration in many areas is the mapped value  60% Little mechanism to reduce this in the deposition models, so the flux uncertainty will be greater in almost all cases.

Summary: Scale effects on deposition terrain: valley v hilltop (rain, wind, temperature …) stochastic rainfall (even on flat areas) local sources, especially with a cleaner atmosphere Interpolation or modelled concentration uncertainty Deposition/Flux model uncertainty  1)Uncertainty in any statistic which focuses on small ecosystem areas and is derived from a national or European scale model will be large. 2)Any reasonable assessment of uncertainty in the deposition estimates will take a substantial effort.

A possible way forward considers these points:  Focus on specific statistics for which an uncertainty estimate is required.  Modelling studies can give some insight into scale uncertainties.  Accept that predictions for small areas will be extremely uncertain.  Consider a result in probability terms over larger areas and accept the sacrifice, at present, of small area information.  Look to simplifying the structure where possible, for example by smoothing.  Massive simulations are now possible, but are still expensive.  There is no off-the-shelf satisfactory solution.