Experiences in assessing deposition model uncertainty and the consequences for policy application

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Experiences in assessing deposition model
uncertainty and the consequences for policy
application
Rognvald I Smith Centre for Ecology and
Hydrology, Edinburgh
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Concentration (measured) gt MODEL
gt 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
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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 (gt10000 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.
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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
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Spatial interpolation PROBLEM models require
values of parameters and input variables
everywhere.
NH3 concentration driven by local
sources background 1 ?g NH3 m-3 (blue and
green) near source 50 ?g NH3 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. SO4 in rain 30 site
networks kriged interpolation CV about 30 for
most areas magnitude confirmed by other
studies 2 x standard error approximation gt
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.
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• 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
• ?????
• Uncertainty in any statistic which focuses on
  small ecosystem areas and is derived from a
  national or European scale model will be large.
• Any reasonable assessment of uncertainty in the
  deposition estimates will take a substantial
  effort.

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• 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.