Research note for TFIAM, May 2003
Dynamic critical loads in freshwaters and the use of target load functions
in integrated assessment modelling
T Oxley1, H ApSimon1 and A Jenkins2
1
_______________________________________________
Department of Environmental Science and Technology, Imperial College London
2
Centre for Ecology and Hydrology, Wallingford, UK
Introduction
The development of an integrated assessment model at the UK scale, UKIAM,
enables us to use detailed data available for the UK to explore links with dynamic
modelling. In previous modelling at the European scale with the ASAM model we
have concentrated on reducing exceedance of critical loads, using target loads as an
intermediate step. This is based on the very simple criterion that an ecosystem is
“protected” if there is no exceedance- that is the deposition to it is at or below the
specified critical load for that ecosystem: and is “unprotected” where deposition does
exceed the critical load. In this sense a critical load acts as an indicator of systems at
risk, but tells us little about the time scales for recovery or the benefits to be expected
where deposition is reduced below the critical load. However the use of dynamic
modelling provides a way of considering these temporal factors, and where and when
recovery is likely to be achieved in response to a decrease in deposition.
We have concentrated on freshwaters where recovery times are faster than for soils,
and used modelling results from the MAGIC model for representative catchment sites
in different areas of the UK. These are sites where measurement data are available for
model calibration. The MAGIC model simulates the temporal evolution of the
chemical composition and ion fluxes through soils and to surface waters in response
to defined changes in current and future deposition. In particular it calculates the
ANC (acid neutralising capacity) which is a useful indicator with respect to ecosystem
health. The MAGIC model has been extensively applied and tested over a 17 year
period at many sites and in many regions round the world (Cosby et al. 2001). Overall
the model has proved to be robust, reliable and useful in a variety of scientific and
environmental activities.
A recent ICP-Waters report has addressed the use of dynamic modelling of surface
waters (Jenkins et al. 2002) in considering the impact of emission reductions, with
extensive illustrations from the MAGIC model. This paper introduces initial ideas on
using such data in the UKIAM model, based on the use of target functions as
described below.
Target load functions for freshwaters
As indicated above the ANC is an indicator of ecosystem health. For example with a
negative value of ANC fish such as trout are not present, but they are increasingly
likely to be found in a healthy state as ANC increases to around 20. An indicator for
recovery is therefore provided by attainment of a specified value of ANC lying
somewhere between zero and twenty. It is then possible to apply the MAGIC model
to calculate what reductions in deposition would be required to improve the ANC to
this value by a specified year- for example 2050.
This leads to the concept of a target load function for a freshwater body, which may
be represented graphically (see figure 1) to indicate the combination of sulphur and
nitrogen deposition that will result in attainment of a specified ANC value by a
specific target year. This looks similar to a critical load function, and may be applied
in a similar way in integrated assessment modelling. However in order to achieve
recovery the levels of sulphur and nitrogen deposition will be lower than the critical
load values. Thus whereas using the critical load approach in integrated assessment
modelling there is no incentive to reduce deposition further in areas where critical
loads have been achieved, this will not be the case using the target load functions until
recovery is implied within a specified time.
S deposition
Critical load
function
Target load
function
N deposition
Figure 1. Comparison of critical load function as used in integrated assessment
modelling hitherto, and target load function to achieve recovery to a specific ANC
value by a specified year.
Optimisation of abatement in the UKIAM model
The UKIAM model has been developed as a more flexible version of the ASAM
model used in parallel with the RAINS model to study scenarios at the European scale
during development of the Gothenburg protocol (Oxley et al 2003). Thus starting
from a pattern of deposition or concentration corresponding to a set of base case
emissions, UKIAM investigates the benefit of alternative abatement steps resulting
from corresponding changes in exposure of sensitive receptors of different types, and
compares the ratio of the total benefit to the cost for each abatement option to select
the most cost-effective. The selected step is implemented and removed from the list of
available options, and the process is repeated to obtain a prioritised sequence of
abatement options, with simultaneous information on the improved situation at
specified levels of overall expenditure. The current version addresses SO2, NOx, NH3
and PM10, and the benefits of reducing sulphur and nitrogen deposition with respect to
acidification and eutrophication, and PM10 exposure.
The model currently uses a 5x 5 km grid resolution, and distinguishes
deposition to different types of surface within each grid square in accordance with
different types of ecosystem ( for example forest, heathland etc) as well as the total
deposition more relevant to a catchment affecting surface waters. Mapping of
concentrations and deposition is based on the FRAME model of CEH Edinburgh for
sulphur and nitrogen species and on the PPM model of Imperial College for the
primary PM10. Abatement measures and costs are incorporated for individual major
sources, and on a sectoral basis down to a county level of resolution for other sources.
UK ecosystem dependent critical load data assembled by CEH Monkswood for seven
ecosystem types at a 1x1 km resolution, can be used to define exceedance functions
by comparing deposition with critical loads for acidification and eutrophication.
Benefit functions can then be defined as in the European scale assessments in terms of
reduction of exceedance of critical loads, or of some intermediate target loads closing
the gap between the base case deposition and critical loads. However the flexibility to
incorporate different data for specific sensitive ecosystems, and different criteria for
their protection and recovery, allows deeper analysis of the environmental benefits, as
well as the role of specific sources or sectors.
Application of target load functions for freshwaters in UKIAM
The UKIAM model has the capability to differentiate between different types of
ecosystems and treat them separately with different weightings. Here we shall
consider only the case of freshwaters and ignore other ecosystems and impacts. Data
has been provided from MAGIC calculations to specify target load functions for 2
separate ANC values (0 and 20) with two different recovery periods- up to 2050 and
up to 2020. This currently covers freshwaters in six sensitive regions of the UK (The
Cairngorms, Dartmoor, Galloway, Lake District, South Pennines, Wales).
Figure 2 illustrates such a set of four target load functions for one specific site.
The larger the target ANC, and the shorter the time for recovery the stricter are the
limits on deposition. The dots illustrate the current deposition, and that for a scenario
corresponding to the Gothenburg protocol emission reductions.
Figure 2: Target load functions to achieve ANC=0 or ANC=20 by 2020 or 2050
We can use these data in different ways. Clearly in the diagram above the
simple scenario used to represent the scaling of deposition according to the
Gothenburg protocol yields an improvement getting close to a return to ANC=0 by
2050, whereas continuing deposition at the present rate would be far from that goal.
However since there is an improved probability of full recovery if the ANC increases
even more towards ANC=20, there can still be a benefit in crossing into the
intermediate zone between the two target load functions. The time delay before such
recovery can also be investigated by using target load functions for different years, in
this case 2020 and 2050, though other years could be used as appropriate.
Currently we are investigating three different approaches to using this data in
integrated assessment with UKIAM:First method: In the first approach we have used straightforward substitution of a
target load function instead of a critical load function. By comparison with
different magnitudes of gap closure, the targets can be made more difficult to
attain by moving from the ANC=0 target towards the ANC= 20 target load
function. The benefit of reducing either nitrogen or sulphur deposition is taken as
the reduction in exceedance of the specified target load function for each of the
freshwater sites. This exceedance is defined as the perpendicular distance of the
deposition from the target load function when plotted as illustrated in the figure
above, but falls to zero when the target load function is attained. Thus there is no
benefit in going beyond the target load function in this situation. However by
setting a harder target to meet, and attempting to converge towards it, we can see
how other less stringent criteria for recovery are met with increasing levels of
expenditure .
Second method: In the second method we can use a combination of target load
functions for two different recovery times, in the example above for the years
2020 and 2050. In principle this could be extended to a greater range of recovery
times with more isolines for different dates. This extension allows us to set
different weightings on different recovery times. Thus for example we can set a
high priority on recovery to a given ANC by 2050, and lower the priority on
further reduction beyond this point until it becomes zero if the 2020 time horizon
is met. In this case we use target load functions for a single value of ANC.
Third method: In the third method we can use a combination of target load
functions for different values of ANC of 0 and 20, to cover a range of probabilities
of full recovery. In this case we set a high priority on eliminating exceedance of
the ANC=0 target load function, and a diminishing priority on further reduction
once this has been achieved as deposition converges towards the target load
function for ANC=20. This accounts for some continued benefit in going beyond
the ANC=0 target .
Preliminary results
Preliminary runs have been undertaken to explore this linking of integrated
assessment modelling with dynamic modelling of surface waters to changes in
deposition. In these runs we have given equal weighting to each surface water site,
recognising that in some cases there may be several sites in a single 5x5 km grid
square. We have also used simplified representative target load functions for each
region to explore the techniques before more extensive calculations for each
individual site are undertaken. In some runs we have restricted abatement to SO2 and
NOx only, ignoring more variable changes due to local reduction of reduced nitrogen.
The UKIAM has been run concentrating entirely on attaining target load
functions for freshwaters, and ignoring other aspects of acidification and
eutrophication. This results in some changes in the resulting priorities for abatement.
In the base case we have currently assumed starting from emissions in the year 1998,
the cost curve data indicate some cheaper steps for reduction of nitrogen emissions
than for sulphur. Thus the first steps implemented are often for nitrogen emissions.
However the greater benefit of sulphur reduction as compared with nitrogen in
reducing exceedance of the target load functions for fresh waters subequently puts
more emphasis on sulphur reduction, despite the greater costs. This remains the same
when different target load functions are used.
As indicated above the ASAM model still produces strategies converging
towards the desired goals even though these are not attainable. It is still too early to
assess whether these strategies remain stable at a given level of cost when different
targets of ANC and recovery times are set. Correspondingly there is little value yet in
exploring the more sophisticated approaches until we have more comprehensive data
in the model (or of comparing more rigorously with strategies aiming at critical loads
until we have new updated data in UKIAM later this year).
Numerically the UKIAM can generate plots for individual sites along the lines
of figure 2, showing the change in status of the site in relation to the different target
load functions as the run proceeds with increasing abatement costs. We are currently
considering how to summarise such data statistically either for particular regions or
the whole country, and how we can reflect uncertainties. Now that the model is
operational there are many aspects that we can investigate further.
Discussion
At present all results are very preliminary as we are expanding and improving the data
used in UKIAM, but the above illustrates how we are currently linking dynamic
modelling and integrated assessment. However two differences from the critical load
approach are already apparent. The first is that there will be continued emphasis on
reducing deposition in certain areas where critical loads are already achieved which
may affect the relative emphasis on certain sources contributing to deposition on those
areas. The second is that there tends to be less relative emphasis on reducing nitrogen
deposition as compared with sulphur in protecting freshwaters. This is because of the
capacity to retain nitrogen in soils and organic matter for substantial periods before
saturation is reached, reducing the capacity for leaching and transfer to freshwater
bodies. Again this will affect the abatement strategies derived as compared with use
of a critical load approach.
References
Cosby, B.J., Ferrier, R.C., Jenkins, A. and Wright, R.F. 2001. Modelling the effects of
acid deposition: refinements, adjustments and inclusion of nitrogen dynamics in the
MAGIC model. Hydrology and Earth System Sciences 5(3), 499 – 517.
Jenkins, A., Larssen, T., Moldan, F., Posch, M. and Wright, R.F. 2002. Dynamic
Modelling of Surface Waters: Impact of Emission Reduction – Possibilities and
Limitations. ICP Waters Report 70/2002, NIVA, Oslo, Norway. 42pp.
Oxley T ApSimon HM Dore A, Hall J, Heywood E, Gonzales del Campo T, and
Warren R (2003) Optimisation of acid abatement strategies using ecosystem specific
critical load exceedances: the UK Integrated Assessment Model. Report UK National
Focal Centre for Integrated Assessment Modelling, Imperial College London.