E. Wolanski . B. King . S. Spagnol
from those areas with complex topography reveal extremely complex, if not chaotic,
distributions. Standard measurements in biology and chemistry, e.g. triplicate samples
at a number of sites, are meaningless, as we have shown for the case of coral eggs and
fine sediment concentration.
How then to use the scientific information from field data and computer models to
help coastal management? This question remains unresolved. However engineering
experience in dealing with oil spill predictions provides a clue to a likely new approach.
In such man-made crises modellers use a wide range of current predictions for different scenarios of winds and tides to calculate the probability of an impact and the severity of an impact should one occur. An example of risk assessment modelling using
chaotic inputs to the model is shown in Fig. 20. This shows the predicted region "at
risk" from an oil spill in Surat Thani harbour in the Gulf of Thailand, when strong and
do~inant winds from the southwest are blowing. Under these conditions, it is suggested
that about 150 km of coastline are at some risk of impact. The most probable fate of an
oil spill under these conditions is shown in black which will see the oil slick at sea for
many days allowing time for weathering and dilution of the oil. These data are then
superimposed on a map of coastal resources, such as ports, seagrass, mangroves,
beaches and rocky shores, to predict probabilities of environmental impacts at various locations along the coast. Managers can use this information to develop a statistically-driven strategy to cope with an oil spill. The situation is relatively simple because
the original environment essentially has zero background oil.
A similar strategy could be applied to faecal coliforms from sewage discharges because they are naturally absent from the environment.
It is much more difficult to predict environmental impacts in a complex topography from other human activities such as dredging, structure engineering, spoil dumping and nutrient discharges. One reason for this is that, contrary to purely anthropogenic wastes such as oil, a topographically complex environment naturally exhibits high
variability in parameters that man can influence, such as currents, suspended sediment,
nutrients and metals. At our study sites chaos or random environmental fluctuations
dominate the system and this variability is under-estimated by numerical models. The
status of modelling environmental impacts in a complex topography is insufficient to
reliably predict the response of a naturally chaotic environment to additional disturbances from man.
The risk assessment approach is promising. So far it has mainly been applied to
quantify physical effects from waste discharges from man. Present marine eutrophication models (e.g. Gray 1996) and deterministic management models that are derived
from them (e.g. Done et al. 1997), neglect the oceanographic and biological chaos and
may generate misleading answers. Indeed, ecological processes are known (e.g. McCook
1994) to be influenced by a very large number of factors, both physical (e.g. oceanography and climate) and biological (e.g. recruitment and interaction between species
or species assemblages). Many of these processes vary chaotically, so that ecological
outcomes may be largely unique to a particular set of circumstances (e.g. Underwood
and Denley 1984; Foster 1990; McCookand Chapman 1997). This in turn means that
prediction of a biological impact is intrinsically uncertain, aside from any difficulties
with detection rationales.
This does not mean that modelling is not useful. It is helpful because it can predict the concentration of substances (e.g. mud, metals, nutrients and larvae) under vari-
from those areas with complex topography reveal extremely complex, if not chaotic,
distributions. Standard measurements in biology and chemistry, e.g. triplicate samples
at a number of sites, are meaningless, as we have shown for the case of coral eggs and
fine sediment concentration.
How then to use the scientific information from field data and computer models to
help coastal management? This question remains unresolved. However engineering
experience in dealing with oil spill predictions provides a clue to a likely new approach.
In such man-made crises modellers use a wide range of current predictions for different scenarios of winds and tides to calculate the probability of an impact and the severity of an impact should one occur. An example of risk assessment modelling using
chaotic inputs to the model is shown in Fig. 20. This shows the predicted region "at
risk" from an oil spill in Surat Thani harbour in the Gulf of Thailand, when strong and
do~inant winds from the southwest are blowing. Under these conditions, it is suggested
that about 150 km of coastline are at some risk of impact. The most probable fate of an
oil spill under these conditions is shown in black which will see the oil slick at sea for
many days allowing time for weathering and dilution of the oil. These data are then
superimposed on a map of coastal resources, such as ports, seagrass, mangroves,
beaches and rocky shores, to predict probabilities of environmental impacts at various locations along the coast. Managers can use this information to develop a statistically-driven strategy to cope with an oil spill. The situation is relatively simple because
the original environment essentially has zero background oil.
A similar strategy could be applied to faecal coliforms from sewage discharges because they are naturally absent from the environment.
It is much more difficult to predict environmental impacts in a complex topography from other human activities such as dredging, structure engineering, spoil dumping and nutrient discharges. One reason for this is that, contrary to purely anthropogenic wastes such as oil, a topographically complex environment naturally exhibits high
variability in parameters that man can influence, such as currents, suspended sediment,
nutrients and metals. At our study sites chaos or random environmental fluctuations
dominate the system and this variability is under-estimated by numerical models. The
status of modelling environmental impacts in a complex topography is insufficient to
reliably predict the response of a naturally chaotic environment to additional disturbances from man.
The risk assessment approach is promising. So far it has mainly been applied to
quantify physical effects from waste discharges from man. Present marine eutrophication models (e.g. Gray 1996) and deterministic management models that are derived
from them (e.g. Done et al. 1997), neglect the oceanographic and biological chaos and
may generate misleading answers. Indeed, ecological processes are known (e.g. McCook
1994) to be influenced by a very large number of factors, both physical (e.g. oceanography and climate) and biological (e.g. recruitment and interaction between species
or species assemblages). Many of these processes vary chaotically, so that ecological
outcomes may be largely unique to a particular set of circumstances (e.g. Underwood
and Denley 1984; Foster 1990; McCookand Chapman 1997). This in turn means that
prediction of a biological impact is intrinsically uncertain, aside from any difficulties
with detection rationales.
This does not mean that modelling is not useful. It is helpful because it can predict the concentration of substances (e.g. mud, metals, nutrients and larvae) under vari-