Uncertainty about mid to long term
impacts of climate change will continue to make the construction of
probability density functions for impacts problematic (Adger et al.,
2009). Due to this uncertainty in climate models at the scales needed
for adaptation decisions, optimal adaptation decision making should be
abandoned in favour of robust decision making. Robust-decision making
entails running a large amount of scenarios and analysing alternatives
over these scenarios on a given set of criteria. It does not require
probabilities attached to the different scenarios. This way options can
be eliminated which do not perform well in projected futures, even when
the likelihoods of future evolutions are not well known.
For
example, Wilby and Dessai (2010) apply robust decision-making to
address the question of ranking adaptation options in the water sector
in Wales and the UK. The method identifies options that address policy
goals in the current climate, then tests the sensitivity of the
outcomes of these options across a large number of future scenarios.
Cost-benefit analysis is used to identify options, where the benefits
exceed costs across a wide range of scenarios of future impacts of
climate change; these are robust options. Those measures that have a
negative benefit-cost ratio for some projected future climate are not
considered robust. They find that measures that are flexible and permit
updating according to future conditions are more likely to be robust to
future climate changes; though there may be other robust options that
are not flexible.
In some cases model-based
approaches have also been used to identify robust adaptation options,
and these approaches are also applicable to other contexts. Lempert and
Groves (2010) used the Robust Decision Making (RDM) quantitative
decision-analytic process in conjunction with the Inland Empire
Utilities Agency (IEUA) to determine appropriate adaptation options for
the water management agency. RDM is designed for use in a context of
uncertainty, as is the case with climate change. It uses simulation
models to assess the performance of agency plans over thousands of
plausible futures, using statistical “scenario
discovery” algorithms to concisely summarize those futures
where the plans fail to perform adequately, and use these resulting
scenarios to help decision makers understand the vulnerabilities of
their plans and assess the options for ameliorating these
vulnerabilities. For IEUA, the RDM analysis suggests the agency's
current plan could perform poorly and lead to high shortage and water
provisioning costs under conditions of: (1) large declines in
precipitation, (2) larger-than-expected impacts of climate change on
the availability of imported supplies, and (3) reductions in
percolation of precipitation into the region's groundwater basin.
Including adaptivity in the current plan eliminates 72% of the
high-cost outcomes. Accelerating efforts in expanding the size of one
of the agency's groundwater banking programs and implementing its
recycling program, while monitoring the region's supply and demand
balance and making additional investments in efficiency and stormwater
capture if shortages are projected provides one promising robust
adaptive strategy — it eliminates more than 80% of the
initially-identified high-cost outcomes.
Exemplary methods and tools
| Name | Description | References
|
| Robust decision-making for ranking adaptation options in the water sector
| Wilby and Dessai (2010) apply robust decision-making to address the question of ranking adaptation options in the water sector in Wales and the UK. The method identifies options that address policy goals in the current climate, then tests the sensitivity of the outcomes of these options across a large number of future scenarios. Cost-benefit analysis is used to identify options, where the benefits exceed costs across a wide range of scenarios of future impacts of climate change; these are robust options. Those measures that have a negative benefit-cost ratio for some projected future climate are not considered robust. They find that measures that are flexible and permit updating according to future conditions are more likely to be robust to future climate changes; though there may be other robust options that are not flexible.
| Wilby, R.L., Dessai, & S. (2010). Robust adaptation to climate change. Weather, 65(7), 180-185.
|
