or expected climatic stimuli or other effects, which moderates harm or
exploits beneficial opportunities.” Adaptive management (AM) is a structured, iterative process of optimal
decision making that focuses on improving management policies and practices by learning from the outcomes
of implemented management strategies (Pahl-Wostl 2007). AM is particularly beneficial for addressing
uncertainty
/media/loftslag/Gareth_James_Lloyd_(DHI,_Dk).pdf
policies and practices by learning from
the outcomes of implemented
management strategies (Pahl-Wostl et
al., 2007; Huntjens et al., 2011)
Adaptive management is learning to
manage by managing to learn
(Bormann et al., 1993)
Adaptive management is a structured,
iterative process of optimal decision
making in the face of uncertainty, with
an aim to reducing uncertainty over
time via system
/media/loftslag/Henriksen-AM.pdf
milestones, which are achieved by implementing adaptive
measures according to the frame. In an iterative process measures for different goals could be identified,
allowing a comparison of measures and identifying robust measures. Comparing the scenarios presented
here results in that the measure of e.g. reducing nutrients is robust and thus should be prioritised to be
implemented. This work
/media/loftslag/Group2-report.pdf
to be reduced in order to correct the error. This argumentation
needs to be applied in an iterative fashion at the end of each hydrological year so that response
time estimates tV from Equation (13) are consistent with an a priori physical estimate of the
response time of the glaciers. This procedure is not trivial to apply in practice. At the first time
steps, the numerator and denominator of Equation (13
/media/ces/ces-glacier-scaling-memo2009-01.pdf
as numerous partially iterative steps. The adaptive process is laid out in
a way intended to help designers determine the objectives of the participation process and the initial design
context, and make preplanning choices that eventually lead to the selection of suitable participation
mechanisms. There are also design tools that facilitate this work. We discuss how our findings are largely
/media/loftslag/vonKorff_etal-2010.pdf
by stakeholders and linked to quantitatively developed scenarios (mathematical model results) in
an iterative procedure. Alcamo [21] describes a ten-step approach that is being adopted by a growing number of global, European,
and local studies (e.g. [2,22–24]). Crucial to the Story-And-Simulation approach is the iteration between stakeholder-determined
storylines and expert-driven model runs to ensure
/media/loftslag/Kok_et_al._TFSC_published_2011.pdf
the design of
the process, and for inclusion of new knowledge and
merging ideas. Iterative, ongoing evaluation also
made it possible to include adaptive management
elements in the design process and to have sufficient
flexibility to make follow-up changes during the
process. At the same time, it was possible to
organize a stable and reliable process that fostered
continued participation during
/media/loftslag/Daniell_etal-2010.pdf
) describes a 10-step approach where narrative
storylines are developed and linked to dynamic models in an
iterative procedure. Stories are developed by a stakeholder panel
consisting of the relevant actors in the region under study, while
models are developed and applied by experts. Examples of global
exercises that have used an approach similar to Story-and-
Simulation include the Millennium
/media/loftslag/Kok_JGEC658_2009.pdf
process in which the effect of adopted water management measures must be monitored and
adjusted in an iterative way as new information and technology gradually become available
under changing and uncertain external impacts, such as climate change. This paper
identifies and characterises uncertainty as it occurs in the different stages of the IWRM
process with respect to sources, nature and type
/media/loftslag/VanderKeur_etal-2008-Uncertainty_IWRM-WARM.pdf