common reference. They not only provide a neutral frame for discussion, they also
allow researchers and practitioners to achieve common goals without compromising
individual objectives: The former conduct research to deepen their scientific understanding, while the latter gain scientific evidence to support their decisions or justify
their actions. With this in mind, the PIREN-Seine’s choice of expert-based models
can be understood as a way of maintaining the boundaries of science and practice,
since emphasis on models as research tools subject to scientific validation and
backed by the recognized expertise of PIREN-Seine researchers maintains a sense
of credibility, legitimacy and trust.
But they pose just as many limitations and challenges. Scenarios are simplified
representations of the world with uncertainties, despite steady progress over the past
decades. The communication of modelling results must be clear about this, as the
results generate passionate debates within society. While modelling results may be
perceived as scientifically objective, the choice of what is modelled and how it is
modelled (e.g. parameters considered, processes taken into account), as well as how
the results are interpreted and used, is inherently subjective. Also, the generation of
model knowledge and the simulation products potentially usable by practitioners
need to be issued in a ‘timely’ manner in relation to the way the problems arise
among the epistemic community. Those works that are not put on the policy agenda
of the river basin authority might not be relevant and, even less, used. The
co-construction of scenarios potentially explored by models is a good way to
alleviate this difficulty, since it allows for collective reflection on possible futures.
The models have made it possible to envisage a paradigm shift (e.g. the transition
to organic farming, source control and reduction of pollutant emissions,
single vs. multiple sewage treatment plants in the Paris region), but they may
encounter some socio-economic rationales. In some sectors, such as agriculture,
models have not yet produced signals strong enough to generate drastic transformations in practices or to overcome resistance to change. Also, model outputs should
not contribute to the depoliticization of public decision by transferring decisions to
purely technical committees. As a last point, dependence on expertise, the lack of
available and/or reliable data, changing regulatory requirements demanding added
functionality from the models and issues of propriety remain fundamental limitations
to modelling. As a result, the Seneque model is no longer being used directly by the
AESN, which now prefers to use an à la carte approach requesting modelling results
from PIREN researchers when needed. The SIAAP, by contrast, has expressed a
desire in recent years to move towards methods of real-time control and artificial
intelligence involving the development of complex assimilation technologies [32]. It
is up to researchers and their partners to see how they will develop their common
patterns of practice, while the majority of operational partners still consider models
to be important support tools.
402
C. Carré et al.
allow researchers and practitioners to achieve common goals without compromising
individual objectives: The former conduct research to deepen their scientific understanding, while the latter gain scientific evidence to support their decisions or justify
their actions. With this in mind, the PIREN-Seine’s choice of expert-based models
can be understood as a way of maintaining the boundaries of science and practice,
since emphasis on models as research tools subject to scientific validation and
backed by the recognized expertise of PIREN-Seine researchers maintains a sense
of credibility, legitimacy and trust.
But they pose just as many limitations and challenges. Scenarios are simplified
representations of the world with uncertainties, despite steady progress over the past
decades. The communication of modelling results must be clear about this, as the
results generate passionate debates within society. While modelling results may be
perceived as scientifically objective, the choice of what is modelled and how it is
modelled (e.g. parameters considered, processes taken into account), as well as how
the results are interpreted and used, is inherently subjective. Also, the generation of
model knowledge and the simulation products potentially usable by practitioners
need to be issued in a ‘timely’ manner in relation to the way the problems arise
among the epistemic community. Those works that are not put on the policy agenda
of the river basin authority might not be relevant and, even less, used. The
co-construction of scenarios potentially explored by models is a good way to
alleviate this difficulty, since it allows for collective reflection on possible futures.
The models have made it possible to envisage a paradigm shift (e.g. the transition
to organic farming, source control and reduction of pollutant emissions,
single vs. multiple sewage treatment plants in the Paris region), but they may
encounter some socio-economic rationales. In some sectors, such as agriculture,
models have not yet produced signals strong enough to generate drastic transformations in practices or to overcome resistance to change. Also, model outputs should
not contribute to the depoliticization of public decision by transferring decisions to
purely technical committees. As a last point, dependence on expertise, the lack of
available and/or reliable data, changing regulatory requirements demanding added
functionality from the models and issues of propriety remain fundamental limitations
to modelling. As a result, the Seneque model is no longer being used directly by the
AESN, which now prefers to use an à la carte approach requesting modelling results
from PIREN researchers when needed. The SIAAP, by contrast, has expressed a
desire in recent years to move towards methods of real-time control and artificial
intelligence involving the development of complex assimilation technologies [32]. It
is up to researchers and their partners to see how they will develop their common
patterns of practice, while the majority of operational partners still consider models
to be important support tools.
402
C. Carré et al.