n s
gh II
Chen
-Rod
s, S. E
and, Aus
ity, Gold
t, Fort Co
tuting processes which support communication and interactions; and by employing software technology
which enables easy model expansion and re use to gain an economy of scale and reduce development
costs. A final group of perhaps more problematic challenges relate to how the success of EDSS ought to be
q Position papers aim to synthesise some key aspect of the knowledge platform for environmental modelling and software issues. The review process is twofold e a normal
external review process followed by extensive review by EMS Board members. See the Editorial in Volume 21 (2006).
* Corresponding author. International WaterCentre, Level 16 333 Ann Street, Brisbane, QLD 4000, Australia. Tel.: þ61 7 3123 7766; fax: þ61 7 3103 4574.
Contents lists available at SciVerse ScienceDirect
Environmental Modelling & Software
journ
Environmental Modelling & Software 26 (2011) 1389e1402E-mail address: b.mcintosh@watercentre.org (B.S. McIntosh).USDA Forest Service, Northern Research Station, 705 Spear Street, South Burlington, USA
eUSDA-FS, Rocky Mountain Research Station, Missoula Forestry Sciences Lab, Missoula, MT, USA
fUrban Water Balance Unit, Climate and Water Division, Bureau of Meteorology, Melbourne, Australia
gUFZ Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Leipzig, Germany
hDepartment of Civil, Environmental and Geomatic Engineering (CEGE), University College, London, United Kingdom
iDepartment of Computer Science, University of Regina, Regina, Saskatchewan, Canada
j Integrated Catchment Assessment and Management Centre, National Centre for Groundwater Research and Training, The Australian National University, Canberra, Australia
kDecision and Information Sciences, Wageningen UR, Wageningen, The Netherlands
l Integrated Land Use Systems Group, Macaulay Institute, Aberdeen, United Kingdom
mHydroEcological Engineering Advanced Decision Support Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
n Laboratory of Chemical and Environmental Engineering, Universitat de Girona, Spain
o Leibniz-Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
pMaralte B.V., Crown Business Center, Leiden, The Netherlands
qDepartment of Land Engineering, Piazza d’Armi, University of Cagliari, Cagliari, Italy
r Potsdam Institute for Climate Impact Research (PIK), Potsdam, Germany
sRIKS bv., Maastricht, The Netherlands
tCatalan Institute for Water Research (ICRA), Girona, Spain
u Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Switzerland
v Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, The Netherlands
a r t i c l e i n f o
Article history:
Received 9 August 2011
Received in revised form
19 September 2011
Accepted 26 September 2011
Available online 2 November 2011
Keywords:
Environmental decision support systems
EDSS
Information systems
Decision-making
Software development
Adoption
Use
a b s t r a c t
Despite the perceived value of DSS in informing environmental and natural resource management, DSS
tools often fail to be adopted by intended end users. By drawing together the experience of a global group
of EDSS developers, we have identified and assessed key challenges in EDSS development and offer
recommendations to resolve them. Challenges related to engaging end users in EDSS development
emphasise the need for a participatory process that embraces end users and stakeholders throughout the
design and development process. Adoption challenges concerned with individual and organisational
capacities to use EDSS and the match between EDSS and organisational goals can be overcome through
the use of an internal champion to promote the EDSS at different levels of a target organisation; co-
ordinate and build capacity within the organisation, and; ensure that developers maintain focus on
developing EDSS which are relatively easy and inexpensive to use and update (and which are perceived
as such by the target users). Significant challenges exist in relation to ensuring EDSS longevity and
financial sustainability. Such business challenges may be met through planning and design that considers
the long-term costs of training, support, and maintenance; revenue generation and licensing by insti-Position paper
Environmental decisio
best practicesq
B.S. McIntosh a,b,*, J.C. Ascou
S. Cuddy j, A.J. Jakeman j, S.
N.W.T. Quinnm, I. Rodriguez
M. Wrobel r, H. van Delden
a International Water Centre, Brisbane, Queensl
b Smart Water Research Centre, Griffith Univers
cUSDA-ARS, Agricultural Systems Research Uni
d1364-8152/$ e see front matter
2011 Elsevier Ltd.
doi:10.1016/j.envsoft.2011.09.009upport systems (EDSS) development e Challenges and
c, M. Twery d, J. Chewe, A. Elmahdi f, D. Haase g, J.J. Harou h, D. Hepting i,
j, A. Kassahun k, S. Lautenbach g, K. Matthews l, W. Merritt j,
a n,t, S. Sieber o, M. Stavenga p, A. Sulis q, J. Ticehurst j, M. Volk g,
l-Sawah j, A. Rizzoli u, A. Voinov v
tralia
Coast, Queensland, Australia
llins, CO, USA
al homepage: www.elsevier .com/locate/envsoftAll rights reserved.

can
eas
llen
des
ce,
nh
en
g the
“agenda setting” by Rogers, 2003); (ii) “desi
of alternative strategies, plans, or options f
identified during the intelligence gatherin
process of evaluating alternatives and “cho
Courtney (2001), Gorry and Morton’s (1971)
to distinguish between structured, semi-st
define
odeltured decision contexts, and then to
systems that help to deal with decision-ma
phase (intelligence, design or choice) was segn” or the development
or solving the problem
g phase, and (iii) the
osing”. As described by
original innovation was
ructured, and unstruc-
DSS as computer-aided
king where at least one
(2006) goes some way towards providing such a guide but delib-
erately does not examine user interfacing, usability or the embed-
ding of models into decision support systems e the primary
concerns here.
The aim of this paper is to fill these two gaps; to improve EDSS
development practice through collating and critically assessing the
professional experience of a global group of EDSS developers (the
authors) from academia, government, and business. This will be(what we will term ‘decision phases
ligence” for the purpose of identifyinthe gathering of “intel-
need for change (called
recommendations that can help to overcome these difficulties and
complications. The ‘ten iterative step’ model of Jakeman et al.distinguished three main phases of organisational decision-making
’) e (i)
generally), and (ii) offers good system development practiceevaluated. Whilst success
culties of definition and m
outcomes. To tackle the cha
concerned with promoting
promoting EDSS acceptan
recommendations should e
help facilitate the achievem
1. Environmental DSS (EDSS) e premise and promise
The environmental and social challenges of the late twentieth
and early twenty-first centuries are complex and intertwined by
nature, and global in extent. Responding to such contemporary
environmental and social challenges requires change e change in
patterns of consumption, processes of production, methods of
resource management, and ways that we value other species and
future generations. Faced with such drivers for change, scientific
rationality has emerged as a prominent force in environmental
policy and management worldwide. The need to formulate new
policy objectives and implementation options, and to change the
way in which we manage our environment and resource-using
activities on the basis of robust analysis and evidence, has
become well accepted. In conjunction with this rise of rationality,
there has been a global growth in the supply of suitable tools and
technologies to support policy assessment in various ways,
accompanied by a similar but variable growth in demand for
different types of decision support tools (Nilsson et al., 2008).
It is within the necessity to do things differently created by
contemporary drivers of environmental change that the concept of
the Decision Support System (DSS) fits, as technology to assist in
the comparative assessment and selection of options for change.
More specifically, the effort to develop technologies that inform
environmental policy and management organisations in the search
for solutions to complex problems has resulted in the development
of what has been termed environmental DSS or EDSS (Guariso and
Werthner, 1989; Rizzoli and Young, 1997). It is not our objective to
provide a comprehensive reviewof the history of DSS development,
but rather, in line with the recognised focus of this journal
(Casagrandi and Guariso, 2009), to push forward our understanding
of EDSS use and potential. The history of DSS can be found else-
where in both extended (McCown, 2002a) and more succinct
(Courtney, 2001) forms. However, before we begin to characterise,
dissect, and re-formulate practices appropriate for remedying
current challenges in EDSS development and use, it is important
that the reader appreciate the initial intentions (the premise and
promise) of DSS technology which first emerged almost four
decades ago. As a leading journal in the field, further information
on EDSS can be found in Environmental Modelling and Software.
The concept of the DSS was developed by Gorry and Morton
(1971) by building on the work of Herbert Simon (1960) whose
work focused on organisational decision-making. Simon (1960)
B.S. McIntosh et al. / Environmental M1390mi- or unstructured.be framed relatively easily in terms of interactions with end users, diffi-
urability emerge in relation to the extent to which EDSS achieve intended
ges described, the authors provide a set of best practice recommendations
ign for ease of use, design for usefulness, establishing trust and credibility,
and starting simple and small in functionality terms. Following these
ance the achievement of successful EDSS adoption, but more importantly,
t of desirable social and environmental outcomes.

2011 Elsevier Ltd. All rights reserved.
So what constitutes a semi- or unstructured decision context?
Pidd (2003) elaborates decisions into three categories along
a continuum of structured to unstructured e from puzzles (with
agreeable formulations and solutions) through problems (with
agreeable formulations and arguable solutions) to messes (with
arguable formulations and solutions) (see McIntosh et al., 2005 or
Oliver and Twery, 1999). The distinction between categories makes
explicit the fact that decisions involve problem formulation as well
as solution generation and selection, and that both dimensionsmay
be contested. Contested decision formulations exist where the
nature of the problem is disagreed, e.g. is water scarcity a conse-
quence of poor water infrastructure, poor resource management or
profligate demand behaviour? Contested solutions are where
different views exist on which option (or set of options with
differing emphases) is the best to solve a given problem e.g. should
water demand be reduced by pricing, conservation education,
water use bans, or providing grants for xeriscaping gardens? Taking
these distinctions, DSS were originally intended to be computer-
aided systems to support one or more phases of decision-making
where either the decision formulation was agreeable but the
solution arguable (semi-structured), or the formulation and solu-
tion were both arguable (unstructured).
In addition to helping the process of structuring and resolving
what action to takewhen knowledge about the nature and impact of
problems (and how best to tackle them) is uncertain and contested,
DSS and more specifically EDSS are meant to improve the trans-
parency of decision formulation and solution. Transparent because
rational explanations can be provided to support decisions, and
because the user/stakeholder/citizen can reproduce the decision
procedure, play with the weights, and perform sensitivity analysis to
assess decision strength and robustness. However, as is well docu-
mented elsewhere, there are significant concerns about the uptake
and actual use of EDSS and related technologies (Diez and McIntosh,
2009, 2011; Lautenbach et al., 2009; Oxley et al., 2004; Elmahdi et al.,
2006). A brief sampling of existing literature on EDSS reveals various
case studies on designing and building real-world EDSS (e.g. Cortés
et al., 2000; Poch et al., 2004; Twery et al., 2005; Argent et al.,
2009; Elmahdi and McFarlane, 2009), and a more limited literature
on evaluating EDSS (e.g. Inman et al., 2011).
In general, the literature does not provide a guide for EDSS
researchers/developers, stakeholders, and policy makers that: (i)
describes the problems and challenges faced by EDSS developers
(as opposed to information systems, or IS, developers more
ling & Software 26 (2011) 1389e1402achieved by: