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Las Tablas de Daimiel, together with other wetlands in La
Mancha, Spain, situated in the Upper Guadiana Basin (Fig.
1), has been catalogued as a Biosphere Reserve Area since
1981 as part of the UNESCO Man and the Biosphere pro-
gramme. Between the mid-1970s and late 1980s, over
150 000 hectares of new irrigation areas were established,
mainly as a result of private initiative. The average recharge
rate of groundwater in the western La Mancha aquifer in the
Upper Guadiana Basin is estimated to be between 200 and
500 million m
Mancha, Spain, situated in the Upper Guadiana Basin (Fig.
1), has been catalogued as a Biosphere Reserve Area since
1981 as part of the UNESCO Man and the Biosphere pro-
gramme. Between the mid-1970s and late 1980s, over
150 000 hectares of new irrigation areas were established,
mainly as a result of private initiative. The average recharge
rate of groundwater in the western La Mancha aquifer in the
Upper Guadiana Basin is estimated to be between 200 and
500 million m
3
per year, in dry and wet years respectively.
Re
charge also depends on the depth of the water table
(Martínez-Cortina & Cruces 2003). Abstraction reached
600 million m
600 million m
3
per year by the end of the 1980s. Up to this
time a total of 3000-5000 million m
3
of the Upper Gua -
diana Basin aquifer's water reserves was withdrawn (Bromley
et al. 2000; Lopéz-Geta et al. 2006). The intensive use of
groundwater has been a main factor for the improvement of
the social and economic situation in this region, with a pop-
ulation of about half a million people, and where the agri-
cultural sector is very important (Llamas et al. 2006).
Water-table drawdown due to the intensive abstraction of
groundwater for irrigation has caused severe negative impacts
on wetlands, streams and rivers, and has resulted in a lower-
ing of groundwater levels by up to 50 m. The main conflicts
in the area are between farmers and conservationists, between
central, regional and local government water agencies, and
between small farmers and big farmers. The conflicts began
about three decades ago (Llamas 1988) and have not yet been
settled. In 2001 the Spanish Parliament asked the Govern -
ment to present a hydrological plan for the Upper Guadiana
Basin within one year. More than 20 draft proposals have
been presented, the last one in 2006 with a budget of almost
four billion Euros. This proposal has been met with strong
opposition from most farmer lobbies.
et al. 2000; Lopéz-Geta et al. 2006). The intensive use of
groundwater has been a main factor for the improvement of
the social and economic situation in this region, with a pop-
ulation of about half a million people, and where the agri-
cultural sector is very important (Llamas et al. 2006).
Water-table drawdown due to the intensive abstraction of
groundwater for irrigation has caused severe negative impacts
on wetlands, streams and rivers, and has resulted in a lower-
ing of groundwater levels by up to 50 m. The main conflicts
in the area are between farmers and conservationists, between
central, regional and local government water agencies, and
between small farmers and big farmers. The conflicts began
about three decades ago (Llamas 1988) and have not yet been
settled. In 2001 the Spanish Parliament asked the Govern -
ment to present a hydrological plan for the Upper Guadiana
Basin within one year. More than 20 draft proposals have
been presented, the last one in 2006 with a budget of almost
four billion Euros. This proposal has been met with strong
opposition from most farmer lobbies.
The Guadiana Basin is one of seven transboundary case
studies of the EU NeWater research project (
New Approaches
for Adaptive Water Management under Uncertainty ). The prin -
cipal water-management issues in the project are addressed
by adaptive and integrated water-resource manage ment. This
includes uncertainty and risk mitigation, gov er n ance, cross-
sectoral integration, scale analysis, information management,
for Adaptive Water Management under Uncertainty ). The prin -
cipal water-management issues in the project are addressed
by adaptive and integrated water-resource manage ment. This
includes uncertainty and risk mitigation, gov er n ance, cross-
sectoral integration, scale analysis, information management,
stakeholder participation, financial aspects, system resilience
and vulnerability. One work block in the NeWater project
has the task of translating research outputs into tools for
practitioners and end-users. As part of this effort, Bayesian
belief networks (Bns) were selected as one possible tool to be
developed as an aid to stakeholder participation in integrated
assessment of gaps, being a suitable tool for dialogue in order
to identify gaps in water-resource management functions,
gaps to meet the goals of the EU Water Framework Directive
and to analyse management potentials and constraints.
and vulnerability. One work block in the NeWater project
has the task of translating research outputs into tools for
practitioners and end-users. As part of this effort, Bayesian
belief networks (Bns) were selected as one possible tool to be
developed as an aid to stakeholder participation in integrated
assessment of gaps, being a suitable tool for dialogue in order
to identify gaps in water-resource management functions,
gaps to meet the goals of the EU Water Framework Directive
and to analyse management potentials and constraints.
The purpose of this paper is to describe the testing of Bns
as a tool for participatory integrated assessment and adaptive
and integrated water-resource management in the Upper
Guadiana Basin.
and integrated water-resource management in the Upper
Guadiana Basin.
Participatory integrated assessment
Participatory integrated assessment can be considered a form
of participatory policy analysis, which aims to support the
policy process by designing and facilitating policy debate and
argument. Assessment is integrated when it draws on a
broader set of knowledge domains than are represented in the
of participatory policy analysis, which aims to support the
policy process by designing and facilitating policy debate and
argument. Assessment is integrated when it draws on a
broader set of knowledge domains than are represented in the
Bayesian belief networks as a tool for participatory
integrated assessment and adaptive groundwater
management: the Upper Guadiana Basin, Spain
integrated assessment and adaptive groundwater
management: the Upper Guadiana Basin, Spain
Hans Jřrgen Henriksen, Per Rasmussen, John Bromley, Africa de la Hera Portillo
and M. Ramón Llamas
and M. Ramón Llamas
© GEUS, 2007.
Geological Survey of Denmark and Greenland Bulletin
13, 69-72. Available at:
www.geus.dk/publications/bull
69
Fig. 1. Location of the Guadiana Basin in Spain. The Upper Guadiana
Basin includes Las Tablas Daimiel and upstream areas in the Castilla-La
Mancha region. Major rivers indicated.
research product of a single discipline. Assessment is distin-
guished from disciplinary research by its purpose: to inform
policy and decision-making, rather than to advance knowl-
edge for its intrinsic value (Hisschemöller et al. 2001).
guished from disciplinary research by its purpose: to inform
policy and decision-making, rather than to advance knowl-
edge for its intrinsic value (Hisschemöller et al. 2001).
A wide range of methods and techniques can be drawn
from social psychology, policy sciences, decision analysis and
anthropology (Hisschemöller et al. 2001) for high-level par-
ticipatory integrated assessment. Some of these, like brain-
storming or decision seminars, although well established, are
of limited value for integrated water-resource management
because a proper understanding of the spatial and temporal
variation and the complexity within river basins requires a
modelling approach (Croke et al. 2007). According to
Jakeman & Letcher (2003) the tools for participatory inte-
grated assessment must:
anthropology (Hisschemöller et al. 2001) for high-level par-
ticipatory integrated assessment. Some of these, like brain-
storming or decision seminars, although well established, are
of limited value for integrated water-resource management
because a proper understanding of the spatial and temporal
variation and the complexity within river basins requires a
modelling approach (Croke et al. 2007). According to
Jakeman & Letcher (2003) the tools for participatory inte-
grated assessment must:
1. be problem-focussed, using an iterative, adaptive ap
-
proach that links research to policy;
2. possess an interactive, transparent framework that en -
hances communication;
3. be enriched by stakeholder involvement and dedicated to
adoption;
4. connect complexities between the natural and human en -
vironment, recognising spatial dependencies, feedbacks
and impediments; and
and impediments; and
5. attempt to recognise essential lacking knowledge.
Jakeman & Letcher (2003) list several tools for participatory
integrated assessment, e.g. system dynamics, Bns, metamod-
els, risk assessment approaches, coupled component models,
agent-based models and expert systems. Here Bns are in focus
as a tool for adaptive and integrated water management in the
Upper Guadiana Basin test case.
integrated assessment, e.g. system dynamics, Bns, metamod-
els, risk assessment approaches, coupled component models,
agent-based models and expert systems. Here Bns are in focus
as a tool for adaptive and integrated water management in the
Upper Guadiana Basin test case.
Bayesian belief networks
A Bayesian belief network (Bn) is a type of decision support
system based on probability theory which implements Bayes'
rule of probability (Jensen 2002; Bromley 2005). This rule
shows mathematically how existing beliefs can be modified
with the input of new evidence. Bns organise the body of
knowledge in a given area by mapping out relationships
among key variables and encoding them with numbers that
represent the extent to which one variable is likely to affect
another.
system based on probability theory which implements Bayes'
rule of probability (Jensen 2002; Bromley 2005). This rule
shows mathematically how existing beliefs can be modified
with the input of new evidence. Bns organise the body of
knowledge in a given area by mapping out relationships
among key variables and encoding them with numbers that
represent the extent to which one variable is likely to affect
another.
Bns have gained a reputation for being a powerful tech-
nique to model complex problems involving uncertain knowl -
edge and uncertain impacts of causes. Ideally, Bns are a
technique to assist decision-making that is especially helpful
when there is scarcity and uncertainty in the data used in
making the decision and the factors are highly interlinked, all
edge and uncertain impacts of causes. Ideally, Bns are a
technique to assist decision-making that is especially helpful
when there is scarcity and uncertainty in the data used in
making the decision and the factors are highly interlinked, all
of which makes the problem very complex. The graphical
nature of Bns facilitates formal discussion of the structure of
the proposed model. Furthermore, the ability of Bns to
describe the uncertain relationships between variables is ideal
to describe the relationship between events, which may not
be well understood.
nature of Bns facilitates formal discussion of the structure of
the proposed model. Furthermore, the ability of Bns to
describe the uncertain relationships between variables is ideal
to describe the relationship between events, which may not
be well understood.
Bns help water managers, stakeholders and scientists (1) to
visualise and recognise, in the face of complexity and uncer-
tainty, the relationships between different actions and conse-
quences; (2) to make learning about water-resource systems
more efficient; and (3) to encourage the involvement of social
and political values in water-resource management (e.g.
Henriksen et al. 2007a, b). Furthermore, it has been judged
that Bns are an excellent tool for integrating different
domains, e.g. socio-economy, hydrology and groundwater
quality data of different knowledge types (monitoring data,
models and expert opinions; Henriksen et al. 2007a). Here
the guidelines from the MERIT project (Bromley 2005) can
help support a successful and efficient involvement of stake-
holders in the participatory integrated assessment process, a
process which is demanding to run due to multiple frames
and opposing interests.
tainty, the relationships between different actions and conse-
quences; (2) to make learning about water-resource systems
more efficient; and (3) to encourage the involvement of social
and political values in water-resource management (e.g.
Henriksen et al. 2007a, b). Furthermore, it has been judged
that Bns are an excellent tool for integrating different
domains, e.g. socio-economy, hydrology and groundwater
quality data of different knowledge types (monitoring data,
models and expert opinions; Henriksen et al. 2007a). Here
the guidelines from the MERIT project (Bromley 2005) can
help support a successful and efficient involvement of stake-
holders in the participatory integrated assessment process, a
process which is demanding to run due to multiple frames
and opposing interests.
Design for testing the enhanced Bayesian
belief network tool
belief network tool
A test of an enhanced Bn tool is being undertaken in the
Upper Guadiana Basin as part of the NeWater project. The
test involves the construction of a Bn to represent the man-
agement of groundwater levels in the region, taking into
account the social, economic, hydrological and ecological
consequences of alternative irrigation and groundwater man-
agement scenarios (Table 1).
Upper Guadiana Basin as part of the NeWater project. The
test involves the construction of a Bn to represent the man-
agement of groundwater levels in the region, taking into
account the social, economic, hydrological and ecological
consequences of alternative irrigation and groundwater man-
agement scenarios (Table 1).
In November 2006, an initial workshop was held at the
Geological Survey of Spain (IGME) in Madrid with partici-
pants from the case study group. During this workshop a pre-
liminary Bn for the Upper Guadiana Basin was developed by
Bn experts from IGME, the University Complutense de
Madrid, the Geological Survey of Denmark and Greenland,
and the Centre of Ecology and Hydrology, Wallingford, UK,
pants from the case study group. During this workshop a pre-
liminary Bn for the Upper Guadiana Basin was developed by
Bn experts from IGME, the University Complutense de
Madrid, the Geological Survey of Denmark and Greenland,
and the Centre of Ecology and Hydrology, Wallingford, UK,
70
together with a representative of the water managers of the
basin responsible for water planning in relation to implemen-
tation of the EU Water Framework Directive.
basin responsible for water planning in relation to implemen-
tation of the EU Water Framework Directive.
A joint workshop with all stakeholders tOFinalise the net-
work has been planned for the first half of 2007. The process
and method for constructing the Bn in the Upper Guadiana
Basin test will follow the MERIT guidelines (Bromley 2005).
In the following we present the preliminary Bn and the
hypotheses relating to the use of the tool in participatory
integrated assessment and adaptive management in the
Guadiana Basin.
and method for constructing the Bn in the Upper Guadiana
Basin test will follow the MERIT guidelines (Bromley 2005).
In the following we present the preliminary Bn and the
hypotheses relating to the use of the tool in participatory
integrated assessment and adaptive management in the
Guadiana Basin.
Results of testing Bayesian belief networks
for adaptive water management
for adaptive water management
The initial step in network design was to establish the space
and time boundaries of the system being modelled. It was
agreed to restrict the model to the Upper Guadiana Basin,
and a one-year time period for groundwater level and socio-
economic consequences was decided. The pilot Bn for the
Upper Guadiana Basin case which emerged from this process
is shown in Fig. 2. The network deals with the way in which
different management actions influence irrigation water use,
groundwater level, crop pattern, farmers' income, wetland
recovery, productivity and employment in the region (Fig. 2).
Included among the potential actions that might be taken
are: (1) acquisition of water rights; (2) law enforcement; (3)
and time boundaries of the system being modelled. It was
agreed to restrict the model to the Upper Guadiana Basin,
and a one-year time period for groundwater level and socio-
economic consequences was decided. The pilot Bn for the
Upper Guadiana Basin case which emerged from this process
is shown in Fig. 2. The network deals with the way in which
different management actions influence irrigation water use,
groundwater level, crop pattern, farmers' income, wetland
recovery, productivity and employment in the region (Fig. 2).
Included among the potential actions that might be taken
are: (1) acquisition of water rights; (2) law enforcement; (3)
common agricultural programmes (CAP) subsidies; and (4)
annual management plans. Climate and the initial state of
the aquifer are included as control factors. The indicators
(objectives) in the network include: (1) groundwater levels;
(2) impact on wetland recovery; (3) agricultural productivity;
(4) farmers' income; and (5) levels of employment in the
region. When running the Bn, combinations of actions can
be selected and calculated.
annual management plans. Climate and the initial state of
the aquifer are included as control factors. The indicators
(objectives) in the network include: (1) groundwater levels;
(2) impact on wetland recovery; (3) agricultural productivity;
(4) farmers' income; and (5) levels of employment in the
region. When running the Bn, combinations of actions can
be selected and calculated.
It is hypothesised that Bns fully support four of the five
requirements proposed by Jakeman & Letcher (2003) for
participatory integrated assessment (Table 2). One require-
ment, the representation of spatial dependencies, is only
partly supported (e.g. input to the decision-making about
which specific wetlands that will be recovered by a certain
increase in groundwater level has to be evaluated using a
groundwater model). However, as stated by Pascual (2005):
"the beauty of Bns lies in their explanatory power: observa-
tions about any node generates knowledge about all other
nodes, providing one with a tool to draw transparent, ration -
al inferences in a probabilistic world". This illustrates that the
tool can be used for diagnosis and social learning.
participatory integrated assessment (Table 2). One require-
ment, the representation of spatial dependencies, is only
partly supported (e.g. input to the decision-making about
which specific wetlands that will be recovered by a certain
increase in groundwater level has to be evaluated using a
groundwater model). However, as stated by Pascual (2005):
"the beauty of Bns lies in their explanatory power: observa-
tions about any node generates knowledge about all other
nodes, providing one with a tool to draw transparent, ration -
al inferences in a probabilistic world". This illustrates that the
tool can be used for diagnosis and social learning.
Bns allow targeted modelling, participatory integrated
assessment and strong support for sense and decision-making
in cases with multiple frames (e.g. when stakeholders perceive
their environment differently, and frame and construct their
world in different ways) that create ambiguous situations and
conflicting interests hindering sustainable solutions for man-
in cases with multiple frames (e.g. when stakeholders perceive
their environment differently, and frame and construct their
world in different ways) that create ambiguous situations and
conflicting interests hindering sustainable solutions for man-
71
Fig. 2. Preliminary Bayesian network for the Upper Guadiana Basin. The objectives of the Bayesian network are to analyse the way in which different
management actions will influence irrigation water use, change in groundwater level, crop pattern, farmers' income, wetland recovery, productivity
and employment.
agement of the environment. The tool and the probability
tables (numbers) are not easily understood if not properly
explained. Thus, training and introduction to the tool and
the statistical background behind Bns is important (Table 2).
tables (numbers) are not easily understood if not properly
explained. Thus, training and introduction to the tool and
the statistical background behind Bns is important (Table 2).
Acknowledgements
The work reported from the NeWater project has been financially sup-
ported by the European Commission under contract number 511179
(GOCE). Integrated project in Priority 6.3 Global Change and Ecosystems
in the 6th EU Framework Programme.
ported by the European Commission under contract number 511179
(GOCE). Integrated project in Priority 6.3 Global Change and Ecosystems
in the 6th EU Framework Programme.
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Authors' addresses
H.J.H. & P.R.,
Geological Survey of Denmark and Greenland, Řster Voldgade 10, DK-1350 Copenhagen K, Denmark.
E-mail:
hjh@geus.dk
J.B., Oxford University Centre for the Environment, South Parks Road, Oxford OX1 3QY, UK.
A.d.l.H.P. , Geological Survey of Spain, 23 Rios Rosas, ITGE-E 28003 Madrid, Spain.
M.R.L., University of Complutense, Ciudad Universitaria, 28040 Madrid, Spain.
J.B., Oxford University Centre for the Environment, South Parks Road, Oxford OX1 3QY, UK.
A.d.l.H.P. , Geological Survey of Spain, 23 Rios Rosas, ITGE-E 28003 Madrid, Spain.
M.R.L., University of Complutense, Ciudad Universitaria, 28040 Madrid, Spain.
72
