An integrated modelling framework for simulating regional

Transcription

Available online at www.sciencedirect.com
Environmental Modelling & Software 23 (2008) 1095e1121
www.elsevier.com/locate/envsoft
An integrated modelling framework for simulating regional-scale
actor responses to global change in the water domain
R. Barthel a,*, S. Janisch b, N. Schwarz c, A. Trifkovic a, D. Nickel a, C. Schulz d, W. Mauser e
a
Institute of Hydraulic Engineering, Universität Stuttgart, Pfaffenwaldring 7a, D-70569 Stuttgart, Germany
Institute of Computer Science, Ludwig-Maximilians-Universität Munich, Oettingenstrasse 67, D-80538 Munich, Germany
c
UFZ e Helmholtz Centre for Environmental Research, Department of Computational Landscape Ecology, Permoserstrasse 15, D-04318 Leipzig, Germany
d
Centre for Environmental Systems Research, University of Kassel, Kurt-Wolters-Strasse 3, D-34131 Kassel, Germany
e
Faculty for Geosciences, Ludwig-Maximilians-Universität Munich, Luisenstrasse 37, D-80333 Munich, Germany
b
Received 16 November 2006; received in revised form 8 February 2008; accepted 14 February 2008
Available online 1 April 2008
Abstract
Within coupled hydrological simulation systems, taking socio-economic processes into account is still a challenging task. In particular,
systems that aim at evaluating impacts of climatic change on large spatial and temporal scales cannot be based on the assumption that infrastructure, economy, demography and other human factors remain constant while physical boundary conditions change. Therefore, any meaningful simulation of possible future scenarios needs to enable socio-economic systems to react and to adapt to climatic changes. To achieve this it is
necessary to simulate decision-making processes of the relevant actors in a way which is adequate for the scale, the catchment specific
management problems to be investigated and finally the data availability. This contribution presents the DEEPACTOR approach for representing
such human decision processes, which makes use of a multi-actor simulation framework and has similarities to agent-based approaches. This
DEEPACTOR approach is embedded in DANUBIA, a coupled simulation system comprising 16 individual models to simulate Global Change impacts
on the entire water cycle of the Upper Danube Catchment (Germany, 77,000 km2). The applicability of DANUBIA and in particular the DEEPACTOR
approach for treating the socio-economic part of the water cycle in a process-based way is demonstrated by means of concrete simulation models
of the water supply sector and of the domestic water users. Results from scenario simulations are used to demonstrate the capabilities and
limitations of the approach.
Ó 2008 Elsevier Ltd. All rights reserved.
Keywords: Climate change; Water supply; Domestic water use; Integrated water resources management; Coupled simulation; Social simulation; Actors;
Framework technology; Regional scale model
Software availability
The framework, the simulation models and the results
discussed in this article rely, amongst others, on software
developed by various project partners of GLOWA-Danube
(www.glowa-danube.de) within the first two phases of the
project (2001e2007). One of the main aims of the third
project period (2007e2010) is to convert the complete
system into an open source project which will be available
* Corresponding author. Tel.: þ49 711 685 66601; fax: þ49 711 685 66600.
E-mail address: [email protected] (R. Barthel).
1364-8152/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.envsoft.2008.02.004
to the public at the end of the third project phase (April
2010) at the latest.
1. Introduction
Suppose a decision maker or, more generally, a stakeholder
wants to know if the available water resources in a large, diverse
catchment will still meet the water demands of various water
users in 30 years’ time, assuming that the worst predictions of
Global Climate Models (GCM) downscaled to regional
conditions come true. More specifically, this decision maker
wants to know where and when in the catchment problems are
R. Barthel et al. / Environmental Modelling & Software 23 (2008) 1095e1121
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to be expected in order to plan counter measures and to allocate
appropriate financial resources. In order to answer these questions, the decision maker needs tools to predict the development
of (a) the state of the catchment’s natural water resources, (b) the
water demands of the different consumers under a changed
climate and (c) the state of the water supply infrastructure and
its ability to provide for adequate water supply. All these
predictions need to be spatially and temporally explicit, if the
‘where’ and the ‘when’ questions are to be addressed.
Physical models (hydrological, hydraulic, groundwater
etc.) are traditionally used to predict the spatially and
temporally explicit state of water resources. The traditional
prediction of demands relies on data driven methods such as
trend extrapolation, regression analysis, time series analysis,
rule and expert knowledge based systems, etc. However, all
methods that neglect the existing feedbacks (see Fig. 1)
between demand and supply and the various options of adaptation the consumers have, are unrealistic or even meaningless.
As indicated in Fig. 1, Climate Change will affect not only
the state of resources but also the ways and the magnitude of
consumption (e.g. introduction of irrigation if the climate gets
dryer). Changing consumption will in turn lead to an additional
change of the resources state; the resources state might
influence the attitude towards consumption or invoke legally
defined restrictions for consumption. Consumers might then
adapt their consumption to decreasing resources (save water)
and/or water suppliers might increase resources by, for example, importing water from neighbouring catchments. Furthermore, the number of consumers might decrease e.g. because
water intensive industries close down. Finally, it must be taken
into account that different consumers will react differently to
changes according to their possibilities, their preferences and
their location. Therefore, an attempt to simulate such complex
systems with their numerous mutual interdependences needs to:
e Integrate the social and physical side of the water cycle
within a catchment. We suggest that both sides should
be simulated by the most appropriate state of the art
models and are doubtful whether combined approaches
where natural science modelling concepts are imposed to
Climate Change
Natural Resources:
Groundwater, Surface
Water, Water Imports
Political Change
Land Use Change
Supply: S(D)
Demand: D(S)
Consumers / Actors:
Water Supply
Companies
Households, Farmers,
Industry, Tourism
Demographic Change
Fig. 1. Schematic view of the influences of changing outer boundary
conditions (‘Global Change’) on the demand/supply relation in a catchment
where demand is a function of supply and vice versa.
social systems or concepts known from social sciences
are used to form simplified conceptual representations of
natural systems are appropriate (see Section 2).
e Rely on a realistic representation of the options how actors
(water suppliers and water consumers) may react to
changes.
e Allow for a differentiated treatment of actors, as the
options and preferences of different actors can vary
when reacting to change, and similar actors can react
differently at different locations.
1.1. Scope and organisation of this article
Without attempting to cover all dependencies shown in
Fig. 1, this article presents an approach to include feedback
and adaptation by simulating responses of the socio-economic
framework to Global Change by using ‘actors’ to simulate
human decision makers at various levels from individual
households to communities and large companies. Note, that
although our approach follows ideas from the field of agentbased social simulation we use the term ‘actor’ instead of
‘agent’ to avoid name clashes with the term ‘software agents’,
which have a more specific meaning in the computer sciences
(see discussion in Drogoul et al., 2002). Hereby we focus upon
the domestic water consumption and the drinking water supply
infrastructure. The developed approach will hence be referred
to as the DEEPACTOR approach that is embedded in the DANUBIA
simulation system (described in Section 4). To summarise, the
approach presented here is
e Based on a fully coupled simulation system that allows for
the parallel interactive simulation of physical and socioeconomic processes and their interactions.
e Part of an integrated approach which considers the full
water cycle from the clouds to the groundwater and its
interaction with the socio-economic framework.
e Applicable on the regional (¼large catchment) scale and
targeted on the evaluation of long-term scenarios, mainly
based on Global Climate scenarios but also in a wider
sense on Global Change scenarios including socioeconomic change. It is therefore meant to show potential
regional impacts of Global Change on larger spatial and
temporal scales rather than to analyse human or institutional behaviour on an individual level.
The central focus of this paper is the actual application of
the DEEPACTOR approach to concrete management problems
in the Upper Danube catchment.
Before going into the details of DANUBIA and the DEEPACTOR
approach, the problem which is described in this introduction
section on a rather specific and conceptual level will be described
from a more general point of view in the context of integrated
water management on the regional scale in Section 2. Section 2
thereby explains the context in which the DEEPACTOR approach
is meant to be applied. In Section 3 we briefly discuss and compare approaches presented by other authors which follow partly or
completely the same ideas. Section 4 describes the background
of the GLOWA-Danube project within which the DEEPACTOR
approach was developed, as this background is needed to understand both the approach itself and its application. Our approach
is described and illustrated in Section 5 by the abstract features
of the DEEPACTOR framework together with the concrete simulation models WaterSupply and Household. Section 6 presents
the application of the DEEPACTOR approach with the selected
model implementations in the Upper Danube catchment. The
results of three climate scenario simulations in combination
with three water management scenarios and the options for
a validation of the approach are discussed. Finally, in Section
7 the main conclusions are summarised.
2. Integrated water resources management and
the role of human actors
Management of water (and land1) resources has always
been led by the two fundamental questions: What is needed
and what is available? In the past decades, however, the
manner of dealing with these basic questions has changed
quite essentially. The driving factors for the respective changes
in water management can be summarised as follows:
1. Sustainability and the long-term management perspective:
Instead of aiming mainly at satisfying present human
needs, management is additionally concerned with the
welfare of future societies. Sustainability has become
a key issue in planning the development and exploitation
of resources (IUCN et al., 1980; UN, 1992).
2. The integration of multiple objectives of different decision
makers and stakeholders: Whereas water management
used to be led by economic considerations, it is now
widely accepted that a range of economic, environmental
and social impacts needs to be considered. Impacts of
the changing state of an exploited resource on the state
of dependent systems, e.g. water-dependent ecosystems,
and on the quality of human life are taken into account
(UNESCO, 1987; Bouwer, 2002).
3. Consideration of all scales from local to regional2 to
global: A rapid technological evolution allows for the
compensation of local deficits by transfer of water over
large distances. At the same time human mobility and
flexibility has increased so that water-related management
may not focus on local aspects only (UNESCO, 1987; Döll
et al., 2003).
4. The recognition of Global Change3: Former management
was often based on the assumption that the long-term
1
In this article we will refer to water resources management only, even if the
presented DEEPACTOR-approach and DANUBIA as a whole integrate water and
land use management.
2
Regional in the context of this article: River basins >10,000 km2, countries, states, larger political entities.
3
Here understood as the sum of physical, social, political and economic
changes that affect the environment at present and in the future on at least a regional scale.
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average boundary conditions of natural systems (temperature, rainfall, percolation, etc.) were more or less constant.
The widespread recognition of Climate Change in the last
years has aroused awareness that this assumption might
lead to dangerous mismanagement. Additionally environmental and social impacts of water management measures
have become more evident and more pronounced in
modern societies (Biswas, 1996; Arnell, 1998; IPCC,
2001; Bolwidt, 2005; Solomon et al., 2007).
5. Increased prediction capabilities: The availability of
increasingly sophisticated optimisation models and the
possibility to run simulation models for large and complex
systems, together with a growing availability of observed
data increases the applicability and predictability of
modelling tools (e.g. Loucks et al., 1981; Wilson, 1999;
Yurdusev, 2002).
The changes of objectives and options listed above, which
for a large part are combined in the European Water Framework Directive (European Commission, 2000), clearly demand
new approaches for dealing with the management of water
resources. Management must consider a multitude of aspects
and objectives, larger spatial areas and, finally, the variability
of natural and social boundary conditions. It is clear that
such a multifaceted management requires the integration of
expert knowledge from many disciplines: the effects of Global
Climate Change on water and land resources cannot be evaluated on the basis of one discipline alone. Therefore management approaches are required that integrate the relevant
disciplines and make use of existing knowledge and models
and that deal with management problems on the appropriate
scale.
A very comprehensive discussion of most of the aspects
discussed in the above listed enumeration can be found in
(Giupponi et al., 2006). Integrated assessment and management, multi-objective decision making and the involvement
of human actors and stakeholders are topics which are
extensively discussed in works of Tony Jakeman, Claudia
Pahl-Wostl and their co-authors (Pahl-Wostl, 2002, 2005,
2007a,b; Jakeman and Letcher, 2003; Letcher et al., 2004,
2007; Jakeman et al., 2006).
2.1. Process-based simulation of human
actors in the water sector
Dealing with uncertainty of assumptions, models and data
are a major concern of IWRM under conditions of Global
(Climate) Change and a crucial aspect for the reliability and
acceptance of model results (Bogardi and Kundzewicz,
2002; Brugnach et al, 2007; Krysanova et al., 2007). An
accepted way of reducing uncertainty or showing the influence
of uncertain processes on model results is by modelling the
actual processes. Today, it is a common understanding that
the less conceptual (i.e. derived through trend extrapolation,
regression analysis, time series analysis, rule and expert
knowledge based systems, fuzzy logic, etc.) a model is and
the more realistic individual processes are represented, the
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better a model can be used in decision making and for stakeholder discussions (Hauhs, 1990; Ewen and Parkin, 1996;
Croke and Jakeman, 2001; Bathurst et al., 2004; Ewen
et al., 2006; Jakeman et al., 2006). In water resources management, natural processes (e.g. groundwater flow) have long
been treated as physical based as possible. Social processes
on the other hand (e.g. changes of water demand per capita)
have mainly been dealt with on a highly conceptual basis.
Under conditions of Global Change, the use of conceptual
approaches to model social processes is questionable, as
they largely ignore that human decisions and behaviour are
changed as a result of (Global) change (see Fig. 1). Human decisions made in reaction to changing natural conditions (e.g.
water consumption or the expansion of a water supply network) change the actors’ environment and form a new basis
for future decisions (Pahl-Wostl, 2002, 2007a). Furthermore,
human behaviour not only adapts to change but also undergoes
an evolution that might be forced by external drivers or be random (Grothmann and Patt, 2005). Therefore it seems judicious
to extend the idea of process-based modelling to the socioeconomic components of an integrated simulation system in
an effort to reduce their uncertainty and to improve their transparency. Mechanisms need to be provided for social systems
to respond to changes and for capturing the effects of these
responses upon the environment. Process-based in that context
means capturing the triggers, options, and temporal and spatial
aspects of an actor’s reaction in a direct, transparent and
realistic way.
3. Related work: Comparison with other approaches
Predicting future water availability and water demand has
always been one of the central questions in water resources
management. Accordingly, many approaches for simulating
and predicting the state of resources and the evolution of
demands, amongst them also many agent-based simulation
approaches, exist (see below). The specific problem at hand
(Section 1) requires (a) an integration of natural and socioeconomic processes, (b) the consideration of multiple actors
with different options and preferences, (c) a regional scale focus, (d) distributed, spatially and temporarily explicit models.
It is therefore not meaningful to compare the developed
DEEPACTOR approach to approaches that deal with demand or
supply predictions on a sectoral basis and approaches that
are dedicated to describe small-scale systems in a discrete4
way or lumped regional models. As already pointed out there
is a growing demand for integrated management and
assessment of resources on a regional scale (UNESCO,
1987; GWP, 2000; Bouwer, 2002; Quinn et al., 2004) which
has accordingly brought upon a number of regional integrated
research projects and approaches (e.g. Scoccimarro et al.,
1999; Gaiser et al., 2003, 2007; Krysanova et al., 2007;
Rodgers et al., 2007). None of these projects has to our
4
Discrete here means modelling systems on a technically or personally explicit level, i.e. real infrastructure, people or institutions.
knowledge come up with a fully coupled system or model
that represents responses of the socio-economic framework
in a process-oriented way. Simulation models that address
the environmental impacts and socio-economic effects together in a fully integrated way were for example presented
by Wu (1995), Kirshen et al. (1995), Watkins et al. (2004)
and Yamout and El-Fadel (2005), however with a focus on
smaller systems on the local scale. Athanasiadis et al. (2005)
and López-Paredes et al. (2005) provide regional and integrated models for water management, including both water
suppliers or municipalities and area residents. Although
implemented as agent-based models, both approaches do not
explicitly represent the processes involved in the case of water
scarcity on the supply and on the demand side. Moss et al.
(2000) sketch a promising approach within the FIRMA project
that explicitly includes policy makers and consumers. However, this approach seems to be not sufficiently specific with
respect to the explicit representation of important variables
such as water demand. The negotiation process simulated in
the approach of Thoyer et al. (2001) and the model of
Espinasse and Franchesquin (2005) focus on a small local
scale. Berger et al. (2007) and Feuillette et al. (2003) describe
integrated approaches to model water management which are
not applicable to central European water management due to
water resources being a major influence on farming and
therefore on household income in both cases.
In view of this brief discussion which of course cannot
reflect all agent-based or integrated simulation approaches in
the water sector, we are not aware of any approach that would
fulfil the requirements explained in Section 2 in a problem
context as exemplified in Section 1.
4. Project background: GLOWA-Danube
GLOWA-Danube (www.glowa-danube.de) is one of five
projects within the GLOWA-programme (www.glowa.org).
Within GLOWA, integrated approaches for carrying out Global
Change research are compared using six catchments in different climates on different continents. The central aim of GLOWA
is to provide an integrated approach for describing, modelling,
and forecasting physical, social, economic, and political
processes related to the hydrological cycle, in particular with
regard to Global Change on the river basin scale, in order to
meet the requirements of modern IWRM.
In GLOWA-Danube, the Upper Danube watershed was
selected as a representative regional-scale test catchment in
the temperate mid-latitudes, covering an area of approx.
77,000 km2 (Fig. 2). GLOWA-Danube equally considers the
influence of natural changes in the ecosystem, such as Climate
Change, and social changes, e.g. changes in land use or water
consumption. The central objective of GLOWA-Danube is the
joint development and application of the Decision Support
System (DSS) DANUBIA (Ludwig et al., 2003; Mauser and
Strasser, 2005).
The GLOWA-Danube consortium comprises a universitybased network of roughly 40 scientists from the fields of meteorology, hydrology, hydrogeology, hydraulic engineering,
0
5
10
15
20
25
1099
30
60
60
The Upper
Danube
55
Basin
55
#
Germany
Czech Republic
50
50
#
Passau
Switzerland
45
45
Austria
Italy
#
40
40
DANUBE
35
35
0
5
10
15
20
25
30
Fig. 2. Location of the Upper Danube Basin, the investigation area of GLOWA-Danube.
plant ecology, environmental psychology, environmental
economics, agricultural economics, glaciology, remote sensing, tourism research and computer sciences. The project
started in January 2001 and will terminate in April 2010.
The first phase of the project (2001e2004) was dedicated to
the development of the DANUBIA prototype. In the second
project phase (2004e2007), the consolidation and application
of DANUBIA, i.e. scenario evaluation, stakeholder involvement,
decision making, and practical water and land use management support were in the centre of the research activities. In
addition the process-based simulations in the socio-economic
sector were further developed. The third phase (2007e2010)
has the main objectives of:
(a) making DANUBIA practically available for the management
of water and land resources in the Upper Danube catchment, and
(b) making DANUBIA and all its components available and
usable for scientists and end users under an open source
licence.
4.1. The Upper Danube catchment
The Upper Danube catchment is a heterogeneous,
mountainous catchment with altitudes ranging from 287 to
4049 m a.s.l., precipitation between 650 and >2000 mm/a,
evaporation between 450 and 550 mm/a, discharge between
150 and 1.600 mm/a, average annual temperature between
4.8 and þ9 C and approximately 10.8 million
inhabitants. The last century showed a rapid transgression
from a mainly agricultural society to a highly industrialised
high-tech economy (mainly IT, automotive, chemistry).
However, industrial activities are quite focussed in few
urban agglomerations leaving vast parts of the country to
traditional farming and tourist activities. The latter in
particular are a major source of income (e.g. skiing in the
Alps) and therefore a major concern of GLOWA-Danube
(Sax, et al., 2007). The parallel evolution of a high tech
industry on the one hand and the need for preserving the
nature for agriculture and recreation purposes on the other
hand forms a major source of conflicting interests. DANUBIA
was developed as an instrument to help decision makers and
stakeholders to solve such conflicts.
Water resources management in the Upper Danube is
complex, partly because the watershed area extends over
a number of countries (Fig. 2), but even more due to the
very distributed nature of the water supply system. Well
over a 2400 municipalities are supplied by roughly 2000
independent water supply companies, leading to a highly
distributed scheme of supplier consumer relations (Barthel
et al., 2005; Nickel et al., 2005). Water supply in the public
sector is mainly (90%) groundwater based (Barthel and Trifkovic, 2007; Barthel et al., 2008).
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4.2. The DANUBIA simulation system and framework
DANUBIA currently comprises a total of 16 disciplinary
simulation models, which are coupled to each other in order
to exchange data dynamically during runtime. As depicted in
the UML 2.0 diagram (UML: see e.g. Rumbaugh et al.,
2005) of Fig. 3, main components to group domain-related
simulation models are used. Natural science simulation
models address physical processes concerned with the natural
water cycle and water availability whereas so-called actor
models address demography, water consumption and supply
infrastructure in the investigation area (compare component
actor in Fig. 3) simulate the socio-economic aspects of the water cycle using an ‘agent-based’ social simulation framework.
The system is used to evaluate the sustainability of future
water resources management alternatives, and to evaluate
consequences of IPCC derived climate scenarios for the period
from 2000 to 2100. Table 1 summarises purpose and scale of
the socio-economic simulation models of DANUBIA which can
make use of the DEEPACTOR approach.
To facilitate the smooth integration of the different
simulation models, DANUBIA makes use of an object-oriented
framework approach (D’Souza and Wills, 1999). Following
this approach, the development of the individual simulation
models is based on common modelling and implementation
frameworks. In the following we briefly describe DANUBIA’s
developer framework which provides the basic simulationrelated model features required for all model implementations
of DANUBIA.
In DANUBIA, the simulation area (Upper Danube Basin, see
Fig. 2) is modelled by a fixed 2-dimensional grid comprising
cells of 1 1 km. Each cell can be identified by a unique
ID and is modelled by an object that has a state determined
by attributes, for example elevation. Apart from such general
cell attributes common to all simulation models, there are
also model-specific cell attributes, for example soil temperature in case of the simulation model Soil (Fig. 3). Following
an object-oriented approach, the cells may also perform
calculations in the form of functions computed over local
values of the particular cell. For this reason we call the cells
proxels, an acronym of ‘process pixel’.
The values of the model-specific proxel attributes are
subject to the data exchange between different coupled
simulation models, which is formally specified by interfaces.
A DANUBIA interface gathers the methods used for data
exchange, one for each parameter, and is accompanied by
a specification of allowed data ranges. For each simulation
model, there is an interface specifying its required import
data and an export interface specifying the data which is
provided, i.e. computed by this model. The main purpose of
these specifications is to ensure the consistent interconnection
of the different simulation models of the project partners
within DANUBIA during runtime.
DANUBIA provides concepts and an implementation for the
treatment of simulation time. On the one hand, any simulation model participating in a coupled simulation follows the
same abstract simulation cycle of getting data, internal
computation, provision of data, getting data and so forth.
On the other hand, each model defines an individual local
time step, such as 1 h, 1 day, 1 month and so forth, depending
on the particular process to be modelled. Therefore, while
a 1 h model runs 24 cycles, a concurrently executed 1 day
«component»
Atmosphere
AtmoStations
AtmoSat
AtmoMM5
«component»
Actor
«component»
Landsurface
Biological
RadiationBalance
Snow
Soil
Surface
«component»
Rivernetwork
RModel
Demography
Economy
Farming
Household
Tourism
WaterSupply
«component»
Groundwater
GroundwaterFlow
Fig. 3. Main components and interfaces of the coupled simulation system DANUBIA. Components comprise at least one simulation model (listed in the lower section
of the boxes).
Table 1
Socio-economic models within DANUBIA: objectives, number of actors and actor types
Model
Main objectivea: to predict the
spatial (1 km2) and temporal
(monthly) distribution of:
Demographyb
Economy
Farming
Tourism
Demographic changes
Industrial water demand
Agricultural water demand
Water demand and development of
tourism infrastructure (e.g. skiing
resorts, golf courses, hotels)
State of water supply systems, state
of water resources, groundwater and
surface water withdrawal, distribution
of water to consumers (see Section 5.4)
Domestic water demand, water related
consumer satisfaction, use of water
saving technology (see Section 5.5)
WaterSupply
Household
a
b
# Actors
# Actor
Types
w1350
w40000
w1050
1
28
8
w2000
2
w50000
5
Only the main water related objectives relevant in this article are listed.
Currently not using the DEEPACTOR approach.
model runs one cycle only. In order to coordinate the simulation models in the coupled system such that mutual exclusion
of data exchange with respect to individual time steps is
taken into account, a coordination mechanism was developed,
whose concepts, design, implementation and formal verification is detailed in Barth et al. (2004), Hennicker and Ludwig
(2005) and Hennicker and Ludwig (2006) and will not be
further discussed here.
5. The DEEPACTOR approach
In contrast to physical processes, social processes are often
the result of complex considerations, which include individual
possibilities and preferences along with characteristic behaviour (Parunak et al., 1998; Axtell, 1999). In order to better support the modelling and simulation of this kind of entity in
DANUBIA, the DEEPACTOR framework was developed, providing
a common conceptual and architectural basis for the modelling
and implementation of the socio-economic simulation models
in GLOWA-Danube. Following their concrete requirements, the
framework applies an approach to agent-based simulation in
social sciences (Davidsson, 2002; Macy and Willer, 2002;
Gilbert and Troitzsch, 2005), which in turn is based upon
agent concepts of (distributed) artificial intelligence (Weiss,
1999; Russell and Norvig, 2003). Deciding entities such as
individuals, organisations, companies and so forth are explicitly modelled and simulated as ‘actors’. An actor observes its
environment and selects plans to execute as a reaction to its
observations (for a slightly different definition see Pahl-Wostl,
2005). Thereby different actors may have different courses-ofaction as well as varying preferences, represented by their
individual plans and their type-specific decision procedure.
Besides providing the modelling support for simple reactive
actors up to more complex deciding entities, which would in
principle also be supported by toolkits and libraries for
agent-based social simulation such as Repast (North et al.,
1101
2006), MASON (Luke et al., 2004) or Swarm (Minar et al.,
1996), the DEEPACTOR framework also provides an integration
with the developer framework and the runtime environment of
DANUBIA. Therefore concrete DEEPACTOR models may be
integrated and coupled with any other simulation model of
GLOWA-Danube (see Fig. 3) using the same basic concepts
and mechanisms for coordinated exchange of spatially explicit
data (via formally specified interfaces) as for any other simulation model of DANUBIA.
Both the DANUBIA and the DEEPACTOR frameworks apply
object-oriented techniques to provide the basic building blocks
for model implementations in the form of interfaces, abstract
base classes and predefined relationships. A concrete
DEEPACTOR model may implement different specialisations
using different concrete subclasses of the abstract base classes
(examples are described in Sections 5.4 and 5.5), each of them
implementing inherited abstract methods in a different, typespecific way. In the following, the most important static
elements of the framework are explained. After that, possibilities for the implementation of model computations on the one
hand and actor decisions on the other hand are described. It
must be stressed that some of the provided framework features
are optional. Therefore its concrete application, or maybe
better, its reification, may well differ between different
simulation models.
5.1. Static structure of model implementations
Fig. 4 shows a conceptual view of the interfaces, base
classes and relationships that are relevant for the development
of a DEEPACTOR model. The upper part shows the most important elements of the DANUBIA developer framework which are
inherited by the DEEPACTOR framework shown in the lower part
of the figure.
Any DEEPACTOR model implementation (specialisation of
AbstractActorModel) is a DANUBIA model (AbstractDanubiaModel) following the same core concepts
as natural-science model implementations, i.e. it comprises
a set of proxels representing the simulation area (AbstractProxel), specifies import and export interfaces for the data
exchange with coupled simulation models (DanubiaInterface), defines a local timestep and finally proceeds
in a cyclic fashion with the implementations of data import
(getData), local computation (compute) and data export
(provide). The cyclic execution is coordinated by the
runtime environment of DANUBIA according to the particular
model timestep. DEEPACTOR models extend the concepts of
a DANUBIA model, amongst others, by a number of new
methods that are executed cyclically. In order to integrate
these extensions transparently for the runtime environment,
the DEEPACTOR framework maps invocations of methods of
the base class AbstractDanubiaModel to calls of
methods of base classes AbstractActorModel, AbstractActor, AbstractPlan and AbstractAction
as well as methods of concrete DEEPACTOR framework classes
such as the implementation of sensors or the history. For
example the sensors are triggered to update their data directly
1102
DanubiaInterface
*
AbstractDanubiaModel
AbstractProxel
elevation
...
timestep
DanubiaInterface
getData()
compute()
provide()
...
Danubia Developer Framework
DeepActor Framework
AbstractActorModel
getData()
preCompute()
postCompute()
provide()
Sensor
data
events
...
AbstractPlan
rating
* isActive()
*
computeRating()
*
AbstractActor
id
location
collaborators
History
execPlans
failedPlans
...
1
*
AbstractAction
query()
options()
filter()
export()
period
isApplicable()
execute()
Fig. 4. Central interfaces and classes of the DANUBIA and the DEEPACTOR developer framework.
after getData of the model implementation is executed,
compute is implemented by an invocation of preCompute
followed by calls to options and filter for all actors, and
finally completed by an invocation of the model’s postCompute. Last but not least the actors’ histories are triggered to
update their state just before the model implementation of
provide is executed.
An actor represents an entity capable of reacting to
system changes in an individual way, i.e. it is capable of
making decisions. The individuality is thereby achieved by
individual properties and preferences of each actor object.
Fig. 5 demonstrates schematically how the individual properties and preferences are assigned. It has to be noted that,
since the simulation area in DANUBIA is represented by
a set of 1 1 km proxel objects, an actor in the most cases
does not explicitly represent a real person or organisation but
rather an abstract, effective ‘average’ of real actors located
on one or more proxels which in turn define the environment
of this actor. To show an example of averaging actors, if 100
real households exist in a proxel (model cell) for which 30%
of the households belong to the ‘conservative’ household
actor type and 70% belong to the ‘progressive’ household
actor type, then the corresponding proxel will host two
actors. The domestic water demand on such a grid cell is
then computed as a weighted average of the water demand
of these two actor types (see Section 5.5).
As depicted in Fig. 4, one of the basic actor properties is its
location, usually defined by a set of proxel object IDs.
Also an actor may define a number of collaborators,
i.e. other actor objects of the same DEEPACTOR model that
this actor needs to collaborate with. The concept of collaborators allows establishing or simulating social network aspects.
These basic properties may be assigned in concrete actor types
and additional properties such as an attribute to store the
number of wells may be declared.
System Changes /
Boundary Conditions
Actor
Changes of water demand /
availability
WSC
• Type
• Type: long distance water
supply
• has properties
• has properties: No. of wells
• has options
• has options: drill new wells,
buy/import water
• has preferences
• has preferences: use own
resources, use groundwater
Fig. 5. Schematic diagram of an actor: Left: abstract actor, right: a specific water supply company (WSC) actor as used in the DANUBIA model ‘WaterSupply’
(see Section 5.4). The specific actor will react to system changes (e.g. changes
of groundwater available for extraction) in a specific way depending on its
properties, options and preferences.
The plans (AbstractPlan) of an actor represent its
course-of-actions and its options (Fig. 5). A plan in turn refers
to a set of actions (AbstractAction), each of them explicitly modelling some state modification associated with the
execution of the particular plan. Individual type-specific
preferences may then be encoded by the type-specific implementation of a plan selection algorithm within filter and
options.
Based on the DEEPACTOR framework, a whole variety of
decision algorithms can be implemented, ranging from simple
habits or heuristics to elaborate cognitive decisions. To
simplify both priority-based decision procedures and decision
algorithms based on the Multiattribute Utility Theory (Russell
and Norvig, 2003), a plan may store a rating attribute. In
the former case, this value may be fixed initially and kept
constant during runtime. In the latter case a function
(computeRating) may be implemented which dynamically
computes a new rating value in each time step. Plans are activated and deactivated during the decision procedure of an actor. The actions associated with activated plans (isActive)
are executed (execute). An activated plan may fail to be
executed if its actions were not executable, e.g. due to time
period constraints (period, isApplicable) or the like.
A number of sensors (Sensor) allows an actor to observe
its environment by directly reading raw data or by being
notified about the occurrence of pre-defined events. An
event is a fixed model-specific interpretation of raw data. In
fact, sensors merely encapsulate the data import facilities of
an actor, adding the possibility of qualitative interpretation
using model-specific events. Currently the framework provides
three types of sensors. The proxel sensor enables importing
proxel values computed within other simulation models. The
constraint sensor allows retrieving constraints, e.g. legal
requirements needing to be observed and which may have
an impact on the decisions to be made. The actor sensor
enables importing data from other actor objects of the same
model. The value of the attribute collaborators specifies
exactly which actors these are.
Finally, each actor disposes of a history (History),
tracing successful and failed plan execution of previous time
steps by storing the corresponding plan IDs (execPlans,
failedPlans), therefore providing the possibility to implement actors with some form of learning capabilities. The
history base class may also be subject to different extensions
for different concrete actor type implementations.
5.2. Decision procedure and model computation of
model implementations
An actor decides within each time step of the particular
simulation model. The decision incorporates four fundamental
methods (see Fig. 4 also):
1. query. The actors observe its environment via the three
sensor types described above.
2. options. According to the actors’ characteristics and
preferences available plans are loaded and made available
1103
for the succeeding decision in the filter step. This includes
the recalculation of the rating of all loaded plans
(computeRating).
3. filter. Implements the definite plan selection. After this
step the set of actions of each selected plan is executed
which usually results in the (re-)computation of some
proxel or actor attributes. Afterwards, the final plan execution status is stored in the history.
4. export. The results of actors relevant for their collaborators are stored to be available via the actor sensor in the
next time step.
The decision process of actors is embedded within computations of a model’s ‘main’ class (specialisation of AbstractActorModel). After having imported data from other
models (getData), the instance of this class may optionally
prepare (preCompute) and post-process (postCompute)
its actors’ decisions, allowing for example the aggregation of
some fine-grained results before providing them to other
DANUBIA simulation models (provide). Note that the possibility to pre- and post-process within the same simulated
time step allows implementing a macro level of simulation
which dynamically takes into account the results of the
‘agent-based’ part of the simulation model, as described in
(Duboz et al., 2003), amongst others. The pre- and post
processing steps of the two chosen model implementations
discussed in the next sections will not be addressed in full
detail here as they are based on rather simple, yet many
calculations and were in principle described in previous publications (e.g. Barthel et al., 2005; Ernst et al., 2005).
5.3. Interaction of water supply company and
household actors
The models Household and WaterSupply were chosen as
examples from the 6 available model implementations (see
Table 1 and Fig. 3), since the water supply system and the
domestic users form the ‘core’ of water management in
many regions. WaterSupply is a model of the water supply
sector comprising water extraction, treatment and distribution.
Household is a model for estimating the water usage of
households under changing environmental conditions.
Before describing the models, we will briefly introduce the
general idea of how the two models interact. This can best be
demonstrated for a situation of water scarcity. Within GLOWADanube ‘water shortage’ is not understood as a technical issue
only, but also and foremost as a term related to sustainability
of water resources and the good ecological status of natural
systems. It is obvious that responses to water shortage are
actor- and actor type-specific. Large water supply companies
can increase their capacity by tapping new sources and by
importing water from other areas while small companies
usually only have the first option if they don’t collaborate
with other companies. The response options of water supply
companies are generally rather constrained by legal restrictions and economic considerations. Consumers in the domestic
sector on the other hand may reduce their water demand by
1104
reducing the irrigation of gardens or changing bathing and
showering habits. Here, legal and economic constraints are
much less important for the behaviour of individual
households than for public water suppliers. Whereas certain
households might adjust their habits only to an actual physical
water shortage (no more water from the tap) others will obey
much earlier to appeals to save water (see below).
It is within this framework of different options and
preferences where the interesting interaction between water
suppliers and water consumers takes place. Both sides will
react to system changes (here decreasing resources) in typical
ways. However, only the water supply companies have the
data and knowledge to assess the current and future situation
whereas consumers rely on secondary information. Therefore
the response of the two actors (suppliers and consumers) will
take place in a stepwise and distributed way: First, water
supply companies will try to make a prediction on the future
development of the available resources. Minor problems will
usually be overcome by a temporal increase of capacities
(using strategic reserves etc.) without letting the public
know. If it is foreseeable that resources and reserves together
will not be enough to meet the demand and no significant
replenishment of the resources can be expected before the
crisis approaches, the public will be informed and encouraged
to save water. This usually happens in different stages: early
warnings (information on potential upcoming shortages),
direct appeals to save water, restriction of certain water
uses and finally periodical system shut downs. What exactly
the coping and information strategy of a specific water supply
company looks like and how a specific consumers reacts (i.e.
if he follows the appeal and obeys the restrictions) depends
on the individual options and preferences (see Sections 5.4
and 5.5).
As described above, the interface between the two actors
(supplier and consumer) is asymmetric. The suppliers have
the full knowledge of the system whereas the individual
consumers simply consume without being fully aware of
how they influence the system. In DANUBIA, the Household
model (consumers) sends the demands per model cell and
time step to the WaterSupply model whereas the WaterSupply
models provides ‘information’ on the state of the water supply
system. This information is provided in a condensed form
using so-called ‘flags’. Flags assume integer values from 1
(good) to 5 (catastrophic). The WaterSupply model calculates
the flags at each time step based upon a set of physical
parameters (see below). With respect to the models described
hereafter (WaterSupply and Household), two different types of
flags are of importance:
1. The groundwater quantity (GQN) and quality (GQL) flags
describe the system state of the groundwater resources in
a defined zone. The flags are computed for a complete
groundwater body, i.e. an array of proxels with similar
hydrogeological properties.
2. The drinking water quantity (DQN) flag describes the state
of the water supply system. A DQN flag is valid for the
supply area of a water supply company.
The calculation of the GQN flag is a moderately complex
procedure that relies on the results of the DANUBIA models
Soil, Rivernetwork and Groundwater (Fig. 3). In principle
the GQN flag calculation is based on an analysis of the
changes in groundwater recharge, groundwater level and river
discharge (baseflow) with respect to reference conditions. The
description of the approach would require a sound explanation
of the aforementioned hydrological models and an introduction into the hydrological and hydrogeological characteristics
of the catchment. Such a description is given in (Barthel and
Trifkovic, 2007).
DQN flags indicate the quantitative state of the drinking
water resources at the disposal of the water suppliers. It is
a water supplier evaluation of the quantitative changes in
availability of drinking water resources that is further committed to the water users in terms of states from 1 to 4. Essentially, DQN flags represent the simple and standardised way
to transfer information about the states of the resources and
available supplies that in the real world occur through public
media, governmental announcements or the like. Real world
analogies for the DQN flag concept can mainly be found in
Australia (e.g. Victorian Water, 2005).
Fig. 6 shows the principles of data exchange between the
models that provide the data for the flag calculation and the
flag exchange between the models WaterSupply and
Household.
5.4. The WaterSupply model
The WaterSupply model simulates the provision of water
from sources to consumers. Having a focus on the public
drinking water supply of the Upper Danube Catchment, the
sectoral goal of WaterSupply is to identify regions that could
experience water stress in the future, resulting either from
climate-induced or water-quality-related changes on the
supply side or demand-side changes, e.g. quantitative, qualitative, spatial and temporal changes in water consumption.
WaterSupply aims to demonstrate the ability of present water
supply schemes to compensate such changes and to evaluate
the effects of different interventions to solve or prevent
problems arising as a result of change. In this context it is
important to point out that not the technical infrastructure of
individual supply companies is modelled, but rather the supply
areas, the important groundwater and surface water bodies
used, the degree of use, cooperation between companies, and
long-distance water transportation schemes. The model is
therefore neither a prediction tool of future water supply
infrastructure nor a design tool on the company level. It can
only serve for the evaluation of the water supply strategies
on a catchment level. Within these described limitations,
WaterSupply does foresee typical response mechanisms on
a company basis to compensate for change, e.g. the change
of preferences regarding resources used or a stronger reliance
upon long-distance water supply. In order to achieve its goals,
WaterSupply reads the quantitative state of water resources at
pre-defined spatial and temporal units, interprets water availability according to sustainability requirements of predefined
«component»
Soil
WaterSupplyToGW
WaterSupplyToHH
getGroundwaterWithdrawal()
...
«component»
Groundwater
1105
getDQNFlag()
...
GroundwaterToWS
«component»
WaterSupply
getGroundwaterRecharge()
...
«component»
Household
HouseholdToWS
getDrinkingWaterDemand()
...
RivernetworkToWS
«component»
Rivernetwork
getRiverDischarge()
...
HouseholdToRN
getWasteWaterQuantity()
...
Fig. 6. Some interface details for the coupled data exchange of natural science and socio-economic simulation models.
natural parameters such as groundwater recharge or river
water discharge and evaluates the ability of the present water
supply schemes to comply with the dynamics of user demand.
WaterSupply seeks then both to simulate typical behaviour of
supply companies within the limitations specified above and to
identify critical regions for which further, more extensive or
fundamental adaptation of the water supply scheme will
become necessary under changing boundary conditions.
5.4.1. Static structure of the WaterSupply model
WaterSupply is a strictly object-oriented simulation model
that implements the decision making of water supply companies based on the DEEPACTOR framework. Fig. 7 provides
a structural overview of the model implementation.
At the core of the model is the distinction between water
supply companies (WaterSupplyCompany, WSC) and
communities (Community, COM). Furthermore, we distinguish between local WSC, which act on the community
ConsumerProxel
has consumers
level(CommunityWSC), and regional WSC, representing
all forms of grouped and joined suppliers that transfer water
among communities (RegionalWSC) (Fig. 8). These actor
types are equipped with different preferences and different
course-of-actions in the form of plans and actions.
Communities represent the consumers and are modelled
explicitly because they are the legally responsible entities
for water supply in Germany. By the summation of imported
water demands from other socio-economic models (Table 1),
a COM knows where and how much water is consumed.
Furthermore, a COM knows from which WSC it is primarily
served; typically these are local suppliers. In contrast,
a WSC possesses information regarding extraction sites, water
rights, raw water quality and collaborating WSC. Extraction
sites are mainly groundwater wells where water rights (capacity constraints) are defined by site-specific extraction limits set
by the water authorities. The basis of WaterSupply is the
comparison of demands obtained by COM with the existing
DeepWSC
Community
activates plan
supply
WaterSupplyProxel
access
BusinessAsUsual
has sources
WaterSupplyCompany
ExpandCapacities
SourceProxel
flags
has aquifiers
Zones
Fig. 7. Structural overview of the WaterSupply implementation.
...
1106
AbstractActorModel
AbstractActor
DeepWaterSupply
AbstractPlan
AbstractAction
BusinessAsUsual
BusinessAsUsual
CommunityWSC
ExpandCapacities
CalculateCapacityReq
RegionalWSC
TapNewResources
ExpandExistResources
CrisisManagement
TapNewResources
DeepWSC
timestep = MONTH
EmergencyMeasures
Fig. 8. Specialisation of the DEEPACTOR framework’s base classes in the WaterSupply implementation.
and potential supply capacity evaluated for the current state of
the resources. The result of the above mentioned comparison
triggers a ‘decision’ on behalf of the particular WSC actor.
To ‘decide’ means here to select and execute an appropriate
plan, consisting of a predefined set of actions with the aim
of satisfying water demands either by exploiting existing
capacities or by opening new ones. The selection of a plan
is not only dependent on demand and state of the resources
but also on the type of the WSC (local or regional), its location
and its collaborators.
5.4.2. Decision procedure and model computation
of the WaterSupply model
During model initialisation, static model and actor
attributes are set and dynamic attributes are initialised. Static
attributes that are not modified during the simulation are for
example existing water sources, company supply areas or
transport links among companies. Source’s capacities, withdrawal values or the state of the resources need to be modified
during run time and are therefore examples for dynamically
assigned attributes.
After initialisation, the model follows the common DANUBIA
computation cycle of data import, computation and data export
with a time step of one month. In getData, the demands
calculated and exported by the Actor models Household,
Farming, Economy and Tourism. Output data such as groundwater level, river discharge or groundwater recharge is
imported from the natural science models Groundwater, Rivernetwork and Soil. The data imported from the natural science
models is used to assess the state of the supply side, i.e. the
quantitative and qualitative status of the groundwater and
surface water resources (GQN flag). The output of the Actor
models is used to calculate the total drinking water demands
that accrue at each time step (preCompute). Based on the
comparison among the available supplies and demands needed
at each time step (preCompute), the decision-making ability
of the water suppliers is activated in order to respond to
potential future problems and to make structural changes
accordingly. In technical terms this translates to the execution
of the DeepWSC implementation of the actor methods
specified by the DEEPACTOR framework (see Section 5 and
Fig. 4, AbstractActor). The single steps are implemented
as described in the following:
1. query. The necessity for change arises either from growing demands, which surpass the present capacity, or from
a quantitative degradation of one or more of the present
sources in use, which leads to a capacity reduction. These
are perceived by the WaterSupply actors via their sensors.
The actors use the proxel sensor to import natural science
data and the actor sensor to import data from collaborating
WSC.
2. options. According to the WSC type (CommunityWSC or RegionalWSC) and the characteristics
(recourses at disposal, potential collaborators etc.), preferences and the available plans are loaded.
3. filter. The decision procedure to select, activate and
execute one of the available plans is depicted in the
UML activity diagram in Fig. 9. The parameters
necessary for plan selection are supplier type, demand, usable capacity, collaborators, number of potential sources,
GQN flags of sources and potential sources. A detailed description of this procedure can be found in (Barthel et al.,
submitted for publication). After selection of a plan, its set
of actions is executed which results in the modification of
the water supply characteristics such as an expansion of
existing sources, tapping of new sources, or relieving or
closing overused sources, thus increasing the demands
sent to a collaborating regional or long-distance supplier.
4. export. The results of WaterSupply actors relevant for
the collaborators of the WSC are stored to be available
via the actor sensor in the next time step.
Finally, the changes of the water supply structure and its
view of the state of the resources are actualised (postCompute) and made visible to the other DANUBIA simulation
models (provide). The main results are the actual
withdrawal quantities exported to the Rivernetwork and
1107
DeepWSC::filter
check for collaborator
[collaborator = 1]
[else]
check capacity
[total use cap / total init cap = 1]
[else]
check GQF 1
[else]
check capacity index
[else]
[no. of sources with GQF 1,2 < 1]
select plan 1
[no. sources with GQF 1,2 >= 1]
select plan 1
check GQF 1 potential
select plan 1
select plan 2
[total dem / total use cap < 0.9]
[else]
[no. pot res with GQF 1,2 >= 1]
check GQF 1
[else]
select plan 2
check GQF 3
[no. sources with GQF 1,2 >= 3]
select plan 4
select plan 2
[else]
select plan 3
Fig. 9. The filter step in the decision procedure of the WaterSupply actors.
Groundwater models as well as DQN flags exported to other
Actor models (Fig. 6).
5.5. The Household model
The main purpose of the Household model is to simulate
the water use of domestic households under changing climatic
conditions, showing plausible reactions of households to water
scarcity or rising temperatures. Simulation results include
water demand, wastewater and the water-related satisfaction
of households. In order to calculate the water demand, ten
water-usages such as showering, toilet flushing or laundry
washing are distinguished. Within the Household model,
Household actors decide upon these ten specific water uses.
The model sums up the respective water demands and the
overall demand is sent to the WaterSupply model (Fig. 6).
The most important import parameters for the Household
actors’ decision procedure are the drinking water price and
the DQN flags, both provided by the model WaterSupply
(Fig. 6), and the air temperature, computed within the
component Atmosphere (Fig. 3). The four DQN flag levels
are interpreted as shown in Table 2.
Table 2
Interpretation of DQN flag values in the Household model
DQN flag value
General interpretation by Household Actors
1
2
No problems reported
Multiple reports in the local newspaper about
potential water supply problems
Public appeal to save water issued by the mayor
Official restrictions for water use
3
4
The ‘deciding entity’ of the model is the household, not
an individual person, because water-related behaviours
such as the installation of water-use technologies or the
frequency of laundry and dish washing are related to entire
households. Households are categorised according to the
sociological concept of lifestyles (Bourdieu, 1984). For
the model implementation, the Sinus Sociovision lifestyle
model (www.sociovision.com) was chosen. Sinus Sociovision divides the German population into ten so-called
Sinus-MilieusÒ. Each milieu is described with general
values, typical behavioural patterns and socio-demographic
data. Microm, a marketing company cooperating with Sinus
Sociovision, provides spatially explicit data for the SinusMilieus in Germany. Therefore, the simulation model
uses a specific distribution of Sinus-Milieus for each
proxel. According to empirical studies conducted within
the project GLOWA-Danube, these ten Sinus-Milieus are
aggregated to five clusters (Socially Leading Milieus,
Post-Materialists, Mainstream, Traditional Milieus, and
Hedonistic Milieus) whose characteristics provide the basis
for the modelling of the five different Household actor
types. Therefore, each inhabited grid cell hosts five Household actors, with each actor representing all households of
that specific type.
Furthermore, the model applies the framework concept of
collaborators to simulate a social network between households: An artificial social network was generated to link
similar household types. The specific Household actors take
the behaviour of their collaborators into account when deciding upon buying water-related technologies such as rain
harvesting systems.
1108
5.5.1. Static structure of the Household model
The model implementation specialises the base classes of
the DEEPACTOR framework as depicted in Fig. 4. The class
DHHActor serves as a base class integrating all Household
actors. Household actor types are determined dynamically
according to their initially assigned profile. Each proxel
contains an actor object for each of the household types existing in this proxel. The amount of households of the same type
is a fraction value assigned to the respective actor object. It is
computed using the spatial distribution of Sinus-Milieus and
the total amount of households imported from the DANUBIA
model Demography, which is part of the main component
Actor in Fig. 3. Each Household actor has type-specific
characteristics, which stem from various empirical studies
undertaken within the project.
For each water usage, several plans (ShowerHead,
ShowerFreq, etc.) and their corresponding actions are
implemented (Fig. 10). The plans have several characteristics,
which are important for the decision making of Household
actors. For example, characteristics for the decision regarding
shower frequency are the amount of water used, the financial
costs and the modernity of the plan, representing in how far
the specific plan complies with modern hygienic habits such
as taking a shower every day. Each plan has a corresponding
action (ShowerHeadAction, ShowerFreqAction,
etc.), which facilitates the computation of the drinking water
demand.
5.5.2. Decision procedure and model computation
in the Household model
Domestic water-use comprises of both habits and deliberate
decision making The Household model addresses both kinds
of behaviour which can be explained nicely using the example
of showering: Shower frequency and length are normally
a matter of habit and not of deliberate decision, while the
installation of a new showerhead requires a conscious decision
process. Habits are chosen in each time step if there are no
extraordinary events such as high temperatures or water
scarcity. At the same time, households decide consciously
upon plans for water-related technologies in each time step.
The UML activity diagram in Fig. 11 provides a high-level
overview of the resulting decision procedure.
AbstractActorModel
DHHModel
AbstractActor
At the beginning of a simulation, the Household model is
initialised, providing the Household actors with values for
habits, preferences, starting values for water-related technologies, and socio-demographic characteristics. For each time
step, all necessary data from other DANUBIA models are imported (getData). Next, the Household model computes
the water-related satisfaction and the water demand of the
public sector (preCompute). Water-related satisfaction is
based upon the level and number of DQN flags. When computing the water demand for the public sector, the model takes the
population of the proxels into account.
After preCompute, the decisions for all Household actors
are calculated by the DHHActor implementation of the abstract framework methods inherited from AbstractActor
(see Fig. 4):
1. query. Each Household actor perceives its local physical
and social environment via its sensors, taking into account
air temperature, drinking water price, DQN flags, and the
behaviour of its collaborators.
2. options. Certain events can trigger a deliberate decision
for normally habitual behaviours, e.g. a DQN flag on
a proxel prompts Household actors on that proxel to
make a conscious decision about their showering frequency with a possible reduction of water-use.
3. filter. In this step, every Household actor calculates its
deliberate decisions and decides upon water-related
technologies. For that, they use two different decision
algorithms depending on actor type and technology (e.g.
Post-Materialists and Social Leaders are highly motivated
and therefore always use a deliberate decision, while
Traditional actors only use this kind of decision if they
think about buying a rain harvesting system, else they
use a simple decision heuristic. Details on the installation
of water-related technologies are given in Schwarz and
Ernst (in press). Furthermore, actors may decide upon
normally habitual behaviour if outer circumstances (as
evaluated in options) trigger such a decision. For example, if actors decide upon shower frequency deliberately because of very high air temperatures or water
scarcity, they evaluate all plans within the plan group
‘shower frequency’ (to shower twice a day, once a day,
AbstractPlan
AbstractAction
ShowerHead
ShowerHeadAction
ShowerFrequency
ShowerFrequAction
BathFrequency
BathFrequAction
DHHActor
timestep = MONTH
...
...
Fig. 10. Specialisation of the DEEPACTOR framework’s base classes in the Household implementation.
1109
DHHActor::options + filter
for each of 10 water uses: check events for "breaking the habit" or "(re)install innovation"
[habit]
[conscious decision]
[innovation]
a certain % of actors
(re)installs its innovations
[water use]
get plan groups
for current water use
perform habitual behaviour
get plan groups
for current innovation
calculate utility values
perform conscious decision
calculate fraction values
Fig. 11. High-level description of options and filter in the decision procedure of the Household actors.
every second day, once a week, or not at all). A Multiattribute Utility approach is used to evaluate these normally
habitual behaviours. Situational circumstances (e.g. the
level and number of DQN flags in the present and the
last time steps) are taken into account by enhancing
the importance of single factors entering the decision
process (e.g. environmental concerns in the case of DQN
flags). Each plan in the plan group executes its corresponding action, which assigns related consequences to the
proxel proportionally to the calculated utility value.
4. export. This step allows every actor to make his behaviour or installation visible to his neighbours and friends
(i.e. the collaborators).
After these computation steps considering the behaviour of
the specific Household actors, the main class DHHModel
sums up the water demand of all Household actors and the
public drinking water demand on a specific proxel to provide
both the overall drinking water demand and the amount of
waste water produced (postCompute). Finally, the Household model provides this data for other DANUBIA models via
its export interface (provide).
6. Application of the DEEPACTOR approach
Applicable results of WaterSupply and Household according to the principle aims of GLOWA-Danube and DANUBIA are
outputs that can be used to identify and visualise water supply
and consumption-related tendencies and developments in the
Upper Danube Basin under conditions of Global Change,
based on scenario calculations (2000e2100) i.e. to:
predict the general situation of water supply in the basin
(good, stable, endangered, unsafe),
identify critical regions and the rate at which critical
developments arise (slow, rapid),
predict overuse and ecological risks,
sketch middle-term trends in water consumption, and to
show possible reactions of domestic households to Climate
Change.
The presentation of the scenario results shows representative clippings that display some of the principles and the
capabilities of the developed methodology. Full scenario
simulations of the DANUBIA system provide an extremely large
amount of outputs in the range of 100 parameters which can
each be presented in maps, time series, tables and so forth.
The GLOWA-Danube online Atlas5 provides a platform for all
interested in a wider facet of results.
6.1. Climate scenarios used
The results presented here were obtained by applying the
DANUBIA system to three climate scenarios which are explained
briefly in Table 3.
The ‘business as usual scenario’ (Table 3) was created
using a stochastic procedure to derive long time series of
synthetic future climate data6 from measured historical
records. To compile a future meteorological data set spanning
the next 100 years, the procedure considers measured relations
between temperature and rainfall, applies a random variation
of temperature, overlays a trend (here 2.7 K/100a which corresponds to a IPCC B2 scenario), and selects the appropriate
time slice from the given basic population of measurements
(30 years of DWD (German Weather Service) recordings). It
should noted that this methodology has the following disadvantages: reduced representation of auto-correlation, lacking
consideration of potential changes in extreme values and an increase of the relative error of determination when approaching
the edges of distributions. However, for the present purposes,
the advantages clearly dominate: physical consistency of
5
http://www.glowa-danube.de/atlas, currently (March 2008) still restricted
to members of the research consortium.
6
The full scale approach for generating regional climate data is a downscaling of GCM results using MM5 integrated in the Atmosphere component of
DANUBIA (see Fig. 3) and detailed in Früh et al. (2007).
1110
Table 3
Climate scenarios used for simulation runs of the DANUBIA system
Scenario name
Description
Comment
Business as usual scenario
IPCC B2 type scenario generated using a
stochastic climate scenario generator (see below)
Observed data from 1970 to 2003 (reference period),
yet using starting conditions (physical models)
as for the other scenarios
An extremely dry ‘scenario’ generated by rearranging
the respective 5 hottest and driest months from
the 1970 to 2003 reference period
Temperature increase of 2.7 K/100a; only the
first 35 years are used here
35 years; used for model validation; optimistic
in the sense that all current GCM predictions are
‘warmer’ than the conditions in this period
35 years; used to force significant model reactions and
to show the sensitivity of the system. Rather unrealistic
and in the pessimistic range of current GCM results
Optimistic scenario
(also validation period)
Pessimisticdworst case scenario
meteorological input data, meteorological model inputs within
a validated range, methodological consistency for ensemble
simulations, possible validation versus baseline scenarios;
consistent spatial resolution of input data.
Fig. 12 shows a comparison of precipitation, which is the
main relevant driver of the system for the three different
scenarios described in Table 3.
6.2. Results of the WaterSupply model
Generally, the following outputs of WaterSupply (amongst
others) can be used for further analysis:
e Changes to the GQN flag in space and time, indicating
changes in natural water availability as a consequence of
Global Change. Changes for the worse indicate that ecological constraints or sustainability criteria are endangered.
e Changes to the DQN flag in space and time, indicating
changes in technical water availability as a consequence
of selected water management scenarios. Changes for
the worse indicate demand management measures, appeals
for more conscience use of water or capacity problems of
the water supply structure.
e Plan execution (choice of plans and actions to be taken) of
the WaterSupply actors as an indication of the behaviour
of the water supply sector as a consequence of Global
Changes. The choice of plans can be interpreted as an
identification of ‘bottlenecks’ of the system and a suggestion of the possible solution at the same time according to
some predefined water management orientation.
In the following, examples for each of these three result
types will be presented. Fig. 13 shows the spatial distribution
of the GQN flag at the end the simulation period for all three
climate scenarios.
More interesting than the situation at one single time step is
the temporal evolution of the flags during the simulation
period. Unfortunately a combined presentation of spatially
and temporarily distributed results is not possible in a printed
medium. Therefore, Fig. 14 shows time series of spatially
aggregated values of the GQN flag for the whole catchment
from all three scenarios.
3200
Precipiation [m3/s]
3000
2800
2600
2400
2200
2000
1800
1600
2005
2010
2015
2020
2025
2030
2035
2040
Time
P 'business as usual'
P 'b.a.u.' mov.av.5a
Linear (P 'business as usual')
Linear (P 'pessimistic')
P 'pessimistic'
P 'optimistic'
Linear (P 'optimistic')
Fig. 12. Comparison of precipitation (P) for three different climate scenarios (Table 3); annual sums and linear trends are shown for all scenarios, the moving
average (5 years, central) is shown for the business as usual (b.a.u.) scenario only.
1111
Fig. 13. Spatial distribution of the GQN flags for the whole catchment on July 2039 for the 3 climate scenarios: optimistic (left), business as usual (middle) and
pessimistic (right).
of the model availability to implement different actor’s
behaviour three exemplary behaviour modes of the WaterSupplyCompanies are shown in Table 4.
Although each WSC actor may have different behaviour,
for simplicity reasons three scenarios in which all WSC actors
behave in the same mode are presented here. The DQN flags
are obtained by comparing exerted water demands with the
state of the available supplies and maximal withdrawal source
capacities on the WSC basis. Beside technical limitations of
available sources, the behaviour mode of WSC actors has
The GQN flags will not be discussed further as they form
rather an input to the decision-making process of the actors
than an output. With respect to the DEEPACTOR approach, the
DQN flags are more interesting as they express different
behaviour or preferences of WSC actors. The interpretation
of changes in the state of the groundwater bodies can vary
depending on the sensibility (i.e. do they care about the
environment or not) of the Water Supply Companies and their
willingness to communicate these changes to the water users
(household in this case). For the purpose of the demonstration
4
1400
1200
Average GQN
800
600
2
400
Groundwater Recharge [m3/s]
1000
3
200
1
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
0
Time
Average GQN "Business as usual"
Average GQN "Optimistic"
Average GQN "Pessimistic"
GWR Buisness as usual MovAv 3a
GWR Optimistic MovAv 3a
GWR Pesimistic MovAv 3a
Fig. 14. The quantitative state of the groundwater resources expressed with the GQN flags as consequence of the changes in groundwater recharge (GWR, moving
Averages 3 years, right y-axis, upper part of the figure) where 1 means ‘good’ state and 5 means ‘bad’ state or identified changes to the worse in the available
quantity for all three climate scenarios (Table 3).
1112
Table 4
Behaviour modes of WSC actors used in the scenario simulations
Behaviour mode
Description
Ignorant
WSC actors disregard the GQN flag, do not
communicate these changes to the water users
and try to satisfy water demands by implementing
technical measures for greater water extraction
without paying any attention to sustainability
and ecological issues
WSC actors consider changes in natural state of
the water resources as very important, communicate
these directly to the users and try to identify
management options in order to protect the
sustainable state of the water resources
WSC actors respect environmental aspects in general
but behave at the same time pragmatic and
economically oriented. It therefore can be seen as a
middle (normal) course between the two extremes
‘ignorant’ and ‘sensitive’
Sensitive
Middle
a prevailing role in the calculation of the DQN flags. Similar to
the GQN flags (see above), the DQN flags are presented
spatially distributed in snapshot maps from the end of the
simulation period (Fig. 15) and temporarily distributed as
time series of aggregated flag values (Fig. 16) for the three
management scenarios.
Looking at the average values of the DQN in Fig. 16, the
‘ignorant’ behaviour of the WSC causes reporting of
constantly ‘good’ DQN flag values to the water users, which
in turns provokes no actions from the user side. The ‘middle’
and the ‘sensitive’ behaviour of WSC report the changes in the
state of the water resources to the water users, thereby
demanding their participation in the identification of the
sustainable management solutions.
The behaviour of the WSC actor can be expressed through
the selection of the different plans (see Fig. 8). Fig. 17
summarises the number of WSC that executed a certain plan
over the simulation period for the three management scenarios
(Table 4):
Fig. 17 shows that most companies select the Plan 1
(BusinessAsUsual) most of the time. This is directly related
to the good state of the water resources in much of the investigation area. It should be noted that the initially predefined
water demand and provision of water is higher than the actual
water demands exerted on water supply companies. Therefore,
the WSC actors must adjust their capacities at the beginning of
the simulation, either by selecting Plan 2 (‘ExpandCapacities’)
or Plan 4 (‘CrisisManagement’). For the few WSC actors that
do not have enough potential sources to expand, Plan 4
remains the only option. In these cases, the use of Plan 4
does not indicate the real water stress in the area but is a consequence of data unavailability regarding existing water
supply capacities. These initial increases of the available
capacity proved to be enough for the provision of enough
water throughout the rest of the simulation period. The
changes in the availability of the water resources are small
enough to be satisfied from the existing technical capacities
and only small upgrades have been identified as necessary
(small variations in the selection of Plan 1 and Plan 2).
6.3. Results of the Household model
The Household model also provides a large variety of result
data on various aggregation levels, e.g.
seasonal changes of water demand within the catchment
showing reactions of households to air temperature;
discontinuous changes of water demand due to DQN flags,
stemming from both changing, formerly habitual waterrelated behaviour and an enhanced installation of watersaving technologies;
continuous changes of water demand due to the installation of water-related innovations, leading to a slight
reduction of water demanddthese changes can be tracked
back to the various actor types showing different adoption
of these innovations.
With the three chosen climate scenarios (Table 3) and the
three different water supply management scenarios (Table 4),
the number of scenarios (‘storylines’) from the viewpoint of
the domestic water users represented by the Household model
Fig. 15. Spatial distribution of the DQN flags for the whole catchment on July 2039 for the business as usual climate scenario (Table 3) and the three behaviour
modes of water supply companies (Table 4): sensitive (left), middle (middle) and ignorant (right).
1113
4
2000
1900
1800
1700
1600
3
1500
1400
1200
1100
2
1000
900
800
Average DQN
Number of DQN
1300
700
600
1
500
400
300
200
100
0
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
0
Time
DQN=3
DQN=2
DQN=1
DQN=4
Average DQN "sensitive"
Average DQN "middle"
Average DQN "ignorant"
Fig. 16. The quantitative state of the water supply system expressed with DQN flag as a consequence of the three water supply management scenarios (Table 4).The
average of all the flag values for the catchment and the total number of flags of a category are shown for all three management scenarios.
2000
4
1900
1800
1700
1600
1500
3
1300
1200
1100
1000
2
900
800
Average DQN
Number of Plans
1400
700
600
500
1
400
300
200
100
0
2038
2037
2036
2035
2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
0
Time
Plan 1
Plan 2
Average DQN "middle"
Plan 3
Plan 4
Average DQN "sensitive"
Average DQN "ignorant"
Fig. 17. Summary distribution of the Plans for the whole catchment over the whole simulation and average DQN flags for all three water supply management
scenarios (Table 4).
1114
demand in urban than in rural areas which does not
necessarily mean that people in big cities consume more
water.
When exploring temporal dynamics, domestic water
demand clearly shows seasonal changes in all scenario runs
(Fig. 20), mostly due to increased showering with higher air
temperatures. In addition, discontinuous variability in water
demand can be detected. Differences in domestic drinking
water demand between the scenarios ‘ignorant’ and ‘sensitive’
and between ‘ignorant’ and ‘middle’(see lower lines of
Fig. 20) are reactions to the different scenarios of DQN flags
provided by WaterSupply. This variability is due to the
reaction of households to DQN flags: Households perceive
flags and interpret them as newspaper articles, appeals to
save water or even restrictions on water use (Table 2). According to their characteristics, households (1) reduce their water
demand by constricting e.g. shower frequency, bathing or
cleaning, (2) are further encouraged to buy water-saving
technologies (see below).
Over the years, the water demand decreases (see Fig. 20,
upper line). This is the result of two factors. First, the simulated water scarcity results in some regions in a reduced
drinking water demand because Household actors lower
their water use in response to the DQN flag. The second
and main reason is the installation of more water-efficient
technologies (e.g. water-saving showerheads, toilet tanks,
and washing machines). In the scenario runs, water-saving
technologies further diffuse in the catchment area, e.g. rain
harvesting systems almost double from about 6.4% to 12%
12
3
10
8
6
2
4
Air temperature (mean) [°C]
2
0
2035
2033
2031
2029
2027
2025
2023
2021
2019
2017
2015
2013
2011
2009
1
2007
Average of DQN flag [ ]; Precipitation [(m3/s)/1000]
is nine. For that reason the following discussion of the results
will be based on the business as usual scenario (Table 3),
which is assumed to be the most consistent and likely
climate scenario. In order to emphasise the interaction
between the models WaterSupply and Household, population
and water prices were kept constant for all simulation runs.
The main driving forces for the Household model are the
DQN flags from the WaterSupply model and the air temperature, which are shown in Fig. 18. According to the strategy
chosen by the water supply companies, domestic Households
are confronted with different levels of public awareness
regarding water availability (in Fig. 18 shown as the average
value of the DQN flag for the whole catchment). In the
scenario ‘ignorant’, almost no communication of problems
in water availability takes place, while in the scenario ‘sensitive’ nearly constant communication regarding these issues
can be detected throughout the simulated area. In the scenario
‘middle’, which, from the three water supply management
scenarios is probably the most likely one, the communication
between WSC and Household remains in average on a very
low level between ‘no problems reported’ and ‘newspaper
reports’ (Table 2). A climax can be observed in the 2020ies,
a period much dryer than average (see Fig. 12).
The resulting domestic drinking water demand is spatially
explicit, indicating high demands in Munich (agglomeration
in the centre of the catchment) and a few larger cities
(Fig. 19). In Germany, domestic water use and small business
water consumption are combined to one value in the official
statistics. Therefore, one finds a higher per capita water
Time
DQN "ignorant"
T mov. av. 3a
P mov. av. 3a
DQN "sensitive"
Linear (T mov. av. 3a)
DQN "middle"
Linear (P mov. av. 3a)
Fig. 18. Main driving forces for the Household model (air temperature [T] and DQN flagsdadditionally precipitation [P] is shown as an indirect driver) for the
three water supply company management scenarios (Table 4), all based on the business as usual climate scenario (Table 3). An ‘ignorant’ WSC behaviour causes
a constant transmission of DQN flags with the value 1 (‘no problems reported’), i.e. all problems are solved on the WSC level (Table 4). For T and P moving
averages and linear Trends are shown; P values were divided by 1000.
1115
Domestic drinking water demand [m3/s]
of households as simulated by the Household
model (december 2036, scenario "ignorant").
uninhabited
0.001 - 0.005
0.005 - 0.01
more than 0.01
Fig. 19. Spatially explicit domestic drinking water demand as simulated by the Household model. As an exemplary result, the demand per proxel of December
2036 (scenario ‘ignorant’dTable 4) is depicted.
in 2036. Furthermore, households react to water scarcity
when deciding upon their water use technologies: Example
results for the diffusion of rain harvesting systems show
(Fig. 21) that Household actors respond differently to
DQN flags. Post-Materialists and Social Leaders always
choose rain harvesting systems when they have the opportunity, therefore adoption cannot increase due to DQN flags.
Hedonistic actors are the most responsive because they do
3
19
2
16
15
14
1
13
12
11
0
Difference "ignorant"-"sensitive"
DWD "ignorant"
Difference "ignorant"-"middle"
2035
2033
2031
2029
2027
2025
2023
2021
2019
2017
2015
2013
2011
2009
10
2007
Differences in DWD [m3/s]
17
DWD ign. mov. av. 12month
Fig. 20. Domestic drinking water demand (DWD) of households in the three scenario runs (Table 4).
DWD in scenario "ignorant" (sum) [m3/s]
18
1116
0.8
0.6
0.92
0.4
0.2
tic
is
on
H
ed
ns
tre
al
on
ad
iti
ad
Le
al
ci
am
s
s
er
ts
lis
So
"
"
nt
ra
nt
ra
no
ig
no
"ig
ia
"-
-"
er
ve
e"
m
at
iti
dl
st
0.14
0.01
s
en
0
id
"m
Po
0.07
0.01
"s
0
0
0.09
0
M
ai
0
Tr
Differences in installed
rain harvesting systems [%]
1
Fig. 21. Comparison of the number of rain harvesting system installations of all five household actor types. Differences between installation numbers for the three
scenarios according to Table 4 are shown in %. The differences are rather low because installation of rain harvesting systems is only considered when building
a new house or during a major reconstruction.
not install rain harvesting systems at all if there are no
problems in water scarcity.
6.4. Validation of the DEEPACTOR framework and
the DEEPACTOR models
The development of validation strategies is a research topic
of its own within GLOWA-Danube and will not be treated here in
depth. Generally, within fully integrated and multidisciplinary
systems such as DANUBIA, the objective of validation cannot
be the validation of single models or even single process
descriptions alone but more the validation of the coupled
system as a whole. Here we will not discuss the validation of
natural science models, which follows in principle the traditional ‘comparison observed-simulated’ approach,7 but the
validation options for results of the DEEPACTOR models in the
socio-economic sector. For the socio-economic models within
the actor component (Fig. 3, Table 1) data that can be used
for validation is generally less accurate, meaningful and reliable than equivalent data that can be used in the natural science
sector. Typical socio-economic model output variables would
for example be domestic water consumption (model Household), household income (Economy), crop yields/prices (Farming), population (Demography) overnight stays (Tourism) and
groundwater withdrawal (WaterSupply) (see Table 1). Regarding observed values for these data two main problems exist:
(a) The spatial and temporal accuracy of the available observed
data is low and data are often unreliable (e.g. agricultural
statistics are on district level so that values relate to areas
7
In fact, the validity of the natural science model output has a major impact
on the validity and uncertainty of the results of the dependent socio-economic
models, but to shorten the discussion we assume that the inputs are ‘correct’.
of 1000e5000 km2 and are mainly based on random
sampling, domestic water consumption is collected on
a community level annually and no distinction is made
between domestic and small business consumption, etc.).
(b) Much ‘socio-economic’ data are not available to the public
or are very difficult to obtain. Data are not collected
systematically on a countrywide level, but on a ‘problem
oriented’ basis, i.e. for certain areas and for certain periods
only. The results of such data collections may be stored at
various locations (private companies, authorities on
different levels) and are subject to data protection. Finally,
much data are privately owned.
With respect to the DEEPACTOR approach and its model
implementations WaterSupply and Household described in
this article, data belonging to the categories described above is
only interesting for the intermediate level of results. More interesting would be data that can directly be related to the results
which are relevant with respect to the main objectives of these
models, i.e. results that can directly be used for decision making
or in stakeholder discussion. Interesting results of this category
would be the DQN flag or the water-related satisfaction of
domestic consumers. As was shown in the previous sections,
such results are highly influenced by the preferences and options
that were assigned to the respective actors. Unfortunately little is
known about the ‘behaviour’ of water supply companies or the
development of water consumption behaviour under conditions
of Global Change. It is therefore very difficult to validate the
respective model results in a traditional way.
This general problem of socio-economic models that rely on
a large degree on assumption about actor responses to change is
amplified in the Upper Danube Catchment because problems
with water quantity have been almost unknown during the last
2e3 decades and water availability problems from earlier
periods (e.g. in 1976, reported in BAYLFW, 1979) cannot be
compared to the current situation because the infrastructure has
been extended considerably since this time.
As a result, many important outputs of DANUBIA, in particular
from the socio-economic sector, cannot be validated in the
classical sense on the basis of measured data. The only means
for validation is to include expert knowledge and consumer
experiences, e.g. the judgement from water supply company
managers, local water authorities or consumer opinions
collected using questionnaires (see e.g. Dow et al., 2007). In
order to use such soft data, it is necessary to use transparent,
process-based models (see Section 2). Only then can subjective
and uncertain parameters of the calculation be meaningfully
discussed and adjusted according to the advice of local experts.
Again it must be pointed out that the socio-economic
DEEPACTOR models’ outputs depend directly on the natural
science models outputs and therefore inherit the respective
models’ errors and uncertainties. The DANUBIA system as
a whole relies to a large degree on the quality and uncertainty
of the drivers, i.e. foremost the climate scenarios. The scenarios used should therefore be regarded as a variety of possible
futures (see Brewer, 2007) and the simulation results therefore
as a way to explore ranges of possible developments within the
boundaries of such future scenarios. The results are not
predictions and the DSS DANUBIA does not propose solutions
but rather provides a basis for discussing potentially upcoming
problems and the keys to avoid or manage them.
The means of validation that can be applied are often indirect
and not of numerical type. The first validation option is the comparison of intermediate results to observed values. In the case of
the Household model, this would be for example the domestic
drinking water demand (per community). In the case of the WaterSupply model, it would be the groundwater withdrawal (per
source). The matches between modelled and observed groundwater withdrawal rates for example are reasonably good but
with limitations that can be contributed to the weak data availability and the large scale of the models (see Barthel et al., 2005).
Second, with respect to the flag values, where no measured
values exist, model simulations for the reference period 1970
to 2003 (Table 3) can be compared to verbal descriptions of
the state of groundwater resources and the water supply system
in the literature. In the case of the upper Danube Catchment,
such evaluations are rather general, yet valuable for the parameterisation and a comparison of the model results for ‘normal’ periods (e.g. BAYLFW, 1980, 1990, 1996; BAYSTMLU, 1994).
For such ‘normal situations’ and ‘normal behaviour’ of the actors
in the past, model results of the hydrological models and subsequently the WaterSupply model show a good status of groundwater resources and an optimal performance (i.e. no shortages) of
the water supply system if a ‘normal’ behaviour of the water
supply companies is assumed. On the other hand, if rather drastic
climatic conditions are forced (see pessimistic scenario, Table 3)
problems develop in exactly those regions with unsuitable
hydrological conditions that are outlined by the administrative
groundwater reports mentioned above. For such water scarcity
situations, the extremely hot and dry years 1976 and 2003 can
be used for reference. In those years ecological (rivers, wetlands)
and economical (navigation, hydropower) damage and some
1117
short-term, local water scarcity in parts of the catchment could
be observed. Reports on these events form a valuable basis for
the parameterisation and further discussion of models and
approaches (BAYSTMUGV, 2003; BAYLFW, 1979, 1980,
1990; BUWAL, 2004; Eybl, 2004; LUBW, 2004). Finally, for
the validation of ‘behaviour’, telephone interviews with 1026
randomly selected households were carried out. The interviews
were based on the results of a questionnaire sent to 1000
households in a smaller test area earlier on. More than 2000 questionnaires were sent to WaterSupply companies (Nickel et al.,
2005). The questionnaire results formed valuable information
to develop the models but will not be further discussed here.
7. Conclusions
The DEEPACTOR approach described in this article was
developed within the framework of the large-scale research
project GLOWA-Danube and is integrated in the very complex
simulation system DANUBIA. Our conclusions must therefore
very briefly address the project and the DANUBIA system as
a whole before we comment on the DEEPACTOR approach and
the respective DEEPACTOR model results.
7.1. The integrative perspective of GLOWA-Danube
The hydrological cycle is a complex system, playing an
important role for many human activities. In turn many human
activities affect and change the hydrological cycle significantly. A main challenge is the fact that human activities adapt
to change and can therefore not be represented as static, repetitive processes. What is needed is a certain degree of ability to
react to changes by making decisions that are not predefined
by trivial input/output functions. In GLOWA-Danube, the
DEEPACTOR approach and its generic implementation, the DEEPACTOR framework, were developed to tackle this challenge.
The framework provides a common conceptual and architectural basis for the development of the socio-economic simulation models in the coupled simulation system of DANUBIA.
A very important aspect hereby is the integration of decision
makers, stakeholders and experts. Models that provide multidisciplinary results and show the interdependencies of the relevant
processes are helpful tools to foster this integration. Models in
that context should not necessarily be seen as the tools that
provide the management solutions but as means to raise awareness of how natural and social systems interact and what the
social, environmental and economic costs of human interference
with the natural system are. A main challenge thereby is to find
the balance between an appropriate, realistic and accurate representation of multifaceted, complex natural and social systems on
the one hand, and on the other hand to provide results that satisfy
the demands of disciplinary experts as well as the demands of
stakeholders and decision makers in all required fields.
7.2. The DEEPACTOR framework
The DEEPACTOR framework as an object-oriented extension
is a specialisation of the DANUBIA developer framework. Its
1118
design follows the specific requirements for an ‘agent-based’
social simulation approach as given by the socio-economic
simulation models of GLOWA-Danube. Along this line, the
framework provides a common generic architecture, a modelling framework with a multitude of basic, but mostly optional,
features to be used within the implementation of the concrete
simulation models. An important architectural aspect is the explicit modelling of actor, plan and action. Even though in the
case of the Household model, the actors’ types are designated
only implicitly by an assigned profile, there are explicit subclasses for plan and action types providing a direct representation of the conceptual course-of-actions. In the case of the
WaterSupply model, there are two actor types explicitly distinguished by their available plans and their type-specific decision procedure. In both model applications the direct and
explicit modelling of types (subclasses) provides both a clear
specification of the current model structure and the possibility
for incremental modular extensions in future model versions.
The flexibility of the framework design is illustrated by the
quite different model implementations Household and WaterSupply. For example, both simulation models make use of
the basic actor features location and collaborators,
but, in the case of the WaterSupply model, the actors are
located on more than one proxel, whereas the Household
actors are defined as one per type (milieu) and proxel object.
The collaborators of WaterSupply actors model ‘real’ collaborators of a WSC for providing water, whereas collaborators of
Household actors are used to simulate an artificial social
network and the diffusion of water-related technologies.
From a more general point of view, the application of the
DEEPACTOR approach is advantageous for the water supply
sector because it facilitates a flexible and realistic response
to system changes. Scenarios can be defined easily by adjusting actor types and preferences; critical states can be identified
without having to model the infrastructure of individual WSC
explicitly. Moreover, the modular and extendable model
structure enables participatory model design and simulation,
as explained and discussed e.g. in Ramanath and Gilbert
(2004), providing an effective tool for analysing effects of
different course-of-actions of water supply companies with
respect to response to Global Change scenarios. In contrast,
the simulation model of water-related behaviour of domestic
households does not aim to provide adjustable parameters in
the context of a realistic and direct model of environmental
circumstances. Rather in this case the application facilitates
the bottom-up approach to social modelling. Based upon the
data provided by Sinus Sociovision, the model enables the
explanation of observable macro phenomena with a low
resolution of type dependent decision algorithms and local
rules for social network generation, both formulated on the
proxel-level. Therefore, in contrast to a purely equation-based
model, the model can be more easily communicated to
stakeholders and is, in general, more comprehensible due to
the conceptually and technically explicit model structure.
Finally, the embedding into DANUBIA as an extension of an
already existing abstract base class allows benefiting from the
treatment of (a) the simulation area as a grid of proxel objects,
(b) its method and mechanism for data exchange during
runtime via formally specified interfaces and (c) its concept
of simulation time with a consistent dynamic integration of
model-specific time steps. As a consequence any DEEPACTOR
model may be executed and coupled to any other DANUBIA
simulation model completely transparent for the rest of the
system. In fact, this integration is one of the reasons not to
build on existing toolkits such as Repast (North et al.,
2006), MASON (Luke et al., 2004) or Swarm (Minar et al.,
1996). It would be by far not trivial and even confusing to
embed them into DANUBIA, since the particular base classes
and modelling elements usually are more general, having their
own notions and concepts of simulation area, time and data
exchange, i.e. of concepts which are fixed in DANUBIA. In
summary the DEEPACTOR framework and also the developer
framework of DANUBIA can be seen to be more special cases
of general frameworks developed elsewhere. Both have been
designed and implemented with focus on the requirements
of the simulation models of GLOWA-Danube. We belief that
this approach is most promising since it seems to be an appropriate compromise between generality and specificsdit provides starting points without restricting to much and it
restricts enough to allow for useful generic implementations
on the framework level.
7.3. The WaterSupply and Household
DEEPACTOR model implementations
The two models chosen can only partially represent the
complex processes and structures that determine the patterns
of water supply and consumption in a large heterogeneous
domain such as the Upper Danube catchment. The current
model implementations should be regarded as preliminary
versions to test and show the capabilities of the approach.
The models are not yet fully validated and not all options
offered by the DEEPACTOR framework are used. In that respect,
the greatest advantage of the approach lies in the fact that it is
easily extendable in terms of plans, actions, preferences and so
forth in a consistent way without the need of significant
changes to the model structure. This was demonstrated for
example by assigning different reaction schemes of water supply companies toward change state of groundwater resources.
Our results have been discussed from two points of view.
First, we evaluated and compared the application of the DEEPACTOR approach and the DEEPACTOR framework, respectively,
by means of the models WaterSupply and Household. Second,
we briefly presented domain-specific results of a scenario
simulation, primarily to illustrate the functionality of the
coupled models WaterSupply and Household within DANUBIA.
In addition to the specific outcome of the models (DQN flags
and domestic water demand), the chosen plans and actions of
actors as underlying mechanisms were analysed. It was shown
on hand of a very limited choice of results that both models
provide a spatially and temporally explicit, process based
way of simulation response of main actors in the water sector
to Global Change. They show very clearly where and when
undesirable developments might occur in a relatively simple
way that allows for an easy detection and explanation of
causeeeffect relations. Rather than forming black boxes, the
processes leading to system changes are controlled by a limited
set of intuitively understandable preferences, options and
triggers of model actors that are simplified but still very
similar to the actors in the real world. The results are therefore
very well suited to form a basis for active participation of
decision makers and stakeholders in coming to discussions
of how we should prepare for possible developments which
may be brought upon by Global Change.
7.4. Applying DANUBIA and the DEEPACTOR
approach elsewhere
The decision support system DANUBIA and the DEEPACTOR
approach presented in this article are a compromise between
the size and the complexity of heterogeneous natural systems
in large catchments, the complexity of human behaviour, the
high degree of inherent uncertainty in both natural systems
and human society and the need to realistically and meaningfully evaluate the impact of Global Change on the environment and human welfare. The aim of DANUBIA is to describe
the water cycle and its physical and socio-economic components as a whole and not so much to describe individual
sectoral processes. Experts from different disciplines may
therefore find the representation of their discipline over-simplified. DANUBIA was developed for use on a very high
administrative level (governmental institutions on state, country or river basin level), where knowledge, data and financial
resources to set up and run the required models are available.
It was developed as a generic system that is transferable and
reusable but not necessarily scalable. It can be applied everywhere, but an application must be based on a high-level
political decision and respective financial resources to allow
its implementation, since the volume of data and financial
resources needed to parameterise the individual models are
quite extensive. An application to smaller scale ‘test case
studies’ is technically possible but largely meaningless since
it contradicts the regional scope of the approach.
7.5. Outlook
Within the last 7 years the multidisciplinary GLOWA-Danube
consortium has developed the highly complex integrated
simulation system DANUBIA to describe the water cycle of a regional scale river basin in all its aspects. DANUBIA has until
now been developed and validated to a degree that allows its
use by the research consortium to perform a large variety of
scenario calculations. The next step is to use the software to
tackle concrete management questions, defined by the main
water authorities8 of the basin. A stakeholder process, mediated by IFOK,9 has now been initiated. Water authorities’
8
The LfU Bayern, the state wide responsible authority for all water and environmental questions, is now a member of the research consortium.
9
http://www.ifok.de/en/home/.
1119
and Stakeholders’ views and expertise will now be included
in the system to improve its applicability. The final aim is to
provide a system in the context of an open source project
which is operational outside the research consortium at the
end of the third project phase (April 2010).
Acknowledgements
GLOWA-Danube is funded by the BMBF (Bundesministerium für Bildung und ForschungdGerman Federal Ministry
of Education and Research). We would like to thank all
governmental organisations, private companies and others
who supported our work by providing data, models, advice
or additional funding. We would like to thank our colleagues
from the partner projects within GLOWA-Danube for the cooperation throughout the last six years. Our special thanks go to
Rolf Hennicker and Andreas Ernst, who supported the present
study directly as leaders of the sub-projects and contributed in
a number of discussions to the concepts of the DEEPACTOR
approach, and to Roman Seidl who kindly supported scenario
runs of the Household model.
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