**3. Adaptive monitoring and information system**

430 Environmental Monitoring

of change, i.e. slow changing variables and fast changing variables. Scenario simulation can draw attention to the role of the slow-changing variables in influencing system dynamics (Walker et al., 2006). The categorisation of variables according to speed of change can be used to program the frequency of data collection, making it easier to identify each variable's

The integration between monitoring and modelling has to be considered as an iterative process. In fact, while models can simulate system dynamics, allowing the identification of key variables, the availability of new data allows the revision and updating of models. Moreover, the speed of change of the variables can also be considered iterative. Indeed, variables classified as slow changing in the model may be identified as fast changing by the monitoring system. In this case, the monitoring sample interval has to be changed. Thus,

Simulation of system dynamics facilitates the identification of thresholds, which can be broadly defined as a breakpoint between two states of a system. When a threshold is exceeded, a change in system function and structure results. Such changes regard the nature and extent of feedback, resulting in changes of directions of the system itself. The changes can be reversible, irreversible or effectively irreversible (Walker et al., 2006). Two different types of thresholds can be defined, i.e. positive and negative. A positive threshold represents a desirable change in the state of the system. Such a change can be due to implemented management actions. A negative threshold can be considered as the starting point of a non-acceptable system trajectory. The recognition of these thresholds is particularly important in the case of irreversible changes. In this situation, actions are needed in order to avoid exceeding the threshold. The integration between monitoring and modelling provides information about the current state and the future trajectory of the

The position of the threshold is strictly linked to past experience. There are no examples where a new kind of threshold has been predicted before it has been experienced. Typically, the identification of thresholds is based on an analysis of systems similar to the one under investigation (Walker and Meyers, 2004). To this aim, a database is going to be implemented to collect empirical data on possible regime shifts in socio-ecological systems (Walker and Meyers, 2004). Some authors suggest using variances in variable trends to detect an impending system change (Brock and Carpenter, 2006). Integrating these two different approaches can be very useful. In other words, the existing experience regarding regime shifts, coming both from other systems and from the tacit knowledge of experienced and highly skilled people, can be structured and included in the system model. The variance can be calculated using monitoring data and the position of the

Integrating system modelling and monitoring iteratively highlights the importance of collecting information on trends. In fact, the availability of time series of data on the different variables allows the behaviour of the system variables and the trajectory of the system to be defined. The detection of trends can support the revision of the hypothesis concerning system dynamics, which is at the basis of the models. For these reasons it is fundamental to develop a monitoring system which is sustainable over time. To this aim, two important issues needs to be addressed, i.e. the need firstly to increase the adaptability of the monitoring system to policy and learning processes, and secondly to reduce monitoring costs through the adoption of scientific and technical innovation in information

clearly a re-assessment process is needed both in models and in monitoring.

trend.

system.

collection.

threshold can be changed.

Considering the issues described in the previous section, the conceptual architecture of a monitoring system for AM was defined (figure 1). From now onward, we refer to this system as Adaptive Monitoring Information System (AMIS).

Fig. 1. AMIS conceptual architecture. The figure has been adapted from the Information cycle elaborated by Timmerman and others (2000), to emphasise the two learning processes.

As described previously, the basis for AMIS design is the conceptual model of the system, which simplifies the system and makes the key components and interactions explicit. The definition of this model is based on the integration between a participatory process, allowing experienced stakeholders to provide their understanding of the system, and models able to simulate future scenarios. The conceptual model is structured using the integration between Cognitive Maps and Causal Loop Diagrams.

Two different conceptual models, i.e. the "water management conceptual model" and the "information management conceptual model" are defined as the basis of AMIS. The former concerns the interpretation of the problem considered, while the latter concerns the information needed to solve the problem considered, and the "frames" used to interpret the information (Pahl-Wostl, 2007; Kolkman et al., 2005).

The AMIS architecture consists of four main boxes, i.e. Conceptual model elicitation, Design, Data collection and Interpretation. The links between them represent the iterative process of monitoring design, which is at the basis of AMIS. The figure was elaborated starting from the information cycle developed by Timmerman et al. (2000). This cycle depicts a framework where information users and producers communicate information needs that link the

Monitoring Information Systems to Support Adaptive Water Management 433


users and producers - Data are publicly available and

specific audiences

over disciplines

into policy support **Additional needs for AM** 

learning.

accessible






important as the results: the focus is on


management activities evaluating the system changes, and measuring progress towards participatory defined goals.

assessing the effectiveness of


**Current monitoring practices Needs for IWRM** 






disciplinary needs

will be produced - Data accessibility is limited

information users



Table 1. Comparison among current, IWRM and AM monitoring

Learning aspects in the AMIS are not about the monitoring as a simple process or its data, but about an increase of the system understanding, communication between stakeholders to influence decision making (McIntosh et al., 2006). While giving floor to and later using knowledge, concerns, demands, and expertise from different points of view, which result from a stakeholder involvement, one will indeed achieve better decision making with more alternatives of choice on the one hand, and a broader and more balanced acceptance of the

To initiate and later-on ensure learning processes using a monitoring system, all relevant stakeholder groups need access to it. Being involved when objectives are defined, data and processes transparently observed, stakeholders get enabled to learn about variables and

against target values.

policy implementation.

**3.1 Learning process using AMIS** 

decision making in management.

provided

specialised

monitoring and decision processes. The monitoring program needs to be adapted to the different stages of the policy definition process, because each stage requires different types of information (Cofino, 1995; Ward, 1995) to make water management and governance adaptive.

Two possible learning processes can be identified. The first one concerns the water management conceptual model. Once information has been examined, a perspective is developed, and an insight is gained and integrated into the conceptual model itself (Kolkman et al., 2005). Information may prove initial models to be wrong and support the debate between actors, which may lead to a revision of models, through reflection and negotiation, in a social learning process. This learning may, in turn, support changes in the water management conceptual model. Moreover, feedback on management actions may generate new questions or new insights. This may make the originally agreed upon information appear inadequate, resulting in new information needs. Thus, the information needed to support a decision process evolves according to the actors' learning process, leading to revision/adaptation in monitoring strategies and data interpretation.

The second learning process relies on feedback from applied monitoring practices. As a result of experience in implementing the monitoring program and assessing its results, adaptation to monitoring may be needed (Cofino, 1995; Smit, 2003). The causes for adaptation can be found within monitoring practices: too little attention may have been spent on specifying the information needs; the information needs may have been specified in such a way that no adequate information can be produced from it, or so that it does not reflect the actual information users' needs; the selected indicators may not adequately measure what they are purported to measure; or the strategy to collect information may not have produced the right information. Furthermore, the available budgets may restrict the number of indicators that can be measured or the intensity of the network in terms of locations and frequency. New information sources may become available (e.g. progress in remote sensing technologies, etc.).

To this aim, an important innovation in AMIS concerns data collection methods. AM often results in a demand to monitor a broad set of variables, with prohibitive costs if the monitoring is done using only traditional methods of measurement. This is particularly true in developing countries, where financial and human resources are limited. In these areas, the monitoring network may cover only small part of the territory or the grid may be too sparse, making the monitoring data unsuitable for the decision process. Furthermore, traditional monitoring is costly, reducing its sustainability over time. The resulting works may be still valuable as one-off assessments, but they do not provide information about the trends of environmental resources and the evolution of environmental phenomena. Thus, the outcomes of environmental policies are often difficult to assess.

To deal with these issues, AMIS is based on the integration of alternative sources of knowledge. Thus, AMIS can be considered as the shared platform through which traditional monitoring information and innovative information sources (e.g. remote sensing monitoring, community monitoring, etc.) are integrated. Therefore, AMIS is able to adapt to data and information availability, supporting adaptive management even in data poor regions.

In Table 1, a comparison between the conventional approach and monitoring to support IWRM and AM is proposed.

monitoring and decision processes. The monitoring program needs to be adapted to the different stages of the policy definition process, because each stage requires different types of information (Cofino, 1995; Ward, 1995) to make water management and governance

Two possible learning processes can be identified. The first one concerns the water management conceptual model. Once information has been examined, a perspective is developed, and an insight is gained and integrated into the conceptual model itself (Kolkman et al., 2005). Information may prove initial models to be wrong and support the debate between actors, which may lead to a revision of models, through reflection and negotiation, in a social learning process. This learning may, in turn, support changes in the water management conceptual model. Moreover, feedback on management actions may generate new questions or new insights. This may make the originally agreed upon information appear inadequate, resulting in new information needs. Thus, the information needed to support a decision process evolves according to the actors' learning process, leading to revision/adaptation in monitoring strategies and data

The second learning process relies on feedback from applied monitoring practices. As a result of experience in implementing the monitoring program and assessing its results, adaptation to monitoring may be needed (Cofino, 1995; Smit, 2003). The causes for adaptation can be found within monitoring practices: too little attention may have been spent on specifying the information needs; the information needs may have been specified in such a way that no adequate information can be produced from it, or so that it does not reflect the actual information users' needs; the selected indicators may not adequately measure what they are purported to measure; or the strategy to collect information may not have produced the right information. Furthermore, the available budgets may restrict the number of indicators that can be measured or the intensity of the network in terms of locations and frequency. New information sources may become available (e.g. progress in

To this aim, an important innovation in AMIS concerns data collection methods. AM often results in a demand to monitor a broad set of variables, with prohibitive costs if the monitoring is done using only traditional methods of measurement. This is particularly true in developing countries, where financial and human resources are limited. In these areas, the monitoring network may cover only small part of the territory or the grid may be too sparse, making the monitoring data unsuitable for the decision process. Furthermore, traditional monitoring is costly, reducing its sustainability over time. The resulting works may be still valuable as one-off assessments, but they do not provide information about the trends of environmental resources and the evolution of environmental phenomena. Thus, the outcomes of environmental policies are often

To deal with these issues, AMIS is based on the integration of alternative sources of knowledge. Thus, AMIS can be considered as the shared platform through which traditional monitoring information and innovative information sources (e.g. remote sensing monitoring, community monitoring, etc.) are integrated. Therefore, AMIS is able to adapt to data and information availability, supporting adaptive management even in data poor

In Table 1, a comparison between the conventional approach and monitoring to support

adaptive.

interpretation.

remote sensing technologies, etc.).

difficult to assess.

IWRM and AM is proposed.

regions.


Table 1. Comparison among current, IWRM and AM monitoring

### **3.1 Learning process using AMIS**

Learning aspects in the AMIS are not about the monitoring as a simple process or its data, but about an increase of the system understanding, communication between stakeholders to influence decision making (McIntosh et al., 2006). While giving floor to and later using knowledge, concerns, demands, and expertise from different points of view, which result from a stakeholder involvement, one will indeed achieve better decision making with more alternatives of choice on the one hand, and a broader and more balanced acceptance of the decision making in management.

To initiate and later-on ensure learning processes using a monitoring system, all relevant stakeholder groups need access to it. Being involved when objectives are defined, data and processes transparently observed, stakeholders get enabled to learn about variables and

Monitoring Information Systems to Support Adaptive Water Management 435

In this section some technical aspects related to the adaptive degree of AMIS are described. Firstly, AMIS should be flexible and able to incorporate new information and data, of different type and with different formats. Using a relational database (RDBMS) is a sound basis to be open for new information requirements, because it is very flexible and extendable. The information can be well structured and redundancy can be avoided. The

To satisfy the information needs of various user groups according to their knowledge of environmental system behaviour, different types of information for different purposes must be produced. One important aim of the AMIS is to provide the user with various methods and predefined algorithms to produce information. AMIS should provide the user with user-friendly predefined methods and algorithms to produce information, such as data visualisation tools as well as automatically generated information from incoming data.

user can create new tables and link them to the existing database.

**3.2 Technical adaptability of an AMIS** 

Fig. 2. Technical components of AMIS.

interactions of "their own" systems and "their own" decisions which could lead to a revision or adaptation of management decisions (Pahl-Wostl, 2007. Further, this creates the feeling that stakeholders "buy in" into the product, that the monitoring system is "their" and therefore deserves more credibility (McIntosh et al., 2006). According to recent approach, the involvement of stakeholders can be extended to monitoring activities and not only to the design phase. The use of local knowledge enhances the understanding of environmental system, particularly in data poor areas. Moreover, adopting a community-based approach to monitoring can promote the public awareness of environmental issues.

Thus the intensive dialogue between science and many different stakeholders offers the opportunity for a mutual development, assessment, enhancement and implementation of new or already existing concepts, methods and tools, and helps improve the quality and acceptance of the decisions that are made. Last not least when using success-stories in management, based on the AMIS design, for the further development and enhancement of the monitoring system, the learning cycle is closed.

The following criteria, implemented into an AMIS, are indispensable to serve as a learning tool (cf. McIntosh et al., 2006):

