The basis for good water resources management is adequate data implying that monitoring is a prerequisite for any work related to the implementation of the Water Framework Directive and the Groundwater Directive. Data alone do, however, only provide information of the current state of the environment, e.g. a chemical concentration, at a specific location and at the time at which the data was collected. This information is, in itself, of very little use. If more measures of the compound have been taken a time series can be constructed and obvious trends in the concentration levels may be revealed. The data do, however, not explain the cause for this trend. To extract this kind of information from the data set one needs to interpret the data based on an understanding of the physical system, that is, we formulate a conceptual understanding (or conceptual model) of the system from which we explain the data. As such we add knowledge to the data, which is necessary if we want to formulate future management strategies based on observations. Natural systems are, however, very complex with several processes occurring simultaneously and interacting. It is therefore very difficult to fully comprehend and separate the different processes, their interaction and their consequences by interpretation of field data alone. In this context, models can be seen as a formal description of knowledge of the hydrological system studied, describing the spatial and temporal coherence in the system.
Monitoring data and models provide complementary information of the system. The model captures physical knowledge about the systems behaviour and monitoring provides information from the actual system including phenomena which are not included in the model. It may be argued that “modelling without monitoring data is guesswork” and “monitoring without models is a waste of money”.
Modelling and monitoring activities can support each other in many ways. On the one hand useful modelling is not possible without appropriate data that must originate from monitoring. On the other hand modelling can support monitoring activities through different activities such as (a) quality assurance of monitoring data; (b) knowledge-based interpolation and extrapolation in time and space; (c) establishment of conceptual models with causal relationships; (d) assessing effects of anthropogenic activities e.g. in relation to programme of measures; and (e) designing of monitoring systems (Hřjberg et al., 2007).
The overall objective of the present report is to analyse and discuss state-of-the-art on existing data in relation to model applications for research purposes and for implementation of the Water Framework Directive and the Groundwater Directive. More specifically the objectives are:
The report is not intended to be a research report, but rather a synthesis of existing knowledge and experience as it has emerged during the series of workshops held within the Harmoni-CA project.
The target audience is policy makers, water managers and professionals involved in implementation of the Water Framework Directive and the Groundwater Directive.
In the process of implementing the Water Framework Directive (WFD) the issue of data is extremely important. The WFD asks for ambitious goals for the (water) environment with the ultimate objective to obtain good quantitative and qualitative status of all waters in Europe by 2015. Achieving this calls for the solution of a large number of technical tasks, all involving either using already collected data or collecting new data sets: water bodies need to be delineated, classified and possibly grouped; water districts must be characterised, anthropogenic effects evaluated, trends identified, pressures and impacts assessed, programmes of measures established and evaluated, etc.
The WFD is supported by a number of Common Implementation Strategy (CIS) Guidance Documents (GD) of which the most relevant in connection to data is the Monitoring GD (EC 2003). This GD prescribes 4 types of monitoring:
As the WFD and the supportive CIS Guidance documents have a generic approach, they do not necessarily provide extensive lists of data or explicit recommendations on spatial and temporal resolution etc. (Blind and de Blois, 2003). Further they do not give explicit recommendations on how to store data in order to fulfil international conventions prescribing freely available data (UNECE 1998; EC 2007). A more detailed discussion of the data requirements for the WFD implementation is given in Section 2.2.
In addition to the data from the WFD monitoring the WFD planning process that will be completed with the preparation of River Basin Management plans require a variety of additional data, most of which typically already exist. These additional data include system data describing physical and chemical characteristics as topography, river geometry, soil types, soil hydraulic properties and geology. Finally, more detailed data than formally requested in the WFD monitoring are often available from stakeholders such as water supply companies, agriculture, industry and citizens. All these additional data are crucial for modelling studies.
The new water management requirements posed by the WFD call for the development and application of new tools and methods. Comprehensive European and national research programmes have been initiated to support this need for new operational approaches. Many of the research projects funded in this connection require a substantial amount of data, both existing data and new types of data. Research projects collecting existing data are often faced with imposed conditions restricting their ability to make the data freely available to other users after project completion.
In this context it is clear that already collected data will play an important role at the same time as the need for collecting new data is obvious. Thus state-of-the-art on data collection is very relevant in terms of the following issues:
The question on how much data are required for modelling is classical and has been debated at length during several decades. In the modelling community lack of data is often used as an ‘excuse’ for poor model performance. Some modellers claim that lack of data or lack of quality in data is the main constraint for practical use of existing modelling tools (Steenstra et al, 2004; Kamphorst et al 2005) and for progress in development of improved process descriptions for models (Beven, 1996). Additional data will often improve the performance of models, and therefore it can be argued from a scientific point of view that there is always scope for improvements and there will never be data enough.
Similarly, lack of data is often by water managers used as a reason for not applying complex models, but instead using simple tools (Steenstra et al, 2004).
A general discussion on the adequacy of data for modelling is given in Section 2.3 and other sections in Chapter 2. More specifically, the issue of data availability for modelling is discussed in Chapter 3.
Using and extracting information from already collected data is often the first step in water management. This requires that the water manager can acquire the existing data within the area of interest. But the task is not finished when the data are identified. Existing data are not always easily accessible to interested users. The five main constraints are:
Further discussion of data accessibility is given in Chapter 4.