Qui ci vaIntegrating Coastal Zone Management and River Basin Management, an application of GIS for the River Elbe Management (Germany)

C. Nunneri (1) , J. Hofmann (2)

(1) GKSS Forschungszentrum, Geesthacht (DE)
(2) Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin (DE)

The new approach of the EU-funded Project EUROCAT (EUROpean CATchments, catchment changes and their impact on the coast, 5th Framework Programme, http://www.iia-cnr.unical.it/EUROCAT/project.htm) is the integrated view of the coastal waters and the river catchment as one system, thus integrating River Basin Management (IRBM) and Coastal Zone Management (CZM), as targeted in the Water Framework Directive (WFD).
This paper presents a methodological approach and its practical application to the Elbe case study as an example. The aim is to collect and integrate available information about sources, fluxes and concentration levels of different compounds flowing from the catchment to the coastal zone through the river systems and integrate this information with natural science models and socio-economic tools in order to develop a practical management tool for decision-makers. Altogether the Elbe study area comprises the river basin of 148,268 km2 including the coastal strip of 10 km wide starting from Haringvliet (Netherlands) until the Danish border (Fig. 1).

DPSIR-approach and the role of scenarios as a framework
The DPSIR-approach of the European Environment Agency is the analytical tool selected to handle complex interactions between the socio-economic (humankind processes) and the natural system (ecosystem processes). Human activities may cause some impact on the (coastal) environment and potentially damage the (coastal) ecological integrity.
Such complex human-ecosystem interactions can be observed by dividing them into five variables: (1) Drivers and (2) Pressures resulting by socio-economic development; (3) State of and (4) Impact on the environment and finally (5) societal Response (policy measures) to such unwanted impacts (table 1). Scenarios represent possible futures shaped by different human activities, which may cause an impact on the environment. Scenarios are used for assessing possible socio-economic development paths, which are the origin of drivers and pressures (e.g. change in land-use or agricultural production patterns). Thus ecosystems will experience different (intensities of) pressures, and, consequently, impacts on their integrity and functionality under alternative scenarios. Furthermore scenarios represent alternative futures, in which society might think and behave differently. This change of societal values through the scenarios is the key for connecting political issues, lifestyles and social values (i.e. social processes) with environmental processes: environmental risk perception and therewith willingness to pay for reducing it (interpretation of the precautionary principle) will considerably influence the feasibility of emission reduction measures (Nunneri et al., 2002).

DRIVER PRESSURE STATE IMPACT RESPONSE
Agriculture
Fertilizers
Manure		 Nutrient input (N,P)

Genetic and species Diversity
Changing N-P ratio

Imbalance in genetic and species diversity Adopt new farming methods in the catchment (less fertilisers)
Protect endangered species
Land use Sedimentation/ Erosion

Morphological change of sea bed and the coastline

 Morphological change of sea bed and the coastline Adopt new land use methods
Energy production Construction of wind mill parks
Open coast (social cultural benefits)		 Changes in the open view Environmental impact assessment
Industrialization Discharge of compounds Concentration Changes in the level
Diseases		 Implement (int.) national policy measures
Urbanization Sewage, waste water, housing Morphology of the coast and sea bed
Ecological groups
Natural coastal dynamics
 Changes in the morphology, ecology and naturalness of the environment  
Fishing (coast based) Landings of fish
No. of vessels and engine sizes		 Spawning
Biomass
Mortality
Recruitment rate		 Changes in
Spawning
Biomass Mortality
Recruitment rate		 Regulation of Total allowable Catch (TAC)
Technical measures
Transport (shipping in general)

Discharge of toxic compounds
Accidents
Litter

 Number of oil slicks
Number of oil beach birds		 Changes in the
- number of oil slicks
- number of oil beach birds 		Transport restriction
Marine inspection
Recreation Pollution of surface water
Disturbance of marine mammals		 Rest places for marine mammals
Clean coastal waters		 Changes in the
- rest places for marine mammals
- clean coastal waters		  

Table 1. DPSIR variables for the Elbe catchment

Attempts towards a harmonised model system
An essential part of the project is to analyse the response of the coastal seas to past, present and future changes in fluxes of nutrients and contaminants from the catchment.
Through GIS application the gathered information can be used for further analysis in order to address the following questions:

  1. What are the quantities and chemical attributes of riverine fluxes to the coastal zone of water, sediment, nutrients and contaminants?
  2. What are the physical, chemical, and biological controls , including natural and anthropogenic, of these fluxes?
  3. What are the feedbacks of changes in drainage basins on human society and on biogeochemical cycles?

In order to address these questions, the following models will be used:

The outcome of the MONERIS model will be used as an input to the ERSEM model in terms of fluxes. MONERIS can help to identify the diffusion path of these substances, thus highlighting more effective policy and management measures in order to achieve the desired standards. A major challenge of the REBCAT (Rhine and Elbe catchment) study is the definition of transfer functions between river loads and concentrations in the sea. The transfer function between MONERIS and ERSEM was validated for the year 1995. The calculated Elbe river loadings in 1995 for TN, Nitrate, Ammonium, TP and Phosphate (calculated totals for annual loads) of the two models (MONERIS vs. ERSEM) were compared and show only minor deviations suggesting a good coincidence. One example of the simulated net primary production [g C/cm2/year] in the upper 30 metres in the North Sea is presented in Fig. 2. In addition, simulated Phosphate concentrations (mmol/m3) and Chlorophyll concentrations (mg/m3) in the upper 30 metres of the North Sea were calculated. There was strong evidence for the influence of suspended matter (SPM) during springtime in the German Bight showing that in this season the phytoplancton growth is light limited.
An artificial stepwise reduction will be carried out by applying the ERSEM model. The effects of these hypothetical reductions to the eutrophication in the coastal waters will be shown by load response curves and GIS created maps for the different emission pathways. One example for the present situation of nitrogen emissions via different pathways is given in Fig. 3. Note that the entire Elbe basin is regionalized according to the coordination regions of the water framework directive.
Based on these results the model MONERIS will be applied for reduction measures in the catchment to answer the question: How can different measures lead towards low probability of blue-green algal blooms occurrence?

The Responses (measures for reducing emissions)
The crucial choice of optimal measures (or measure packages) for river basin management will employ a multi criteria analysis (MCA), in which the expected effects of reduced emissions upon the coastal-zone ecosystem (ERSEM results) will be related with the expected costs of reduction and other non-monetary benefits or costs connected. An essential role in evaluating the effect of management measures is played by a participatory approach involving interest groups (Governmental and Non-governmental Institutions) situated in the Elbe catchment (Nunneri & Hofmann, in preparation). The participatory part of the project is essential for keeping the whole project on a policy-relevant track and determining possible conflicts, feasible measures and criteria for choosing the optimal management solution (measure package). The GIS based visualization of the results from reduction scenarios will be an important tool for decision makers.

Fig. 1 - The Elbe catchment and the coastal zone of the German Bight. The relevant boxes (COCOA based) for the application of the ecosystem model ERSEM are given with the numbers 59, 69 and 78

Fig. 2 - Simulated net primary production [g C/cm2/year] in the upper 30 metres in the North Sea for the model period 1995 (Lenhart 2003)

Fig. 3 - Nitrogen emissions during the period 1998-2000 via various pathways

References