Construct the Simulation Model

Subtask Description:
Construct the simulation model
Conduct a hindcast simulation with policy or other change.

Action points of the implementation:
The model should now be running in a logical manner; however it now needs to be validated using hindcasting. This involves undertaking a simulation run on the model using historical driving data as a scenario and comparing the outputs from the model to what occurred in the real world. If these simulations do not match with the observed data it may be necessary to re-examine the model to discover where errors lie.

Result: A calibrated and validated simulation model of the system.

Søndeledfjorden, Norway

Policy Issue:
Increase local economic benefits from tourism, while minimizing negative impacts on the local coastal cod stock and conflicts with local users of the fjord system

Human Activities:
Tourism (2 nd home and non local), commercial fisheries, aquaculture (potential)

General Information:
Touristic activities in the area encompass general recreation and tourism, development of cottages with access to sea, fishing tourism, environmental tourism and development of the local aquarium. This is translated into development of new beaches and harbors, examples of activities that can harm habitats, biodiversity, recruitment of marine organisms and annual yield in local fisheries. The characteristics of the recreational use of the coas tal zone area make the pressure highly seasonal. The main stakeholder concerns are connected to the impacts of the recreational fishing and of the touristic development in the state of the system.

Example of Implementation:
Given tourist numbers for hotels and camping/cabin-rental, for which official statistics exist, the base values of relevant variables have been set accordingly. To get meaningful variables for the policy issue, and that also make the links between the economic, ecological and social components sensible, some variables have had to be constructed (conflict indicator and landscape quality index). Parameters for these are highly uncertain, but qualitative effects of the changes will (very likely) be of the correct sign. For the effect of the cod stock status on tourist-day numbers the “best guess” was chosen for data on catch rates for fishing tourists and 2 nd home owners in Norway. For some of the other variables, only data from other/larger regions were available (including daily expenditure by tourist category, use of 2 nd homes per year, investment and maintenance costs for 2 nd homes).

Based on these, what was considered as a “best guess” on values / influence on tourist-day numbers or expenditures was chosen. For the effect of GDP on tourist-days a simple statistical analysis was made of annual GDP growth rates for both the OECD (as these make up the major tourist-groups to Norway), and Norway itself (most tourists to Risør are Norwegian), against growth rates in tourist numbers to Norway for the period 1990-2007.

From this Norwegian GDP growth rates was selected as the most relevant variable (explaining about 35% of the variation in tourist numbers’ growth rate).

It is highly recommended that the forcing data and data used for model comparison have been measured during a major policy change in order to test that the model responds correctly to this change.

Contact: Erlend Moksness