||This paper proposes approaches to modeling visitor flows in the context of weather and outdoor recreation. The nature conservation area and area under investigation the Lobau, which is a part of the Danube Floodplains National Park, lies in close proximity to the large conurbation of Vienna, the capital city of Austria. This circumstance presents the managers and researchers of the Lobau with a variety of challenging problems, due to the high number of visitors and the multifaceted visitor structure. An ecologically and economically sustainable management of the recreation and conservation area Lobau requires a profound knowledge of the uses visitors make of this area and a reliable prediction of the potential numbers of visitors. The investigation of the prognostic model is based on the results of a visitor monitoring project. Within this project, video-cameras were installed at several entrance points to the Lobau to monitor recreational activities throughout one year. The prognostic models were based on the dependence of the daily number of visitors on external factors such as weather and day of the week. Using a linear regression, these relationships were investigated and used to predict visitor loads. For the model, a distinction was made between workdays and weekends and/or holidays. The weather was considered in a very differentiated way: Meteorological elements, i.e. air temperature, cloud cover, precipitation, appear directly as parameters in the models as well as indirectly in thermal comfort indices, e.g. the Physiological Equivalent Temperature (PET). Reliable models can be obtained for the daily totals of visitors as well as for specific user groups with high visitor loads, i.e. hikers and bikers. The day of the week has the greatest influence on the daily totals of visitors as well as on individual user groups. The numbers of bikers and hikers depend heavily on the Physiological Equivalent Temperature. The effects of precipitation and cloud cover during the preceding seven days are small. The usage patterns of joggers and dog walkers are more difficult to model as they are less influenced by the day of the week and weather related factors.