Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.

A probabilistic model estimating oil spill clean-up costs - a case study for the Gulf of Finland

Existing models estimating oil spill costs at sea are based on data from the past, and they usually lack a systematic approach. This make them passive, and limits their ability to forecast the effect of the changes in the oil combating fleet or location of a spill on the oil spill costs. In this paper we make an attempt towards the development of a probabilistic and systematic model estimating the costs of clean-up operations for the Gulf of Finland. For this purpose we utilize expert knowledge along with the available data and information from literature. Then, the obtained information is combined into a framework with the use of a Bayesian Belief Networks. Due to lack of data, we validate the model by comparing its results with existing models, with which we found good agreement. We anticipate that the presented model can contribute to the cost-effective oil-combating fleet optimization for the Gulf of Finland. It can also facilitate the accident consequences estimation in the framework of formal safety assessment (FSA).

Drought risk assessment as changes in the potential stand yield classes and potential forest production for Sitka spruce, Scots pine, and pedunculate oak over the next 80 years in the Great Britain

Probabilistic climate data have become available for the first time through the UK Climate Projections 2009, so that the risk of tree growth change can be quantified. We assess the drought risk spatially and temporally using drought probabilities and tree species vulnerabilities across Britain. We assessed the drought impact on the potential yield class of three major tree species (Picea sitchensis, Pinus sylvestris, and Quercus robur) which presently cover around 59% (400,700 ha) of state-managed forests, across lowland and upland sites. Here we show that drought impacts result mostly in reduced tree growth over the next 80 years when using b1, a1b and a1fi IPCC emissions scenarios. We found a maximum reduction of 94% but also a maximum increase of 56% in potential stand yield class in the 2080s from the baseline climate (1961-1990). Furthermore, potential production over the national forest estate for all three species in the 2080s may decrease due to drought by 42% in the lowlands and 32% in the uplands in comparison to the baseline climate. Our results reveal that potential tree growth and forest production on the national forest estate in Britain is likely to reduce, and indicate where and when adaptation measures are required. Moreover, this paper demonstrates the value of probabilistic climate projections for an important economic and environmental sector.

Projections for Land Use Change in Brazil:2000-2050, links to files in ArcGIS shapefile format

This data set contains the inputs and the results of the REDD+ Policy Assessment Centre project (REDD-PAC) project (http://www.redd-pac.org), developed by a consortium of research institutes (IIASA, INPE, IPEA, UNEP-WCMC), supported by Germany's International Climate Initiative. Taking a new land use map of Brazil for 2000 as input, the research team used the global economic model GLOBIOM to project land use changes in Brazil up to 2050. Model projections show that Brazil has the potential to balance its goals of protecting the environment and becoming a major global producer of food and biofuels. The model results were taken into account by Brazilian decision-makers when developing the country's intended nationally determined contribution (INDC).