Tiivistelmä.

Dynamic models provide a key input to the forthcoming assessment of the Gothenburg Protocol and for input to any further emission reduction agreements by establishing the time-scale of the ecosystem recovery from acidification. Data from six sites belonging to the UN ECE ICP Integrated Monitoring Programme have been used here to test the MAGIC model predictions against several years of observations and to make predictions into the future assuming the agreed emission reductions under current legislation plans are achieved by 2010. A close match between observed and simulated under this rigorous test of the model using best available site information and input-output data clearly demonstrates that the model captures the key controls on mean annual surface water chemistry.

The Times (Obituaries, 4 August 2008) reported that “John Thornes was one of the most eminent and influential physical geographers of his generation.” John’s keen interest in understanding landform processes and evolution was furthered through a variety of methods and informed across a range of disciplinary boundaries. In particular he pushed for better integration of monitoring, theoretical and simulation modelling, field and laboratory experimentation and remote sensing techniques. Although dominated by an interest in the Mediterranean region and problems of land degradation, his research activities ranged across a number of time scales and with other environmental perspectives. This collection of papers reflects this wide range of John’s interests through the recent work of scientists and professionals most strongly influenced by his rigorous training or leadership. The thematic focus of the book, which runs through all of the main contributions, is the integration of different methodologies and the application of this approach to improved understanding of natural systems and the development of appropriate strategies for environmental and resource management. Short overviews of John’s contributions to geomorphological research are also presented to provide context for the origins of this book.

Tiivistelmä.

When talking about modelling it is natural to talk about simulation. Simulation is the imitation of the operation of a real-world process or systems over time. The objective is to generate a history of the model and the observation of that history helps us understand how the real-world system works, not necessarily involving the real-world into this process. A system (or process) model takes the form of a set of assumptions concerning its operation. In a model mathematical and logical assumptions are considered, and entities and their relationship are delimited. The objective of a model – and its respective simulation – is to answer a vast number of “what-if” questions. Some questions answered in this book are: What if the power distribution system does not work as expected? What if the produced ships were not able to transport all the demanded containers through the Yangtze River in China? And, what if an installed wind farm does not produce the expected amount of energyt? Answering these questions without a dynamic simulation model could be extremely expensive or even impossible in some cases and this book aims to present possible solutions to these problems.

A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.