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\"Completely updated and expanded new edition of this widely cited book, Modelling Forest Growth and Yield, 2nd Edition synthesizes current scientific literature, provides insights in how models are constructed, gives suggestions for future developments, and outlines keys for successful implementation of models.The book describes current modeling approaches for predicting forest growth and yield and explores the components that comprise the various modeling approaches. It provides the reader with the tools for evaluating and calibrating growth and yield models and outlines the steps necessary for developing a forest growth and yield model\"--

The only book dedicated to physiologically-based pharmacokinetic modeling in pharmaceutical science Physiologically-based pharmacokinetic (PBPK) modeling has become increasingly widespread within the pharmaceutical industry over the last decade, but without one dedicated book that provides the information researchers need to learn these new.

This study investigates the added value of operational radar with respect to rain gauges in obtaining high-resolution daily rainfall fields as required in distributed hydrological modeling. To this end data from the Netherlands operational national rain gauge network (330 gauges nationwide) is combined with an experimental network (30 gauges within 225 km²). Based on 74 selected rainfall events (March–October 2004) the spatial variability of daily rainfall is investigated at three spatial extents: small (225 km²), medium (10 000 km²), and large (82 875 km²). From this analysis it is shown that semivariograms show no clear dependence on season. Predictions of point rainfall are performed for all three extents using three different geostatistical methods: (i) ordinary kriging (OK; rain gauge data only), (ii) kriging with external drift (KED), and (iii) ordinary collocated cokriging (OCCK), with the latter two using both rain gauge data and range-corrected daily radar composites—a standard operational radar product from the Royal Netherlands Meteorological Institute (KNMI). The focus here is on automatic prediction. For the small extent, rain gauge data alone perform better than radar, while for larger extents with lower gauge densities, radar performs overall better than rain gauge data alone (OK). Methods using both radar and rain gauge data (KED and OCCK) prove to be more accurate than using either rain gauge data alone (OK) or radar, in particular, for larger extents. The added value of radar is positively related to the correlation between radar and rain gauge data. Using a pooled semivariogram is almost as good as using event-based semivariograms, which is convenient if the prediction is to be automated. An interesting result is that the pooled semivariograms perform better in terms of estimating the prediction error (kriging variance) especially for the small and medium extent, where the number of data points to estimate semivariograms is small and event-based semivariograms are rather unstable.

Modeling of integrated urban water systems (IUWS) has seen a rapid development in recent years. Models and software are available that describe the process dynamics in sewers, wastewater treatment plants (WWTPs), receiving water systems as well as at the interfaces between the submodels. Successful applications of integrated modeling are, however, relatively scarce. One of the reasons for this is the lack of high-quality monitoring data with the required spatial and temporal resolution and accuracy to calibrate and validate the integrated models, even though the state of the art of monitoring itself is no longer the limiting factor. This paper discusses the efforts to be able to meet the data requirements associated with integrated modeling and describes the methods applied to validate the monitoring data and to use submodels as software sensor to provide the necessary input for other submodels. The main conclusion of the paper is that state of the art monitoring is in principle sufficient to provide the data necessary to calibrate integrated models, but practical limitations resulting in incomplete data-sets hamper widespread application. In order to overcome these difficulties, redundancy of future monitoring networks should be increased and, at the same time, data handling (including data validation, mining and assimilation) should receive much more attention.

Mixed modelling is very useful, and easier than you think! Mixed modelling is now well established as a powerful approach to statistical data analysis. It is based on the recognition of random-effect terms in statistical models, leading to inferences and estimates that have much wider applicability and are more realistic than those otherwise obtained. Introduction to Mixed Modelling leads the reader into mixed modelling as a natural extension of two more familiar methods, regression analysis and analysis of variance. It provides practical guidance combined with a clear explanation of the underlying concepts. Like the first edition, this new edition shows diverse applications of mixed models, provides guidance on the identification of random-effect terms, and explains how to obtain and interpret best linear unbiased predictors (BLUPs).   It also introduces several important new topics, including the following: * Use of the software SAS, in addition to GenStat and R. * Meta-analysis and the multiple testing problem. * The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this book provides an ideal introduction to mixed modelling for final year undergraduate students, postgraduate students and professional researchers. It will appeal to readers from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, archaeology and geography. Praise for the first edition: \"One of the main strengths of the text is the bridge it provides between traditional analysis of variance and regression models and the more recently developed class of mixed models...Each chapter is well-motivated by at least one carefully chosen example...demonstrating the broad applicability of mixed models in many different disciplines...most readers will likely learn something new, and those previously unfamiliar with mixed models will obtain a solid foundation on this topic.\"—Kerrie Nelson University of South Carolina, in American Statistician, 2007

Agriculture is strongly affected by changes in soil hydrology as well as by changes in land use and management practices and the complex interactions between them. This book aims to expand our knowledge and understanding of these interactions on a watershed scale, using soil hydrology models, and to address the consequences of land use and management changes on agriculture from a research perspective. Case studies illustrate the impact of land use and management practices on various soil hydrological parameters under different climates and ecosystems.

Modern city dwellers are largely detached from the environmental effects of their daily lives. The sources of the water they drink, the food they eat, and the energy they consume are all but invisible, often coming from other continents, and their waste ends up in places beyond their city boundaries. Cities as Sustainable Ecosystems shows how cities and their residents can begin to reintegrate into their bioregional environment, and how cities themselves can be planned with nature's organizing principles in mind. Taking cues from living systems for sustainability strategies, Newman and Jennings reassess urban design by exploring flows of energy, materials, and information, along with the interactions between human and non-human parts of the system. Drawing on examples from all corners of the world, the authors explore natural patterns and processes that cities can emulate in order to move toward sustainability. Some cities have adopted simple strategies such as harvesting rainwater, greening roofs, and producing renewable energy. Others have created biodiversity parks for endangered species, community gardens that support a connection to their foodshed, and pedestrian-friendly spaces that encourage walking and cycling. A powerful model for urban redevelopment, Cities as Sustainable Ecosystems describes aspects of urban ecosystems from the visioning process to achieving economic security to fostering a sense of place.

Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data.