Abstract
Mathematical models are increasingly used to represent, understand, predict and manage environmental systems. In recent years, increasing data availability and computing power have induced dramatic changes in the way environmental models are developed and used. Models are being constructed at increasingly high resolution and complexity, while computer-based simulations allow for unprecedented uses of environmental models and data. Nonetheless, the complexity of environmental systems and the uncertainty in environmental data still pose a number of challenges in the construction, validation and use of environmental models. In this chapter, the relation between observations, modelling and numerical computing, and their implications in the environmental domain will be discussed through several examples. Without pretending to be exhaustive, the selection of examples mainly aim at highlighting the variety of contexts, application domains and modelling purposes affected by new computing technology.
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Notes
- 1.
The first quantitative population growth model can be traced back to the Essay on the Principle of Population by Thomas Robert Malthus in 1798. Another classical topic of ecology is the prey-predator dynamics, that was first modeled by Alfred J. Lotka and Vito Volterra in the 1920s.
- 2.
Uncertainty stems from multiple sources including inaccuracy of measuring devices, errors introduced by preprocessing, problems of sampling. The quantification of such uncertainty has become an important research field. Some examples from the water systems domain are given in [15–17]. Mittelbach et al. [17] provides an example of problems arising in the calibration of measuring devices. They investigate the accuracy of soil moisture sensors and show that in field conditions none of the evaluated sensors has a level of performance consistent with the respective manufacturer specifications developed in laboratory conditions. Baldassarre and Montanari [16] investigates preprocessing errors and define a methodology for quantifying the uncertainty of river flow data, which are obtained from river stage data by application of rating curves. They show that in their case study, overall errors may be as large as 42.8 % (at the 95 % confidence level) with an average value of 25.6 %. An example of sampling difficulties is given in [15] when discussing river quality modelling, where monitoring programs have sampling frequency and location designed for regulation purposes and often inadequate to capture the system dynamics as required for successful model identification.
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Pianosi, F. (2014). Computational Models for Environmental Systems. In: Amigoni, F., Schiaffonati, V. (eds) Methods and Experimental Techniques in Computer Engineering. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-319-00272-9_1
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