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Hydroinformatics techniques in eco-environmental modelling and management

机译：生态环境建模与管理中的水文信息学技术

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The high complexity of aquatic ecosystems and the multiple processes involved, make the development of ecohydraulics and eco-environmental models a challenging subject. Conventionally, computer-based models use a mathematical formulation for the processes involved which are then solved by numerical methods. These models are often derived based on the assumption of spatial homogeneity and conservation principles of mass, momentum and energy. Development of these models often demands a clear understanding of the processes involved. However, the above assumptions are easily violated when spatial heterogeneity, individual species behaviour and local interactions play a significant role in the system dynamics. In particular for eco-environmental systems, knowledge on local interactions that determine the overall system behaviour is not always available. Although the rapid advances of data-driven techniques have recently made great contributions to water-environment related research, data on ecosystems are often quite limited, which restricts the application of data mining methods to eco-environmental system modelling. In addition, no modelling - also not black-box modelling - can be undertaken without having at least some understanding of the basic processes and mechanisms involved. It is always advisable to start exploring any dataset using conventional statistical techniques, as elaborated in this paper for a case study on Western Xiamen Bay, China. Neural network trimming was then used to establish the dominant factors; it was shown that a relatively simple ANN model was quite capable of capturing the essential features, provided the right input parameters are chosen. Examples of integrated approaches to ecohydraulics modelling coupling formulations with cellular automata and physical equations with fuzzy rules are presented for applications on eutrophication modelling of Taihu Lake in China, competitive growths and colonization of two underwater macrophytes in Lake Veluwe in The Netherlands, and forecasting of algal blooms in the Dutch coastal waters on the North Sea. A mussel dynamics model developed for the Upper Mississippi River in the USA demonstrates the feasibility of individual based modelling in ecosystem dynamics. Numerical models are quite capable of simulating the abiotic aquatic environment, including complicated fluid flow and transport mechanisms. However, when it comes to simulating the biotic and ecosystem dynamics, the interaction of individual species with their environment, as well as the interactions amongst species, has to be taken into account. The future of ecohydraulics and eco-environmental modelling thus seems to lie in the integration of different paradigms and techniques, which is the core content of the hydroinformatics discipline.
机译：水生生态系统的高度复杂性和所涉及的多个过程，使生态液压技术和生态环境模型的开发成为一个具有挑战性的课题。常规地，基于计算机的模型对所涉及的过程使用数学公式，然后通过数值方法求解。这些模型通常是基于空间均质性以及质量，动量和能量守恒原理的假设得出的。这些模型的开发通常需要对所涉及的过程有清楚的了解。但是，当空间异质性，个体物种行为和局部相互作用在系统动力学中起重要作用时，很容易违反上述假设。特别是对于生态环境系统，并非总是可以获得有关确定整体系统行为的局部交互作用的知识。尽管最近数据驱动技术的飞速发展为与水环境相关的研究做出了巨大贡献，但有关生态系统的数据通常非常有限，这限制了数据挖掘方法在生态环境系统建模中的应用。此外，在没有至少对所涉及的基本过程和机制有一定了解的情况下，无法进行建模，也不能进行黑盒建模。始终建议使用常规统计技术开始探索任何数据集，如本文针对中国厦门西湾的案例研究所述。然后使用神经网络修整来确定主导因素。结果表明，只要选择正确的输入参数，一个相对简单的ANN模型就能够捕获基本特征。提出了将生态系统水力模型与细胞自动机耦合公式和具有模糊规则的物理方程耦合的综合方法的实例，这些方法可用于中国太湖的富营养化建模，荷兰维鲁韦湖的两种水下大型植物的竞争生长和定殖以及藻类预报在北海的荷兰沿海水域盛开。为美国密西西比河上游开发的贻贝动力学模型证明了基于个体的模型在生态系统动力学中的可行性。数值模型非常有能力模拟非生物水生环境，包括复杂的流体流动和运输机制。但是，在模拟生物和生态系统动态时，必须考虑单个物种与其环境的相互作用以及物种之间的相互作用。因此，生态液压技术和生态环境建模的未来似乎在于整合不同的范式和技术，这是水信息学学科的核心内容。

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