Challenges and opportunities for integrating lake ecosystem modelling approaches
Challenges and opportunities for integrating lake ecosystem modelling approaches
复制标题
DOI：
10.1007/s10452-010-9339-3
复制
发表时间：
2010-09-01
期刊：
影响因子：
1.8
通讯作者：
Janse, Jan H.
中科院分区：
文献类型：
--
作者：
Mooij, Wolf M.;Trolle, Dennis;Janse, Jan H.
在过去的四十年里，已经开发和出版了大量的湖泊生态系统模型。我们确定了在这一领域取得进一步进展的两个挑战。其中一个挑战是避免开发更多的模型，主要是遵循别人的概念（“重新发明轮子”）。另一个挑战是避免只关注一种模式，而忽视已经出现的新的和多样化的方法（“眼光狭隘”）。在本文中，我们的目标是提高认识现有的模型和知识的并发方法在湖泊生态系统建模，不涵盖所有可能的模型工具和途径。首先，我们提出了各种各样的建模方法。为了说明这些方法，我们对相当任意选择的特定模型集进行了简要描述。我们处理静态模型（稳态和回归模型），复杂的动态模型（CAEDYM，CE-QUAL-W2，德尔夫特3D-ECO，LakeMab，LakeWeb，MyLake，PCLake，PALSH，萨尔莫），结构动态模型和最小动态模型。我们还讨论了一组的方法，都可以被归类为基于个人的：超个人模型（Piscator，魅力），生理结构模型，阶段结构模型和traitbased模型。我们简要地提到遗传算法，神经网络，卡尔曼滤波器和模糊逻辑。之后，我们放大，作为一个深入的例子，在湖泊生态系统模型PCLake和相关模型（PCLake元模型，希拉湖模型，IPH-TRIM 3D-PCLake）的几十年的发展和应用。在讨论中，我们认为，虽然每种方法和模型的历史发展是可以理解的，因为它的“主导原则”，有很多机会结合起来的方法。我们的观点是，一个单一的“正确”的方法并不存在，也不应该去争取。相反，多种建模方法，同时适用于一个给定的问题，可以帮助开发一个综合的观点湖泊生态系统的功能。最后，我们提出了一系列具体的建议，这些建议可能有助于湖泊生态系统模型的进一步发展。
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.