ABSTRACT

Estuaries are the receiving environment for catchment-derived contaminants, the fate of which depends on the interplay between the estuarine geomorphology and hydrodynamics. In large estuaries, biophysical processes are spatially and temporally-diverse, which makes understanding and managing the impact of human activities challenging. Here we use two common modelling approaches to explore the advantages and limitations of biophysical modelling as a tool for limit setting in a large barrier-enclosed estuary in New Zealand. The model shows the large spatial variation in water quality associated with low upper harbour flushing. Variations can also be attributed to spatial variation in processes (such as denitrification). Although the non-linear interactions between processes within these models can limit the value of using specific detail of outputs for decision making, the general patterns and sensitivities can be used to define areas, explore connectivity, and provide some information when monitoring data is lacking. Even in a deterministic modelling environment, it can very difficult to attribute water quality variations output at one location to the loading that caused these variations. While biophysical modelling will likely remain a core tool for informing management, any future development of limit setting methods for estuaries should recognise the inherent constraints we describe here.

摘要

河口是集水区污染物的接收环境，其命运取决于河口地貌和水动力之间的相互作用。在大型河口，生物物理过程在空间和时间上是多样化的，这使得理解和管理人类活动的影响具有挑战性。在这里，我们使用两种常见的建模方法来探索生物物理建模作为新西兰大型屏障封闭河口限制设置工具的优势和局限性。该模型显示了与低上海港冲刷相关的水质的巨大空间变化。变化也可归因于过程中的空间变化（例如反硝化）。尽管这些模型中流程之间的非线性相互作用可能会限制使用输出的特定细节进行决策的价值，但一般模式和敏感性可用于定义区域、探索连通性并在缺乏监测数据时提供一些信息。即使在确定性建模环境中，也很难将某个位置的水质变化输出归因于导致这些变化的负载。虽然生物物理建模可能仍然是为管理提供信息的核心工具，但河口限制设置方法的任何未来发展都应该认识到我们在此描述的固有限制。即使在确定性建模环境中，也很难将某个位置的水质变化输出归因于导致这些变化的负载。虽然生物物理建模可能仍然是为管理提供信息的核心工具，但河口限制设置方法的任何未来发展都应该认识到我们在此描述的固有限制。即使在确定性建模环境中，也很难将某个位置的水质变化输出归因于导致这些变化的负载。虽然生物物理建模可能仍然是为管理提供信息的核心工具，但河口限制设置方法的任何未来发展都应该认识到我们在此描述的固有限制。