| 1. |
- Janssen, Annette B. G., et al.
(författare)
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Exploring, exploiting and evolving diversity of aquatic ecosystem models : a community perspective
- 2015
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Ingår i: Aquatic Ecology. - : Springer Science and Business Media LLC. - 1386-2588 .- 1573-5125. ; 49:4, s. 513-548
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Tidskriftsartikel (refereegranskat)abstract
- Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5-10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary.
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| 2. |
- Lin, Shuqi, et al.
(författare)
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Multi-Model Machine Learning Approach Accurately Predicts Lake Dissolved Oxygen With Multiple Environmental Inputs
- 2024
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Ingår i: Earth and Space Science. - : American Geophysical Union (AGU). - 2333-5084. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- As a key water quality parameter, dissolved oxygen (DO) concentration, and particularly changes in bottom water DO is fundamental for understanding the biogeochemical processes in lake ecosystems. Based on two machine learning (ML) models, Gradient Boost Regressor (GBR) and long-short-term-memory (LSTM) network, this study developed three ML model approaches: direct GBR; direct LSTM; and a 2-step mixed ML model workflow combining both GBR and LSTM. They were used to simulate multi-year surface and bottom DO concentrations in five lakes. All approaches were trained with readily available environmental data as predictors. Indices of lake thermal structure and mixing provided by a one-dimensional (1-D) hydrodynamic model were also included as predictors in the ML models. The advantages of each ML approach were not consistent for all the tested lakes, but the best one of them was defined that can estimate DO concentration with coefficient of determination (R2) up to 0.6-0.7 in each lake. All three approaches have normalized mean absolute error (NMAE) under 0.15. In a polymictic lake, the 2-step mixed model workflow showed better representation of bottom DO concentrations, with a highest true positive rate (TPR) of hypolimnetic hypoxia detection of over 90%, while the other workflows resulted in, TPRs are around 50%. In most of the tested lakes, the predicted surface DO concentrations and variables indicating stratified conditions (i.e., Wedderburn number and the temperature difference between surface and bottom water) are essential for simulating bottom DO. The ML approaches showed promising results and could be used to support short- and long-term water management plans.
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| 3. |
- Teurlincx, Sven, et al.
(författare)
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A perspective on water quality in connected systems : modelling feedback between upstream and downstream transport and local ecological processes
- 2019
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Ingår i: Current Opinion in Environmental Sustainability. - : Elsevier BV. - 1877-3435 .- 1877-3443. ; 40, s. 21-29
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Tidskriftsartikel (refereegranskat)abstract
- Food production for a growing world population relies on application of fertilisers and pesticides on agricultural lands. However, these substances threaten surface water quality and thereby endanger valued ecosystem services such as drinking water supply, food production and recreational water use. Such deleterious effects do not merely arise on the local scale, but also on the regional scale through transport of substances as well as energy and biota across the catchment. Here we argue that aquatic ecosystem models can provide a process-based understanding of how these transports by water and organisms as vectors affect - and are affected by - ecosystem state and functioning in networks of connected lakes. Such a catchment scale approach is key to setting critical limits for the release of substances by agricultural practices and other human pressures on aquatic ecosystems. Thereby, water and food production and the trade-offs between them may be managed more sustainably.
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