17:15 〜 19:15
[AOS14-P05] 気候変動シナリオを組み合わせた気候変動が将来的に湖沼に与える全球的評価に向けた１次元多層水質評価モデルの作成
キーワード：湖沼、気候変動、水質評価モデル
This study aims to develop a one-dimensional, multi-layer water quality evaluation model to more accurately simulate the physical and chemical processes occurring in lake environments under the influence of climate change. Freshwater resources play a crucial role in the sustainable development of human society and the global environment. Lakes, in particular, hold multifaceted value as sources of drinking water, providers of ecosystem services, reservoirs of biodiversity, foundations for agriculture and industry, and venues for tourism and recreation. However, in recent years, global climate change has severely impacted lake ecosystems. Rising water temperatures have intensified thermal stratification, inhibiting mixing between upper and lower layers. This has led to decreased dissolved oxygen concentrations in bottom layers and an increased occurrence of harmful algal blooms.
The baseline model for this study, the Pamolare two-layer model, divides a lake into an upper and a lower layer. While it has demonstrated a certain degree of accuracy in reproducing water temperature and nutrient distributions, its limited spatial resolution and inability to adapt to rapid meteorological changes present significant challenges. To address these limitations, this study develops a multi-layer model that allows for a more detailed analysis of vertical physical and chemical processes in lakes. By increasing the number of layers beyond the conventional two-layer structure, the model enables dynamic adjustment of diffusion coefficients based on stratification stability and incorporates the latest meteorological data to more accurately reflect real-world environmental conditions. Furthermore, by integrating future projections, such as the IPCC climate change scenarios, the model aims to serve as a robust simulation tool capable of comprehensively assessing long-term changes in lake environments due to climate change.
For validation, simulations were conducted on Lake Suwa for the period 2012–2016. The results indicated that while key seasonal phenomena, such as summer stratification formation and winter holomixis, were reproduced to some extent, the model failed to fully capture the strong summer stratification observed in nature. Additionally, while surface-layer dissolved oxygen concentrations generally aligned with observational data, the reproduction of hypoxic conditions in deeper layers remains insufficient. Although adjustments to growth rates and light limitation factors helped suppress phytoplankton blooms, interactions with zooplankton and their effects on nitrogen and phosphorus concentrations remain inadequately represented.
These results highlight that the model, in its current form, does not fully replicate real-world environmental conditions, particularly in terms of water temperature, dissolved oxygen, nitrogen cycling, and phytoplankton dynamics, underscoring the need for further refinement. Future improvements will focus on enhancing the representation of stratification intensity and material circulation, stabilizing material balances, and improving overall model accuracy. Additionally, expanding model applications beyond Lake Suwa and validating its performance across various lakes, both domestically and internationally, will be essential for increasing its versatility. By further integrating future climate prediction models, such as IPCC scenarios, this study aims to provide more precise indicators for assessing the long-term impacts of climate change on lake environments, thereby contributing to environmental management and policy formulation.
In summary, this study proposes a novel multi-layer model that overcomes the limitations of conventional models, enabling detailed simulations of physical and chemical processes in lake environments under changing climatic conditions. The model’s applicability and future potential provide a valuable foundation for further research and development in the field of lake ecosystem modeling and climate impact assessment.
The baseline model for this study, the Pamolare two-layer model, divides a lake into an upper and a lower layer. While it has demonstrated a certain degree of accuracy in reproducing water temperature and nutrient distributions, its limited spatial resolution and inability to adapt to rapid meteorological changes present significant challenges. To address these limitations, this study develops a multi-layer model that allows for a more detailed analysis of vertical physical and chemical processes in lakes. By increasing the number of layers beyond the conventional two-layer structure, the model enables dynamic adjustment of diffusion coefficients based on stratification stability and incorporates the latest meteorological data to more accurately reflect real-world environmental conditions. Furthermore, by integrating future projections, such as the IPCC climate change scenarios, the model aims to serve as a robust simulation tool capable of comprehensively assessing long-term changes in lake environments due to climate change.
For validation, simulations were conducted on Lake Suwa for the period 2012–2016. The results indicated that while key seasonal phenomena, such as summer stratification formation and winter holomixis, were reproduced to some extent, the model failed to fully capture the strong summer stratification observed in nature. Additionally, while surface-layer dissolved oxygen concentrations generally aligned with observational data, the reproduction of hypoxic conditions in deeper layers remains insufficient. Although adjustments to growth rates and light limitation factors helped suppress phytoplankton blooms, interactions with zooplankton and their effects on nitrogen and phosphorus concentrations remain inadequately represented.
These results highlight that the model, in its current form, does not fully replicate real-world environmental conditions, particularly in terms of water temperature, dissolved oxygen, nitrogen cycling, and phytoplankton dynamics, underscoring the need for further refinement. Future improvements will focus on enhancing the representation of stratification intensity and material circulation, stabilizing material balances, and improving overall model accuracy. Additionally, expanding model applications beyond Lake Suwa and validating its performance across various lakes, both domestically and internationally, will be essential for increasing its versatility. By further integrating future climate prediction models, such as IPCC scenarios, this study aims to provide more precise indicators for assessing the long-term impacts of climate change on lake environments, thereby contributing to environmental management and policy formulation.
In summary, this study proposes a novel multi-layer model that overcomes the limitations of conventional models, enabling detailed simulations of physical and chemical processes in lake environments under changing climatic conditions. The model’s applicability and future potential provide a valuable foundation for further research and development in the field of lake ecosystem modeling and climate impact assessment.