This study presents a one-dimensional, multi-layer water quality model capable of assessing lake water quality under climate change, even in regions where bathymetric data are limited. Freshwater lakes provide essential ecosystem services—supplying drinking water, supporting biodiversity, enabling food production, and offering recreation. However, climate warming intensifies thermal stratification, weakens vertical mixing, depletes deep-water oxygen, and promotes harmful algal blooms.
Conventional two-layer models (e.g., Pamolare) simulate temperature and nutrient dynamics with limited precision and rely on fixed parameters and detailed depth profiles. In contrast, the proposed model replaces the rigid epilimnion–hypolimnion split with a flexible stack of dynamically updated layers, whose thicknesses and turbulent diffusion coefficients are computed using Richardson number–based stability diagnostics.
When bathymetric data are unavailable, the model estimates an effective mean depth using basic statistics (e.g., surface area and volume) or satellite-derived products. It then selects an appropriate number of layers to maintain vertical resolution, eliminating the need for detailed lakebed maps.
An integrated ecological sub-module simulates nitrogen, phosphorus, dissolved oxygen, phytoplankton, zooplankton, detritus, and dissolved organic matter. The model accepts hourly meteorological forcing from global reanalysis datasets or satellite observations. IPCC AR6 scenario data are read directly, allowing for future projections regardless of local data richness.
Model validation using Lake Suwa, Japan (2012–2016), successfully reproduced spring turnover timing and summer stratification onset. Surface dissolved oxygen trends also aligned with observations. However, the model underestimated stratification strength, leading to underestimation of hypolimnetic oxygen depletion. Calibration of phytoplankton growth and light attenuation reduced overestimated blooms, but nutrient feedbacks and sediment–water fluxes remain under-resolved. Consequently, achieving simultaneous accuracy across temperature, oxygen, nutrients, and plankton dynamics still requires further calibration.
Planned enhancements include advanced turbulence closure schemes, explicit sediment flux modeling, size-structured plankton functional types, assimilation of satellite-derived chlorophyll, and automated multi-objective calibration. These upgrades aim to improve global applicability, especially in data-poor lakes.
Testing in morphologically and climatically diverse systems will assess model transferability. Coupling with downscaled CMIP6 scenarios will support long-term risk assessments. Ultimately, the model seeks to provide indicators of hypoxia and bloom risks, inform aeration and nutrient reduction strategies, and support climate adaptation planning—helping safeguard freshwater ecosystem services in a warming world.
This framework provides a practical approach to scenario-based assessment of lakes worldwide, offering key input for evaluating future drinking water supply and ecosystem health under changing climate conditions.