Watershed modeling considering parameter uncertainty to evaluate future physical water risks

For beverage manufacturers that utilize local water resources, it is crucial to understand future physical water risks caused by climate change to ensure business continuity and appropriate disclosure to investors, customers, and other stakeholders. Recently, free and widely used water risk screening tools have become available, such as Aqueduct 4.0 (WRI, 2023) and Water Risk Filter (WWF, 2024). However, because of their global-scale design, they may not accurately represent site-specific hydrological water cycle processes, including local water use conditions and surface water-groundwater interactions. Thus, their outputs may not be consistent with historical experience or local perceptions.

Watershed modeling is a useful tool for representing local hydrological water cycle processes and quantitatively evaluating future water risks. Nevertheless, watershed model parameters are inherently uncertain, and future prediction simulations with a single parameter set may lead to either underestimation or overestimation of water risk metrics. Therefore, in order to assess water risk more effectively, it is necessary to develop a framework that can identify feasible parameter combinations (multiple solutions) while taking into account parameter uncertainty.

In this study, we developed a watershed model that considers parameter uncertainty to quantify future physical water risks in the Phu Sai River Basin, Rayong Province, Thailand (approximately 280 km²), which is a concern due to the increase in water risks based on Aqueduct 4.0. A watershed modeling tool GETFLOWS was applied, which can simulate surface water and groundwater flow simultaneously.

The required data for watershed modeling was classified into hydrological observations, meteorology, land use/land cover, topography, geology, and water use. Our primary source of data was public data, including global datasets for meteorology, land use/land cover, and topography, as well as Thai government datasets for geology and water use. Regarding hydrological observation data used for model validation, in addition to existing data released by the Thai government, field measurements of river discharge and groundwater levels were conducted to improve model accuracy. Model performance for 2015–2025 was evaluated using the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and correlation coefficient.

The first step was to identify a parameter set that can accurately reproduce the hydrological observations through manual calibration. The next step was to create realistic parameter ranges and conduct sensitivity analyses to extract parameters that have a significant impact on simulated river discharge and groundwater levels. For the selected parameters, we generated 100 parameter combinations using Latin hypercube sampling and ultimately identified two parameter sets that showed high agreement with hydrological observations based on the NSE, RMSE, and correlation coefficient. Obtaining multiple solutions could help us evaluate the spread of future water risk predictions caused by parameter uncertainty.

In future work, we plan to conduct future prediction simulations using climate projection datasets such as NEX-GDDP-CMIP6 v2.0 (NASA, 2025) and to quantitatively evaluate future physical water risks based on risk assessment metrics such as required river discharge and groundwater level thresholds.