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Abstract Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing andin situdata for validation.In situmonitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data. Here, we address the challenge of synthesizingin situlake temperature data by creating a standardized database of near-surface and subsurface measurements from 134 sites across 29 large North American lakes, with the primary goal of supporting an ongoing lake model validation study. We utilize data sources ranging from federal agency repositories to local monitoring group samples, with a collective historical record spanning January 1, 2000 through December 31, 2022. Our database has direct utility for validating simulations and forecasts from operational numerical weather prediction systems in large lakes whose extensive surface area may significantly influence nearby weather and climate patterns. 

Comprehensive assessments of hydrological components are crucial for enhancing operational water supply simulations. However, hydrological models are often evaluated based on their surface flow simulations, while the validation of subsurface and groundwater components tends to be overlooked or not well documented. In this study, we evaluated the outputs of two hydrological models, the Large Basin Runoff Model (LBRM) and the Weather Research and Forecasting – Hydrological modeling extension package (WRF-Hydro), for potential implementation in operational water balance forecasting in the Great Lakes region. We examined the simulated hydrological variables including surface (e.g. snow water equivalent, evapotranspiration, and streamflow), subsurface (e.g. soil moisture at different layers), and groundwater components with observed or reference data from ground-based stations and remotely sensed images. The findings of this study provide valuable insights into the capabilities and limitations of each model. These findings contribute to more informed water management strategies for the Great Lakes region.