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Climate model output is often downscaled to grids of moderately high spatial resolution (~4–6-km grid cells). Such projections have been used in numerous hydrological impact assessment studies at watershed scales. However, relatively few studies have been conducted to assess the impact of climate change on the hydrodynamics and water quality in lakes and reservoirs. A potential barrier to such assessments is the need for meteorological variables at subdaily time scales that are downscaled to in situ observations to which lake and reservoir water quality models have been calibrated and validated. In this study, we describe a generalizable procedure that utilizes gridded downscaled data; applies a secondary bias-correction procedure using equidistance quantile mapping to map projections to station-based observations; and implements temporal disaggregation models to generate point-scale hourly air and dewpoint temperature, wind speed, and solar radiation for use in water quality models. The proposed approach is demonstrated for six locations within New York State: four within watersheds of the New York City water supply system and two at nearby National Weather Service stations. Disaggregation models developed using observations reproduced hourly data well at all locations, with Nash–Sutcliffe efficiency greater than 0.9 for air temperature and dewpoint, 0.4–0.6 for wind speed, and 0.7–0.9 for solar radiation.

A Practical Approach to Developing Climate Change Scenarios for Water Quality Models