When to conduct an isotopic survey for lake water balance evaluation in highly seasonal climates

One‐time or short‐term lake water isotopic surveys are often employed to evaluate regional lake water balance. However, it can be difficult to determine the optimal time‐window for sampling to obtain a representative long‐term perspective of lake water balance in settings influenced by seasonal variations in precipitation, evaporative loss, glacial/snow meltwater, and larger seasonal shifts in isotopic composition of precipitation. This is especially true for areas of the Tibetan Plateau that are influenced by the summer Indian monsoon. Although high‐frequency sampling is always preferred as the most rigorous approach to characterize the water budget of lakes or watersheds, this may be impractical in remote regions and over large spatial scales. To assess the potential sensitivity of isotope balance characterization to seasonal variability, we used a weekly lake water isotope data set acquired over a period of 3 years on the Tibetan Plateau to evaluate the potential inaccuracies that might have arisen from using isotopic data collected during narrower time‐windows. For this assessment, we use weekly isotopic data collected during the study and assume that these sampling events were stand‐alone one‐time surveys. We then demonstrate the sensitivity of the isotope balance method in this setting, particularly for the rainy season that significantly underestimated the evaporation/inflow. In contrast, isotopic composition of the lake water was found to be more representative of long‐term conditions when sampled in October on the Tibetan Plateau. To broaden our evaluation of seasonality effects over a range of climatic zones, published high‐frequency isotopic data were also compiled, and a similar assessment was carried out for selected regions of the world. The synthesized data and model outputs, which confirm pronounced variations in lake water isotopic composition and evaporation/inflow across a range of seasonal climates, were used to determine optimal sampling windows for these specific regions.