Human or climate? Differentiating the anthropogenic and climatic drivers of lake storage changes on spatial perspective via remote sensing data

Lakes are an essential part of the terrestrial water system in which storage changes are controlled by water balance and human impact. Although there are some attempts to define storage changes on a global scale, examination of spatial relations is poorly quantified. In this study, therefore, lake storage changes have been investigated using remote-sensing-derived data around the globe. Hence, 372 artificial/natural lakes were obtained, covering between 1992 and 2019. Watersheds belong to river was extracted via HydroSHED data. Based on watershed, dominant climate types were determined via Köppen-Geiger classification. Similarly, the areal average CRU TS v.4.05 monthly gridded precipitation time series and human footprint data based on watersheds were obtained to understand the drivers of lake storage changes. The nonparametric Mann-Kendall and Sen's slope trend analyses were applied to the storage changes in order to determine long-term increases and decreases. A bivariate map was constructed between storage changes trend vs precipitation trend and human footprint to reveal the drivers of lake storage changes in terms of spatial aspects. The trend analysis and bivariate map results show that North America, the East African Highlands, and the Tibet plateau are important increasing hotspots, where precipitation is a significant driver for storage oscillations, except for the Tibet plateau. Besides, the Brazilian Highlands, Pacific Mountain System, and Intermontane of conterminous USA are other decreasing hotspots in which human footprint and decreasing precipitation collectively affect these changes. Furthermore, results clearly show that anthropogenic influence is low in the northern and mountainous areas, and storage changes have a linear relationship with precipitation. In contrast, intense human climate interaction influences lake changes in plains areas and arid/temperate climates (p < 0.05).