Lake and river temperature regimes at global scale derived from remote sensing imagery and geospatial modelling

Lake and river water temperatures provide a framework for understanding the ecological, biogeochemical, and physical functioning of these aquatic ecosystems. In particular, knowledge of the current global distribution of lake and river thermal characteristics can serve as a critical baseline to assess impacts of projected future changes. However, site-specific observations of lake and river temperatures are not readily available for most locations in the world and are especially scarce for small lakes and streams. This presentation provides an overview of several assessments at global scale and in high spatial resolution to estimate the surface water temperatures of all lakes and rivers worldwide as contained in the HydroATLAS database.

First, the seasonality of lake surface temperatures was derived from Landsat 8 thermal radiance observations between 2013 and 2021 for ~1.4 million lakes in the world that are ≥10 ha in surface area; resulting in a dataset termed LakeTEMP. Furthermore, mixing regime types were estimated for all lakes with a deterministic, physically-based model using the satellite-derived, lake-specific surface temperatures of LakeTEMP combined with other lake properties (ice cover, transparency, wind, solar radiation, and mean lake depth); resulting in a dataset termed LakeMIX. LakeTEMP and LakeMIX fill a crucial spatial data gap in large-scale limnological research, especially for the incorporation of small lakes and understudied geographies of remote regions. The data are in an analysis-ready format and freely available at https://www.hydrosheds.org/products/laketemp.

Second, we developed a global high-resolution model to estimate the long-term monthly average water temperatures of every river reach within the global digital river network of HydroATLAS, representing all rivers and streams exceeding either 10 km² in upstream catchment area or 100 L/sec in average flow. The hybrid model uses a geostatistical approach to estimate the water temperature of headwater streams based on air temperature as well as a physical model component that includes streamflow routing and snow and groundwater contributions.

Our global, high-resolution water temperature datasets are intended to serve as a baseline that can aid in large-scale assessments of lake and river ecosystem conditions by categorizing different thermal regime types. Results are globally consistent and expected to be particularly valuable as first-order proxies in remote and data sparse regions, but less adequate for smaller scale studies due to local inaccuracies.