Spatial extrapolation of stream thermal peaks using heterogeneous time series at a national scale

Abstract

Spatial reconstruction of stream temperature is relevant to water quality standards and fisheries management, yet large, regional scale datasets are rare because data are decentralized and inharmonious. This data discordance is a major limitation for understanding thermal regimes of riverine ecosystems. To overcome this barrier, we first aggregated one of the largest stream temperature databases on record with data from 1700 individual stations over 9 years from 2009-2017 (ng€Combining double low lineg€45g€000g€000 hourly measurements) across France (areag€Combining double low lineg€552g€000g€km2). For each station, we calculated a simple, ecologically relevant metric - the thermal peak - that captures the magnitude of summer thermal maximums. We then used three statistical models to extrapolate the thermal peak to nearly every stream reach in France and Corsica (ng€Combining double low lineg€105g€800) and compared relative model performances with an air temperature metric. In general, the hottest thermal peaks were found along major rivers, whereas the coldest thermal peaks were found along small rivers with forested riparian zones and strong groundwater inputs and were located in mountainous regions. Several key predictors of the thermal peak emerged, including drainage area, mean summer air temperature, minimum monthly specific discharge, and vegetation cover in the riparian zone. Despite differing predictor importance across model structures, we observed strong concordance among models in their spatial distributions of the thermal peak, suggesting its robustness as a useful metric at the regional scale. Finally, air temperature was found to be a poor proxy for the stream temperature thermal peak across nearly all stations and reaches, highlighting the growing need to measure and account for stream temperature in regional ecological studies.