资源环境科技发展态势分析平台

River temperature exerts a critical control on habitat for aquatic biota. As the climate warms in eastern Canada, threats to habitats of cold‐water species will increase, underpinning the necessity to develop an understanding of landscape‐scale, thermal regimes of flowing waters. We assessed the performance of spatial statistical network (SSN) models of river temperature using high resolution thermal infrared imagery (0.6 m) and LiDAR (1 m) compared to NASA's Shuttle Radar Topography Mission (SRTM ‐ 30 m) topographic data, and interrogate LiDAR derived fine‐scale models (3 ha) to describe groundwater connectivity to surface waters in catchments with shallow overburden and varied bedrock geology. LiDAR improved model performance in a catchment underlain by a homogeneous, high hydraulic conductance bedrock (Cains River), but did not improve model performance in a catchment with heterogeneous bedrock and variable hydraulic conductance (North Pole Stream). We hypothesize that differences in bedrock conductance modified topographic controls on subsurface flows and discharge patterns to the rivers, and thus produced the mixed performance of the SSN models. At finer scales, river reaches in steep valleys incising high conductance bedrock produced groundwater discharge which was absent in incised valleys with low conductance bedrock. These findings indicate that while topography exerts an important control on landscape‐scale hydrological processes, geologic setting is a similarly important influence on hydrological processes. We suggest the inclusion of a 3^rd dimension of spatial autocorrelation, representative of the vertical plane that captures the geologic setting, would broaden the geographic applicability of spatial statistical models for river temperature studies.