LAUSR

Riparian tree planting is increasingly being used as a strategy to shade river corridors and offset the impact of climate change on river temperature. Because the circumstances under which tree planting generates the greatest impact are still largely unknown, researchers are increasingly using process-based models to simulate the impacts of tree planting (or felling) on river temperature. However, the high-resolution data on existing riparian tree cover needed to parameterise these models can be difficult to obtain, especially in datasparse areas. In this paper, we compare the performance of a river temperature model parameterised with a range of different tree cover datasets, to assess whether tree cover data extracted from readily available GIS databases or coarser (ie. 2 – 5 m) digital elevation products are able to generate river temperature simulations approaching the accuracy of higher resolution SfM or LiDAR datasets. Our results show that model performance for simulations incorporating these data is generally degraded in relation to LiDAR/SfM inputs, and that tree cover data from ‘alternative’ sources can lead to unexpected temperature model outcomes. We subsequently use our model to simulate the addition/removal of riparian tree cover from alongside the river channel. Simulations indicate that the vast majority of the ‘shading effect’ is generated by tree cover within the 5 m zone immediately adjacent to the river channel, a key finding with regards to developing efficient riparian tree planting strategies. These results further emphasise the importance of incorporating the highest-possible resolution tree cover data when running tree planting/clearcutting scenario simulations.