School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom.
Marine Scotland Science, Freshwater Fisheries Laboratory, Faskally, Pitlochry PH16 5LB, United Kingdom.
Sci Total Environ. 2019 Aug 15;678:326-340. doi: 10.1016/j.scitotenv.2019.04.229. Epub 2019 May 8.
Climatic warming will increase river temperature globally, with consequences for cold water-adapted organisms. In regions with low forest cover, elevated river temperature is often associated with a lack of bankside shading. Consequently, river managers have advocated riparian tree planting as a strategy to reduce temperature extremes. However, the effect of riparian shading on river temperature varies substantially between locations. Process-based models can elucidate the relative importance of woodland and other factors driving river temperature and thus improve understanding of spatial variability of the effect of shading, but characterising the spatial distribution and height of riparian tree cover necessary to parameterise these models remains a significant challenge. Here, we document a novel approach that combines Structure-from-Motion (SfM) photogrammetry acquired from a drone to characterise the riparian canopy with a process based temperature model (Heat Source) to simulate the effects of tree shading on river temperature. Our approach was applied in the Girnock Burn, a tributary of the Aberdeenshire Dee, Scotland. Results show that SfM approximates true canopy elevation with a good degree of accuracy (R = 0.96) and reveals notable spatial heterogeneity in shading. When these data were incorporated into a process-based temperature model, it was possible to simulate river temperatures with a similarly-high level of accuracy (RMSE <0.7 °C) to a model parameterised using 'conventional' LiDAR tree height data. We subsequently demonstrate the utility of our approach for quantifying the magnitude of shading effects on stream temperature by comparing simulated temperatures against another model from which all riparian woodland has been removed. Our findings highlight drone-based SfM as an effective tool for characterising riparian shading and improving river temperature models. This research provides valuable insights into the effects of riparian woodland on river temperature and the potential of bankside tree planting for climate change adaptation.
气候变暖将使全球河流温度上升，这将对适应冷水的生物产生影响。在森林覆盖率低的地区，河流温度升高通常与河岸缺乏遮荫有关。因此，河流管理者提倡在河岸种植树木，作为降低极端温度的一种策略。然而，河岸遮荫对河流温度的影响在不同地区差异很大。基于过程的模型可以阐明林地和其他驱动河流温度的因素的相对重要性，从而更好地理解遮荫效果的空间变异性，但描述这些模型所需的河岸树木覆盖的空间分布和高度仍然是一个重大挑战。在这里，我们提出了一种新的方法，该方法结合了从无人机获取的运动结构（Structure-from-Motion，SfM）摄影测量技术来描述河岸冠层，并结合基于过程的温度模型（Heat Source）来模拟树木遮荫对河流温度的影响。我们的方法应用于苏格兰阿伯丁郡迪河的一条支流吉尔诺克溪（Girnock Burn）。结果表明，SfM 可以很好地近似真实树冠高度（R = 0.96），并揭示了遮荫的显著空间异质性。当将这些数据纳入基于过程的温度模型时，就可以以与使用“传统”LiDAR 树高数据参数化的模型类似的高精度（RMSE <0.7°C）来模拟河流温度。然后，我们通过比较模拟温度与另一个去除所有河岸林地的模型来证明我们的方法在量化遮荫对溪流温度的影响程度方面的效用。我们的研究结果突出了基于无人机的 SfM 在描述河岸遮荫和改进河流温度模型方面的有效性。这项研究为河岸林地对河流温度的影响以及河岸树木种植对气候变化适应的潜力提供了有价值的见解。
