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Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition

机译:基于测量的土壤水分和植物物种组成的地形湿度指数计算指导

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Soil moisture controls environmental processes and species distributions, but it is difficult to measure and interpolate across space. Topographic Wetness Index (TWI) derived from digital elevation model is therefore often used as a proxy for soil moisture. However, different algorithms can be used to calculate TWI and this potentially affects TWI relationship with soil moisture and species assemblages. To disentangle insufficiently-known effects of different algorithms on TWI relation with soil moisture and plant species composition, we measured the root-zone soil moisture throughout a growing season and recorded vascular plants and bryophytes in 45 temperate forest plots. For each plot, we calculated 26 TWI variants from a LiDAR-based digital terrain model and related these TWI variants to the measured soil moisture and moisture-controlled species assemblages of vascular plants and bryophytes. A flow accumulation algorithm determined the ability of the TWI to predict soil moisture, while the flow width and slope algorithms had only a small effects. The TWI calculated with the most often used single-flow D8 algorithm explained less than half of the variation in soil moisture and species composition explained by the TWI calculated with the multiple-flow FD8 algorithm. Flow dispersion used in the FD8 algorithm strongly affected the TWi performance, and a flow dispersion close to 1.0 resulted in the TWI best related to the soil moisture and species assemblages. Using downslope gradient instead of the local slope gradient can strongly decrease TWI performance. Our results clearly showed that the method used to calculate TWI affects study conclusion. However, TWI calculation is often not specified and thus impossible to reproduce and compare among studies. We therefore provide guidelines for TWI calculation and recommend the FD8 flow algorithm with a flow dispersion close to 1.0, flow width equal to the raster cell size and local slope gradient for TWI calculation.
机译:土壤水分控制环境过程和物种分布,但难以衡量和跨空间内插。因此,来自数字高度模型的地形湿度指数(TWI)通常用作土壤水分的代理。然而,不同的算法可用于计算TWI,这可能影响与土壤水分和物种组装的TWI关系。为了解开不同算法与土壤水分和植物物种组成的不同算法的不良效果,我们在整个生长季节的根区土壤水分测量,在45个温带森林地块中记录了血管植物和血糖植物。对于每种曲线,我们计算了基于激光雷达的数字地形模型的26个TWI变体,并将这些TWI变体相关给测量的血管植物和苔藓植物和苔藓植物的土壤水分和水分控制物种组合。流量累积算法确定了TWI预测土壤水分的能力,而流量宽度和斜率算法仅具有小效果。用最常用的单流D8算法计算的TWI解释了用多流FD8算法计算的TWI解释的土壤湿度和物种组成的一半。 FD8算法中使用的流动分散体强烈影响TWI性能,与1.0接近1.0的流动分散导致与土壤水分和物种组合有关的TWI。使用Downslope梯度而不是本地斜率梯度可以强烈降低TWI性能。我们的结果清楚地表明,用于计算TWI的方法会影响研究结论。然而,通常没有指定TWI计算,因此无法在研究中复制和比较。因此,我们为TWI计算提供了指导方针,并推荐FD8流量算法,该流量分散接近1.0,流量等于光栅单元尺寸和TWI计算的局部斜率梯度。

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