We present a set of new geomorphometric variables that express landscape position relative to breaklines in hillslopes, and test whether these variables are of value in explaining soil property variation in three study sites in the United States, the Netherlands, and Spain. Underlying this work is the recognition that slope breaks, such as cliff lines, lynchets, and large slumped blocks, are associated with processes that affect soil formation around them. For each study site, we digitized slope breaks and calculated vertical and horizontal distance to the nearest lower and higher slope breaks, as well as the relative position between multiple slope breaks, the slope increase at the next higher slope break, and the slope decrease at the next lower slope break. We then assessed the value of these geomorphometric variables in the prediction of slope properties by simple linear regression. At each study site, models were fitted to existing soil observations using a traditional set of geomorphometric variables, and the traditional set plus our newly developed variables.

Model comparison indicated that the new variables substantially improved model fit and reduced model error for the site in the United States (Kansas, n = 100), improved model fit but did not reduce model error for the site in the Netherlands (Limburg, n = 192), and did not solve model overfitting issues for the small dataset in Spain (Malaga, n = 66).

我们提出了一组新的地貌测量变量，这些变量表示相对于山坡断裂线的景观位置，并测试这些变量在解释美国、荷兰和西班牙三个研究地点的土壤特性变化方面是否有价值。这项工作的基础是认识到斜坡断裂，如悬崖线、山丘和大型坍塌块，与影响其周围土壤形成的过程有关。对于每个研究地点，我们将坡折点数字化并计算到最近的较低和较高坡折点的垂直和水平距离，以及多个坡折点之间的相对位置，坡度在下一个较高坡折点处增加，坡度在下一个较低的斜坡中断。然后，我们通过简单的线性回归评估了这些地貌变量在预测斜坡特性中的价值。在每个研究地点，使用一组传统的地貌测量变量和传统的一组加上我们新开发的变量，将模型拟合到现有的土壤观测值。

模型比较表明，新变量显着改善了模型拟合并减少了美国站点的模型误差（堪萨斯州，n = 100），改进了模型拟合，但并未减少荷兰站点的模型误差（林堡，n = 192），并且没有解决西班牙小数据集的模型过度拟合问题（马拉加，n = 66）。