Estimating organic surface horizon depth for peat and peaty soils across a Scottish upland catchment using linear mixed models with topographic and geological covariates
Estimating organic surface horizon depth for peat and peaty soils across a Scottish upland catchment using linear mixed models with topographic and geological covariates
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使用具有地形和地质协变量的线性混合模型估计苏格兰高地流域泥炭和泥炭土的有机表面层深度
DOI：
10.1111/sum.12596
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发表时间：
2020
影响因子：
3.8
通讯作者：
Finlayson A
中科院分区：
文献类型：
--
作者：
Finlayson A
为了评估和保护泥炭和泥炭质土壤提供的生态系统服务，需要准确估计表层有机层的深度。本研究使用线性混合模型（LIFE）来测试地形（海拔，坡度，方面）和浅表地质参数如何有助于提高苏格兰高地流域的深度估计。使用来自283个研究中心的平均（n= 5）深度数据（代表完整的协变量范围）校准LIGO，并根据验证数据集进行检验。使用最大似然法估计模型，并使用Akaike信息准则检验将协变量迭代添加到具有恒定固定效应的模型中是否有益。海拔、坡度和某些地质类别都被确定为有用的协变量。在添加随机效应（即残差的空间建模）后，仅具有常数固定效应的模型的RMSE降低了24%。向具有地形协变量（固定效应=常数、坡度、海拔）的模型添加随机效应使RMSE降低了13%，而向具有地形和地质协变量（固定效应=常数、坡度、海拔、某些地质类别）的模型添加随机效应仅使RMSE降低了3%。因此，大部分的空间模式的深度解释的固定效应在后一个模型。这项研究有助于不断增长的研究基础，证明广泛可用的地形（以及地质）数据集，具有全国覆盖范围，可以包括在空间模型，以提高有机层深度估计。
In order to evaluate and protect ecosystem services provided by peat and peaty soils, accurate estimations for the depth of the surface organic horizon are required. This study uses linear mixed models (LMMs) to test how topographic (elevation, slope, aspect) and superficial geology parameters can contribute to improved depth estimates across a Scottish upland catchment. Mean (n= 5) depth data from 283 sites (representing full covariate ranges) were used to calibrate LMMs, which were tested against a validation dataset. Models were estimated using maximum likelihood, and the Akaike Information Criterion was used to test whether the iterative addition of covariates to a model with constant fixed effects was beneficial. Elevation, slope and certain geology classes were all identified as useful covariates. Upon addition of the random effects (i.e. spatial modelling of residuals), the RMSE for the model with constant‐only fixed effects reduced by 24%. Addition of random effects to a model with topographic covariates (fixed effects = constant, slope, elevation) reduced the RMSE by 13%, whereas the addition of random effects to a model with topographic and geological covariates (fixed effects = constant, slope, elevation, certain geology classes) reduced the RMSE by only 3%. Therefore, much of the spatial pattern in depth was explained by the fixed effects in the latter model. The study contributes to a growing research base demonstrating that widely available topographic (and also here geological) datasets, which have national coverage, can be included in spatial models to improve organic horizon depth estimations.
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