4.5 Article

Soil erosion modelling of burned and mulched soils following a Mediterranean forest wildfire

期刊

SOIL USE AND MANAGEMENT
卷 39, 期 2, 页码 881-899

出版社

WILEY
DOI: 10.1111/sum.12884

关键词

calibration; erosion model; event scale; plot scale; postfire management; soil loss

向作者/读者索取更多资源

Soil erosion models applied to burned forests in different global regions can be unreliable due to a lack of verification data. This study evaluated three erosion models and found that an optimized MMF model was the most accurate way to estimate soil loss in burned forests, providing valuable insight for land managers.
Soil erosion modelling applied to burned forests in different global regions can be unreliable because of a lack of verification data. Here, we evaluated the following three erosion models: (1) Water Erosion Prediction Project (WEPP), (2) Morgan-Morgan-Finney (MMF) and (3) Universal Soil Loss Equation-Modified (USLE-M). Using field plots that were either untreated or mulched with straw, this study involved observations of soil loss at the event scale at a burned pine forest in Central Eastern Spain. The erosion predictions of the three models were analysed for goodness-of-fit. Optimization of the MMF model with a new procedure to estimate the C-factor resulted in a satisfactory erosion prediction capacity in burned plots with or without the mulching treatment. The WEPP model underestimated erosion in the unburned areas and largely overestimated the soil loss in burned areas. The accuracy of soil loss estimation by the USLE-M model was also poor. Calibration of the curve numbers and C-factors did not improve the USLE-M model estimation. Therefore, we conclude that an optimized MMF model was the most accurate way to estimate soil loss and recommend this approach for in Mediterranean burned forests with or without postfire mulching. This study gives land managers insight about the choice of the most suitable model for erosion predictions in burned forests.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据