Improved USLE-K factor prediction: A case study on water erosion areas in China

Soil erodibility (K-factor) is an essential factor in soil erosion prediction and conservation practises. The major obstacles to any accurate, large-scale soil erodibility estimation are the lack of necessary data on soil characteristics and the misuse of variable K-factor calculators. In this study...

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Main Authors: Bin Wang, Fenli Zheng, Yinghui Guan
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2016-09-01
Series:International Soil and Water Conservation Research
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2095633916300648
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spelling doaj-70318f15439946f39e2666eca4e8ddd32021-02-02T01:18:52ZengKeAi Communications Co., Ltd.International Soil and Water Conservation Research2095-63392016-09-014316817610.1016/j.iswcr.2016.08.003Improved USLE-K factor prediction: A case study on water erosion areas in ChinaBin Wang0Fenli Zheng1Yinghui Guan2School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR ChinaState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, Shaanxi 712100, PR ChinaSchool of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, PR ChinaSoil erodibility (K-factor) is an essential factor in soil erosion prediction and conservation practises. The major obstacles to any accurate, large-scale soil erodibility estimation are the lack of necessary data on soil characteristics and the misuse of variable K-factor calculators. In this study, we assessed the performance of available erodibility estimators Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), Erosion Productivity Impact Calculator (EPIC) and the Geometric Mean Diameter based (Dg) model for different geographic regions based on the Chinese soil erodibility database (CSED). Results showed that previous estimators overestimated almost all K-values. Furthermore, only the USLE and Dg approaches could be directly and reliably applicable to black and loess soil regions. Based on the nonlinear best fitting techniques, we improved soil erodibility prediction by combining Dg and soil organic matter (SOM). The NSE, R2 and RE values were 0.94, 0.67 and 9.5% after calibrating the results independently; similar model performance was showed for the validation process. The results obtained via the proposed approach were more accurate that the former K-value predictions. Moreover, those improvements allowed us to effectively establish a regional soil erodibility map (1:250,000 scale) of water erosion areas in China. The mean K-value of Chinese water erosion regions was 0.0321 (t ha h)·(ha MJ mm)−1 with a standard deviation of 0.0107 (t ha h)·(ha MJ mm)−1; K-values present a decreasing trend from North to South in water erosion areas in China. The yield soil erodibility dataset also satisfactorily corresponded to former K-values from different scales (local, regional, and national).http://www.sciencedirect.com/science/article/pii/S2095633916300648Erodibility assessmentErodibility mapK-valueModelingChina
collection DOAJ
language English
format Article
sources DOAJ
author Bin Wang
Fenli Zheng
Yinghui Guan
spellingShingle Bin Wang
Fenli Zheng
Yinghui Guan
Improved USLE-K factor prediction: A case study on water erosion areas in China
International Soil and Water Conservation Research
Erodibility assessment
Erodibility map
K-value
Modeling
China
author_facet Bin Wang
Fenli Zheng
Yinghui Guan
author_sort Bin Wang
title Improved USLE-K factor prediction: A case study on water erosion areas in China
title_short Improved USLE-K factor prediction: A case study on water erosion areas in China
title_full Improved USLE-K factor prediction: A case study on water erosion areas in China
title_fullStr Improved USLE-K factor prediction: A case study on water erosion areas in China
title_full_unstemmed Improved USLE-K factor prediction: A case study on water erosion areas in China
title_sort improved usle-k factor prediction: a case study on water erosion areas in china
publisher KeAi Communications Co., Ltd.
series International Soil and Water Conservation Research
issn 2095-6339
publishDate 2016-09-01
description Soil erodibility (K-factor) is an essential factor in soil erosion prediction and conservation practises. The major obstacles to any accurate, large-scale soil erodibility estimation are the lack of necessary data on soil characteristics and the misuse of variable K-factor calculators. In this study, we assessed the performance of available erodibility estimators Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE), Erosion Productivity Impact Calculator (EPIC) and the Geometric Mean Diameter based (Dg) model for different geographic regions based on the Chinese soil erodibility database (CSED). Results showed that previous estimators overestimated almost all K-values. Furthermore, only the USLE and Dg approaches could be directly and reliably applicable to black and loess soil regions. Based on the nonlinear best fitting techniques, we improved soil erodibility prediction by combining Dg and soil organic matter (SOM). The NSE, R2 and RE values were 0.94, 0.67 and 9.5% after calibrating the results independently; similar model performance was showed for the validation process. The results obtained via the proposed approach were more accurate that the former K-value predictions. Moreover, those improvements allowed us to effectively establish a regional soil erodibility map (1:250,000 scale) of water erosion areas in China. The mean K-value of Chinese water erosion regions was 0.0321 (t ha h)·(ha MJ mm)−1 with a standard deviation of 0.0107 (t ha h)·(ha MJ mm)−1; K-values present a decreasing trend from North to South in water erosion areas in China. The yield soil erodibility dataset also satisfactorily corresponded to former K-values from different scales (local, regional, and national).
topic Erodibility assessment
Erodibility map
K-value
Modeling
China
url http://www.sciencedirect.com/science/article/pii/S2095633916300648
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