Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
Volume 21, issue 3
Hydrol. Earth Syst. Sci., 21, 1491–1514, 2017
https://doi.org/10.5194/hess-21-1491-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Vegetation changes under a changing environment and the impacts...

Hydrol. Earth Syst. Sci., 21, 1491–1514, 2017
https://doi.org/10.5194/hess-21-1491-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 Mar 2017

Research article | 10 Mar 2017

An integrated probabilistic assessment to analyse stochasticity of soil erosion in different restoration vegetation types

Ji Zhou1,2, Bojie Fu1,2, Guangyao Gao1, Yihe Lü1, and Shuai Wang1 Ji Zhou et al.
  • 1State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Science, Chinese Academy of Science, Beijing 100085, People's Republic of China
  • 2University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China

Abstract. The stochasticity of soil erosion reflects the variability of soil hydrological response to precipitation in a complex environment. Assessing this stochasticity is important for the conservation of soil and water resources; however, the stochasticity of erosion event in restoration vegetation types in water-limited environment has been little investigated. In this study, we constructed an event-driven framework to quantify the stochasticity of runoff and sediment generation in three typical restoration vegetation types (Armeniaca sibirica (T1), Spiraea pubescens (T2) and Artemisia copria (T3)) in closed runoff plots over five rainy seasons in the Loess Plateau of China. The results indicate that, under the same rainfall condition, the average probabilities of runoff and sediment in T1 (3.8 and 1.6 %) and T3 (5.6 and 4.4 %) were lowest and highest, respectively. The binomial and Poisson probabilistic model are two effective ways to simulate the frequency distributions of times of erosion events occurring in all restoration vegetation types. The Bayes model indicated that relatively longer-duration and stronger-intensity rainfall events respectively become the main probabilistic contributors to the stochasticity of an erosion event occurring in T1 and T3. Logistic regression modelling highlighted that the higher-grade rainfall intensity and canopy structure were the two most important factors to respectively improve and restrain the probability of stochastic erosion generation in all restoration vegetation types. The Bayes, binomial, Poisson and logistic regression models constituted an integrated probabilistic assessment to systematically simulate and evaluate soil erosion stochasticity. This should prove to be an innovative and important complement in understanding soil erosion from the stochasticity viewpoint, and also provide an alternative to assess the efficacy of ecological restoration in conserving soil and water resources in a semi-arid environment.

Publications Copernicus
Download
Short summary
We constructed an integrated probabilistic assessment to describe, simulate and evaluate the stochasticity of soil erosion in restoration vegetation in the Loess Plateau. We found that morphological structures in vegetation are the source of different stochasticities of soil erosion, and proved that the Poisson model is fit for predicting erosion stochasticity. This assessment could be an important complement to develop restoration strategies to improve understanding of stochasticity of erosion.
We constructed an integrated probabilistic assessment to describe, simulate and evaluate the...
Citation