Articles | Volume 9, issue 6
https://doi.org/10.5194/se-9-1507-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-9-1507-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2018
Research article |
| 20 Dec 2018
| 20 Dec 2018
Soil erodibility and its influencing factors on the Loess Plateau of China: a case study in the Ansai watershed
State Key Laboratory of Earth Surface Processes and Resources Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Institute of Land Surface System and Sustainable Development, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Earth Surface Processes and Resources Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Institute of Land Surface System and Sustainable Development, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Lizhi Jia
State Key Laboratory of Earth Surface Processes and Resources Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Institute of Land Surface System and Sustainable Development, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Stefani Daryanto
State Key Laboratory of Earth Surface Processes and Resources Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Institute of Land Surface System and Sustainable Development, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Xiao Zhang
State Key Laboratory of Earth Surface Processes and Resources Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Institute of Land Surface System and Sustainable Development, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Yanxu Liu
State Key Laboratory of Earth Surface Processes and Resources Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Institute of Land Surface System and Sustainable Development, Faculty of
Geographical Science, Beijing Normal University, Beijing 100875, China
Related authors
Jonathan Rizzi, Ana M. Tarquis, Anne Gobin, Mikhail Semenov, Wenwu Zhao, and Paolo Tarolli
Nat. Hazards Earth Syst. Sci., 21, 3873–3877, https://doi.org/10.5194/nhess-21-3873-2021, https://doi.org/10.5194/nhess-21-3873-2021, 2021
N. A. Nyathi, W. Zhao, and W. Musakwa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 809–816, https://doi.org/10.5194/isprs-annals-V-3-2020-809-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-809-2020, 2020
Ting Hua, Wenwu Zhao, Yanxu Liu, and Yue Liu
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2019-122, https://doi.org/10.5194/nhess-2019-122, 2019
Manuscript not accepted for further review
Short summary
Short summary
We used the RUSLE model and geographical detector method to evaluate and identify the dominant factors and variability in the Yellow River basin. We found that in the period of low rainfall and vegetation coverage, the interaction of rainfall and slope can enhance their impact on the distribution of soil erosion, while the combination of vegetation and slope was the dominant interacting factor in other periods, and the dominant driving factors of soil erosion variability were rainfall.
Jingyi Ding, Wenwu Zhao, Stefani Daryanto, Lixin Wang, Hao Fan, Qiang Feng, and Yaping Wang
Hydrol. Earth Syst. Sci., 21, 2405–2419, https://doi.org/10.5194/hess-21-2405-2017, https://doi.org/10.5194/hess-21-2405-2017, 2017
Short summary
Short summary
In this study, we focused on exploring the spatial distribution and temporal variation of desert riparian forests and their influencing factors based on field experiment and remote sensing data. Our result revealed how the environmental factors shape the spatial distribution and temporal variation of desert riparian forest in the downstream Heihe river. The results of this study provide support for the effective restoration of desert riparian forest in the hyperarid zone.
Xuening Fang, Wenwu Zhao, Lixin Wang, Qiang Feng, Jingyi Ding, Yuanxin Liu, and Xiao Zhang
Hydrol. Earth Syst. Sci., 20, 3309–3323, https://doi.org/10.5194/hess-20-3309-2016, https://doi.org/10.5194/hess-20-3309-2016, 2016
Short summary
Short summary
In this study, we focused on analyzing the variation and factors influencing deep soil moisture content based on a soil moisture survey of the Ansai watershed. Our results revealed the variation characteristics of deep soil moisture and its controlling mechanism at a moderate scale. The results of this study are of practical significance for vegetation restoration strategies and the sustainability of restored ecosystems.
Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, and Fang Zhao
Earth Syst. Sci. Data, 16, 2449–2464, https://doi.org/10.5194/essd-16-2449-2024, https://doi.org/10.5194/essd-16-2449-2024, 2024
Short summary
Short summary
To fill the gap in the gridded industrial water withdrawal (IWW) data in China, we developed the China Industrial Water Withdrawal (CIWW) dataset, which provides monthly IWWs from 1965 to 2020 at a spatial resolution of 0.1°/0.25° and auxiliary data including subsectoral IWW and industrial output value in 2008. This dataset can help understand the human water use dynamics and support studies in hydrology, geography, sustainability sciences, and water resource management and allocation in China.
Zhiwei Yang, Jian Peng, Yanxu Liu, Song Jiang, Xueyan Cheng, Xuebang Liu, Jianquan Dong, Tiantian Hua, and Xiaoyu Yu
Earth Syst. Sci. Data, 16, 2407–2424, https://doi.org/10.5194/essd-16-2407-2024, https://doi.org/10.5194/essd-16-2407-2024, 2024
Short summary
Short summary
We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global coverage and an extensive time series spanning March 2000 to October 2022 with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. GloUTCI-M can enhance our capacity to evaluate thermal stress experienced by the human, offering substantial prospects across a wide array of applications.
Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, and Jian Peng
Earth Syst. Sci. Data, 14, 5651–5664, https://doi.org/10.5194/essd-14-5651-2022, https://doi.org/10.5194/essd-14-5651-2022, 2022
Short summary
Short summary
We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT) through the calculation of wet-bulb temperature, data quality control, infilling missing values, and homogenization. The GSDM-WBT covers the complete daily series of 1834 stations from 1981 to 2020. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, which could better support the studies on global and regional humid heat events.
Jonathan Rizzi, Ana M. Tarquis, Anne Gobin, Mikhail Semenov, Wenwu Zhao, and Paolo Tarolli
Nat. Hazards Earth Syst. Sci., 21, 3873–3877, https://doi.org/10.5194/nhess-21-3873-2021, https://doi.org/10.5194/nhess-21-3873-2021, 2021
N. A. Nyathi, W. Zhao, and W. Musakwa
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 809–816, https://doi.org/10.5194/isprs-annals-V-3-2020-809-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-809-2020, 2020
Ting Hua, Wenwu Zhao, Yanxu Liu, and Yue Liu
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2019-122, https://doi.org/10.5194/nhess-2019-122, 2019
Manuscript not accepted for further review
Short summary
Short summary
We used the RUSLE model and geographical detector method to evaluate and identify the dominant factors and variability in the Yellow River basin. We found that in the period of low rainfall and vegetation coverage, the interaction of rainfall and slope can enhance their impact on the distribution of soil erosion, while the combination of vegetation and slope was the dominant interacting factor in other periods, and the dominant driving factors of soil erosion variability were rainfall.
Jingyi Ding, Wenwu Zhao, Stefani Daryanto, Lixin Wang, Hao Fan, Qiang Feng, and Yaping Wang
Hydrol. Earth Syst. Sci., 21, 2405–2419, https://doi.org/10.5194/hess-21-2405-2017, https://doi.org/10.5194/hess-21-2405-2017, 2017
Short summary
Short summary
In this study, we focused on exploring the spatial distribution and temporal variation of desert riparian forests and their influencing factors based on field experiment and remote sensing data. Our result revealed how the environmental factors shape the spatial distribution and temporal variation of desert riparian forest in the downstream Heihe river. The results of this study provide support for the effective restoration of desert riparian forest in the hyperarid zone.
Xuening Fang, Wenwu Zhao, Lixin Wang, Qiang Feng, Jingyi Ding, Yuanxin Liu, and Xiao Zhang
Hydrol. Earth Syst. Sci., 20, 3309–3323, https://doi.org/10.5194/hess-20-3309-2016, https://doi.org/10.5194/hess-20-3309-2016, 2016
Short summary
Short summary
In this study, we focused on analyzing the variation and factors influencing deep soil moisture content based on a soil moisture survey of the Ansai watershed. Our results revealed the variation characteristics of deep soil moisture and its controlling mechanism at a moderate scale. The results of this study are of practical significance for vegetation restoration strategies and the sustainability of restored ecosystems.
Related subject area
Subject area: The evolving Earth surface | Editorial team: Critical zone science | Discipline: Soil science
Stability of soil organic matter in Cryosols of the maritime Antarctic: insights from 13C NMR and electron spin resonance spectroscopy
Influence of slope aspect on the microbial properties of rhizospheric and non-rhizospheric soils on the Loess Plateau, China
Assessment of soil erosion vulnerability in the heavily populated and ecologically fragile communities in Motozintla de Mendoza, Chiapas, Mexico
Simulating carbon sequestration using cellular automata and land use assessment for Karaj, Iran
Polycyclic aromatic hydrocarbon in urban soils of an Eastern European megalopolis: distribution, source identification and cancer risk evaluation
Evgeny Abakumov and Ivan Alekseev
Solid Earth, 9, 1329–1339, https://doi.org/10.5194/se-9-1329-2018, https://doi.org/10.5194/se-9-1329-2018, 2018
Ze Min Ai, Jiao Yang Zhang, Hong Fei Liu, Sha Xue, and Guo Bin Liu
Solid Earth, 9, 1157–1168, https://doi.org/10.5194/se-9-1157-2018, https://doi.org/10.5194/se-9-1157-2018, 2018
Short summary
Short summary
Slope aspect significantly but differently affected the soil microbial biomass carbon and phospholipid fatty acid contents.
Soil carbon and nitrogen have the largest effect on the soil microbial properties.
The rhizospheric effect caused significant difference between rhizospheric and non-rhizospheric soil microbial properties.
Slope aspect affected the mechanisms driving the structure of microbial communities in a micro-ecosystem environment.
Selene B. González-Morales, Alex Mayer, and Neptalí Ramírez-Marcial
Solid Earth, 9, 745–757, https://doi.org/10.5194/se-9-745-2018, https://doi.org/10.5194/se-9-745-2018, 2018
Short summary
Short summary
Physical aspects and knowledge of soil erosion in six rural communities in Chiapas, Mexico, were assessed. Average erosion rates estimated using the RUSLE model ranged from 200 to 1200 ha−1 yr−1. Most erosion rates were relatively high due to steep slopes, sandy soils, and bare land cover. The results of a knowledge, attitudes, and practices (KAP) survey showed that some communities with high erosion rates also had less knowledge of and more negative attitudes towards soil erosion management.
Ali Khatibi, Sharareh Pourebrahim, and Mazlin Bin Mokhtar
Solid Earth, 9, 735–744, https://doi.org/10.5194/se-9-735-2018, https://doi.org/10.5194/se-9-735-2018, 2018
Short summary
Short summary
The speed of land use changes is much higher than in the past, which led to many changes in the environment and ecological processes. These changes cause some changes in the climate, the amount of pollution and biodiversity. Monitoring and modeling historical situation of the region can be used to anticipate the negative effects of these changes in order to protect resources. Agriculture class will be faced with a huge reduction of carbon sequestration because of expansion of residential areas.
George Shamilishvily, Evgeny Abakumov, and Dmitriy Gabov
Solid Earth, 9, 669–682, https://doi.org/10.5194/se-9-669-2018, https://doi.org/10.5194/se-9-669-2018, 2018
Cited articles
Bagarello, V., Stefano, C. D., Ferro, V., Giordano, G., Iovino, M., and
Pampalone, V.: Estimating the USLE soil erodibility factor in Sicily, south
Italy, Appl. Eng. Agric., 28, 199–206, https://doi.org/10.13031/2013.41347, 2012.
Bonilla, C. A. and Johnson, O. I.: Soil erodibility mapping and its
correlation with soil properties in Central Chile, Geoderma, 189–190,
116–123, https://doi.org/10.1016/j.geoderma.2012.05.005, 2012.
Bryan, R. B., Govers, G., and Poesenb, S. R. A.: The concept of soil
erodibility and some problems of assessment and application, Catena, 16,
393–412, https://doi.org/10.1016/0341-8162(89)90023-4, 1989.
Cerdà, A.: Soil aggregate stability under different Mediterranean
vegetation types, Catena, 32, 73–86, https://doi.org/10.1016/S0341-8162(98)00041-1,
1998.
Cerdà, A., Keesstra, S. D., Rodrigo-Comino, J., Novara, A., Pereira, P.,
Brevik, E., Gimenez-morera, A., Fernandez-raga, M., Pulido, M., Prima, S. D.,
and Jordán, A.: Runoff initiation, soil detachment and connectivity are
enhanced as a consequence of vineyards plantations, J. Environ. Manage., 202,
268–275, https://doi.org/10.1016/j.jenvman.2017.07.036, 2017.
Chen, X. Y. and Zhou, J.: Volume-based soil particle fractal relation with
soil erodibility in a small watershed of purple soil, Environ. Earth Sci.,
70, 1735–1746, https://doi.org/10.1007/s12665-013-2261-y, 2013.
Fang, X., Zhao, W., Wang, L., Feng, Q., Ding, J., Liu, Y., and Zhang, X.:
Variations of deep soil moisture under different vegetation types and
influencing factors in a watershed of the Loess Plateau, China, Hydrol. Earth
Syst. Sci., 20, 3309–3323, https://doi.org/10.5194/hess-20-3309-2016, 2016.
Feng, Q., Zhao, W. W., Qiu, Y., Zhao, M. Y., and Zhong, L. N.: Spatial
heterogeneity of soil moisture and the scale variability of its influencing
factors: a case study in the Loess Plateau of China, Water, 5, 1226–1242,
https://doi.org/10.3390/w5031226, 2013.
Feng, X. M., Fu, B. J., Lu, N., Zeng, Y., and Wu, B. F.: How ecological
restoration alters ecosystem services: an analysis of carbon sequestration in
China's Loess Plateau, Sci. Rep.-UK., 3, 28–46, https://doi.org/10.1038/srep02846,
2013.
Ferreira, V., Panagopoulos, T., Andrade, R., Guerrero, C., and Loures, L.:
Spatial variability of soil properties and soil erodibility in the Alqueva
reservoir watershed, Solid Earth, 6, 383–392,
https://doi.org/10.5194/se-6-383-2015, 2015.
Fu, B. J., Zhao, W. W., Chen, L. D., Zhang, Q. J., Lü, Y. H., Gulinck,
H., and Poesen, J.: Assessment of soil erosion at large watershed scale using
RUSLE and GIS: a case study in the Loess plateau of China, Land Degrad. Dev.,
16, 73–85, https://doi.org/10.1002/ldr.646, 2005.
Fu, B. J., Wang, Y. F., Lü, Y. H., He, C. S., Chen, L. D., and Song, C.
J.: The effects of land use combination on soil erosion-a case study in Loess
Plateau of China, Prog. Phys. Geog., 33, 793–804,
https://doi.org/10.1177/0309133309350264, 2009.
Fu, B. J., Liu, Y., Lü, Y. H., He, C. S., Zeng, Y., and Wu, B. F.:
Assessing the soil erosion control service of ecosystems change in the Loess
Plateau of China, Ecol. Complex., 8, 284–293,
https://doi.org/10.1016/j.ecocom.2011.07.003, 2011.
Fu, B. J., Wang, S., Liu, Y., Liu, J. B., Liang, W., and Miao, C. Y.:
Hydrogeomorphic ecosystem responses to natural and anthropogenic changes in
the Loess Plateau of China, Annu. Rev. Earth Planet Sci., 45, 223–243,
https://doi.org/10.1146/annurevearth-063016-020552, 2017.
Huang, J., Wang, J., Zhao, X. N., Li, H. B., Jing, Z. L., Gao, X. D., Chen,
X. L., and Pute, W.: Simulation study of the impact of permanent groundcover
on soil and water changes in jujube orchards on sloping ground, Land Degrad.
Dev., 27, 946–954, https://doi.org/10.1002/ldr.2281, 2016.
Hussein, M. H.: A sheet erodibility parameter for water erosion modeling in
regions with low intensity rain, Hydrol. Res., 44, 1013–1021,
https://doi.org/10.2166/nh.2013.029, 2013.
Igwe, C. A.: Erodibility of soils of the upper rainforest zone, southeastern
nigeria, Land Degrad. Dev., 14, 323–334, https://doi.org/10.1002/ldr.554, 2003.
Kiani, F. and Ghezelseflo, A.: Evaluation of soil erodibility factor(k) for
loess derived landforms of Kechik watershed in Golestan Province, North of
Iran, J. Mt. Sci.-Engl., 13, 2028–2035, https://doi.org/0.1007/s11629-015-3702-8,
2016.
Li, P., Li, Z. B., and Zheng, Y.: Effect of different elevation on soil
physical-chemical properties and erodibility in dry-hot valley, Bull. Soil
Water Conserv., 31, 103–107, https://doi.org/10.13961/j.cnki.stbctb.2011.04.034, 2011
(in Chinese with English abstract).
Lin, F., Zhu, Z. L., Zeng, Q. C., and An, S. S.: Comparative study of three
different methods for estimation of soil erodibility K in Yanhe Watershed of
China, Acta Pedologi Sinica., 54, 1136–1146,
https://doi.org/10.11766/trxb201611290469, 2017. (in Chinese with English abstract)
Liu, S. L., Guo, X. D., Lian, G., Fu, B. J., and Wang, J.: Multi-scale
analysis of spatial variation of soil characteristics in Loess Plateau-case
study of Hengshan County, J. Soil Water Conserv., 19, 105–108,
https://doi.org/10.13870/j.cnki.stbcxb.2005.05.026, 2005 (in Chinese with English
abstract).
Lü, Y. H., Fu B. J., Feng X. M., Zeng, Y., Liu, Y., Chang, R. Y., Sun,
G., and Wu, B. F.: A policy-driven large scale ecological restoration:
quantifying ecosystem services changes in the Loess Plateau of China, PLoS
ONE, 7, 17–28, https://doi.org/10.1371/journal.pone.0031782, 2012.
Mandal, U. K., Warrington, D. N., Bhardwaj, A. K., Bar-Tal, A., Kautsky, L.,
Minz, D., and Levy, G. J.: Evaluating impact of irrigation water quality on a
calcareous clay soil using principal component analysis, Geoderma, 144,
189–197, https://doi.org/10.1016/j.geoderma.2007.11.014, 2008.
Manmohan, J., Singh, Krishan L., Khera, P. S.: Selection of soil physical
quality indicators in relation to soil erodibility, Arch Acker Pfl Boden, 58,
657–672, 2012.
Mwaniki, M. W., Agutu, N. O., Mbaka, J. G., Ngigi, T. G., and Waithaka, E.
H.: Landslide scar/soil erodibility mapping using Landsat TM/ETM+ bands 7
and 3 normalised difference index: A case study of central region of Kenya,
Appl Geogr., 64, 108–120, https://doi.org/10.1016/j.apgeog.2015.09.009, 2015.
Parajuli, S. P., Yang, Z., and Kocurek, G.: Mapping erodibility in dust
source regions based on geomorphology, meteorology, and remote sensing, J.
Geophys. Res.-Earth, 119, 1977–1994, https://doi.org/10.1002/2014JF003095, 2015.
Sanchis, M. P. S., Torri, D., Borselli, L., and Poesen, J.: Climate effects
on soil erodibility, Earth Surf. Proc. Land, 33, 1082–1097,
https://doi.org/10.1002/esp.1604, 2012.
Sepúlveda-Lozada, A., Geissen, V., Ochoa-Gaona, S., Jarquin-Sanchez, A.,
dela-Cruz, S. H., Capetillo, E., Zamora- Cornelio, L. F., and Revista, D. B.
T.: Influence of three types of riparian vegetation on fluvial erosion
control in Pantanos de Centla, Mexico, Rev. Biol. Trop., 57, 1153–1163,
2009.
Shi, D. M., Chen, Z. F., Jiang, G. Y., and Jiang, D.: Comparative study on
estimation methods for soil erodibility K in purple hilly area, J.
Beijing Forest. Univ., 34, 32–38, 2012 (in Chinese with English
abstract).
Shirazi, M. A., Hart, J. W., and Boersma, L.: A unifying quantitative
analysis of soil texture: improvement of precision and extension of scale,
Soil Sci. Soc. of Am. J., 52, 181–190,
https://doi.org/10.2136/sssaj1988.03615995005200010032x, 1988.
Tang, F. K., Cui, M., Lu, Q., Liu, Y. G., Guo, H. Y., and Zhou, J. X.:
Effects of vegetation restoration on the aggregate stability and distribution
of aggregate-associated organic carbon in a typical karst gorge region, Solid
Earth, 7, 141–151, https://doi.org/10.5194/se-7-141-2016, 2016.
Taylor, K.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000jd900719, 2001.
Torri, D., Poesen, J., and Borselli, L.: Predictability and uncertainty of
the soil erodibility factor using a global dataset, Catena, 31, 1–22,
https://doi.org/10.1016/S0341-8162(01)00175-8, 1997.
Vaezi, A. R., Hasanzadeh, H., and Cerda, A.: Developing an erodibility
triangle for soil textures in semi-arid regions, NW Iran, Catena, 142,
221–232, https://doi.org/10.1016/j.catena.2016.03.015, 2016a.
Vaezi, A. R., Abbasi, M., Bussi, G., and Keesstra, S.: Modeling sediment
yield in semi-arid pasture Micro Catchments, NW Iran, Land Degrad. Dev., 28,
1274–1286, https://doi.org/10.1002/ldr.2526, 2016b.
Wang, A. J and Li, Z. G.: Spatial distribution of soil erodibility in Upper
Yangtze River Region, Adv. Mater Res., 610–613, 2944–2947, 2012.
Wang, D. C., Zhang, G. L., Pan, X. Z., Zhao, Y. G., Zhao, M. S., and Wang, G.
F.: Mapping soil texture of a plain area using fuzzy-c-means clustering
method based on land surface diurnal temperature difference, Pedosphere, 22,
394–403, https://doi.org/10.1016/S1002-0160(12)60025-3, 2012.
Wang, B., Zheng, F. L., Römkens, M. J. M., and Darboux, F.: Soil
erodibility for water erosion: A perspective and Chinese experiences,
Geomorphology, 187, 1–10, https://doi.org/10.1016/j.geomorph.2013.01.018, 2013a.
Wang, B., Zheng, F. L., and Römkens, M. J. M.: Comparison of soil
erodibility factors in USLE, RUSLE2, EPIC and Dg models based on a Chinese
soil erodibility database, Acta Agr. Scand. B-S. P., 63, 69–79,
https://doi.org/10.1080/09064710.2012.718358, 2013b.
Wang, G. Q., Fang, Q. F., Wu, B. B., Yang, H. C., and Xu, Z. X.: Relationship
between soil erodibility and modeled infiltration rate in different soils, J.
Hydrol., 528, 408–418, https://doi.org/10.1016/j.jhydrol.2015.06.044, 2015.
Wei, H., Zhao, W. W., and Wang, J.: Research process on soil erodibility,
Chin. J. Appl. Ecol., 28, 2749–2759, https://doi.org/10.13287/j.1001-9332.201708.011,
2017a (in Chinese with English abstract).
Wei, H., Zhao, W. W., and Wang, J.: The optimal estimation method for K value
of soil erodibility: A case study in Ansai Watershed, Science of Soil and
Water Conservation, 15, 52–62, https://doi.org/10.16843/j.sswc.2017.06.007, 2017b (in
Chinese with English abstract).
Williams, J. R.: The erosion-productivity impact calculator (EPIC) model: A
case history, Phil. Trans. R. Soc. B., 329, 421–428,
https://doi.org/10.1098/rstb.1990.0184, 1990.
Wischmeier, W. H., Johnson, C. B., and Cross, B. V.: Soil erodibility
nomograph for farmland and construction sites, J. Soil Water Conserv., 26,
189–193, https://doi.org/10.2307/3896643, 1971.
Wischmeier, W. H. and Smith, D. D.: Predicting rainfall erosion losses-a
guide to conservation planning, United States, Dept. of Agriculture Handbook,
available at: http://eprints.icrisat.ac.in/id/eprint/8473 (last access:
22 October 2012), 537, 1978.
Wu, L., Liu, X., and Ma, X.: Application of a modified distributed-dynamic
erosion and sediment yield model in a typical watershed of a hilly and gully
region, Chinese Loess Plateau, Solid Earth, 7, 1577–1590,
https://doi.org/10.5194/se-7-1577-2016, 2016.
Xu, X. L., Ma, K. M., Fu, B. J., Song, C. J., and Liu, W.: Relationships
between vegetation and soil and topography in a dry warm river valley, SW
China, Catena, 75, 138–145, https://doi.org/10.1016/j.catena.2008.04.016, 2008.
Yu, Y., Wei, W., Chen, L. D., Jia, F. Y., Yang, L., Zhang, H. D., and Feng,
T. J.: Responses of vertical soil moisture to rainfall pulses and land uses
in a typical loess hilly area, China, Solid Earth, 6, 595–608,
https://doi.org/10.5194/se-6-595-2015, 2015.
Zhang, K. L., Cai, Y. M., Liu, B. Y., and Jiang, Z. S.: Evaluation of soil
erodibility on the Loess Plateau, Acta Ecol. Sin., 21, 1687–1695,
https://doi.org/10.3321/j.issn:1000-0933.2001.10.018, 2001 (in Chinese with
English abstract).
Zhang, K. L., Shu, A. P., Xu, X. L., Yang, Q. K., and Yu, B.: Soil
erodibility and its estimation for agricultural soils in China, J. Arid
Environ., 72, 1002–1011, https://doi.org/10.1016/j.jaridenv.2007.11.018, 2008.
Zhang, W. T., Yu, D. S., Shi, X. Z., Zhang, X. Y., Wang, H. J., and Gu, Z.
J.: Uncertainty in prediction of soil erodibility K-factor in subtropical
China, Acta Pedol. Sin., 46, 185–191,
https://doi.org/10.3321/j.issn:0564-3929.2009.02.001, 2009 (in Chinese with
English abstract).
Zhao, M. Y., Zhao, W. W., and Liu, Y. X.: Comparative analysis of soil
particle size distribution and its influence factors in different scales: a
case study in the Loess hilly-gully area, Acta Ecol. Sin., 35, 4625–4632,
https://doi.org/10.5846/stxb201311272828, 2015 (in Chinese with English abstract).
Zhao, W. W., Fu, B. J., and Chen, L. D.: A comparison between soil loss
evaluation index and the C-factor of RUSLE: a case study in the Loess Plateau
of China, Hydrol. Earth Syst. Sci., 16, 2739–2748,
https://doi.org/10.5194/hess-16-2739-2012, 2012.
Short summary
Soil erodibility (K) is one of the key factors of soil erosion. Selecting the optimal estimation method of soil erodibility is critical to estimate the amount of soil erosion, and provide the base for sustainable land management. This research took the Loess Plateau of China as a case study, estimated soil erodibility factor with different methods, selected the best texture-based method to estimate K, and aimed to understand the indirect environmental factors of soil erodibility.
Soil erodibility (K) is one of the key factors of soil erosion. Selecting the optimal estimation...
