Spatiotemporal dynamics of soil erosion risk for Anji County, China

Jiang, Zhenlan; Su, Shiliang; Jing, Changfeng; Lin, Shengpan; Fei, Xufeng; Wu, Jiaping

doi:10.1007/s00477-012-0590-0

Cited by 24 publications

(14 citation statements)

References 68 publications

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“…Soil erosion estimation and its spatial distribution can be calculated using combination of many techniques such as remote sensing, Geographic Information System (GIS) (Ghosh et al 2013;Haile & Fetene 2012;Jiang et al 2012;Khanal et al 2013;Kim et al 2012;Mhangara et al 2012;Ozsoy et al 2012;Patel & Kathwas 2011). In the literature, it is possible to see a wide application of universal soil loss equation (USLE) in soil erosion modelling in larger areas with acceptable accuracy (Millward & Mersey 1999;Wang et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Khosrokhani

Pradhan

2013

Geomatics, Natural Hazards and Risk

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The main purpose of this paper is to assess and predict soil erosion and its dynamism observation at Kuala Lumpur metropolitan city using universal soil loss equation (USLE). To acquire USLE factors, multi-source datasets such as rainfall data of the past 20 years, DEM map, SPOT5 image and geological map of the study area were used to calculate spatial soil erosion of Kuala Lumpur metropolitan city. Results indicated that the highest spatial coverage (61.9%) occurred in very low and the lowest spatial coverage (3.8%) in severe soil loss class. Furthermore, a landslide inventory map of the same area was overlaid on the resultant soil erosion layer to demonstrate their correlation. A frequency ratio (FR) method was applied to determine the relationship between occurred landslides and each soil erosion class. The results indicated that the highest and the lowest FR equal to 0.8 and 2.1 and observed in very low and severe soil loss classes, respectively. Temporal assessment of soil erosion indicated that there is a decrement of soil loss content in severe class from 2000 to 2010. The result of the prognosis analysis showed that the spatial coverage of the study area in severe soil loss class equals to 48%. This result proved the significance of land use/land cover to curb soil erosion. The resultant maps may help the planners and managers to take proper decision for the conservation of soil as a noteworthy natural resource.

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Section: Introductionmentioning

confidence: 99%

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Khosrokhani

Pradhan

2013

Geomatics, Natural Hazards and Risk

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“…This empirical model was introduced over 50 years ago (Wischmeier and Smith 1978) and the Revised Universal Soil Loss Equation (RUSLE) was created in the early 1990s (Renard et al 1991). The RUSLE has been the most widely used model to predict soil erosion (Van Rompaey et al 2001;Pruski and Nearing 2002;Yang et al 2003;Angima et al 2003;Fernandez et al 2003;Jiang et al 2012). The USLE/RUSLE equation depends on five factors related to climate (i.e., rainfall runoff erosivity [R]), soil type (i.e., erodibility factor [K]), topography (i.e., slope steepness and length factor, [LS]), land cover type (cover management factor [C]), and cultivation management (i.e., conservation practice factor [CP]).…”

mentioning

confidence: 99%

Potential impacts of climate change on soil erosion vulnerability across the conterminous United States

Segura¹,

Sun²,

McNulty³

et al. 2014

Journal of Soil and Water Conservation

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Rainfall runoff erosivity (R) is one key climate factor that controls water erosion. Quantifying the effects of climate change-induced erosivity change is important for identifying critical regions prone to soil erosion under a changing environment. In this study we first evaluate the changes of R from 1970 to 2090 across the United States under nine climate conditions predicted by three general circulation models for three emissions scenarios (A2, A1B, and B1) from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Then, we identify watersheds that are most vulnerable to future climate change in terms of soil erosion potential. We develop a novel approach to evaluate future trends of R magnitude and variance by incorporating both the rate of change with time as well as the level of agreement between climatic projections. Our results show that mean decadal R values would increase with time according to all nine climatic projections considered between 1970 and 2090. However, these trends vary widely spatially. In general, catchments in the northeastern and northwestern United States are characterized by strong increasing trends in R, while the trends in the midwestern and southwestern United States are either weak or inconsistent among the nine climatic projections considered. The northeastern and northwestern United States will likely experience a significant increase in annual variability of R (i.e., increase in extreme events). Conversely the variability of R is unlikely to change in large areas of the Midwest. At the watershed scale (8-digit Hydrologic Unit Code), the mean vulnerability to erosion scores vary between -0.12 and 0.35 with a mean of 0.04. The five hydrologic regions with the highest mean vulnerability to erosion are 5, 6, 2, 1, and 17, with values varying between 0.06 and 0.09. These regions occupy large areas of Ohio, Maryland, Indiana, Vermont, and Illinois, with mean erosion vulnerability score statewide above 0.08. Future watershed management aiming at reducing soil erosion should focus on areas with the highest soil erosion vulnerability identified by this study.

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“…The Tiaoxi watershed is a typical one in the developed region of southeastern China [6, 39], which is close to the country’s economic center. Although this work focused on soil landscape pattern changes in response to rural anthropogenic activity, there may exist other factors occasionally linked to soil sealing, such as soil erosion increase, surface water penetration decrease and subsequent increase of surface runoff from sealed surface.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, in Anji county (within the Tiaoxi watershed) rural anthropogenic activities were associated with soil erosion [39] that controls the discharge of nitrogen and phosphorus from agricultural fields and build-up areas. Eutrophication in rivers and lakes has been a concern across the country during the past decade [39, 40].…”

Section: Discussionmentioning

confidence: 99%

Soil Landscape Pattern Changes in Response to Rural Anthropogenic Activity across Tiaoxi Watershed, China

et al. 2016

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Soil sealing (loss of soil resources due to extensive land covering for the purpose of house building, road construction etc.) and subsequent soil landscape pattern changes constitute typical environmental problems in many places worldwide. Previous studies concentrated on soil sealing in urbanized regions, whereas rural areas have not been given sufficient attention. Accordingly, this paper studies soil landscape pattern dynamics (i.e., landscape pattern changes in response to rural anthropogenic activities) in the Tiaoxi watershed (Zhejiang province, eastern China), in which surface sealing is by far the predominant component of human forcing with respect to environmental change. A novel approach of quantifying the impacts of rural anthropogenic activities on soil resources is presented. Specifically, quantitative relationships were derived between five soil landscape pattern metrics (patch density, edge density, shape index, Shannon’s diversity index and aggregation index) and three rural anthropogenic activity indicators (anthropogenic activity intensity, distance to towns, and distance to roads) at two landscape block scales (3 and 5 km) between 1985 and 2010. The results showed that the Tiaoxi watershed experienced extensive rural settlement expansion and high rates of soil sealing. Soil landscapes became more fragmented, more irregular, more isolated, and less diverse. Relationships between soil landscape pattern changes and rural anthropogenic activities differed with the scale (spatial and temporal) and variable considered. In particular, the anthropogenic activity intensity was found to be the most important indicator explaining social development intensity, whereas the other two proximity indicators had a significant impact at certain temporal interval. In combination with scale effects, spatial dependency (correlation) was shown to play a key role that should be carefully taken into consideration in any relevant environmental study. Overall, the findings of this work suggest that soil sealing can be a critical human forcing issue with considerable consequences deserving serious attention by the experts, the public and the government alike.

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Spatiotemporal dynamics of soil erosion risk for Anji County, China

Cited by 24 publications

References 68 publications

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

Potential impacts of climate change on soil erosion vulnerability across the conterminous United States

Soil Landscape Pattern Changes in Response to Rural Anthropogenic Activity across Tiaoxi Watershed, China

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