Figure 1. Location of the study area
Data collection
We used Sentinel 2A and Landsat 5–8 series satellite data downloaded by
the United States Geological Survey USGS (https://www.usgs.gov/) to draw
the 2010–2020 land use type map, divided into five categories:
cropland, building, forestland, grassland, and water. Vegetation
information was also extracted to obtain the time series of vegetation
coverage in the Jiuyuangou watershed from 2010 to 2020. The 12.5 m
resolution DEM data was downloaded through NASA
(https://search.asf.alaska.edu/#/) to extract topographic parameters of
the watershed and the slope length and slope factor. We downloaded the
daily rainfall dataset from 1960 to 2020 from the National
Meteorological Data Center (https://data.cma.cn/) and performed a
spatial interpolation of meteorological data based on this data to
obtain the 2010–2020 rainfall erosivity layer of the basin. The soil
physicochemical properties of the watershed were obtained from the FAO
250 m resolution global soil texture survey data
(https://soilgrids.org/). We used Google high-resolution historical
imagery to identify soil and water conservation tillage measures in the
watershed from 2010 to 2020.
Model implementation
Liu et al. (2002) innovatively proposed the vegetation measure (B
factor), engineering measure (E factor), and tillage measure (T factor)
according to the actual soil and water conservation situation on the
Loess Plateau and combined them with the RUSLE equation to establish a
Chinese soil erosion prediction model suitable for the Loess Plateau:
where A is the soil erosion modulus based on the CSLE model (1 t
ha–1 yr–1=100 t
km–2 yr–1), R is rainfall
erosivity
(MJ∙mm·ha–1∙h–1∙yr–1),
K is soil erodibility factor
(t∙ha∙h–1·ha–1∙MJ–1∙mm–1),
L is slope length factor, S is slope factor, B is vegetation coverage
measure factor, E is water and soil conservation engineering measure
factor, and T is water and soil conservation tillage measure factor.
We obtained the rainfall erosivity grid layer using the rainfall
erosivity model based on daily rainfall data from meteorological
stations around the Jiuyuangou watershed. For soil erodibility factor (K
factor) in the Loess Plateau region, most studies have adopted the soil
erosion and productivity impact estimation model (EPIC; Williams et al.,
1983), which uses soil organic matter and particle composition for
estimation. Based on 12.5m ALOS DEM data
(https://www.earthdata.nasa.gov/), the LS_TOOL slope length and slope
factor calculation software developed by Zhang et al. (2017) was used to
complete the terrain factor calculations, and ArcGIS 10.7 was used to
generate a raster layer of slope length and slope factors. In this
study, based on the ‘Technical Regulations for Dynamic Monitoring of
Regional Soil Erosion’ from the Department of Soil and Water
Conservation, Ministry of Water Resources, China, the vegetation
coverage measure (B factor) was calculated using the vegetation coverage
combined with land use type data and monthly rainfall erosion ratio with
month as the time step. The E factor of the watershed was obtained by
interpreting Google historical imagery within the study area. The raster
calculator in ArcGIS was used to assign the T factor to the
corresponding slope grading map and obtain the T factor map of soil and
water conservation tillage measures.
Land cover and land use changes and soil erosion changes
(2010–2020)
For soil erosion change and LULCC assessment, we used the transition
matrix method, where diagonal values show the 2010–2020 stable area of
LULC and soil erosion grades. The soil erosion modulus map and LULC map
combined with the transition matrix help us understand the spatial and
temporal evolution of land use patterns and soil erosion grades in the
Jiuyuangou watershed. As per Gilani et al (2021), we stipulate soil
erosion transformations from lower to higher erosion grades as ‘loss’
and higher to lower erosion grades as ‘gain’. For changes in the spatial
pattern of land use, we specify the conversion of other land use types
to cropland as ‘loss’ and cropland to other land use types as ‘gain’. On
this basis, we conduct a binary variable spatial correlation analysis to
determine how the two variables change simultaneously in the whole
watershed space (Nandi and Shakoor, 2010). Finally, we produced a
bivariate choropleth to understand the spatial pattern of soil erosion
and LULCC and the association of gain, loss, and no change between 2010
and 2020.