Zokor activity promotes soil water infiltration in the Mu Us sandy land of northern Shaanxi,China

Zokors are common subterranean rodents that inhabit agricultural fields, shrublands, and grasslands in the arid and semi-arid regions of China. Zokor burrowing activities can alter soil structure and affect soil hydrological processes;however, there are few studies regarding their effects on soil preferential flow in the Mu Us sandy land. An evaluation of the effects of zokor disturbance on their habitat and soil water is important for understanding the ecological role of zokors in the soil ecosystem of the Mu Us sandy land. A field dye-tracing experiment was conducted in the Gechougou watershed on the southeastern edge of the Mu Us sandy land to investigate the effect of zokor burrowing activity on soil preferential flow characteristics. Our results showed that the density of zokor tunnels was the highest(0.40–0.46 m m^(-2)) under 30%–50% vegetation coverage and that the tunnels were approximately 3 cm from the surface.Both stained area ratio and stained path number were higher at sites with zokors than without zokors. Stained path widths were 10–80 and > 80 mm at zokor-harboring sites exhibiting homogeneous flow and heterogeneous finger flow, respectively. In the absence of zokors, homogeneous flow and highly interacted macropore flow were predominant. Soil water content below the zokor tunnels was higher than that above the tunnels. Moderate disturbance of soil structure by zokor activity facilitated soil water infiltration. These results enabled a better understanding of the effect of soil fauna on soil structure and hydrological processes and provided recommendations for ecological construction and renovation in arid and semi-arid regions.

Mapping soil erodibility in southeast China at 250 m resolution:Using environmental variables and random forest regression with limited samples

Soil erodibility(K factor)mapping has been accomplished mainly by soil map-linked or geo-statistical interpolation.However,the resulting maps usually have coarse spatial resolution at a regional scale.The objectives of this study were a)to map the K factors using a set of environmental variables and random forest(RF)model,and b)to identify the important environmental variables in the predictive mapping on a regional scale.We collected 101 surface soil samples across southeast China in the summer of 2019.For each sample,we measured the particle size distribution and organic matter content,and calculated the K factors using the nomograph equation.The hyperparameters of RF were optimized through 5-fold cross validation(m_(ay)=2,n_(tree)=500,p=63),and a digital map with 250 m resolution was generated for the K factor.The lower and upper limits of a 90% prediction interval were also pro-duced for uncertainty analysis.It was found that the important environmental variables for the K factor prediction were relief,climate,land surface temperature and vegetation indexes.Since the existing K factor map has an average polygonal area of 6.8 km^(2),our approach dramatically improves the spatial resolution of the K factor to 0.0625 km^(2).The new method captures more distinct differences in spatial details,and the spatial distribution of the K factor derived from RF prediction followed a similar pattern with kriging interpolation.This suggests the presented approach in this study is effective for mapping the K factor with limited sampling data.

Simulating soil erodibility in southeastern China using a sequential Gaussian algorithm

Soil erodibility(K)is a key factor of soil erosion,and its appropriate quantification and interpolation are vitally important for soil and water conservation.The traditional point-represent-polygon approaches and common kriging method for the estimation of K,however,do not sufficiently represent the original data.The objectives of this study were to simulate the spatial distribution of K using a sequential Gaussian algorithm and analyze the uncertainty in evaluating the risk of soil erodibility in southeastern China.We determined 101 sampling points in the area and collected disturbed soil samples from the 0-20 cm layer at each point.Soil properties were determined,and K was calculated using five common models:the EPIC(Erosion/Productivity Impact Calculator),approximate nomograph,Torri,Shirazi,and Wang models.Among the chosen models,the EPIC model performed the best at estimating K(KEPIC),which ranged from 0.019 to 0.060 t ha h(ha MJ mm)^(-1),with a mean of 0.043 t ha h(ha MJ mm)^(-1).The KEPIC was moderately spatially variable and had a limited spatial structure,increasing from south to north in our study area,and all spatial simulations using the cooperative kriging(CK)interpolation and the sequential Gaussian simulation(SGS)with 10,25,50,100,200,and 500 realizations had acceptable accuracies.The CK interpolation narrowed the range,and the SGS maintained the original characteristics of the calculated data.The proportions of the risk area were 38.0% and 10.1%,when the risk probability for K was 60% and 80%,respectively,and high risk areas were mostly located in the north.The results provide scientific guidance for managing the risk of soil erodibility in southeastern China.