After decades of comprehensive management, the hydrology and sediment dynamics of the middle Yellow River Basin have changed significantly. However, the comprehensive evaluation of the water and sediment regimes and its potential attribution at the regional scale are limited. In this study, the hydrological characteristics and in‐reach sediment budget in the Coarse Sandy Hilly Catchments (CSHC) region and their potential attribution were investigated by using data from hydrological stations in the mainstream and tributaries and data from soil and water conservation measures (SWCMs) in different river reaches. The results indicated that the runoff and sediment load of the mainstream showed obvious spatial heterogeneity, both of which revealed a very significant decreasing trend from 1956 to 2018 (P < 0.01). The spatial heterogeneity of sediment load along the mainstream was more obvious than that of streamflow, and the Toudaoguai and Fugu stations had the largest difference in sediment transport trends. During the P1 period (1956‑1968), the streamflow of each reach was sufficient, and the riverbeds of the mainstream and channel on both banks were subject to serious scouring. In the P2‑P3 period (1969‑2018), in‐reach water decreased continuously, and the mainstream began to show a process from slight scouring to no scouring and even sediment deposition. Except for climate change being the main factor in streamflow reduction during the P2 period (1969‑1999) at the Fugu and Wubao stations, anthropogenic activities were the primary factors leading to the reduction in streamflow and sediment load in the CSHC region. Under the influence of the same measures, the sediment reduction rate in this region was greater than the runoff reduction rate. Furthermore, the SWCMs in different reaches indicated great diversity in the benefits of water and sediment reduction. Therefore, zoning regulation of the CSHC region should be carried out in the future, and the sustainable development of the regional vegetation ecosystem should be maintained.
The soil erodibility factor is the key index to evaluate regional potential erosion degree and predict soil erosion. Therefore, it is very meaningful to explore the response mechanism of soil erodibility to external environmental factors on the Loess Plateau after ecological restoration. In this study, three soil erodibility factors (K‐USLE, K‐shira, and K‐torri), a comprehensive soil erodibility index (CSEI), different vegetation indices, and five topographic parameters (Topo) in two typical watersheds were selected to achieve this goal. The results revealed that vegetation indices (VI), soil properties (SP), and Topo were significantly affected by regional location and land use types. Vegetation restoration can reduce soil erodibility factors, but the reduction capacity of different land use types varies greatly. SP, VI, and Topo can explain more than 70% of variances in soil erodibility. The partial least squares‐structural equation modelling (PLS‐SEM) illustrated that SP had the largest and significant direct impact on soil erodibility (standard path coefficients (SPC) were −0.935 and −0.895, respectively). Vegetation indirectly inhibited the soil erodibility of the two watersheds by improving SP (SPC were 0.228 and 0.437, respectively), while the indirect impact of Topo mainly interferes with soil erodibility by adjusting vegetation type and spatial distribution (SPC were −0.566 and −0.292, respectively). Furthermore, CSEI was significantly affected by land use, and significantly associated with soil texture, soil organic carbon (SOC), mean weight diameter (MWD), normalized green‐red difference index (NGRDI), and vegetative index (VEG) in both watersheds. The soil in the Xinshui River watershed was more vulnerable to erosion than that in the Zhujiachuan watershed. It is of great significance to explore the response mechanism of soil erodibility to multi‐scale factors to understand the impact of external environmental evolution on soil erosion and sediment yield on the Loess Plateau.
Denitrification, as the main nitrogen (N) removal process in farmland drainage ditches in coastal areas, is significantly affected by saline-alkali conditions. To elucidate the effects of saline-alkali conditions on denitrification, incubation experiments with five salt and salt-alkali gradients and three nitrogen addition levels were conducted in a saline-alkali soil followed by determination of denitrification rates and the associated functional genes (i.e., nirK/nirS and nosZ Clade I) via N2/Ar technique in combination with qPCR. The results showed that denitrification rates were significantly decreased by 23.83–50.08%, 20.64–57.31% and 6.12–54.61% with salt gradient increasing from 1 to 3‰, 8‰, and 15‰ under 0.05‰, 0.10‰ and 0.15‰ urea addition conditions, respectively. Similarly, denitrification rates were significantly decreased by 44.57–63.24% with an increase of the salt-alkali gradient from 0.5 to 8‰. The abundance of nosZ decreased sharply in the saline condition, while a high salt level significantly decreased the abundance of nirK and nirS. In addition, the increase of nitrogen concentration attenuated the reduction of nirK, nirS and nosZ gene abundance. Partial least squares regression (PLSR) models demonstrated that salinity, dissolved oxygen (DO) in the overlying water, N concentration, and denitrifying gene abundance were key determinants of the denitrification rate in the saline environment, while pH was an additional determinant in the saline-alkali environment. Taken together, our results suggest that salinity and high pH levels decreased the denitrification rates by significantly inhibiting the abundance of the denitrifying genes nirK, nirS, and nosZ, whereas increasing nitrogen concentration could alleviate this effect. Our study provides helpful information on better understanding of reactive N removal and fertilizer application in the coastal areas.
Soil saturated hydraulic conductivity (Ks) is a key soil hydraulic property that determines the hydrological cycle of check dam–dominated catchment areas. However, Ks data are lacking due to the difficulty of directly measuring this variable in deep soil layers. In this study, 45 soil profiles (0–200 cm) in 15 check dams in three typical watersheds (Xinshui River, Zhujiachuan, and Kuye River) in a hilly gully region on the Chinese Loess Plateau were selected, and a total of 586 soil samples were collected along the soil profiles. Backpropagation neural network (BPNN) and support vector regression (SVR) models based on the genetic algorithm (GA) were tested, and pedotransfer functions for Ks estimation were established for check dams on the Loess Plateau. Basic soil characteristics, such as soil depth, sand, silt, clay, soil organic matter, and bulk density, were adopted as the model inputs to estimate Ks. Combinations of these parameters could be used to suitably estimate Ks, and the models were found to require relatively few soil characteristics to achieve similar accuracy. In comparison to GA‐BPNN, the GA‐SVR model attained good practicability and was more stable in Ks prediction (the geometric mean error ratio was between 0.942 and 1.101; RMSE was between 0.069 and 0.073). Our research can make some contributions to the solution of land restoration and watershed governance on the Chinese Loess Plateau.
Studying the distribution patterns and controlling mechanisms of soil organic carbon (SOC) based on the comprehensive performance of vegetation restoration and check dams at the watershed scale is important for understanding carbon cycling processes in nature. Two typical watersheds (Xinshui River and Zhujiachuan watershed) of the Loess Plateau were selected to evaluate the factors affecting the change in SOC content, and then the key factors were considered in the genetic algorithm‐support vector regression (GA‐SVR) model to predict SOC content. The results showed that the topography, vegetation, and soil characteristics had significant effects on the SOC content in the upland hillslopes, while the SOC content in the check dams was significantly affected by depth and soil characteristics. The soil organic carbon storage (TSOC) in the check dams could be evaluated and predicted by the vegetation index (NGRDI) and area of the subwatershed. The GA‐SVR model had good prediction accuracy and stable performance in predicting SOC content. According to the model simulation results, bulk density (BD), mean weight diameter (MWD), elevation, NGRDI, clay ratio (CR), and slope could be used to predict the surface SOC content of the Loess Plateau. Furthermore, depth, CR, MWD, BD, and median particle size (D50) could be applied in the model to predict the SOC content at different depths in the check dams. This study explored the potential control factors of SOC content and predicted SOC content from multiple angles, which can provide basic support for the study of the carbon sequestration on the Loess Plateau.
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