The investigation of soil quality indices as affected by groundwater irrigation is of great significance to prevent and control soil degradation mainly in semi-arid steppe. Research has recently shown that irrigation can significantly improve biomass production of grasslands, but has yet to address the impact of irrigation on its soil quality. Here we determined to explore the effects of groundwater irrigation on soil quality of typical steppe dominated by Leymus chinensis and Stipa krylovii vegetation in the Xilin Gol grassland of China. Analysis of 11 different soil parameters at depths of 0-20 cm, 20-40 cm and 40-60 cm after a year of irrigation showed that irrigation did not pose soil salinity and sodicity risk. Average values for sodium adsorption ratio (SAR), pH and electrical conductivity (EC) all fell below standard limits. Compared with the control samples, soil organic matter (SOM) increased while available nitrogen (AN), available potassium (AK) and available phosphorus (AP) tended to decrease, although not significantly. Principal component analysis found that EC, SOM and pH form a minimum data set (MDS) for assessing soil quality of irrigated land. Irrigation led to slightly higher SQI values for Leymus chinensis grassland but did not significantly change SQI values for Stipa krylovii grassland. The results of this study can inform sustainable management of grasslands in semi-arid areas and other studies of steppe ecosystems.

Soil wind erosion is one of the critical drivers of land degradation in the black soil region of northeastern China. In this study, 137Cs background value and soil wind erosion rate was studied by using 137Cs nuclide tracing technique in the black soil region of northeastern China, and wind erosion characteristics such as background value and soil erosion rate over the years were clarified, which has guiding significance for soil and water conservation and agricultural sustainable development in the black soil region of northeast China. The mean area activity of the Hilltop area was 1541 Bq·m-2, which was lower than the background value of 1879.7 Bq·m-2 in the study area. At the top of the hill, soil erosion occurred to some extent. The 137Cs profile has distributional characteristics that can be divided into three categories. Most of the 137Cs area activity in Hilltop area is evenly distributed in 0-15 cm, and the 137Cs area activity below 15 cm decreased gradually until it disappeared. The spatial difference of wind erosion rate is significant in the study area, mainly between 7.17-10.13 t·hm-2a-1. The average wind erosion rate of hilltops with different tillage quality depths has little difference. The average wind erosion rate is 8.60 t·hm-2a-1, which belongs to mild erosion. However, its erosion rate is higher than the tolerable soil loss of 2 t·hm-2a-1 in the black soil region of northeast China. As a result, soil erosion control in the black soil region of northeast China still requires attention.

With the increasing frequency extreme weather events associated with climate change, real-time monitoring of rainfall intensity is critical for water resource management, disaster warning, and other applications. Traditional methods, such as ground-based rain gauges, radar, and satellites, face challenges like high costs, low resolution, and monitoring gaps. This study proposes a novel real-time rainfall intensity monitoring method based on deep learning and audio signal processing, using acoustic features from rainfall to predict intensity. Conducted in the semi-arid region of Northwest China, the study employed a custom-designed sound collection device to capture acoustic signals from raindrop-surface interactions. The method, combining multi-feature extraction and regression modeling, accurately predicted rainfall intensity. Experimental results revealed a strong linear relationship between sound pressure and rainfall intensity (r = 0.916, R² = 0.838), with clear nonlinear enhancement of acoustic energy during heavy rainfall. Compared to traditional methods like CML and radio link techniques, the acoustic approach offers advantages in cost, high-density deployment, and adaptability to complex terrain. Despite some limitations, including regional and seasonal biases, the study lays the foundation for future improvements, such as expanding sample coverage, optimizing sensor design, and incorporating multi-source data. This method holds significant potential for applications in urban drainage, agricultural irrigation, and disaster early warning.