The double-ring infiltrometer is widely used to measure soil unsaturated hydraulic conductivity in the field. The scale effect of the inner and outer ring size (especially the inner one) affects the measurement results. In the semi-arid steppe, where water is scarce and transportation is inadequate, studying the scale effect caused by the inner-ring diameter of the infiltrometer can reduce the test consumption on the premise of ensuring the test accuracy. In this paper, a total of 190 double-ring infiltration tests with different inner-ring diameters (15, 20, 25, 30, and 40 cm) and 0.33 times outer buffer index were carried out at 38 sites with five soil types in the Xilin river basin, China. Results showed that: (1) When comparing the simulated parameters of six infiltration models, parameters increased with the increase of the infiltrometer inner diameter, but the trend gradually slowed down, indicating that the increase of the infiltrometer inner diameter would weaken the influence of the infiltrometer scale effect. However, the infiltrometer with an inner diameter of 40 cm is not enough to completely overcome the scale effect. (2) Through principal component analysis, the infiltration process is mainly affected by the particle size and the initial moisture content. (3) The soil infiltration map based on infiltration tests was more practical than the soil type map, which can provide a theoretical basis for ecological and soil restoration in the future.

In this work, we use the gridded precipitation dataset (with a resolution of 0.5° × 0.5°) of the eastern part of inland river basin of Inner Mongolian Plateau from 1961–2015 as the basis and adopt the methods of climatic diagnosis (e.g., the Modified Mann-Kendall method, principal component analysis, and correlation analysis) to analyze the spatial and temporal variations of six extreme precipitation indices. Furthermore, we analyzed the relationship between El Niño–Southern Oscillation (ENSO) events and the observed extreme precipitation. The results indicated that the gridded dataset can be used to describe the precipitation distribution in our study area. In recent 55 years, the inter-annual variation trends of extreme precipitation indices are generally dominated by declination except for the maximum precipitation over five days (RX5DAY) and the heavy precipitation (R95P), in particular, the decreasing regions of consecutive dry days (CDD) accounts for 91% of the entire basin, 17.28% of which is showing the significant downward trend. Contrary to CDD, the spatial distribution of the other five indices is gradually decreasing from northeast to southwest, and the precipitation intensity (SDII) ranges from 3.8–5.3 mm·d⁻¹, with relatively small spatial differences. To some extent, CDD and R95P can used to characterize the extreme precipitation regimes. Moreover, the number of days with heavy precipitation (RR10), SDII, and R95P are more susceptible to the ENSO events. In addition, the moderate El Niño event may increase the probability of CDD, while the La Niña events may increase the risk of the heavy rainfall regime in the study area.

Grazing removal rate of grasses needs to be determined for various climate conditions to address eco-environmental concerns (e.g., desertification) related to steppe grassland degradation. The conventional approach, which requires survey data on animal species and heads as well as grass consumption per individual animal, is too costly and time-consuming to be applied at a watershed scale. The objective of this study was to present a new approach that can be used to estimate grazing removal rate with no requirement of animal-related data. The application of this new approach was demonstrated in a Eurasian semiarid typical-steppe watershed for an analysis period of 2000 to 2010. The results indicate that the removal rate tended to become larger, but its temporal variation tended to become smaller, from the upstream to downstream. Averaged across the watershed, the removal rate ranged from 63.9 to 401.0 g DM m⁻² (or 22.4 to 60.9%) during the analysis period. As expected, the removal rate in an atmospherically wetter year was higher than that in an atmospherically drier year. Nevertheless, none of the eleven analysis years had a removal rate higher than the threshold value of 65%, above which the risk of grassland degradation would become much greater.

This study aims to achieve coordination between regional economic development and ecological environmental protection and to mitigate issues such as competition for water use among industries and significant disparities between water supply and demand. A multi-water-source, multi-user, and multi-objective optimal water resource allocation model was developed for Ordos City using general water allocation and simulation (GWAS). This model was applied to optimize water resource allocation on a monthly scale for various users across different administrative units (banners) in both short- and long-term planning periods. The results indicate that Ordos City’s allocated water volume for 2025 and 2030 is projected to be 1833.36 × 10⁶ m³ and 1963.44 × 10⁶ m³, respectively, with an overall water shortage rate of 5.46% and 5.67%, respectively. Water shortages are predicted in Dongsheng District, Dalad Banner, Etuoke Banner, Hangjin Banner, and Wushen Banner, primarily during the agricultural water usage period from March to November. The regional water supply structure was notably optimized, with a gradual decrease in the proportion of groundwater in the total water supply and a corresponding increase in the supply of surface water and unconventional water. These changes effectively improve local groundwater overexploitation and enhance the water supply efficiency. The research findings could offer valuable theoretical and technical support for the development and utilization of water resources, as well as for adjustments in the population–economic–industrial structure of Ordos City. Additionally, this study could provide scientific references for optimizing water resource allocation in other water-deficient cities in arid and semi-arid areas of the Yellow River Basin.