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.

Groundwater-dependent ecosystems (GDEs) refer to ecosystems that require partial or complete access to groundwater to maintain their ecological structure and functions, providing very important services for the health of land, water, and coastal ecosystems. However, regional identification of GDEs is still difficult in areas affected by climate change and extensive groundwater extraction. To address this issue, taking the Langxi River basin (LRB), one of the lower tributaries of the Yellow River in north China, as an example, we propose a four-diagnostic-criteria framework for identifying the GDEs based on remote sensing, geographic information system (GIS) data dredging, and hydrogeological survey data. Firstly, the potential GDE distributions are preliminarily delineated by the topographic features and the differences in terms of vegetation water situation and soil moisture at the end of the dry and wet seasons. On this basis, according to the given GDE identification criteria, three main types of GDEs in the basin, including the stream-type GDEs (S-GDEs), vegetation-type GDEs (V-GDEs), and karst-aquifer-type GDEs (K-GDEs), are further determined by comparing the relationship between groundwater table, riverbed elevation, and vegetation root development depth and through surveys of karst springs and aquifers. Following this, the GDEs are mapped using the spatial kernel density function, which can represent the characteristics of spatial aggregation distribution. Results show that the potential GDEs are mainly distributed in plain areas, with a small part in hilly areas, reflecting the moisture distribution status of waters, vegetation, and wetlands in the basin that possibly receive groundwater recharge; however, the true GDEs are concentrated in the riverine and riparian zone, the vegetation-related wetland, and the scattered karst spring surroundings which groundwater directly moves toward and into. In order to verify the reliability of GDE distributions, the study verified the determination of GDEs through hydrological rhythm analysis, as well as through the analysis of the hydrochemical characteristics of various waterbodies in the basin and of ecohydrological signals such as groundwater invertebrates. The hydrological rhythm analysis in the Shuyuan section showed that the proportion of base flow to river flow is about 54.15 % and that S-GDEs still receive spring water recharge even in the extremely dry season. Furthermore, the analysis of hydrochemical sampling from the karst aquifer, the Quaternary aquifer, the spring water, and the surface reservoir water reveals that GDEs are also relished by groundwater. More importantly, we also found a distinctive ecohydrological signal of GDEs is the presence of millimeter-sized groundwater fauna living in the different types of GDEs. In addition, the study suggests that the use of isotope and environmental DNA technology to analyze the hydrological–sediment–biological connectivity between groundwater and GDEs is the future development direction of this field.

Due to multi-source data information fusion, the precision of eco-hydrology models is improving rapidly. In particular, the fractional vegetation coverage (FVC) is of great significance in the remote sensing monitoring of surface parameters. In this study, downscaling inversion was performed using Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) data from Land Remote-Sensing Satellite (System, Landsat-8) and RVI-Freeman data from Radarsat-2 with polarization decomposition, incorporating the scattering entropy (H) and anisotropy (α). Further, modified vegetation indices (mVIs) and corresponding calculation methods were developed to describe the FVC precisely. Two deep learning (DL) methods were used for mVI optimization. The results showed that the inclusion of H and α greatly facilitated FVC estimation and that the mVIs and DL provided higher accuracies and smaller errors than the previous methods (NDVI or RVI). HαmRVI, one of the mVIs, had the highest accuracy in FVC simulation using a vegetation index, and the particle swarm optimization neural network (PSONN) achieved the best performance. The FVC was then predicted with 8 m resolution using the mVIs and PSONN, demonstrating that the proposed method effectively compensates for the fluctuations in high-FVC valley wetlands caused by high water content, avoids overestimation in grasslands, and provides great detail while retaining the original regional variations.