The Huaihe River Basin is particularly vulnerable to climate change. This paper first evaluated interpolation methods for different meteorological elements, followed by an assessment of the simulation performance of various Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Models (GCMs) for these elements. We then applied the Improved Quantile Mapping (IQM) method for bias correction of the GCMs. Finally, we analyzed the characteristics of future climate change in the Huaihe River Basin. The results show the following: (1) The radial basis function interpolation method is the most effective for rainfall, while Kriging performs best for air temperature. (2) The HadGEM2-AO model provides the most accurate rainfall simulations, MIROC-ESM best simulates maximum air temperature, and HadGEM2-ES is most effective for minimum air temperature. (3) The IQM method outperforms other approaches for bias correction of climate variables in the basin. (4) Future projections show an increase in both rainfall and air temperature, with more pronounced rises under the RCP8.5 scenario. Additionally, rainfall and maximum air temperature show considerable spatial variation across emission scenarios, while minimum air temperature consistently exhibits an upward trend.

To overcome the limitations imposed by the anisotropy and heterogeneity of natural loess, this study establishes a novel quantitative macro–micro correlation framework for investigating the deformation mechanisms of artificial structural loess (ASL). ASL samples were prepared by mixing remolded loess with cement (0–4%) and NaCl (0–16%), followed by static compaction (95% degree) and 28-day curing (20 ± 2 °C, >90% RH) to replicate the structural properties of natural loess under controlled conditions. An integrated experimental methodology was employed, incorporating consolidation/collapsibility tests, particle size analysis, X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP). A three-dimensional nonlinear model was proposed. The findings show that intergranular cementation, particle size distribution, and pore architecture are the main factors influencing loess’s compressibility and collapsibility. A critical transition from medium to low compressibility was observed at cement content ≥1% and moisture content ≤16%. A strong correlation (Pearson |r| > 0.96) was identified between the mesopore volume ratio and the collapsibility coefficient. The innovation of this study lies in the establishment of a three-dimensional nonlinear model that quantitatively correlates key microstructural parameters (fractal dimension value (D), clay mineral ratio (C), and large and medium porosity (n)) with macroscopic deformation indicators (porosity ratio (e) and collapsibility coefficient (δ_s)). The measured data and the model’s output agree quite well, with a determination coefficient (R²) of 0.893 for porosity and 0.746 for collapsibility, verifying the reliability of the model. This study provides a novel quantitative tool for loess deformation prediction, offering significant value for engineering settlement assessment in controlled cementation and moisture conditions, though its application to natural loess requires further validation.

The application research of ground change detection based on multi-temporal 3D models is attracting more and more attention. However, the conventional methods of using UAV GPS-supported bundle adjustment or measuring ground control points before each data collection are not only economically costly, but also have insufficient geometric accuracy. In this paper, an automatic geometric-registration method for multi-temporal 3D models is proposed. First, feature points are extracted from the highest resolution texture image of the 3D model, and their corresponding spatial location information is obtained based on the triangular mesh of the 3D model, which is then converted into 3D spatial-feature points. Second, the transformation model parameters of the 3D model to be registered relative to the base 3D model are estimated by the spatial-feature points with the outliers removed, and all the vertex positions of the model to be registered are updated to the coordinate system of the base 3D model. The experimental results show that the position measurement error of the ground object is less than 0.01 m for the multi-temporal 3D models obtained by the method of this paper. Since the method does not require the measurement of a large number of ground control points for each data acquisition, its application to long-period, high-precision ground monitoring projects has great economic and geometric accuracy advantages.

Accurate short-term forecasts of meteorological parameters remain challenging in mountainous small watersheds because sparse observations and global background fields do not adequately capture rapid local water vapor variations. Previous studies indicate that assimilating GNSS-derived PWV can improve regional-scale forecasts of precipitation and near-surface meteorological variables. However, its incremental value in mountainous small watersheds remains uncertain. Complex terrain, sparse local observations, and rapid water vapor variability may limit the transferability of findings obtained over larger domains. To address this setting-specific gap, we assess the impact of GNSS-derived PWV assimilation on short-term numerical forecasts for the Shahe watershed and its surrounding area in Hanzhong, China. Using the Weather Research and Forecasting Data Assimilation (WRFDA) system, the MET experiment assimilated conventional meteorological observations from four GNSS meteorological (GNSS-Met) stations, whereas MET + PWV additionally assimilated GNSS-derived PWV from the same four stations. The 129 CMA meteorological stations were reserved exclusively for independent forecast verification. During the selected precipitation period, adding GNSS-derived PWV reduces hourly precipitation RMSE by 4.7% compared with assimilating conventional meteorological observations alone. It also increases the threat score (TS) and probability of detection (POD) by 3.8% and 5.0%, respectively. Moreover, adding GNSS-derived PWV reduces the mean RMSEs of air temperature and relative humidity by 10.2% and 8.0%, respectively, while the corresponding reduction in surface pressure RMSE is 2.9%. During the selected non-precipitation period, adding GNSS-derived PWV to conventional meteorological-observation assimilation reduces the 1–6 h RMSEs of air temperature, relative humidity, and surface pressure by 6.0–8.9%, 8.3–9.8%, and 4.2–4.3%, respectively. For the selected precipitation and non-precipitation cases, the results indicate that adding GNSS-derived PWV to conventional meteorological observations from the same four GNSS-Met stations provides an additional local moisture constraint and improves short-term forecasts of multiple meteorological parameters in the Shahe watershed and its surrounding area.