Inner Mongolia, a typical arid and semi-arid region in northern China, has undergone significant ecological transformation over the past two decades through climate shifts and large-scale ecological restoration projects. However, the relative contributions of climate and anthropogenic drivers to these ecological changes have not been sufficiently quantified. This study presents a comprehensive quantitative evaluation of the relative contributions of climate change, land conversion, and ecological management to changes in four critical ecosystem services—carbon sequestration, hydrological regulation, soil and water conservation, and windbreak and sand fixation—between 2001 and 2020. Using the residual trend method—a technique to separate climate-driven from human-induced effects—we further decomposed human influence into land conversion and management components. The results show that climate change was the primary driver, enhancing carbon sequestration and hydrological regulation but negatively impacting erosion control, with contributions often over 90%. In contrast, human activities had more spatially variable effects; while land conversion improved several services, it also heightened the vulnerability of sand fixation functions. The analysis further revealed ecosystem-type-specific responses, where grasslands and deserts responded better to management measures and forests and croplands showed greater improvements from land conversion. These findings offer crucial insights into the differentiated mechanisms and outcomes of ecological interventions, providing a scientific basis for optimizing restoration strategies and achieving sustainable ecosystem governance in climate-sensitive regions.

River ice is an important part of the cryosphere, and effective monitoring of ice thickness information is essential for river ice research. The task of preventing and controlling winter ice damage in the Yellow River is severe, and it is significant to explore the applicability of different ice thickness estimation methods in this region. There is a lack of inversion studies on the thickness of the Yellow River ice. In this study, a typical reach of the Yellow River was selected as the research area. Based on the measured ice thickness data for many years, the applications of river ice thickness estimation models based on remote sensing and air temperature were explored respectively in this area. The results show the VV polarized backscatter coefficient of Sentinel-1 SAR data has the highest correlation with the measured ice thickness, with a Pearson correlation coefficient of 0.702. SAR ice thickness inversion results are helpful to identify ice jam location. The simulation accuracy of the air temperature model for ice thickness is better than that of the SAR model. Long-term air temperature-based ice thickness estimates show the maximum ice thickness in the study area is thinning at a rate of -0.7 cm a decade.

This study investigates the critical impact of incipient sediment motion on sediment transport estimation and riverbed evolution prediction. In this research, we examine the effects of ice cover on the vertical distribution of flow velocity, establishing a mathematical relationship between the vertical average flow velocities in open channel and ice-covered flows. This leads to the derivation of a formula for incipient motion velocity under ice cover. Additionally, the study analyzes the riverbed evolution process under ice jam conditions. The proposed formula is applicable to both open channel and ice-covered flows, effectively capturing the characteristics of incipient sediment motion for non-cohesive and cohesive sediments. The calculated incipient motion velocities closely align with the empirical data from existing literature. The study reveals that the roughness of ice cover significantly influences the incipient motion velocity of sediment, with higher ratios of ice cover roughness to riverbed roughness promoting sediment initiation under more favorable hydraulic conditions. Furthermore, the riverbed beneath ice jams experiences significant scouring. Field observations indicate that when ice jams form in localized sections of the river, the displacement of the main flow can substantially increase flow velocity in areas away from the ice jam, leading to scouring in non-ice-jammed areas and sedimentation in ice-jammed areas. The uneven distribution of ice jam is likely a critical factor contributing to discrepancies between theoretical predictions and observed outcomes. The complexity and limited data associated with the initiation of cohesive sediments pose challenges in validating the proposed formula for these sediment types.

Ice velocity is a critical parameter in river ice monitoring, playing a vital role in analyzing ice blockages, the freeze-up process, and ice jam locations. It provides essential references for freeze-up forecasting. This study utilized UAV (Unmanned Aerial Vehicle) remote sensing imagery from December 2023, during the ice run period, combined with the Pyramidal Lucas-Kanade Optical Flow Algorithm to quantify ice velocity distribution across multiple bends during the Yellow River’s ice run period. The study identified low ice velocity cross-sections at bend apexes, where velocity decreased sharply, leading to ice floe accumulation and an increased probability of ice jams. Significant spatial variations in ice velocity within bends were observed. Curvature and constriction ratios were identified as key factors influencing ice velocity distribution. Bends with high curvature and significant constriction should be prioritized for monitoring and early warning. The study also revealed a correlation between the low ice velocity cross-section at the apex of Shisifenzi Bend and the location of the initial ice jam during the ice run period, suggesting its potential as a scientific guide for ice jam risk forecasting. These findings provide valuable insights for freeze-up forecasting and flood prevention in the Yellow River.

Mathematical models and methods serve as fundamental tools for studying ice-related phenomena in the Yellow River. River ice is driven and constrained by hydrometeorological and geographical conditions, creating a complex system. Regarding the Yellow River, there are some uncertainties that manifest in unique features in this context, including ice–water–sediment mixed transport processes and the distribution of sediment both within the ice and on its surface. These distinctive characteristics are considered to different degrees across different scales. Mathematical models for Yellow River ice developed over the past few decades not only encompass models for the large-scale deterministic evolution of river ice formation and melting, but also uncertainty parameter schemes for deterministic mathematical models reflecting the Yellow River’s particular ice-related characteristics. Moreover, there are modern mathematical results quantitatively describing these characteristics with uncertainty, allowing for a better understanding of the unique ice phenomena in the Yellow River. This review summarizes (a) universal equations established according to thermodynamic and hydrodynamic principles in river ice mathematical models, as well as (b) uncertainty sources caused by the river’s characteristics, ice properties, and hydrometeorological conditions, embedded in parametric schemes reflecting the Yellow River’s ice. The intractable uncertainty-related problems in space–sky–ground telemetric image segmentation and the current status of mathematical processing methods are reviewed. In particular, the current status and difficulties faced by various mathematical models in terms of predicting the freeze-up and break-up times, the formation of ice jams and dams, and the early warning of ice disasters are presented. This review discusses the prospects related to the uncertainties in research results regarding the simulation and prediction of Yellow River ice while also exploring potential future trends in research related to mathematical methods for uncertain problems.