This study explores the water consumption and greenhouse gas (GHG) emissions in the Yarkand River Basin, focusing on their dynamic interactions across industrial sectors. Utilizing environmental input–output analysis (IOA), the CROPWAT model, and life cycle assessment (LCA), we quantified the historical evolution of physical and virtual water cycles in relation to the water–carbon nexus. Our findings reveal that the planting industry, particularly the production of export-oriented, water-intensive crops like cotton, significantly contributes to both blue and green water consumption, exacerbating regional water scarcity. The persistent external market demand drives this over-extraction, further strained by the basin’s limited water retention capabilities. Although advancements have been made in reducing the per-unit water footprint of crops, total water consumption continues to rise due to agricultural expansion, intensifying pressure on blue water resources. Additionally, agricultural GHG emissions have surged, driven by increased electricity consumption, heavy fertilizer use, and escalating soil N₂O emissions. In light of these challenges, our research underscores the critical need for integrated resource management strategies that align with sustainable development goals. By promoting efficient water allocation within the agricultural sector and diversifying crop structures downstream, we can enhance ecosystem resilience and reduce environmental degradation. Furthermore, the advancement of value-added agricultural processing and the implementation of innovative water conservation technologies are essential for fostering economic sustainability. These strategies not only mitigate the environmental impacts associated with agricultural practices but also strengthen the region’s adaptive capacity in the face of climate change and fluctuating market demands. Our findings contribute to the broader discourse on sustainable agricultural practices, emphasizing the interconnectedness of water management, climate resilience, and economic viability in arid regions.

A mobile ultrasonic stratified flow velocity measurement device, which utilizes a pair of ultrasonic transducers, was characterized by its low power consumption and the ability to measure multi-layer flow velocities in channels, offering advantages in water measurement applications in agricultural irrigation areas. The study combined experimental research with computational modeling to investigate the impact of ultrasonic propagation time in downstream and upstream flows and the effect of measurement position on flow velocity prediction. Traditional time-difference methods were found to be less effective for calculating water flow velocities using the proposed mobile ultrasonic transducers. By employing the AdaBoost algorithm with decision tree algorithms as weak classifiers, the relative error of the testing set was <5% in 85.71% of cases, achieving an R² value of 0.951, an RMSE value of 0.071, an MSE value of 0.795, and an MAE value of 0.654. The experimental results demonstrated that the use of the AdaBoost algorithm from machine learning for the mobile ultrasonic stratified flow velocity measurement device was feasible and effective.