Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management policies, it is necessary to evaluate the evolution of industrial water use efficiency and its influencing factors in China. Given the high sensitivity and accuracy of the stochastic frontier analysis (SFA) model for efficiency assessment, the Tobit model is more suitable for regression analyses of truncated data. This study employed the SFA–Tobit panel model to evaluate the industrial water use efficiency of provinces in China from 2003 to 2021. The results indicate that national industrial water use efficiency improved from 0.41 to 0.65 during the study period. All provinces showed significant improvements, with developed provinces exhibiting higher industrial water use efficiency than undeveloped provinces. Regionally, the eastern areas demonstrated superior industrial water use efficiency compared to the western regions, with the central regions having the lowest overall water use efficiency. Moreover, the efficiency gap between regions has been narrowing. The national industrial water-saving potential is estimated at 31.306 billion cubic meters, with Jiangsu province having the highest saving potential at 3.709 billion cubic meters. In comparison, Beijing has the lowest at just 32,000 cubic meters. The Tobit regression results reveal that economic development and technological progress positively contribute to increased industrial water use efficiency. In contrast, water use intensity, openness, and urbanization levels negatively impacted the improvement of industrial water use efficiency. Therefore, it is necessary to increase investment in technological innovation, strictly control industrial water intensity, appropriately balance import and export trade with urbanization levels, and promote sustainable economic development. This study can provide effective support for the subsequent green transformation of China’s industry.

Real-time precipitation monitoring through satellite remote sensing represents a critical technological frontier for operational hydrology in data-scarce mountainous regions. Following a comprehensive evaluation of reanalysis precipitation products in the downstream Yarlung Zangpo watershed, this investigation advances understanding by systematically assessing eight satellite-based precipitation retrieval algorithms against ground truth observations from 18 meteorological stations (2014–2022). Multi-temporal performance analysis employed statistical metrics including correlation analysis, root mean square error, mean absolute error, and bias assessment to characterize algorithm reliability across annual, monthly, and seasonal scales. Representative monthly spatial analysis (January, April, July) and comprehensive 12 month × 18 station heatmap visualization revealed pronounced seasonal performance variations and elevation-dependent error patterns. Satellite retrieval algorithms demonstrated systematic underestimation tendencies, with observational precipitation averaging 2358 mm/yr, substantially exceeding remote sensing estimates across six of eight products. IMERG_EarlyRun and IMERG_LateRun achieved optimal performance with annual correlation coefficients of 0.41/0.37 and minimal bias (relative bias: −3.0%/1.4%), substantially outperforming other products. Unexpectedly, IMERG_FinalRun exhibited severe deterioration (correlation: 0.37, relative bias: −73.8%) compared to Early/Late Run products despite comprehensive gauge adjustment, indicating critical limitations of statistical correction procedures in data-sparse mountainous environments. Temporal analysis revealed substantial year-to-year performance variability across all products, with algorithm accuracy strongly modulated by annual precipitation characteristics and underlying meteorological conditions. Station-level assessment demonstrated that 100% of stations showed underestimation for IMERG_FinalRun versus balanced patterns for IMERG_EarlyRun/LateRun (53% underestimation, 47% overestimation), confirming systematic gauge-adjustment failures. Supplementary terrain–precipitation analysis indicated GSMaP_MVK_G shows superior spatial pattern representation, while IMERG_LateRun excels in capturing temporal variations, suggesting multi-product integration strategies for comprehensive monitoring. Comparative assessment with previous reanalysis evaluation establishes that satellite products offer superior real-time availability but exhibit greater temporal variability compared to model-based approaches’ consistent performance. IMERG_EarlyRun and IMERG_LateRun are recommended for operational real-time applications, GSMaP_MVK_G for terrain-sensitive spatial analysis, and reanalysis products for seasonal assessment, while IMERG_FinalRun and FY2 require substantial improvement before deployment in high-altitude watershed management systems.

The lower Yarlung Zangbo River Basin’s Great Bend region, characterized by extreme topography and intense orographic precipitation processes, presents significant challenges for accurate precipitation estimation using reanalysis products. Therefore, this study evaluates four widely used products (ERA5-Land, MSWEP, CMA, and TPMFD) against station observations (2014–2022) in this critical area. Performance was rigorously assessed using correlation analysis, error metrics (RMSE, MAE, RBIAS), and spatial regression. The region exhibits strong seasonality, with 62.1% of annual rainfall occurring during the monsoon (June-October). Results indicate TPMFD performed best overall, capturing spatiotemporal patterns effectively (correlation coefficients 0.6–0.8, low RBIAS). Conversely, ERA5-Land significantly overestimated precipitation, particularly in rugged northeast areas, suggesting poor representation of orographic effects. MSWEP and CMA underestimated rainfall with variable temporal consistency. Topographic analysis confirmed slope, aspect, and longitude strongly control precipitation distribution, aligning with classical orographic mechanisms (e.g., windward enhancement, lee-side rain shadows) and monsoonal moisture transport. Spatial regression revealed terrain features explain 15.4% of flood-season variation. TPMFD most accurately captured these terrain-precipitation relationships. Consequently, findings underscore the necessity for terrain-sensitive calibration and data fusion strategies in mountainous regions to improve precipitation products and hydrological modeling under orographic influence.

Bias correction of global climate model (GCM) simulations is usually required for hydrological impact studies due to the coarse resolution and systematic biases of these simulations. Commonly used bias correction methods are applied at sites independently while ignoring the spatial correlation of variables, which may cause unreasonable hydrological simulation. To solve this problem, a stochastic multisite bias correction (SMBC) method is proposed for hydrological impact studies. It first uses the Daily Bias Correction (DBC) method to correct the distribution of variables, and then the distribution-free shuffle algorithm and Markov chain are used to generate spatial correlation of variables. The performance of this method is compared with the DBC method for hydro-meteorological impact studies in the Xiangjiang River Basin. The results show that the DBC method inherits the bias of the temporal sequence of precipitation and spatial correlation of GCM simulated variables. The mean absolute error (MAE) of the spatial correlation is between 0.25 and 0.38 and between 0.36 and 0.39 for the simulated precipitation occurrence and daily precipitation amount, respectively, while the MAE for the probability of two stations having wet days/dry days simultaneously is around 0.07. The SMBC method effectively reproduces spatial correlation of observations, with the MAE of the above indexes around 0.02. In hydrological simulation, the SMBC method has much better performance in reproducing the return period of the maximum consecutive day for streamflow and the maximum consecutive day’s streamflow. The averaged streamflow process is also well represented. Overall, the SMBC method efficiently reproduces the distribution and spatial correlation of variables, thereby generating more accurate hydrology simulations.