Effect of chitosan nanocarriers on the interaction between cimetidine and bovine serum albumin and its regulation of protein conformation - Shijun Yu; Jiawei Huang; Haiting Sun; Jiali Gu (Chemico-Biological Interactions, 2026)

Trilobatin is associated with reduced lipid accumulation in multiple biological models: insights from transcriptomics, molecular docking, and molecular dynamics simulations - Mingyue Xu; Litao Wang; Qi Gu; Wanmei Zhou; Guosheng Liu; Chenlu Wang; Jie Yang; Jiandong Wang; Xinlin Zhang; Haiting Tian; Yujie Fu (Naunyn-Schmiedeberg's Archives of Pharmacology, 2026)

A semi-supervised LSTM framework for spatiotemporal downscaling of GRACE-derived terrestrial water storage anomalies to improve flood monitoring in the Yarlung Tsangpo River basin - Ying Hu; Xiaoming Cai; Yue-Ping Xu; Haiting Gu; Jingkai Xie; Sirui Dai (Journal of Hydrology, 2026)

Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting - Li Liu; Xiao Liang; Yue-Ping Xu; Yuxue Guo; Quan J. Wang; Haiting Gu (Journal of Hydrology, 2024)

Increasing parameter identifiability through clustered time-varying sensitivity analysis - Lu Wang; Yue-Ping Xu; Jiliang Xu; Haiting Gu; Zhixu Bai; Peng Zhou; Hongjie Yu; Yuxue Guo (Environmental Modelling & Software, 2024)

Multi-objective robust optimization of reservoir operation for real-time flood control under forecasting uncertainty - Xinting Yu; Yue-Ping Xu; Haiting Gu; Yuxue Guo (Journal of Hydrology, 2023)

Estimating the added value of GRACE total water storage and uncertainty quantification in seasonal streamflow forecasting - Li Liu; Jingkai Xie; Haiting Gu; Yue-Ping Xu (Hydrological Sciences Journal, 2022)

Assessing climate change impacts on streamflow and sediment load in the upstream of the Mekong River basin - Di Ma; Budong Qian; Haiting Gu; Zhilin Sun; Yue‐Ping Xu (International Journal of Climatology, 2021)
Assessments of climate change impacts on streamflow and sediment processes are essential for developing science‐based sustainable watershed management plans. We assessed climate change impacts on streamflow and sediment load in the upstream of the Mekong River Basin, as a case study. Future climate scenarios including an ensemble‐mean climate scenario (EnM scenario) were generated based on 20 GCMs in CMIP5, using a stochastic weather generator (LARS‐WG) coupled with a distribution‐free shuffle procedure. The SWAT model was applied to simulate changes in streamflow and sediment load for the future period 2071–2100 under RCP8.5 with respect to the baseline period 1971–2000. Results show that mean monthly maximum and minimum temperature were projected to increase by all the 20 GCMs, with an ensemble‐mean increase of 4.6–5.7°C and 4.2–5.8°C across the 12 months, respectively. An increase in mean annual precipitation (3.4–55.8%) and streamflow (1.0–72.7%) was also projected by all GCMs. However, projected changes in sediment load were not consistent. One half of the GCMs projected an increase (5.2–53.2%) in annual sediment load while the other half projected a decrease (5.1%–43.2%). In each month, at least three‐quarters of the GCMs projected an increase in monthly streamflow. For monthly sediment load, an increase in May to July was projected by over half of the GCMs, while a decrease was projected by a majority of the GCMs for other months. Our results indicate large uncertainties in streamflow and sediment projections under climate change, demonstrating the need to use multi‐model ensembles in climate change impact studies. Moreover, it was found that the streamflow and sediment loads simulated using the EnM scenario were often close to the ensemble means simulated using the 20 GCMs, which implies that the single EnM scenario has the potential of effectively and efficiently estimating the ensemble means of projections in a multi‐model ensemble.

How well do the ERA‐Interim , ERA ‐5, GLDAS ‐2.1 and NCEP‐R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau? - Li Liu; Haiting Gu; Jingkai Xie; Yue‐Ping Xu (International Journal of Climatology, 2021)
Snow and glacier are important components in the hydrological cycle of the Tibetan Plateau (TP). Air temperature, as the main driver in freezing and thawing processes, becomes vital for hydrological modelling and prediction in this region. Due to a sparse ground gauging network, spatial density of air temperature measurement is insufficient for hydro‐meteorological studies. Therefore, the aim of this study is to identify the best representative temperature data for hydrological applications from four widely used reanalysis products, including ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2, with reference to in situ measurements and gridded snow depth from the year 2008–2017 over the entire TP. To reduce errors, Bayesian Joint Probability (BJP) approach based on K‐Nearest Neighbour (KNN) classification algorithm (KNN‐BJP) is proposed to post‐process gridded reanalyses. The results indicate that all the reanalysis datasets provide highly correlated but cold biased air temperature. The correlation ecoefficiency is greater than 0.85. The cold biases are near −3 ° C and mainly distributed in the southeastern TP. Bias in daily maximum temperature during Spring is greater than −8 ° C for most stations. ERA‐Interim is found to have the closest agreement with in situ measurements, closely followed by GLDAS‐2.1. KNN‐BJP is found to be effective within a distance smaller than 5° . After post‐processing, the prominent underestimation is efficiently corrected with Bias near 0. RMSE is markedly reduced to be smaller than 2.5 ° C . The post‐processed ERA‐5 and GLDAS‐2.1 are as accurate as ERA‐Interim, but able to provide more detailed information for extreme events due to their finer spatial resolution. Thus, ERA‐5 and GLDAS‐2.1 are more recommended to represent air temperature in the TP. Snow depth as complementary reference data is able to present spatial variance of air temperature. Our study can help alleviate the problem of sparse air temperature data over the TP.

Do model parameters change under changing climate and land use in the upstream of the Lancang River Basin, China? - Di Ma; Yue-Ping Xu; Weidong Xuan; Haiting Gu; Zhilin Sun; Zhixu Bai (Hydrological Sciences Journal, 2020)

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province, China - Weidong Xuan; Chong Ma; Lili Kang; Haiting Gu; Suli Pan; Yue-Ping Xu (Theoretical and Applied Climatology, 2017)