Precipitation is crucial for managing water resources in the Three River Headwaters (TRH) region of the Tibetan Plateau (TP). Gridded precipitation datasets across the TRH region exhibit significant discrepancies in their results. Previous studies have primarily focused on assessing average or extreme precipitation for a single dataset or several datasets. In this study, based on the observed gridded precipitation dataset (CN05.1), a comprehensive evaluation of the climatic features and extreme precipitation across the TRH region from 1983 to 2014 is performed by employing two gauge-based gridded datasets (GPCC and CRU), two satellite-derived precipitation datasets (P-CDR and IMERG), and two reanalysis precipitation datasets (ERA5 and CRA40). The results show that all datasets are consistent in reproducing the climatology, interannual variability, and annual cycle of precipitation in the TRH region. However, the different datasets exhibit significant discrepancies in characterizing the long-term trends and extreme precipitation events. P-CDR and GPCC provide a good representation of the spatial variability of the annual mean climatology. ERA5 and CRU are more reliable in capturing interannual variabilities. The long-term trends can be closely described by employing CRU. P-CDR and GPCC exhibit higher skills in terms of the annual cycle. P-CDR performs better than IMERG for daily precipitation in terms of probability distributions and other assessment metrics. P-CDR and IMERG have advantages and disadvantages in characterizing the nine extreme precipitation indices. This study demonstrates a comprehensive comparison method using multiple precipitation datasets to gain essential insight into the strengths and weaknesses of various datasets across the TRH region.

Ensemble precipitation forecasts (EPFs) can help to extend lead times and provide reliable probabilistic forecasts, which have been widely applied for streamflow predictions by driving hydrological models. Nonetheless, inherent biases and under-dispersion in EPFs require post-processing for accurate application. It is imperative to explore the skillful lead time of post-processed EPFs for summer streamflow predictions, particularly in mountainous regions. In this study, four popular EPFs, i.e., the CMA, ECMWF, JMA, and NCEP, were post-processed by two state of art methods, i.e., the Bayesian model averaging (BMA) and generator-based post-processing (GPP) methods. These refined forecasts were subsequently integrated with the Xin’anjiang (XAJ) model for summer streamflow prediction. The performances of precipitation forecasts and streamflow predictions were comprehensively evaluated before and after post-processing. The results reveal that raw EPFs frequently deviate from ensemble mean forecasts, particularly underestimating torrential rain. There are also clear underestimations of uncertainty in their probabilistic forecasts. Among the four EPFs, the ECMWF outperforms its peers, delivering skillful precipitation forecasts for 1–7 lead days and streamflow predictions for 1–4 lead days. The effectiveness of post-processing methods varies, yet both GPP and BMA address the under-dispersion of EPFs effectively. The GPP method, recommended as the superior method, can effectively improve both deterministic and probabilistic forecasting accuracy. Moreover, the ECMWF post-processed by GPP extends the effective lead time to seven days and reduces the underestimation of peak flows. The findings of this study underscore the potential benefits of adeptly post-processed EPFs, providing a reference for streamflow prediction over mountain river basins.

Runoff simulation is an ongoing challenge in the field of hydrology. Process-based (PB) hydrological models often gain unsatisfactory simulation accuracy due to incomplete physical process representations. While the deep learning (DL) models demonstrate their capacity to grasp intricate hydrological response processes, they still face constraints pertaining to the representative training data and comprehensive hydrological observations. In order to provide unobservable hydrological variables from the PB model to the DL model, this study constructed hybrid models by feeding the output variables of the PB model (BTOP) into the DL model (LSTM) as additional input features. These variables underwent feature dimensionality reduction using the feature selection method (Pearson Correlation Coefficient, PCC) and the feature extraction method (Principal Component Analysis, PCA) before input into LSTM. The results showed that the standalone LSTM performed well across the basin, with NSE values all exceeding 0.70. The hybrid models enhanced the simulation performance of the standalone LSTM. The NSE values increased from 0.75 to nearly 0.80 in a sub-basin. Lastly, if the BTOP output is directly fed into LSTM without feature dimensionality reduction, the model’s accuracy significantly decreases due to noise interference. The NSE value decreased by 0.09 compared to the standalone LSTM in a sub-basin. The results demonstrated the effectiveness of PCC and PCA in removing redundant information within hydrological variables. These findings provide new insights into incorporating physical information into LSTM and constructing hybrid models.

The middle reaches of the Yangtze River are a crucial breeding habitat for four major Chinese carps. The ecohydrological characteristics of their spawning grounds are crucial factors influencing spawning for these species: black carp (Mylopharyngodon piceus), grass carp (Ctenopharyngodon idella), silver carp (Hypophthalmichthys molitrix), and bighead carp (Aristichthys nobilis). To investigate the impact of ecohydrological processes within the Three Gorges Reservoir on spawning, this study focused on the spawning grounds of the four major Chinese carps in the Yichang–Yidu section of the Yangtze River. By identifying key ecohydrological indicators and leveraging hydrological and spawning monitoring data from 2013 to 2024, the response characteristics of the four major Chinese carps’ spawning to these hydrological processes were analyzed. The results showed that the key ecohydrological indicators currently stimulating spawning for the four major Chinese carps are the fish-perceived daily flow rate increase, the fish-perceived cumulative flow rate increase, and the daily flow rate increase. These three indicators are significantly positively correlated with the scale of spawning for the four major Chinese carps. The thresholds for spawning at least 20% of the annual spawning total are: a perceived daily flow increase (P_da) of 4.52–36.05%; a perceived cumulative flow increase (P_cu) of 36.15–180.23%; and a daily flow increase (Q_av) of 588–2825 m³/s. The optimal water temperature for the reproduction of the four major Chinese carps is 21–23 °C. Overall, since the Three Gorges Reservoir’s normal operation, the frequency and scale of spawning for the four major Chinese carps have been highest during periods of rising water. It is recommended that, within the corresponding thresholds, ecological operation be conducted twice a year, once in mid-June and once in early July or late June. Daily flow increases can be controlled within the range of 588–2000 m³/s. This study analyzed the correlation between eco-hydrological indicators and the stimulation of spawning of the four major Chinese carps, providing optimized flow ranges and habitat conditions for ecological operation, which is conducive to promoting the reproduction and spawning of the four major Chinese carps in the Yichang–Yidu spawning grounds in the middle reaches of the Yangtze River.