Ten years after the Predictions in Ungauged Basins (PUB) initiative was put forward, known as the post-PUB era (2013 onwards), reducing uncertainty in hydrological prediction in ungauged basins still receives considerable attention. This integration or optimization of the traditional regionalization approaches is an effective way to improve the river discharge simulation in the ungauged basins. In the Jialing River, southwest of China, the regression equations of hydrological model parameters and watershed characteristic factors were firstly established, based on the block-wise use of TOPMODEL (BTOP). This paper explored the application of twelve regionalization approaches that were combined with the spatial proximity, physical similarity, integration similarity, and regression-augmented approach in five ungauged target basins. The results showed that the spatial proximity approach performs best in the river discharge simulation of the studied basins, while the regression-augmented regionalization approach is satisfactory as well, indicating a good potential for the application in ungauged basins. However, for the regression-augmented approach, the number of watershed characteristic factors considered in the regression equation impacts the simulated effect, implying that the determination of optimal watershed characteristic factors set by the model parameter regression equation is a crux for the regression-augmented approach, and the regression strength may also be an influencing factor. These findings provide meaningful information to establish a parametric transfer equation, as well as references for the application in data-sparse regions for the BTOP model. Future research should address the classification of the donor basins under the spatial distance between the reference basin and the target basin, and build regression equations of model parameters adopted to regression-augmented regionalization in each classification group, to further explore this approach’s potential.

Based on the bibliometric and data visualization analysis software Citespace, this study carried out document statistics and information mining on the Web of Science database and characterized the distributed hydrological model knowledge system from 1986 to 2019. The results show a few things: (1) from 1986 to 2019, the United States and China accounted for 41% of the total amount of publications, and they were the main force in the field of distributed hydrological model research; (2) field research involves multiple disciplines, mainly covering water resources, geology, earth sciences, environmental sciences, ecology and engineering; (3) the frontier of field research has shifted from using distributed hydrological models in order to simulate runoff and nonpoint source environmental responses to the coupling of technologies and products that can obtain high-precision, high-resolution data with distributed hydrological models. (4) Affected by climate warming, the melting of glaciers has accelerated, and the spatial distribution of permafrost and water resources have changed, which has caused a non-negligible impact on the hydrological process. Therefore, the development of distributed hydrological models suitable for alpine regions and the response of hydrological processes to climate change have also become important research directions at present.

Based on the daily temperature data from 26 meteorological stations in the Minjiang River Basin, for 1961 to 2016, the temporal trend and spatial distribution of extreme temperature in this region were analyzed using 16 extreme temperature indices. The results show that in terms of time variation, determined using linear trend analysis and a Mann–Kendall trend test, the warm and day indices mainly show an upward trend while the cold and night indices mainly show a downward trend in the entire basin. Among them, FD0, TN10, and CSDI significantly decrease at −1.3, −2.9, and −1.1 d/10a, respectively. TN90, TX90, SU25, and TR20 significantly increase at 3.0, 2.6, 2.1, and 2.2 d/10a, respectively. The crop growth period in the basin showed a significant increasing trend at 1.4 days/decade, while the diurnal temperature range showed a nonsignificant increasing trend at 0.03 days/decade. On comparing the change range of each index, it was found that the change range of the warm index is greater than that of the cold index, while the change range of the day index is smaller than that of the night index; thus, the change trends of the maximum and minimum temperatures in the whole basin are not obvious. Analysis of the changing trend at each station showed that the relative index of the extreme temperature in the basin has good climatic consistency in terms of spatial distribution. The distributions of the absolute and extreme indices are not uniform, which is consistent with the change in elevation in the basin. Further, the diurnal temperature range in the upper reaches of the basin is greater than that in the middle and lower reaches. However, because of the more obvious upward trends of the day and warm indices in the middle and lower reaches, and the more obvious downward trends of the night and cold indices in the upper reaches, the daily temperature differences in the upper, middle, and lower reaches of the Minjiang River Basin tend to be consistent. Therefore, the precipitation in the Minjiang River Basin shows a significant decreasing trend; thus, the basin shows a trend of drying and warming.

Block-wise use of TOPMODEL with the Muskingum–Cunge method (BTOPMC) is a physically based distributed hydrological model with five parameters that quantitatively reflect basin physical features, including soil type, vegetation, and land use of each grid-cell. In order to determine the sensitive model parameters and related variables more reasonably and efficiently, and to improve the model’s practical applicability and simulation accuracy, BTOPMC was integrated with the Uncertainty Quantification Python Laboratory (UQ-PyL) and used in the Fuji River Basin of Japan, by which qualitative and quantitative sensitivity analysis (SA) of variables related to the BTOPMC parameters was performed, and the sensitive ones were optimized by shuffled complex evolution (SCE-UA). The results showed that optimizing only the sensitive variables related to the three sensitive parameters of BTOPMC can ensure simulation accuracy with higher optimization efficiency, which indicates that the BTOPMC model could be applied more simply while guaranteeing the reliability of modeling.

Based on the Standardized Precipitation Index (SPI) and copula function, this study analyzed the meteorological drought in the upper Minjiang River basin. The Tyson polygon method is used to divide the research area into four regions based on four meteorological stations. The monthly precipitation data of four meteorological stations from 1966 to 2016 were used for the calculation of SPI. The change trend of SPI1, SPI3 and SPI12 showed the historical dry-wet evolution phenomenon of short-term humidification and long-term aridification in the study area. The major drought events in each region are counted based on SPI3. The results show that the drought lasted the longest in Maoxian region, the occurrence of minor drought events was more frequent than the other regions. Nine distribution functions are used to fit the marginal distribution of drought duration (D), severity (S) and peak (P) estimated based on SPI3, the best marginal distribution is obtained by chi-square test. Five copula functions are used to create a bivariate joint probability distribution, the best copula function is selected through AIC, the univariate and bivariate return periods were calculated. The results of this paper will help the study area to assess the drought risk.