Parameter sensitivity analysis is a significant part of quantifying model uncertainty, effectively identifying key parameters, and improving the efficiency of parameter optimization. The Soil and Water Assessment Tool (SWAT) model was applied to the upper Heihe River basin (UHRB) in China to simulate the monthly runoff for 11 years (1990–2000). Four typical sensitivity analyses, namely, the Morris screening, Sobol analysis, Fourier amplitude sensitivity test (FAST), and extended Fourier amplitude sensitivity test (EFAST), were used to determine the critical parameters affecting hydrological processes. The results show that the sensitivity parameters defined by the four methods were significantly different, resulting in a specific difference in the simulation effect of the SWAT model. The reason may be the different sampling process, sensitivity index, and calculation principle of each method. The snow-melt base temperature (SMTMP) and snowfall temperature (SFTMP) related to the snow-melt process, the available water capacity of the soil layer (SOL_AWC), saturated hydraulic conductivity (SOL_K), depth from the soil surface to the bottom of the layer (SOL_Z), moist bulk density (SOL_BD), deep aquifer percolation fraction (RCHRG_DP), and threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) related to the soil water and groundwater movement, baseflow alpha factor for bank storage (ALPHA_BNK) related to the base flow regression, and average slope steepness (HRU_SLP) are all very sensitive parameters. The 10 key parameters were optimized 100 times with the sequential uncertainty fitting procedure version 2 (SUFI-2). The Nash–Sutcliffe efficiency coefficient (NSE), Kling–Gupta efficiency coefficient (KGE), mean square error (MSE), and percentage bias (PBIAS) were 0.89, 200, 8.60, and 0.90, respectively. The simulation results are better than optimizing the sensitive parameters defined by the single method and all the selected parameters. The differences illustrate the rationality and importance of parameter sensitivity analysis for hydrological models and the synthesis of multiple approaches to define sensitive parameters. These conclusions have reference significance in the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model.

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.

In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them with the inverse distance weighting, ordinary kriging, linear regression residual inverse distance weighting and linear regression residual kriging interpolation methods. The TRMM 3B43 V7 satellite precipitation information, which is processed by the latest revision algorithm, is used as the auxiliary variable for ground site precipitation interpolation along with latitude and elevation. Our results show that: (1) adding the TRMM 3B43 V7 satellite precipitation data as an auxiliary variable significantly improves the interpolation accuracy of the linear regression equation and SVM model; (2) the support vector machine hybrid interpolation method obtains superior interpolation results compared to the inverse distance weighting method, ordinary kriging method and linear regression hybrid interpolation method; (3) the interpolation accuracy of the SVM hybrid interpolation method depends on the SVM fitting degree, so we should choose a suitable fitting accuracy rather than the highest fitting accuracy; (4) the linear regression equation has a greater degree of dependency on the TRMM data than the SVM. The SVM accepts the TRMM data information while better maintaining its independence, taking into account that the TRMM data linear regression and linear regression hybrid interpolation method are not suitable for TRMM data evaluation.

Improving the accuracy of runoff simulations is a significant focus of hydrological science for multiple purposes such as water resources management, flood and drought prediction, and water environment protection. However, the simulated runoff has limitations that cannot be eliminated. This paper proposes a method that integrates the hydrological and time series models to improve the reliability and accuracy of simulated runoffs. Specifically, the block-wise use of TOPMODEL (BTOP) is integrated with three time series models to improve the simulated runoff from a hydrological model of the Zhou River Basin, China. Unlike most previous research that has not addressed the influence of runoff patterns while correcting the runoff, this study manually adds the hydrologic cycle to the machine learning-based time series model. This also incorporates scenario-specific knowledge from the researcher’s area of expertise into the prediction model. The results show that the improved Prophet model proposed in this study, that is, by adjusting its holiday function to a flow function, significantly improved the Nash–Sutcliffe efficiency (NSE) of the simulated runoff by 53.47% (highest) and 23.93% (average). The autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) improved the runoff but performed less well than the improved Prophet model. This paper presents an effective method to improve the runoff simulation by integrating the hydrological and time series models.

The NPS pollution is difficult to manage and control due to its complicated generation and formation mechanism, especially in the data sparse area. Thus the ECM and BTOPMC were, respectively, adopted to develop an easy and practical assessment method, and a comparison between the outputs of them is then conducted in this paper. The literature survey and field data were acquired to confirm the export coefficients of the ECM, and the loads of TN and TP were statistically analyzed in the study area. Based on hydrological similarity, runoff data from nearby gauged sites were pooled to compensate for the lack of at-site data and the water quality submodel of BTOPMC was then applied to simulate the monthly pollutant fluxes in the two sections from 2010 to 2012. The results showed agricultural fertilizer, rural sewage, and livestock and poultry sewage were the main pollution sources, and under the consideration of self-purification capacity of river, the outputs of the two models were almost identical. The proposed method with a main thought of combining and comparing an empirical model and a mechanistic model can assess the water quality conditions in the study area scientifically, which indicated it has a good potential for popularization in other regions.

The movement of water flow usually has history and path dependence. Fractional calculus is very suitable for describing the process with memory and hereditary properties. In this study, the order of the differential equation in the Nash confluence system was extended from integer order to fractional order. On the basis of the Laplace transform, the fractional instantaneous unit hydrograph was obtained, which was used to describe the long-term memory of the basin confluence system. Furthermore, the enhanced TOPMODEL (FTOP) model was obtained by applying the fractional instantaneous unit hydrograph as the surface runoff calculation. Taking Chengcun Basin in China as an example, the FTOP model was used to simulate the daily runoff and 22 floods from 1989 to 1996. The simulation results were compared with two original TOPMODEL models (the NTOP and ITOP models). The results show that in the daily runoff simulation, the Nash–Sutcliffe efficiency (NSE), relative error (RE), and root mean square error (RMSE) of the FTOP model were 0.82, −11.14%, and 15.25 m³/s, respectively, being slightly better than the other two TOPMODEL models. According to the hydrologic frequency curve, the measured daily runoff was divided into different flow levels. It was found that the FTOP model can improve the simulation effect of the medium-flow (frequency between 10% and 50%) and low-flow (frequency more than 50%) sections to a certain extent. In the flood simulation, the average runoff depth relative error (RDRE), peak discharge relative error (PDRE), peak occurrence time error (POTE), and NSE of 22 floods were 1.99%, 14.06%, −1.27, and 0.88, respectively, indicating that the simulation effect had been improved. Especially in NSE, the improvement was more prominent, meaning that the FTOP model can better simulate the flooding process. However, the flood peak and runoff depth simulation effect were not significantly improved. These conclusions indicate that the confluence method using the fractional instantaneous unit hydrograph as the TOPMODEL model can improve the simulation effect.