Accurate assessment of spatial and temporal precipitation is crucial for simulating hydrological processes in basins, but is challenging due to insufficient rain gauges. Our study aims to analyze different precipitation interpolation schemes and their performances in runoff simulation during light and heavy rain periods. In particular, combinations of different interpolation estimates are explored and their performances in runoff simulation are discussed. The study was carried out in the Pengxi River basin of the Three Gorges Basin. Precipitation data from 16 rain gauges were interpolated using the Thiessen Polygon (TP), Inverse Distance Weighted (IDW), and Co-Kriging (CK) methods. Results showed that streamflow predictions employing CK inputs demonstrated the best performance in the whole process, in terms of the Nash–Sutcliffe Coefficient (NSE), the coefficient of determination (R²), and the Root Mean Square Error (RMSE) indices. The TP, IDW, and CK methods showed good performance in the heavy rain period but poor performance in the light rain period compared with the default method (least sophisticated nearest neighbor technique) in Soil and Water Assessment Tool (SWAT). Furthermore, the correlation between the dynamic weight of one method and its performance during runoff simulation followed a parabolic function. The combination of CK and TP achieved a better performance in decreasing the largest and lowest absolute errors compared to any single method, but the IDW method outperformed all methods in terms of the median absolute error. However, it is clear from our findings that interpolation methods should be chosen depending on the amount of precipitation, adaptability of the method, and accuracy of the estimate in different rain periods.

Flash floods in mountainous areas have become more severe and frequent as a result of climate change and are a threat to public safety and social development. This study explores the application of distributed hydrological models in flash floods risk management in a small watershed in Sichuan Province, China, and aims to increase early warning lead time in mountainous areas. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was used to simulate the flash flood process and analyze the variation in flood hydrographs. First, the HEC-HMS model was established based on geospatial data and the river network shape, and eight heavy rainfall events from 2010 to 2015 were used for model calibration and validation, showing that the HEC-HMS model was effective for the simulation of mountain floods in the study area. Second, with the assumption that rainfall and flood events have the same frequency, the flood hydrographs with different frequencies (p = 1%, 2%, 5%, and 10%) were calculated by the HEC-HMS model. The rising limbs of the flood hydrographs were significantly different and can be divided into three parts (0–5 h, 6–10 h, and 11–15 h). The rising rate of the flood stage for each part of the flood hydrograph increases in multiples. According to the analysis of the flood hydrographs, two critical early warning indicators with an invention patent were determined in the study: the flood stage for immediate evacuation and the rising rate. The application of the indicators in the study shows that it is feasible to advance the time of issuing an early warning signal, and it is expected that the indicators can offer a reference for flash flood early warning in the study area and other small watersheds in mountainous areas.

Dramatic changes in land use/cover (LULC) patterns have taken place in the Three Gorges Reservoir Area (TGRA) after the construction of the Three Gorges Dam, which have led to hydrological and environment alterations. In this study, eight land use scenarios from 1980 to 2018 were used to evaluate the impact of LULC changes on runoff and nutrient load in the TGRA, using a validated version of the Soil & Water Assessment Tool (SWAT) model. Firstly, we analyzed the LULC characteristic. During the 38-year period, the LULC pattern showed an increase in forestland and a decrease in cropland. The cropland mainly changed into forestland. Construction land realized growth by encroaching mainly on cropland and forestland. Secondly, the temporal–spatial characteristics of runoff and nutrient load were analyzed. In the TGRA, surface runoff and nutrient load exhibited significant tempo-spatial heterogeneity. The runoff depth and the total nitrogen (TN) and total phosphorus (TP) loads increased through 1980 to 2018, and 2005 was a turning point. After 2005, the annual average change rate was larger than before 2005. The area with a larger runoff depth was mainly distributed in the head and middle region as well as on the left bank of the TGRA. The middle and tail region of the TGRA generated relatively higher TN and TP loads. Lastly, the contributions of LULC types on runoff and nutrient load were explored. Forestland had the highest contribution rate to surface runoff, followed by cropland. Cropland had the highest contribution rate to TN and TP, follow by forestland. This study can provide a better understanding of the hydrological consequences of LULC changes and help watershed management in the TGRA.

Traditional hydrodynamic models face the significant challenge of balancing the demands of long prediction spans and precise boundary conditions, large computational areas, and low computational costs when attempting to rapidly and accurately predict the nonlinear spatial and temporal characteristics of fluids at the basin scale. To tackle this obstacle, this study constructed a novel deep learning framework with a hydrodynamic model for the rapid spatiotemporal prediction of hydrodynamics at the basin scale, named U-Net-ConvLSTM. A validated high-fidelity hydrodynamic mechanistic model was utilized to build a 20-year hydrodynamic indicator dataset of the middle and lower reaches of the Han River for the training and validation of U-Net-ConvLSTM. The findings indicate that the $R^2$ value of the model surpassed 0.99 when comparing the single-step prediction results with the target values. Additionally, the required computing time fell by 62.08% compared with the hydrodynamic model. The ablation tests demonstrate that the U-Net-ConvLSTM framework outperforms other frameworks in terms of accuracy for basin-scale hydrodynamic prediction. In the multi-step-ahead prediction scenarios, the prediction interval increased from 1 day to 5 days, while consistently maintaining an $R^2$ value above 0.7, which demonstrates the effectiveness of the model in the missing boundary conditions scenario. In summary, the U-Net-ConvLSTM framework is capable of making precise spatiotemporal predictions in hydrodynamics, which may be considered a high-performance computational solution for predicting hydrodynamics at the basin scale.

The Three Gorges Project (TGP) has greatly enhanced the heterogeneity of the underlying surface in the Three Gorges Reservoir Area (TGRA), thereby affecting the hydrologic processes and water quality. However, the influence of the differences of underlying surfaces on the hydrologic processes and water quality in the TGRA has not been studied thoroughly. In this research, the influence of the heterogeneity of landscape pattern and geographical characteristics on the spatial distribution difference of hydrologic processes and water quality in the different tributary basins of the TGRA was identified. The TGRA was divided into 23 tributary basins with 1840 sub-basins. The spatial differentiation of the hydrologic processes and water quality of the 23 tributary basins was examined by the Soil and Water Assessment Tool (SWAT). The observed data between 1 January 2010 and 31 December 2013 were used to calibrate and validate the model, after which the SWAT model was applied to further predict the runoff and water quality in the TGRA. There are 25 main model parameters, including CN2, CH_K2 and SOL_AWC, which were calibrated and validated with SWAT-Calibration and Uncertainty Procedures (SWAT-CUP). The landscape patterns and geomorphologic characteristics in 23 tributary basins were investigated and spatially visualized to correlate with surface runoff and nutrient losses. Due to geographical difference, the average total runoff depth (2010–2013) in the left bank area (538.6 mm) was 1.4 times higher than that in the right bank area (384.5 mm), total nitrogen (TN) loads in the left bank area (6.23 kg/ha) were 1.9 times higher than in the right bank area (3.27 kg/ha), and total phosphorus (TP) loads in the left bank area (1.27 kg/ha) were 2.2 times higher than in the right bank area (0.58 kg/ha). The total runoff depth decreased from the head region (553.3 mm) to the tail region (383.2 mm), while the loads of TN and TP were the highest in the middle region (5.51 kg/ha for TN, 1.15 kg/ha for TP), followed by the tail region (5.15 kg/ha for TN, 1.12 kg/ha for TP) and head region (3.92 kg/ha for TN, 0.56 kg/ha for TP). Owing to the different spatial distributions of land use, soil and geographical features in the TGRA, correlations between elevation, slope gradient, slope length and total runoff depth, TN and TP, were not clear and no consistency was observed in each tributary basin. Therefore, the management and control schemes of the water security of the TGRA should be adapted to local conditions.