Satellite precipitation products (SPPs) have advanced remarkably in recent decades. However, the bias correction of SPPs still performs unsatisfactorily in the case of a limited rain-gauge network. This study proposes a new real-time bias correction approach that includes three steps to improve the precipitation quality with limited gauges and facilitate the hydrological simulation in the Min River Basin, China. This paper employed 66 gauges as available ground observation precipitation, Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) as the historical precipitation to correct Global Satellite Mapping of Precipitation NOW (GNOW) and Global Satellite Mapping of Precipitation NRT (GNRT) in 2020. A total of 1020 auto-rainfall stations were used as the benchmark to evaluate the original and corrected SPPs with six criteria. The results show that the statistic and dynamic bias correction method (SDBC) improved the SPPs significantly and the cumulative distribution function matching method (CDF) could reduce the overcorrection error from SDBC. The inverse error variance weighting method (IEVW) integrations of GNOW and GNRT did not have noticeable improvement as they use similar hardware and software processes. The corrected SPPs show better performance in hydrological simulations. It is recommended to employ different SPPs for integration. The proposed bias correction approach is significant for precipitation estimation and flood prediction in data-sparse basins worldwide.

Surface mounds and depressions are the basic patterns of microtopography. Their geometric forms and physical properties affect rainfall infiltration, runoff generation and runoff confluence process. In this study, soil beds were set up with seven different types of microtopography to study the effects of surface mounds and depressions on runoff. They were the control check (CK), alternate mounds (AM), continuous mounds (CM), alternate depressions (AD), continuous depressions (CD), alternate mounds and depressions (AMD) and continuous mounds and depressions (CMD). There was only one microtopography type for monomorphic surface relief (MSR) while two for compound surface relief (CSR). All soil beds were exposed under 60, 90 or 120 mm/h rainfall intensity for 90 min. The main results are as follows: surface mounds could promote surface runoff, triggering and shortening runoff generation time, while surface depressions showed contrary results. Whether there was an interval between mounds or depressions also affected the characteristics of runoff. The runoff generation time was 3.8–5.0 times higher for continuous slope than for interval slope, while the runoff yield and runoff coefficient both decreased by approximately 40%. CSR can significantly neutralize the flow-promoting effects of the mounds and the flow-inhibiting effects of the depressions, making the runoff yield and runoff process present a neutral state between the mounds and depressions. CSR prolongs runoff generation time from 1–10 min of MSR to 5–16 min. The runoff yield of CSR presented as 0.12, between 0.17 for mounds and 0.10 for depressions, and so did the runoff coefficient and hydrodynamic parameters. In addition, with rainfall intensity increased, the runoff pattern of CSR and MSR became more similar to each other, and the retarding effects of topography on overland flow were more effective.

In view of the uncertainty of inflow of cascade reservoirs, an implicit stochastic joint scheduling function model for cascade hydropower stations based on support vector machine (SVM) was established. Taking the cascade hydropower station on the lower reaches of the Yalong River as an example, according to the long series of optimized scheduling simulation operation data, the Gauss radial basis (BRF) kernel function is utilized by LIBSVM for the scheduling function fitting of the cascade reservoirs in the lower reaches of the Yalong River. Besides, combined with particle swarm optimization algorithm, the support vector machine (SVM) model parameters c (penalty coefficient) and g (relaxation coefficient) were optimized. Eventually, the optimized scheduling function model was used for the cascade hydropower station simulation operation. The results show that compared with the existing scheduling technology, the nonlinear SVM scheduling function is better than the linear regression model, and the effect of the nonlinear SVM scheduling function is equivalent to that of the threshold regression model. Therefore, the SVM-based Implicit Stochastic Scheduling method can provide references for the actual operation of the cascade hydropower station.