Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards

• Quantified the uncertainty in the radar and GSMaP QPEs in a China’s hilly humid region. • Investigated the radar and GSMaP QPEs utility for integrated flood and landslide forecasting. • Developed a dynamic bias correction method to improve the radar and GSMaP QPEs. • Explored the impacts of time interval and spatial resolution on the bias-correction performance. It is important to develop the integrated flood and landslide modeling system driven by radar and satellite to predict these hazards to mitigate their damages. In this study, we investigated the utility of the C-band, one-polarization radar quantitative precipitation estimation (QPE) and the Global Satellite Mapping of Precipitation (GSMaP) satellite QPE for the integrated prediction of floods and landslides in two hilly basins of southern Shaanxi Province of China. We further developed a dynamic bias correction to reduce uncertainty in radar and satellite QPEs using gauge observations and explored the impacts of gauge density and spatial resolution of QPE on the effectiveness of bias correction. Our results show that the radar and GSMaP QPEs have respective large negative and positive biases. The bias-correction method has significantly improved the quality of both radar and GSMaP QPEs and the associated accuracies in the simulated hydrological processes and slope stability. The bias-correction method with a correction time interval of 24 h can achieve the optimal results for both radar and GSMaP QPEs. Although gauge density and spatial resolution impact the accuracy of the bias-corrected methods for both radar and GSMaP, inclusion of the observations from even a small number of rain gauges will be helpful for reducing the uncertainty in the radar and satellite QPEs.