Storm floods have become a major disaster threatening our lives and affecting socioeconomic development. The frequency of storm floods and the severity of the damage are beyond comparison with most other disasters. China is located in the East Asian monsoon belt and is the country with the most frequent flooding caused by heavy rainfall and the most severe damage in the world, with more than half of China’s regions suffering from various degrees of flooding. With the development of the national economy and society and the gradual expansion and concentration of population size, the economic loss and death toll per unit of the affected area increase [
1,
2,
3,
4], resulting in higher requirements for flood prevention and control capacity building. Effective flood forecasting is an important means to reduce losses and impact. It can not only help to gain a valuable window period for emergency response, but also provide a scientific and reliable decision basis for flood control and dispatch, thereby achieving great socioeconomic benefits in disaster prevention and mitigation.
Precipitation is the key input data source for flood simulation, and high-quality live precipitation data and forecast products are extremely important for improving hydrological forecast accuracy. In practical hydrological applications, the spatial resolution of precipitation data needs to reach 10 km and below, and the temporal resolution needs to reach 1 h to satisfy the requirements of flood forecasting. Precipitation estimation with high spatial and temporal resolution has also become a research hotspot at home and abroad. Some precipitations have a high degree of spatial and temporal variability due to various factors, such as geographic undulating conditions, atmospheric circulation changes, and different subsurfaces [
5,
6]. Currently, three main ways are available to obtain precipitation information. First, rainfall stations observe precipitation. At present, complex networks of rainfall stations have been built worldwide, but many mountainous or less developed areas still have sparse networks of rainfall stations and greatly lack precipitation observation data, thereby causing difficulty in accurately reflecting the spatial structure of precipitation and satisfying the application requirements of high-precision hydrological simulation, especially flood simulation. Second, the satellite remote sensing inversion of precipitation is not limited by complex terrain, high altitude, and other harsh environments. It provides precipitation data with a continuous spatial and temporal distribution, wide spatial coverage, and finer resolution; it is convenient and efficient to obtain. The spatial and temporal resolution and accuracy of Quantitative Precipitation Estimation (QPE) products for the inversion of precipitation in satellite remote sensing systems have been further improved [
7,
8]. Some examples include the widely used Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) precipitation product [
9], the more mature Climate Prediction Center Morphing Technique (CMORPH) precipitation product [
10], the more sophisticated Climate Prediction Center Morphing Technique (CMORPH precipitation product) [
11], the widely used Global Precipitation Measurement (GPM) precipitation product [
12,
13], Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) precipitation products [
14], and other products that have been used for a long time in water resource conservation and application, drought and flood control, and climate change [
15,
16,
17]. However, although satellite inversion precipitation has a high spatial and temporal resolution, its accuracy still has a certain gap compared with ground-based rain gauge observations [
18]. Third, in radar-estimated precipitation, the efficient data transmission and jigsaw network play a good role in enhancing the early warning and monitoring services of sudden disaster weather systems. However, the inversion of precipitation data by radar still has some problems [
19], such as the need for a higher spatial and temporal resolution and better spatial continuity of precipitation data, a more accurate integration of radar inversion of precipitation with other precipitation data, the further optimization of the algorithm of radar inversion of precipitation, and the improvement in data assimilation techniques to improve the assimilation of remotely sensed water cycle variables.
Therefore, the further enhancement in the integration of these multiple sources of information, such as ground observations, radar measurements, and satellite remote sensing, is necessary, and their analysis, study, and application are important aspects of the current development of basin hydrological simulation and prediction forecasting techniques [
18]. The National Weather Service’s Office of Hydrologic Development and NOAA’s National Severe Storms Laboratory jointly launched the National Mosaic and Multisensor QPE Project (NMQ) working pilot program with a view to improving the resolution and accuracy of precipitation data. With the support of this program, the fusion techniques of radar inversion, satellite remote sensing, ground observation, and other multisource data have developed rapidly [
20]. The vast size of China and the very complex topography have caused practical difficulties in radar networking, which has created difficulties in developing satellite–radar observation triple-source fusion precipitation products [
21,
22]. The Meteorological Information Center of the China Meteorological Administration developed a multisource data fusion suitable for the distribution of a regional precipitation observation station network [
23,
24,
25,
26,
27], as well as data-fused ground observation, radar inversion, and satellite remote sensing precipitation. This fused precipitation is more spatially representative than any single-source precipitation data and the ground–satellite two-source precipitation fusion product (two-source fusion) [
24,
28]. However, currently, these data are mainly used in the meteorological field; the applications in other fields are still relatively few and small, mainly for the analysis of a few regions [
29,
30]. This fused precipitation is more spatially representative than any single-source precipitation data and ground–satellite two-source precipitation fusion products [
24,
28], but currently, these data are mainly used in the meteorological field, and the applications in other fields are still relatively few and small, mainly for the analysis of few regions. The study area is the Qingyi River (13,000 km
2), a typical watershed in the southwest mountainous region. The main tributaries of the Qingyi River are fan-shaped, and most of the floods in the basin tend to converge at the same time, resulting in frequent floods in the mainstream of the Qingyi River, characterized by the steep rise and fall of floods. The area is mountainous, and its geological conditions are fragile, which is very likely to cause geological disasters. At present, the research on flood forecasting in the Qingyi River basin is still weak, and the high-precision flood forecasting in this region is greatly important for water conservancy projects, flood control, and water resource management. We use CMPA precipitation and hourly observed precipitation from meteorological stations to drive the distributed hydrological model, the block-wise use of the topographic-based hydrologic model (TOPMODEL) (BTOPMC), and separately calibrate its parameters to study the effects of different precipitation products on the accuracy of flood forecasting in a typical watershed in the southwest mountainous region. Meanwhile, because CMPA precipitation data incorporate precipitation information from ground stations, the previous use of ground-observed precipitation to verify the accuracy of precipitation products becomes inapplicable, and the use of the runoff flood forecast effectiveness test can be an effective solution to test the quality of precipitation products.
Figure 1 shows the research framework for this research paper.