## 8 Future Studies and Shortcomings  
For future studies, the quality of IMERG should be improved to provide more precise half-hourly precipitation estimates, especially for territories with dry climates, as well as high latitudes and altitudes. Correspondingly, more studies are required to assess the performance of GPM IMERG against other satellite and reanalysis products to find the advantages and limitations of the GPM satellite. Furthermore, other precipitation specific properties such as spatial and temporal heterogeneity, spatial and temporal intermittence, extreme variability and multi-fractal comparisons is necessary to overcome substantial uncertainties rainfall estimations [57; 58; 59]. [PERSON] et al. [60] also depicted in their review that the rainfall variability may need wider range of conditions and scenario based in observational datasets for urban hydrological basins, which need proper analysis for development of better hydrological response system with higher sensitivity.  
For nowcasting, in order to obtain better precipitation nowcasting results, the precipitation data of GPM IMERG product and rain gauge can be blended, such as kriging with external drift and the double-kernel smoothing methods. The characteristics of uncertainty and error of new generation of geosynchronous satellite products launched by the National Aeronautics and Space Administration (NASA) as GPM IMERG and National Oceanic and Atmospheric Administration (NOAA) will open new perspective for nowcasting and data assimilation. Moreover, such products should be further explored to encourage the application and development of products such as IMERG and geostationary lightning mapper sensors, respectively, and their associative algorithms in the recent era. This forecasting technique is extremely important especially for issuing flash flood warnings several hours in advance. Heavy rainfall is especially frequent in the Yangtze River delta area of southern China. In the future, more case studies can be conducted in this area in order to enhance the accuracy of nowcasting and forecasting precipitation models.  
**Author Contributions:** Conceptualization: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], F.G., X.T., and [PERSON]; methodology: [PERSON], [PERSON], [PERSON], [PERSON], and X.T.; investigation: [PERSON], [PERSON], and [PERSON]; writing--original draft preparation: [PERSON], [PERSON], and [PERSON]; writing--review and editing: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; supervision: [PERSON], [PERSON], [PERSON], and [PERSON]; funding acquisition: [PERSON], [PERSON], [PERSON], and [PERSON] and Z.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors acknowledge the support given by the Research Fund (RDH-101-2020-0044) of Xi'an Jiaotong-Liverpool University Urban and Environmental Studies University Re-search Center.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** We would like to thank [PERSON] for his helpful suggestions and data support in this study. Gratitude is also due to the Optimizing Water Streams Project funded by the Research Centre, Xi'an Jiaotong-Liverpool University.  
**Conflicts of Interest:** The authors declare no conflict of interest.