*4.5. Flash Flood Risk Assessment*

The risk proposed by the United Nations refers to the expected loss of people's lives, property, and economic activities caused by a specific natural disaster in a certain area and a given time period [103]. Therefore, the flash flood risk analysis is obtained by combining hazard analysis and vulnerability analysis. Different from flash flood susceptibility analysis and flash flood disaster analysis, some flash flood risk analyses considered the factors of city and climate change [41–43]. The different geomorphic processes and hydraulic behaviors of the watershed are controlled by its morphometric characteristics [49]. Therefore, morphometric analyses are frequently used in flash flood risk analysis [31,44,45]. Table 6 summarizes the representative literature on flash flood risk assessment in terms of analytical methods and factors.


**Table 6.** For flash flood risk assessment.

#### **5. Discussion**

In the past 20 years, the application of remote sensing and GIS technology in flash flood research has made great progress, mainly reflected in the increasingly abundant multisource remote sensing data sources, GIS strong spatial analysis ability, and coupling ability with hydrological and hydrodynamic models. However, the uncertainty of the data and model is still a huge challenge for future research. How to obtain real-time or quasi-real-time accurate simulations and reduce the uncertainty of data input (such as precipitation, land use, evaluation unit division, etc.) and model output is the goal of future research. To date, in many areas, through remote sensing data sources, GIS, and hydrological coupling models, a large number of studies and analyses have been carried out on flash flood susceptibility analysis, flash flood disaster impact assessment, and flash flood hazard identification. Most of the experimental results show that the established or improved model is effective for the experimental area, but as to whether the model can be applied in other areas, the universality of the model needs further verification.

For flash flood forecasting, with the development of meteorological satellite technology and radar-based rainfall forecast technology, more accurate and real-time precipitation data can be used in flash flood forecasting, and after precipitation data from multiple sources are acquired, the precipitation data can be corrected via the correction model.

For the impact of flash flood disaster assessment, with the development of data association analysis and multimodel coupling technology, the impact of flash floods on the regional ecology and environment can be rapidly and quantitatively assessed.

For flash flood susceptibility assessment, at present, most of the susceptibility zoning maps belong to static mapping and cannot show the inundation depth and advance speed. Future studies should combine machine learning with the hydrodynamic model to complete the dynamic susceptibility mapping of flash flood disasters. Then, a 2D model will be researched and developed to obtain the inundation depth and advance speed.

For flash flood risk assessment and hazard area identification, mapping flash flood disaster maps and flash flood risk maps relies on various adjustment factors that represent the physical characteristics of the study area. Due to the influence of data precision, data volume, size of the study area, and the authors' subjective choices, there are some

differences among the adjustment factors selected in these papers, and the weights of the adjustment factors are not always the same. Even in regions with similar geological conditions, whether the adjustment factors selected in other areas can be used and their weights need to be further discussed and verified. It is hoped that there will be a set of systematic rules in the future so that adjustment factors and corresponding weights can be selected for regions with different sizes and different physical characteristics, according to their conditions, to obtain better results.

#### **6. Conclusions**

In this study, the related literature on remote sensing and GIS applied in the field of flash flood disasters was systematically analyzed. Then, a visualization analysis of the literature was adopted to perform keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Finally, several main subfields of the application of remote sensing and GIS in flash floods were summarized, including flash flood forecasting, the impact of flash flood assessment, flash flood susceptibility assessment, flash flood risk assessment, and the identification of flash flood hazard areas, which makes our study different from the previous review of remote sensing and geographical information application to natural disasters. The main conclusions are as follows: (1) through the analysis of the time zone map, the appearance of keywords can be roughly divided into five stages. (2) Analyzing the burst of keywords in 248 articles, we found that current research focuses on reducing uncertainty, and reducing the uncertainty of flash flood forecasting is the basis for real-time accurate simulation. (3) Through the co-cited analysis of 248 articles, 7 clusters were obtained. Among them, there were three highly co-cited articles from 2012 to 2015, which are landmark studies. Therefore, from this review, various applications of remote sensing and GIS in the field of flash floods and specific opportunities and challenges in different fields can be found.

**Author Contributions:** L.D. and H.L. drafted the manuscript and were responsible for the research design, experiment, and analysis. L.M. and C.L. reviewed and edited the manuscript. L.L., N.L., Z.Y., and Y.Y. supported the data preparation and the interpretation of the results. All of the authors contributed to editing and reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the National Key R&D Program of China(2019YFC1510700), the National Natural Science Foundation of China (41701499), the funding provided by the Alexander von Humboldt-Stiftung, the Sichuan Science and Technology Program (2018GZ0265), the Geomatics Technology and Application Key Laboratory of Qinghai Province, China (QHDX-2018-07), the Major Scientific and Technological Special Program of Sichuan Province, China (2018SZDZX0027), and the Key Research and Development Program of Sichuan Province, China (2018SZ027, 2019-YF09-00081-SN).

**Institutional Review Board Statement:** Not applicable for studies not involving humans or animals.

**Informed Consent Statement:** Not applicable for studies not involving humans.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

