# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 5 Conclusions  
The L4 combination observation can be used to analyze the ionosphere delay, multipath error, and parameter retrieval. However, whether the characteristic of L4 will be influenced during the flood has not been researched in depth. Thus, by using real datasets collected during the flood, this research investigated and analyzed the impact of the flood on GPS and GLONASS satellites on L4 observations.  
First, the relationship between the multipath error, flood, and L4 combination observation is illustrated in theory. The multipath error performs more severely during the flood because the reflection coefficient of water is three times larger than that of dry soil. Furthermore, after adopting the averaging and filtering methods, the multipath error can be reflected by L4 combination observation. Thus, the influence of flood on L4 observation can be analyzed by the residuals of L4. To ensure the accuracy of comparison results, the ground track repeat period of GPS and GLONASS satellites is investigated in the view of the sky plot. The results indicate that the one and eight solar days should be used to analyze the influence of flood on L4 for GPS and GLONASS satellites, respectively.  
Real data sets collected during the flood in Zhengzhou, China from DOY 193 to DOY 204, 2021 are used. The flood appeared on DOY 202. Experimental results show that the influence of flood on L4 observation is obvious, and the RMS of L4 residuals during the flood performs significantly larger than that of without flood. For example, the average RMS increase rate of all GPS satellites under flood can reach approximately 109.53% compared to without flood, and even the smallest RMS increase rate is approximately 23.53%. In terms of GLONASS satellites, the average RMS increase rate is 43.65%, while the maximum RMS increase rate is 79.84%. In addition, the influence of rainfall and hardware device is analyzed, and the results indicate that the influence of these two elements is very small and can be ignored. Based on the above analysis, it can be drawn that the increase in L4 residuals during floods is mainly caused by floods and not by other factors.  
In conclusion, the influence of flood on L4 combination observation of GPS and GLONASS satellites is obvious, and the fluctuation of L4 performs more severely during the flood. Thus, when L4 observation is used to mitigate multipath error or parameter retrieval (such as soil moisture or vegetation water content) under a flood environment, the influence of flood should be considered.  
[PERSON] worked out technical details; [PERSON] proposed the idea; [PERSON] wrote the main manuscript; [PERSON] and [PERSON] drew the figures; [PERSON] provided the data set. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Zhejiang Provincial Natural Science Foundation of China under grant No.LQ22D040001.  
Not applicable.  
The data set used in this study is collected from the Infrastructure of National Earthquake Data Center, China. Readers in China can apply for the data set from the Agency of Crustal Movement Observation Network of China on their own, and the website is: [[http://data.earthquake.cn](http://data.earthquake.cn)]([http://data.earthquake.cn](http://data.earthquake.cn)).  
We appreciate the author [PERSON] for providing the data sets. Thanks to the author [PERSON], and hope he and his good friend can pass the exam of postgraduate. In addition, thanks to the editors and reviewers for their contributions to the improvement of this manuscript.  
The authors declare no conflict of interest.