# Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea
## 4 Discussions  
As we have noted before, precise real-time GNSS processing is a prerequisite for incorporation of coastal GNSS-arrays into tsunami early warnings. In this context, we assessed the potential capability of using real-time BDS to detect on-land co-seismic displacements caused by submarine rupture and addressed the special contribution from combined BDS/GPS solutions as well. According to our estimations from Section 2, horizontal accuracies of single BDS and GPS at the test site were 1.1 cm and 1.2 cm, respectively, while the joint BDS/GPS accuracy reached about 0.7 cm. For vertical component, BDS/GPS improved accuracy from about 2 cm to about 1.0 cm. In terms of current accuracy, BDS is close to GPS in the Asia-Pacific region, and the advantage of fusion of BDS and GPS is clear. Nonetheless, our simulation of the Mw = 8.0 earthquake did not show any notable difference in the case of BDS-only or BDS/GPS joint inversion (compare Figure 4b,c). For an earthquake with a magnitude of Mw = 8.0, near field deformation can be as large as tens of centimeters (Figure 4a); with respect to this scale of deformation, the signal-to-noise ratio (SNR) improvement by joint BDS/GPS processing is not clear in this case. However, joint BDS/GPS processing may show its advantage for smaller but still tsunamigenic earthquakes. Consider a scenario of a smaller earthquake. Figure 6 presents a forward model as well as the two source inversion models for an Mw = 7.5 earthquake at the Manila trench (compare Figure 6 to Figure 4). Earthquakes of this magnitude will not pose a significant tsunami threat for the coasts of China and Taiwan but can still be dangerous in the near field (Figure 7). In this case, simulated co-seismic displacements are significantly smaller (Figure 6a), and the better signal-to-noise ratio of the joint BDS/GPS real-time processing results in better restoration of the original slip distribution (compare Figure 6b,c) and, therefore, in better tsunami forecasting for the near field (compare vertical bars on Figure 7 and refer to the supplementary Table S1 for more detailed information). Figure 6: (**a**) Assumed slip distribution (colored dots) and correspondent co-seismic surface deformation for an event with a magnitude of Mw = 7.5 rupturing along the Northern Manila mega-thrust. Red arrows show horizontal displacements computed at the virtual BDS/GPS network (black triangles); (**b**) Colored dots show slip distribution as inverted from the simulated BDS-displacements (note, synthetic BDS-displacements include real-time processing noise). Additionally shown is the resulting vertical deformation; (**c**) Same as (b), but for the inversion of the joint BDS/GPS displacements. Notice that our models are simple and generic enough to illustrate the main message of the present study: Tsunamigenic earthquakes at the Manila trench will trigger significant co-seismic deformation at the Luzon Island. Deformation amplitudes will be far above modern real-time GNSS resolution. That is why it makes sense to employ coastal GNSS-arrays for tsunami early warnings in the Southern China Sea. At the same time, authors understand that there are many open questions regarding practical incorporation of GNSS component into any particular TEWS. For example, we opted for static inversion of final co-seismic displacements. Alternatively, some very recent studies [48, 49] showed that static inversions have a tendency to over-smoothen slip distribution, which, in turn, can result in the under-prediction of tsunami heights. Kinematic (waveform) inversions demonstrate better spatial resolution and have a clear advantage when static displacements are small. On the other hand, static inversions are fast and robust, and use less data and a smaller number of control parameters. The problem of optimal choice of methods and algorithms is not, of course, restricted to static _vs_. kinematic inversion.