Accurate and seamless coastal water level observations are crucial for monitoring climate change, mean sea level, and storm surge. Although the water level changes have been measured using local measurement instruments at coastal sites for centuries, the spatial distribution of these sites is typically limited to locations with infrastructure. For example, Alaska has significant gaps in the spatially varying tidal information so that more supports are needed for coastal mapping and storm surge preparedness. GNSS-Reflectometry (GNSS-R) is being investigated as tool for water level monitoring. By calculating the phase delay of the GNSS radio signals reflected by the water surface, the temporal variation of the water level can be observed. The advantage of this system is twofold: 1) It is non-contact and measures water levels as it is based on remote sensing technique. 2) resulting water level measurements are tied directly to a global reference frame that it significantly contributes to the consistent vertical datum. In this study, we processed data from three Continuously Operating Reference Stations in California (CACC), Louisiana (CALC), and Alaska (AT01), which are operated by NGS and UNAVCO. The resulting water levels were compared with observations from stations in the NOAA’s National Water Level Observation Network (NWLON). The CALC station was selected specifically for performance evaluation during hurricane Harvey. The GNSS-R results show a strong agreement with the published observations and datums from the NWLON stations. The peak storm surge induced by hurricane Harvey is clearly observed in the data from CALC. Tidal datums computed from CALC data are within 6cm of published. Data from the AT01, located in St. Michael Alaska, show tidal characteristics not represented in the published predictions, which are based on data collected between 1891 and 1899. The AC01 results are very encouraging considering the expected challenges due to limited GNSS observations and strong ionospheric activities in high latitudes. The results from this study show many promising applications for GNSS-R derived water levels, such as tidal datum determination, predictions and validation of vertical datum separation models.