This study investigated long-term observations from three pairs of closely spaced, continuously operating reference stations (CORS): STVI and VITH (2008–2014, 0.6 km apart), BYSP and PRHL (2008–2014, 3.2 km apart), and CRO1 and VIKH (1995–2014, 23 km apart), in the region of Puerto Rico and Virgin Islands. The Global Positioning System (GPS) antennas of STVI, BYSP, and CRO1 are mounted on ground-based monuments. The GPS antennas of VITH, PRHL, and VIKH are mounted on building-based monuments. This study indicated that there is no considerable difference between building-based and ground-based CORS with regard to the precision of daily positions and the reliability of long-term site velocities. The amplitude of thermal movements associated with daily temperature changes and the wind drift associated with air pressure change from a 2-story concrete building fell below the detectable levels of the present high-precision GPS technique determined by Precise Point Positioning (PPP) processing. Further ...

A variety of climate factors influence the precision of the long‐term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long‐term GNSS monitoring records, this study combines the extended seven‐parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local‐scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long‐term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long‐term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long‐term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit‐less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS‐derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long‐term GNSS monitoring.

From many sources, we develop an ocean hazard classification scheme (OHCS) based on the collection of historical and projected ocean hazards data at 302 locations along Hawaii’s state coastal highways. The OHCS identifies ocean hazards impacting coastal built infrastructure, i.e. roadways. In the OHCS, we first rank the vulnerability of: sea level rise; waves; shoreline change; tsunami; and storm surge. Next, using our developed OHCS, provide the vulnerability ranking for all five variables combined. We find the highest OHCS to be on Molokai, the island that has the highest OHCS numbers for most of the island. For the majority of state highway locations in Hawaii, we find the highest vulnerability is from storm surge, with tsunami threat being the second largest contributor. Sea level rise should also be considered a contributor since higher sea levels contribute to more extreme storm surge and tsunami inundation. Although the OHCS is applied towards roads in our study, our method can be applied towards any coastal island-based built infrastructure vulnerability scheme. This is an important tool in planning for future construction projects or identifying which hazards to focus on in more detailed assessments, such a probabilistic risk assessment in a more localized location.