Numerical models are key tools to characterize hydrodynamical patterns of coastal environments and anticipate the potential effects of hazardous and extreme events, anthropogenic intervention or climate change. In this work, the openTELEMAC-MASCARET modelling system was selected to represent the dynamics of the Minho estuary, a very shallow estuary located at the Northwestern Iberian Peninsula coast. Calibration and validation results confirm the accuracy of the numerical tool, with small root mean square errors, close to null bias and the close to unit correlation and skill coefficients obtained for water level and currents velocity at several estuarine locations. The obtained results depict a tide dominated estuary with a delay in the tide phase and a marked asymmetry in the tide curve that increases upriver. Additionally, an upstream diminution of M₂ and an upstream augmentation of M₄ was observed, classifying this estuary as flood-dominated. The represented current patterns show that variations in the intensities of the main driving forces alter the behaviour of the hydrodynamical patterns within the estuary, with a clear dependence on bathymetric and topographic characteristics. During flood events, larger estuarine regions become submerged due to the low margins and the wetland characteristics, highlighting the need for accurate numerical models that can be used as a decision-making support tool for effective and integrated estuarine management.

We have been focused on the exploitation of the limits of the performance of low cost sensors, used in an integrated way, both for classical and geodetic navigation. In this work, the navigation performance of the Android smartphone’s IMU was assessed thoroughly. The raw IMU data from two smartphones, “Xiaomi 8” and “Honor Play” were recorded, using different sampling rates, and compared with the measurements, recorded simultaneously from higher grade IMU’s. For assessing the navigation capabilities of the smartphones IMU sensors, two kinematic tests were designed. In these tests, the smartphones IMU data were processed using a 15-state Kalman Filter developed by our group, while the reference IMU data were processed using a commercial software. The details of the comparisons between the positioning, velocity and attitude solutions are discussed. The results show that the navigation solutions of both smartphones are quite close to that of the reference IMUs’, showing differences of around 0.2 m in positioning, 0.3 m/s in velocity, and 1° in roll and pitch. The quality of the yaw estimation, with an accuracy of a few degrees, is clearly worse than that of the other attitude parameters. However, the results of the second test reveal that using a higher data rate setting for the IMU samplings, it is possible to improve the solution. The assessment study of the smartphone’s IMUs performed in this work can be useful for the development of many different applications, including the exploitation for Autonomous Vehicles, Intelligent Transport Systems, and Smart Cities.

Antarctica has a significant impact on global climate change. However, to draw climate change scenarios, there is a need for meteorological data, such as water vapor content, which is scarce in Antarctica. Global navigation satellite system (GNSS) networks can play a major role in overcoming this problem as the tropospheric delay that can be derived from GNSS measurements is an important data source for monitoring the variation of water vapor content. This work intends to be a contribution for improving the estimation of the zenith tropospheric delay (ZTD) obtained with the latest global pressure–temperature (GPT3) model for Antarctica through the use of long short-term-memory (LSTM) and radial basis function (RBF) neural networks for modifying GPT3_ZTD. The forecasting ZTD model is established based on the GNSS_ZTD observations at 71 GNSS stations from 1 January 2018 to 23 October 2021. According to the autocorrelation of the bias series between GNSS_ZTD and GPT3_ZTD, we predict the LSTM_ZTD for each GNSS station for period from October 2020 to October 2021 using the LSTM day by day. Based on the bias between LSTM_ZTD and GPT3_ZTD of the training stations, the RBF is adopted to estimate the LSTM_RBF_ZTD of the verified station, where the LSTM_ZTD represents the temporal forecasting ZTD at a single station, and the LSTM_RBF_ZTD represents the predicted ZTD obtained from space. Both the daily and yearly RMSE are calculated against the reference (GNSS_ZTD), and the improvement of predicted ZTD is compared with GPT3_ZTD. The results show that the single-station LSTM_ZTD series has a good agreement with the GNSS_ZTD, and most daily RMSE values are within 20 mm. The yearly RMSE of the 65 stations ranges from 6.4 mm to 32.8 mm, with an average of 10.9 mm. The overall accuracy of the LSTM_RBF_ZTD is significantly better than that of the GPT3_ZTD, with the daily RMSE of LSTM_RBF_ZTD significantly less than 30 mm, and the yearly RMSE ranging from 5.6 mm to 50.1 mm for the 65 stations. The average yearly RMSE is 15.7 mm, which is 10.2 mm less than that of the GPT3_ZTD. The LSTM_RBF_ZTD of 62 stations is more accurate than GPT3_ZTD, with the maximum improvement reaching 76.3%. The accuracy of LSTM_RBF_ZTD is slightly inferior to GPT3_ZTD at three stations located in East Antarctica with few GNSS stations. The average improvement across the 65 stations is 39.6%.