Comparative Analysis of GEDI’s Elevation Accuracy from the First and Second Data Product Releases over Inland Waterbodies

Spaceborne LiDAR altimetry has been demonstrated to be an essential source of data for the estimation and monitoring of inland water level variations. In this study, water level estimates from the Global Ecosystem Dynamics Investigation (GEDI) were validated against in situ gauge station records over Lake Geneva for the period between April 2019 and September 2020. The performances of the first and second releases (V1 and V2, respectively) of the GEDI data products were compared, and the effects on the accuracy of the instrumental and environmental factors were analyzed in order to discern the most accurate GEDI acquisitions. The respective influences of five parameters were analyzed in this study: (1) the signal-over-noise ratio (SNR); (2) the width of the water surface peak within the waveform (gwidth); (3) the amplitude of the water surface peak within the waveform (A); (4) the viewing angle of GEDI (VA); and (5) the acquiring beam. Results indicated that all these factors, except the acquiring beam, had an effect on the accuracy of GEDI elevations. Nonetheless, using VA as a filtering criterion was demonstrated to be the best compromise between retained shot count and water level estimation accuracy. Indeed, by choosing the shots with a VA ≤ 3.5°, 74.6% of the shots (after an initial filter) were retained with accuracies similar to choosing A > 400 (46.2% retained shots), SNR > 15 dB (63.3% retained shots), or gwidth < 10 bins (46.5% of retained shots). Finally, the comparison between V1 and V2 elevations showed that V2, overall, provided elevations with a more constant, but higher, bias and fewer deviations to the in situ data than V1. Indeed, by choosing GEDI shots with VA ≤ 3.5°, the unbiased RMSE (ubRMSE) of GEDI elevations was 27.1 cm with V2 (r = 0.66) and 42.8 cm with V1 (r = 0.34). Results also show that the accuracy of GEDI (ubRMSE) does not seem to depend on the beam number and GEDI acquisition dates for the most accurate GEDI acquisitions (VA ≤ 3.5°). Regarding the bias, a higher value was observed with V2, but with lower variability (54 cm) in comparison to V1 (35 cm). Finally, the bias showed a slight dependence on beam GEDI number and strong dependence on GEDI dates.

Does More Moisture in the Atmosphere Lead to More Intense Rains?

It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatter plot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moistures in the atmosphere is that there is a more chance to happen, rather than of an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm/6h to a lesser extent. The result is derived from an analysis of 33 local rain–gauge station data and a shared sounding over Friuli Venezia Giulia, North–East Italy.

Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above: (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

Internal Variability Role on Estimating Sea Level Acceleration in Fremantle Tide Gauge Station

Low frequency internal signals bring challenges to signify the role of anthropogenic factors in sea level rise and to attain a certain accuracy in trend and acceleration estimations. Due to both spatially and temporally poor coverage of the relevant data sets, identification of internal variability patterns is not straightforward. In this study, the identification and the role of low frequency internal variability (decadal and multidecadal) in sea level change of Fremantle tide gauge station is analyzed using two climate indices, Pacific Decadal Oscillation (PDO) and Tripole Interdecadal Pacific Oscillation (TPI). It is shown that the multidecadal sea level variability is anticorrelated with corresponding components of climate indices in the Pacific Ocean, with correlation coefficients of −0.9 and −0.76 for TPI and PDO, respectively. The correlations are comparatively low on decadal time scale, −0.5 for both indices. This shows that internal variability on decadal and multidecadal scales affects the sea level variation in Fremantle unequally and thus, separate terms are required in trajectory models. To estimate trend and acceleration in Fremantle, three trajectory models are tested. The first model is a simple second-degree polynomial comprising trend and acceleration terms. Low passed PDO, representing decadal and interdecadal variabilities in Pacific Ocean, added to the first model to form the second model. For the third model, decomposed signals of decadal and multidecadal variability of TPI are added to the first model. In overall, TPI represents the low frequency internal variability slightly better than PDO for sea level variation in Fremantle. Although the estimated trends do not change significantly, the estimated accelerations varies for the three models. The accelerations estimated from the first and second models are statistically insignificant, 0.006 ± 0.012 mm yr−2 and 0.01 ± 0.01 mm yr−2, respectively, while this figure for the third model is 0.018 ± 0.011 mm yr−2. The outcome exemplifies the importance of modelling low frequency internal variability in acceleration estimations for sea level rise in regional scale.

First Results of Undersea Muography with the Tokyo-Bay Seafloor Hyper-Kilometric Submarine Deep Detector MAGMA-HKMSDD Collaboration

Tidal measurements are of great significance since they may provide us with essential data to apply towards protection of coastal communities and sea traffic. Currently, the tide gauge stations, satellite-based, pressure-based and ultrasonic-based techniques are commonly used. However, with these techniques, sensors have to be either floated on the sea surface or sunk onto the seafloor. The former option makes it difficult to conduct the tide measurements in high traffic maritime sea channels, and the latter option requires the construction of undersea electric and telecommunication infrastructures to support real time monitoring. On the other hand, with muography, sensors can be located underneath the seafloor inside, for example, an undersea tunnel where the electric and telecommunication infrastructures are more readily available. In this work, the world’s first under-seafloor particle detector array called the Tokyo-bay Seafloor Hyper-Kilometric Submarine Deep Detector (TS-HKMSDD) was deployed underneath the Tokyo-Bay seafloor for conducting submarine muography. Time-sequential muographic data were converted to the tidal levels above the detector and compared with the data acquired from the nearby tide gauge station. The results were consistent, indicating that submarine muography could be an alternative tool for tide measurements. We anticipate that if the length of the TS-HKMSDD is extended from 100 m to a full-scale as large as 9.6 km to provide continuous tidal information along the tunnel, muography will become an established standard, demonstrating its effectiveness as practical tide monitor for this heavy traffic waterway in Tokyo and in other important sea traffic areas worldwide.

Improving assessment of flood inundation of Navsari (India) via open-source data and HEC-RAS model

&lt;p&gt;Flooding seems to be the most widespread and common catastrophe in a tropical country such as India. Efficient rainfall, industrial development, huge population, the effect of the tide, and urban growth are actual reasons for flooding in urban coastal regions. Navsari, the city of Gujarat, located 19 km upstream of the Arabian Sea. The city has experienced a devastating flood on 4rth August 2004. Flash flooding and maximum discharge estimated at the Mahuva gauge station of about 8836 m3/sec were responsible for a disaster that resulted in massive damage to property and lives. A two dimensional (2D) flood simulation model is carried out to assessment of flood inundation in an urban coastal area. HEC-RAS is one of the most popular open-source hydraulic software having 2D capabilities including GIS features. In the present study, the distance between the Mahuva gauge station to the Arabian sea was considered for flood inundation assessment, whereas the SRTM 30 m DEM was used for grid generation for Navsari city. The inflow hydrograph was used as the upstream boundary condition, and normal depth was used as the downstream boundary condition during the 4th August 2004 flood event. The unsteady flow simulation was performed and validated for the year of 2004 flood event. The simulated outcomes show that major areas such as Viraval, Kachiawad, Jalalpore, near Railway station, Kaliawad, Tavdi village, and Near TATA School were flooded with 2-4 m depth. Furthermore, the simulated result demonstrates that, if the discharge exceeds 8836 m3/sec in the area of a floodplain, it may take 11 to 13 hours to make the city inundated. The R&lt;sup&gt;2 &lt;/sup&gt;value for the model is 0.9679, which shows that the observed value is the best match with the simulated value. The research study illustrates the accurate flood inundation assessment in the urban coastal area using open-source 2D HEC-RAS model. The present study described the applicability of open-source data and model in flood inundation assessment. The study will fill the gap of flood assessment through 2D HEC-RAS model worldwide areas, which are situated nearby coastal region, accompanied by the benefits of open-source dataset and model.&lt;/p&gt;