Search Results (73)

Floods and droughts are natural disasters that disrupt livelihoods and destroy the environment, with floods constituting up to 40% of all natural disasters globally. South Sudan has experienced severe, recurrent flooding for decades, with two-thirds of the country affected. An integrated flood management system is urgently needed to mitigate impacts and improve community resilience. This requires understanding the inundation process and analyzing flood causes and characteristics. This research leverages data from the Climate Hazards Center InfraRed Precipitation with Station (CHIRPS v2.0) to examine rainfall patterns and analyze trends in annual total precipitation (PRCPTOT), days with precipitation ≥ 20 mm (R20 mm), and simple precipitation intensity (SDII) at the basin scale. It also incorporates Nile River flow data from the Mangala station and Lake Victoria water levels from satellite altimetry. Findings indicate decreasing trends in PRCPTOT, R20 mm, and SDII in Jonglei and Unity States, but increasing trends in river flows and Lake Victoria levels. The Global Surface Water dataset reveals increased water surface areas in these states. These findings suggest that river flow trends oppose rainfall patterns, indicating that local rainfall is not the primary contributor to the recurrent flooding in the area. Full article

Global mean sea level has been rising steadily since the early 1990s, yet regional sea level changes exhibit complex spatial variability that frequently contrasts with global trends. Investigating sea level variations in semi-enclosed basins such as the Black Sea is crucial for elucidating regional responses to climate change and characterizing its unique spatiotemporal evolution patterns. In this study, we employ satellite altimetry (SA) data to study sea level changes, spatial variability, and seasonal patterns in the Black Sea over eight distinct time periods with temporally correlated noise, and our results show good consistency with existing studies. The results show that sea level changes are non-linear over time and exhibit spatial variability in the Black Sea. The estimated sea level trend fluctuates over brief intervals, but extended time series provide reduced uncertainty in the trend and more precise estimation over a 28-year time series. The annual amplitude and phase derived from virtual altimetry data (1993–2020) exhibit a distinct seasonal pattern, with peak sea levels typically occurring between November and February. Furthermore, to reduce the uncertainty induced by noise in the sea surface height (SSH) time series, principal component analysis (PCA) was utilized to denoise the SSH data from 1993 to 2020, yielding a sea level trend of 1.76 ± 0.56 mm/yr. Denoising reduced the trend uncertainty by 57%, decreased the root mean square error of the SSH series by 5.06 mm, and decreased the annual amplitude by 23.35%. Full article

Sea level rise due to climate change poses an increasing threat to coastal ecosystems, infrastructure, and human settlements. However, accurately estimating sea level changes in regions without tide gauge observations remains a major challenge. While satellite altimetry provides wide spatial coverage, its accuracy diminishes near coastlines. In contrast, tide gauges offer high precision but are spatially limited. This study aims to develop an artificial neural network-based model for estimating relative sea level changes in coastal regions where tide gauge data are unavailable. Unlike conventional forecasting approaches focused on future time series prediction, the proposed model is designed to learn spatial distribution patterns and temporal rates of sea level change from a fusion of satellite altimetry and tide gauge data. A normalization scheme is applied to reduce inconsistencies in reference levels, and Bayesian optimization is employed to fine-tune hyperparameters. A case analysis is conducted in two coastal regions in South Korea, Busan and Ansan, using data from 2018 to 2023. The model demonstrates strong agreement with observed tide gauge records, particularly in estimating temporal trends of sea level rise. This approach effectively compensates for the limitations of satellite altimetry in coastal regions and fills critical observational gaps in ungauged areas. The proposed method holds substantial promise for coastal hazard mitigation, infrastructure planning, and climate adaptation strategies. Full article

This study analyzes sea-level changes in the Yellow and Bohai Seas from 1993 to 2023 based on satellite altimetry data. After reconstructing the gridded sea-level data using local mean decomposition (LMD), the annual mean sea level was estimated at 28.86 mm, with an average rise rate of 2.21 mm per year (mm/a). Temporal and spatial variations were examined through nonlinear least squares fitting to capture interannual variability and decadal amplitude distributions. Empirical orthogonal function (EOF) analysis identified the first three modes, explaining 90.40%, 2.78%, and 1.47% of the total variance, respectively, and their spatial patterns and temporal coefficients were derived. The first mode was strongly correlated with sea surface temperature (SST) and precipitation, showing distinct spatial structures. Temperature and salinity profiles revealed a decadal-scale trend of increasing temperature and decreasing salinity with depth. Seasonal variations of sea-level anomaly (SLA) were evident, with mean values and trends of −11.47 mm (2.19 mm/a) in spring, 57.12 mm (2.29 mm/a) in summer, 75.68 mm (2.24 mm/a) in autumn, and −13.90 mm (2.11 mm/a) in winter. Seasonal correlations among SLA, SST, salinity, and precipitation were assessed, highlighting interannual amplitude variations. This integrated analysis provides a comprehensive understanding of the dynamics and drivers of sea-level fluctuations, offering insights for future research. Full article

Determining the sea surface height using Global Navigation Satellite System (GNSS) buoys is an important method for satellite altimetry calibration. The buoys observe the absolute height of the sea surface using GNSS positioning technology, which is then used to correct the systematic deviation of the altimeter of the orbiting satellite. Due to the challenging observational conditions, such as significant multipath errors in GNSS code observation and complex variations in buoy position and attitude, gross errors in GNSS buoy positioning reduce the accuracy and stability of the calculated sea surface heights. To accurately detect and remove these gross errors from GNSS coordinate time series, the complementary ensemble empirical mode decomposition (CEEMD) method and the interquartile range (IQR) method were adopted to enhance the accuracy and stability of GNSS sea surface altimetry. Firstly, the raw GNSS sequential coordinate series are decomposed into main terms, such as trend contents and periodic contents, and high-frequency noise terms using the CEEMD method. Subsequently, the high-frequency noise terms of the GNSS coordinate series are regarded as the residual sequences, which are used to detect gross errors using the IQR method. This approach, which integrates the CEEMD and IQR methods, was named CEEMD-IQR and enhances the ability of the traditional IQR method to detect subtle gross errors in GNSS coordinate time series. The results indicated that the CEEMD-IQR method effectively detected gross errors in offshore GNSS coordinate time series using GNSS buoys, presenting a significant enhancement in the gross error detection rate of at least 35.3% and providing a “clean” time series for sea level measurements. The resulting GNSS sea surface altimetry accuracy was found to be better than 1.51 cm. Full article

This study evaluates the performance of the ICESat-2 ATL13 altimetry product for estimating water levels in 182 Canadian lakes by integrating satellite-derived observations with in situ gauge measurements and applying spatial filtering using the HydroLAKES dataset. The analysis compares ATL13-derived lake surface elevations with hydrometric data from national monitoring stations, providing a robust framework for assessing measurement accuracy. Statistical metrics—including root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE)—are employed to quantify discrepancies between the datasets. Importantly, the application of HydroLAKES-based filtering reduces the mean RMSE from 1.53 m to 1.40 m, and the further exclusion of high-error lakes lowers it to 0.96 m. Larger and deeper lakes exhibit lower error margins, while smaller lakes with complex shorelines show greater variability. Regression analysis confirms the excellent agreement between satellite and gauge measurements (R² = 0.9999; Pearson’s r = 0.9999, n = 182 lakes, p < 0.0001). Temporal trends reveal declining water levels in 134 lakes and increasing levels in 48 lakes from 2018 to 2024, potentially reflecting climatic variability and human influence. These findings highlight the potential utility of ICESat-2 ATL13 altimetry for large-scale inland water monitoring when combined with spatial filtering techniques such as HydroLAKES. Full article

The regional sea level budget in the East China Sea (ECS) was investigated with satellite gravimetry, altimetry, steric and sediment datasets over the period from April 2002 to December 2022. The “sediment effect” due to the difference between the change in sediment mass and the displaced original seawater should be removed from the total mass change observed by satellite gravimetry data to accurately estimate the manometric sea level change associated with the variations in seawater mass. We divided the whole ECS region into sediment and nonsediment areas. After accurately estimating the manometric sea level change, specifically the change in seawater mass, the ECS regional sea level budget could be closed within a 2-sigma uncertainty. Our results revealed that the linear trends of the regional mean sea level change in the ECS can be attributed mainly to the change in the manometric sea level (3.06 mm/year), followed by the steric component (0.44 mm/year), which contributes only ~12.57% of the total ECS regional mean sea level change rate observed via satellite altimetry. The linear trend residuals of the ECS regional sea level budget ranged from −0.12 mm/year to 0.10 mm/year, all within a 2-sigma uncertainty. Full article

This study analyzes sea level changes (SLCs) in China seas and their adjacent ocean (CSO) using data from the TOPEX/Poseidon and Jason-1/2/3 satellite altimetry missions from 1993 to 2022. A 30-year time series of sea level anomalies (SLAs) is established, with trends, spatial distribution, and periodicities analyzed through least squares linear fitting, Kriging interpolation, and wavelet analysis. The average yearly sea level rise in the CSO is 3.87 mm, with specific rates of 4.15 mm/yr in the Bohai Sea, 3.96 mm/yr in the Yellow Sea, 3.54 mm/yr in the East China Sea, and 4.09 mm/yr in the South China Sea. This study examines the spatiotemporal variations in SLAs and identifies an annual primary cycle, along with a new periodicity of 11 years. Utilizing 30 years of satellite observation data, particularly the newer Jason-3 satellite data, this reanalysis reveals new findings related to cycles. Overall, the research updates previous studies and provides valuable insights for further investigations into China’s marine environment. Full article

The sea ice extent in the Weddell Sea exhibited a positive trend from the start of satellite observations in 1978 until 2016 but has shown a decreasing trend since then. This study analyzes seasonal and interannual variations in sea ice thickness using ICESat-2 laser altimetry data over the Weddell Sea from 2019 to 2022. Sea ice thickness was calculated from ICESat-2’s ATL10 freeboard product using the Improved Buoyancy Equation. Seasonal variability in ice thickness, characterized by an increase from February to September, is more pronounced in the eastern Weddell sector, while interannual variability is more evident in the western Weddell sector. The results were compared with field data obtained between 2019 and 2022, showing a general agreement in ice thickness distributions around predominantly level ice. A decreasing trend in sea ice thickness was observed when compared to measurements from 2003 to 2017. Notably, the spring of 2021 and summer of 2022 saw significant decreases in Sea Ice Extent (SIE). Although the overall mean sea ice thickness remained unchanged, the northwestern Weddell region experienced a noticeable decrease in ice thickness. Full article

The development of satellite altimetry has significantly advanced the application of satellite Earth observation technologies in surface water monitoring, resulting in a substantial body of research. Although numerous reviews have summarized progress in this field, their analyses are often limited in scope and fail to provide a systematic, quantitative assessment of the current research prospects and trends. To address this gap, we utilize CiteSpace and VOSviewer bibliometric software to analyze 13,500 publications from the WOS database, spanning the years from 1988 to 2022. Our analysis focused on publication volume, authorship, collaboration networks, and content. We also compare data from Google Scholar and Scopus to validate the reliability of our dataset. Our findings indicate a steadily growing research potential in this field, as evidenced by trends in publication volume, authorship, journal influence, and disciplinary focus. Notably, the leading journals are primarily in the realm of remote sensing, while key disciplines include geology, remote sensing science, and oceanography. Keyword analysis revealed current research hotspots such as sea-level rise, snow depth, and machine learning applications. Among various water body types, research on glaciers ranks second only to ocean studies. Furthermore, research focus areas are shifting from large oceanic regions like the Pacific and Atlantic Oceans to significant inland water bodies, notably the Tibetan Plateau and the Amazon basin. This study combines qualitative and quantitative methods to analyze vast amounts of information in the field of surface water monitoring by satellite altimetry. The resulting visualizations provide researchers with clear insights into the development trends and patterns within this domain, offering valuable support for identifying future research priorities and directions. Full article

Gravity measurements have important applications in geophysics, resource exploration, geodesy, and inertial navigation. The range of classical gravimetry includes laser interferometer (LI)-based absolute gravimeters, spring relative gravimeters, superconducting gravimeters, airborne/marine gravimeters, micro-electromechanical-system (MEMS) gravimeters, as well as gravity satellites and satellite altimetry. Atomic gravimetry is a new absolute gravity measurement technology based on atom interferometers (AIs) and features zero drift, long-term stability, long-term continuous measurements, and high precision. Atomic gravimetry has been used to measure static, marine, and airborne gravity; gravity gradient; as well as acceleration to test the weak equivalence principle at the China Space Station. In this paper, classical gravimetry is introduced, and the research progress on static and airborne/marine atomic gravimeters, space AIs, and atomic gravity gradiometers is reviewed. In addition, classical and atomic gravimetry are compared. Future atomic gravimetry development trends are also discussed with the aim of jointly promoting the further development of gravity measurement technologies alongside classical gravimetry. Full article

Lake ice thickness (LIT) is a sensitive indicator of climate change, identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic-aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow–ice and ice–water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LIT and FFSAR_LIT retracker, respectively. We found that LIT retrievals from Sentinel-6 high-resolution SAR data at different posting rates are fully consistent with the LIT estimations obtained from thermodynamic lake ice model simulations and from low-resolution mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, demonstrating the continuity between LRM and SAR LIT retrievals. By comparing the Sentinel-6 SAR LIT estimates to optical/radar images, we found that the Sentinel-6 LIT measurements are fully consistent with the evolution of the lake surface conditions, accurately capturing the seasonal transitions of ice formation and melt. The uncertainty in the LIT estimates obtained with Sentinel-6 UFSAR data at 20 Hz is in the order of 5 cm, meeting the GCOS requirements for LIT measurements. This uncertainty is significantly smaller, by a factor of 2 to 3 times, than the uncertainty obtained with LRM data. The FFSAR processing at 140 Hz provides even better LIT estimates, with 20% smaller uncertainties. The LIT retracker analysis performed on data at the higher posting rate (140 Hz) shows increased performance in comparison to the 20 Hz data, especially during the melt transition period, due to the increased statistics. The LIT analysis has been performed over two representative lakes, Great Slave Lake and Baker Lake (Canada), demonstrating that the results are robust and hold for lake targets that differ in terms of size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions. Full article

This study assesses the variability of coastal extreme sea levels globally by utilizing nearly three decades of along-track, multi-mission satellite altimetry data. An altimetry-based global coastal database of the non-tidal residual sea level component has been produced. The climate variability of extremes is modeled through a parametric, non-stationary statistical model. This model captures intra-annual, inter-annual and long-term variations in non-tidal residual return levels. Comparisons with tide gauge data demonstrate the ability of altimetry data to capture the variability of coastal extreme sea levels. Our findings reveal a greater complexity in the monthly variability patterns of non-tidal residual extremes in tropical latitudes, often exhibiting multiple storm periods, contrasting with coasts in extratropical latitudes, which are mostly controlled by a winter–summer pattern. This study also highlights the significant influence of established climate circulation patterns on sea level extremes. The positive phase of the Arctic Oscillation pattern leads to increases of over 25% in non-tidal residual return levels in Northwestern Europe with respect to a neutral phase. Furthermore, return levels in the western coast of Central America could be 50% higher during El Niño compared to La Niña. Our results show a robust increasing trend in non-tidal residual return levels along most global coastlines. A comparative analysis shows that variations during the 1995–2020 period were primarily driven by intra-annual variations. Full article

The expected acceleration in sea level rise (SLR) throughout this century poses significant threats to coastal cities and low-lying regions. Since the early 1990s, high-precision multi-mission satellite altimetry (SA) has enabled the routine measurement of sea levels, providing a continuous 30-year record from which the mean sea level rise (global and regional) and its variability can be computed. The latest reprocessed product from CMEMS span the period from 1993 to 2020, and have enabled the acquisition of accurate sea level data within the coastal range of 0–20 km. In order to fully utilize this new dataset, we establish a global virtual network consisting of 184 virtual SA stations. We evaluate the impact of different stochastic noises on the estimation of the velocity of the sea surface height (SSH) time series using BIC_tp information criterion. In the second step, the principal component analysis (PCA) allows the common mode noise in the SSH time series to be mitigated. Finally, we analyzed the spatiotemporal characteristics and accuracy of sea level change derived from SA. Our results suggest that the stochasticity of the SSH time series is not well described by a combination of random, flicker, and white noise, but is best described by an ARFIM/ARMA/GGM process. After removing the common mode noise with PCA, about 96.7% of the times series’ RMS decreased, and most of the uncertainty associated with the computed SLR decreased. We confirm that the spatiotemporal correlations should be accounted for to yield trustworthy trends and reliable uncertainties. Our estimated SLR is 2.75 ± 0.89 mm/yr, which aligns closely with recent studies, emphasizing the robustness and consistency of our method using virtual SA stations. We additionally introduce open-source software (SA_Tool V1.0) to process the SA data and reduce noise in surface height time series to the community. Full article

This study presents the first azimuth cutoff analysis in Synthetic Aperture Radar (SAR) altimetry, aiming to assess its applicability in characterizing sea-state dynamics. In SAR imaging, the azimuth cutoff serves as a proxy for the shortest waves, in terms of wavelength, that can be detected by the satellite under certain wind and wave conditions. The magnitude of this parameter is closely related to the wave orbital velocity variance, a key parameter for characterizing wind-wave systems. We exploit wave modulations exhibited in the tail of fully-focused SAR waveforms and extract the azimuth cutoff from the radar signal through the analysis of its along-track autocorrelation function. We showcase the capability of Sentinel-6A in deriving these two parameters based on analyses in the spatial and wavenumber domains, accompanied by a discussion of the limitations. We use Level-1A high-resolution Sentinel-6A data from one repeat cycle (10 days) globally to verify our findings against wave modeled data. In the spatial domain analysis, the estimation of azimuth cutoff involves fitting a Gaussian function to the along-track autocorrelation function. Results reveal pronounced dependencies on wind speed and significant wave height, factors primarily determining the magnitude of the velocity variance. In extreme sea states, the parameters are underestimated by the altimeter, while in relatively calm sea states and in the presence of swells, a substantial overestimation trend is observed. We introduce an alternative approach to extract the azimuth cutoff by identifying the fall-off wavenumber in the wavenumber domain. Results indicate effective mitigation of swell-induced errors, with some additional sensitivity to extreme sea states compared to the spatial domain approach. Full article