Stephen John Chuter

Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.

Flood risk mapping requires detailed river bathymetry, yet these data are unavailable for most rivers. We propose a new algorithm to estimate river bathymetry from multitemporal observations of water height by the Surface Water and Ocean Topography (SWOT) mission. The robustness of bathymetry estimates were evaluated against detailed in situ data on a narrow river (40–70 m width) that is often below SWOT's mission specification. Typical SWOT errors, not always flagged by the SWOT processing algorithm, could severely impact bathymetry estimates. However, a simple “Tulip” shaped misfit function could largely mitigate for these and was especially important along narrow sections of the river. SWOT River Single Pass node (∼200 m reaches) height errors against in situ gauge levels with stringent quality filtering were 0.16 m, approaching the mission accuracy requirements for much larger 1 km ² waterbodies. Derived channel bed elevations were physically plausible (∼1 m error) when estimated at a spatial resolution below half the river backwater length. A flood model built from SWOT‐derived bathymetry had commensurate accuracy to a model based on high‐resolution sonar bathymetry. For flooding in 2000, the model had a Critical Success Index of 0.88 against airborne SAR and absolute differences from gauged levels ranging from 0.14 to 0.31 m across the event.

Traditional nadir altimeters struggle with coastal water surface elevation (WSE) measurement and fine‐scale river‐estuary interactions, due to land‐water signal interference and their wide inter‐track spacing. The wide‐swath Surface Water and Ocean Topography (SWOT) mission, using a new Ka‐band radar interferometer, aims to address these issues by delivering 2D WSE measurements with unprecedented spatial resolution, accuracy, and precision. However, the mission's effectiveness in coastal WSE retrieval and its error characteristics remain unverified. This study leverages gauge and airborne LiDAR data to validate SWOT's WSE in the Bristol Channel and Severn Estuary. Assuming error‐free in situ data, SWOT ocean products exhibit a standard deviation of difference (STD) of 13 cm within a 3 km radius of tide gauges. Compared to LiDAR, SWOT's PIXC measurements have STD of 37 cm, improving to 14 cm over 100 m grids and 9 cm over 1 km ² areas. This meets the SWOT science requirement of 10 cm STD at 1 km ² scale and extends satellite‐based WSE monitoring into complex coastal environments.

Flood risk management and mapping require detailed river bathymetry information. However, these data are difficult to obtain for most rivers. Methods have been proposed to estimate bathymetry from the water surface elevation, which is much easier to observe. The Surface Water and Ocean Topography (SWOT) mission provides these water surface height measurements with unprecedented spatial and temporal resolution for rivers wider than ~50m, which might vastly increase the scope for bathymetry estimation. This paper evaluates SWOT water height observations on a small river (40-70m width) towards the limit of SWOT’s mission specification, where we expect errors in SWOT data to be relatively challenging to work with. We find an agreement at the node level (~200m reaches) of 0.16m against in situ gauge levels with stringent quality filtering, approaching the mission accuracy requirements for much larger 1 km2 waterbodies. An algorithm is developed to estimate 1D river bathymetry from SWOT water heights, which takes steps to mitigate SWOT and model errors using a tulip-shaped objective function. Channel bed elevations were physically plausible (~1m error) when estimated at a spatial resolution below ½ the river backwater length. A flood model built from SWOT-derived bathymetry had commensurate accuracy to a model based on high-resolution sonar bathymetry for a flood in 2000 with a Critical Success Index score of 0.87 for the SWOT model extent, and 0.88 for the terrestrial model inundation extent, and similar performance of stage water level simulation (mean absolute differences in water levels <0.63m (range 0.11-0.63m) across the simulated event).

Sea level rise is one of the greatest socio-economic impacts of climate change in the 21st Century. Whilst global mean sea level is an essential climate variable (ECV) for assessing the integrated response of the Earth system to climate change, regional sea level variability is of primary concern for policy-making decisions and the development of adaptation strategies in coastal localities. Redistribution of terrestrial mass, in the form of hydrological and land ice mass fluxes, partly drives this regional sea level variability due to its impact on the Earth’s gravity, rotation and deformation (GRD), termed ‘Sea Level Fingerprints’ or Barystatic-GRD fingerprints. With increasing mass losses projected from ice sheets and glaciers over the coming centuries, the magnitude and relative contribution of these Barystatic-GRD fingerprints to regional sea level change are expected to increase. As a result, accurately quantifying this phenomenon and its uncertainty is critical when assessing contemporary and future regional sea level variability.Current contemporary Barystatic-GRD fingerprints are typically either calculated using a single mass loading observation source or provide discontinuous coverage since 1992 (the satellite altimetry era). Here, we present a continuous monthly Barystatic-GRD fingerprint product from 1992-2017, computed from an ensemble of mass loadings derived from differing observation techniques. To achieve this, we use the Ice Sheet and Sea Level Model (ISSM) sea level equation solver, which uses a finite element approach to solving the sea level equation at high spatial-temporal resolution, whilst maintaining computational efficiency. This enables us to use an ensemble modelling framework, ensuring the computed Barystatic-GRD fingerprint encompasses the variability between differing observation techniques. Additionally, it allows us to propagate the observation uncertainties into the fingerprint uncertainty in a robust manner. As well as the total Barystatic-GRD fingerprint, we assess the contribution of individual terrestrial components (Antarctica, Greenland, Glaciers, and hydrological mass change). This work is part of the Fingerprinting Approach to Close Regional Sea Level Budgets using ESA-CCI (FACTORS), a European Space Agency Climate Change Initiative Research Fellowship.

Understanding and predicting environmental phenomena often requires the construction of spatio-temporal statistical models, which are typically Gaussian processes. A common assumption made on Gaussian processes is that of covariance stationarity, which is unrealistic in many geophysical applications. In this article, we introduce a deep-learning-inspired approach to construct descriptive nonstationary spatio-temporal models by modeling stationary processes on warped spatio-temporal domains. The warping functions we use are constructed using several simple injective warping units which, when combined through composition, can induce complex warpings. A stationary spatio-temporal covariance function on the warped domain induces covariance nonstationarity on the original domain. Sparse linear algebraic methods are used to reduce the computational complexity when fitting the model in a big data setting. We show that our proposed nonstationary spatio-temporal model can capture covariance nonstationarity in both space and time, and provide better probabilistic predictions than conventional stationary models in both simulation studies and on a real-world data set.

The Totten and Moscow University glaciers, located in East Antarctica, contain 5.1 m sea-level equivalent of ice and have been losing mass over recent decades. Using ICESat-2 laser altimetry repeat track analysis and satellite radar interferometry from Sentinel-1a/b synthetic aperture radar (SAR) images, we mapped the grounding line (GL) locations of these two glaciers between 2017 and 2021. By comparing the 2017–2021 GL measurements with historic GLs, we detected pervasive GL retreats along the ice plains at the glacier central trunk of Totten Glacier Ice Shelf (TGIS) and Moscow University Ice Shelf (MUIS). The GL retreated 3.51±0.49 km at TGIS, while it retreated 13.85±0.08 km at MUIS from 1996. Using CryoSat-2 radar altimetry, we found that the observed GL retreats are coincident with high thinning rates, in addition to high ice velocities, indicating a mass loss pattern dominated by ice dynamics. We also identified two tide-modulated ocean channels on Totten Glacier Eastern Ice Shelf (TGEIS) and Moscow University Western Ice Shelf (MUWIS), where the ocean channel widths are highly correlated with the differential tidal amplitudes. The opening of the MUWIS ocean channel connects the two previously separated TGIS and MUIS systems, which might open a pathway for the warm modified Circumpolar Deep Water to enter the main MUIS cavity and facilitate further GL retreat.

The Totten and Moscow University glaciers, located in East Antarctica, contain 5.1 m sea-level equivalent of ice and have been losing mass over recent decades. Using ICESat-2 laser altimetry repeat track analysis and satellite radar interferometry from Sentinel-1a/b SAR images, we mapped the grounding line locations of these two glaciers between 2017 and 2021. We detected pervasive grounding line retreat along the ice plains at the glacier central trunk of Totten Glacier Ice Shelf (TGIS) and Moscow University Ice Shelf (MUIS), where the GL retreated 3.51 ± 0.49 km and 13.85 ± 0.08 km during 1996–2020, respectively. Using CryoSat-2 radar altimetry, we found that the observed grounding line retreats are coincident with high thinning rates and high ice velocities, indicating a mass loss pattern dominated by ice dynamics. We also identified two tide-modulated ocean channels on the Totten eastern ice shelf and the Moscow University western ice shelf, where the ocean channel widths are highly correlated with the tidal range. The opening of the Moscow University western ice shelf channel connects the two previously separated TGIS and MUIS systems, which might open a pathway for the warm modified circumpolar deep water entering the main MUIS cavity and facilitate further grounding line retreat.