The SWOT satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Here, we present the first discharge estimates from SWOT during the initial orbital configuration of the mission. A preliminary accuracy assessment from April of 2023 shows results consistent with pre-launch expectations: SWOT discharge can track dynamics without any gauge information. It captures discharge magnitude correctly in some cases but has bias in others. Correlations in a set of representative cases of SWOT performance ranged from 0.42 to 0.89, while the normalized root mean squared error was between 4.6% and 67.5%. Although we were very limited in the selection of river reaches, the feasibility of estimating river discharge from SWOT and these promising initial results are encouraging. As SWOT data are improved by reprocessing, we argue that the outlook for SWOT discharge is bright.

Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modelling methods, this paper presents a new global ~30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies – ranging from locally collected event water levels, to national inventories of engineering flood maps – we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ~0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ~0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1-2.6), we show end-of-century global flood hazard increases are limited to 9% (likely range -6–29%); this is within the likely climatological uncertainty of -8–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5-8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7–109%) increase by 2100. This work verifies the fitness-for-purpose of this new-generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment.

The forthcoming Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new datasets for both gaged and ungaged basins. SWOT discharge products will provide discharge for all river reaches wider than 100 m, but at lower accuracy and temporal resolution than what is possible in situ. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present here for the first time a complete estimate of SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge timeseries. We expect that discharge uncertainty will be less than 30% for two thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge timeseries will be dominated by random error and are expected to be estimated to within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

Geodetic altimeters provide unique observations of the river surface longitudinal profile due to their long repeat periods and densely spaced ground tracks. This information is valuable for calibrating hydraulic model parameters, and thus for producing reliable simulations of water level for flood forecasting and river management, particularly in poorly instrumented catchments. In this study, we present an efficient calibration approach for hydraulic models based on a steady-state hydraulic solver and CryoSat-2 observations. In order to ensure that only coherent forcing/observation pairs are considered in the calibration, we first propose an outlier filtering approach for CryoSat-2 observations in data-scarce regions using simulated runoff produced by a hydrologic model. In the hydraulic calibration, a steady-state solver computes the WSE profile along the river for selected discharges corresponding to the days of CryoSat-2 overpass. In synthetic calibration experiments, the global search algorithm generally recovers the true parameter values in portions of the river where observations are available, illustrating the benefit of dense spatial sampling from geodetic altimetry. The most sensitive parameters are the bed elevations. In calibration experiments with real CryoSat-2 data, validation performance against both Sentinel-3 WSE and in-situ records is similar to previous studies, with RMSD ranging from 0.43 to 1.14 m against Sentinel-3 and 0.60 to 0.73 against in-situ WSE observations. Performance remains similar when transferring parameters to a one-dimensional hydrodynamic model. Because the approach is computationally efficient, model parameters can be inverted at high spatial resolution to fully exploit the information contained in geodetic CryoSat-2 altimetry.

Flood inundation modelling across large data sparse areas has been increasing in recent years, driven by a desire to provide hazard information for a wider range of locations. The sophistication of these models has steadily advanced over the past decade due to improvements in remote sensing and modelling capability. There are now several global flood models (GFMs) that seek to simulate water surface dynamics across all rivers and floodplains regardless of data scarcity. However, flood models in data sparse areas lack river bathymetry because this cannot be observed remotely, meaning that a variety of methods for approximating river bathymetry have been developed from uniform flow or downstream hydraulic geometry theory. We argue that bathymetry estimation in these models should follow gradually varying flow theory to account for both uniform and nonuniform flows. We demonstrate that existing methods for bathymetry estimation in GFM’s are only accurate for kinematic reaches and are unable to simulate unbiased water surface profiles for reaches with diffusive or shallow water wave properties. The use of gradually varied flow theory to estimate bathymetry in a GFM reduced water surface profile errors by 66% and eliminated bias due to backwater effects. For a large-scale test case in Mozambique this reduced flood extends by 40% and floodplain storage by 79% at the 1 in 5 year return period. The results have significant implications for the role floodplains play in attenuating river discharges because previous GFM’s based on uniform flow theory will overstate the role of the floodplain.

This paper reports a new and significantly enhanced analysis of US flood hazard at 30m spatial resolution. Specific improvements include updated hydrography data, new methods to determine channel depth, more rigorous flood frequency analysis, output downscaling to property tract level and inclusion of the impact of local interventions in the flooding system. For the first time we consider pluvial, fluvial and coastal flood hazards within the same framework and provide projections for both current (rather than historic average) conditions and for future time periods centred on 2035 and 2050 under the RCP4.5 emissions pathway. Validation against high quality local models and the entire catalogue of FEMA 1% annual probability flood maps yielded Critical Success Index values in the range 0.69-0.82. Significant improvements over a previous pluvial/fluvial model version are shown for high frequency events and coastal zones, along with minor improvements in areas where model performance was already good. The result is the first comprehensive and consistent national scale analysis of flood hazard for the conterminous US for both current and future conditions. Even though we consider a stabilization emissions scenario and a near future time horizon we project clear patterns of changing flood hazard (-3.8 to +16% changes in 100yr inundated area at 1° scale), that are significant when considered as a proportion of the land area where human use is possible or in terms of the currently protected land area where the standard of flood defence protection may become compromised by this time.

Flood models typically produce flood maps with constant return periods in space, without considering the spatial structure of flood events. At a large scale, this can lead to a misestimation of flood risk and losses caused by extreme events. A stochastic approach to global flood modelling allows the simulation of sets of flood events with realistic spatial structure that can overcome this problem, but until recently this has been limited by the availability of gauge data. Previous research shows that simulated discharge data from global hydrological models can be used to develop a stochastic flood model of the United States (Wing et al., 2020) and suggests that the same approach can potentially be used to build large scale stochastic flood models elsewhere but this has not so far been tested. This research therefore focuses on using discharge hindcasts from global hydrological models to drive stochastic flood models in different areas of the world. By comparing the outputs of these simulations to a gauge-based approach, we analyse how a model-based approach can simulate spatial dependency in large scale flood modelling outside of well-gauged territories such as the US. Based on data availability we selected different areas in Australia, South Africa, South America and Europe for the analysis. The results of this research show that the performance of a model-based approach in the different continents is promising and the errors are comparable to the results obtained in the United States by Wing et al. (2020). In the United States, with this magnitude of errors, the loss distribution obtained using the model-based approach is near identical to the one produced by the gauge-based method. This suggests that this method could be used in other regions to characterize losses. Using a network of synthetic gauges with data from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data-scarce regions and loss exceedance curves where exposure and vulnerability data are available.

A reach-scale high resolution digital elevation model (DEM) of the Congo’s main stem bathymetry is presented. The Bathymetry DEM covers a multichannel reach of the main stem situated in the Cuvette Centrale, and is developed from a series of in-situ measurements of bathymetry, water surface elevation and discharge that were obtained during a CRuHM fieldtrip in summer 2017. The main stem’s complex network of channel threads requires a bathymetry modelling methodology that is capable of intelligently interpolating the raw bathymetry measurements. The methodology must also estimate a significant portion of the bathymetry, since it is not feasible to measure the entire extent of the massive and complex channel network that this study reach is comprised of. This methodology is also presented. Remote sensing from satellites is increasingly being used to resolve the scarcity of contemporary hydrological and hydrographic measurements in the Congo Basin. However, river channel bathymetry information cannot yet be reliably obtained from remote sensing methods. This is problematic since river channel representation has been shown to be an essential input into a hydraulic model. Analyses of satellite observations suggest that, relative to other global rivers, in-channel flows on the Congo’s main stem represent a relatively large proportion of total flows through the river-floodplain system. This implies the Congo’s in-channel bathymetry may play a relatively large role in controlling Congo main stem hydrodynamics. When used in a hydraulic model, the bathymetry DEM presented here will provide new information on Congo in-channel hydraulics and the extent to which bathymetry controls the Congo’s middle reach hydrodynamics. It will help better quantify the capacity of the Congo main stem channels through the Cuvette Centrale, and thus provide further insights into the extent to which the main stem channel floods in this region. It is also intended to be used for testing simplified methods of Congo bathymetry representation that are necessary for larger scale hydraulic models.

Flood disasters have regularly been reported in the Congo Basin with significant damages to human lives, food production systems and infrastructure. Losses incurred by these damages are huge and represent a major challenge for economic expansion in developing nations. In the Congo River Basin, where availability of in-situ data is a significant challenge, new approaches are needed to investigate flood risks and enable effective management strategies. This study uses recently developed global flood prediction data in order to produce flood risk maps for the Congo River Basin, where flood information currently does not exist. Flood hazard maps that estimate fluvial flooding at a grid cell resolution of 3 arc-seconds (~ 90 m), gridded population density data of 1 arc-second (~ 30 m) spatial resolution, and a spatial layer of infrastructure dataset are used to addresses flood risk at the scale of the Congo Basin. The global flood data provides different return periods of exposure to flooding in the Congo Basin and identifies flood extents. The risk analysis results are presented in terms of the percentage of population and infrastructure at flood risk for six return periods (5, 10, 20, 50, 75 and 100 year). Of the 525 administrative territories, 374 are AGU Books

The Congo Basin exhibits tremendous heterogeneities, out of which it emerges as an intricate system where complexity will vary consistently over time and space. Increased complexity in the absence of adequate knowledge will always result in increased uncertainties. One way of simplifying this complexity is through an understanding of organisational relationships of the landscape features, which is termed here as catchment classification. The need for a catchment classification framework for the Congo Basin is obvious given the basin’s inherent heterogeneities, the ungauged nature of the basin, and the pressing needs for water resources management that include the quantification of current and future supplies and demands, which also encompass the impacts of future changes associated with climate and land use, as well as water resources operational policies. The need is also prompted by many local-scale management concerns within the basin. This study uses an a priori approach to determine homogenous climatic-physiographic regions that are expected to underline dominant hydrological processes characteristics. A set of 1740 catchment units are partitioned across the whole basin, based on a set of comprehensive criteria, including natural break of the elevation gradient (199 units), inclusion of socio-economic and anthropogenic systems (204 units), and water management units based on traditional nomenclature of the rivers within the basin (1337 units). The identified catchment units are used to assess existing datasets of the basin physical properties, necessary to derive descriptors of the catchments characteristics. An unsupervised classification, based on Hierarchical Agglomerative Cluster algorithm is used, that yields 11 homogenous groups that are consistent with the current perceptual understanding of the Congo Basin physiographic and climatic settings. These regions represent therefore an a priori classification that will be further used to derive functional relationships of the catchments, necessary to enable hydrological prediction and water management in the basin.

The Congo River provides potential for socio-economic growth at the regional scale, but with limited information on the river dynamics it is difficult for basin countries to benefit from this potential, and to invest in the development of water resources. In recent years, the number of hazards related to navigation and flooding has sharply increased, resulting in high loss of human lives as well as economic losses. Associated problems of river management in the Congo also include inefficiency in hydropower production, an increase in rate of river sedimentation and land use changes. Accurate information is needed to support adequate management strategies such as prediction of navigation water levels and sediment movement, and assessment of environmental impacts and engineering implications of water resources infrastructure. Modelling approaches and space observations have been used to understand the Congo River dynamics, but their effective application has proved difficult due to a lack of ground-based observational data for validation. Recent developments in data capture with acoustic Doppler technologies have considerably improved measurements of river dynamics. As well measuring river discharge, they also allow the analysis of the multiple hydrodynamic features occurring in fluvial systems. This paper presents the results of field measurement campaigns carried out in the middle reach of the Congo River and the Kasai tributary using state of the art measurement technology (ADCP, Sonar, GNSS) for investigation of large rivers. The measurements relate to river flow at multiple transects, river bathymetry, static and continuous water surface elevation, and targeted sediment sampling along the river. The paper provides a descriptive summary of the measurement results, a discussion on the application and performance of the equipment used in the Congo River, and lessons for future use of this equipment for measurements of large rivers in a data scarce environment such as the Congo Basin.

We propose a machine learning-based approach to estimate the flood defense standard (FDS) for ungauged sites. We adopted random forest regression (RFR) to characterize the relationship between the observed FDS and ten explanatory factors contained in publicly available datasets. We compared RFR with multiple linear regression (MLR) and demonstrated the proposed approach in the conterminous United States (CONUS) and England, respectively. The results showed the following: (1) RFR performed better than MLR, with a Nash–Sutcliffe efficiency (NSE) of 0.82 in the CONUS and 0.73 in England. A negative NSE when using MLR indicated that the relationship between the FDS and each explanatory factor did not obey an explicit linear function. (2) River flood factors had higher importance than physical and socio-economic factors in the FDS estimation. The proposed approach achieved the highest performance using all factors for prediction and could not provide satisfactory predictions (NSE < 0.6) using physical or socio-economic factors individually. (3) We estimated the FDS for all ungauged sites in the CONUS and England. Approximately 80% and 29% of sites were identified as high or highest standard (> 100-year return period) in the CONUS and England, respectively. (4) We incorporated the estimated FDS in large-scale flood modeling and compared the model results with official flood hazard maps in three case studies. We identified obvious overestimations in protected areas when flood defenses were not taken into account; and flood defenses were successfully represented using the proposed approach.