Advances in geodetic altimetry instruments are providing more accurate measurements, thus enabling satellite missions to produce useful data for narrow rivers and streams. Altimetry missions produce spatially dense land and water surface elevation (WSE) measurements in remote areas where in situ data are scarce that can be combined with hydraulic and/or hydrodynamic models to simulate WSE and estimate discharge. In this study, we combine ICESat-2 (Ice, Cloud and land Elevation Satellite) land and water surface elevation measurements with a low-parameterized hydraulic calibration to simulate WSE and discharge without the need for surveyed cross-sectional geometry and a rainfall–runoff model. ICESat-2 provides an opportunity to map river cross-sectional geometry very accurately, with an along-track resolution of 0.7 m, using the ATL03 product. These measurements are combined with the inland water product ATL13 to calibrate a steady-state hydraulic model to retrieve unobserved hydraulic parameters such as river depth or the roughness coefficient. The low-parameterized model, together with the assumption of steady-state hydraulics, enables the application of a global search algorithm for a spatially uniform parameter calibration at a manageable computational cost. The model performance is similar to that reported for highly parameterized models, with a root mean square error (RMSE) of around 0.41 m. With the calibrated model, we can calculate the WSE time series at any chainage point at any time for an available satellite pass within the river reach and estimate discharge from WSE. The discharge estimates are validated with in situ measurements at two available gauging stations. In addition, we use the calibrated parameters in a full hydrodynamic model simulation, resulting in a RMSE of 0.59 m for the entire observation period.

Surface velocity is traditionally measured with in situ techniques such as velocity probes (in shallow rivers) or Acoustic Doppler Current Profilers (in deeper water). In the last years, researchers have developed remote sensing techniques, both optical (e.g., image-based velocimetry techniques) and microwave (e.g., Doppler radar). These techniques can be deployed from Unmanned Aerial Systems (UAS), which ensure fast and low-cost surveys also in remotely-accessible locations. We compare the results obtained with a UAS-borne Doppler radar and UAS-borne Particle Image Velocimetry (PIV) in different rivers, which presented different hydraulic–morphological conditions (width, slope, surface roughness and sediment material). The Doppler radar was a commercial 24 GHz instrument, developed for static deployment, adapted for UAS integration. PIV was applied with natural seeding (e.g., foam, debris) when possible, or with artificial seeding (woodchips) in the stream where the density of natural particles was insufficient. PIV reconstructed the velocity profile with high accuracy typically in the order of a few cm s⁻¹ and a coefficient of determination (R²) typically larger than 0.7 (in half of the cases larger than 0.85), when compared with acoustic Doppler current profiler (ADCP) or velocity probe, in all investigated rivers. However, UAS-borne Doppler radar measurements show low reliability because of UAS vibrations, large instrument sampling footprint, large required sampling time and difficult-to-interpret quality indicators suggesting that additional research is needed to measure surface velocity from UAS-borne Doppler radar.

Accurate river hydraulic characterization is fundamental to assess flood risk, parametrize flood forecasting models, and develop river maintenance workflows. River hydraulic roughness and riverbed/floodplain geometry are the main factors controlling inundation extent and water levels. However, gauging stations providing hydrometric observations are declining worldwide, and they provide point measurements only. To describe hydraulic processes, spatially distributed data are required. In situ surveys are costly and time-consuming, and they are sometimes limited by local accessibility conditions. Satellite earth observation (EO) techniques can be used to measure spatially distributed hydrometric variables, reducing the time and cost of traditional surveys. Satellite EO provides high temporal and spatial frequency, but it can only measure large rivers (wider than ca. 50 m) and only provides water surface elevation (WSE), water surface slope (WSS), and surface water width data. UAS hydrometry can provide WSE, WSS, water surface velocity and riverbed geometry at a high spatial resolution, making it suitable for rivers of all sizes. The use of UAS hydrometry can enhance river management, with cost-effective surveys offering large coverage and high-resolution data, which are fundamental in flood risk assessment, especially in areas that difficult to access. In this study, we proposed a combination of UAS hydrometry techniques to fully characterize the hydraulic parameters of a river. The land elevation adjacent to the river channel was measured with LiDAR, the riverbed elevation was measured with a sonar payload, and the WSE was measured with a UAS radar altimetry payload. The survey provided 57 river cross-sections with riverbed elevation, and 8 km of WSE and land elevation and took around 2 days of survey work in the field. Simulated WSE values were compared to radar altimetry observations to fit hydraulic roughness, which cannot be directly observed. The riverbed elevation cross-sections have an average error of 32 cm relative to RTK GNSS ground-truth measurements. This error was a consequence of the dense vegetation on land that prevents the LiDAR signal from reaching the ground and underwater vegetation, which has an impact on the quality of the sonar measurements and could be mitigated by performing surveys during winter, when submerged vegetation is less prevalent. Despite the error of the riverbed elevation cross-sections, the hydraulic model gave good estimates of the WSE, with an RMSE below 3 cm. The estimated roughness is also in good agreement with the values measured at a gauging station, with a Gauckler–Manning–Strickler coefficient of M = 16–17 m^1/3/s. Hydraulic modeling results demonstrate that both bathymetry and roughness measurements are necessary to obtain a unique and robust hydraulic characterization of the river.

Climate change intensifies the occurrence of severe flood events, increasing the demand for flood modeling studies. Hydrodynamic models can effectively represent flood events, but they are limited by the quality of available observations. Accurate topographic elevation is essential to replicate channel-floodplain interaction. Elevation is normally retrieved using satellite-based DEMs. However, freely available DEMs have a low spatial resolution, which is a limitation in identifying small-scale channel features in complex floodplain topography. In addition, these products can present issues such as vertical offset, random noise, or vegetation biases. These issues can lead to large errors when used in hydraulic modeling to simulate water levels and inundation extent. FABDEM is a 1 arcsec DEM, that removes forest and building artifacts from Copernicus DEM, but to map complex floodplain topography, finer resolution is needed. ICESat-2 mission offers a large spatial coverage with an along-track resolution down to 70 cm in the ATL03 product. This data product has shown great potential when mapping river topography and identifying small-scale channel features. ATL03 can be used as a control point dataset, to correct biases and refine DEMsTo improve the accuracy of 2D hydraulic models, FABDEM was corrected on selected floodplain areas using supplementary data and machine learning methods. Artificial Neural Network (ANN) was used in the correction of FABDEM. This regression algorithm can predict differences between FABDEM floodplain elevation and ATL03 reference elevation, inputting data from Sentinel-2 and water occurrence maps produced from spectral and SAR imagery. The output floodplain elevation has a reduced vertical offset and a spatial resolution of 10 m, which can detect small-scale channel features. Flood inundation was simulated using the updated DEM. The high computational cost of 2D hydraulic models is a limitation when using discharge time series. To deal with computational cost, discharge classes were defined to represent different inundation scenarios that provide a good indicator for flood risk management, and steady-state inundation patterns were simulated for each discharge class.The method is demonstrated in a section of the Upstream Yellow River characterized by large floodplains with complex topography, and small-scale channels. Discharge observations from the Jimai in-situ station are used to define discharge classes. The discharge classes are defined by calculating the exceedance probability of a discharge value. The inundation scenarios are simulated for high flow discharge values for an exceedance probability of 25% (Q25) and 10% (Q10), and medium flow discharge values with 50% (Q50), and are compared with the corresponding water occurrence map produced from spectral and SAR imagery for the given discharge class. The critical success index (CSI) of the inundation map improves by about 5% using FABDEM corrected version for Q10 and Q25, and about 4% for Q50. In addition, we observe a consistent Bias reduction of about 20% for Q10 and Q25.

Compared to traditional river surface velocity measurement techniques such as in-situ point measurements with electromagnetic current meters, remote sensing techniques are attractive because measurements are fast, low cost and contactless. Based on Unmanned Aerial Systems (UAS) equipped with optical equipment (e.g., HD camera) and Doppler radar, surface velocity can be efficiently measured with high spatial resolution. UAS-borne Doppler radar is particularly attractive, because it is suitable for real-time velocity determination and has fewer limitations (no seeding of the flow required, no daylight required, works for both narrow and wide rivers).In this paper, videos from a UAS RGB video camera were analysed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a 24 GHz continuous wave Doppler radar (e.g., Geolux RSS-2-300) at multiple waypoints across the river. Different from previous processing methods, which only considered the processed velocity from Doppler radar itself, we propose an algorithm for picking the correct river surface velocity from the raw data. The algorithm fits two alternative models to the raw data average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peaks model.Results indicate that river flow velocity and drone-induced propwash velocity can be found in the river’s lower flow velocity portions (i.e., surface velocity between 30 cm/s and 80 cm/s), while the drone-induced velocity can be neglected in fast and highly turbulent flows (i.e., surface velocity > 80 cm/s). To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of electromagnetic velocity sensor data (OTT MF Pro) with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river flow velocity.

Hydraulic characterization of river reaches is fundamental for flood risk assessment and flood forecasting. Hydraulic models can translate discharge into water levels, with measurements of river topography, water level and hydraulic roughness. Traditionally, these measurements are taken with in-situ surveys that are normally costly and time consuming when large or spatially distributed datasets are required, and difficult to retrieve in some locations. Remote sensing solutions have been widely used in the last years to measure inland water topography and water levels, reducing the time and cost of traditional surveys. Satellite earth observations can measure inland water bodies with high temporal and spatial frequency, but they only work in large rivers, can have limited accuracy and cannot measure the submerged portion of the river. UAS techniques offer high-resolution measurements of river topography, including river bathymetry and water level in medium-sized streams that are too big to wade through, offering a good opportunity to recover a full river hydraulic characterization. UAS techniques have been widely used in hydrologic surveys, especially in smaller streams where satellite-based solutions are unfeasible due to the measurement sparsity. In addition, these techniques are very versatile, offering different types of measurements depending on the payload attached to the airborne system. River bathymetry can be retrieved using sonar or water penetrating radar (WPR), which provides depth measurements that, combined with water level measurements, can be used to calculate bathymetry. Moreover, land elevation can be measured with a LiDAR payload, providing topographic information on the river edges and adjacent floodplains. Radar altimeters can also provide water level measurements at a very high spatial resolution and accuracy. These data-sets can be used together to calibrate hydraulic roughness, which cannot be observed at the scale needed. In this study, we propose a new UAS data acquisition technique for full hydraulic characterization of a river reach combining sonar bathymetry, LiDAR elevation of the land adjacent to the river and radar water level measurements to calibrate a hydraulic model. The method is demonstrated in a reach of Ryå stream in Jammerbugt, Denmark. This stream has a river width of around 10 meters and is characterized by dense vegetation in the surroundings, with deep areas where it is not possible to wade through. The bathymetry is observed using a sonar payload that measures depth in contact with water and water level measurements from RTK. The sonar depth is acquired in 1 day with a quasi-continuous UAS flight that measured 54 cross-sections separated by 100 meters. The land elevation is measured using a LiDAR in scanning mode, which gives measurements for 8 km of the river reach in less than half a day. The water level measurements were taken with a radar altimeter payload for 8 km of the river reach. The topographic and water level measurements are used in a hydraulic model to calibrate hydraulic roughness by estimating water levels that are compared with observed water level from radar altimetry.