Peter Bauer-Gottwein’s research while affiliated with Technical University of Denmark and other places

Using Unoccupied Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS‐borne Doppler radar is particularly attractive because it is suitable for real‐time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross‐sections (XSs) were surveyed within a 10 km stretch of Rönne River in Sweden. Ground‐truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at one m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space‐Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity based on Gaussian models with: (a) one peak, and (b) two peaks. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone‐induced propwash velocity can be neglected in fast and highly turbulent flows. 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 OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS‐borne Doppler radar could reliably measure the river surface velocity.

The ICESat-2 and SWOT satellite earth observation missions have provided highly accurate water surface slope (WSS) observations in global rivers for the first time. While water surface slope is expected to remain constant in time for approximately uniform flow conditions, we observe time varying water surface slope in many river reaches around the globe in the ICESat-2 record. Here, we investigate the causes of time variability of WSSs using simplified river hydraulic models based on the theory of steady, gradually varied flow. We identify bed slope or cross section shape changes, river confluences, flood waves, and backwater effects from lakes, reservoirs, or the ocean as the main non-uniform hydraulic situations in natural rivers that cause time changes of WSSs. We illustrate these phenomena at selected river sites around the world, using ICESat-2 data and river discharge estimates. The analysis shows that WSS observations from space can provide new insights into river hydraulics and can enable the estimation of river discharge from combined observations of water surface elevation and WSSs at sites with complex hydraulic characteristics.

Recent advancements in satellite Earth observation (EO) technology have significantly improved the accuracy and density of data available for monitoring rivers and streams, as well as for diagnosing errors in hydraulic models. Laser and radar altimetry missions, such as ICESat-2 (Ice, Cloud, and Land Elevation Satellite) and SWOT (Surface Water and Ocean Topography), offer high-resolution measurements of land and water surface elevations (WSE) in remote areas where in-situ data are limited. In this study, we implemented a workflow to assess the accuracy of simulated WSE and evaluate the performance of hydraulic models in the Chao Phraya (CPY) River, using WSE data from ICESat-2 and SWOT. The evaluation of ICESat-2, SWOT, and simulated WSE from the model, compared to in-situ data, resulted in root mean square error (RMSE) values of 0.34 m, 0.35 m, and 0.37 m, respectively. Despite this, both ICESat-2 and SWOT data proved effective for error detection and performance evaluation along the CPY River in point, profile, and spatial map comparisons, with overall RMSE values of 0.36 m and 0.33 m, respectively, when compared with simulated WSE. This paper demonstrates that ICESat-2 and SWOT are valuable tools for diagnosing errors and improving hydraulic model performance, providing critical insights for river monitoring and model validation.

Digital elevation models (DEMs) are essential datasets, particularly for flood inundation mapping in one-dimensional (1D) to two-dimensional (2D) flood models. Given the current uncertainties stemming from changes in weather patterns affecting flooding, reducing inaccuracies in flood models is imperative. This study aims to enhance the performance of 1D-2D flood models using satellite Earth observation (EO) data in the lower Chao Phraya (CPY) basin. It introduces two workflows applied to upgrade the 1D-2D flood model: DEM analysis and flood map analysis. The DEM analysis workflow evaluates 10 DEM products (LDD, JICA, merged LDD-JICA, ASTER GDEM V3, STRMv3, MERIT, GLO30, FABDEMv1-2, TanDEM-X, and TanDEM-EDEM) using satellite laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) according to standard criteria for DEM selection as input to the flood model. Findings indicate that the merged LDD-JICA and FABDEMv1-2 DEMs exhibit the highest level of accuracy, with root mean square error (RMSE) values of 1.93 and 1.95 m, respectively. The flood map analysis workflow involves comparing flood extent maps derived from multi-mission satellite datasets, and simulated flood maps. This study utilizes surface water extent (SWE) maps from the WorldWater project, obtained from the Sentinel-1 and Sentinel-2 imaging satellites, and flood maps from the Geo-Informatics and Space Technology Development Agency (GISTDA) in Thailand to verify flood maps produced by the 1D-2D flood model. The results reveal that the flood maps from the 1D-2D flood model tend to overestimate flood extent, with a critical success index (CSI) range of 0.072 – 0.230. Our study demonstrates the potential to enhance the skill of 1D-2D flood models using satellite EO data, thereby improving the reliability of flood inundation predictions.

Forecasting flood and drought events requires accurate modeling tools. Hydraulic river models are based on estimates of riverbed geometry which are traditionally collected in situ. The novel Ice, Cloud and Land Elevation Satellite 2 [ICESat‐2] lidar altimetry mission with 6 simultaneous high‐resolution laser beams provides the opportunity to define river cross‐section geometries as well as observe water surface elevation [WSE] and water surface slope spatially resolved along the river chainage. This paper describes a method to utilize terrain altimetry and water surface slope estimates to define complete river geometries from ICESat‐2 data products, using the diffusive wave approximation to calculate depth in the submerged section not penetrated by the lidar. Exemplifying the method, cross‐sections are defined for a stretch of the Mekong River. Hydrodynamic model results of the stretch are compared with ICESat‐2 WSE estimates and in situ gauging station time series. Insights in river characteristics from satellite imagery and the ICESat‐2 slope estimates allow for fine‐tuning of the cross‐sections using spatially varying Manning numbers. The final model achieves a root mean square error against the ICESat‐2 WSE of 0.676 m and average Kling‐Gupta Efficiency against gauging station time series of 0.880. The method is limited by the diffusive wave approximation resulting in inaccurate cross‐section estimates in sections with supercritical flow or significant acceleration. Errors can be identified from ICESat‐2 WSE estimates and reduced with additional cross‐sections. Combined with hydrological models, the method will allow for cross‐section definition without in situ data.

The ICESat-2 and SWOT satellite earth observation missions provide highly accurate water surface slope (WSS) observations in global rivers for the first time. While water surface slope is expected to remain constant in time for approximately uniform flow conditions, we observe time varying water surface slope in many river reaches around the globe in the ICESat-2 record. Here, we investigate the causes of time variability of WSS using simplified river hydraulic models based on the theory of steady, gradually varied flow. We identify bed slope or cross section shape changes, river confluences, flood waves and backwater effects from lakes, reservoirs, or the ocean as the main hydraulic phenomena causing time changes of WSS in rivers. We illustrate these phenomena at selected prototypical river sites around the world. These sites show that WSS observations can provide new insights into river hydraulics and can enable estimation of river discharge from water level observations at sites with complex hydraulic characteristics.

Using Unmanned Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS-borne Doppler radar is particularly attractive because it is suitable for real-time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross-sections (XSs) were surveyed within a 10 km stretch of Rönne Å in Sweden. Ground-truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at 1 m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peak model. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone-induced propwash velocity can be neglected in fast and highly turbulent flows. 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 OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river surface velocity.

Using Unmanned Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS-borne Doppler radar is particularly attractive because it is suitable for real-time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross-sections (XSs) were surveyed within a 10 km stretch of Rönne Å in Sweden. Ground-truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at 1 m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peak model. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone-induced propwash velocity can be neglected in fast and highly turbulent flows. 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 OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river surface velocity.