Xinqi Hu

Discharge is a fundamental hydrological variable, yet often unavailable under hard‐to‐access or critical conditions, hindering understanding of ungauged catchment hydrology. Unoccupied Aerial Systems (UAS) offer promising contactless approaches for monitoring inland waters under such conditions. Numerous studies have assessed UAS discharge estimation, but few have focused on evaluating fully contactless approaches across diverse river conditions. To address this gap, we present a UAS‐based framework for estimating discharge using contactless measurements of water surface velocity, water surface elevation, water surface slope and river bathymetry, supplemented by satellite observations and external DEMs where needed. Four surface‐to‐bulk velocity conversion methods: 0.85 coefficient, log law, power law and entropy method were applied. UAS discharge estimation accuracy was benchmarked at 46 cross‐sections across four river systems with gauge records. We quantified uncertainty propagation from each input variable, assessed their relative contributions, and performed an error propagation experiment to simulate high‐bias measurement scenarios. To identify factors controlling performance, we calculated correlations between discharge error, uncertainty metrics, and cross‐sectional properties. Across most cross‐sections, at least one method achieved discharge error less than 30%, though no single method consistently outperformed the others. The 0.85 method was robust with respect to input uncertainty, with performance patterns across methods linked to morphology: 0.85 and entropy methods performed generally better at larger rivers, while high water surface slopes reduced the accuracy of the log law and power law methods. Correlation analysis revealed that surface velocity uncertainty dominated discharge uncertainty in deep rivers, whereas water surface elevation was more influential in shallow ones.

River discharge remains critically ungauged across much of the globe, limiting the accuracy of flood forecasting and constraining climate adaptation strategies. To address this, we propose a novel framework that integrates occasional Unoccupied Aerial Systems (UAS) with satellite Earth observations. Specifically, we construct a high-resolution hydraulic model of the Torne River in northern Scandinavia by combining a steady gradually varied flow (SGVF) solver with riverbed geometry extracted from UAS-based water-penetrating radar (WPR). The model is calibrated using four in-situ measured discharge–water surface elevation (WSE) snapshots from the Surface Water and Ocean Topography (SWOT) mission to estimate spatially variable, depth-dependent Manning’s roughness coefficients via automated optimization. This calibration enables river discharge to be estimated solely from satellite altimetry data (e.g., SWOT, Sentinel-3, and ICESat-2). Our approach demonstrates high accuracy and operational feasibility, achieving a mean absolute relative error of only 6.15 % when validated against in situ gauge measurements. Remarkably, the model successfully reconstructed an extreme 100-year flood event observed by ICESat-2, with an error of just 2.59 %. This framework provides a scalable and transferable approach for accurately estimating river discharge in virtually any reach observable by SWOT, in combination with one-off or occasional UAS hydrometry surveys.

River discharge remains critically ungauged across much of the globe, limiting the accuracy of flood forecasting and constraining climate adaptation strategies. To address this, we propose a novel framework that integrates occasional Unoccupied Aerial Systems (UAS) with satellite Earth observations. Specifically, we construct a high-resolution hydraulic model of the Torne River in northern Scandinavia by combining a steady gradually varied flow (SGVF) solver with riverbed geometry extracted from UAS-based water-penetrating radar (WPR). The model is calibrated using four in-situ measured discharge–water surface elevation (WSE) snapshots from the Surface Water and Ocean Topography (SWOT) mission to estimate spatially variable, depth-dependent Manning’s roughness coefficients via automated optimization. This calibration enables river discharge to be estimated solely from satellite altimetry data (e.g., SWOT, Sentinel-3, and ICESat-2). Our approach demonstrates high accuracy and operational feasibility, achieving a mean absolute relative error of only 6.15 % when validated against in situ gauge measurements. Remarkably, the model successfully reconstructed an extreme 100-year flood event observed by ICESat-2, with an error of just 2.59 %. This framework provides a scalable and transferable approach for accurately estimating river discharge in virtually any reach observable by SWOT, in combination with one-off or occasional UAS hydrometry surveys.

River discharge remains critically ungauged across much of the globe, limiting the accuracy of flood forecasting and constraining climate adaptation strategies. To address this, we propose a novel framework that integrates occasional Unoccupied Aerial Systems (UAS) with satellite Earth observations. Specifically, we construct a high-resolution hydraulic model of the Torne River in northern Scandinavia by combining a steady gradually varied flow (SGVF) solver with riverbed geometry extracted from UAS-based water-penetrating radar (WPR). The model is calibrated using four in-situ measured discharge–water surface elevation (WSE) snapshots from the Surface Water and Ocean Topography (SWOT) mission to estimate spatially variable, depth-dependent Manning’s roughness coefficients via automated optimization. This calibration enables river discharge to be estimated solely from satellite altimetry data (e.g., SWOT, Sentinel-3, and ICESat-2). Our approach demonstrates high accuracy and operational feasibility, achieving a mean absolute relative error of only 6.15 % when validated against in situ gauge measurements. Remarkably, the model successfully reconstructed an extreme 100-year flood event observed by ICESat-2, with an error of just 2.59 %. This framework provides a scalable and transferable approach for accurately estimating river discharge in virtually any reach observable by SWOT, in combination with one-off or occasional UAS hydrometry surveys.

Discharge observations mainly rely on gauged water levels through rating curves (RC), whose reliable establishment requires long term measurements that are often unavailable due to high maintenance costs, complex terrain, and political reasons. As a result, many basins worldwide remain ungauged, making RC estimation particularly challenging. Recent advances in remote sensing, including satellite altimetry, provide new opportunities for discharge and RC estimation in ungauged basins. However, several challenges remain, including parameter equifinality in discharge inversion from water level, oversimplified assumptions of channel resistance and cross-sectional instability in morphologically active rivers. While Unmanned Aerial Systems (UAS) enable retrieval of channel geometry in complex and hard-to-reach river reaches which imposes an important constraint to mitigate parameter equifinality in hydrodynamic modeling, a systematic assessment of how UAS and remote sensing observations can be combined to reliably estimate rating curves in fully ungauged basins remains lacking.Funded by European Union's Horizon Europe project UAWOS (Unoccupied Airborne Water Observing System), this study presents a RC estimation framework specifically for ungauged basins using multisource remote sensing, UAS data, and a coupled lumped rainfall–runoff and one-dimensional hydrodynamic model. The model is fully forced and calibrated using remote sensing and UAS observations only. To address parameter equifinality, we first perform a temporal-scale dependent parameter sensitivity analysis to assess the identifiability of model parameters given availability of different remote sensing observations. Based on the sensitivity results, a multi-staged Bayesian calibration strategy is introduced, in which each observation type constrains only the parameter subspace supported by its information content. Isar River, Germany was chosen to test and evaluate the feasibility of the proposed methodology.Overall, the proposed framework provides a transferable theoretical and technical pathway for estimating RC in ungauged river basins, demonstrating the potential of combining UAS and remote sensing data to derive RC, without relying on prior discharge measurements and offering implications for estimation of ungauged catchments.

Accurate measurement of river surface velocity is essential for hydrological monitoring, flood forecasting, and water resource management. In contrast with traditional in-situ point measurements using electromagnetic current meters, remote sensing techniques offer significant advantages for river surface velocity estimation, including rapid data acquisition, lower operational costs, and contactless operation. Based on Unmanned Aerial Systems (UAS) equipped with Doppler radar becomes more attractive due to it is suitable for real-time velocity determination and has fewer limitations.The UAS-borne Doppler radar estimates river surface velocity by detecting the frequency shift of backscattered microwaves, with the drone operating at a controlled hover altitude of approximately 4 meters above the water surface during field measurements. However, the propeller wash (propwash) generated by the UAS distorts the radar return signal, resulting in a composite velocity measurement that combines the true river surface flow with the locally induced airflow velocity. To isolate the true river velocity, we introduce a bidirectional observation scheme in which the same surface footprint is measured from two opposing directions. The Continuous Wavelet Transform (CWT) algorithm is employed to extract the mixed velocity components from each directional dataset. By analytically reconciling these bidirectional measurements, the downwash contribution is effectively removed, thereby yielding a refined estimate of the true river surface velocity.In this study, river surface velocity was analysed across more than 50 cross-sections spanning four distinct rivers, covering a broad velocity range from 20 cm/s to 250 cm/s. To validate the velocity estimates obtained from the UAS-Doppler radar and CWT method, comparisons were made against in-situ measurements collected using instruments such as the OTT MF Pro, Acoustic Doppler Current Profilers (ADCP), and flow trackers. Quantitative analysis confirmed that the UAS-Doppler radar system provides reliable river flow velocity measurements while offering enhanced efficiency in post-processing workflows.

River discharge remains critically ungauged across much of the globe, limiting the accuracy of flood forecasting and constraining climate adaptation strategies. To address this, we propose a novel framework that integrates occasional Unoccupied Aerial Systems (UAS) with satellite Earth observations. Specifically, we construct a high-resolution hydraulic model of the Torne River in northern Scandinavia by combining a steady gradually varied flow (SGVF) solver with riverbed geometry extracted from UAS-based water-penetrating radar (WPR). The model is calibrated using four in-situ measured discharge–water surface elevation (WSE) snapshots from the Surface Water and Ocean Topography (SWOT) mission to estimate spatially variable, depth-dependent Manning’s roughness coefficients via automated optimization. This calibration enables river discharge to be estimated solely from satellite altimetry data (e.g., SWOT, Sentinel-3, and ICESat-2). Our approach demonstrates high accuracy and operational feasibility, achieving a mean absolute relative error of only 6.15% when validated against in situ gauge measurements. Remarkably, the model successfully reconstructed an extreme 100-year flood event observed by ICESat-2, with an error of just 2.59%. This framework provides a scalable and transferable approach for accurately estimating river discharge in virtually any reach observable by SWOT, in combination with one-off or occasional UAS hydrometry surveys.

Unoccupied Aerial Systems offer promising opportunities for monitoring inland water hydrometry. UAS-borne Doppler radar, water-penetrating radar, and radar altimetry enable contactless observation of key hydraulic variables, providing valuable data under hard-to-access or critical conditions. However, a systematic assessment of UAS-based river discharge estimation across diverse river types is still lacking. To address this gap, we present a framework for estimating discharge from UAS-derived water surface velocity, water surface elevation, and river bathymetry with local water surface slope computed from the water level profile. Four surface-to-bulk velocity conversion methods: 0.85 method, log law method, power law method and entropy method were applied. The overall accuracy of UAS discharge estimation algorithm was benchmarked at 46 cross-sections across four river systems with gauge records. We quantified uncertainty propagation from each input variable, assessed their relative contributions, and performed an error propagation experiment with ±30% perturbations to simulate high-bias measurement scenarios. To identify factors controlling performance, we calculated Spearman’s rank correlations between discharge bias, uncertainty metrics, and cross-sectional properties. Results show that, across most sites, at least one method achieved discharge bias within 30%, though no single method consistently outperformed the others. The 0.85 method proved most robust overall, with performance patterns linked to morphology: 0.85 and entropy methods generally better at larger rivers, while high water surface slopes reduced the accuracy of the log law and power law methods. Correlation analysis revealed that surface velocity uncertainty dominated to discharge uncertainty in deep rivers, whereas water surface elevation was more influential in shallow ones.