Daniel Wennerberg

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.

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.

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.

River discharge plays a crucial role in hydrologic studies and water resource management. Accurate discharge estimations enable significant advancements in scientific research and water-related decision-making processes. Given that discharge is the product of flow area and flow velocity, traditional in situ measurements of river discharge typically require detailed data on river water level, river bathymetry, and the bulk velocity of the river cross-section. However, such manual measurements are time-consuming and impractical in certain situations, such as rivers in remote, hard-to-access regions or those experiencing extreme high-flow events. With the increasing availability of technical and computational resources, remote sensing offers significant potential for improving discharge estimation. Among these technologies, Unmanned Aerial Systems (UAS) have emerged as a valuable solution for improving discharge estimation. Their low cost, high accuracy, and ability to cover large areas make them particularly effective for monitoring in remote or hard-to-access locations. While numerous studies have developed and demonstrated the feasibility of UAS-based discharge estimation algorithms, their evaluations are often limited to specific sites. Thus, questions remain regarding the adaptability of these algorithms across diverse river systems.Funded by European Union's Horizon Europe project UAWOS (Unoccupied Airborne Water Observing System), This work focuses on evaluating the performance of UAS-based discharge estimation algorithms across a diverse set of cross-sections to enhance their generalizability. Specifically, this work seeks to address the following key questions: 1, how well the discharge algorithm performs based on UAS velocimetry, bathymetry and water surface elevation across different cross-sections? 2, which input datasets and river characteristics may limit the performance, and how sensitive they are? 3, how can we improve the discharge estimation strategies?To answer the questions above, we applied several bulk velocity estimation models on UAS hydrometry parameters to calculate the river discharge among various types of rivers: Rønne Å River (Sweden), Isar River (Germany), Po River (Italy), Orco River (Italy), and Torne River (Sweden). We utilized various in situ discharge measurements to assess the accuracy of our algorithms and investigated how specific cross-sectional properties affect performance. We further systematic analysed the uncertainty from the inputs and models, and discovered strategies to optimize the discharge estimation results by utilizing Bayesian inference. Overall, this study shows that advanced UAS hydrometry technique is an accurate and reliable way for river discharge estimation, providing valuable insights for hydrological studies and water resource management.

With the increasing frequency of extreme weather events, such as river flooding, there is a growing need for more cost-effective and efficient methods for hydrometric river monitoring. Moreover, traditional in-situ hydrometric surveys often face challenges when applied to remote or hard-to-access river locations. Therefore, we investigated the potential of using Unoccupied Aerial Systems (UAS) hydrometry surveys to develop a hydraulic model for extracting rating curves, which can then be used to derive discharge from satellite altimetry-based Water Surface Elevation (WSE) measurements.This study employed UAS-borne Water Penetrating Radar (WPR) to map river bathymetry, while Digital Elevation Models (DEM) were used to extract the non-submerged portions along the WPR cross section. A Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) receiver provided ground truth WSE measurements. Additionally, the pixel cloud data product of the Surface Water and Ocean Topography (SWOT) satellite mission was used to extract WSE along the river. Furthermore, based on the cross sectional geometry information, we constructed two one-dimensional hydraulic models, one using a steady gradually-varied flow solver and the other using the MIKE+ hydrodynamic solver.The study site is located along the Torne River in northern Scandinavia, which forms part of the national border between Sweden and Finland. From September 3rd to 9th, 2024, surveys were conducted at 23 field sites distributed across two areas of interest along the river. Manning's numbers for the river reaches were calibrated against WSE observations derived from the SWOT pixel cloud dataset using the steady gradually-varied flow solver. Hydraulic models were employed to construct rating curves at chainage locations where observations from the Sentinel-3 satellite mission were available at two defined virtual stations: Övertorneå and Pello. These rating curves were subsequently used to convert WSE observations by the SWOT pixel cloud and Sentinel-3 to discharge, enabling the construction of a river discharge time series.

The snowpack stores a substantial part of the seasonal freshwater in cold environments, impacting catchment runoff generation and timing. Alterations of the seasonal snowpack may affect the availability of water resources, with implications for energy production, relying on meltwater from mountain catchments. Spatial and temporal variability of snow processes at multiple scales challenges snowpack monitoring, snow volume estimations and runoff predictions. Drone acquisition techniques have emerged as a new methodology for snowpack monitoring to obtain dense and high spatial-resolution snow data. This study uses drone observations to estimate snow depth close to the accumulation peak in the Överuman catchment, Northern Sweden. We compared the snow depth average and distribution in the catchment areas where drone acquisitions occurred and in the whole catchment. We explored the use of topographic and wind shelter factors and different machine learning methods to obtain snow depth maps of the entire catchment. We further evaluated the impact of aggregating snow depth data at various spatial resolutions on snow spatial distribution and runoff. Results show high correlations of snow depth, especially with wind shelter factors, which are among the selected predictors in cross-validation, together with topographic roughness at a fine spatial scale. Drone observations provided valuable insights into the snow depth variability to improve process understanding and model development.