Estimating Reservoir Release Using Multi-Source Satellite Datasets and Hydrological Modeling Techniques

Study regionAkaki is a headwater catchment of Awash River Basin that hosts the capital city of Ethiopia, Addis Ababa. The area encompasses several agglomerated towns, water supply, and hydropower reservoirs and is characterized by a chain of mountains and floodplains. Due to basin rainfall, and the expansion of urbanized areas, the catchment is frequently affected by flooding. Study focusThis study evaluates dynamic Bayesian Model Averaging (BMA) approach to improve rainfall estimation over the catchment by blending four high-resolution satellite rainfall estimate (SRE) products. Using daily data (2003–2019) observed at thirteen stations as a reference, seven statistical metrics served to assess the point and spatial scale accuracy of the rainfall estimates. New hydrological insightsMain findings from this study are: (i) the blended product outperformed the individual SRE products by notably improving correlation with in-situ observed rainfall, and reducing the error of the estimated rainfall, (ii) the blended and individual SRE products performed better in the highlands than the lowlands of the catchment, and (iii) the amount of daily rainfall during the main-rainy season was mostly overestimated by the individual SRE products but was fairly estimated by the blended product. This study showed the nonexistence of surpassing individual SRE products and emphasized the blending of several products for gaining optimal results from each product.

Approximately 70% of South Korea’s land is mountainous, and as expressways and national highways have expanded across this terrain, extensive roadside slopes have been formed. Although various reinforcement and stabilization measures were applied during road construction, failures, such as collapses and settlements, continue to occur due to heavy rainfall, typhoons, and other extreme weather events. In recent years, South Korea has initiated research efforts to utilize imagery acquired from Synthetic Aperture Radar (SAR) satellites to detect long-term ground displacements along roadside slopes and support preparedness for potential geohazards. This study assessed the detectability of ground displacements on road slopes by considering the motion direction and incidence angle characteristics of SAR satellites over the Korean Peninsula. SAR satellites can acquire high-resolution images regardless of daylight or weather conditions. Furthermore, interferometric techniques (InSAR) enable the generation of digital elevation model (DEM) with sub-meter accuracy and ground displacement estimations with millimeter-level precision. However, as radar satellites observe the Earth at oblique angles relative to their flight direction and employ side-looking geometry perpendicular to the line of sight, geometric distortion may occur in areas with highly irregular terrain. In such conditions, shadowing effects—including layover and radar shadow can arise where radar signals are unable to reach. Steep road slopes are particularly susceptible to these limitations, and accurate observation may be impossible depending on the satellite’s motion direction and incidence angle. Therefore, before applying SAR data to slope-monitoring studies, it is essential to assess the feasibility of observations for the target slopes. In this study, the motion and observation characteristics of the Sentinel-1B satellite which continuously acquires imagery across the Korean Peninsula at approximately 12 day intervals were analyzed to evaluate the feasibility of observing road slopes nationwide. To evaluate the SAR observation feasibility for road slopes in South Korea, the evaluation was conducted in three stages: 1. Road-slope information, road networks, and other required datasets were compiled and converted into geospatial (Geographic Information System) data formats. 2. SAR imagery covering the Korean Peninsula was collected, and satellite motion direction and incidence angles were derived from image header files and metadata, then converted into raster datasets. 3. The relative spatial relationship between the road-slope data and satellite-observation data was analyzed, and based on these results, the observation feasibility of each road slope was evaluated considering the effects of the SAR satellite’s motion direction and incidence angle. As a result, the study evaluated the SAR observation feasibility for approximately 27,000 road slopes across South Korea. Considering the orbit and incidence-angle characteristics of the Sentinel-1B satellite, it was found that about 66% of all slopes were located within the satellite’s effective imaging range.AcknowledgementThis research is based upon work supported by Korea Institute of Civil Engineering & Building Technology(KICT), Project No.20250285-001

Synthetic Aperture Radar (SAR) satellite power system has the characteristics of high power, pulse power supply and fast response speed. Based on the combination of domestic and foreign SAR satellite power supply structures, a SAR satellite power supply system scheme with a composite bus architecture is proposed in this paper. By adding battery discharge regulators (BDR) between the platform bus and the SAR load bus as a grid-connected controller, the SAR load bus can supply power to the platform load, and the solar array and battery pack can be used efficiently while achieving dual bus output. Aiming at the composite bus, this paper also proposes an efficient high-power filter technology. Finally, the ground test results verify the correctness of the scheme, which can meet the different power supply requirements of the SAR satellite stable and pulse load equipment.

Synthetic Aperture Radar (SAR) satellites are complex systems that usually require long development periods and lot of investment. In 2013, a study of an innovative bi-static SAR concept based on a passive, receive-only Companion Satellite (CS) to a full SAR satellite was performed by ESA and the Industry to design a small, low-cost and fast-track implementation satellite to fly with SAOCOM-1 satellite that acts as an illuminator. SAOCOM is a constellation of two L-band SAR satellites (1A and 1B) under development in Argentina (CONAE). The CS was initially conceived to be launched together with the 1B satellite onboard a Falcon-9 rocket in 2019 and fly in formation to acquire the echoes from its SAR payload. Thus, in addition to the performance requirements, the launch conditions and the challenging implementation schedule drove the design of SAOCOM-CS, forcing the use of components with high technology readiness levels. These led to the CS payload design, including the L-band SAR antenna and the external synchronization antenna for a non-collaborative bistatic SAR mission.

The definition of the objective function for hydrological model calibration and goodness-of-fit metrics for validation are crucial in characterising model performance and driving model development. Ideally, model developers and users should identify the most suitable metric to characterize model performance based on their specific use cases and objectives. The benefits of this are evident; for example, the optimal objective function to calibrate a hydrological model only used for flood forecasting should be different from the optimal one to be used for a model focusing only on low flows. However, in practice, most hydrological models are used for multiple applications and a standard generalist function is adopted for their calibration. The two most widely used generalist functions are the Nash-Sutcliffe Efficiency (NSE) and the Kling-Gupta Efficiency (KGE), in its standard and modified versions. While the NSE is a simple normalization of the mean square error (MSE), the KGE overcomes some of the NSE limitations based on the decomposition of the MSE into three components, i.e., the error in the mean simulated streamflow, the relative variability, and the linear correlation between simulations and observations. Still, KGE presents some limitations, including a large sensitivity to outliers and an assumption of linearity and normality in the error distribution, which are impactful especially for the characterization of performances over low flows. Some alternative calibration functions have been proposed in the literature to overcome these limitations, but no calibration function overcomes the traditional two options (NSE and KGE) in improving the simulation along the whole range of the flow duration curve. Here we present the results of an extensive comparison of hydrological models calibrated and validated with multiple functions, including new variants and combinations of KGE and information-theory metrics, that can be suitable to characterize the performance over high, low, and regime flows. Two hydrological models (GR4J and Open Source LISFLOOD) were calibrated with several alternative calibration functions over more than 200 catchments in Europe with a varied range of hydroclimatic conditions. Both models were evaluated using multiple metrics, including use-case specific hydrological signatures focusing on flood characteristics, average regime and low flows. Based on our analysis, a new function is proposed which combines the three KGE components with an additional component based on the Jensen-Shannon Divergence. The performance of the two models calibrated with the new function is shown to outperform the standard KGE and NSE over low flows with minimal change in performance over regimes and high flows. This study shows that more effort should be devoted to the choice of the optimal calibration function for hydrological model applications when aiming to improve specific aspects of model performance.

Abstract. Tropospheric water vapor is one of the most important trace gases of the Earth's climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)2. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021).

The development of high resolution mapping models of forest attributes based on employing machine or deep learning techniques has increasingly accelerated in the last couple of years. The consequence of this is the widespread availability of multiple sources of information, which can either lead to a potential confusion, or to a possibility to get an "extended” insight into the state of our forests by interpreting these sources jointly. This contribution aims at addressing the latter, by relying on the Bayesian model averaging (BMA) approach.BMA is a method that can be used in building a consensus from an ensemble of different model predictions. It can be seen as weighted mean of different predictions with weights reflecting the predictive performances of different models, or as a finite mixture model which estimates the probability that each observation from the independent validation dataset has been generated by one of the models belonging to the ensemble. BMA can thus be used to diagnose and understand the difference in the predictions and to possibly interpret them.The predictions in our case are the forest canopy height estimations for the metropolitan France coming from 5 different AI models [1-5], while the independent validation dataset comes from the French National Forest Inventory (NFI) disposing with some 6000 plots per year, distributed across the territory of interest. For every plot we have several measurements/estimations of the forest canopy height out of which the following two are considered in this study: h_m – the maximum total height (from the tree's base level to the terminal bud of the tree's main stem) measured within the plot, and h_dom – the average height of the seven largest dominant trees per hectare.In this contribution we present for every considered plot the dominant model with respect to both references i.e. the model having the highest probability to be the one generating measurements/estimations at NFI plot (h_m and h_dom). We present as well as the respective inter-model and the intra-model variance estimations, allowing us to propose a series of hypotheses concerning the established differences between predictions of individual models in function of their specificities.[1] Schwartz, M., et al.: FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and Global Ecosystem Dynamics Investigation (GEDI) data with a deep learning approach, Earth Syst. Sci. Data, 15, 4927–4945, 2023, https://doi.org/10.5194/essd-15-4927-2023[2] Lang, N., et al.: A high-resolution canopy height model of the Earth, Nat Ecol Evol 7, 1778–1789, 2023. https://doi.org/10.1038/s41559-023-02206-6[3] Morin, D. et al.: Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process, Remote Sens., 14, 2079. 2022, https://doi.org/10.3390/rs14092079[4] Potapov, P., et al.: Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sensing of Environment, 253, 2021, https://doi.org/10.1016/j.rse.2020.112165.[5] Liu, S. et al.: The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe, Sci. Adv. 9, eadh4097, 2023, 10.1126/sciadv.adh4097.

Synthetic Aperture Radar (SAR) satellite an active sensor offering unique high spatial resolution regardless of weather conditions can operate both day and night time with wide area coverage. Therefore, SAR satellite can be used for monitoring ship on sea surface. This study showed on an alternative method for ship detection of SAR data using Pi-SAR-L2 (L-band, JAXA-Airborne SAR) data. The ship detection method is this study was consisted of eight main stages. After the Pi-SAR data was registered and speckle was filtered, then the land was masked using SRTM-DEM (Shuttle Radar Topography Mission-Digital Elevation Model) data since most ship detectors produced false detections when it applied to land areas. A ship sample image was then selected (cropped). The next step was to detect some unique keypoints of ship sample image using Speeded Up Robust Features (SURF) detector. The maximum distance (‘MaxDist’) of keypoints was also calculated. The same detector was then applied to whole Pi-SAR imagery to detect all possible keypoints. Then, for each detected keypoint, we calculated distance to other keypoint (‘Dist’). If ‘Dist’ was smaller than ‘MaxDist’, then we marked these two (or more) keypoints as neighboring keypoints. If the number of neighbor keypoints was equal or greater than two, finally we marked these keypoints as ‘Detected Ship’ (draw rectangle and show its geographic position). Results showed that our method can detect successfully 32 ‘possible ships’ from Pi-SAR-L2 data acquired on the area of North Sulawesi, Indonesia (August 8, 2012).

In this study, we developed and evaluated a hybrid framework for reservoir inflow forecast. This framework is unprecedented, which integrates new quasi-globally available observation-, satellite-, or model-based datasets using machine learing models to forecast inflow at the local scale. Under this framework, we compared random forests, gradient boosting machine, extreme learning machine, M5-cubist, elastic net, as well as their multi-model ensemble using Bayesian model averaging (BMA), and tested contributions from different input datasets, including retrospective forecast (reforecast) from florecast oriented low ocean resolution (FLOR) dynamic climate model, satellite-based hydrologic products, and climate phenomenon information. The performance was evaluated using Kling–Gupta efficiency (KGE) and correlation coefficient (R) in two headwater reservoirs, Harris reservoir in the humid Alabama–Coosa–Tallapoosa river basin and the Navajo reservoir in the arid Upper Colorado River Basin (UCRB). The results showed that for the Harris reservoir, the BMA combining five models with antecedent inflow and satellite-based hydrologic information as model inputs provided the best performance (KGE = 0.66, R = 0.76). For the Navajo reservoir, the gradient boosting machine model with all variables combined as input showed the best performance (KGE = 0.76, R = 0.83). Satellite-based soil moisture and evaporation consistently showed significant contributions to the inflow forecast. Benefits from climate indices and FLOR reforecast varied by locations, with more benefits coming from climate indices than FLOR potential evaporation reforecast at the Navajo reservoir in UCRB. Given the global coverage of the model inputs, our approach can be potentially applicable to improve reservoir inflow forecasts in different regions of the world.

Electroencephalography (EEG) source localization in epileptology continues to be a challenge for neuroscientists. A number of inverse solution (IS) methodologies have been proposed to solve this problem, and their advantages and limitations have been described. In the present work, a previously developed IS approach called Bayesian model averaging (BMA) is introduced in clinical practice in order to improve the localization accuracy of epileptic discharge sources. For this study, 31 patients with the diagnosis of partial epilepsies were studied: 14 had benign childhood epilepsy with centrotemporal spikes and 17 had temporal lobe epilepsy (TLE). The underlying epileptic sources were localized using the BMA approach, and the results were compared with those expected from the clinical diagnosis. Additional comparisons with results obtained from 3 of the most commonly used distributed IS methods for these purposes (minimum norm [MN], weighted minimum norm [WMN], and low-resolution electromagnetic tomography [LORETA]) were carried out in terms of source localization accuracy and spatial resolutions. The BMA approach estimated discharge sources that were consistent with the clinical diagnosis, and this method outperformed LORETA, MN, and WMN in terms of both localization accuracy and spatial resolution. The BMA was able to localize deeper generators with high accuracy. In conclusion, the BMA methodology has a great potential for the noninvasive accurate localization of epileptic sources, even those located in deeper structures. Therefore, it could be a promising tool for clinical practice in epileptology, although additional studies in other types of epileptic syndromes are necessary.

<p>The Multipurpose Airborne Sensor Carrier (MASC) is a fixed-wing unmanned aircraft system (UAS) that has been continuously developed and used for in-situ, high-resolution flight measurements of atmospheric variables such as wind, temperature, humidity as well as trace gas and particle concentrations by the Environmental Physics group at University of Tübingen. The most recent innovation in the MASC-series is the MASC-V type vertical takeoff and landing UAS. It has been designed in cooperation with ElevonX d.o.o.. Compared to its predecessor, MASC-3, it can automatically takeoff and land on small patches of land while carrying an identical atmospheric measurement payload. This capability, complemented by an enhanced safety and operational concept, allows for deployment in offshore applications. Particularily, MASC-V has demonstrated safe operation beyond visual line of sight (BVLOS) from the remote safety pilot in offshore applications within the EUs new legal framework introduced in 2020.</p><p>Before its first offshore mission, MASC-V underwent a system validation against a meteorological tower at the German Weather Service (DWD) Observatory site at Falkenberg, Germany. Offshore measurements were conducted from the German offshore island Heligoland at the Testfield for Maritime Technologies in cooperation with the Fraunhofer Institute for Applied Material Science in September 2021. The goal of the Heligoland campaign was to validate the remote sensing of sea surface wind measurements by Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 formation at low flight altitudes (20 m - 30 m). SAR satellites can deliver detailed wind data over large areas such as the German Bight including for example wind farm wake effects. Direct validation of these results is difficult with other in-situ techniques. Buoys and measurement towers or platforms can provide stationary data. Aerial measurements with manned aircraft are only possible at higher altitudes. The new UAS data provide the first aerial in-situ SAR validation measurement at low altitude. Additionally, we have demonstrated the capabilities of VTOL fixed-wing UAS for vertical profiling as well as to operate tens of kilometers away from ground personell over open water.</p>

<p>The Multipurpose Airborne Sensor Carrier (MASC) is a fixed-wing unmanned aircraft system (UAS) that has been continuously developed and used for in-situ, high-resolution flight measurements of atmospheric variables such as wind, temperature, humidity as well as trace gas and particle concentrations by the Environmental Physics group at University of Tübingen. The most recent innovation in the MASC-series is the MASC-V type vertical takeoff and landing UAS. It has been designed in cooperation with ElevonX d.o.o.. Compared to it<span>s</span> predecessor, MASC-3, it can automatically takeoff and land on small patches<span> of land</span> while carrying an identical atmospheric measurement payload. This capability, complemented by an enhanced safety and operational concept, allows <span>for </span>deployment in offshore applications. Particularily, MASC-V has demonstrated safe operation beyond visual line of sight (BVLOS) from the remote safety pilot in offshore applications within the EUs new legal framework introduced in 2020.</p><p><span>Before its first offshore mission, MASC-V underwent a full system validation against a meteorological tower at the German Weather Service (DWD) Observatory site at Falkenberg, Germany. Offshore measurements were conducted from the German offshore island Heligoland at the Testfield for Maritime Technologies in cooperation with the Fraunhofer Institute for Applied Material Science in September 2021. The goal of the Heligoland campaign was to validate the remote sensing of sea surface wind measurements by Synthetic Aperture Radar (SAR) satellites of the Sentinel-1 formation at low flight altitudes (20 m - 30 m). SAR satellites can deliver detailed wind data over large areas such as the German Bight including for example wind farm wake effects. Direct validation of these results is difficult with other in-situ techniques. Buoys and measurement towers or platforms can provide stationary data. Aerial measurements with manned aircraft are only possible at higher altitudes. The new UA</span><span>S</span><span> data provide the first aerial in-situ SAR validation measurement at low altitude. Additionally, we have demonstrated the capabilities of VTOL fixed-wing UA</span><span>S </span><span>for vertical profiling as well as</span><span> to operate tens of kilometers away from ground personell over open water.</span></p>

Accurate rainfall-runoff analysis is vital for flood prediction, water resources management, and climate impact assessment. While data-driven hydrological models such as Long Short-Term Memory (LSTM) networks have shown promise, developing a globally applicable framework that is accurate, interpretable, and computationally efficient remains a grand challenge, primarily because most catchments worldwide are ungauged. We address this by employing HYdrologic Prediction with multi-model Ensemble and Reservoir computing (HYPER). This hybrid method combines Bayesian Model Averaging (BMA), a multi-model ensemble, with Reservoir Computing (RC), a type of machine learning model. The framework infers model weights for ungauged basins by linking catchment attributes to the model weights learned from gauged basins. While this model has previously demonstrated higher accuracy and lower uncertainty compared to LSTMs, particularly when training data is limited, its global applicability remains unassessed. Therefore, in this study, we evaluate the global applicability of HYPER using a pseudo-ungauged approach, where gauged basins are treated as ungauged for validation. We challenge the conventional assumption that more data is better by investigating whether selecting a strategic subset of gauged basins for training outperforms using the entire available dataset. Initial experiments revealed that prediction accuracy remained robust regardless of whether 90 % or only 3 % of available basins were used for training. Furthermore, training on basins from a single, hydrologically similar region often yielded higher accuracy than training on a diverse multi-regional dataset. To identify the optimal training subset, we compared three distinct data selection methods: 1) Greedy selection, which identifies donor basins by selecting the nearest neighbors within the static catchment attribute state space; 2) Physics-Informed selection, which calculates the distance between target and candidate basins while applying heavier penalty weights to slope and aridity to strictly enforce physical similarity; and 3) Meta-Learning, which utilizes a Random Forest to learn the relationship between attribute differences and model weight correlations, subsequently predicting donor basins expected to have the highest weight correlation with the target. While all three methods outperformed the baseline of using all available data (Kling-Gupta Efficiency (KGE): 0.12), the Physics-Informed and Meta-Learning approaches achieved the highest consistency and accuracy. Even when only 5 out of 1,505 basins were used for training, these methods achieved KGE scores of 0.26 and 0.31, respectively, effectively bridging the performance gap toward fully gauged basins (KGE: 0.54). These findings demonstrate that for global prediction in ungauged regions, data quality, especially the strategic selection of training basins, is more important than data quantity, marking a step towards robust, globally applicable runoff analysis.

This paper briefly discusses the nature, the causes and the role of predictive uncertainty in flood forecasting and proposes a novel approach to its estimation. Following the definition of predictive uncertainty, its importance in the decision process is highlighted in relation to the different sources of errors (model, parameter, observations, boundary conditions) that affect flood forecasting. Moreover, the paper briefly analyses the importance of using a full predictive uncertainty, obtained by marginalising the parameter uncertainty, instead of the predictive uncertainty conditional to a single parameter set. Finally, a new Model Conditional Processor (MCP) for the assessment of predictive uncertainty is then proposed as an alternative to the Hydrologic Uncertainty Processor (HUP) introduced by Krzysztofowicz as well as to the Bayesian Model Averaging (BMA) approach due to Raftery et al. The new MCP approach, which aims at assessing, and possibly reducing, predictive uncertainty, allows combination of the observations with one or several models’ forecasts in a multi‐Normal space, by transforming observations and model forecasts in a multivariate Normal space by means of the Normal Quantile Transform. The results of the new approach are shown for the case of the River Po in Italy, and compared with the results obtainable both with the HUP and the BMA.

<p>In recent years, the number of large-scale hydrological forecasting systems has been steadily growing. This may lead to regions having numerous models spatially overlapping each other. Some of these regions have what we will refer to as a regional, more specialized, model for the area that performs generally better than their large-scale counterpart, considering the coarser spatial resolution and sometimes lack of calibration of the latter. Our work explored the possibility of using simple methods to retrieve hydrological information from a large-scale model, namely the National Surface and River Prediction System (NSRPS) that will eventually cover the Canadian territory, in order to improve the forecasts from a local system, namely the Système de Prévision Hydrologique (SPH) that covers most of the province of Quebec. Outputs from the two forecasting systems were thus combined using methods including the simple mean, a weighted average in which the weights are optimized either using the Kling-Gupta Efficiency (KGE) or the Continuous Ranked Probability Score (CRPS) as cost functions, or weights calculated from the residual errors of the forecasts. Bayesian Model Averaging (BMA) was also explored to combine the ensemble forecasts from both systems. The results show that it is possible to improve the local hydrological forecasts by using simple weighted combinations with forecasts from the large-scale system. Performance was assessed using many well-known criteria such as the Nash-Sutcliffe Efficiency (NSE), KGE and CRPS. Results were averaged over the 61 available gauging stations and analyzed at lead times ranging from 3 to 120 hours. We observed improvements in all criteria for lead times over 60 hours as well as no loss in performance at any lead times. Finally, the methods were also used in a leave-one-out setup to simulate performance on ungauged basins. The performance gain for ungauged basins is similar to that of the gauged basins, hinting at the fact that these simple methods could also improve forecasts in more remote territories where no measurements are available.</p>