06 Fakultät Luft- und Raumfahrttechnik und Geodäsie
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Item Open Access Spaceborne river discharge from a nonparametric stochastic quantile mapping function(2021) Elmi, Omid; Tourian, Mohammad J.; Bárdossy, András; Sneeuw, NicoThe number of active gauges with open‐data policy for discharge monitoring along rivers has decreased over the last decades. Therefore, spaceborne measurements are investigated as alternatives. Among different techniques for estimating river discharge from space, developing a rating curve between the ground‐based discharge and spaceborne river water level or width is the most straightforward one. However, this does not always lead to successful results, since the river section morphology often cannot simply be modeled by a limited number of parameters. Moreover, such methods do not deliver a proper estimation of the discharge's uncertainty as a result of the mismodeling and also the coarse assumptions made for the uncertainty of inputs. Here, we propose a nonparametric model for estimating river discharge and its uncertainty from spaceborne river width measurements. The model employs a stochastic quantile mapping scheme by, iteratively: (a) generating realizations of river discharge and width time series using Monte Carlo simulation, (b) obtaining a collection of quantile mapping functions by matching all possible permutations of simulated river discharge and width quantile functions, and (c) adjusting the measurement uncertainties according to the point cloud scatter. We validate our method over 14 different river reaches along the Niger, Congo, Po Rivers, and several river reaches in the Mississippi river basin. Our results show that the proposed algorithm can mitigate the effect of measurement noise and also possible mismodeling. Moreover, the proposed algorithm delivers a meaningful uncertainty for the estimated discharge and allows us to calibrate the error bars of in situ discharge measurements.Item Open Access Hydrogeodesy : a Bayesian perspective(2025) Tourian, Mohammad J.; Sneeuw, Nico (Prof. Dr.)While historically focused on local scales, modern hydrologic studies have increasingly adopted a global perspective, recognizing water as a finite resource and the interconnection between regions. This global perspective puts hydrology within the water cycle framework, offering a comprehensive view of water dynamics across regions and scales. Despite this framework’s conceptual clarity, accurately quantifying the global water cycle remains challenging due to the complexity of capturing localized and large-scale patterns, variations in topography, climate, and land use, as well as temporal variability. These complexities hinder comprehensive measurements, resulting in knowledge gaps around key water cycle components, including river discharge, surface water storage, soil moisture dynamics, and subsurface water storage and flow. Inspired by the existing knowledge gaps in the water cycle, an emerging field known as Hydrogeodesy comes to the forefront. Hydrogeodesy is the discipline that uses terrestrial and primarily spaceborne geodetic data, both geometric and gravimetric, to support global water cycle quantification. Utilizing technologies such as satellite altimetry, gravimetry, imaging, InSAR, GNSS, and GNSS-Reflectometry, hydrogeodesy offers direct or indirect measurements of key water cycle components, including terrestrial water storage, and river discharge, significantly advancing our understanding of water dynamics. Despite advancements in spaceborne geodetic sensors, hydrogeodesy faces challenges such as limitations in the spatiotemporal resolution of satellite measurements, measurement uncertainties, unobserved variables, inconsistencies in background models, and the difficulty of separating aggregated measurements. Possible solutions to these challenges involve combining different data types, including satellite, ground-based observations, and model outputs, to benefit from their complementary strengths. However, this presents its own challenges, as it requires reconciling datasets with varying resolutions, accuracies, and temporal scales. To address some of the challenges listed above, Bayesian approaches offer viable solutions by providing probabilistic interpretations and uncertainty quantification. Bayesian approaches offer a robust framework for updating prior knowledge with new data to yield a posterior distribution, enabling a probabilistic interpretation and explicit uncertainty estimation of parameters. This is especially valuable in hydrogeodesy, where parameters like river discharge, soil moisture, and groundwater storage are often estimated indirectly and carry substantial uncertainties. This habilitation thesis provides a foundational discussion on Bayesian modeling and statistics and demonstrates the versatility and power of Bayesian methods in enhancing our understanding of water cycle components by presenting three distinct Bayesian applications in hydrogeodesy. The first study applies a Bayesian approach, specifically the Kalman filter, to estimate river discharge using spaceborne geodetic measurements. In hydrogeodetic studies, the Kalman filter and dynamic systems are especially valuable, as they enable the integration of multiple data sources and the continuous updating of estimates with incoming measurements. This is particularly beneficial for river systems, which inherently function as a dynamic system. To assess this potential, a method is introduced that uses the cyclostationary properties of discharge as prior information, while observed altimetric discharge data provide the likelihood. Together, these yield a posterior providing an unbiased daily discharge estimate. The method is applied to the Niger River basin and its main tributaries and validated against in situ data from 18 gauges. Results show a high average Correlation Coefficient (CC) of 0.9 and an average relative Root Mean Squared Error (RMSE) and bias of 15%. This method effectively estimates daily river discharge across entire basins and shows promise for global application, especially in data-scarce regions. With satellite altimetry data from multiple virtual stations and historical discharge data, daily discharge estimates with an error under 20% could be attainable in many river basins worldwide. The growing availability of spaceborne geodetic data, such as that provided by SWOT, further enhances this potential by delivering comprehensive measurements of river height and width, along with global discharge estimates. In most real-world applications, including hydrogeodesy, the Gaussianity assumption required by the Kalman filter does not hold, limiting its applicability. Inspired by this challenge, and motivated by the need to overcome the limitations of the poor spatial resolution of the GRACE and GRACE-FO missions, the second study proposes a Bayesian method to downscale GRACE data, proposing a nonparametric method to infer the posterior distribution directly, without any assumption for the likelihood or posterior. The prior distribution is obtained based on GRACE data values using the monthly variation of GRACE data. To model the likelihood functions, copulas are employed to capture dependencies among multivariate distributions. Monthly empirical copulas are constructed and fitted to analytical copulas, conditioned on specific quantile values, reflecting the dependency between GRACE and fine-scale data. A key advantage of this copula-supported Bayesian approach is its capacity to represent uncertainties in both data and models, even with variable input quality. The proposed downscaling approach is applied to the Amazon Basin, utilizing four different fine-scale datasets: WGHM, PCR-GLOBWB, SURFEX-TRIP, and the ensemble of flux data and soil moisture data from GLEAM and ASCAT. Validation is conducted against two independent datasets: space-based Surface Water Storage Change (SWSC) and GPS-observed Vertical Crustal Displacement Change (VCDR). In SWSC validation, downscaled results capture spatial variations in river storage with high CC and a relative RMSE of 26%. VCDR validation involves two analyses: comparing GPS-VCDR with TWSF-based VCDR using Green’s function convolution, where downscaled products yield RMSE values between 2.27 and 5.65 mm/month, outperforming input fine-scale data with 14 mm/month RMSE. In terms of CC, downscaled results achieve an average value of -0.81 versus -0.73 for the input. The proposed Bayesian framework effectively downscales GRACE data, with performance highly dependent on input data quality. The copula-supported Bayesian approach offers valuable uncertainty quantification even with inconsistent input data. This method aids in understanding water storage variations in small catchments, supporting local hydrological studies, and can be applied to other water cycle parameters as an alternative to traditional methods. Although a direct posterior is obtained for each grid cell in the downscaling study, spatial dependencies among neighboring grid cells are not considered. Graphical models are particularly well-suited for capturing such spatial dependencies. To address this limitation - and inspired by the challenge of noisy water level estimates from satellite altimetry over inland water bodies - the third study presents a Bayesian approach that formulates a probabilistic graphical model known as a Markov Random Field (MRF), with a Maximum A Posteriori estimation of the MRF (MRF-MAP) as the objective. There to improve inland altimetry, a retracking method is proposed. Unlike conventional retracking methods that target a single waveform point, a holistic approach by identifying retracking lines within 2D radargrams, treating the radargram as a segmented image. This segmentation divides the radargram into Front and Back segments, resembling a binary image segmentation task. The proposed MRF-MAP framework uses spatial dependencies as prior information, with the likelihood based on the temporal evolution of pixel labels across groundtrack cycles. Two temporal energy functions are applied: 1D, based solely on pixel intensity, and 2D, which includes both intensity and bin values, with the posterior probability maximized using the maxflow algorithm. The maxflow algorithm is then applied to obtain MAP solution, yielding a segmented radargram where the retracking line is defined as the boundary between segments. The proposed retracker method is applied to both pulse-limited and SAR altimetry datasets across nine U.S. lakes and reservoirs with varying altimetry characteristics. Validated against in situ data, the proposed method improves RMSE by approximately 0.25 m with the 1D temporal energy function and 0.51 m with the 2D function. The main advantage of the proposed method is its robustness against unexpected waveform variations, making it especially valuable for complex radargrams where conventional retrackers often deliver outliers. By integrating both spatial and temporal information, this method offers a more comprehensive understanding of the data and has broad applicability, such as improving the classification of SWOT pixel cloud points by incorporating spatiotemporal detail. Through these case studies, the thesis illustrates the advantages of Bayesian approaches in improving the accuracy and reliability of hydrological estimates - such as river discharge, terrestrial water storage, and water level measurements - derived from spaceborne geodetic sensors. By integrating theoretical insights with practical applications, the thesis demonstrates how Bayesian methods can effectively improve spatiotemporal resolution, obtain uncertainties, enhance data fusion, and accommodate the complexities inherent in hydrological systems. This combination of foundational knowledge and real-world examples shall establish a base for advancing the use of Bayesian approaches in hydrogeodetic research and beyond. Moreover, by highlighting the challenges in hydrogeodesy, this thesis provides a clear direction for future research and development in the field. It emphasizes critical areas requiring attention, such as improving the spatial and temporal resolution of hydrological estimates, addressing inherent uncertainties in geodetic observations, and developing more effective methods for assimilating diverse data sources. The thesis encourages the refinement of geodetic data processing techniques and the adoption of probabilistic frameworks, such as Bayesian modeling, in future work. Building upon the work presented here, future studies can ultimately achieve more accurate and reliable insights into the Earth's systems.Item Open Access The potential of EO data for enhanced flood monitoring and forecasting : a consortium assessment(2026) Tarpanelli, Angelica; Massari, Christian; Revilla-Romero, Beatriz; Tourian, Mohammad J.; Saemian, Peyman; Elmi, Omid; Scherer, Daniel; Pedinotti, Vanessa; Kittel, Cecile; Benveniste, Jérôme; Bauer-Gottwein, Peter; Ciabatta, Luca; Chewning, Connor; Barbetta, Silvia; Filippucci, Paolo; Cantoni, Èlia; Dettmering, Denise; Andersson, Jafet; Gal, Laetitia; Gustafsson, David; Hundecha, Yeshewatesfa; Larnicol, Gilles; Larnier, Kevin; Nielsen, Karina; Paris, Adrien; Sadki, Malak; Schwatke, Christian; Tamagnone, Paolo; Vrettou, Artemis; Douch, Karim; Volden, Espen; Schumann, GuyThe monitoring and modeling of riverine floods have been covered extensively in the scientific literature with a substantial number of scientific contributions related to calibration/validation of hydraulic and hydrological models and assimilation of Earth Observation (EO) data into them. These models, when used for flood forecasting purposes, rely heavily on ground-based hydrological networks along with numerical weather models which, particularly in data-scarce regions, are often challenged by data sparsity. In these situations, EO data offer a viable solution to enhance the skill of these flood forecasting systems by providing global-scale observations of key hydrological variables such as precipitation, soil moisture, river discharge, water levels, and flood extent. This manuscript reviews and discusses the capability of these EO data in enhancing flood forecasting systems, by analyzing their accuracy, lead time, and reliability, while at the same time highlighting key challenges such as data latency, spatial–temporal resolution trade-offs, and model assimilation constraints. By leveraging recent advancements in remote sensing, data assimilation techniques, and artificial intelligence, EO-based flood forecasting has the potential to bridge existing observational gaps, particularly in vulnerable regions. The paper also outlines future research directions and technological developments needed to maximize the impact of satellite data in operational flood forecasting systems.Item Open Access Crop water productivity mapping and benchmarking using remote sensing and Google Earth Engine cloud computing(2022) Ghorbanpour, Ali Karbalaye; Kisekka, Isaya; Afshar, Abbas; Hessels, Tim; Taraghi, Mahdi; Hessari, Behzad; Tourian, Mohammad J.; Duan, ZhengScarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49-0.55 (kg/m3) for irrigated wheat, 0.27-0.34 for rainfed wheat, 1.7-2.2 for apples, 1.2-1.7 for grapes, 5.5-6.2 for sugar beets, and 0.67-1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.Item Open Access Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM)(2025) Saemian, Peyman; Elmi, Omid; Stroud, Molly; Riggs, Ryan; Kitambo, Benjamin M.; Papa, Fabrice; Allen, George H.; Tourian, Mohammad J.River discharge is a crucial measurement, indicating the volume of water flowing through a river cross-section at any given time. However, the existing network of river discharge gauges faces significant issues, largely due to the declining number of active gauges and temporal gaps. Remote sensing, especially radar-based techniques, offers an effective means to this issue. This study introduces the Satellite Altimetry-based Extension of the global-scale in situ river discharge Measurements (SAEM) data set, which utilizes multiple satellite altimetry missions and estimates discharge using the existing worldwide networks of national and international gauges. In SAEM, we have explored 47 000 gauges and estimated height-based discharge for 8730 of them, which is approximately 3 times the number of gauges of the largest existing remote-sensing-based data set. These gauges cover approximately 88 % of the total gauged discharge volume. The height-based discharge estimates in SAEM demonstrate a median Kling–Gupta efficiency (KGE) of 0.48, outperforming current global data sets. In addition to the river discharge time series, the SAEM data set comprises three more products, each contributing a unique facet to better usage of our data. (1) A catalog of virtual stations (VSs) is defined by certain predefined criteria. In addition to each station's coordinates, this catalog provides information on satellite altimetry missions, distance to the discharge gauge, and relevant quality flags. (2) The altimetric water level time series of those VSs are included, for which we ultimately obtained good-quality discharge data. These water level time series are sourced from both existing Level-3 water level time series and newly generated ones within this study. The Level-3 data are gathered from pre-existing data sets, including Hydroweb.Next (formerly Hydroweb), the Database of Hydrological Time Series of Inland Waters (DAHITI), the Global River Radar Altimetry Time Series (GRRATS), and HydroSat. (3) SAEM's third product is rating curves for the defined VSs, which map water level values into discharge values, derived using a nonparametric stochastic quantile mapping function approach. The SAEM data set can be used to improve hydrological models, inform water resource management, and address nonlinear water-related challenges under climate change. The SAEM data set is available from https://doi.org/10.18419/darus-4475 .Item Open Access Application of spaceborne geodetic sensors for hydrology(2013) Tourian, Mohammad J.; Sneeuw, Nico (Prof. Dr.-Ing.)How much freshwater do we have on land? How is the freshwater cycle changing with time? Actually, we can not properly answer these questions as our knowledge of the spatial and temporal dynamics of the hydrological cycle is limited. The lack of knowledge is mainly induced by shortage of observational evidence, which motivates the objective of this study: the monitoring of the hydrological cycle using spaceborne geodetic sensors. Among the current space geodetic sensors, GRACE and satellite altimetry are the two active mission concepts, that can capture part of the hydrological cycle. However, monitoring the hydrological cycle using these two sensors is challenging. Satellite altimetry is investigated as an independent spaceborne sensor that provides the water level and discharge time series. An algorithm is developed to improve the quality of water level time series over inland water surfaces. This algorithm particularly deals with the challenges of resolution and uncertainty of altimetry. The obtained altimetric water level time series is validated against in situ measurements showing about 10% improvement in accuracy of the time series. Moreover, this study proposes an algorithm to reduce the random noise from pre-retracked data. The algorithm combines the results of different retrackers and provides water level time series with reduced noise level. The validation shows a significant reduction of noise level and a clear improvement in correlation with in situ measurements. Moreover, this study proposes a statistical approach based on quantile functions to infer a functional relationship between altimetric water level and in situ river discharge without the need for synchronous data sets. This method is based on a scatter diagram of quantile functions, in which the probability-coordinate is eliminated. In contrast, the conventional methods for simultaneous measurements operate directly on time series and eliminate the time-coordinate. The results show that the proposed methodology provides the same range of error as the common conventional empirical method. The good performance of the statistical approach supports the usage of altimetry to salvage pre-satellite altimetry discharge data and turn them into active use for the satellite altimetry time frame. In addition, a stochastic process model is implemented to (i) deal with the data outages in altimetric discharge, (ii) provide a scheme for data assimilation and (iii) smooth the discharge estimation. The model benefits from the cyclostationary behaviour of the discharge and is combined with the estimated discharge from altimetry and available in situ measurements to form a linear dynamic system. The dynamic system is solved using the Kalman filter, that provides an unbiased discharge with minimum variance. The error level of the results is comparable to the empirical approach. In this study, the utility of GRACE data as sensor of hydrological water storage changes is shown to be limited by the following challenges: consistency, resolution, separability and uncertainty. The challenge of inconsistency is addressed by developing two filters for hydrological and hydro-meteorological water storage changes, which lead to a better correlation with GRACE mass storage changes. The challenges of separability and resolution are not specifically investigated in this study, yet their consequences, which appear in different forms of uncertainties is investigated. To deal with the GRACE uncertainties, an algorithm is developed to detect outliers in monthly solutions. The outliers have been corrected by replacing them by an inter-annual monthly mean of the respective month. The results conclude that outlier identification and correction must be performed before further assimilation of GRACE products into hydrological or hydro-meteorological analysis. Further, a longrange correlation has been identified as another source of uncertainty in GRACE monthly solutions. EOF analysis is employed to identify the zonal behaviour of the GRACE C20 errors as the responsible source for the long-range correlation. It is considered as an error source because its residual contains tidal aliasing errors instead of white noise. Therefore, to reduce the uncertainties in GRACE monthly solutions, tidal aliasing errors are also investigated. Primary and secondary tidal aliasing errors of main tidal constituents, S1, S2, P1, K1, K2, M2, O2, O1 and Q1 are identified in GRACE monthly solutions. The effect of tidal aliasing error is estimated using a least squares Fourier analysis indicating errors up to 22mm over the globe. In general, after dealing with GRACE’s challenges and achieving a data set without outliers, long-range correlation and tidal aliasing errors, the noise level of GRACE is quantified. The quantification shows a variation between 2–20mm/month over different parts of the globe, with higher values over tropical and boreal regions. The results specifically confirm that small catchments in the tropics contain more noise contamination. It is also shown that a lower noise level of a catchment does not necessarily lead to a better correlation of GRACE with hydro-meteorological signal. Finally, the joint performance of spaceborne geodetic sensors for estimating the actual evapotranspiration ETa is assessed. There, two approaches are introduced to estimate ETa using the results of GRACE and satellite altimetry. The results of both approaches are compared with different models and their ensemble mean. All in all, given the obtained relative discrepancy, the methods seem to be a viable way for determining ETa for most non-desert catchments containing hot and warm summers.Item Open Access Remote sensing-based extension of GRDC discharge time series : a monthly product with uncertainty estimates(2024) Elmi, Omid; Tourian, Mohammad J.; Saemian, Peyman; Sneeuw, NicoThe Global Runoff Data Center (GRDC) data set has faced a decline in the number of active gauges since the 1980s, leaving only 14% of gauges active as of 2020. We develop the Remote Sensing-based Extension for the GRDC (RSEG) data set that can ingest legacy gauge discharge and remote sensing observations. We employ a stochastic nonparametric mapping algorithm to extend the monthly discharge time series for inactive GRDC stations, benefiting from satellite imagery- and altimetry-derived river width and water height observations. After a rigorous quality assessment of our estimated discharge, involving statistical validation, tests and visual inspection, results in the extension of discharge records for 3377 out of 6015 GRDC stations. The quality of discharge estimates for the rivers with a large or medium mean discharge is quite satisfactory (average KGE value > 0.5) however for river reaches with a low mean discharge the average KGE value drops to 0.33.The RSEG data set regains monitoring capability for 83% of total river discharge measured by GRDC stations, equivalent to 7895 km 3 /month.Item Open Access A machine learning approach for total water storage anomaly eXtension back to 1980 (ML-TWiX)(2026) Saemian, Peyman; Tourian, Mohammad J.; Douch, Karim; Foster, James; Gou, Junyang; Wiese, David; AghaKouchak, Amir; Sneeuw, NicoWe present ML-TWiX, a global dataset of monthly total water storage anomalies (TWSA) reconstructed from 1980 to 2012, provided on a 0.5 ° × 0.5 ° global grid. While the GRACE and GRACE Follow-On satellite missions have provided valuable observations of global TWSA, their combined record spans just over two decades, limiting their utility for long-term climate and hydrological studies. ML-TWiX extends the GRACE-era record into the pre-GRACE period by learning from global hydrological and land surface model simulations using an ensemble of three machine learning models: Random Forest, XGBoost, and Gaussian Process Regression. The three machine learning models were independently used to reconstruct TWSA, and their outputs were subsequently combined through ensemble averaging to produce a unified product with spatially explicit uncertainty estimates. We validated ML-TWiX against multiple independent datasets, including satellite laser ranging, storage deduced from the water mass balance closure, and global mean sea level budget estimates. It provides a continuous reconstruction of global TWSA, enabling a wide range of applications in hydrology, climate science, and water resource assessment.Item Open Access A probabilistic approach to characterizing drought using satellite gravimetry(2024) Saemian, Peyman; Tourian, Mohammad J.; Elmi, Omid; Sneeuw, Nico; AghaKouchak, AmirIn the recent past, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor GRACE Follow‐On (GRACE‐FO), have become invaluable tools for characterizing drought through measurements of Total Water Storage Anomaly (TWSA). However, the existing approaches have often overlooked the uncertainties in TWSA that stem from GRACE orbit configuration, background models, and intrinsic data errors. Here we introduce a fresh view on this problem which incorporates the uncertainties in the data: the Probabilistic Storage‐based Drought Index (PSDI). Our method leverages Monte Carlo simulations to yield realistic realizations for the stochastic process of the TWSA time series. These realizations depict a range of plausible drought scenarios that later on are used to characterize drought. This approach provides probability for each drought category instead of selecting a single final category at each epoch. We have compared PSDI with the deterministic approach (Storage‐based Drought Index, SDI) over major global basins. Our results show that the deterministic approach often leans toward an overestimation of storage‐based drought severity. Furthermore, we scrutinize the performance of PSDI across diverse hydrologic events, spanning continents from the United States to Europe, the Middle East, Southern Africa, South America, and Australia. In each case, PSDI emerges as a reliable indicator for characterizing drought conditions, providing a more comprehensive perspective than conventional deterministic indices. In contrast to the common deterministic view, our probabilistic approach provides a more realistic characterization of the TWS drought, making it more suited for adaptive strategies and realistic risk management.Item Open Access A Kalman filter approach for estimating daily discharge using space‐based discharge estimates(2025) Ke, Siqi; Tourian, Mohammad J.; Sneeuw, Nico; Frasson, Renato Prata de Moraes; Paiva, Rodrigo C. D.; Durand, Michael; Gleason, Colin; Elmi, Omid; Malaterre, Pierre‐Olivier; David, CédricThe SWOT satellite mission is the first to conduct a global survey of the Earth's surface waters, measuring water surface height, river width, and water surface slope, based on which river discharge is estimated. At mid‐latitudes, the repeat orbit design of SWOT only allows a sampling of twice per repeat cycle, which is considered too low for most hydrological applications. To address the spatiotemporal limitations of SWOT, we develop a method that assimilates SWOT observations across continuous reaches within a single‐branch river network to obtain daily discharge estimates. Our model‐free assimilation method provides a linear dynamic system that includes a process model based on a physically based spatiotemporal discharge correlation model and observation equations utilizing SWOT products. We solve this dynamic system using a simple Kalman filter in the time domain, assimilating SWOT observations and incorporating the physically based prior to estimate daily discharge. Since SWOT discharge products were not yet available during the period of this research, we used synthetic SWOT data sets, introducing random and systematic errors through Monte Carlo simulation. The validation of the estimated discharge against true discharge over all test cases leads to a median correlation as high as 0.95, a median NSE for residuals (mean‐removed discharge) as high as 0.81, and a median relative bias as low as 5%, respectively. These promising results suggest that daily discharge for continuous reaches in a river network can be obtained through our data assimilation framework.