# Satellite Requirements to Capture Water Propagation in Earth's Rivers

A. Cerbelaud, C H David, T. Pavelsky, S. Biancamaria, Pierre-André Garambois, C. Kittel, F. Papa, P. Bates, M J Tourian, M. Durand, et al.

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To cite this version:

A. Cerbelaud, C H David, T. Pavelsky, S. Biancamaria, Pierre-André Garambois, et al., Satellite Requirements to Capture Water Propagation in Earth's Rivers. Reviews of Geophysics, In press, 63 (3), 10.1029/2024rg000871. http://hal-05213277

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Distributed under a Creative Commons Attribution 4.0 International License
# Reviews of Geophysics®

## REVIEW ARTICLE

10.1029/2024RG000871

**Key Points:**

* Recent efforts regarding the type of remote sensing observations that could enhance representation of river dynamics are reviewed

* Priorities are to resolve narrow rivers, optimize signals, ensure daily revisit time, leverage data assimilation and measure water velocity

* We recommend a hydrology-focused system with a cutting-edge satellite for advanced data and a cost-effective constellation for dynamics

**Correspondence to:**

A. Cerbelaud and C. H. David,
arnaud.cerbelaud@jpl.nasa.gov;
cedric.david@jpl.nasa.gov

**Citation:**

Cerbelaud, A., David, C. H., Pavelsky, T., Biancamaria, S., Garambois, P.-A., Kittel, C., et al. (2025). Satellite requirements to capture water propagation in Earth's rivers. *Reviews of Geophysics*, 63, e2024RG000871. https://doi.org/10.1029/2024RG000871

Received 20 DEC 2024
Accepted 30 JUN 2025

**Author Contributions:**

**Conceptualization:** A. Cerbelaud, C. H. David, T. Pavelsky, S. Biancamaria, P.-A. Garambois, C. Kittel, F. Papa, P. Bates, M. J. Tourian, M. Durand, R. Frasson, H. Oubanas, M. Simard, G. Schumann, G. Allen, A. Tarpanelli, S. Wongchuig, J. Wade, M. Tom, P.-O. Malaterre, C. J. Gleason, J.-F. Crétaux, K. Nielsen, O. Elmi, C. Schwatke, D. Scherer, M. Bonnema, K. Verma, K. Andreadis, N. Picot, J. Benveniste

**Data curation:** A. Cerbelaud

**Formal analysis:** A. Cerbelaud, C. H. David, J. Benveniste

**Funding acquisition:** C. H. David, S. Biancamaria

© 2025. Jet Propulsion Laboratory, California Institute of Technology and The Author(s). Government sponsorship acknowledged.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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## Satellite Requirements to Capture Water Propagation in Earth's Rivers

A. Cerbelaud¹ ID, C. H. David¹ ID, T. Pavelsky² ID, S. Biancamaria³ ID, P.-A. Garambois⁴ ID, C. Kittel⁵ ID, F. Papa³ ID, P. Bates⁶ ID, M. J. Tourian⁷ ID, M. Durand⁸ ID, R. Frasson¹ ID, H. Oubanas⁹ ID, M. Simard¹ ID, G. Schumann⁶,¹⁰ ID, G. Allen¹¹ ID, A. Tarpanelli¹², S. Wongchuig³ ID, J. Wade¹, M. Tom¹ ID, P.-O. Malaterre⁹ ID, C. J. Gleason¹³ ID, J.-F. Crétaux³,¹⁴, K. Nielsen¹⁵, O. Elmi⁷, C. Schwatke¹⁶ ID, D. Scherer¹⁶ ID, L. Gal¹⁷, A. Paris¹⁷ ID, A. Dasgupta¹⁸, S. Munier¹⁹ ID, D. Feng²⁰ ID, P. Bauer-Gottwein²¹,²² ID, F. Hossain²³ ID, D. Yamazaki²⁴ ID, B. Kitambo²⁵,²⁶ ID, S. Mischel²⁷, M. Bonnema¹, K. Verma¹⁹ ID, K. Andreadis¹³ ID, N. Picot¹⁴ ID, and J. Benveniste²⁸

¹Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA, ²Department of Earth, Marine and Environmental Sciences, University of North Carolina, Chapel Hill, NC, USA, ³Université de Toulouse, LEGOS (CNES/CNRS/IRD/UT3), Toulouse, France, ⁴INRAE, RECOVER, Aix-Marseille University, Aix-en-Provence, France, ⁵DHI A/S, Hørsholm, Denmark, ⁶School of Geographical Sciences, University of Bristol, Bristol, UK, ⁷Institute of Geodesy, University of Stuttgart, Stuttgart, Germany, ⁸School of Earth Sciences, and Byrd Polar and Climate Research Center, The Ohio State University, Columbus, OH, USA, ⁹UMR G-EAU - AgroParisTech, BRGM, Cirad, IRD, INRAE, Institut Agro Montpellier, Univ Montpellier, Montpellier, France, ¹⁰RSS-Hydro, Kayl, Luxembourg, ¹¹Virginia Polytechnic Institute and State University, Blacksburg, VA, USA, ¹²CNR/IRPI, Perugia, Italy, ¹³University of Massachusetts, Amherst, MA, USA, ¹⁴CNES, Toulouse, France, ¹⁵Department of Space Research and Technology, Technical University of Denmark, Lyngby, Denmark, ¹⁶Deutsches Geodätisches Forschungsinstitut der Technischen Universität München (DGFI-TUM), Munich, Germany, ¹⁷Hydro-Matters, Toulouse, France, ¹⁸Institute for Hydraulic Engineering and Water Resource Management, RWTH Aachen University, Aachen, Germany, ¹⁹CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France, ²⁰College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH, USA, ²¹Technical University of Denmark, Kongens Lyngby, Denmark, ²²University of Copenhagen, Copenhagen, Denmark, ²³Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA, ²⁴Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan, ²⁵Congo Basin Water Resources Research Center (CRREBaC), Lubumbashi, Democratic Republic of the Congo, ²⁶Faculty of Sciences, Department of Geology, University of Lubumbashi (UNILU), Lubumbashi, Democratic Republic of the Congo, ²⁷Global Runoff Data Centre (GRDC), Federal Institute of Hydrology (BfG), Koblenz, Germany, ²⁸COSPAR, Paris, France

**Abstract** The water in Earth's rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving accurate knowledge of surface water storage and fluxes to support water resources management and water-related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space-time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model-data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50–100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1–3 days observation intervals, (d) leveraging data assimilation and multi-satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology-focused, multi-mission observing system comprising: (a) a cutting-edge single or dual-satellite mission for advanced surface water measurements, and (b) a constellation of cost-effective satellites targeting dynamic processes.

**Plain Language Summary** Understanding how water moves through Earth's rivers is essential for managing water resources and mitigating water-related disasters. Limited on-the-ground data make satellite remote sensing a vital tool, but the specific capabilities such as the type, frequency and scale of observation, the spatial resolution, and the measurement accuracy needed to capture river dynamics remain uncertain. We
**Investigation:** A. Cerbelaud, C. H. David, T. Pavelsky, S. Biancamaria, P.-A. Garambois, C. Kittel, F. Papa, P. Bates, M. J. Tourian, M. Durand, R. Frasson, H. Oubanas, M. Simard, G. Schumann, G. Allen, A. Tarpanelli, S. Wongchuig, J. Wade, M. Tom, P.-O. Malaterre, C. J. Gleason, J.-F. Crétaux, K. Nielsen, O. Elmi, C. Schwatke, D. Scherer, L. Gal, A. Paris, A. Dasgupta, S. Munier, D. Feng, P. Bauer-Gottwein, F. Hossain, D. Yamazaki, B. Kitambo, S. Mischel, J. Benveniste

**Methodology:** A. Cerbelaud, C. H. David

**Project administration:** C. H. David

**Resources:** A. Cerbelaud, C. H. David

**Software:** A. Cerbelaud

**Supervision:** C. H. David

**Validation:** A. Cerbelaud

**Visualization:** A. Cerbelaud, C. H. David, F. Papa

**Writing – original draft:** A. Cerbelaud, C. H. David, T. Pavelsky, S. Biancamaria, P.-A. Garambois, C. Kittel, F. Papa, P. Bates, M. J. Tourian, M. Durand, R. Frasson, H. Oubanas, M. Simard, G. Schumann, G. Allen, A. Tarpanelli, S. Wongchuig, J. Wade, M. Tom, P.-O. Malaterre, C. J. Gleason, J.-F. Crétaux, K. Nielsen, O. Elmi, C. Schwatke, D. Scherer, L. Gal, A. Paris, A. Dasgupta, S. Munier, D. Feng, P. Bauer-Gottwein, F. Hossain, D. Yamazaki, B. Kitambo, S. Mischel, J. Benveniste

**Writing – review & editing:**

A. Cerbelaud, C. H. David

highlight recent advances in using remote sensing to study the propagation of water in rivers and identify key priorities for progress. These priorities include resolving narrow rivers (finer than 50–100 m in width), gaining a better knowledge of required measurement accuracy relative to hydrologic variability, achieving frequent observations (1–3 days), and leveraging more data assimilation and multi-satellite approaches. Innovative satellite technologies are also needed to measure variables critical for estimating water flux and discharge such as water velocity. To address these challenges, the recommended approach involves a dual observing system with (a) a cutting-edge satellite mission for advanced surface water measurements and (b) a constellation of cost-effective satellites to monitor dynamic river processes. This combination has the potential to transform the monitoring and modeling of water propagation in rivers, providing the tools necessary to improve water management and disaster response on a global scale.

# 1. Introduction

Monitoring rivers is increasingly vital in the Anthropocene (Gudmundsson et al., 2021) as climate change is modifying the stability of the water cycle globally (Milly et al., 2008), exacerbating weather extremes (Blöschl, Hall, et al., 2019; Intergovernmental Panel on Climate Change (IPCC), 2023; Perkins-Kirkpatrick et al., 2024) with contrasting effects regionally (Vörösmarty et al., 2000, 2010). Global river systems face multiple urgent environmental and societal challenges. River flooding is expected to increase with the changing climate, leading to devastating damages to human life and property (Alifu et al., 2022; Milly et al., 2002). Water resources and river biodiversity are threatened by human population growth and global environmental change (Vorosmarty et al., 2010). The management of transboundary basins is challenged by historical, legal, and cultural differences among neighbor nations (UNEP, 2016) or regions (Anilkumar et al., 2024). Nutrient exports through rivers are primarily responsible for dead zones in the coastal oceans (Diaz & Rosenberg, 2008). River deltas are increasingly vulnerable to coastal hazards (Zhu et al., 2024) as declining sediment supply, storm surges and climate change alter their sediment budget (Hou et al., 2024; Nienhuis et al., 2020).

From a community process in 2018, Blöschl, Bierkens, et al. (2019) identified 23 unsolved problems in hydrology, mostly focused on the process-based understanding of hydrological variability and causality at all space and time scales. Problems associated with river systems emphasized their response to climate and environmental change, spatial variability, and human interactions, highlighting the dynamics of runoff and extreme events and the need for improved data collection, modeling, and communication. Satellite remote sensing, especially hydrogeodesy, shows high potential to help solve some of these 23 unsolved questions (Jaramillo et al., 2024). Question 16 reads: “How can we use innovative technologies to measure surface and subsurface properties, states and fluxes at a range of spatial and temporal scales?” Indeed, a major challenge in river science is bridging the scale gap (Figure 1): field instruments provide detailed, continuous geometric and hydraulic information at the cross-section level, even for small streams, but lack scalability potential, while airborne and satellite remote sensing can offer broad observations of flow propagation and change in large rivers at the reach, basin and continental scale but mostly misses fine-scale dynamics. Although large rivers receive much of the attention, small, headwater streams, which comprise 70%–80% of river network length, play vital ecological and hydrological roles but are often under-monitored, poorly delineated, and inadequately protected (Wohl, 2017). Accurate and frequent observations of these small water bodies at broad spatial scales is essential for protecting biodiversity and sustaining ecosystem services vital to human well-being (Ferreira et al., 2023).

Blöschl, Bierkens, et al. (2019) also highlighted the need to interface these scientific questions with society. Enhancing our understanding of water propagation in rivers at the global scale serves invaluable benefits across two key societal domains: (a) Water Resources Management (WRM) and (b) Flood Forecasting and Mitigation (FFM). WRM deals primarily with ensuring equitable access to clean water for various purposes such as consumption, sanitation, agriculture, transportation, industrial use, ecosystem services, environmental preservation, and energy generation, and thus includes the critical monitoring of droughts to anticipate water scarcity challenges (Mekonnen and Hoekstra, 2016). FFM approaches seek to minimize damage and safeguard lives and infrastructure during extreme weather events, as well as allow for damage assessment for insurance claims and improved preparedness for future events. While WRM mostly relates to the low-frequency variability of river systems (i.e., slow changes), involving the comparison between present-time conditions and long-term statistics (e.g., mean, standard deviation and quantiles of streamflow), FFM relies on the analysis of high-frequency
<figure><img src="image_4.png" /><figcaption>**Figure 1.** River networks across scales, examples of linked measurement systems, and their articulation with some of the major challenges inspired by the unsolved problems in hydrology identified in Blöschl, Bierkens, et al. (2019). Satellite and airborne swath shapes are illustrative, and swath colors are arbitrary. Basemaps are courtesy of the USGS/NASA, Copernicus, Maxar Technologies, and Airbus © 2025.</figcaption></figure>

dynamics (i.e., rapid changes) with the definition of annual exceedance probabilities, recurrence intervals or the detection of peak flow events (often defined with regards to high quantiles of discharge) and the estimation of their magnitude, extent and duration. However, since high-flow events can expand beyond the streambed by overtopping riverbanks, the monitoring of river discharge alone is sometimes insufficient to fully characterize them. WRM and FFM are vast research topics characterized by ongoing efforts to expand data availability, enhance the accuracy of methodologies, and improve the efficacy of adaptation interventions (Speight et al., 2021; Tellman et al., 2021). For instance, Speight et al. (2021) recently highlighted the data needs for surface water flood forecasting and stressed the value of diverse sources, including field, airborne, remote sensing, and crowdsourced data. While such observations have effectively supported flood and water resource forecasting in regions like Europe, they still remain too limited and/or more difficult to obtain in tropical and subtropical areas of South America, Asia and Africa. In some of these regions, sparse gauging networks and remote sensing constraints, such as persistent cloud cover and dense vegetation, continue to pose challenges, despite recent progress (Papa et al., 2023).

Water propagation in rivers refers to the movement and distribution of water across open channels, driven by processes such as precipitation, infiltration, runoff, and groundwater flow, and influenced by factors like topography, river bathymetry, channel bed friction and hydraulic structures (e.g., reservoir operations). Even though the amount of freshwater stored in the world's rivers (~2 × 10³ km³) only represents 0.01% of all liquid freshwater on Earth (~24,000 × 10³ km³), which itself accounts for 1.5% of all of Earth's water (~1,400,000 × 10³ km³), the transport of water from rivers to the ocean (~45.5 × 10³ km³·y⁻¹) represents the largest liquid water flow out of the continental balance (Oki and Kanae, 2006).

To gain a detailed understanding of the propagation of water across space and time in global hydrographic networks, key remotely observable variables to measure include river width (*W*), water surface elevation (*H*),
slope (S), and velocity (u), all of which are closely linked to river discharge (Q, also termed streamflow, or water flow). The frequent monitoring of these variables provides critical insights for water management and related services, including flood protection, navigation, and energy production (Dobriyal et al., 2017). Integrating all upstream water cycle dynamics into one variable (Dai & Trenberth, 2002), river discharge is one of four main components of the continental water balance equation (Dorigo et al., 2021; Milly et al., 2005), and serves as the ultimate indicator of water propagation in space and time.

River discharge is usually determined in situ with a current meter or an Acoustic Doppler Current Profiler by measuring channel cross-sectional area and water velocity profiles inferred from doppler shift using ultrasonic waves (Mueller et al., 2013). However, flow velocity profiles are rarely measured continuously in situ per se and surface velocity profiles (extracted from flow videos using tracking techniques) only provide partial information (Le Coz et al., 2010). Instead, discharge is traditionally estimated at gauging stations using height (also termed stage) measurements from limnimetric scales or pressure transducers at locations where channel cross-section is known/measured. A rating curve, also known as a stage-discharge ($H \rightarrow Q$) relationship (Equation 1), is typically established through multiple simultaneous height-discharge measurements over time, using a typical power-law formula:

$$Q = aD^b \qquad (1)$$

Where a and b are parameters to be estimated. D refers to the water depth and is equal to $H - Z_0$. Water surface height H can be estimated at a gauge or by remote sensors, and $Z_0$ is defined as the elevation of zero-flow (from a reference that depends on the measuring instrument). Rating curves are affected by known hysteresis effects and regularly need to be recalibrated due to changes in the streambed (e.g., erosion, deposition, growth of submerged vegetation). In addition, they can fail to extrapolate flow conditions outside the range of calibration due to their low frequency of measurement and to extreme conditions (Le Coz et al., 2014). In backwater affected reaches, where the energy slope of the flow is variable, stage-fall-discharge rating curves are more relevant (Malou et al., 2021; Mansanarez et al., 2016; Paris et al., 2016).

Similarly, discharge Q can be approximated at a given reach from river width (W, see figure 2.1 from Brisset et al. (2018) for a representation of a river cross-section), instead of height, where H exhibits smaller variations than W, for instance with similar power-law $W \rightarrow Q$ relationships (Equation 2; Gleason et al., 2014; Leopold and Maddock, 1953; Moody and Troutman, 2002) such as:

$$Q = \alpha W^{\beta} \qquad (2)$$

Where $\alpha$ and $\beta$ are parameters to be estimated. These functional scaling laws Equations 1 and 2 are often referred to as hydraulic geometry relationships (Leopold and Maddock, 1953). In practice, the coefficients a and $\alpha$ and exponents b and $\beta$ are estimated using repeated field measurements at a single river cross-section (called at-a-station hydraulic geometry). These parameters are also related through relationships between observations at a fixed quantile of discharge (called downstream hydraulic geometry) or at all quantiles of discharge (called at-many-stations hydraulic geometry) between cross-sections from many distributed locations along a river (see Gleason and Smith, 2014). When both H and W are measurable jointly, and provided that the water surface slope S can be reasonably approximated along with assumptions about the channel bed's roughness coefficient (n) and morphology/bathymetry, empirical formulas like Manning's equation (among other possible parameterizations) or physics-based relationships such as the 1D Saint-Venant equations can be used to estimate discharge in open channels (Barré de Saint Venant, 1871; Manning, 1891). The so-called low Froude Manning Strickler model involving water surface slope S (cf. Garambois and Monnier, 2015) simplifies Manning's equation for a wide rectangular river cross-sectional area $A \sim W * D$ where $W \gg D$, into:

$$Q \sim VA \sim 1/n WD^{5/3}S^{1/2} \qquad (3)$$

Equation 3 describes the approximation of the cross-sectional average water velocity V (which can be calculated from surface velocity and assumptions on the velocity profile shape; Smart and Biggs, 2020) from W, D, S, and n. Surface water velocity u is therefore also considered a key river observable, helping to constrain the estimation of discharge (Masafu et al., 2023).
Although water level is the only river-related variable that is measured both frequently and accurately by in situ sensors (Fekete et al., 2015)—having played a significant historical role in hydrologic science—global gauge height measurements remain severely limited and their spatiotemporal coverage has declined in recent years, with placement bias toward large rivers and developed regions (Hannah et al., 2011; Krabbenhoft et al., 2022; Ruhi et al., 2018). Despite significant international efforts, the availability of river observations for research and decision-making on a global scale has been further constrained by limitations in data sharing across political boundaries (Global Runoff Data Center GRDC, 2024). At the present time, a considerable number of watersheds remain ungauged, and even gauged river systems sometimes fail to observe extreme flows when instruments break and/or are washed away when hit by flood waves and debris.

The hydraulic visibility yielded by single or multi-satellite measurements, that is, “the potential to depict a hy-
drological response and surface hydraulic variabilities within a river section or network via remote sensing”
(Garambois et al., 2017; Rodríguez et al., 2020), can provide valuable information for estimating discharge from
river surface observables. A range of remote sensing techniques, including optical and Synthetic Aperture Radar
(SAR), often in conjunction with digital elevation models (DEM), as well as altimetry instruments, have been
developed to observe surface water extent (e.g., providing river width W) and water surface elevation WSE (H,
which can also lead to water surface slope S) of global rivers from satellites. These measurements allow the
estimation of discharge using algorithms (referred to as RSQ algorithms) mainly based on or inspired by
Equations 1–3, with more or fewer parameters (Alsdorf et al., 2007; Bjerklie et al., 2005; Durand et al., 2023;
Gleason and Durand, 2020; Gleason and Wang, 2015; Kouraev et al., 2004; Lin et al., 2023; Malou et al., 2021;
Papa et al., 2012a; Paris et al., 2016). Other RSQ techniques involve more comprehensive full 1D Saint-Venant
hydraulic models to invert model parameters from H, W and/or S measurements (e.g., HiVDI, Larnier et al., 2021;
or SIC4DVar, Oubanas et al., 2018a). Nevertheless, dynamic bathymetry-friction parameters, which are unob-
servable from space, underlie all discharge laws and need to be calibrated (Garambois et al., 2020; Garambois and
Monnier, 2015; Gejadze and Malaterre, 2017; Larnier et al., 2021). Other methods using signal ratios (e.g.,
calibration/measurement ratio) based on brightness temperature from passive microwave radiometers or near-
infrared reflectance from optical sensors between selected river locations and nearby footprints not affected by
surface water have also been described as proxies of river discharge (Brakenridge et al., 2005, 2007, 2023; Papa
et al., 2007, 2008; Tarpanelli et al., 2013b; Yoon et al., 2022). These ratios are strongly correlated with water
propagation in rivers (from surface area expansion and/or sediment load changes with discharge; Filippucci
et al., 2022). A key aspect of discharge estimation from satellite hydraulic visibility lies in the spatiotemporal
sparsity of observations relative to the scale and characteristic time of the river as studied using identifiability
maps by Brisset et al. (2018) and Pujol et al. (2020). As a result, selecting the appropriate satellite systems
(considering, e.g., orbit, swath width, sensor wavelength) is critical to ensure adequate observation coverage and
accuracy. Together, water extent, river width, slope, height, brightness temperature and reflectance ratios, and
their variability, all relate to river discharge, and follow patterns of space-time propagation of water that are
described by momentum and continuity equations such as the 1D Saint-Venant system noted above.

While satellite remote sensing can be seen as a valuable tool to complement in situ observations and modeling endeavors (Jaramillo et al., 2024) with implications for both WRM and FFM at the global scale, most recent efforts have relied on opportunistic signals (Bates et al., 2014), with river hydrology benefitting from instruments initially designed mostly to observe oceans or the land surface (e.g., Sentinel-1,-2,-3,-6, Landsat series, Jason-1,-2,-3, Soil Moisture Active Passive, and Soil Moisture and Ocean Salinity satellites). Developed from three decades of legacy in altimetry, the recent Surface Water and Ocean Topography (SWOT) mission is the first exception, with inland waters being one of two primary focuses of the mission alongside physical oceanography. Despite the urgent aforementioned challenges faced by global river systems and the launch of SWOT, it surprisingly remains unclear how often rivers should be monitored in both space and time, what channel size we need to be able to resolve, and what variables of interest from satellites (and with which precision) are best suited to capture the space-time propagation of water and the dynamics in water storage within rivers (Papa and Frappart, 2021).

The objective of this paper is therefore to break from an opportunistic perspective in the study of river system
dynamics using available observations and to instead shift towards an active definition of observational needs
with the aim of informing the design of global measurement systems. We provide a summary of past and ongoing
efforts designed to reveal the satellite “measurement requirements” that could lead to a more accurate under-
standing and representation of water propagation through Earth's rivers and to propose additional paths forward
towards this goal.
We begin by briefly summarizing the current remote sensing technologies and methodologies used to study Earth's rivers. Given the already extensive reviews of measurement in previous literature (Fassoni-Andrade et al., 2021; Gleason and Durand, 2020; Jaramillo et al., 2024; Smith, 1997; Tarpanelli et al., 2023), we specifically focus on the implications for observational needs such as retrieval accuracy, spatial scale and frequency of acquisition. Then, we assess the current capabilities in capturing global river dynamics from space and outline the measurement requirements, focusing on revisit period, spatial sampling, spatial resolution, latency time, and key variables of interest. Finally, from the current state of methodologies, their reported precision and the missions involved, we provide recommendations for future satellite systems and research in remote sensing to capture water propagation in Earth's rivers.

## 2. Current Technologies, Methodologies, and Precision for Satellite Remote Sensing of Rivers

In the last few decades, satellite data has increasingly been used to monitor rivers at the global scale (Alsdorf et al., 2007; Berry et al., 2005; Birkett, 1998; Bjerklie et al., 2003; Calmant et al., 2008; Chawla et al., 2020; Crétaux et al., 2015; Famiglietti et al., 2015; Lettenmaier et al., 2015; Smith, 1997; Wu et al., 2023). Prior to describing the state-of-knowledge in requirements to capture global river dynamics from space, a summary of current river hydraulic variables estimation and RSQ capabilities is necessary. Given that these capabilities are influenced by the scale of the rivers being observed, we define, for the purpose of this study, small/narrow rivers as 30–100 m wide (Czuba & Allen, 2023) and medium rivers as 100–500 m wide. This definition is close to the characterization by Meybeck (1996) used by Biancamaria et al. (2017) and, in accordance with Frasson, Pavelsky, et al. (2019), corresponds to mean discharges from ~10 to 100 m³/s for small/narrow rivers and ~100 to 2,000 m³/s for medium rivers. Section 2.1 discusses some of the instruments capable of deriving river hydraulic variables from space such as width, elevation, and slope, along with their accuracy (Table 1). Section 2.2 describes empirical algebraic flow laws used to relate remote sensing-derived river hydraulics to discharge as well as other approaches and instruments used to compute proxies of discharge from space, with their reported spatiotemporal capabilities and precision (Table 2). Methods based on the full 1D Saint-Venant equations are also briefly discussed. Approaches involving surface water velocity measurements from space are not yet as widely developed and available and are identified as next frontiers in Section 4. Figure 2 illustrates the main satellite instruments and approaches that have been used to observe rivers, monitor their changes and estimate discharge to capture water propagation.

### 2.1. Deriving River Hydraulics Variables From Space

#### 2.1.1. Surface Water Extent and River Width

Water surface areas can be measured using imaging sensors onboard satellites, thereby giving access to lake and river extents (inundated areas) and river top widths globally. By leveraging the contrast in reflectance between water and other land surfaces in the near and shortwave infrared, optical sensors allow the monitoring of water bodies using classification techniques (Huang et al., 2018a, 2018b; e.g., SurfWater, Peña-Luque et al., 2021; or NASA OPERA Dynamic Surface Water eXtent. products, 2023). The Modified Normalized Difference Water Index (Xu, 2006) is particularly efficient at identifying water extents and can be computed using past/recent high-resolution multispectral missions like Landsat or Sentinel-2. A combination of multiple water indices has been advised by Schwatke et al. (2019) to provide optimal classification accuracy in an innovative automated approach. Global surface water time series include the data set produced by Pickens et al. (2020), or the Global Surface Water data set (Pekel et al., 2016) developed by the European Commission's Joint Research Centre employing the entire archive of Landsat 5, 7, and 8 imagery over more than 30 years. Another example is the Global WaterPack database that uses 20 years of daily images from the Moderate Resolution Imaging Spectroradiometer (MODIS) (Klein et al., 2024). Elmi et al. (2016) and Elmi and Tourian (2023) demonstrated that using a region-based classification algorithm, while incorporating spatial and temporal correlations alongside pixel values, can significantly enhance the accuracy of river surface water extent extraction from satellite images. However, the substantial computational demands of this approach pose a significant challenge to its application on a global scale. Despite the great capabilities of optical imagers to classify water, they cannot acquire surface water reflectance through clouds or at night, nor in regions covered by dense vegetation such as the tropical forests of the Amazon or Congo. Synthetic aperture radars (SAR) overcome these limitations while also allowing for the detection of surface water extents (Dasgupta, Grimaldi, Ramsankaran, Pauwels, Walker, et al., 2018; Dasgupta, Grimaldi, Ramsankaran, Pauwels, Walker, et al., 2018; Giustarini et al., 2013; Hess et al., 2003; Twele et al., 2016).
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Schematic illustration of the main satellite instruments and approaches used to monitor river spatiotemporal dynamics from space. Satellite swath shapes are illustrative, and swath colors are arbitrary. Basemap is courtesy of the USGS/NASA and Copernicus © 2025.</figcaption></figure>

Surface water extent measurements can then allow the computation of river widths using river centerline algorithms (Marshak et al., 2020; Pavelsky and Smith, 2008). Uncertainties on the estimation of river widths depend mostly on the precision in water pixel classification (supervised or not), the instrument's spatial resolution, and the radiometric accuracy in the spectral bands of interest. For instance, the Global River Widths from Landsat (GRWL) width measurements feature a mean Root Mean Square Error (RMSE) of 25.2 m relative to in situ width measurements at mean discharge (Allen & Pavelsky, 2018). These measurements were limited to rivers wider than 30 m at Landsat's spatial resolution (and wider than 90 m to obtain reliable changes in width in Feng et al., 2022). Accuracy requirements for SWOT width estimates have been set to 15% for evaluated water body and river reaches (for a more detailed discussion on other SWOT measurements, see Section 2.1.2). The relative precision in width estimation naturally depends on the ratio between the instrument's spatial resolution and the targeted river's width.

Beyond determining river width, remote sensing imagers also serve the distinct purpose of mapping flood inundation extents for damage assessment in FFM (Dasgupta, Grimaldi, Ramsankaran, Pauwels, & Walker, 2018; Giustarini et al., 2013; Munasinghe et al., 2023; Zwele et al., 2016). Flood extent maps can then be integrated in hydraulic models for improved simulation capacity (e.g., Wood et al., 2016; as can stage data, e.g., Schumann et al., 2009) or used in hydrodynamic model calibration, assimilation and validation (e.g., DassFlow 2D, Hostache et al., 2010; TELEMAC-2D, Nguyen et al., 2022; or LISFLOOD-FP, Bates et al., 2010, 2004; Bates & De
<table><caption>**Table 1**
Main Remote Sensing Instruments Used to Derive River Hydraulics Variables From Space</caption><thead><tr><th>Technology</th><th>Swath/<br>Footprint<br>size</th><th>Pixel size/<br>Spatial<br>resolution</th><th>Spatial gap<br>between tracks</th><th>Revisit per orbit<br>period at the equator</th><th>Hydraulic variable</th><th>Minimum accurate<br>detectable size</th><th>Minimum<br>detectable<br>change</th><th>Challenges</th></tr></thead><tbody><tr><td>Optical imagery</td><td>25–<br>2,330 km</td><td>1–250 m</td><td>None</td><td>1–8 days</td><td>River Width (W)</td><td>3 pixels</td><td>~1 pixel</td><td>Water classification<br>(clouds and night<br>for optical)</td></tr><tr><td>SAR imagery</td><td>10–500 km</td><td>1–100 m</td><td>None</td><td>6–14 days</td><td>River Width (W)</td><td></td><td></td><td></td></tr><tr><td>Radar nadir altimetry<br>(LRM, SAR,<br>FFSAR)</td><td>10–20 km</td><td>0.5–<br>300 m × 10–<br>20 km</td><td>80–315 km</td><td>10–35 days</td><td>Water Surface<br>Elevation (WSE, H)</td><td>~200–1,000 m, not<br>clearly defined</td><td>~10–30 cm</td><td>WSE retracking,<br>zero-flow WSE</td></tr><tr><td>Laser nadir altimetry</td><td>13 m</td><td>0.7 m × 13 m</td><td>10–30 km</td><td>91 days</td><td>WSE (H), Slope (S)</td><td>~1 m</td><td>&lt;10 cm</td><td></td></tr><tr><td>Radar wide-swath<br>altimetry</td><td>120 km</td><td>Pixel<br>cloud (~10 m)<br>Node, reach<br>(200 m, 10 km)</td><td>None</td><td>~11 days</td><td>River Width (W),<br>WSE (H), Slope (S)</td><td>~50–100 m</td><td>&lt;10 cm</td><td>Water classification and<br>flow algorithms</td></tr></tbody></table>

Roo, 2000; Dasgupta et al., 2021, 2020; Neal et al., 2012). The first remote sensing-based operational global flood mapping and monitoring service based on Sentinel-1 SAR, the Global Flood Monitoring service integrated into the Global Flood Awareness System (GloFAS, Alfieri et al., 2020; Harrigan et al., 2023) within the Copernicus Emergency Management Service, has been a substantial step forward in FFM in recent years (Krullikowski et al., 2023). More information about complementary modeling approaches is given in Section 4.3.

### 2.1.2. Water Surface Elevation, River Height, and Slope

Radar nadir altimeters have been used for more than three decades to estimate the instantaneous elevation of the ocean surface below the vertical position of the satellite with an accuracy of a few centimeters (e.g., since the pioneering missions such as Seasat, TOPEX/Poseidon, ERS-1). Nadir altimeters are active systems using processing techniques referred to as conventional (i.e., Low Resolution Mode, LRM) and delay-Doppler (SAR) modes. Radar altimetry also makes it possible to measure the altitude of large inland bodies of water (Birkett, 1994, 1998; Birkett et al., 2002; Kitambo et al., 2022). Hydrologists have thus used them opportunistically to obtain elevations of rivers at virtual stations, that is, where the satellite ground tracks cross a water body (Calmant & Seyler, 2006; Coss et al., 2020; Roux et al., 2010). Radar echoes reflected by the surface inside a given footprint back to the altimeter can be processed using waveform retracking algorithms (e.g., Ice-1, Offset Centre of Gravity; Wingham et al., 1986) to access water surface elevation (WSE) $H = D + Z_0$ above the geoid, also termed water surface height (Biancamaria et al., 2017; Calmant et al., 2013; Cretaux et al., 2017; Zakharova et al., 2020). $Z_0$ is the zero-flow elevation parameter, that is, the elevation of the bottom of the riverbed above the geoid. Uncertainty in deriving WSE from nadir altimeters originates from multiple sources including atmospheric disturbance, environmental correction, size of the radar beam (footprint), acquisition and processing technique (e.g., LRM or unfocused/fully-focused SAR mode), on-board tracking system (e.g., DIODE/DEM or Open-Loop Tracking Command; Le Gac et al., 2021), range resolution, orbit determination, geometric configuration of the virtual station, complexity of the scene, and retracking algorithm. Current nadir altimetry missions with ~10–20 km radar beam diameters are estimated to provide WSE for rivers down to a few hundred meters in width, even though no fixed minimum width can truly be defined as signal processing depends on numerous local conditions and the capacity to interpret the return signal (Biancamaria et al., 2017; Fang et al., 2025). Among the currently active radar altimetry constellations, Sentinel-3 capabilities for river monitoring are described, amongst others, by Tarpanelli et al. (2021, 2020) and Kittel et al. (2021b). Good precision in WSE were reported in local studies for recent satellites, for example, RMSE ≤30 cm for both Jason-3 by Biancamaria et al. (2018) and Sentinel-3 by Kittel et al. (2021b) and Halicki and Niedzielski (2022). Additionally, when correcting for the river surface slope, which is important in small upstream reaches, WSE RMSE from these two missions was improved by ~2–6 cm on average (Halicki et al., 2023; Scherer et al., 2022). A median RMSE of ~50 cm was found in a global assessment of Sentinel-3 by Jiang et al. (2023). Validation for ENVISAT and Saral/AltiKa missions led to similar results, with RMSE of 40 and 25 cm respectively, for rivers ranging from 30 m to 4 km in width in the Congo River
Basin (Paris et al., 2022). It is worth noting that all these results were obtained using retrackers that were not
developed initially for observing rivers. Recent advances in “river-dedicated” retracking shall improve our ca-
cacity to estimate valuable time series of WSE, mostly for peaky signals, representative of small rivers (Fang
et al., 2025; Tourian et al., 2025). The most recent radar nadir altimetry mission, Sentinel-6 MF, benefits from a
Fully-Focused SAR (FFSAR) processing technique (first applied to CryoSat-2 data) that has recently been shown
to deliver improved and denser measurements of WSE with RMSE ≤10 cm (Domingo et al., 2025). Under certain
geometric conditions, longitudinal river profiles with 10 m along-track resolution were obtained over 10–20 km
transects using off-nadir information (Boy et al., 2023; Ehlers et al., 2025). However, unlike swath-based sensors,
nadir altimeters have limited spatial coverage in the across-track direction (no measurements in the spatial gap
between ground tracks) and have historically been limited to 10-day revisit periods at virtual stations, at best.

Despite an even longer repeat orbit of 369 days, which is a priori undesirable for river monitoring, CryoSat-2 with
its SAR Interferometric Radar Altimeter has also been leveraged to map river surface longitudinal profiles. An
average RMSE of 38 cm for water levels was reported against in situ observations on the Po river (Schneider
et al., 2018b). The high spatial coverage and sub-cycle period of 30 days resulting from CryoSat-2 geodetic orbit
have shown potential for water propagation monitoring during peak flows in the Himalayas (Villadsen
et al., 2015; see Section 4.3 for more on CryoSat-2 in the context of modeling). In addition to radar altimeters,
laser altimeters have also been used to measure WSE of inland water bodies. With much higher across- and along-
track resolution than radar altimeters, a new contribution from ICESat-2 (0.7 m) is the estimation of fine-scale
cross-sectional geometry using accurate elevation transects. Despite the low revisit frequency of these systems
(91 days for ICESat-2), the dense ground sampling pattern (~30 km between reference ground tracks) has been
leveraged to parameterize basin-scale hydraulic models, that is, to calibrate unobserved hydraulic parameters, for
continental-scale rivers (Bauer-Gottwein et al., 2023; Coppo Frias et al., 2023; Musaeus et al., 2024).

The ongoing SWOT mission benefits from a long legacy of radar nadir altimetry and interferometry (Alsdorf
et al., 2000, 2007; Alsdorf & Lettenmaier, 2003; Farr et al., 2007; Fu et al., 2024; Rosen et al., 2000) and is the
first wide-swath radar altimetry mission that was specifically designed to observe inland water surfaces in
addition to oceans. The Ka-band Radar Interferometer (KaRIn) of SWOT allows for unprecedented spatial
coverage through 2-dimensional radar images encompassing two 50 km swaths compared to traditional nadir-
pointing 1-dimensional radar traces in previous missions (Peral et al., 2024). Wide-swath radar interferometry
gives access to river width W, water surface elevation H and slope S jointly (Biancamaria et al., 2016). Addi-
tionally, the triangulation of radar echoes provides a much finer spatial resolution (resolving rivers down to
50–100 m wide or even less, to be confirmed by ongoing evaluation efforts) and a better control of uncertainty
over the measurements. This allows for SWOT accuracy requirements of 10 cm for WSE and 1.7 cm/km for slope
when averaged over ~1 km² (Desai, 2018). Preliminary results suggest that these requirements are being met (Fu
et al., 2024; Kica et al., 2025). Beyond SWOT, water surface slope has also been estimated for decades from other
sources including DEMs (Cohen et al., 2018; LeFavour and Alsdorf, 2005) assuming terrain slope is equal to
water surface slope, multi-mission altimetry (Schwatke et al., 2024) and spaceborne lidar (e.g., ICESat-2; Scherer
et al., 2023). Water surface slope also plays a crucial part in discharge estimation (through water velocity) since Q
can greatly differ for a given (H, W) pair with varying S values (see Equation 3 modified Manning's equation)
such as in rising or declining discharge conditions (stage-discharge hysteresis).

WSE data from past and ongoing altimetry missions have been processed by multiple organizations, including but
not limited to the National and Aeronautics Space Administration (NASA), the European Space Agency (ESA),
the French Space Agency (CNES), Universität Stuttgart and Deutsches Geodätisches Forschungsinstitut der
Technischen Universität München (DGFI-TUM), and have been made available to the public through platforms
like River&Lake (Berry et al., 2005), NASA's Physical Oceanography Distributed Active Archive Center (PO.
DAAC, 2024), Hydroweb and then Hydroweb.next (Crétaux et al., 2011; Gasnier et al., 2023; Santos da Silva
et al., 2010), HydroSat (Tourian et al., 2022) and DAHITI (Schwatke et al., 2015).

A summary of some of the instrument technologies used to derive river hydraulic variables from existing satellite
missions such as width, elevation, and slope is given in Table 1.

## 2.2. Approximation of Discharge From Space-Derived River Hydraulic Quantities or Direct Signals

River discharge depends non-linearly on river surface hydraulic quantities that are observable from space, and can
be estimated using empirical algebraic flow laws with more or fewer parameters to be estimated. Two main types
<table><caption>**Table 2**
Main Remote Sensing of Discharge Methods, Their Scalability and Associated Challenges</caption><thead><tr><th>Remote sensing of discharge (RSQ) method</th><th>Notional normalized or relative root mean square error (RMSE)</th><th>Scalability</th><th>Challenges</th></tr></thead><tbody><tr><td>W → Q</td><td>~10%–50%</td><td>Where discharge estimates are available (i.e., in gauged or well simulated basins)</td><td>Requires first guess/time series of discharge estimates for calibration (e.g., in situ)</td></tr><tr><td>H → Q</td><td>~10%–30%</td><td>Potentially global</td><td>Solving ill-posed hydrodynamic system</td></tr><tr><td>(W,H,S) → Q</td><td>~10%–25%</td><td>Where ratio between water extent and image resolution is appropriate</td><td>Requires careful selection of satellite gauging station</td></tr><tr><td>C/M → Q</td><td>~20%–50%</td><td></td><td></td></tr></tbody></table>

Note. Some methods require calibration against discharge estimates, while others solve the McFLI problem (Gleason et al., 2017). The latter approaches are more complex to implement and often exhibit lower accuracies, but offer the potential for broader applicability, even in the absence of reference in situ discharge.

of methodologies co-exist. While some approaches require calibration based on in situ or modeled reference discharge, other methods can be used to estimate discharge only from the satellite observations (Mass conserved Flow-Law Inversion or McFLI methods; Gleason et al., 2017). These McFLI methods can be applied to ungauged basins; however, their accuracy is not guaranteed, as discharge inversion from surface observations and unknown bathymetry/friction parameters remains an ill-posed problem (Andreadis et al., 2025). Because the latter approaches are not constrained by in situ discharge estimates, they can inherently exhibit lower performance accuracies. Overall, the diversity of performance metrics used to evaluate discharge algorithms makes inter-comparison challenging. Since the accuracy assessments described in the following subsections are almost exclusively based on concurrent satellite and in situ measurements, they only reflect performances over a few data points per month (depending on the satellites' revisit frequencies), and high performance metrics are therefore no guarantee that the true overall variability of discharge is captured.

### 2.2.1. Discharge Estimation From River Width Alone

Where discharge changes are accompanied by flow width and water surface area expansions and contractions, $W \rightarrow Q$ rating curves have initially been calibrated using simultaneous ground measurements of streamflow and SAR-based surface water extent (Smith et al., 1995, 1996). Global power-law relationships involving river width $W$ such as the Moody and Troutman (2002) equation $W = 7.2 Q^{0.5}$ can be used as a first approximation of bankfull width mean discharge with no need for prior $Q$ values (Frasson, Pavelsky, et al., 2019). At-Many-Station Hydraulic Geometry (AMHG) $W \rightarrow Q$ relationships were then established from widths estimated using Landsat images (Gleason et al., 2014; Gleason and Smith, 2014; Gleason and Wang, 2015) with 26%–41% relative RMSE (RRMSE) agreement between AMHG discharge retrievals and in situ gauge observations on 34 gauged rivers worldwide. It was also shown that paired coefficients $a$ and $b$ in Equation 1 or $\alpha$ and $\beta$ in Equation 2 are linearly related along a given river, with AMHG correlations being on average lower for $W \rightarrow Q$ relationships compared to $H \rightarrow Q$ relationships (Gleason and Smith, 2014). First guess values on mean discharge (e.g., from GRADES model simulations; Lin et al., 2019) have been used with optimized local algorithms like the updated Bayesian At-many-stations hydraulic geometry-Manning's algorithm geoBAM (Brinkerhoff et al., 2020; Feng et al., 2021) to fit power-law relationship based on $log(W)$. In a global study, “off-the-shelf” geoBAM was found to be “successful” for 1/3 of reaches among 3,078 sites (correlation >0.5 and KGE >0), while 2/3 required more refined priors (Lin et al., 2023). Other studies have leveraged discharge records from gauges to calibrate $W \rightarrow Q$ relationships. For example, missing records at 2,168 gauge locations worldwide (from a combination of national and international data sets) were filled with daily discharge estimates from a river width-based rating curve technique (using Landsat/Sentinel-2 images and available gauge data as discharge prior), featuring a median relative bias of 1.4% and a median KGE of 0.46 (Riggs et al., 2023). To deal with non-simultaneous measurements of in situ discharge and satellite-based river width, $W \rightarrow Q$ relationships were fitted using a stochastic quantile mapping function, revealing Normalized RMSE (NRMSE) <10% on the Niger, 23% on the Congo and 50% on the Po river using the Global Surface Water data set (Elmi et al., 2021). Using this same methodology, more than 3,000 inoperative GRDC stations were recently extended with both altimetry data and Landsat images, with average KGE values greater than 0.5 (Elmi et al., 2024).
The value of width-based RSQ has thus been demonstrated at the global scale (including for high-frequency monitoring) with varying accuracies ranging from ~10% to 50% at up to daily temporal resolution (Table 2). Such methods have, however, been shown to have limited performance in cloud-prone regions (where optical sensors are useless) and in cases such as wide rivers and/or rivers with low width variability (Lin et al., 2023; e.g., rivers with steep banks which rather show changes in water surface elevation with changes in discharge). Width is also a poor indicator of discharge variations in river reaches where the artificial control of water flow disrupts the natural relationship between width and discharge (e.g., urban areas).

### 2.2.2. Discharge Estimation From Water Surface Elevation Alone

The most direct adaptation of traditional in situ discharge estimates to RSQ is the calibration of $H \rightarrow Q$ relationships from space-based elevation measurements. Equation 1 $Q = a(H - Z_0)^b$ has been used for at least two decades at virtual stations from altimetry data (Birkinshaw et al., 2010; Kouraev et al., 2004; Leon et al., 2006; Papa et al., 2010). To transform WSE information into discharge at a specific location, the three parameters a, b and $Z_0$ in Equation 1 must be calibrated using known or assumed values of discharge. These reference values can originate from in situ stations. Even though concurrent gauge- and satellite-based measurements are not necessarily available (both in space and time), some studies have established rating curves using the closest in situ stations, with NRMSE ~20% on the Ob river and the Ganges-Brahmaputra (Kouraev et al., 2004; Papa et al., 2012a; Zakharova et al., 2020). A routing model was used by Tarpanelli et al. (2013a) to overcome the synchronicity limitation by inferring downstream discharge at virtual stations from in situ upstream data to compute rating curves at the exact location of the virtual station, with NRMSE ~30% on the Po river. Tourian et al. (2017, 2013) have also proposed a nonparametric approach, making use of quantile functions of the measurements, and without assuming any specific mathematical form (like linear, power-law, etc.) for the $H \rightarrow Q$ relationships (as for the $W \rightarrow Q$ ones later derived by Elmi et al., 2021). They found NRMSE <20% in multiple major rivers using this quantile matching methodology (Tourian et al., 2013), and an average correlation of 0.95, NRMSE of 12% and NBIAS of 10% over the Niger basin (Tourian et al., 2017). Discharge simulations can also provide reference discharge values (e.g., Garambois et al., 2017; Getirana and Peters-Lidard, 2013; Malou et al., 2021; Paris et al., 2016), albeit model outputs need to be extensively validated prior to the fitting of the rating curve. For instance, Paris et al. (2022, 2016) reported NRMSE of ~10% in the Amazon and Congo River basins using the rainfall-runoff MGB (Modelo Hidrológico de Grandes Bacias; Paiva, Buarque, et al., 2013; Paiva et al., 2011) model as reference discharge. Such accuracy was reported not only on large rivers and main streams but also for hundred-meter-wide rivers.

These accuracy assessments suggest that elevation-based discharge estimation methods are more accurate than width-based approaches, with accuracies ranging from ~10% to 30% (Table 2). However, they suffer from the low-frequency revisit of altimeters (10 days at best for nadir missions) compared to imagery-derived water extent-based discharge methods (Table 1). In addition, most studies investigating the accuracy of $H \rightarrow Q$ relationships are only local. The estimation of discharge at the global scale from altimetry missions alone is still in its infancy (e.g., Elmi et al., 2024; Saemian et al., 2025; or the ESA Climate Change Initiative Discharge project, 2024), even though global WSE data sets are already freely available. Finally, the classic bijective $H \rightarrow Q$ rating curve concept (Equation 1) performs poorly upstream of river confluences, regulated reservoirs and dams, near the coast and on low-gradient streams because of backwater effects. Satellite altimetry observations of water surface elevation and slope have revealed stage-slope-discharge relationships for these locations (Liu et al., 2023; Malou et al., 2021).

### 2.2.3. Discharge Estimation From River Width, Elevation, and Slope

In addition to $W \rightarrow Q$ and $H \rightarrow Q$ calibrated methods, multi-mission endeavors have been carried out to leverage both width and WSE measurements from satellites to estimate discharge, hence partly solving hysteresis effects observed in both width-only and elevation-only methods (e.g., Huang et al., 2018a, 2018b). Inspired by a form of Manning's resistance equation established by Bjerklie et al. (2003), Birkinshaw et al. (2014) computed daily discharge from ERS-2/ENVISAT altimeters and Landsat satellites with NSE values of 0.90 on the Mekong and 0.86 on the Ob rivers. ENVISAT- and Jason-2-derived WSE and MODIS- and Landsat-derived river width changes were used on several river sites globally by Sichangi et al. (2018, 2016) to derive estimates of discharge from Manning's equation (NRMSE < 25%). Discharge was computed using the same equation with altimetry data and SWORD (the SWOT mission River Database, Altenau et al., 2021) static river widths and slopes at 71% of 230 sites in the Mississippi river basin with $KGE > 0.32$ compared to USGS in situ data (Gehring et al., 2022). Recently,
ICESat-2 river surface slopes and 20 years of satellite altimetry observations were combined with high-resolution satellite imagery from PlanetScope via a hypometric function to derive discharge with a median NRMSE of 12% in 27 river reaches globally (Scherer et al., 2024). We wish to highlight that these approaches differ in that they do not necessarily require reference discharge data for direct calibration, but instead require estimating parameters such as hydraulic friction and channel bathymetry. Among others (e.g., Durand et al., 2024), these studies thus demonstrated the benefits of pairing both river width from imagers and WSE from altimeters to derive discharge estimates, with ≤25% relative accuracy (Table 2), although the addition of river widths was found to provide only marginal improvements (a few %) over using elevation only by Sichangi et al. (2016). A bias related to the assessment of discharge algorithms near gauges, where water stage usually varies much more than width with flow variations, may explain why width is often found to be less relevant than WSE for discharge estimation.

With its joint measurements of width, elevation, and slope, SWOT has introduced a new perspective to RSQ in the past years (preemptive and ongoing studies). In addition to the observed products, multiple investigations have been conducted to prepare for the production of an official discharge product at 200,000+ reaches globally (Altenau et al., 2021; Andreadis et al., 2020; Bonnema et al., 2016; Durand et al., 2014, 2016, 2023; Frasson et al., 2021; Garambois and Monnier, 2015; Larnier et al., 2021; Nickles et al., 2020; Oubanas et al., 2018a, 2018b; Paiva et al., 2015; Papa et al., 2012b). The following breakdown of the modified Manning's equation (Durand et al., 2014) is used in most cases for the basic form of flow laws underlying SWOT discharge algorithms:

$$Q \sim 1/n(A_0 + \delta A)^{\frac{5}{3}} W^{-\frac{2}{3}} S^{\frac{1}{3}} \quad (4)$$

With $A = A_0 + \delta A$, $\delta A$ being the cross-sectional area anomaly estimated by SWOT from the combined elevation and width measurements. $n$ and $A_0$ (the cross-sectional area under the observed anomaly) are unobserved by SWOT and are referred to as flow law parameters. These parameters are estimated, alongside discharge, using the SWOT discharge algorithms, which include but are not limited to, GeoBAM (Brinkerhoff et al., 2020; Gleason et al., 2014; Gleason and Smith, 2014), SAD (Andreadis et al., 2020), HiVDI (Larnier et al., 2021), MetroMan (Durand et al., 2014), MOMMA (previously MFG; Bonnema et al., 2016), and SIC4DVar (Gejadze et al., 2022; Oubanas et al., 2018a). The six SWOT discharge algorithms have investigated a wide range of methodologies involving different complexities of hydraulic models, inversion approaches and data assimilation. Interested readers are referred to Durand et al. (2023, 2016) and Frasson et al. (2021) for a complete overview of the research topic in the context of SWOT. Accuracy of discharge estimates from Confluence (SWOT's Science Team computational framework for discharge estimation) is expected to be approximately 30% or less for 2/3 of global SWORD reaches and be dominated by bias (Andreadis et al., 2025; Durand et al., 2023). SWOT discharge will be provided with an average revisit time of 11 days (3–21 days depending on latitude) over most of the globe. Although SWOT's spatial coverage is unprecedented for surface waters, its average revisit period does not offer a significant improvement over existing nadir altimetry systems (except at high latitudes thanks to overlapping measurements).

### 2.2.4. Discharge Estimation Using Direct Measurements From Passive Sensors

Concurrent to the development of physics-based discharge estimation techniques, approximations of discharge directly from satellite signals (i.e., not through a hydraulic quantity-based relationship) have been established using ratios between wet and neighboring dry pixel data, called calibration/measurement (C/M) ratios. One of these methods involves passive microwave radiometers and leverages the strong contrast in brightness temperature between water (wet pixel, lower $T_{b,m}$) and adjacent land (dry pixel, higher $T_{b,c}$). For this purpose, satellite instruments such as SSM/I, AMSR-E, SMAP, and SMOS have been used to compute microwave C/M ratios (Brakenridge et al., 2007, 2023; Kugler et al., 2019, 2023, 2024; Yoon et al., 2022) and temporal reflectance indices (Yoon et al., 2023, 2025). For instance, Kugler et al. (2024) established correlations of approximately 0.75 between SMAP-based (Soil Moisture Active and Passive, using L-band) discharge estimates and in situ observations at 150 satellite gauging reaches globally.

The same approach to observe riverine inundation increase (land cover change) has been implemented with multispectral optical imagers and uses the expected reflectance C/M ratio in the near infrared to represent river flow variations with width and sediment transport variations (Brakenridge et al., 2005). Variations in reflectance have been computed using MODIS, with correlations with in situ discharge up to 0.6 on the Po river (Tarpanelli
et al., 2013b) or greater than 0.95 on the Niger-Benue River (Tarpanelli et al., 2017), and using Sentinel-2 with NSE ~0.5 on the Po and Tiber rivers (Filippucci et al., 2022). Although Tarpanelli et al. (2017) revealed that altimetry is more accurate in estimating river discharge (by 10%-30% on the Niger river), they highlighted the significantly higher temporal resolution of MODIS (1-2 days) as more helpful for Flood Forecasting and Mitigation. Although passive microwave instruments overcome some of the limitations of optical sensors (nighttime measurements and penetration through clouds, so that all acquisitions are exploitable) and feature much higher revisit frequency (1 to a few days) compared to altimeters, their spatial resolution is very coarse (10-50 km), making them suitable only for monitoring events characterized by large inundation extents. Recently, an innovative regional gauging method based on a similar concept of water fraction in discharge-sensitive areas used both Sentinel-1 SAR and Sentinel-2 optical data to derive discharge with 20%-30% relative accuracy (Lin et al., 2024).

These C/M methods heavily rely on the ratio between river width and image resolution, as the methods described in Section 2.2.1. Medium-resolution instruments (e.g., MODIS, 250-1,000 m) yield better results (correlation coefficient *R* > 0.7) for wide, low-gradient river systems with large and variable discharge, and poorer results for narrow rivers in steep, high-relief terrain with smaller and less variable discharge (Van Dijk et al., 2016). However, higher resolution satellites like Sentinel-2 (10 m) show greater potential compared to MODIS for medium rivers such as the Po River (300 m wide) (Filippucci et al., 2022). To address the inherent limitations of optical imagery during high flow events (e.g., clouds), the scientific community is currently exploring integrated approaches that combine concurrent passive (C/M ratio technique) and active (altimetry) remote sensing signals to provide near-daily discharge estimates, leveraging the strengths of both methods (Tarpanelli et al., 2015, 2019).

A compact summary of some of the main remote sensing methods used to compute proxies of river discharge from space is given in Table 2.

To guide the readers and help them navigate the current methodologies, data and measurement technologies available at the time of writing, we provide a (non-exhaustive) summary of existing global open-access capabilities to capture river dynamics from space (Table 3).

## 3. Capturing River Dynamics From Space

Much work has been accomplished in understanding how, and with what precision, rivers can be observed using records from past and current satellite systems (optical and SAR imagery, conventional, SAR/FFSAR and wide-swath altimetry, passive microwave radiometry). However, none of these systems have been specifically formulated for river hydrology applications, except SWOT for part of its instrument design and orbital specifications (shared with physical oceanography requirements). An active definition of the observational needs to inform the design of a global measurement system dedicated to river dynamics is still lacking. The objective of this third section is to first provide a simple definition of variables characterizing channel dynamics and water propagation in river networks, followed by a review on the ability of current satellite systems to capture these processes with respect to river size and variability.

### 3.1. Definition of Variables Related to River Dynamics and Water Propagation

Prior to discussing the capabilities of current instruments and methods, and the measurement requirements for future planned endeavors, we define simple selected variables and quantities that relate river characteristics or observables with flow dynamics and the propagation of water in open channels (Figure 3).

The defined variables can relate to any of the following hydrologic-related variables X: river width W, water surface elevation H, C/M ratio, surface water velocity u or discharge Q. Contrary to width, elevation or velocity, the C/M ratio is not a hydrological variable per se. However, it has been shown to be directly correlated to discharge dynamics (Brakenridge et al., 2005; Tarpanelli et al., 2013b). For the purpose of this study, we wish to discuss the following measurable quantities: the mean X̄, the quantiles Xp (p = 0,...,99 percentiles), the standard deviation Xstd, the number Np, duration Tp and length Lp of peak flow events (i.e., when X > Xp with Xp a peak threshold), the propagation time τp of peak flow waves, and their speed of propagation known as celerity c, which is linearly related to flow velocity u in a wide rectangular channel (Lighthill and Whitham, 1955) (Figure 3). We choose these quantities as they are some of the simplest and most common examples of hydrologic quantities of interest for studying river dynamics, although others could be added. As shown in Figure 3, smaller magnitude and higher variability characterize upstream low-order streams, while higher magnitude and lower-frequency
<caption>**Table 3**</caption>

Non-Exhaustive List of Major Open-Source Repositories, Data Sets and Products, Tools and Algorithms Related to Satellite Remote Sensing for Global-Scale Data Analysis and Modeling of River System Dynamics

<table><thead><tr><th>Data repositories</th><th>Relevance and use</th><th>Examples of products available</th></tr></thead><tbody><tr><td>NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC)</td><td rowspan="4">Surface water level, slope, extent, river discharge</td><td>SWOT, OPERA DSWx, GRRATS</td></tr><tr><td>Hydroweb.next</td><td>SWOT, THEIA nadir altimetry and imagery</td></tr><tr><td>HydroSat</td><td>Nadir altimetry and imagery, RSEG, SAEM discharge</td></tr><tr><td>Database for Hydrological Time Series of Inland Waters (DAHITI)</td><td>SWOT, nadir altimetry and imagery</td></tr></tbody></table>

<table><thead><tr><th>Data sets and products</th><th>Relevance and use</th><th>Satellites/instruments involved</th></tr></thead><tbody><tr><td>Global River Widths from Landsat (GRWL)</td><td rowspan="2">River characteristics (e.g., width)</td><td>Landsat</td></tr><tr><td>SWOT River Database (SWORD)</td><td>Landsat, SRTM</td></tr><tr><td>Global Flood Monitoring service (GFM/GloFAS)</td><td></td><td>Sentinel-1</td></tr><tr><td>Global Surface Water (GSW)</td><td rowspan="2">Surface water extent and flood monitoring</td><td>Landsat</td></tr><tr><td>Global WaterPack (GWP)</td><td>MODIS</td></tr><tr><td>Global Flood Detection System</td><td>Flood event detection</td><td>AMSR-E</td></tr><tr><td>Extension of global river gauges (Riggs et al., 2023)</td><td></td><td>Landsat, Sentinel-2</td></tr><tr><td>Landsat + geoBAM (Lin et al., 2023)</td><td></td><td>Landsat</td></tr><tr><td>Remote Sensing-Based Extension of GRDC Discharge (RSEG; Elmi et al., 2024)</td><td rowspan="2">Global river discharge</td><td>Multiple nadir altimeters and Landsat</td></tr><tr><td>Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM; Saemian et al., 2025)</td><td>Multiple nadir altimeters</td></tr><tr><td>SWORD of Science (SoS)</td><td></td><td>SWOT</td></tr></tbody></table>

<table><thead><tr><th>Tools and algorithms</th><th>Relevance and use</th><th>Satellites/instruments involved</th></tr></thead><tbody><tr><td>Klein et al., 2024; OPERA, 2023; Pekel et al., 2016; Schwatke et al., 2019; SurfWater, 2021</td><td>Water classification</td><td>Imagers</td></tr><tr><td>Ice-1, OCOG, BiST (Tourian et al., 2025; Wingham et al., 1986)</td><td>Water level retracking</td><td>Nadir altimeters</td></tr><tr><td>Bidirectional translations between MERIT-Basins and the SWOT River Database (SWORD) (Wade et al., 2025a)</td><td>Interoperability between SWOT data and hydrologic models</td><td>SWOT</td></tr><tr><td>Markov Chain Monte Carlo rating curve (Paris et al., 2016)</td><td></td><td>Altimeters</td></tr><tr><td>Non-Parametric Quantile Mapping method (Elmi et al., 2021)</td><td></td><td>Altimeters and imagers</td></tr><tr><td>Optimization approach with uncertainty quantification (Scherer et al., 2024)</td><td></td><td></td></tr><tr><td>Remotely Observed Discharge from Effective width Occurrence (RODEO, Riggs et al., 2022)</td><td></td><td>Imagers</td></tr><tr><td>Surrogate River Discharge Model (Yoon et al., 2025)</td><td rowspan="6">Discharge algorithms from remote sensing variables</td><td>Passive microwave radiometers</td></tr><tr><td>geoBAM (Brinkerhoff et al., 2020; Feng et al., 2021)</td><td>SWOT, Imagers + altimeters</td></tr><tr><td>SAD (Andreadis et al., 2020)</td><td></td></tr><tr><td>HiVDI (Larnier et al., 2021)</td><td></td></tr><tr><td>MetroMan (Durand et al., 2014)</td><td></td></tr><tr><td>MOMMA (Bonnema et al., 2016)</td><td></td></tr><tr><td>SIC4DVar (Gejadze et al., 2022; Oubanas et al., 2018a)</td><td></td><td></td></tr></tbody></table>

Note. Acronyms are defined in the table of acronyms. Please refer to the links indicated for further information.
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Space-time hydrograph representation of a hydrologic-related variable *X* = *W*,*H*,*C*,*M*,*u*,*Q* with quantities related to river dynamics.</figcaption></figure>

dynamics are usually observed further downstream. Flow waves in rivers propagate downstream faster than the water itself (kinematic wave celerity *c* ~ 5/3 *u*; Dingman, 1984) and the duration of peak events are related to both the timing of the water inflow (rain or snowmelt and runoff) and the speed of propagation of the wave. The following sections review the capability of current satellites and methods described in Section 2 to estimate these river quantities, under the strong assumption that all variables *X* covary (i.e., are related) with perfect measurements and algorithms. This hypothesis is challenged in Section 3.4 to discuss the relevance of each measurement technology for monitoring river dynamics. In particular, due to nonlinear wave propagation as predicted by the 1D Saint-Venant model, a maximum of water surface elevation *H* at a river section is not necessarily concomitant in time with a maximum of discharge *Q* (in free surface channel flows).

**3.2. Low-Frequency Dynamics and Statistical Distributions of River Flow**

Based on a global-scale analysis of the Global Reach-scale A priori Discharge Estimates for SWOT (GRADES) model simulations of satellite-observable rivers, Sikder et al. (2021) analyzed the current nadir and wide-swath altimetry sampling capabilities both in time and space and demonstrated that they should allow capturing the global long-term distributions of streamflow (X̄, Xp) with reliable accuracy (Figure 4, green checks in hydrographs B and C). This is consistent with a study from Fekete et al. (2012) based on in situ gauges which revealed that annual discharge estimates resulting from a theoretical 10-day sampling rate (e.g., Sentinel-2A's global or Sentinel-6's local revisit frequency) are comparable to what in situ daily rates provide (±5%), at least for relatively large rivers with Q̄ > 100 m³/s. The impact on the global frequency distribution of discharge estimated from the irregular temporal sampling of the SWOT mission is also expected to be very limited compared to daily observations in the Mississippi River basin (Nickles et al., 2019). More recently, Cerbelaud et al. (2024) confirmed for that same basin that the long-term mean, standard deviation and quantiles of hourly in situ discharge (X̄, Xp, Xstd) can all be computed within a limited error range (±2.5%) up to a theoretical 8-day sampling period (Figure 4, green checks). These results however all presuppose that discharge dynamics can be perfectly reproduced from satellite-observable variables.

**3.3. Observability of Narrow Water Bodies**

Assuming perfect measurements and adequate algorithms, the sampling capabilities of current satellite missions (e.g., imagery, altimetry) can thus be considered sufficient to measure the medium- to long-term statistical distributions of river quantities in medium to large observable channels (width >100 m), with applications to WRM (Figure 4). However, many small, low-order streams (width <50–100 m) are still unobservable considering the current resolutions and measurement uncertainties of instruments (Figure 4, red crosses in upstream
<figure><img src="image_7.png" /><figcaption>**Figure 4.** River spatiotemporal dynamics and water propagation in the context of satellite observations. Space-time hydrograph representation of a hydrologic-related variable $X = W, H, C/M, u, Q$ at three illustrative locations A, B and C from upstream to downstream, highlighting observable (green check) and unobservable (red cross) quantities as mostly captured by current satellite systems.</figcaption></figure>

hydrograph A). When filtering the global MERIT-Hydro Basins river network to rivers wider than 50 and 100 m (Wade et al., 2025b), find the number of selected global reaches decreasing from ~2.94 million reaches to 192,339 (6.5% of all reaches) and 89,144 (3.0% of all reaches), respectively. They also indicate that capturing 90% or 95% of global discharge to the ocean (respectively of water storage in rivers) requires accurately resolving rivers wider than 60 m or 30 m (respectively wider than 100 m or 60 m) (Figure 5, see Wade et al. (2025b)). Observing small, narrow rivers at this level with sufficient accuracy is feasible but challenges current satellite technology, such as SWOT.

In particular, imagers and altimeters still fail to observe the dynamics of narrow and/or steep-sided rivers with satisfactory accuracy (e.g., NRMSE $\le 25\%$; Table 2 errors for $(W,H,S) - Q$ methods), although progress has been made with the development of decametric to metric resolution in optical imagery and SAR processing/open-loop tracking modes/interferometry in altimetry (Feng et al., 2019; Fu et al., 2024; Le Gac et al., 2021). Further details on this matter are given in Section 3.4.

### 3.4. Ratio of Signal Accuracy to Hydrological Variability and Relevance of Various Measurement Technologies

Sections 3.2 and 3.3 are based on the assumption that all variables $X$ (width, water surface elevation, C/M ratio, velocity, river discharge) are associated with perfect measurements and algorithms, so that they are all equivalent in representing river dynamics. However, numerous satellite- and instrument-specific limitations apply with respect to (a) the capacity to retrieve a clear signal on a given river segment (i.e., the accuracy in variable estimation, Figure 6), (b) the ability to discern the magnitude of changes occurring at a specific location (Figures 4 and 6) and (c) the ability to convert the signal into proxies for river discharge.

For width retrieval from either optical or SAR imagers, the effective river width that can be accurately extracted is generally assumed to be three times the image resolution (Li et al., 2020). The 30 m resolution of Landsat therefore limits its ability to reliably determine water extent, and thus river width, in rivers at least ~90 m wide (Feng et al., 2022; Pekel et al., 2016; Yamazaki et al., 2015) within <25%-30% accuracy (Allen & Pavelsky, 2018), whereas the 10 m spatial resolution of Sentinel-1 SAR and Sentinel-2 near infrared band 8 allows resolving rivers wider than ~30 m (Li et al., 2020, 2024). The ability to discern the magnitude of width changes $\delta W$ responsible for significant river discharge dynamics can be assumed to be on the order of magnitude of the pixel size (Figure 6). As an illustration, with ~30 and ~10 m precision on a 100 m (respectively 1,000 m) wide river, the relative error on computed discharge could be as high as 50% (respectively 6%) and 20% (respectively
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Proportion of total global discharge to the ocean (blue) and river storage (black) captured given an observational spatial resolution that resolves all rivers wider than a given width. Temporal mean (circles) and mean annual range (squares) are calculated over the 30-year simulation period for each river width scenario and compared to the selected quantity at all global reaches (reproduced from Wade et al., 2025b).</figcaption></figure>

2%) (from the relationships between width and discharge displayed in Frasson, Pavelsky, et al., 2019). Thus, high-frequency dynamics in small low-order rivers with width <100 m may only be accurately observed by high-resolution imagery (pixel size ≤10 m) such as Sentinel-2. However, the ability to track river width dynamics may be improved through spatial averaging over long stretches of rivers, potentially decreasing the width error to sub-pixel sizes at the cost of spatial resolution (Feng et al., 2019; Pavelsky, 2014).

Reflectance-based C/M ratio methods show great promise (even on narrow rivers), benefitting from similar spatial and temporal resolutions as width-based techniques, and will continue to improve with planned land surface missions featuring enhanced resolution, so that measurement accuracy is likely to improve accordingly. However, the use of high-resolution products raises other issues, with relative C/M ratios occasionally not varying with river discharge when small pixels (relative to the river scale) become saturated (Filippucci et al., 2022).

While the relative precision of WSE measurements and the correlation between discharge and WSE is commonly found to be higher compared to that of W or C/M ratios (Elmi et al., 2024; Tarpanelli et al., 2017), the acquisition and processing of a clear signal can be more challenging and less straightforward. With ≤30 cm vertical accuracy on WSE reported from recent missions such as Jason-3 and Sentinel-3 (see Section 2.1.2), the relative error over 1 m (respectively 5 m) water height could be as high as 50% (respectively 10%) on the computed discharge (from a classical $H \rightarrow Q$ coefficient $b = 5/3$, Equation 1). The FFSAR processing of Sentinel-6 as well as the interferometric capabilities of SWOT have brought a paradigm shift with potential accuracies down to ≤10 cm for WSE (Boy et al., 2023; Fu et al., 2024), and thus could lead to errors ≤20% (respectively 3%) for discharge production (30% is mentioned as a target for SWOT discharge inversion algorithms in Durand et al., 2023). High-frequency dynamics in small low-order rivers in the upstream of basins (width <100 m) may therefore only be accurately observed with high-precision altimetry (error ≤ 10 cm), which has been achieved with recent missions.

However, we wish to emphasize that even with high-precision remote sensing measurements, errors in discharge production may still occur due to numerous unobserved or poorly known factors. Increased understanding of river variability relative to measurement accuracy and further analysis of this trade-off is essential for identifying the most suitable signals, relevant methods/processing and algorithms to represent flow variations and reveal the ideal sensors. The continued development of all types of methodologies is thus needed to allow handling every configuration and pattern of river changes as observed from space.
<figure><img src="image_9.png" /><figcaption>**Figure 6.** Water propagation in rivers in the context of satellite observations. River network with examples of width-varying and elevation-varying wetted cross-sections at three illustrative locations A, B and C from upstream to downstream, highlighting observable (green check) and unobservable (red cross) hydraulic variables as mostly captured by current satellite systems. Beyond temporal revisit, the ratio of spatial resolution and signal accuracy of the instruments to the hydrological variability of river variables are key to capturing river quantities and their dynamics depending on the river characteristics (e.g., width). Spatial resolution of imagery and minimum width of channel for accurate retracking of altimetry data (horizontal arrows) are given as illustrations and are not necessarily to scale.</figcaption></figure>

# 4. Current Limitations and Next Frontiers for the Estimation of River Dynamics From Space

Even with recent advances in remote sensing and modeling, many water bodies are still not captured adequately given their characteristics and dynamics (Papa et al., 2023). The objective of this section is to provide a summary of past and ongoing efforts designed to shed light on the type of remote sensing observations and approaches that could lead to a more accurate understanding and representation of water movement through the Earth's hydrographic network. We propose several paths forward (temporal revisit, latency time, distance between tracks, swath, variable of interest) in support of this goal, including a review of gap-filling modeling approaches relying on multi-satellite data and a discussion on surface water velocity measurements from space for more accurate water flux and discharge estimates.

## 4.1. High-Frequency Dynamics and River Flooding

Beyond standard statistics such as mean, quantiles and standard deviation of river-related variables, the study of peak flow events and their propagation in channels is paramount to informing the modeling of rainfall-runoff processes of river systems and advancing Flood Forecasting and Mitigation. High-frequency variability can be studied using multiple satellite-based approaches.

Munasinghe et al. (2023) analyzed surface water extent retrieval globally from optical and SAR multi-imagers merging and showed that the combined revisit of Landsat, Sentinel-2 (two satellites), and Sentinel-1 (two satellites) provides a sampling rate of ~2.4 days. Such temporal sampling with complete spatial coverage was shown to lead to 90% capture of large floods in the Dartmouth Flood Observatory (Brakenridge, 2024) data set based on the detection of inundated areas. However, the study did not address whether these sampling capabilities were sufficient to capture the duration and spatial coverage of flow waves, only their occurrence. Because multispectral and SAR imagers are used for numerous land and coastal applications, multiple constellations exist and provide observations at ≤10 m resolutions (even sub-metric for commercial satellites) with global spatial coverage (swath-based instruments) and sub-weekly (even sub-daily for commercial satellites) temporal sampling. Overall, the only platforms currently capable of monitoring rivers for FFM on a 1- to 5-day basis are optical/SAR imagers and microwave radiometry instruments (at much coarser spatial resolutions). However, the ability to accurately observe quantities such as the number $N_p$, duration $T_p$, and length $L_p$ of peak flow events, as well as their propagation time $\tau_p$, from these platforms remains to be demonstrated. In particular, the high-revisit frequency of optical imagers during flood events is often reduced by cloud cover. Tarpanelli et al. (2022) evaluated through a synthetic study the effectiveness of Sentinel-1 and Sentinel-2 in the detection of flood extents in Europe. With
flood events ranging from some hours to a few days in durations, they estimated that on average 58% of flood events are potentially observable by Sentinel-1A/B (6-day revisit) and only 28% by Sentinel-2A/B (5-day revisit) due to the cloud coverage. Similarly, the coarse spatial resolution of passive microwave radiometers generally does not allow the investigation of flood events at specific river locations, especially in medium and small rivers. Despite all these limitations, Sichangi et al. (2018) demonstrated using only high-revisit MODIS-based river width observations at two spaced stream segments on the Yangtze river that flow propagation celerity *c* can be estimated by computing the time lag between maximum width measurements, yielding a resultant value of 0.96 m/s, consistent with many other studies (e.g., on the Lena river; Smith and Pavelsky, 2008).

In contrast to satellite imagers, current altimetry missions used by hydrologists still do not provide a sufficient temporal sampling to study high-frequency phenomena in small- to medium-sized rivers with sufficient accuracy (Cerbelaud et al., 2024). For instance, SWOT is expected to fail to observe half of the floods occurring during its lifetime (Frasson, Schumann, et al., 2019). In addition, only 22% of SWOT measurements are expected to fall within one day of an event's peak (Nickles et al., 2019). Potential significant deviations in flood magnitude estimations derived from nadir altimetry satellites were suggested due to their coarse temporal revisit. Indeed, 35% (respectively 50%) and 10% (respectively 16%) decreases in discharge are to be expected on the Iowa and the Mississippi/Mackenzie rivers, approximately 5 (respectively 10) days after a flood peak (Gehring et al., 2023). The sparse spatial sampling of the current nadir altimetry missions further limits their ability to capture the dynamics of high-flow events. Overall, it has been shown that the global distribution of the number $N_p$ and duration $T_p$ of peak flow events could not be accurately captured with revisit frequencies greater than 5 days (Sikder et al., 2021), a challenge that is exacerbated when discharge algorithms based on width, elevation, slope, and/or signal ratios lack consistent accuracy. Overall, satellite nadir altimetry currently does not provide revisit periods more frequent than 10 days, which hampers the investigation of the high-frequency-related variables $N_p$, $T_p$, $L_p$, $\tau_p$, and *c* (Figure 4, red crosses).

## 4.2. Required Spatiotemporal Sampling Strategies to Capture Peak Flow Events

Given the current limitations in observing high-frequency river dynamics and occasional events from space, several efforts have recently been made to identify sampling requirements that could lead to improved observation capabilities. According to Gehring et al. (2023), a revisit <5 days is needed to measure flood peak discharge within 35% error for relatively flashy (yet still observable via altimetry) small- to medium-sized rivers like the Iowa river (basin area ~32,500 km²). A typical 5-day time scale for flood events in US rivers has also been determined using harmonic analysis (wavelet transformation) over USGS gauge data (Stroud et al., 2024). These results are all consistent with the findings of Sikder et al. (2021) who also recommended a revisit period <5 days to capture peak flow events and their durations globally from model simulations.

More recently, Cerbelaud et al. (2024) revealed an acute spatial variability in the duration $T_{90}$ of peak flow events (i.e., events above the 90th discharge quantile) in satellite-observable rivers of the Mississippi river basin using USGS hourly gauge data, from 2 days upstream to 30 days downstream, and with a median value of approximately 4 days. They also demonstrated that sampling periods must be a factor of 4 finer than median peak flow event durations $T_{90}$ to estimate durations within ~90% median accuracy. Cerbelaud et al. (2025) then showed, using the same data set of gauges, that the spatial coverage of the peak flow events along rivers, referred to as “length” $L_{90}$, has a median value of approximately 330 km. They also identified a comparable guiding principle in space: the sampling interval must be 4 times finer than the spatial scale of the peak flow event to accurately observe the length of the flow wave, analogous to the principle observed in the time domain. Consequently, Cerbelaud et al. (2025, 2024) recommended that data on rivers of the Mississippi basin be collected at least daily and approximately every 80 km, with implications for intertrack spacing of nadir altimetry missions.

The revisit period needed to capture river temporal dynamics is further exemplified at the global scale in Figures 7 and 8 by combining the results from Sikder et al. (2021), based on the statistical analysis of daily GRADES model simulations (Lin et al., 2019) at 145,000 reaches globally ($\overline{Q} > 150$ m³/s), and the rule derived by Cerbelaud et al. (2024). We here provide a global, river reach-specific, map of estimated recommended revisit period $\Delta T = T_{90}/4$ (Figure 7), as well as exceedance functions by upstream area and river width (Figure 8), to capture high-frequency temporal dynamics in rivers. Table 4 summarizes these main statistics.
<figure><img src="image_10.png" /><figcaption>**Figure 7.** Map of estimated revisit period ΔT (days) at 145,000 river reaches globally ($\overline{Q} > 150 \text{ m}^3/\text{s}$) required to capture peak flow event durations within ~90% accuracy using the rule derived by Cerbelaud et al. (2024), that is, $T_{90}/4$. $T_{90}$ values are from Sikder et al. (2021). Upstream area is from the MERIT-Hydro Basins river network. River upstream area legend refers to river line widths.</figcaption></figure>

The main driver of peak flow event durations, and hence of ΔT, is the upstream area. Small fast responding catchments (<50,000 km²) require a revisit period of less than 1 or 2 days while large slow responding basins (>500,000 km²) require only 5- or 6-day frequency monitoring (Figures 7 and 8, Table 4). Given the distribution of river widths among each upstream area category (% numbers in Table 4), recommended revisit periods by river width category are more challenging to provide (Figure 8, Table 4). However, we estimate that a 1-day revisit enables accurate event duration estimates for 90% of medium rivers, while 2 days resolves the dynamics of 90% of large rivers and 65% of medium rivers. The global median (average) recommended revisit period for Earth's largest rivers using these combined results is 2.9 (3.6) days. This 3-day revisit period corresponds to the median requirement for medium rivers (width <500 m), which represent 50% of the data set (Table 4). It is also approximately what the combined revisit period of Landsat, Sentinel-2, and Sentinel-1 imagery satellites currently provides according to Munasinghe et al. (2023), that is, ~2.4 days. The sampling requirements derived by Cerbelaud et al. (2025, 2024) and Sikder et al. (2021), however, apply to all types of satellite systems (optical and SAR imagery, microwave radiometry, altimetry) that aim to capture the water propagation in Earth's rivers by providing proxies of discharge (e.g., water extent, height, C/M ratio).

In addition to observation, forecast components are also a crucial part of flood mitigation. Global flow waves were estimated to take a median of 6, 4, and 3 days to reach their basin terminus, the next downstream city, and the next downstream dam, respectively (Allen et al., 2018). Therefore, a ≤2-day data latency was advised to support real-time river FFM applications. In addition, the optimal temporal acquisition strategy of SAR-derived flood extent has recently been shown to strongly depend on the reach morphology and flood wave arrival timing. Indeed, Dasgupta et al. (2021) found that a single image acquired at the right time could improve an 8-days ahead forecast by ~95% when assimilated in reaches with large, flat floodplains but limited tidal influence. However, in reaches with narrow valleys, more than 10 images were needed to achieve the same outcome. Since flood waves can occur at any time, the only solution is to acquire data frequently enough to ensure that peak flow events are always observed with near certainty, and with sufficient latency to react in a timely manner. For other related
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Exceedance functions of estimated revisit period $\Delta T$ (days) at 145,000 river reaches globally ($\bar{Q} > 150 \text{ m}^3/\text{s}$) required to capture peak flow event durations within ~90% accuracy using the rule derived by Cerbelaud et al. (2024), that is, $T_{90}/4$, by (a) upstream area and (b) river width. $T_{90}$ values are from Sikder et al. (2021). Upstream area is from the MERIT-Hydro Basins river network. River widths are from the SWORD database.</figcaption></figure>

hydrological applications such as the calibration of static parameters of large-scale river models, high spatial sampling has been found to be more important than temporal resolution and observation accuracy in altimetry (e.g., SARAL's 80 km ground track spacing; Jiang et al., 2019).

Hence, recent studies and our analysis emphasize the need to image the Earth's rivers with both optical and SAR instruments and measure their WSE at least every 1–3 days, ideally at daily frequencies, to monitor higher frequency surface water dynamics even in small rivers. In the meantime, adequate spatial sampling (intertrack spacing for nadir altimeters, swath width for imagers and wide-swath altimeters) must be maintained within the constraints imposed by orbital/instrument trade-off limitations to accurately monitor even the shortest rivers and flow waves. However, achieving a designated spatial sampling (e.g., monitoring every 80 km along rivers)

<caption>**Table 4**</caption>

Median Estimated Revisit Period $\Delta T$ (Days) at 145,000 River Reaches Globally ($\bar{Q} > 150 \text{ m}^3/\text{s}$) Required to Capture Peak Flow Event Durations Within ~90% Accuracy Using the Rule Derived by Cerbelaud et al. (2024), That Is, $T_{90}/4$, by Upstream Area and River Width

<table><thead><tr><th>River width</th><th>Small<sup>a</sup></th><th>Medium</th><th>Large</th><th>Total (days)</th></tr><tr><th>Upstream area</th><th>30–100 m</th><th>100–500 m</th><th>&gt;500 m</th><th></th></tr></thead><tbody><tr><td>0–10,000 km<sup>2</sup></td><td><strong>1.2 (60%)</strong></td><td><strong>1.2 (34%)</strong></td><td>1.6 (6%)</td><td>1.2 (100%)</td></tr><tr><td>10,000–50,000 km<sup>2</sup></td><td><strong>1.9 (25%)</strong></td><td><strong>2.4 (63%)</strong></td><td>2.7 (12%)</td><td>2.3 (100%)</td></tr><tr><td>50,000–100,000 km<sup>2</sup></td><td>3.3 (11%)</td><td><strong>3.5 (62%)</strong></td><td><strong>3.8 (27%)</strong></td><td>3.5 (100%)</td></tr><tr><td>100,000–500,000 km<sup>2</sup></td><td>5.1 (9%)</td><td><strong>4.6 (45%)</strong></td><td><strong>5.1 (46%)</strong></td><td>5.1 (100%)</td></tr><tr><td>500,000–1,000,000 km<sup>2</sup></td><td>NA (&lt;5%)</td><td>9.2 (16%)</td><td><strong>6.5 (81%)</strong></td><td>7.3 (100%)</td></tr><tr><td>&gt;1,000,000 km<sup>2</sup></td><td>NA (&lt;5%)</td><td>8.2 (14%)</td><td><strong>8.1 (83%)</strong></td><td>8.1 (100%)</td></tr><tr><td><strong>Total (days)</strong></td><td><strong>1.6 (26%)</strong></td><td><strong>2.7 (50%)</strong></td><td><strong>4.6 (24%)</strong></td><td><strong>2.9 (100%)</strong></td></tr></tbody></table>

Note. $T_{90}$ values are from Sikder et al. (2021). Upstream area is from the MERIT-Hydro Basins river network. River widths are from the SWORD database. Numbers in parenthesis represent the ratio of reaches featuring a given river width by upstream area category. Median estimated revisit period $\Delta T$ (days) required to capture peak flow event durations globally. Numbers in parenthesis represent the ratio of reaches featuring a given river width by upstream area category. Bold numbers are the most representative by upstream area category. <sup>a</sup>These categories only contain the reaches currently observable by satellites.
globally with a given satellite system is not trivial because of rivers' orientations (North/South vs. East/West) and meanders. Following frequency approaches of the Saint-Venant equations could also help to better quantify the required sampling periods/intervals depending on river characteristics (e.g., Munier et al., 2008).

4.3. Simulating the Propagation of Water in Earth's Rivers With Gap-Filling Modeling Approaches and Multi-Satellite Data Assimilation

To synthetically improve the spatiotemporal sampling capabilities of altimetry data sets and simulate the high-
frequency variability of river systems, densification and assimilation approaches have been introduced by
Tourian et al. (2017, 2016), Nielsen et al. (2022), and Boergens et al. (2019) by hydraulically and statistically
linking virtual stations from multiple altimeters and applying Kalman filters, yielding water level time series at
any location along rivers with up to daily resolution. In particular, Boergens et al. (2019) showed that multi-
mission time series including CryoSat-2 data adequately reflect the general inter-annual flood behavior of the
Mekong River basin with a probability of detection of the flooding of ~80% while Envisat and Jason-2 single-
mission altimetry could only detect around 40% of the flooding correctly. However, these types of methods
are limited in terms of extrapolating rapidly varying peak flow events (e.g., in small and medium rivers) when the
events have not been captured at any of the available virtual stations in the river network.

Alongside remote sensing observations, hydrodynamic, hydrological and machine learning models coupled with river routing models (e.g., TELEMAC-MASCARET, SIC, LISFLOOD-FP, HEC-RAS, DassFlow, MGB, RAPID, CTRIP, MIKE+) have been used to provide spatially seamless estimations of discharge along rivers and fill in the space-time observational gaps. Extensively validated global-scale river discharge simulations are available today with various input data and levels of detail (e.g., meteorological forcing) and at different spatial and temporal resolutions (e.g., GRADES, Lin et al., 2019; GloFAS, Alfieri et al., 2020, Harrigan et al., 2020; GRFR, Yang et al., 2021; GEOGLOWS, Hales et al., 2022; MeanDRS, Collins et al., 2024; DHI-DGM, Murray et al., 2023; GRADES-hydroDL, Yang et al., 2023). Originally used in the atmospheric and oceanic sciences, data assimilation techniques have been applied to further improve the accuracy of hydraulic and hydrological models (e.g., by updating model state and input variables) through the incorporation of observations. While most make use of ground stations, satellite-based discharge assimilation has also recently emerged, using as observation operator a calibrated flow law making the remote sensing observations amenable to hydrological models with simplified routing (Coppo Frias et al., 2023; Garambois et al., 2017, 2020; Jiang et al., 2019; Paiva, Collischonn, et al., 2013; Revel et al., 2021, 2023; Schneider et al., 2018a). The assimilation of geoBAM-based discharge from river width estimations using Landsat was implemented into two hydrologic model simulations (namely GRADES and GloFAS; Feng et al., 2021) over Arctic basins, increasing KGE values from ~0.3 to 0.4. Emery et al. (2022, 2018) presented a data assimilation platform that assimilates into the large-scale ISBA-CTRIP land surface model a punctual river discharge product. This discharge product, derived from ENVISAT and Jason-2's elevation measurements covering the entire Amazon basin, reduced the model's RMSE from 69% to 49%. However, the benefits were constrained by ENVISAT's 35-day revisit interval and Jason-2's sparse spatial sampling. The assimilation of synthetic SWOT data into models has also been anticipated for years, with high expectations (Andreadis et al., 2007; Brisset et al., 2018; Emery et al., 2020; Munier et al., 2015; Oubanas et al., 2018a, 2018b; Pujol et al., 2020; Wongchuig-Correa et al., 2020). Indeed, Wongchuig-Correa et al. (2020) found that SWOT-like discharge assimilation can reduce model errors in daily discharge by up to ~40%. Oubanas et al. (2018a) revealed that relative RMSE of model simulations could be lowered to ~12% at the time of SWOT observations thanks to data assimilation. However, these error values were found 10%–20% higher at the daily scale because of SWOT's sparse temporal sampling (~11 days) compared to the characteristic times of the studied rivers (~5 days). The use of multi-satellite data such as radar altimetry, SAR imagery, passive microwave radiometry (for surface soil moisture estimates), and gravimetry (for total water storage changes), as well as the fusion of all of the above, has been shown to be highly useful for hydrological model calibration (e.g., estimation of channel friction) and assimilation, including in large poorly gauged catchments (Jiang et al., 2019; Kittel et al., 2018; Milzow et al., 2011; Shen et al., 2020; Verma and Indu, 2023; Wongchuig et al., 2023, 2024). Geodetic CryoSat-2 altimetry data with 7.5 km intertrack distance at the equator and drifting daily measurements of WSE during part of its 30-day sub-cycle has also been leveraged locally for hydrodynamic model calibration and assimilation (Jiang et al., 2017; Kittel et al., 2021a; Schneider et al., 2017, 2018a). The estimation of unobserved parameters such as Manning's *n* or the river bathymetry *A*₀ represents a (if not the most) crucial element to better approximate cross-sectional average water velocity *V* and, as a result, river discharge *Q* from the
momentum and continuity equations (Garambois et al., 2017; Garambois and Monnier, 2015; Gejadze et al., 2023; Gejadze and Malaterre, 2016).

However, these assimilation methodologies are all constrained by the spatiotemporal resolution of the provided observations: if the forcing by meteorological data or rainfall-runoff model outputs is not sufficiently accurate, the assimilation can potentially fail to accurately reflect the rapid dynamics (on the order of a day/hours) of low-order river systems. Nonetheless, we emphasize the importance of choosing the right scale/size of the modeled system to incorporate as much information and therefore as much dynamics as possible. For basins that are large enough to witness several SWOT passes per day over different rivers and tributaries, the assimilation of SWOT spatially comprehensive discharge products could be promising in its ability to accurately simulate up to daily variability at the watershed scale. Interestingly, Verma and Indu (2023) suggested that hydrological model calibration (in their case the WRF-Hydro modeling framework) using SWOT-like discharge could perform similarly to daily in situ discharge at regional scales. Sadki (2023) obtained similar results when calibrating a reservoir model (DROP, Sadki et al., 2023) to better reproduce dam releases and consequently river discharge downstream dam-reservoirs. Further experiments will need to be conducted in the coming years to determine the ability of multi-mission and actual SWOT-based model correction/data assimilation to reproduce global, high-frequency (up to a daily scale), river dynamics, including the capture of peak flow events across large, medium and small river reaches.

## 4.4. Surface Water Velocity Measurement for Constraining Discharge Estimates

The aforementioned models, whether empirically- or physically-based, rely on systems of equations involving various river variables that typically include the cross-sectional area anomaly $\delta A$, river width $W$, water surface slope $S$, Manning's channel bed friction coefficient $n$, the cross-sectional area under the observed anomaly $A_0$, and cross-sectional average water velocity $V$, which can be linearly related to surface water velocity $u$ (Chiu, 1989; Smart and Biggs, 2020). The revolutionary KaRIn instrument onboard SWOT enables the concurrent estimation of three key variables to capture water propagation in rivers ($\delta A, W, S$), but the estimation of discharge using flow laws remains under-constrained. Resolving the ill-posed hydrodynamic system with a single satellite requires an additional concurrent variable observation.

Among the remaining unknowns, surface water velocity can be estimated through imaging or radar techniques. Various remote sensing methodologies have been investigated in both rivers and the ocean, including large scale particle image velocimetry introduced by Fujita et al. (1998) and still in development today (Legleiter et al., 2017; Legleiter and Kinzel, 2021), doppler scatterometry (Rodríguez et al., 2018) or Doppler radar (Costa et al., 2000; Fulton et al., 2020a, 2020b; Johannessen et al., 2008; Moiseev et al., 2022), and along-track interferometry (Grünler et al., 2013; Romeiser et al., 2007; Yoshida et al., 2021).

Spaceborne large scale particle image velocimetry requires capturing videos over a few seconds, or a set of consecutive images, of objects or hydrodynamic features moving along with the water. It has been used to measure surface velocity by tracking ice flows (Kääb et al., 2019) or other tracers (Legleiter and Kinzel, 2021; Masafu et al., 2023) in rivers. Radar-based methods have rather historically been applied to retrieve ocean currents, including at the mouth of large estuaries (Yang et al., 2024). The use of along-track radar interferometry to measure ocean surface currents was first demonstrated by Goldstein et al. (1988) and later used with the Shuttle Radar Topography Mission (Romeiser et al., 2005) and TerraSAR-X in coastal regions and over rivers (Romeiser et al., 2007, 2010, 2014). Even though extensive reviews of remote sensing capabilities to measure ocean surface winds, waves and currents are available (Hauser et al., 2023), most along-track interferometry demonstrations over rivers have originated from airborne instruments (Biondi et al., 2020; Nouvel et al., 2008; Yoshida et al., 2021).

While measurements of surface water velocity $u$ from space, with encouraging accuracies (Kääb et al., 2019; Legleiter and Kinzel, 2021), could be used to estimate mean cross-sectional velocity $V$ and significantly improve the accuracy of river discharge (e.g., through constraints in Manning's equation, Equation 3), surface velocity patterns do not necessarily reflect subsurface vertical variations closely, and flux estimation remains challenging when the river cross-sectional shape is unknown. In addition, the associated capabilities and data sets are currently not as globally available (neither in time nor in space) as those based on spaceborne estimates of river width, height and slope. The development of a spaceborne platform equipped with an instrument capable of measuring surface water velocity should constitute a key technological priority to improve our ability to monitor water propagation in Earth's rivers.
5. Conclusion and Recommendations

Over the past two decades, monitoring and modeling have significantly advanced our understanding of Earth's surface waters, as shown by the 300+ papers cited here. Such extensive research has also helped in identifying knowledge gaps. This review focuses on global river dynamics to highlight specific paths forward for satellite observations:

(a) *Fine sensing of narrow water bodies (width < 50–100 m) for global Earth system science.* Surface water storage and fluxes play key roles in Earth's cycles. Recent research (Wade et al., 2025b) indicates that capturing 90% (or 95%) of global seasonal river patterns requires accurately measuring the mean and variability of discharge to the ocean in rivers wider than 60 m (or 30 m) and of storage in rivers wider than 100 m (or 60 m). Observing small, narrow rivers at this level with sufficient accuracy is feasible but challenges current technology, such as SWOT. Improved signal resolution, image processing (e.g., object-based methods, machine learning) and dedicated altimetry retracking (e.g., subwaveform analysis, Fang et al., 2025, or further exploitation of 2D radargrams, Tourian et al., 2025) are all paramount for increased observability. An effort to derive an open-access surface water product from the existing archives of high spatial resolution commercial imagery (e.g., at submetric scales, PlanetScope optical data, or Capella SAR spotlight mode data) could be undertaken for the mapping of narrow rivers. In addition, narrow water bodies are also characterized by small amplitude and rapid changes that challenges current observing systems in terms of accuracy, spatial resolution and temporal sampling.

(b) *Ratio of signal accuracy to hydrologic variability and relevance of various measurement technologies for sensor optimization.* Since surface water-related variables generally co-vary and each measurement technology has unique strengths and limitations, understanding river variability (e.g., width, height) relative to measurement accuracy (e.g., pixel resolution and water classification, WSE retrieval) is crucial. Analyzing this trade-off is essential for identifying the most suitable variables for simplified measurement systems. In addition, determining the most relevant signals (e.g., electromagnetic bands considered in the near and shortwave infrared for optical imagers, X-, C-, L-band for radar imagers or W-, V-, Ka-, Ku-band for altimeters), methods/processing (e.g., image fusion and deep learning in imagery, fully focused SAR and interferometry in altimetry) and algorithms (purpose of the SWOT Discharge Algorithm Working Group or the ESA CCI River Discharge project, 2024) to represent flow variations is key to revealing the ideal sensors.

(c) *Frequent observations to advance global river dynamics.* Recent work (Cerbelaud et al., 2024; Sikder et al., 2021) shows that while current/past 10- to 35-day revisit cycles of altimetry missions are sufficient to capture long-term means and seasonal patterns in rivers, they miss peak event timing and duration. Event detection is essential for understanding wave propagation and for early warning systems (in addition to rainfall forecasting). Building on previous guidelines (Cerbelaud et al., 2024), this review estimates that a 2-day repeat would allow resolving the dynamics of 90% of large rivers (width >500 m) and 65% of medium rivers (100 < width <500 m), while a 1-day revisit would enable accurate event duration estimates for 90% of medium rivers. These revisit frequencies approach multi-imagers capabilities (e.g., Munasinghe et al., 2023) but exceed single-satellite limits. A constellation of cost-effective satellites with a 1- to 3-day revisit could greatly enhance our understanding of medium and large river dynamics including flood and flow propagation processes (e.g., a constellation of nadir altimeters; Figure 9). Optical products with such specifications (including high spatial resolution) already exist, even at the global scale, but only originate from commercial endeavors (e.g., PlanetScope Dove constellation of CubeSats; Junqueira et al., 2021; Valman et al., 2024). We acknowledge that achieving both a high temporal revisit and high spatial resolution to enable observation of smaller rivers and their dynamics globally requires more satellites because of smaller instrument swath (Frasson et al., 2024). The deployment of agile task satellites with roll and pitch capabilities (see Gorr et al., 2024, e.g.) could therefore also be considered, with the objective of observing specific river locations in clear-sky conditions around the time areas of interest experience heavy rainfall. Finally, frequent observations must also go hand in hand with low-latency capabilities to support real-time river flood forecasting and mitigation applications.

(d) *Modeling of river systems and multi-sensor approaches to harness the wealth of satellite data.* The increasing availability of multiple surface water-related remote sensing data has been highly instrumental in improving river discharge estimation and inundation modeling. In the coming years, further experiments will be required to evaluate the effectiveness of multi-mission and SWOT-based model correction/data assimilation in reproducing global, high-frequency river dynamics (up to a daily scale), including the detection of peak flow
<figure><img src="image_12.png" /><figcaption>**Figure 9.** Space and time scales of hydrological features and measurement capabilities of different (current and recommended) satellite systems for the study of river dynamics.</figcaption></figure>

events in large to small river reaches. While SWOT has already been used to study 2024 flood events (Laipelt et al., 2025; Simoes-Sousa et al., 2025; Thurman et al., 2025), these early efforts only begin to tap its full potential. Artificial intelligence will also likely continue to benefit from increasingly available hydrological data to provide global reliable flood forecasting (Nearing et al., 2024). In addition to river discharge monitoring, flood risk management and modeling requires better accuracy in inundation extent retrieval from satellite imagery (>80% overlap with reference data) for many applications, in particular to discriminate between competing models or guide flood risk planning (Bates, 2023; Biggin & Blyth, 1996; Horritt et al., 2001).

Along with modeling and data assimilation systems, the implementation of multi-sensor merging (e.g., several imagers, or in combination with altimeters, microwave radiometers) represents an immense opportunity for hydrology (Durand et al., 2021), in particular to better capture high-frequency high flow events (Munasinghe et al., 2023; Nielsen et al., 2022). The full potential of existing satellite capabilities for observing Earth's rivers remains unexploited, as many of these technologies are not being used to their fullest extent. Indeed, many altimetry instruments in geodetic orbits have been under explored/processed for inland water analysis (e.g., CryoSat-2, Saral/AltiKa, ICESat-2, GEDI; Jiang et al., 2024). They could be particularly useful for rivers with north-south orientation that are currently difficult to observe with exact repeat orbit altimetry missions. Overall, the lack of sufficient Level 3 inland altimetry data limits the development of exhaustive water level monitoring systems and further processing of current altimetry missions should be conducted. In addition, the processing of dual frequency co-located SAR imagery systems (e.g., X-, C- or S- + L-band) could aid the detection of inundation extents across various land covers and improve flood mapping accuracy.
<table><caption>**Table 5**
Recommendations for Future Satellite Systems A + B for River Dynamics Monitoring</caption><thead><tr><th>Type of system</th><th>Options</th><th>Recommendations targeted</th><th>Measured variables</th><th>Spatial resolution</th><th>Spatial coverage</th><th>Revisit period (all satellites)</th><th>Challenges</th></tr></thead><tbody><tr><td>A: SWOT-like</td><td>Wide-swath interferometer</td><td>(a), (b), (d), (e)</td><td>Water extent, river width, WSE, slope, velocity?</td><td>≤30 m</td><td>Global</td><td>≤10 days</td><td>Water classification, latency</td></tr><tr><td rowspan="3">B: Simplified constellation system</td><td>1: Agile task optical imager</td><td>(a), (b), (c), (d)</td><td>Water extent, river width</td><td>≤5-10 m</td><td>Local to global</td><td>1-3 days</td><td>Water classification, clouds, vegetation, shadows</td></tr><tr><td>2: SAR imager</td><td></td><td>Water extent, river width</td><td>≤5-10 m (spotlight mode?)</td><td></td><td></td><td>Water classification (use of dual band SAR?), steep topography</td></tr><tr><td>3: Nadir altimeter</td><td></td><td>WSE</td><td>≤50-100 m (V-band, W-band?)</td><td>Partial (≤80 km intertrack spacing)</td><td></td><td>Retracking in narrow, steep-sided river valleys, land cover</td></tr></tbody></table>

Note. Question marks refer to technological perspectives that could be of benefit for future systems.

(e) New variable measurement for accurate water flux and discharge estimates. In its simplest form, determining river fluxes involves six variables ($u, A_0, \delta A, n, W, S$) and two equations (momentum and continuity). With recent satellites (e.g., SWOT) observing only three variables ($\delta A, W, S$), the problem remains under-constrained, with one degree of freedom. Resolving this with a single satellite requires one more concurrent observation. Of the remaining unknowns ($u, A_0, n$), only surface water velocity could be directly observable, drawing inspiration from documented remote sensing methodologies such as particle image velocimetry, radar doppler scatterometry, along-track interferometry, or Doppler radar, making it a key technological priority to better constrain bathymetry-friction parameters in flow laws.

These recommendations are interconnected and interdependent: monitoring the dynamics of smaller rivers requires an observational strategy with improved understanding of uncertainties and better accuracy, finer spatial resolution and sampling, and more frequent revisit, aided by planned multi-satellite merging and adapted modeling capabilities. Current technology is unlikely to meet all the aforementioned challenges within the budget of a single space agency. However, an international strategy could pool resources to create a multi-mission hydrology-focused observing system that doesn't require excessive inter-platform processing on the user side and does not depend entirely on serendipity for co-located measurements (Table 5). This might include a cutting-edge single or dual-satellite mission for advanced measurements of surface water variables (Type A: SWOT-like), alongside a constellation of cost-effective satellites targeting highly variable processes (Type B, e.g., the SMall Altimetry Satellites for Hydrology concept, SMASH; Benveniste, 2021; Blumstein et al., 2019; similar systems are also praised in oceanography; Li et al., 2022). Such a system could help address urgent scientific and societal challenges facing Earth's hydrographic networks.

Some of the capabilities we advocate for may already be partially achievable through existing or upcoming satellite systems, such as SWOT, Sentinel-3 Next Generation Topography (S3NG-T), and small-satellite imagery constellations. These missions, providing spatiotemporal observations of global river hydraulics variability, may therefore be well-positioned to contribute to answering some of the key questions raised by Blöschl, Bierkens, et al. (2019) across scales (as illustrated in Figure 1). Evaluating the extent to which current and upcoming satellite systems can address these questions will help refine and prioritize observational requirements further. For example, we may eventually be able to process accurate SWOT or S3NG-T observations over rivers with widths narrower than 30 m (Maubant et al., 2025), though such leap is not guaranteed and could only be enabled by improved understanding in measurement phenomenology, error patterns and associated data processing capabilities. Additionally, water extent observations from a commercial imagery constellation could potentially serve as the high temporal revisit companion to SWOT or S3NG-T, but it still remains unclear whether observing surface water extent or elevation provides a more comprehensive and accurate means of tracking flow propagation in the world's rivers. The detailed evaluation of early observations from SWOT could help guide the choice of altimetry or imagery for a constellation. If high-revisit imagery proves insufficient, high-revisit cost-effective constellations should be extended to include altimetry (e.g., Blumstein et al., 2019).
Additionally, for high-frequency satellite data to help advance science and society, there would be value in free and accessible data policies, which may or may not be in line with commercial endeavors. Finally, additional measurements capabilities such as surface water velocity may be acquired using new instrument technology, to be integrated within new designs based on existing complex satellite platforms like SWOT or upcoming missions like S3NG-T. The S3NG-T mission is planned to continue focusing on wide-swath altimetry to provide global spatial coverage of WSE, width and slope at a very fine spatial resolution, with a temporal revisit of approximately 5 days. With a 3-day global revisit and spatial resolution ≤25 m² set as Copernicus' Sentinel-1 SAR Next Generation mission requirements, water extent-based algorithms based on radar imagery may very well emerge as the most suitable among non-altimetry methods for monitoring high-frequency river dynamics in the future.

For a global plan to be sustainable, a commitment to ensure the establishment of long-term monitoring satellite systems, that is, continuity of measurements, operational processing and delivery of exploitable information is required. To optimize the planning of future missions and ensure continuity, it is also essential to derive global products from current constellations (e.g., Jason-3, Sentinel-3, Sentinel-6 MF, ICESat-2), and in particular from SWOT (purpose of the Discharge Algorithm Working Group), with the highest possible degree of reliability to ascertain the present spatiotemporal capabilities and accuracies at various basin scales. Eventually, to bring all the current/future flood data from space rapidly in the hands of those who desperately need them in a manner that is genuinely actionable, we also need to invest in building scalable pipelines to empower the broader community (Dasgupta et al., 2025). Sustainable flood data integration and accessibility also require open data commitments and collaboration across political, economic, and human systems beyond science (Alvarez León & Gleason, 2017; Bennett et al., 2022). To truly save and/or improve lives and maximize the return on public investments in surface water-oriented remote sensing systems, future satellite missions will require proactive engagement, even before launch, with protocols to build user-readiness such as the SWOT early adopter program (Hossain et al., 2022). It is our hope that the progress made toward satellite requirements to capture water propagation in Earth's rivers, as outlined in this paper, will complement other recent communications (Huang et al., 2024) and community efforts in hydrological sciences (Blöschl, Bierkens, et al., 2019) and mirror some of the recommendations for better observational methods in hydrological sciences stated in Beven et al. (2020).

Table of Acronyms and Abbreviations

<table><tr><td>AMSR-E:</td><td>Advanced Microwave Scanning Radiometer for EOS</td></tr><tr><td>BiST:</td><td>Bin-Space-Time</td></tr><tr><td>C/M:</td><td>Calibration/Measurement Ratio</td></tr><tr><td>D:</td><td>Water Depth</td></tr><tr><td>DEM:</td><td>Digital Elevation Model</td></tr><tr><td>DSWx:</td><td>Dynamic Surface Water Extent</td></tr><tr><td>ESA:</td><td>European Space Agency</td></tr><tr><td>FFM:</td><td>Flood Forecasting and Mitigation</td></tr><tr><td>FFSAR:</td><td>Fully-Focused Synthetic Aperture Radar</td></tr><tr><td>geoBAM:</td><td>geomorphically informed Bayesian At-many-stations hydraulic geometry-Manning's</td></tr><tr><td>GRRATS:</td><td>Global River Radar Altimeter Time Series</td></tr><tr><td>H and WSE:</td><td>Water Surface Elevation</td></tr><tr><td>HiVDI:</td><td>Hierarchical Variational Discharge Inversion</td></tr><tr><td>ICESat-2:</td><td>Ice, Cloud, and land Elevation Satellite 2</td></tr><tr><td>KGE:</td><td>Kling-Gupta Efficiency</td></tr><tr><td>LRM:</td><td>Low Resolution Mode</td></tr><tr><td>LSM:</td><td>Land Surface Model</td></tr></table>
<table><tr><td>McFLI:</td><td>Mass conserved Flow-Law Inversion</td></tr><tr><td>MERIT-Basins:</td><td>Multi-Error-Removed Improved Terrain Basins</td></tr><tr><td>MetroMan:</td><td>Metropolis-Manning</td></tr><tr><td>MODIS:</td><td>Moderate Resolution Imaging Spectroradiometer</td></tr><tr><td>MOMMA:</td><td>Modified Optimized Manning Method Algorithm</td></tr><tr><td>NASA:</td><td>National Aeronautics and Space Administration</td></tr><tr><td>NSE:</td><td>Nash-Sutcliffe Efficiency</td></tr><tr><td>OCOG:</td><td>Offset Centre of Gravity</td></tr><tr><td>OPERA:</td><td>Observational Products for End-Users from Remote Sensing Analysis</td></tr><tr><td>PO.DAAC:</td><td>Physical Oceanography Distributed Active Archive Center</td></tr><tr><td>Q:</td><td>River Discharge</td></tr><tr><td>RC:</td><td>Rating Curve</td></tr><tr><td>(N/R) RMSE:</td><td>(Normalized/Relative) Root Mean Squared Error</td></tr><tr><td>RSEG:</td><td>Remote Sensing-Based Extension of GRDC Discharge</td></tr><tr><td>RSQ:</td><td>Remote Sensing of Discharge</td></tr><tr><td>S:</td><td>(water surface) slope</td></tr><tr><td>SAD:</td><td>SWOT Assimilated Discharge</td></tr><tr><td>SAEM:</td><td>Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements</td></tr><tr><td>SAR:</td><td>Synthetic Aperture Radar</td></tr><tr><td>SIC4DVar:</td><td>Simulation and Integration of Control for Canals 4-Dimensional Variational data assimilation</td></tr><tr><td>SMAP:</td><td>Soil Moisture Active Passive</td></tr><tr><td>SMOS:</td><td>Soil Moisture and Ocean Salinity</td></tr><tr><td>SRTM:</td><td>Shuttle Radar Topography Mission</td></tr><tr><td>SSM/I:</td><td>Special Sensor Microwave/Imager</td></tr><tr><td>SWOT:</td><td>Surface Water and Ocean Topography</td></tr><tr><td>Terra-SAR-X:</td><td>Terra Synthetic Aperture Radar in band X</td></tr><tr><td>USGS:</td><td>United States Geological Survey</td></tr><tr><td>W:</td><td>River Width</td></tr><tr><td>WRM:</td><td>Water Resources Management</td></tr><tr><td>WSE and H:</td><td>Water Surface Elevation</td></tr></table>

Data Availability Statement

The data set allowing for the computation of global suggested revisit times for capturing peak flow event durations in rivers are made publicly available for research purposes in Cerbelaud (2024). We follow the NASA Open Science guidelines for our open science practices. © 2025 California Institute of Technology. Government sponsorship acknowledged.
Acknowledgments

The authors thank Prof. Alan Robock for his editorial treatment and Prof. Paola Passalacqua and two other anonymous reviewers for their time and their constructive and thoughtful comments on our manuscript. This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (NASA) including grants from the Terrestrial Hydrology (NNH17ZDA001N-THP), Earth Science U.S. Participating Investigator (NNH20ZDA001N-EUSPI), and Surface Water and Ocean Topography Science Team (NNH23ZDA001N-SWOTST) Programs.

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