# Bidirectional Translations Between Observational and Topography-Based Hydrographic Data Sets: MERIT-Basins and the SWOT River Database (SWORD)

Jeffrey Wade, Cédric H David, Elizabeth H Altenau, Elyssa L Collins, Hind Oubanas, Stephen Coss, Arnaud Cerbelaud, Manu Tom, Michael Durand, Tamlin M Pavelsky

<figure><img src="image_1.png" /></figure>

To cite this version:

Jeffrey Wade, Cédric H David, Elizabeth H Altenau, Elyssa L Collins, Hind Oubanas, et al. Bidirectional Translations Between Observational and Topography-Based Hydrographic Data Sets: MERIT-Basins and the SWOT River Database (SWORD). Water Resources Research, 2025, 61 (5), pp.e2024WR038633. 10.1029/2024wr038633. hal-05094961

HAL Id: hal-05094961

https://hal.inrae.fr/hal-05094961v1

Submitted on 3 Jun 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Water Resources Research®

## DATA ARTICLE

10.1029/2024WR038633

### Special Collection:

Science from the Surface Water and Ocean Topography Satellite Mission

### Key Points:

* Data transfer pathways between hydrographic networks enable interoperability between satellite observations and hydrologic models

* The Multi-Error-Removed Improved Terrain (MERIT)-SWOT River Database (SWORD) data set provides critical links between the SWORD and MERIT-Basins hydrographic networks

* MERIT-SWORD establishes global, bidirectional, one-to-many translations between ~240,000 SWORD and ~277,000 MB river reaches

### Supporting Information:

Supporting Information may be found in the online version of this article.

### Correspondence to:

J. Wade,
jeffwade200@gmail.com

### Citation:

Wade, J., David, C. H., Altenau, E. H., Collins, E. L., Oubanas, H., Coss, S., et al. (2025). Bidirectional translations between observational and topography-based hydrographic data sets: MERIT-basins and the SWORD river database (SWORD). *Water Resources Research*, *61*, e2024WR038633. https://doi.org/10.1029/2024WR038633

Received 19 AUG 2024
Accepted 4 MAY 2025

### Author Contributions:

**Conceptualization:** Jeffrey Wade, Cédric H. David, Elizabeth H. Altenau, Elyssa L. Collins, Stephen Coss, Arnaud Cerbelaud, Manu Tom, Tamlin M. Pavelsky

© 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.

<figure><img src="image_3.png" /></figure>

## Bidirectional Translations Between Observational and Topography-Based Hydrographic Data Sets: MERIT-Basins and the SWOT River Database (SWORD)

Jeffrey Wade¹ ID, Cédric H. David¹ ID, Elizabeth H. Altenau² ID, Elyssa L. Collins²,³,⁴ ID, Hind Oubanas⁵ ID, Stephen Coss⁶,⁷ ID, Arnaud Cerbelaud¹ ID, Manu Tom¹ ID, Michael Durand⁶,⁷ ID, and Tamlin M. Pavelsky² ID

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, ³Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA, ⁴Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA, ⁵INRAE, UMR G-eau, Montpellier, France, ⁶School of Earth Sciences, Ohio State University, Columbus, OH, USA, ⁷Byrd Polar and Climate Research Center, Columbus, OH, USA

**Abstract** The recently launched Surface Water and Ocean Topography (SWOT) Mission is expected to provide transformative observations of water surface elevation, width, and slope and produce derived estimates of discharge for global rivers along rivers in the SWOT River Database (SWORD). However, the hydrographic representation of rivers in SWORD differs from hydrography data sets commonly used for modeling purposes, such as Multi-Error-Removed Improved Terrain (MERIT)-Basins. Here, we develop links between the river networks of SWORD and MERIT-Basins (MB) to enable interoperability between SWOT data products and hydrologic modeling frameworks. This data set, termed MERIT-SWORD, identifies a subset of ~277,000 global MB river reaches that most closely represent the location and extent of the SWORD river network and establishes bidirectional, one-to-many translations between reaches in the two hydrographic data sets. The MERIT-SWORD data set serves to unite SWOT observations with river routing models, allowing for the seamless and standardized assimilation of SWOT vector products into global river simulations and the provision of improved a priori discharge estimates for SWOT discharge computation.

**Plain Language Summary** The Surface Water and Ocean Topography (SWOT) Mission observes the water surface elevation, width, and slope of river reaches described in the SWOT River Database (SWORD). The location and extent of rivers in SWORD differ considerably from other river network data sets that are commonly used for hydrological modeling such as Multi-Error-Removed Improved Terrain (MERIT)-Basins. Here, we present and publicly share the MERIT-SWORD data set which links rivers in SWORD to rivers in MERIT-Basins (MB), and vice versa. These links between river reaches in the two data sets can allow for model simulations to be used as a first guess for SWOT measurements, and for observations from SWOT to be easily transferred to hydrologic modeling frameworks based on the MB river network for data assimilation.

## 1. Introduction

Hydrographic data sets, which provide a digital representation of the location and topological connectivity of river networks, are fundamental to the study of surface water hydrology (Lehner & Grill, 2013; Lehner et al., 2008; Yamazaki et al., 2019). Perhaps most critically, river hydrography standardizes the position of rivers in space, allowing for the geolocation of in situ measurements, remote sensing observations, and model simulations (Yamazaki et al., 2019). High-resolution digital elevation models (DEMs) are often used to produce maps of flow accumulation and flow direction, delineate channels and watershed boundaries, and generate hydrographic networks. Examples include the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS, Lehner et al., 2008) data set, based on the global Shuttle Radar Topography Mission (SRTM, Farr et al., 2007), and the MERIT-Basins (MB) (Lin et al., 2019; Yang et al., 2021) data set, based on Multi-Error-Removed Improved Terrain (MERIT) Hydro topography (Yamazaki et al., 2019). These DEM-based vector networks often lack some of the complexities inherent to riverine systems (braided channels, complex deltas, etc.) but ensure consistent network topology and ease of application. Alternatively, hydrography data sets can also be derived from optical imagery, as is done by the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018), to better map the observed position of river centerlines and allow for increased network
**Data curation:** Jeffrey Wade

**Funding acquisition:** Cédric H. David

**Investigation:** Jeffrey Wade

**Methodology:** Jeffrey Wade

**Resources:** Cédric H. David, Elizabeth H. Altenau, Elyssa L. Collins, Michael Durand, Tamlin M. Pavelsky

**Software:** Jeffrey Wade, Cédric H. David, Hind Oubanas

**Supervision:** Cédric H. David

**Validation:** Jeffrey Wade, Hind Oubanas

**Visualization:** Jeffrey Wade

**Writing – original draft:** Jeffrey Wade

**Writing – review & editing:**

Jeffrey Wade, Cédric H. David, Elizabeth H. Altenau, Elyssa L. Collins, Hind Oubanas, Stephen Coss, Arnaud Cerbelaud, Manu Tom, Michael Durand, Tamlin M. Pavelsky

complexity. The fundamental differences in the representation of geometric complexity in river systems between topography-derived and imagery-derived hydrography data sets inevitably result in discrepancies in the extent and spatial location of river networks. In cases where a research approach necessitates the use of data from multiple hydrographic networks, it is critical to reconcile differences between data sets by translating (i.e., linking) individual river reaches in one network to those in another to enable the transformation of information between networks.

The underlying problem outlined above is particularly relevant to the recently launched Surface Water and Ocean Topography (SWOT) Mission and its associated SWOT River Database (SWORD; Altenau et al., 2021). The SWOT mission uses a novel wide swath Ka-band Radar Interferometer (KaRIn) (Enjolras & Rodriguez, 2009; Peral et al., 2024) to measure the water surface elevation, slope, and extent of global surface waters with an average 10 days revisit frequency (Alsdorf et al., 2007; Biancamaria et al., 2016). For freshwater hydrology applications, these observations are aggregated to river reaches in SWORD, a river vector hydrography product primarily derived from the Global River Widths from Landsat (GRWL) product, which itself is based on optical imagery (Allen & Pavelsky, 2018; Altenau et al., 2021). Due to the resolution limits of Landsat imagery and the underlying GRWL data set, SWORD only contains rivers wider than ~30 m, generally matching SWOT's science goal of observing rivers wider than 50 m (JPL Internal Document, 2018). In isolation, SWOT's simultaneous observations of river elevation, slope, and width, leading to global reach-specific estimates of discharge (Durand et al., 2023), can be expected to provide novel insights into hydrologic dynamics (Biancamaria et al., 2016). When coupled with existing hydrologic models, SWOT discharge estimates observations could supply valuable time-varying constraints on global river flow simulations through data assimilation (Oubanas, Gejadze, Malaterre, Durand, et al., 2018; Oubanas, Gejadze, Malaterre, & Mercier, 2018), while model simulations could supply a priori long-term average discharges used in the calculation of flow law parameters leading to SWOT discharge estimates.

Here, we present a novel data product that reconciles critical differences between SWORD (Altenau et al., 2021) and MB (Lin et al., 2019; Yang et al., 2021), an elevation-derived vector hydrography data set commonly used by global river routing models (Collins et al., 2024). The SWORD and MB river networks differ considerably in their representation of the location and extent of global river reaches, complicating potential synergistic data transfer between SWOT observations and existing hydrologic models. Our data set, which we term “MERIT-SWORD,” seeks to (a) identify a subset of river reaches in MB that directly correspond to related reaches in SWORD, and (b) generate bidirectional, one-to-many links (i.e., translations) between river reaches in SWORD and MB. Through the MERIT-SWORD data set, we seek to standardize the transfer of data between SWORD and MB in the hydrologic community, allowing for the reproducible assimilation of SWOT river observations into hydrologic modeling frameworks.

## 2. Data

### 2.1. SWOT River Database (SWORD)

SWORD (Version 16) provides a set of global river reaches that form the basis for SWOT's vector data products of width, slope, elevation, and future estimates of discharge (Altenau et al., 2021; accessible at https://swor-dexplorer.com). SWOT reaches are primarily derived from the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018), which maps the centerlines of global rivers wider than 30 m using Landsat imagery. SWOT also integrates elevation and flow accumulation data from the DEM-derived MERIT Hydro (Yamazaki et al., 2019) data set to aid in constructing network topology (Altenau et al., 2021) and channel obstructions from the Global River Obstruction Database (Whittemore et al., 2020; GROD) and HydroFALLS (https://wp.geog.mcgill.ca/hydrolab/hydrofalls).

SWORD consists of ~240,000 reaches globally, which in aggregate have a total length of ~2.2 million km. SWOT reaches are organized into 61 hydrologic regions and assigned Pfafstetter codes defined by the nested watershed boundaries of the HydroBASINS data set (Lehner & Grill, 2013; Verdin & Verdin, 1999). Each reach is identified by a unique 11-digit reach_id value consisting of its Pfafstetter code derived from HydroBASINS, a reach number, and its type (Altenau et al., 2021). Five reach types exist in SWORD, including river, lake on river, dam/waterfall, unreliable topology, and ghost reach (Altenau et al., 2021). Although SWOT discharge estimates will only be produced at SWORD reaches of type “river,” our analysis includes SWORD reaches of all types for completeness.
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Conceptual depiction of the Multi-Error-Removed Improved Terrain (MERIT)-SWOT River Database (SWORD) data product, which links river reaches in the SWORD with corresponding reaches in MERIT-Basins.</figcaption></figure>

## 2.2. MERIT-Basins

MERIT Hydro (v0.7) Basins (v1.0), referred to here as MB, is a global vector hydrography data set consisting of ~3 million river reaches and their associated unit catchments (Lin et al., 2019; Yang et al., 2021; accessible at https://www.reachhydro.org/home/params/merit-basins). MB is based on the ~90 m resolution MERIT DEM (Yamazaki et al., 2017) and the accompanying flow direction and flow accumulation maps provided by the MERIT Hydro data set (Yamazaki et al., 2019). Reaches in MB are defined by a 25 km² channelization threshold and grouped into 61 hydrologic regions using the basin boundaries of MERIT Hydro (Lin et al., 2019). We note that the hydrologic regions of MB differ slightly from those used by SWORD, which leverages the HydroBASINS data set to define its regional boundaries (Lehner & Grill, 2013; Lin et al., 2019). Each MB river reach and its corresponding unit catchment are identified by a shared unique 8-digit COMID value. Despite the existence of other global river vector data sets, we opt to base our data product on MB given its existing links to a priori discharge estimates for the SWOT mission (e.g., GRADES; Durand et al., 2023; Lin et al., 2019) and its widespread application to hydrologic modeling and analysis (Collins et al., 2024; Feng et al., 2021, 2022; Lin et al., 2019; Yang et al., 2021; Yin et al., 2024).

# 3. Methodology

## 3.1. Generating the MERIT-SWORD Network

As the MB hydrography is derived from high-resolution topography (Lin et al., 2019; Yamazaki et al., 2019; Yang et al., 2021) while SWORD hydrography is primarily based on the imagery-derived GRWL database (Allen & Pavelsky, 2018), the two networks have numerous differences that introduce challenges to translation efforts (Figure 1). These include, but are not limited to, the spatial location of river centerlines, the extent of the river networks, the boundaries of hydrologic regions, and the representation of multichannel reaches. Additionally, the spatial intersection of SWORD and MB reaches alone is insufficient to establish robust transitions, as many narrow tributaries included in MB overlap, but not are not analogous to, larger SWORD mainstem rivers. Due to these differences, we find it critical to first establish a network of MB reaches that directly correspond to rivers in SWORD (which we term MERIT-SWORD river networks) to ensure that subsequent translations do not include undesired MB reaches. Ultimately, these MERIT-SWORD river networks serve as an intermediate data product to ensure reliable translations between SWORD and MB. We rely heavily on the reach-specific unit catchments provided by MB (Lin et al., 2019; Yang et al., 2021) to account for discrepancies in the spatial location of river centerlines between the two hydrography data sets.

We generate incrementally refined MERIT-SWORD river networks, which are made up of MB reaches that in aggregate match the extent and location of rivers in SWORD for each of the 61 global Pfafstetter Level 2 hydrologic regions (Figure 1). For each unique SWORD reach, we intersect and select the single MB catchment (and its corresponding reach) that contains the largest proportion of the given SWORD reach (Figure 2a). This provides a first approximation of the MB reaches that likely correspond to SWORD reaches. Then, using the topological connections provided by the NextDownID field of MB, we trace all selected reaches downstream to their final outlet (defined here as having a NextDownID value of 0) and add the traced MB reaches to the MERIT-SWORD network (Figure 2b). The tracing procedure ensures that the resulting MERIT-SWORD network is topologically complete and lacks gaps between neighboring reaches. Then, we remove any MB reaches from the traced river network if they are located more than 10 km from the nearest SWORD reach to ensure that the MERIT-SWORD networks match the spatial extent of SWORD (Figure 2c). We finalize the MERIT-SWORD river networks by removing 3,583 global MB reaches from the traced networks that do not correspond to SWORD reaches and were mistakenly included by intersection or tracing (see Table S10 in Supporting Information S1).
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Tracing steps used to generate the Multi-Error-Removed Improved Terrain (MERIT)-SWOT River Database (SWORD) river network from MERIT-Basins (MB) reaches corresponding to SWORD reaches. (a) Initial intersection and selection of MB catchments and SWORD reaches; (b) topological tracing from selected MB reaches to their most downstream outlet; and (c) removal of selected MB reaches more than 10 km from SWORD reaches to form the final MERIT-SWORD network.</figcaption></figure>

Although our methodology is designed to be performed independently on each of the 61 hydrologic regions, mismatches in the boundaries of these regions between the MB and SWORD data sets necessitate additional consideration. We address this misalignment by searching for MB reaches that correspond to SWORD reaches in all MB hydrologic regions that overlap the SWORD region of interest and applying the tracing approach we outline above. Due to this boundary discrepancy, the resulting MB reaches assigned to a single SWORD region, in a small number of cases, originate from multiple MB regions and are therefore not topologically continuous. Additionally, the shapefiles defining the location of MB and SWORD reaches use different sign conventions for longitude values near the antimeridian. To ensure that features from the two networks could be properly intersected, we temporarily modify the longitude coordinates of SWORD reaches that cross the antimeridian.

## 3.2. Translating Between MERIT-Basins and SWORD

Our method for establishing one-to-many bidirectional translations between reaches in SWORD and MB is based on the intersection of SWORD reaches with MB unit catchments, which are linked to MB reaches via shared COMID values (Figure 3). The resulting translations provide, for a given reach in either MB or SWORD, the corresponding reaches in the other data set. We begin by selecting the subset of MB catchments that are associated with reaches in the MERIT-SWORD river networks for each region (described in Section 3.1). Only this subset of MB catchments is intersected with SWORD reaches when identifying translations, ensuring that the numerous tributaries included in MB that do not directly correspond to reaches in SWORD are excluded from the resulting translations. For each SWORD reach, we identify all intersecting eligible MB catchments associated with MERIT-SWORD reaches and rank them based on the partial intersecting length (in meters) of the SWORD reach contained within each catchment (Figure 3). These partial intersecting SWORD lengths form the basis for both the SWORD-to-MB and MB-to-SWORD translations. Finally, we leverage flow accumulation values assigned to reaches in each data set, which reflect the total upstream area contributing to each reach, to confirm the validity of translations between reaches in MB and SWORD. We compare the flow accumulation areas between the intersecting MB catchments and SWORD reaches to confirm that the corresponding reaches are of similar size and can therefore be inferred to represent the same river. Corresponding reaches with flow accumulation values outside of one order of magnitude of each other are not included in the translation. We note that while removing reaches due to flow accumulation considerably improves the resulting translation, the process likely also removes
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Multi-Error-Removed Improved Terrain (MERIT)-SWOT river database (SWORD) reaches are translated to corresponding SWORD reaches (a, b) (and vice versa, from SWORD to MERIT-SWORD) (c, d) through the intersection of the MERIT-Basins (MB) catchments of MERIT-SWORD reaches with each SWORD reach. For a given SWORD reach α, the COMIDs of intersecting MB catchments A and B are ordered by the partial length of SWORD α (m) contained within catchments A and B (a, b). For a given MB reach B and its associated catchment, the reach IDs of intersecting SWORD reaches α and β are ordered by the partial length of SWORD α and β (in km) contained within catchment B.</figcaption></figure>

a limited number of valid translations due anomalous flow accumulations in SWORD caused by errors in network topology. This is a current limitation of our approach that will likely be improved with future updates to SWORD topology.

We provide translations from SWORD to MB by storing all SWORD reach_id values for a given region and relating those reaches to intersecting MB catchments, ranked by their corresponding partial SWORD lengths (Figure 3a). For each SWORD reach, we store these intersecting MB COMID values in columns named mb_1, mb_2,..., etc., while the partial SWORD lengths (m) are stored in columns named part_len_1, part_len_2,..., etc (Figure 3b). Similarly, we translate from MB to SWORD by storing all MB COMID values for a given region and relating those reaches to the SWORD reaches that intersect their corresponding MB catchments (Figure 3c). The reach_id values of the intersecting SWORD reaches are ranked by their partial intersecting length within each MB catchment and are stored in columns named sword_1, sword_2,..., etc., while the partial SWORD lengths (m) are again stored in columns named part_len_1, part_len_2,..., etc (Figure 3d). In cases where a river reach does not have a corresponding reach in the other data set, it is assigned a value of “0” in all columns of the translation table.
To validate that observations or model simulations can be transferred between the two networks via our translational tables with no loss of information, we verify that the MB-to-SWORD translations can be recreated in their entirety using only data contained within SWORD-to-MB translations, and vice versa. We provide abridged example translation tables in Tables S1 and S2 in Supporting Information S1. While our translations between MB and SWORD follow a one-to-many approach, we acknowledge that alternative methods involving modification of river reach geometries, such as used in the Harmonized Prior river-lake database (HarP; Sikder et al., 2024), can enable one-to-one mappings.

### 3.3. MERIT-SWORD Translation Diagnostics

Following translation between MB reaches in MERIT-SWORD and SWORD reaches, we apply several diagnostic tests to assess the quality of translations at each reach. Reaches with valid translations not flagged by any diagnostic tests are assigned a value of “0.” The first diagnostic evaluation ensures that all reaches translated to a single MB or SWORD reach are topologically connected to each other, under the assumption that a continuous river segment should be referenced to a similarly continuous segment from the other hydrographic network. If the reaches translated to a single reach consist of multiple, unconnected segments, we assign a quality flag value of “1” to indicate a topological discontinuity. In some cases, often due to a misalignment between the hydrographic networks, some MB or SWORD reaches do not intersect any features in the other data set and are therefore not assigned a translation. We assign reaches lacking corresponding translations with a diagnostic value of “2.”

When initial translations between MB and SWORD features fail quality evaluations, we discard the translation in question and supplement the diagnostic value of “2” (indicating an absent translation) with additional information (either “21” or “22,” as detailed below). As noted previously, we remove translations from MB and SWORD features if their corresponding flow accumulation values differ by more than one order of magnitude (see Section 3.2). We flag these reaches with a diagnostic value of “21” to indicate that the reaches have translations that are eliminated when validating the similarity of their flow accumulations. Additionally, reaches lacking corresponding translated reaches are particularly common in coastal regions, where the MB and SWORD data sets disagree on the position of the coastline. Most commonly, SWORD reaches extend beyond the coastal endpoints of MB reaches and therefore lack counterparts in the MERIT-SWORD network. When these SWORD reaches are located outside of the coastline defined by MB, which we represent by the boundaries of MB’s 61 hydrologic regions, we flag the reaches with a diagnostic value of “22.” In aggregate, these diagnostic flags identify reaches in the MB-to-SWORD and SWORD-to-MB translations that require additional consideration when transferring data between networks (Table S3 in Supporting Information S1).

## 4. Results

### 4.1. Data Products

Our analysis produces two primary data products, with each consisting of 61 files corresponding to each Level 2 Pfafstetter region. These files include:

* MB-to-SWORD and SWORD-to-MB Translations (NetCDF)

* MB-to-SWORD and SWORD-to-MB Translation Diagnostics (NetCDF)

We also provide both the translation and diagnostic tables in shapefile format, joined to their respective MB and SWORD river vector shapefiles for each region.

### 4.2. MERIT-SWORD Translation Quality

Our MB-to-SWORD and SWORD-to-MB translations supply bidirectional links for information transfer between MB and SWORD reaches. Globally, 9.1% (266,411) of all MB reaches were associated to a corresponding SWORD reach. Of these MB reaches with translations, each reach is on average associated to 1.69 SWORD reaches, with a minimum of 0 and a maximum of 40 translated reaches. MB reaches with many associated SWORD reaches (such as the maximum of 40) typically occur when the SWORD network is subdivided into numerous short segments by a series of dams or obstructions. On average, each SWORD reach corresponds to 2.05 MB reaches, with a minimum of 0 and a maximum of 11 translated reaches. For both MB (excluding reaches not in MERIT-SWORD) and SWORD, the most common number of associated reaches corresponding to each
reach is one. Reaches with only a single translation typically arise when neighboring MB and SWORD reaches differ significantly in length, resulting in one reach being fully contained within the other. Additionally, SWORD reaches classified as type 6 (ghost reaches) often have only a single corresponding MB reach due to their short length.

To identify potential issues with the validity of our translations between MB and SWORD, we also present tables of quality flags for the translations in each region, with flag values equal to “0,” “1,” “2,” “21,” or “22.” As expected, a considerable proportion (90.7%) of MB reaches lack a corresponding SWORD river reach (diagnostic flag of “2”), though most of these reaches are not present in the MERIT-SWORD network due to the far greater extent of the MB river network as compared to SWORD (Figures 1 and 4). Of the 277,036 global MB reaches present in our MERIT-SWORD network, 266,444 (96.2%) have diagnostic flags equal to a value of “0,” confirming that their translations are valid. Similarly, 229,917 (95.1%) of the 241,852 SWORD reaches have translations with diagnostic flags equal to “0.” The diagnostic value indicating that a reach had its translation removed due to mismatches in flow accumulation values (“21”) is most common for both networks (excluding MB reaches not in MERIT-SWORD), with 5,937 (2.1%) flagged reaches in MB-to-SWORD translations and 4,415 (1.8%) flagged reaches in SWORD-to-MB translations. SWORD reaches classified as “river” (type 1) and “dam/waterfall” (type 4) have the most reliable translations, with 97.2% and 98.2% of reaches in those categories having valid translations, respectively (Table S7 in Supporting Information S1). In contrast, translations for the “unreliable topology” SWORD reach class (type 5), which includes coastal rivers, are the least reliable, with only 80.3% of reaches having valid translations. We present summaries of the MERIT-SWORD translation diagnostics based on geographic region and other SWORD reach types in Supporting Information S1 (Tables S5–S7).

## 4.3. MERIT-Basins and SWORD Network Alignment

The accuracy of the translations provided by the MERIT-SWORD product is fundamentally controlled by the spatial correspondence of river reaches in MB and SWORD. Consequently, it is critical to evaluate how the MERIT-SWORD river network, an intermediate data product composed of MB reaches that are directly analogous to SWORD reaches, aligns with the SWORD river network. Overall, the MB reaches comprising the MERIT-SWORD river network provide excellent coverage of the SWORD river network worldwide (see Figure S1 in Supporting Information S1). Globally, the MERIT-SWORD network includes 277,036 MB reaches, with a total aggregate length of 2,448,197 km, compared to the 241,852 reaches and 2,172,425 km of the SWORD network. The greater total length of MB reaches in MERIT-SWORD arises because MB reaches that partially overlap SWORD reaches are included in their entirety in the MERIT-SWORD network. However, this slight overestimation of river extent by MERIT-SWORD does not influence the results of translation between the two networks, as the partial intersecting SWORD lengths included in the translations can be used to scale transferred data values. We present further region-specific summaries of the MERIT-SWORD network in Supporting Information S1 (Tables S2–S4).

The alignment between MB reaches in the MERIT-SWORD network and reaches in the SWORD network depends on several factors. In regions of moderate to high topographic relief, the MERIT-SWORD network generally provides exceptional coverage of SWORD reaches, as MB and SWORD tend to agree on the spatial location of river centerlines in these settings. However, in poorly drained, low-relief regions such as Northern Canada and Siberia, MB reaches are poorly aligned with the position of all SWORD reaches. Additionally, MERIT-SWORD struggles to represent SWORD reaches in multi-channel river systems and complex deltas. This limitation arises because the MB data set (like other DEM-derived hydrographic networks) is designed to only allow a single downstream outlet from each reach, making it incapable of reflecting the multitude of complex river configurations present in SWORD (Altenau et al., 2021). Similarly, many SWORD reaches associated with man-made diversions or canals often do not have corresponding MB reaches. While we provide translations for all five types of SWORD reaches, the MERIT-SWORD network and resulting translations are most reliable for SWORD classified as “river” (see Table S7 in Supporting Information S1). These reaches typically represent undisrupted channels with well-defined topographic drainage. Given the uncertainty in the position of SWORD and MB rivers in coastal and estuarine settings, we advise users of our data set to exercise caution when interpreting translations to SWORD reaches flagged as having unreliable topology (type 5).
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Global translation of long-term mean corrected discharge (m³/s) from the MeanDRS data set (Collins et al., 2024) to SWOT River Database (SWORD) reaches (a). Translations of discharge made using a weighted average based on the relative overlap between individual MERIT-Basins (MB) catchments and SWORD reaches. MeanDRS discharge for all MB reaches is shown for the Mississippi (b1), Amazon (c1), and Congo (d1) basins, and compared to translated MeanDRS discharge along SWORD reaches for the same regions (b2, c2, and d2).</figcaption></figure>

**5. MERIT-SWORD Applications**

The MERIT-SWORD data product facilitates the seamless transfer of hydrologic information between the MB and SWORD hydrography data sets in both directions. At the core of the MERIT-SWORD data set are the MB-to-SWORD and SWORD-to-MB translation tables, which provide a foundation for the future harmonization of river model simulations and SWOT data products. For each reach, the translational tables list all corresponding reaches in the other hydrographic network, along with the associated partial overlapping length of related MB catchments and SWORD reaches. These partial intersecting length values serve an instrumental role in transferring hydrologic information from multiple reaches to a single shared corresponding reach, as they can be used to calculate weighted averages for terms of interest during translation. Additionally, the translation diagnostic files can be
used to assess potential issues in translation arising from network topology errors or to provide explanations for
why reaches may lack corresponding matches in either MB or SWORD.

**5.1. Transferring Hydrologic Information Between MERIT-Basins and SWORD**

Here, we provide two demonstrations of how hydrologic simulations or observations associated with either MB or SWORD can be transferred to the other river network using our MERIT-SWORD data product. First, we show how hydrologic simulations based on elevation-derived river networks such as MB can be seamlessly mapped onto the SWORD river network. As an example, the Mean Discharge Runoff and Storage (MeanDRS; Collins et al., 2024) data set provides global monthly simulations of river discharge and storage from 1980 to 2009 along MB's ~3 million river reaches. Using the SWORD-to-MB translations, we assign simulated discharge values from MeanDRS to corresponding SWORD reaches globally (Figure 4). When multiple MB reaches are associated to a single SWORD reach, we perform a weighted average of the associated MeanDRS discharge values based on the partial intersecting length values of SWORD reaches within each of the MB catchments. Visual inspection of the transfer of MeanDRS discharge simulations to SWORD (Figure 4) suggests that our translations can successfully link the two hydrographic data sets, though gaps do exist in the MERIT-SWORD network due to mismatched flow accumulations between MB and SWORD (see the Amazon and Congo Rivers; Figures 4c and 4d). We illustrate how discharge simulations from MeanDRS or other model data sets can be readily supplied to SWORD, which could then act as reach-specific, temporally-varying a priori discharge estimates to improve the resulting accuracy of SWOT discharge algorithms (Durand et al., 2023).

Similarly, transferring observations of river height, width, slope, and discharge made by SWOT from SWORD to MB would be critical for enabling the assimilation of SWOT observations into hydrologic models. As a simplified example of how this data pipeline could be implemented, we also translate river widths at each SWORD reach derived from GRWL (Allen & Pavelsky, 2018; Altenau et al., 2021) to corresponding reaches in MB (Figure S2 in Supporting Information S1). Again, mirroring the approach above, we perform a weighted average to assign SWORD river widths to MB reaches based on the partial intersecting lengths of SWORD reaches within each of the MB catchments. Although we translate static river widths here, this approach could be easily adapted to transfer SWOT observations along SWORD vector reaches to reaches in the MB river network.

**5.2. Expected Evolution of MERIT-SWORD or Other Similar Translations**

As the first publicly available translation between the SWORD and MB river networks, our MERIT-SWORD data product could be integral to science applications that leverage both SWOT observations and model simulations. As such, MERIT-SWORD may need to evolve as the underlying data products (i.e., SWORD and MB) receive updates over time. While the version of MERIT-SWORD we present here is based on Version 16 of SWORD (Altenau et al., 2021), updates to SWORD reach definition and network topology are expected, necessitating an updated version of our data product. Future updates to SWORD's network topology could remove inconsistencies in the data set's flow accumulation values, which in turn would aid in our method for validation of flow accumulations between SWORD and MB during translation. Additionally, our approach could be adapted to link SWORD to other hydrography data sets, such as the National Hydrography Data set Plus v2 (McKay et al., 2012) and the related National Water Model Hydrofabric (Cosgrove et al., 2024), or HydroSHEDs and the related vector data set HydroRIVERS (Lehner & Grill, 2013).

**6. Conclusion**

Hydrographic networks are essential for the spatial representation of surface water processes, as they allow
observations and simulations of rivers to be consistently mapped onto a standardized river network, yet many
differing hydrography data sets, including SWORD and MB, are used by the hydrologic community. Here, we
present the publicly available MERIT-SWORD data set and its associated open-source software, which identifies
approximately 277,000 of the 3,000,000 total MB river reaches that best correspond to SWORD's ~240,000
global reaches and establishes bidirectional data transfer pathways between MB and SWORD. These validated
links between MB and SWORD provided by the MERIT-SWORD data set will serve a critical role in ensuring
that the transformation insights of the SWOT mission can be effectively integrated with the global modeling
capabilities of the hydrologic community.

Data Availability Statement

All software and data products related to this study are publicly available for research purposes via Wade (2025), Wade et al. (2025), respectively. We follow the guidelines of the NASA TOPS Open Science 101 Curriculum Development Team (2023) for our open science practices. © 2025 California Institute of Technology. All rights reserved.

Acknowledgments

This research was carried out at the Jet
Propulsion Laboratory, California Institute
of Technology, under a contract with the
National Aeronautics and Space
Administration (80NM0018D0004)
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. T. Pavelsky and E.
Altenau were funded under a contract from
the SWOT Project at the Jet Propulsion
Laboratory.

References

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. *Science*, **361**(6402), 585–588. https://doi.org/10.1126/science.aat0636

Alsdorf, D. E., Rodríguez, E., & Lettenmaier, D. P. (2007). Measuring surface water from space. *Reviews of Geophysics*, **45**(2). https://doi.org/10.1029/2006RG000197

Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang, X., Frasson, R. P. D. M., & Bendezu, L. (2021). The surface water and ocean topography (SWOT) mission river database (SWORD): A Global River Network for satellite data products. *Water Resources Research*, **57**(7), e2021WR030054. https://doi.org/10.1029/2021WR030054

Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016). The SWOT mission and its capabilities for land hydrology. *Surveys in Geophysics*, **37**(2), 307–337. https://doi.org/10.1007/s10712-015-9346-y

Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., et al. (2024). Global patterns in river water storage dependent on residence time. *Nature Geoscience*, **17**(5), 1–7. https://doi.org/10.1038/s41561-024-01421-5

Cosgrove, B., Gochis, D., Flowers, T., Dugger, A., Ogden, F., Graziano, T., et al. (2024). NOAA's National Water Model: Advancing operational hydrology through continental-scale modeling. *JAWRA Journal of the American Water Resources Association*, **60**(2), 247–272. https://doi.org/10.1111/1752-1688.13184

Durand, M., Gleason, C. J., Pavelsky, T. M., Prata de Moraes Frasson, R., Turmon, M., David, C. H., et al. (2023). A framework for estimating global river discharge from the surface water and ocean topography satellite mission. *Water Resources Research*, **59**(4), e2021WR031614. https://doi.org/10.1029/2021WR031614

Enjolras, V. M., & Rodriguez, E. (2009). An assessment of a Ka-band radar interferometer mission accuracy over Eurasian rivers. *IEEE Transactions on Geoscience and Remote Sensing*, **47**(6), 1752–1765. https://doi.org/10.1109/TGRS.2008.2006370

Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., et al. (2007). The shuttle radar topography mission. *Reviews of Geophysics*, **45**(2), RG2004. https://doi.org/10.1029/2005RG000183

Feng, D., Gleason, C. J., Lin, P., Yang, X., Pan, M., & Ishitsuka, Y. (2021). Recent changes to Arctic river discharge. *Nature Communications*, **12**(1), 6917. https://doi.org/10.1038/s41467-021-27228-1

Feng, D., Gleason, C. J., Yang, X., Allen, G. H., & Pavelsky, T. M. (2022). How have global river widths changed over time? *Water Resources Research*, **58**(8), e2021WR031712. https://doi.org/10.1029/2021WR031712

JPL Internal Document. (2018). *Surface water and ocean topography mission project science requirements document*. California Institute of Technology.

Lehner, B., & Grill, G. (2013). Global river hydrography and network routing: Baseline data and new approaches to study the world's large river systems. *Hydrological Processes*, **27**(15), 2171–2186. https://doi.org/10.1002/hyp.9740

Lehner, B., Verdin, K., & Jarvis, A. (2008). New global hydrography derived from spaceborne elevation data. *Eos, Transactions American Geophysical Union*, **89**(10), 93–94. https://doi.org/10.1029/2008EO100001

Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global reconstruction of naturalized river flows at 2.94 million reaches. *Water Resources Research*, **55**(8), 6499–6516. https://doi.org/10.1029/2019WR025287

McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., & Rea, A. (2012). NHDPlus version 2: User guide.

NASA TOPS Open Science 101 Curriculum Development Team, Adams, A., Almarzouq, B., Alves Lacerda, M., Alwood, J., Alzahawi, S., et al. (2023). NASA TOPS open science 101 (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10161527

Oubanas, H., Gejadze, I., Malaterre, P. O., & Mercier, F. (2018). River discharge estimation from synthetic SWOT-type observations using variational data assimilation and the full Saint-Venant hydraulic model. *Journal of Hydrology*, **559**, 638–647. https://doi.org/10.1016/j.jhydrol.2018.02.004

Oubanas, H., Gejadze, I., Malaterre, P. O., Durand, M., Wei, R., Frasson, R. P. D. M., & Domeneghetti, A. (2018). Discharge estimation in ungauged basins through variational data assimilation: The potential of the SWOT mission. *Water Resources Research*, **54**(3), 2405–2423. https://doi.org/10.1002/2017WR021735

Peral, E., Esteban-Fernández, D., Rodríguez, E., McWatters, D., De Bleser, J.-W., Ahmed, R., et al. (2024). KaRIn, the Ka-band radar interferometer of the SWOT mission: Design and in-flight performance. *IEEE Transactions on Geoscience and Remote Sensing*, **62**, 1–27. https://doi.org/10.1109/TGRS.2024.3405343

Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Crétaux, J.-F., & Pavelsky, T. M. (2024). HarP: Harmonized Prior River-lake database (SWOT-HarP v1.1) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14205131

Verdin, K. L., & Verdin, J. P. (1999). A topological system for delineation and codification of the Earth's river basins. *Journal of Hydrology*, **218**(1), 1–12. https://doi.org/10.1016/S0022-1694(99)00011-6

Wade, J. (2025) merit-sword (Version v0.4) [Computer software]. GitHub. https://github.com/jswade/merit-sword

Wade, J., David, C. H., Altenau, E. H., Collins, E. L., Oubanas, H., Coss, S., et al. (2025). MERIT-SWORD: Bidirectional Translations Between MERIT-Basins and the SWOT River Database (SWORD) (v0.4) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14675925

Whittemore, A., Ross, M. R. V., Dolan, W., Langhorst, T., Yang, X., Pawar, S., et al. (2020). A participatory science approach to expanding instream infrastructure inventories. *Earth's Future*, **8**(11), e2020EF001558. https://doi.org/10.1029/2020EF001558

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography dataset. *Water Resources Research*, **55**(6), 5053–5073. https://doi.org/10.1029/2019WR024873

Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. *Geophysical Research Letters*, **44**(11), 5844–5853. https://doi.org/10.1002/2017GL072874
Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., et al. (2021). Global reach-level 3-hourly river flood reanalysis (1980–2019). *Bulletin of the American Meteorological Society*, 102(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1

Yin, Z., Lin, P., Riggs, R., Allen, G. H., Lei, X., Zheng, Z., & Cai, S. (2024). A synthesis of global streamflow characteristics, hydrometeorology, and catchment attributes (GSHA) for large sample river-centric studies. *Earth System Science Data*, 16(3), 1559–1587. https://doi.org/10.5194/essd-16-1559-2024