From SWOT Reaches to Model Grids: A Global Solution for Hydrologically Consistent Observation–Model Alignment

The Surface Water and Ocean Topography (SWOT) mission provides the first global measurements of river water surface elevation at reach scale, captured through the vector-based SWORD database. While these observations offer unprecedented spatial detail, most large-scale hydrological models represent rivers on gridded routing networks, creating a structural mismatch that limits the direct use of SWOT data in global analyses. A robust and scalable translation between SWORD reaches and model grid cells is therefore essential for enabling SWOT-based hydrology. Here we present a global, confidence-oriented strategy for aligning SWORD reaches with the 1/12° river network of the CTRIP routing model. The method evaluates candidate associations using several hydrologically meaningful criteria, including geographic proximity, upstream area and basin delineation coherence inherited from MERIT-Hydro, reach morphology, and alignment with D8 flow directions. Each pixel receives a confidence category that distinguishes unambiguous single-reach matches from robust or uncertain multi-reach configurations. This classification provides transparent information on mapping quality and identifies locations where model–observation alignment is intrinsically ambiguous. We demonstrate the performance of the method through a global application at 1/12° resolution. The resulting reach-to-grid associations produce spatially coherent river corridors, consistent basin topology, and near-complete coverage across observable rivers. Diagnostics across continents show that the framework performs reliably in challenging systems such as deltas, braided rivers, and multi-thread channels where simpler geometric approaches commonly fail. The final outputs include confidence-tier maps, reach–pixel match tables, and gridded river masks that translate SWOT’s vector observations into hydrologically meaningful model space. These products provide the community with a ready-to-use, reproducible translation layer that supports a wide range of SWOT-based research activities, including large-scale river characterization, network comparison, uncertainty assessment, and future assimilation experiments. The approach enables consistent use of SWOT observations in global hydrology and opens new avenues for connecting reach-scale satellite measurements with continental-scale hydrological understanding.