Mainstems: A logical data model implementing mainstem and drainage basin feature types based on WaterML2 Part 3: HY Features concepts
Links
- More information: Publisher Index Page (via DOI)
- Data Releases:
- USGS data release - mainstems workflow: HU12 NHDPlusV2 NHDPlus HiRes Matching
- USGS data release - Mainstem Rivers of the World based on MERIT hydrography and Natural Earth names
- USGS data release - Mainstem Rivers of the Conterminous United States
- Open Access Version: Publisher Index Page
- Download citation as: RIS | Dublin Core
Abstract
The Mainstems data model implements the catchment and flowpath concepts from WaterML2 Part 3: Surface Hydrology Features (HY_Features) for persistent, cross-scale, identification of hydrologic features. The data model itself provides a focused and lightweight method to describe hydrologic networks with minimum but sufficient information. The design is intended to provide a model for data integration that can be used for network navigation and persistent hydrologic indexing (hydrographic addressing) functionality. Mainstems is designed to provide long-term stability with minimal maintenance requirements. The data model is not meant to advance hydrologic process representation or uniquely represent geomorphic characteristics. The principle assumption in Mainstems is that all drainage basins have one - and only one - headwater source area and a single mainstem that flows to a single outlet. Using these base feature types, (headwater, outlet, mainstem, and drainage basin) a nested set of drainage basins - and the associated dendritic network of mainstems - can be identified.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Mainstems: A logical data model implementing mainstem and drainage basin feature types based on WaterML2 Part 3: HY Features concepts |
| Series title | Environmental Modelling and Software |
| DOI | 10.1016/j.envsoft.2020.104927 |
| Volume | 135 |
| Year Published | 2021 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | WMA - Integrated Modeling and Prediction Division |
| Description | 104927, 11 p. |
| Google Analytic Metrics | Metrics page |