Preprints
https://doi.org/10.5194/essd-2021-278
https://doi.org/10.5194/essd-2021-278
 
10 Dec 2021
10 Dec 2021
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

A high-resolution inland surface water body dataset for the tundra and boreal forests of North America

Yijie Sui1, Min Feng1,2,3, Chunling Wang1,3, and Xin Li1,2,3 Yijie Sui et al.
  • 1National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
  • 3University of Chinese Academy Sciences, Beijing 100049, China

Abstract. Inland surface waters are abundant in the tundra and boreal forests in North America, essential to environments and human societies but vulnerable to climate changes. These high-latitude water bodies differ greatly in their morphological and topological characteristics related to the formation, type, and vulnerability. In this paper we present an inland surface water body inventory (SWBI) dataset for the tundra and boreal forests of North America. Nearly 6.7 million water bodies were identified, with approximately 6 million (~90 %) of them smaller than 0.1 km2. The dataset provides geometry coverage and morphological attributes for every water body. During this study we developed an automated approach for detecting surface water extent and identifying water bodies in the 10 m resolution Sentinel-2 multispectral satellite data to enhance the capability for delineating small water bodies and their morphological attributes. The approach was applied to the Sentinel-2 data acquired in 2019 to produce the water body dataset for the entire tundra and boreal forests in North America, providing a more complete representation of the region than existing regional datasets, e.g., Permafrost Region Pond and Lake (PeRL). Total accuracy of the detected water extent by SWBI dataset was 96.36 % by comparing to interpreted data for locations randomly sampled across the region. Compared to the 30 m or coarser resolution water datasets, e.g., JRC GSW yearly water history, HydroLakes, and Global Lakes and Wetlands Database (GLWD), the SWBI provided an improved ability on delineating water bodies, and reported higher accuracies in the size, number, and perimeter attributes of water body by comparing to PeRL and interpreted regional dataset. This dataset is available on the National Tibetan Plateau/Third Pole Environment Data Center (TPDC, http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271021 (Feng et al., 2020).

Yijie Sui et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-278', Anonymous Referee #1, 18 Jan 2022
    • AC1: 'Reply on RC1', Yijie Sui, 05 Jun 2022
  • RC2: 'Comment on essd-2021-278', Anonymous Referee #2, 25 Jan 2022
    • AC2: 'Reply on RC2', Yijie Sui, 05 Jun 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-278', Anonymous Referee #1, 18 Jan 2022
    • AC1: 'Reply on RC1', Yijie Sui, 05 Jun 2022
  • RC2: 'Comment on essd-2021-278', Anonymous Referee #2, 25 Jan 2022
    • AC2: 'Reply on RC2', Yijie Sui, 05 Jun 2022

Yijie Sui et al.

Data sets

High resolution inland surface water dataset for the tundra and boreal in North America Feng, M., Sui, Y. https://doi.org/10.11888/Hydro.tpdc.271021

Yijie Sui et al.

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Short summary
The high-latitude water bodies differ greatly in their morphological and topological characteristics related to the formation, type, and vulnerability. In this paper we present an inland surface water body inventory (SWBI) dataset for the tundra and boreal forests of North America, which provided an improved ability on delineating water bodies. Nearly 6.7 million water bodies were identified, with approximately 6 million (~90 %) of them smaller than 0.1 km2.