Assessing the impacts of anthropogenic drainage structures on hydrologic connectivity using high‐resolution digital elevation models

Bhadra, Sourav; Li, Ruopu; Wu, Di; Wang, Guangxing; Rekabdar, Banafsheh

doi:10.1111/tgis.12832

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Cited by 3 publications

References 43 publications

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Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach

Wu,

Li,

Rekabdar

et al. 2023

GIScience & Remote Sensing

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Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach

Wu,

Li,

Rekabdar

et al. 2023

GIScience & Remote Sensing

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Deep Learning-Based Spatial Detection of Drainage Structures using Advanced Object Detection Methods

Jalalipour,

Ayyalasomayjula,

Damrah

et al. 2023

2023 Fifth International Conference on Transdisciplinary AI (TransAI)

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Enhancing hydrologic LiDAR digital elevation models: Bridging hydrographic gaps at fine scales

Wu,

Li,

Edidem

et al. 2024

J American Water Resour Assoc

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High‐resolution digital elevation models (HRDEMs), derived from LiDAR, are widely used for mapping hydrographic details in flat terrains. However, artificial flow barriers, particularly from roads, elevate terrain and prematurely end flowlines. Drainage barrier processing (DBP), such as HRDEM excavation, is employed to address this issue. However, there is a gap in quantitatively assessing the impact of DBP on HRDEM‐derived flowlines, especially at finer scales. This study fills that gap by quantitatively assessing how DBP improves flowline quality at finer scales. We evaluated HRDEM‐derived flowlines that were generated using different flow direction algorithms, developing a framework to measure the effects of flow barrier removal. The results show that the primary factor influencing flowline quality is the presence of flow accumulation artifacts. Quality issues also stem from differences between natural and artificial flow paths, unrealistic flowlines in flat areas, complex canal networks, and ephemeral drainage ways. Notably, the improvement achieved by DBP is demonstrated to be more than 6%, showcasing its efficacy in reducing the impact of flow barriers on hydrologic connectivity.

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Assessing the impacts of anthropogenic drainage structures on hydrologic connectivity using high‐resolution digital elevation models

Abstract: Delineating accurate flowlines using digital elevation models is a critical step for overland flow modeling. However, extracting surface flowlines from high-resolution digital elevation models (HRDEMs) can be biased, partly due to the

Cited by 3 publications

References 43 publications

Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach

Classification of drainage crossings on high-resolution digital elevation models: A deep learning approach

Deep Learning-Based Spatial Detection of Drainage Structures using Advanced Object Detection Methods

Enhancing hydrologic LiDAR digital elevation models: Bridging hydrographic gaps at fine scales

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