Drainage Structure Datasets and Effects on LiDAR-Derived Surface Flow Modeling

Ruopu Li,Xu Li,Zhenghong Tang,Jessie Winter

doi:10.3390/ijgi2041136

Abstract

With extraordinary resolution and accuracy, Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) have been increasingly used for watershed analyses and modeling by hydrologists, planners and engineers. Such high-accuracy DEMs have demonstrated their effectiveness in delineating watershed and drainage patterns at fine scales in low-relief terrains. However, these high-resolution datasets are usually only available as topographic DEMs rather than hydrologic DEMs, presenting greater land roughness that can affect natural flow accumulation. Specifically, locations of drainage structures such as road culverts and bridges were simulated as barriers to the passage of drainage. This paper proposed a geospatial method for producing LiDAR-derived hydrologic DEMs, which incorporates data collection of drainage structures (i.e., culverts and bridges), data preprocessing and burning of the drainage structures into DEMs. A case study of GIS-based watershed modeling in South Central Nebraska showed improved simulated surface water derivatives after the drainage structures were burned into the LiDAR-derived topographic DEMs. The paper culminates in a proposal and discussion of establishing a national or statewide drainage structure dataset.

Highlights

Digital Elevation Models (DEMs) are the most critical datasets to the success of surface hydrologic modeling applications [1,2,3]
A close examination shows that the location of the channels modeled from the topographic digital elevation models (DEMs) did not coincide with the location of the surveyed culverts
The finding of this study supports the hypothesis that burning drainage structures can benefit the simulated surface water derivatives from Light Detection and Ranging (LiDAR)-derived topographic DEMs

Summary

Introduction

Digital Elevation Models (DEMs) are the most critical datasets to the success of surface hydrologic modeling applications [1,2,3]. These datasets can be used to produce critical topographic and hydrologic derivatives, such as slope, aspect and flow accumulation. DEMs were traditionally derived by the US Geological Survey (USGS) photogrammetrically or from topographic maps with relatively coarse resolution (usually > 10 m) and low vertical accuracy (±2.44 m) [5]. LiDAR-derived DEMs have been found to significantly improve the accuracy of wetland-stream connectivity determined at the landscape scale [12] and boost topo-hydric data accuracy [13,14]

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Conclusion