Abstract
Accurate watershed delineation is a precondition for runoff and water quality simulation. Traditional digital elevation model (DEM) may not generate realistic drainage networks due to large depressions and subtle elevation differences in local-scale plains. In this study, we propose a new method for solving the problem of watershed delineation, using the Taihu Basin as a case study. Rivers, lakes, and reservoirs were obtained from Sentinel-2A images with the Canny algorithm on Google Earth Engine (GEE), rather than from DEM, to compose the drainage network. Catchments were delineated by modifying the flow direction of rivers, lakes, reservoirs, and overland flow, instead of using DEM values. A watershed was divided into the following three types: Lake, reservoir, and overland catchment. A total of 2291 river segments, seven lakes, eight reservoirs, and 2306 subwatersheds were retained in this study. Compared with results from HydroSHEDS and Arc Hydro, the proposed method retains crisscross structures in the topology and prevented erroneous streamlines in large lakes. High-resolution Sentinel-2A images available on the GEE have relatively greater merits than DEMs for precisely representing drainage networks and catchments, especially in the plains area. Because of the higher accuracy, this method can be used as a new solution for watershed division in the plains area.
Highlights
Drainage networks are essential to geospatial assessment, basin analysis, and applications for catchment delineation, flow statistics, flood risk assessment, and climate modeling [1,2]
Compared to medium-resolution digital elevation model (DEM) of drainage networks and catchments, the objective of this study is to demonstrate the relative advantages of the proposed method for making precise and accurate watershed delineation in plains areas using Sentinel-2A images available on the Google Earth Engine (GEE), taking the Taihu Basin as a case study
A random sampling method was used to select 500 water surface points and 500 nonwater surface points from Global Land Cover 30 (GLC30), in which the water and nonwater verification points were evenly distributed across the Taihu Basin
Summary
Drainage networks are essential to geospatial assessment, basin analysis, and applications for catchment delineation, flow statistics, flood risk assessment, and climate modeling [1,2]. They play an important role in estimating the transmission of pollution and nutrients [3]. Drainage networks are a fundamental condition for watershed delineation and an indispensable component in hydrological modeling. Many algorithms, including the drainage networks algorithm [8] and the delineating watersheds algorithm [9], are developed to automatically extract essential hydrological features with DEM
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