Watershed delineation using hydrographic features and a DEM in plain river network region

Abstract

Watershed delineation is a required step when conducting any spatially distributed hydrological modelling. Automated approaches are often proposed to delineate a watershed based on a river network extracted from the digital elevation model (DEM) using the deterministic eight‐neighbour (D8) method. However, a realistic river network cannot be derived from conventional DEM processing methods for a large flat area with a complex network of rivers, lakes, reservoirs, and polders, referred to as a plain river network region (PRNR). In this study, a new approach, which uses both hydrographic features and DEM, has been developed to address the problems of watershed delineation in PRNR. It extracts the river nodes and determines the flow directions of the river network based on a vector‐based hydrographic feature data model. The river network, lakes, reservoirs, and polders are then used to modify the flow directions of grid cells determined by D8 approach. The watershed is eventually delineated into four types of catchments including lakes, reservoirs, polders, and overland catchments based on the flow direction matrix and the location of river nodes. Multiple flow directions of grid cells are represented using a multi‐direction encoding method, and multiple outflows of catchments are also reflected in the topology of catchments. The proposed approach is applied to the western Taihu watershed in China. Comparisons between the results obtained from the D8 approach, the ‘stream burning’ approach, and those from the proposed approach clearly demonstrate an improvement of the new approach over the conventional approaches. This approach will benefit the development of distributed hydrological models in PRNR for the consideration of different types and multiple inlets and outlets of catchments. Copyright © 2015 John Wiley & Sons, Ltd.

A combined algorithm for automated drainage network extraction from digital elevation models

“…However, completely flat areas do not normally exist in reality. Usually natural flat areas, for example, plain river network regions, are covered with dense rivers and dotted with lakes and other hydraulic structures (Lai, Li, Lv, Pan, & Fei, ). Such complicated terrain further increases the difficulty of drainage networks extraction.…”

“…Such complicated terrain further increases the difficulty of drainage networks extraction. Ancillary data from DEMs, for example, river and lake networks, are thus recommended to be used in order to improve the accuracy of flow estimation over flat areas (Hutchinson, ; Lai et al, ; Turcotte et al, ; Yamazaki et al, ). Nevertheless, the proposed LCP & TFM algorithm does improve the accuracy of flow estimation over the tested four mathematical surfaces, compared with the other selected flow routing algorithms.…”

“…TFM = triangular form-based multiple flow; LCP = least cost path; SFD = single flow direction; MFD = multiple flow direction; RMSE = root mean square error; ME = mean error; SD = standard deviation. (Lai, Li, Lv, Pan, & Fei, 2016).…”

A method to employ the spatial organization of catchments into semi-distributed rainfall–runoff models

“…Most approaches are based on topography (Band, 1986;Moore and Grayson, 1991;Vogt et al, 2003;Lai et al, 2016) and focus on the extraction of stream networks and the network connectivity, utilizing topology-driven modelling concepts (Beven and Kirkby, 1979;Rodríguez-Iturbe and Valdés, 1979). Particularly the development of the geomorphologic instantaneous unit hydrograph (GIUH) as well as its enhancements like the geomorphological dispersion (Rinaldo et al, 1991;Gupta and Mesa, 1988) require sophisticated stream network derivation and analysis.…”

“…This has been a further step to incorporate remote sensed data in describing the organization of catchments. While the above-described methods are based on gridded digital elevation models (DEMs), other methods try to identify streamlines derived from DEM shapes, producing contour lines (Moore and Grayson, 1991;Lai et al, 2016) that are subsequently used as modelling instances.…”