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Prediction of drainage morphometry using a genetic landscape evolution algorithm
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DOI：10.1080/10106049.2020.1762766.png)
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This study employs geographic information system (GIS) and remote-sensing techniques to predict drainage morphometry parameters and hydrological processes. A new approach, named genetic landscape evolution (GLE), is used to model drainage based on optimal waterway network states to predict future drainage patterns and morphometry. Following the theoretical balance between the forces of uplift and sedimentation, GLE strives to achieve the minimum total energy expenditure of the system. The predicted stream-network landscapes created using the GLE algorithm have the self-similar tree structure of natural stream networks. Morphometric analysis shows that the basin has 6th-level stream orders in the 30 m DEM and 5th-level stream orders in the GLE algorithm-produced DEM. The drainage density value of the basin for the 30-m DEM was 0.933 km/km(2)and for the GLE DEM was 0.877 km/km(2). These drainage densities create coarse drainage textures and therefore, this can be used to predict future soil texture.
Keyword:
Morphometric parameters
hydrological processes
genetic landscape evolution (GLE)
minimum total energy expenditure (MTEE)
digital elevation model (DEM)
geographic information system (GIS)
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