Abstract

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/km² and for the GLE DEM was 0.877 km/km². These drainage densities create coarse drainage textures and therefore, this can be used to predict future soil texture.

摘要

本研究采用地理信息系统 (GIS) 和遥感技术来预测排水形态测量参数和水文过程。一种名为遗传景观演化 (GLE) 的新方法用于根据最佳水道网络状态对排水进行建模，以预测未来的排水模式和形态测量。遵循隆起和沉降力之间的理论平衡，GLE 力求实现系统的最小总能量消耗。使用 GLE 算法创建的预测流网络景观具有自然流网络的自相似树结构。形态计量分析表明，该盆地在 30 m DEM 中具有 6 级流阶，在 GLE 算法生成的 DEM 中具有 5 级流阶。30 米 DEM 的流域排水密度值为 0.933 km/km²和 GLE DEM 为 0.877 km/km ²。这些排水密度会产生粗糙的排水质地，因此，这可以用来预测未来的土壤质地。