High-resolution automated detection of headwater streambeds for large watersheds

Abstract. Streams are defined by the presence of a streambed, which is a linear depression where water flows between discernible banks. The upstream boundary of a stream is called a channel head. Headwater streams, which are small streams at the top of a watershed, account for the majority of the total length of streams, yet their exact locations are still not well known. For years, many algorithms were used to produce hydrographic networks that represent headwater streams with varying degrees of accuracy. Although digital elevation models derived from LiDAR have significantly improved headwater stream detection, the performance of the algorithms with different geomorphic characteristics remains unclear. Here, we address this issue by testing different combinations of algorithms using classification trees. Homogeneous hydrological processes were identified through hydrological classification. The results showed that in shallow soil that mainly consists of till deposits, the algorithms that recreate the surface runoff process provide the best explanation for the presence of a streambed. In contrast, streambeds in thick soil with high infiltration rates were primarily explained by a small-scale incision algorithm. Furthermore, the use of an iterative process that recreates water diffusion made it possible to more accurately detect streambeds than other methods tested, regardless of the hydrological classification. The method developed in this paper shows the importance of considering hydrological processes when aiming to identify headwater streams.