The temporal dynamics of stream networks are vitally important for understanding hydrologic processes including surface water and groundwater interaction and hydrograph recession. However, observations of wet channel networks are limited, especially in headwater catchments. Near‐infrared LiDAR data provide an opportunity to map wet channel networks owing to the fine spatial resolution and strong absorption of light energy by water surfaces. A systematic method is developed to map wet channel networks by integrating elevation and signal intensity of ground returns. The signal intensity thresholds for identifying wet pixels are extracted from frequency distributions of intensity return within the convergent topography extent using a Gaussian mixture model. Moreover, the concept of edge in digital image processing, defined based on the intensity gradient, is utilized to enhance detection of small wet channels. The developed method is applied to the Lake Tahoe area based on eight LiDAR snapshots during recession periods in five watersheds. A power law relationship between streamflow and wetted channel length during recession periods is derived, and the scaling exponent ( ) is within the range of reported values from fieldwork in other regions.

The purpose of this study is to identify the relationship between drainage density and climate, as represented by the climate aridity index, and to understand the relationship between drainage density and drainage area. A total of 121 study sites with low human impact, and a wide range of climate aridity index 0.3 (humid)–10.4 (arid), were selected based on the availability of light detection and ranging (LiDAR) data, producing a digital elevation model (DEM) with a spatial resolution of 1 m. A curvature-based method, incorporating both positive and negative curvature information, was used to extract the valley (drainage) network from the LiDAR-based DEMs. Drainage density and climate aridity index exhibited a monotonically increasing trend, contrary to the previous results that have shown a U-shaped relationship. This discrepancy was caused by the selection of watersheds with extensive human activity in the previous study. One-meter resolution DEM produced greater drainage density than the previous studies with a coarse spatial resolution of 30 m as small valleys are not detectable in low-resolution topography datasets. The discrepancy between the previous study and the current study results encouraged further investigation of the impact of the drainage area (watershed size). A negative correlation between drainage density and drainage area was reconfirmed, while a stronger decreasing trend was observed in arid regions than in humid regions.

This study assesses the characteristics of perennial streams using the dimensionless relationship between streamflow exceedance probability and the wet channel ratio based on a wet channel network extracted from light detection and ranging (LiDAR) data. LiDAR provides topographic data and signals’ intensity in high-resolution and with high accuracy to provide useful information for drainage networks and channel network extraction. In this study, a valley network and wet channel are extracted from LiDAR topographic and signals’ intensity information with a spatial resolution of 1 meter. Based on the available LiDAR data and streamflow observations from across the United States, we selected 30 watersheds with various climate conditions and analyzed the characteristics of their perennial streams. The wet channel ratio and perennial stream ratio were developed to define a perennial stream using the observed streamflow and the identified wet channel. The results of this study are consistent with previous studies on the definition of a perennial stream through transformation into a dimensionless form and confirmed the possibility of applying the wet channel ratio as an alternative parameter to define a perennial stream.