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New high-resolution maps of Hawaiian lava flows highlight complex topographically controlled channel networks. Network geometries range from distributary systems dominated by branching around local obstacles, to tributary systems constricted by topography. We combine 2-D network analysis tools developed for river systems and neural networks with 3-D lidar morphologic analysis and historical records of flow emplacement to investigate both the origins of channel networks and their influence on flow morphology and behavior. We find that network complexity is a function of underlying slope and that the degree of flow branching, network connectivity, and longevity of individual channels all influence the final flow morphology (flow and channel widths and levee heights). Furthermore, because channel networks govern the distribution of lava supply within a flow, changes in the channel topology can dramatically alter the effective volumetric flux in any one branch, which affects both flow length and advance rate. Specifically, branching will slow and shorten flows, while merging can accelerate and lengthen them. Consideration of channel networks is thus important for predicting lava flow behavior and mitigating flow hazards with diversion barriers. Observed relationships between network geometry, flow parameters, and morphology also offer insight into the interpretation of these features elsewhere on Earth and other terrestrial planets.

[1] Lava flow mapping is both an essential component of volcano monitoring and a valuable tool for investigating lava flow behavior. Although maps are traditionally created through field surveys, remote sensing allows an extraordinary view of active lava flows while avoiding the difficulties of mapping on location. Synthetic aperture radar (SAR) imagery, in particular, can detect changes in a flow field by comparing two images collected at different times with SAR coherence. New lava flows radically alter the scattering properties of the surface, making the radar signal decorrelated in SAR coherence images. We describe a new technique, SAR Coherence Mapping (SCM), to map lava flows automatically from coherence images independent of look angle or satellite path. We use this approach to map lava flow emplacement during the Pu‘u ‘Ō‘ō-Kupaianaha eruption at Kīlauea, Hawai‘i. The resulting flow maps correspond well with field mapping and better resolve the internal structure of surface flows, as well as the locations of active flow paths. However, the SCM technique is only moderately successful at mapping flows that enter vegetation, which is also often decorrelated between successive SAR images. Along with measurements of planform morphology, we are able to show that the length of time a flow stays decorrelated after initial emplacement is linearly related to the flow thickness. Finally, we use interferograms obtained after flow surfaces become correlated to show that persistent decorrelation is caused by post-emplacement flow subsidence.