Seismic instruments placed outside of spatially extensive hazard zones can be used to rapidly sense a range of mass movements. However, it remains challenging to automatically detect specific events of interest. Benford's law, which states that first non-zero-digit of given datasets follow a specific probability distribution, can provide a computationally cheap approach to identifying anomalies in large datasets and potentially be used for event detection. Here, we select raw seismic signals to derive the first-digit distribution. The seismic signals generated by debris flows, landslides, lahars, and glacier-lake-outburst floods follow Benford's law, while those generated by ambient noise, rockfalls, and bedload transports do not. Focusing on debris flows, our Benford's-law-based detector is comparable to an existing random forest method for the Illgraben, Switzerland, but requires only single station data and three non-dimensional parameters. We suggest this computationally cheap, novel technique offers an alternative for event recognition and potentially for real-time warnings.

An important component of quantifying bedload transport flux is the identification of the onset of bedload motion. Bedload transport can be monitored with high temporal resolution using passive acoustic methods, e.g., hydrophones. Yet, an efficient method for identifying the onset of bedload transport from long-term continuous acoustic data is still lacking. Benford’s Law defines a probability distribution of the first-digit of datasets and has been used to identify anomalies. We apply Benford’s Law to the three years of acoustic recordings from a stationary hydrophone in the Taroko National Park, Taiwan. Our workflow allows for monitoring bedload motion in near-real-time, and it is convenient for others to reference. Two bedload transport events were identified during the examined period, lasting 17 and 45 hours, accounting for approximately 0.35% of the time per year.

Machine learning can improve the accuracy of identifying mass movements in seismic signals and extend early warning times. However, we lack a profound understanding of the effective seismic features and the limitations of different machine learning models, especially for debris-flow warning. Here, we investigate the importance of seismic features for the binary debris flow classification tasks using two ensemble models: Random Forest (RF) and eXtreme Gradient Boosting (XGB) models. We find that an established approach to training machine learning models for debris flow classification task based on more than seventy seismic signal features may be affected by redundant input information. These seismic features are derived from physical and statistical knowledge of impact sources and are grouped into waveform, spectrum, spectrogram, and network sets. Our results show that only six selected seismic features can perform similarly for the binary debris flow classification task compared to published benchmark results trained with seventy features. Considering models that aim to capture patterns in sequential data rather than focusing on information only in one given window as ensemble models, using the Long Short-Term Memory (LSTM) algorithm does not improve the performance of binary debris flow classification tasks over RF and XGB. As a debris flow alarm task, the LSTM model predicts debris flow initiation more consistently and generates fewer false warnings. Our proposed framework simplifies seismic signal-driven early warning for debris flows and provides an appropriate workflow for identifying other mass movements.

The way Alpine rivers mobilize, convey and store coarse material during high-magnitude events is poorly understood, notably because it is difficult to obtain measurements of bedload transport at the watershed scale. Seismic sensor data, evaluated with appropriate seismic physical models, can provide that missing link by yielding absolute time-series of bedload transport. Low cost and ease of installation allows for networks of sensors to be deployed, providing continuous, watershed-scale insights into bedload transport dynamics. Here, we deploy a network of 24 seismic sensors to capture the motion of coarse material in a 13.4 km2 Alpine watershed during a high-magnitude bedload transport event. First, we benchmark the seismic inversion routine with an independent time-series obtained with a calibrated acoustic system. Then, we apply the procedure to the other seismic sensors across the watershed. Spatially-distributed time-series of bedload transport reveal a relative inefficiency of Alpine watersheds in evacuating coarse material, even during a relatively infrequent high-magnitude bedload transport event. Significant inputs measured for some tributaries were rapidly attenuated as the main river crossed less hydraulically-efficient reaches, and only a comparatively negligible proportion of the total amount of material mobilized in the watershed was exported at the outlet. Cross-correlation analysis of the time-series suggests that a faster moving water wave (re-)mobilizes local material and bedload is expected to move slower, and over shorter distances. Multiple periods of competent flows are likely to be necessary to evacuate the coarse material produced throughout the watershed during individual source-mobilizing bedload transport events.

Large boulders with a diameter of up to several tens of meters are globally observed in mountainous bedrock channel environments. Recent theories suggest that high concentrations of boulders are associated with changes in channel morphology. However, data are scarce and ambiguous, and process-related studies are limited. Here we present data from the Liwu River, Taiwan, showing that channel width and slope increase with boulder concentration. We apply two mass balance principles of bedrock erosion and sediment transport and develop a theory to explain the steepening and widening trends. Five mechanisms are considered and compared to the field data. The cover effect by immobile boulders is found to have no influence on channel width. Channel width can partially be explained by boulder control on the tools effect and on the partitioning of the flow shear stress. However, none of the mechanisms we explored can adequately explain the scattered width data, potentially indicating a long-timescale adjustment of channel width to boulder input. Steepening can be best described by assuming a reduction of sediment transport efficiency with boulder concentration. We find that boulders represent a significant perturbation to the fluvial landscape. Channels tend to adjust to this perturbation leading to a new morphology that differs from boulder-free channels. The general approach presented here can be further expanded to explore the role of other boulder-related processes.