Fire (plus) flood (equals) beach: coastal response to an exceptional river sediment discharge event.

Wildfire and post-fire rainfall have resounding effects on hillslope processes and sediment yields of mountainous landscapes. Yet, it remains unclear how fire-flood sequences influence downstream coastal littoral systems. It is timely to examine terrestrial-coastal connections because climate change is increasing the frequency, size, and intensity of wildfires, altering precipitation rates, and accelerating sea-level rise; and these factors can be understood as contrasting accretionary and erosive agents for coastal systems. Here we provide new satellite-derived shoreline measurements of Big Sur, California and show how river sediment discharge significantly influenced shoreline positions during the past several decades. A 2016 wildfire followed by record precipitation increased sediment discharge in the Big Sur River and resulted in almost half of the total river sediment load of the past 50 years (~ 2.2 of ~ 4.8 Mt). Roughly 30% of this river sediment was inferred to be littoral-grade sand and was incorporated into the littoral cell, causing the widest beaches in the 37-year satellite record and spreading downcoast over timescales of years. Hence, the impact of fire-flood events on coastal sediment budgets may be substantial, and these impacts may increase with time considering projected intensification of wildfires and extreme rain events under global warming.

Blind testing of shoreline evolution models.

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.