Immediate use of CPAP in out-of-hospital treatment of CPE and until CPE resolves after admission significantly improves early outcome compared with medical treatment alone.

Rivers are the fluvial conveyor belts routing sediment across the landscape. While there are proper techniques for continuous estimates of the flux of suspended solids, constraining bedload flux is much more challenging, typically involving extensive measurement infrastructure or labor‐intensive manual measurements. Seismometers are potentially valuable alternatives to in‐stream devices, delivering continuous data with high temporal resolution on the average behavior of a reach. Two models exist to predict the seismic spectra generated by river turbulence and bedload flux. However, these models require estimating a large number of parameters and the spectra usually overlap significantly, which hinders straightforward inversion. We provide three functions contained in the R package “eseis” that allow generic modeling of hydraulic and bedload transport dynamics from seismic data using these models. The underlying Monte Carlo approach creates lookup tables of potential spectra, which are compared against the empirical spectra to identify the best fitting solutions. The method is validated against synthetic data sets and independently measured metrics from the Nahal Eshtemoa, Israel, a flash flood‐dominated ephemeral gravel bed river. Our approach reproduces the synthetic time series with average absolute deviations of 0.01–0.04 m (water depth, ranging between 0 and 1 m) and 0.00–0.04 kg/sm (bedload flux, ranging between 0 and 4 kg/sm). The example flash flood water depths and bedload fluxes are reproduced with respective average deviations of 0.10 m and 0.02 kg/sm. Our approach thus provides generic, testable, and reproducible routines for a quantitative description of key metrics, hard to collect by other techniques in a continuous and representative manner.

Rivers are key features of ecosystems, transferring water, dissolved, and particulate matter across the Earth's surface. Driven by the power of moving water, sediment helps rivers to shape landscapes and contribute to the evolution of river morphology (Leopold et al., 1964). Sediment in rivers is either carried as suspended load or as bedload (rolling, sliding, or saltating on the bed). Bedload contributes to channel changes, such as creating micro-and macroforms, narrowing, widening, shifting, aggrading, and degrading. It also affects riverbed and bank stability (Little & Mayer, 1976). From an engineering perspective, bedload transport and the channel changes that it causes can damage infrastructure and threaten near channel human activities (Badoux et al., 2014;Kondolf et al., 2002). Predictive models are needed to correctly constrain and understand the evolution of river morphology.The construction of predictive models of river morpho-dynamics requires high quality time-resolved quantitative data on bedload flux. In-stream monitoring has been developed to obtain these data, relying on devices such as basket samplers (i.e., portable traps, fixed basket), geophones, hydrophones, and underwater microphones (Ergenzinger & De Jong, 2003). However, these methods remain challenging. Basket samplers require manual maintenance and resolve only parts of an event (Vericat et al., 2006). In addition, they can also introduce a bias because they alter stream flow and transport patterns around them, thereby affecting local transport rates. Acoustic measurements of bedload can be achieved with geophones and hydrophones (Geay et al., 2017(Geay et al., , 2020Habersack et al., 2017;Rickenmann, 2017) calibrated by direct measurements. In addition, geophones require a stable cross section in the streambed. As a result, they are mostly used in small mountain streams. Because in-stream monitoring requires specific channel conditions (basket samplers, geophones, hydrophones), has low temporal resolution (basket samplers), or cannot be deployed during flood conditions (portable traps, e.g., Helley-Smith samplers or small boats carrying acoustic doppler