How Does Precipitation Variability Control Bedload Response Across a Mountainous Channel Network in a Maritime Climate?

Abstract

Key to predicting how a river will respond to a given flow rate is predicting the channel conveyance at the time of the flood (Sturm, 2010). In mountainous watersheds, channel conveyance is maintained by the capacity of the channel to move bedload. If bedload transport capacity falls below the supply rate, the channel fills with sediment and channel conveyance decreases until consummate increases in channel slope balance transport and supply rates (Lane, 1955).At gaged locations, an estimate of bedload transport capacity under historic hydrologic conditions can be determined from a survey of the reach geometry, measurement of bed surface grain size and the hydrograph (Wilcock et al., 2009). For ungauged locations or future climate scenarios, a hydrograph must be approximated. If the hydrograph is modeled from precipitation derived from observation networks like the US National Weather Service Cooperative Observer Network (NWS, 2020) and Livneh et al. (2013) data set, or a future climate meteorology data set such as the Salathé et al. (2014) data set, the precipitation may largely be recorded as daily average values.The frequency of precipitation observations necessary to accurately model floods was described decades ago (Bras, 1979;Eagleson & Shack, 1966;Singh, 1997) but time series of daily average precipitation are still commonly used to model hydrologic response to climate change (Dan et al., 2012;Shrestha et al., 2012), with precipitation assumed to fall at a constant rate over the day for hourly hydrologic simulations. As the understanding of hydrologic processes has improved, detailed physical models have confirmed the necessity of accurate temporal representation of precipitation for flood prediction at the basin outlet (Paschalis et al., 2014). Furthermore, over geologic timescales, precipitation variability has been shown to control erosion rates and channel

Forest management history influences eight decades of shallow landsliding in the northwest Cascade Mountains, USA

“…could be more common during periods of warm and wet conditions but that may be temporally or spatially obscured within our cumulative discharge or monthly precipitation metrics (Keck et al, 2022). This is supported by the observation that peak flows at the local gages we analysed are poorly correlated with each other (evident in Figure 5c), suggesting high spatial variability in storm response across the Skagit watershed may be responsible for variation in landslide response between sub basins (Paulson, 1997).…”

“…Specifically, the regionally warm/wet conditions in the latter decades of the timeseries correlated with increasing No Management landslide volume generation at the multi‐decadal scale (Figure 5) but did not correlate closely with shorter‐term hydrologic indicators (discharge, precipitation) that could be quantitatively linked to No Management landsliding at the photo period scale in most basins (Table 8). This discrepancy may be partially explained by phenomena not well captured by gage data such as the frequency and magnitude of localized extreme weather events (Collins et al, 2020) or rapid changes in snow melt and runoff that could be more common during periods of warm and wet conditions but that may be temporally or spatially obscured within our cumulative discharge or monthly precipitation metrics (Keck et al, 2022). This is supported by the observation that peak flows at the local gages we analysed are poorly correlated with each other (evident in Figure 5c), suggesting high spatial variability in storm response across the Skagit watershed may be responsible for variation in landslide response between sub basins (Paulson, 1997).…”

Observations and computational multi‐phase modelling in tropical river settings show complex channel changes downstream from rainfall‐triggered landslides

“…The results in Figure 8 also show that assumptions on precipitation‐derived discharge methods (as per section 3.2.3) only modestly affect channel morphodynamics modelled by r.avaflow. Sediment transport capacity that has been predicted using shear stress relationships has been observed to vary by several orders of magnitude when the uncertainty associated with modelled rainfall‐derived discharges is considered (Keck et al, 2022). The lower variation shown by our uncertainty analysis is principally caused by r.avaflow predicting a multi‐phase mixture of solid and fluid materials; predictions are thus less sensitive to changes in flow input because of the importance of the solid phase, and energy and momentum exchange between phases.…”