Problemas que enfrentan las Iniciativas de Clúster

In order to identify hydrological thresholds for managing ecological health the exact moment that biomass removal processes begin needs to be recorded. Hoyle et al. (2017) demonstrate that molar action requires the highest grain stresses to occur. Drag and abra- sion processes begin to operate below these stresses. Thresholds for bedload transport, which initiates molar action, therefore offer the most useful target for hydrological limits to manage periphyton abundance as they guarantee the activation of all three removal processes. Measuring site-specific entrainment and transport thresholds is therefore criti- cal for periphyton management. A wide range of approaches have been used to measure bedload transport either by direct measurement or through surrogate measurement tech- niques (Leopold & Emmett, 1976; Reidet al. 1984; Wilcocket al. 1996; Tunnicliffe et al. 2000; Mizuyamaet al. 2010; Rickenmannet al. 2014; Mao et al. 2016). A range of these approaches are discussed below.

1.6.1 Direct Measurement of Bedload

Traditionally, substrate movement was measured by tracking the movement of clasts or trapping of bedload material. Tracer particles are the most widely used method for estimating bedload transport in gravel-bed rivers (e.g. Ashworth & Ferguson, 1989; Death & Winterbourn, 1995). Pit trap and basket type samplers are also common and over- come the recovery limitations of tracer clasts (Reidet al. 1980; Leopold & Emmett, 1997; Bunte & Abt, 2003; Rickenmannet al. 2012). The major limitation of these methods for initiation of motion research is the inability to record the exact timing of clast movement, unless specialist systems such as pressure cells are used in traps, or smart tracers are used for particle tracking (e.g. Rickenmann et al. 2012; Cassel et al. 2017). These are difficult to establish in larger, mobile beds. Quantifying transport rates is also challenging due to

1.6. Identifying the Onset of Motion and Transport Rates

a lack of ability to sample the entire bedload.

1.6.2 Surrogate Measurement of Bedload Transport

Surrogate methods, which use sensors to record the signal created by either the pas- sage of clasts in transport, or the movement of the bedload layer, offer an alternative to the infamously difficult task of directly measuring substrate movement. A large range of these sensors are available and mostly use electromagnetic (Brayshaw, 1985; Tunnicliffe et al. 2000), or acoustic sensors (Lorang & Tonolla, 2014; Tsakiris et al. 2014). They in turn can be active or passive. Active sensors are those which emit their own signal and record properties of the reflected signal, whereas passive sensors record signals generated naturally (Gray et al. 2010). Direct application is where sensors generate a signal when the sediment in transport contacts the sensor. Indirect methods record the bedload with- out direct contact.

1.6.2.1 Passive bedload sensors

Passive sensors have been used in bedload research for over 30 years, and are more commonly used than active sensors. Passive electromagnetic sensors generally comprise of a magnetometer which records changes in the magnetic field produced by either tagged clasts (Reidet al. 1984; Brayshaw, 1985) or clasts with sufficient ferric content (e.g. Tun- nicliffe et al. 2000). Passive acoustic sensors typically consist of either a hydrophone or geophone which record the vibrations caused by clasts contacting other clasts, or contact- ing a resonator (steel plate or pipe) (Lorang & Tonolla, 2014; Mao et al. 2016). Indirect sensors are highly sensitive to contamination from surrounding noise (Lorang & Tonolla, 2014; Barri`ere et al. 2015), severely limiting where they can be used. Attaching the hydrophone to a resonator eliminates considerable noise contamination, but requires the sensor to be installed in the bed where it may be buried or scoured out (Lorang & Tonolla, 2014).

Both electromagnetic and acoustic sensors suffer from limited spatial coverage, limits on clast size detection, and sensitivity to noise. Installing an array of sensors across the channel may overcome the spatial limitations, but will be difficult in larger and/or mobile channels. Clasts as small as 4 mm have been measured using electromagnetic sensors (Tunnicliffe et al. 2000), and filtering techniques have allowed fine and coarse bedload fractions to be separated in acoustic signals (Maoet al. 2016). It is also impossible to dis- cern if recorded clasts originate locally or have been transported from upstream. Passive acoustic sensors have an advantage over electromagnetic sensors as they do not require ferric content in the bedload.

1.6.2.2 Active bedload sensors

There is a growing body of literature demonstrating the potential of active sensors for bedload monitoring, but the application of active sensors has largely been limited to feasibility studies (e.g. Yu et al. 2012; Chen et al. 2014) as opposed to long-term monitoring. Active electromagnetic sensors use radar transceivers to emit a pulse which is reflected from sediment (Shrestha et al. 2005), and the reflected signal (echo) recorded (Grayet al. 2010). Ferric clasts are not required. Active acoustic sensors come in a range of forms, including side-scan sonar (Kenyon & Belderson, 1973; Hamill et al. 2018) and acoustic Doppler current profilers (Milan & Heritage, 2012). Active acoustics emit sound pulses and record the returned signal. These methods can be used to measure bedload

Chapter 1. Towards mechanistic hydrological limits

transport directly by measuring bed movement and velocity, or calculate transport flux by cross-section differencing or dune tracking (Spicer et al. 1997; Traykovski et al. 1998; Shresthaet al. 2005). The major advantage of active sensors is the ability to mount them out of the water column on bridges or cableways (Spiceret al. 1997).

One of the main limitations with active acoustic sensors is the variability of measure- ments (Rennie & Villard, 2004), and noise from suspended sediment (Stark et al. 2014). It is also important to note that bedload velocities recorded by active acoustics may be biased by stationary bed particles and may not represent heterogeneous bedload processes (Stark et al. 2014). Wang et al. (2016) demonstrate the need to incorporate other data including sediment chemical properties, water temperature, and suspended sediment con- centration in radar processing as these parameters can affect the radar signal. This may increase cost, but, the results of Shresthaet al. (2005) suggest radar-based methodologies are worth the extra cost for active monitoring of bedload.