Response to Anonymous Referee # 1 Interactive comment on “ Image analysis for measuring stratigraphy in sand-gravel laboratory experiments ”

Coarse size fraction 3: 3.15-5.60 mm. We considered these fractions as having negligible overlap since the result of the sieve analysis, done to characterize the grain size distribution, indicated only a slight overlap of 1-2% between medium fraction 2 and coarse fraction 3 (Figure 2). The same results were obtained in the case of the painted fractions (Figure 2). As mentioned in the paper the non-overlapping fractions were chosen to facilitate the sieve analysis, which is required to evaluate the image analysis technique. The image analysis method was developed to compare future experimental data with the results of models such as a surface-based transport model (i.e. Wilcock & Crowe 2003), a sediment continuity model for mixed sediment (i.e. Hirano 1971, Ribberink 1987, Blom et al 2008), which require information on the volume fraction content for a range of grain sizes present on the bed. This explains why we do not measure individual grain sizes but rather measure the fraction content of a certain grain size fraction. We will add this information to the manuscript.

2. Further description and references regarding Gilbert deltas will be added to the manuscript in the introduction.
In the following we show a new version for Figure 1.Colour segmentation indicates the division of all the pixels present in an image into a limited number of imposed colour groups (i.e.clusters).
K-means clustering (MacQueen, 1967) is a partitioning method aimed at dividing a set of objects into groups (i.e.clusters) based on their attributes.Each object has a location in space.The distance from this location to a representative point of the cluster (i.e.cluster centre) is used to define to which group the object belongs.Using an iterative algorithm the objects are moved between the clusters until the sum of distances from each object to its cluster centroid is minimized.Although the method can be applied to a very wide range of cases, we use it to divide a set of pixels into colour groups to determine an average value for each colour (i.e.cluster centre).
The above explanations will be added to Section 2.3 "Image analysis" in the manuscript, before explaining the image analysis procedure.
4. Equation 1 was derived to convert each point of the areal grain size distribution, resulting from the image analysis measurement, to the measured volumetric distribution provided by the sieve analysis.The k parameter is an empirical value based on our experimental data.Its value was found by striving for the minimum mean absolute difference between the measured and predicted volume fraction contents of two (i.e.fine and medium) out of three size fractions for 64 sediment samples.
Based on the comments by Referee#2, we have compared the results of our conversion model to the ones of the conversion model proposed by Parker (1991a,b).The latter originates from the conversion model developed by Proffitt (1980) and Diplas and Sutherland (1988) and provides an estimate of the areal fraction content based on a given volume fraction content.The model by Parker (1991a,b) rewritten in the form as presented by Cui andParker (1998b, 2005) is: where F Vi denotes the volume fraction content of size fraction i in a sediment layer [-], F Ai is the areal fraction content of size fraction i in the same sediment layer [-], D i is the grain size of size fraction i [m].
The Parker (1991a,b) conversion model was used, as requested by Referee#2, starting from the areal fraction content, F Ai , resulting from our image analysis, to compute the volume fraction content F Vi .In the reverse form Equation ( 1) is written as: (2) The model proposed by Parker (1991a,b) results in a volumetric distribution that is very similar to the one obtained using our model.6.We thank the reviewer for pointing out the incorrect use of the term "errors".We agree with this comment and we will rephrase the sentence at page 992.
7. We experienced that the sampling with the vacuum cleaner is not size-selective and does not show a preference for the fine fractions.The fact that the sediment is painted helped us to verify the removal of all size fractions.For instance, the coarser yellow particles are clearly distinguishable and it is visible when they are removed by the vacuum cleaner together with the finer fractions.Moreover, the vacuum cleaner is used passing over the same area several times until the 1 cm thick layer is removed.
This helps us to collect all fractions from that layer.Additionally, our results do not suggest that the vacuum cleaner prefers entrainment of the finer fractions.Please note that the finer fractions are underestimated in the volume fraction contents derived by the sieve analysis of the sampled layers.If the vacuum cleaner would have shown preference to entrain the finer fractions, we would have found the opposite result.
Figure 2 shows the comparison between the measured areal and volumetric fraction contents, as well the computed volumetric fractions using the Orrú et al. (2013) model and the Parker (1991a,b) model.

Figure 2
Figure 2 Comparison of the predicted volume fractions by the Orrú et al. (2013) and the Parker (1991a,b) models and the measured volume and areal fractions for Experiment 2, Side sample.