The objective of this study was to discriminate between modern beach subenvironments based on textural characteristics obtained using the graphical (percentile) method, the moment method, and the log‐hyperbolic distribution (LHD). A total of 126 surface sedimentation units were sampled at the nodes of a 21 x 6 rectangular grid (1000 m2) on a carbonate sand beach, Oahu, Hawaii. Sampling was conducted at low energy conditions from the lower foreshore to the backshore. Non‐parametric discriminant analysis was used as an objective tool in defining distinct subenvironments. Confidence bands around the canonical variates derived from the graphic mean, sorting, skewness and kurtosis indicated four separate subenvironments (lower foreshore, mid‐foreshore, upper foreshore and backshore). Three distinct subenvironments were identified using the mean, sorting (standard deviation) and skewness measures derived by the method of moments. A similar subenvironment distinction was obtained using five statistics of the LHD (gamma, γ; nu ν; delta, δ; tau, τ; and xi, ξ). No significant difference was noted in textural characteristics between the upper foreshore and backshore zones, and these zones were grouped into one subenvironment. These results indicate that different process scenarios would be needed to explain different subenvironment partitioning based simply on the approach adopted. Discriminant analysis indicated that fewer subenvironment samples were misclassified and separation distances between subenvironments in bivariate canonical plots were greater for the standard moment measures compared with the statistics derived from fitting the computationally intensive LHD model. Examination of the mass frequency grain size distributions indicated that the LHD was generally the most appropriate model. These observations were confirmed by the hyperbolic shape triangle which indicated that the LHD rather than the more commonly used log‐normal distribution was generally optimal in describing sediments. These results support the use of the LHD statistical measures in subenvironment discrimination over the graphic‐inclusive measures.

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Detailed textural analyses in a carbonate foreshore-backshore coastal envi ronment in Hawaii indicated distinct differences in mean, sorting, skewness, and percent of fines between zones and within zones. A well-developed berm crest partitioned the 1,000 m 2 grid into foreshore (n = 79) and backshore (n = 46) zones. As a group, foreshore sediments were finer, better sorted, and more negatively skewed than backshore sedi ments. This pattern was thought to reflect the decoupling of the foreshore source popula tion by swash-backwash and eolian processes. Spatial variations in texture within the foreshore were also observed, and this led to the subdivision of the foreshore into three zones based on observed textural variations-lower foreshore, mid-foreshore, and upper foreshore. The lower foreshore was found to be statistically coarser, more poorly sorted, and more positively skewed than the other foreshore zones. The mid-foreshore zone was the finest, and the upper foreshore was intermediate in grain size. This patterning was thought to reflect the greatest energy dissipation in the lower foreshore, subsequent com petency decrease of swash runup, and deposition of coarsest particles remaining in trans port in the upper foreshore, accompanied by infiltration losses in the backwash and deposition of the finest materials in the mid-foreshore zone. Traditional bivariate plots of textural parameters for the foreshore and backshore samples indicated that this approach alone would not be useful in distinguishing subenvironments in paleosequence studies. A mean value approach (MVA) was developed and used in combination with published bivariate plots to identify depositional environments using moment statistics. Encouraging results were obtained from Waimanalo Beach sediments and from data published in the literature. The bivariate (textural) suite statistics approach (Tanner, 1991 b) was used to test for correct environmental recognition using the 125 beach samples. Results at first appeared promising, but assessment of literature data illustrated that the original suite dia grams could usefully be expanded. These results support previous statements that textural studies should not be used as the only tool in paleoenvironmental reconstruction. [

Discerning the rainfall spatial heterogeneity is an important issue as using the water isotopic tracer for transit time evaluation, particularly in meso-scale catchments. Here, we checked the rainfall spatial heterogeneity of event 2 and event 3 in terms of the rainfall amount and its isotopic composition. The spatial distribution of rainfall amount of each storm was interpolated via inverse distance weighted method (power parameter is 2) by 4 CWB rain gauges (see Fig. 1 in main text). The relative difference (RD) and the coefficient of variation (CV) are calculated for illustrating the spatial heterogeneity (Fig. S1 and Table S1). Note that RD is defined as the rainfall minus the average rainfall of a specific cell divided by the mean rainfall of the entire catchment. In this figure, the CVs of the total rainfall are 16% and 10%, respectively, for event 2 and 3 (Fig. S1(a) and Fig. S1(b)). Such low CVs indicated that the variation were much less than the mean, showing the rainfall spatial pattern is relatively homogeneous. Additionally, the distribution of RD shows that the western part receives more rainfall and the RD has a variation of approx. ±40% of the average. In sum, the both indicator showed that the typhoon-induced rainfall is short-lived, intense, but its rainfall spatial heterogeneity in meso-scale catchments is not pretty large. Fig. S1. Rainfall spatial heterogeneity of event 2 (a) and event 3 (b). The black points are rainwater sampling sites with δ 18 O value in parentheses.

Transit time with its indicative significance in regulating rainfall-runoff mechanism is a key factor for understanding 10 many biogeochemical processes, but is rarely investigated in steep and fractured mountainous catchments. Mountainous catchments in Taiwan are characterized by active endogenic tectonics and exogenic typhoons and thus provide opportunities to explore the hydrodynamic systems over time. In this study, the hydrometrics and δ 18 O in rain and stream water were sampled by ~3-hour interval for six typhoon events in two mesoscale catchments. The TRANSEP (transfer function hydrograph separation model) and global sensitivity analysis was applied for estimating mean transit time (MTTew) and fraction (Few) of 15 event water and identifying the chronosequent parameter sensitivity. Results show that TRANSEP could satisfactorily simulate the streamflow and δ 18 O change with the efficiency coefficients of from 0.85 to 0.97 and from 0.61 to 0.99, respectively. The MTTew and Few varied from 2 to 11h and from 0.2 to 0.8, respectively. Our MTTew in the meso-scale catchments is similar with that in micro-scale catchments, showing a fast transfer in our steep catchments. The mean rainfall intensity which negatively controls on the MTTew and positively on the Few is a predominant indicator which likely activates preferential flow 20 paths and quickly transfers event water to the stream. Sensitivity analysis among inter-and intra-events suggested that parameter sensitivity is event-depend and time-variant, affirming a nonlinear behavior in event water transfer function and time-variant parameterization should be particularly considered when estimating the MTTew in steep and fractured catchments. IntroductionTransit time chronicling the elapsed time of water from entering a system, traveling through the system to its exit of the system 25 (Stewart and Mcdonnell, 1991) implies information about sources, pathways, and water storage. It also indicates how catchments retain and release water and solutes associated with biogeochemical processes (Stark and Stieglitz, 2000), chemical weathering (Maher and Chamberlain, 2014) and contamination transportation (Kirchner, 2003;Kirchner et al., 2000) Therefore, accurately estimating mean transit time could provide a physical basis for understanding the hydrological behaviors and consequently the biogeochemical effects of anthropogenic disturbances (Turner et al., 2006) and landcover change (Burns et 30