Isotopic Sampling Error due to Throughfall Spatial Variability
Δδ18O(‰)
Amount (TF / Pg)
Fall 2009 were Amount and isotopic composition of throughfall and gross precipitation measured in a Douglas-fir forest in western Oregon, USA. Throughfall was collected for 11 events at 13 collectors with 9.5 cm diameter funnels. A Monte-Carlo process using field data simulated the statistical properties in estimating mean throughfall amount and δ18O associated with various sampling schemes. Sampling schemes used varying number of collectors for determining event and cumulative means (with fixed versus roving collectors; Ritter and Regalado, 2014). We quantified the variability of virtually sampled throughfall deviation from the true mean. Fall 2009 Spring 2010
Precipitation Event
Precipitation Event
Figure 2 Kernel-smoothed histograms of depth (TF/Pg) and δ18O.
Spring NAP Spring NAP
1. School of Renewable Natural Resources, Louisiana State University. Baton Rouge, LA, USA. 3 Spring 2. Global Institute for Water Security, University of Saskatchewan. Saskatoon, SK, CA. 2 Spring SAP
Normalized TF Depth Normalized TF Depth Normalized TF Depth
Normalized TF Depth Normalized TF Depth Normalized TF Depth
Normalized TF Depth Normalized TF Depth Normalized TF Depth
2 Figure 3 2 1
3
18 18 Normalized O Normalized O Normalized O
3 2 4 Figure 2 1 3
Probability Density (unitless)
18 Normalized O Normalized O Normalized O
3 2 4 2 1 3
NAP
4 2 1 3
18
3 ranked by mean 6.0000 2Collectors 0.4 Col 13 1.0000 3 6.0000 2 1
3 2 O(Roving) Individual 3 2 1
Collectors ranked by
Conclusions
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18
5 Effect of sample 1 0 2 1 1 0 0 2 2 size on sample deviation from the mean 0 -1 1 0 0 -1 -1 1 1 18O) for (amount and δ -1 -2 0 -1 -1 -2 -2 0 0 cumulative (fixed or -2 -3 -1 -2 -2 -3 -3 -1 -1 2D Graph 3 0 5 10 15 20 25 3 5 Graph 7 9 3 11 13 roving 0collectors) 5 10or 15 20 25 30 35 -3 -2 -3 -3 -2 -2 1 3 2D 18 18 18 18 18 Collectors ranked by O Collectors ranked by O Collectors ranked by mean O Collectors Ranked by δ O Collection Location Ranked by Throughfall δ O event throughfall. 0 5 10 15 20 25 3 5 7 9 11 13 0 5 10 15 20 25 30 35 -3 -3 -3 1 3 2D 18 Graph 3 1818 18 3 2010 3 0.5 Spring Collectors ranked by O δ O : Individual Location – mean (‰) 3Collectors Collectors ranked by O ranked by mean O Cumulative 0 5 10 15 20 25 3 1 3 Col 513 7 9 11 13 0 5 10 15 20 25 30 35 Amount 18 18 18 O
Normalized d-excess Normalized d-excess Normalized d-excess
δ O/δ H Depleted
Jeffrey J.
2 McDonnell 3
Normalized d-excess Normalized d-excess Normalized d-excess
Enriched
time-scales.
2
Fall
3 2 1 2 1 0
Standard Deviation (‰) (fractional volume)
Methods
18
18 Normalized O Normalized O Normalized O
Redistribution
Figure 1 Objectives Conceptual model of effects • Determine the characteristics and of interception structure of spatiotemporal on spatial variability of throughfall isotopic variability of composition. throughfall • Determine the sampling error amount and associated with different sampling isotopic composition schemes at both event and seasonal
18
Fractionation & Mixing
Evaporation
Precisely and accurately measuring isotope end members is advantageous to hydrologists. In a vegetated landscape, throughfall is more appropriate to use than gross precipitation because interception alters the isotopic composition of precipitation (Kendall and McDonnell., 1993) and results in a throughfall input to soil with spatially varying isotopic composition (Allen et al., 2013).
F.
Keim1
3
Spring SAP Spring SAP
3 2 3 Time-stability 1 Figure 3 Figure 4 plots (Allen et al., 2013). Figure 3 2 1 2 1 0 0 Each point is a single 1 0 1 0 1 0 -1 -1 -1 sample normalized by 0 -1 0 0 mean amount or -1 -1 -2 event -2 -2 18O. δ -1 -2 -1 -2 -1 -2 -3 2D Graph 1 -3 -3 5 10 15 20 25 3 -2 0 5 Graph 7 9 1 11 13 Figure -3 -2 1 3 2D 5 10 of 15 20 25 30 35 -3 -2 04 Histograms -3 Collectors ranked by TF Depth Collectors ranked by TF Depth Collectors Ranked by TF Depth 5 10 15 20 25 3 1 3 2D 5 Graph 7 9 111 13 throughfall 0 5 10 and 15 20 25 30 35 amount -34 0 -3 -3 4 4 Collectors ranked by TF Depth Collectors ranked by TF Depth Collectors Ranked bybyAmount Collectors Ranked TF Depth 18 Location Ranked Depth 0 5 10 15 20 25 3 O. Lines are fitted 1 3 5 7 9 11 13 δ Collection 0 5 10 15 20 by 25 Throughfall 30 35 4 3 Location 4 4 3 3 Amount: Individual meanby (unitless) Collectors/ranked TF Depth Collectors ranked by TF Depth Collectors Ranked by TF Depthnormal distributions. 3 2 18
Background
Allen1, Richard
Normalized d-excess Normalized d-excess Normalized Deviation Standardd-excess
Scott T.
3 3 2
Fall Fall
3 2 3 2 1
Collectors ranked by
O
Event 2.0000 Col 13 • Isotopic composition of throughfall is highly variable 1.0000 2 2 3.0000 6.0000 2 1 Cumulative 1 1 0 0 2.0000 0 representing an important sampling consideration. 1.0000 4.0000 0.2 3.0000 (Fixed) 1 relative isotopic composition 1 1 0 0 0 2.0000 5.0000 -1 -1 • The lack of temporal persistence of -1 4.0000 3.0000 7.0000 0.1 0 0 0 5.0000 decreases the variability in cumulative throughfall. -1 -1 -1 -2 -2 -2 4.0000 8.0000 7.0000 • Using multiple throughfall collectors is recommended for 0 9.0000 5.0000 -1 -1 -1 -2 -2 -2 -3 -3 8.0000 -3 7.0000 10.0000 0 1 2 3 4 5 6 7 8-2 90 10 5 constraining sampling error of isotopic composition. 9.0000 10 15 20 25 30 35 -2 0.5 0 5 10 15 20 25 3 -2 5 7 189 11 13Collection -3 -3 8.0000 -3 1 3 11.0000 Location Ranked by Throughfall d-excess δ O 10.0000 Collectors ranked bynot d-excess Collectors ranked by d-excess • Roving does reduce isotopic error as it does when measuring Collectors ranked by d-excess 0 5 10 15 20 25 30 35 9.0000 5 10 15 20 25 3 5 7 9 11 13 -3 -3 0 -3 10.4 3 11.0000 Collectors ranked by d-excess Collectors ranked by d-excess 10.0000 amount. Collectors ranked by d-excess 0 5 10 15 20 25 30 35 0 5 10 15 20 25 3 1 3 Col 513 7 9 11 13 11.0000 Collectors ranked by d-excess 0.3 11.0000 Collectors ranked by d-excess Collectors ranked by d-excess References Col 13 1.0000 Allen, S.T., Brooks, J.R., Keim, R.F., Bond, B.J., McDonnell, J.J., 2013. The role of pre-event canopy storage in 11.0000 0.2 2.0000 Col 13 throughfall and stemflow by using isotopic tracers. Ecohydrology 10.1002/eco.1408. 1.0000 3.0000 11.0000 Kendall, C., McDonnell, J.J., 1993. Effect of intrastorm isotopic heterogeneities of rainfall, soil water, and 2.0000 0.1 4.0000 groundwater on runoff modelling, in: Peters, N.E., Hoehn, E., Leibundgut, C., Tase, N., Walling, D.E. (Eds.), 1.0000 3.0000 Tracers in Hydrology: Proceedings of an International Symposium Held at Yokohama, Japan, 21-23 July 1993. 2.0000 5.0000 0 4.0000 IAHS Press, Yokohama, Japan, pp. 41–48. 3.0000 6.0000 05.0000 1 2 3 4 5 6 7 8 9 10 Ritter, A., Regalado, C.M., 2014. Roving revisited, towards an optimum throughfall sampling design. Hydrol. 7.0000 4.0000 6.0000Number of Collectors Process. 28, 123–133.
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Figure 5
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