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The potential for reference site resampling in estimating sediment redistribution and assessing landscape stability by the caesium‐137 method
Author(s) -
Sutherland Ross A.
Publication year - 1998
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/(sici)1099-1085(19980615)12:7<995::aid-hyp634>3.0.co;2-f
Subject(s) - statistics , resampling , confidence interval , sampling (signal processing) , environmental science , confidence and prediction bands , mathematics , sample (material) , hydrology (agriculture) , computer science , geology , filter (signal processing) , chromatography , computer vision , chemistry , geotechnical engineering
The objective of this study was to examine a new resampling methodology for estimating reference levels of 137 Cs in uneroded locations. Accurate and precise measurement of 137 Cs is required from reference locations to estimate long‐term ( c . 40 years) sediment redistribution (SRD) and landscape stability. Without reliable long‐term, quantitative erosion data it is extremely difficult for land managers to make optimal decisions to ensure landscape sustainability. To determine the influence of 137 Cs reference site sampling, particularly under‐sampling, on SRD and landscape stability, two statistical approaches were applied to a grid‐based data set. Caesium‐137 inventories in the reference location ( n =36) indicated that data were normally distributed, with a mean inventory of 2150±130 Bq m −2 (±95% confidence band) and a coefficient of variation of 18%. The two approaches used to determine the effect of under sampling included: (1) one‐time random subsampling from the total sample collected, subsamples ranged from n =3 to n =30; from these data means and parametric confidence bands were calculated; and (2) random subsamples ( n =3 to n =36) were selected from the total 137 Cs reference sample, and each subsample was in turn resampled 1000 times with replacement to establish a sampling distribution of means. Thus, an empirically derived mean and 95% confidence bands were established. Caesium‐137 activities determined from each approach were input into equations to estimate SRD from two cultivated fields. Results indicate that the one‐time random sampling approach for subsamples of size ≤12 significantly over‐ or under‐estimated net SRD, particularly from the gently sloping agricultural field. Computer‐intensive resampling produced significantly better estimates of net SRD when compared with the random one‐sample approach, especially when a subsample of size three was used. Landscape stability, based on partitioning the agricultural fields into areas exhibiting erosion, stability and deposition, was better approximated for both fields by applying resampling. © 1998 John Wiley & Sons, Ltd.