Roving revisited, towards an optimum throughfall sampling design

Abstract

Additional aspects regarding the optimum fixed and roving sampling techniques, to those already explored in a previous authors’ throughfall study, are further investigated here. The roving technique consists in the random repositioning, with a frequency f_r, of N throughfall gauges among M positions (M > N), oppositely to the fixed or stationary arrangement where N = M. Both fixed and roving optimum sampling techniques of 100 monitored throughfall events sampled with 200 fixed gauges under a semideciduous tropical rain forest in Panama were investigated by means of Monte-Carlo numerical experiments. Mean dispersion was shown to be always smaller in the roving versus the fixed gauge arrangement, independently of the relocation frequency studied (f_r = 0.1, 0.2, 0.5, 1), such that all roving schemes with N ≥ 50 gauges lay within ±5% of the mean cumulative throughfall. Results indicated that a low variability, high precision, and accuracy are obtained with a modest relocation frequency f_r = 0.2 (i.e. a relocation every five episodes of the original 100 measured events) and N = 30 roving gauges, with no significant improvement worth the extra field work beyond f_r > 0.2 and N >30. Only by increasing the number of roving positions from M < < 200 to M = 200, the precision and accuracy of the mean estimate were improved without comprising additional labour. Hence, a roving sampling scheme which relocates gauges over completely new fresh sites each roving cycle is recommended for future throughfall studies. Finally, we designed an a priori sampling strategy which permitted us to conclude that using only the first 20 out of the total 100 measuring events, for the remaining 80 throughfall field measurements, N = 40 roving gauges (i.e. five time less than the originally 200 gauges displayed) would have been sufficient for ensuring ≤5% error, expressed as percentage of the mean cumulative throughfall. Copyright © 2012 John Wiley & Sons, Ltd.