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Sampling procedures for throughfall monitoring : a simulation study

  • What is the most appropriate sampling scheme to estimate event-based average throughfall? A satisfactory answer to this seemingly simple question has yet to be found, a failure which we attribute to previous efforts' dependence on empirical studies. Here we try to answer this question by simulating stochastic throughfall fields based on parameters for statistical models of large monitoring data sets. We subsequently sampled these fields with different sampling designs and variable sample supports. We evaluated the performance of a particular sampling scheme with respect to the uncertainty of possible estimated means of throughfall volumes. Even for a relative error limit of 20%, an impractically large number of small, funnel-type collectors would be required to estimate mean throughfall, particularly for small events. While stratification of the target area is not superior to simple random sampling, cluster random sampling involves the risk of being less efficient. A larger sample support, e.g., the use of trough-type collectors,What is the most appropriate sampling scheme to estimate event-based average throughfall? A satisfactory answer to this seemingly simple question has yet to be found, a failure which we attribute to previous efforts' dependence on empirical studies. Here we try to answer this question by simulating stochastic throughfall fields based on parameters for statistical models of large monitoring data sets. We subsequently sampled these fields with different sampling designs and variable sample supports. We evaluated the performance of a particular sampling scheme with respect to the uncertainty of possible estimated means of throughfall volumes. Even for a relative error limit of 20%, an impractically large number of small, funnel-type collectors would be required to estimate mean throughfall, particularly for small events. While stratification of the target area is not superior to simple random sampling, cluster random sampling involves the risk of being less efficient. A larger sample support, e.g., the use of trough-type collectors, considerably reduces the necessary sample sizes and eliminates the sensitivity of the mean to outliers. Since the gain in time associated with the manual handling of troughs versus funnels depends on the local precipitation regime, the employment of automatically recording clusters of long troughs emerges as the most promising sampling scheme. Even so, a relative error of less than 5% appears out of reach for throughfall under heterogeneous canopies. We therefore suspect a considerable uncertainty of input parameters for interception models derived from measured throughfall, in particular, for those requiring data of small throughfall events.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Beate Zimmermann, Alexander Zimmermann, Richard M. Lark, Helmut ElsenbeerORCiD
URL:http://www.agu.org/journals/wr/
DOI:https://doi.org/10.1029/2009wr007776
ISSN:0043-1397
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Jahr der Erstveröffentlichung:2010
Erscheinungsjahr:2010
Datum der Freischaltung:24.03.2017
Quelle:Water resources research. - ISSN 0043-1397. - 46 (2010), Art W01503
Organisationseinheiten:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften
Peer Review:Referiert
Name der Einrichtung zum Zeitpunkt der Publikation:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Erd- und Umweltwissenschaften
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