Abstract Short rainfall events contribute to large portions of annual sediment and nutrient exports. Most water quality sampling schemes rely on regularly spaced temporal sampling and increasingly monitoring schemes are including a form of event-based sampling. A typical approach is to sample each event using equal intervals in time using an automatic sampler. The use of this form of sampling is systematic in nature and requires model-based statistics to be analysed correctly. Probabilistic based sampling methods allow for easier and more defendable statistical inference as the assumptions are not based on a model, rather they are based on the sample design. Several probabilistic methods have been developed, however these methods commonly require additional hardware to implement. In this paper we present a method using a stratified random sampling procedure for automatic samplers which does not require any additional hardware. Our approach is to divide the mean event hydrograph into strata based on key features such as the rising and falling limbs. Random sampling is applied within each strata. A problem of this approach is that the length of the event and strata must be defined before each event. We therefore outline how the samples can be post-stratified after each event based on the key hydrological components of each event. The sampling scheme is outlined using continuously sampled electrical conductivity and turbidity data of three events from a creek in south eastern Australia. Limited to 24 samples per event, estimated event mean CIs were within the observed event means for all three events. This method provides a flexible low-cost sampling scheme providing unbiased estimates of key event hydrological components which can be easily adapted by catchment management authorities.

中文翻译：

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使用自动采样器监测基于事件的水质的事后分层随机抽样方案

摘要 短期降雨事件促成了大部分年度沉积物和养分出口。大多数水质采样方案依赖于定期间隔的时间采样，越来越多的监测方案包括一种基于事件的采样形式。典型的方法是使用自动采样器以相等的时间间隔对每个事件进行采样。这种抽样形式的使用本质上是系统的，需要正确分析基于模型的统计数据。基于概率的抽样方法允许更容易和更可靠的统计推断，因为假设不是基于模型，而是基于样本设计。已经开发了几种概率方法，但是这些方法通常需要额外的硬件来实现。在本文中，我们提出了一种对自动采样器使用分层随机采样程序的方法，该方法不需要任何额外的硬件。我们的方法是根据上升和下降肢等关键特征将平均事件水位线划分为层。随机抽样应用于每一层。这种方法的一个问题是必须在每个事件之前定义事件和地层的长度。因此，我们概述了如何根据每个事件的关键水文组成部分在每个事件之后对样本进行后分层。使用来自澳大利亚东南部一条小溪的三个事件的连续采样电导率和浊度数据概述了采样方案。每个事件仅限于 24 个样本，估计的事件平均 CI 均在所有三个事件的观察事件均值范围内。