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Adaptive design improves water and sediment data collection in remote arroyos, suggesting cost-effective solutions.
Methods for collecting water, suspended sediment samples, and streamflow from rivers and streams have been documented in the literature by various science agencies. However, some projects may require data collected in difficult conditions including remote sites, sites that have limited accessibility during storms, and streamflow that occurs rarely. For these studies, low cost, adaptive approaches to sampler instrument design can yield valuable sample and streamflow data that were previously unobtainable. A study to track the source(s) of metals to the San Juan River required water chemistry samples, suspended sediment samples, and streamflow data collected on 28 ephemeral arroyos and five perennial streams. These tributaries to the San Juan River presented many challenges because of their remote locations, inaccessibility during storms, and ephemeral nature. A novel design, modified from previous adaptive instrument designs, was created for this specific project. The design included low cost, readily available materials (i.e. sourced from hardware stores) that allowed for ease of replacement. Three instruments were designed following these design goals: a siphon sampler (water chemistry), sediment trap (suspended sediments), and a stage sensor (water level). These instruments were mounted to a T-post array in the stream channel. This installation approach, collectively named the “sed-chem gage,” was operated at each ephemeral arroyo for two consecutive field seasons. The modified siphon sampler showed good agreement with an automated pumping sampler over 24 replicate samples. All samples were within 2.5 percent difference, with the siphon sampler slightly oversampling when compared with the automated sampler. The instrument design approach, parts list, and results of operation are presented herein. This approach can be used to evaluate water flow and water and sediment chemistry in a large, remote area where traditional techniques and methods may be cost prohibitive.
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Brown et al. (2025) studied this question.
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