Drifters that track their position are important tools in studying the hydrodynamic behavior of rivers. Drifters that can be tracked in real time have so far been rather expensive. Recently, due to the rise of the open-hardware revolution and the associated Arduino ecosystem, both GPS receivers and cellular modems have become available at lower prices to "tinkering scientists", i.e., scientists that like to build their own measurement devices as much as is possible. This article serves two goals. Firstly, we provide detailed instructions on how to build a low-power GPS drifter with local storage and cellular model that we tested in a fieldwork on the confluence of the Chindwin and Ayeyarwady rivers in Myanmar. The device was designed from easily connected off-the-shelf components, allowing construction without a background in electrical engineering. The instructions allow fellow geoscientists to recreate the device. Secondly, we set the following question: has the open-hardware revolution progressed to the point that a low-power GPS drifter that wirelessly transmits its position can be made from open-hardware components by most geoscientists?. We feel this question is relevant and timely as more low-cost open-hardware devices are promoted, but in practice applicability is often restricted to the "tinkering engineer". We argue that because of the plug-and-play nature of the components geoscientists should be able to construct these type of devices. However, to get such devices to operate at low power levels that fieldwork often demands requires detailed (micro)electrical expertise.

Newly developed mobile phone applications in combination with citizen science are used in different fields of research, such as public health monitoring, environmental monitoring, precipitation monitoring, noise pollution measurement and mapping, earth observation. In this paper, we present a low-cost water quality mobile phone measurement technique combined with sensor and test strips, and reported the weekly-collected data of three years of the Ayeyarwady River system by volunteers at seven locations and compared these results with the measurements collected by the lab technicians. We assessed the quality of the collected data and their reliability based on several indicators, such as data accuracy, consistency, and completeness. In this study, six local governmental staffs and one middle school teacher collected baseline water quality data with high temporal and spatial resolution. The quality of the data collected by volunteers was comparable to the data of the experienced lab technicians for sensor-based measurement of electrical conductivity and transparency. However, the lower accuracy (higher uncertainty range) of the indicator strips made them less useful in the Ayeyarwady with its relatively small water quality variations. We showed that participatory water quality monitoring in Myanmar can be a serious alternative for a more classical water sampling and lab analysis-based monitoring network, particularly as it results in much higher spatial and temporal resolution of water quality information against the very modest investment and running costs. This approach can help solving the invisible water crisis of unknown water quality (changes) in river and lake systems all over the world.