Stream water sampling during hydrological events is important, but also challenging, because stream water chemistry can vary largely during events and the number of samples which can be taken is limited due to practical constrains. This project will contribute towards better observations of the temporal variability of stream water chemistry (including isotope composition) by intelligent sampling strategies. This will support both hydrological research and monitoring. The objectives of this project are (i) the identification of optimal sampling distribution during events based on perfect information (i.e., in hindsight) to maximize the information gained by a limited number of water samples, (ii) the formulation of a sampling strategy under real conditions, where sampling decisions have to be made based on (uncertain) forecasts and (iii) the development of a technical implementation for the instrumentation of a forecast-based sampling based on low-power wireless sensor networks.
In environmental sciences field locations are often remote, difficult - and at times dangerous - to access (time-restricted access). Sensor-logger units and water samplers, which record data automatically, can partly overcome these restrictions. For water samplers the triggering of the sampling is a core issue. Typically, the onset of sampling is initiated by a single on-site sensor (e.g. when reaching/exceeding a water level threshold). Up-to-date, water samplers have not been linked to each other or integrated in a sensor network (e.g. on-site water level sensors, off-site rain gauges and upstream water quality sensors). This project intends to integrate a suit of water samplers installed along a stream into a sensor network installed in the same catchment. This approach allows new possibilities of sampling, as the applied sampling strategy (e.g. triggering of the samplers) can be step-wise adapted, coordinated, and optimised based on online data generated by the catchment-wide sensor network. As a result of this project, water quantity sensor data can be linked to water quality sampling data on a new level, enabling scientists to implement sampling strategies based on online-interpretation of sensor data (e.g. by a hydrological or environmental model).
Objective: Historic structures are often of extraordinary architecture, design or material. The conservation of such structures for next European generations is one of the main future tasks. To conserve historic structures it is more and more required to understand the deterioration processes mainly caused by the environment. In certain cases continuous monitoring systems have been installed to obtain information about the deterioration processes. However, most of these monitoring systems were just weather or air pollution data acquisition systems and use only basic models for data analysis. The real influence of the environment to the structure or the structural material is often unaccounted for. That means that the structural resistance is just calculated from the measurements and not determined by sufficient sensors. Another aspect is the fact that most monitoring systems require cabling, which is neither aesthetically appealing nor in some cases applicable due to the needed fastening techniques. The proposed project aims at the development of competitive tools for practitioners which goes beyond the mere accumulation of data. Smart monitoring systems using wireless sensor networks, new miniature sensor technologies (e.g. MEMS) for minimally invasive installation as well as smart data processing will be developed. It will provide help in the sense of warnings (e.g. increase of damaging factors) and recommendations for action (e.g. ventilation or heating on/off, etc.) using data fusion and interpretation that is implemented within the monitoring system. The development will consist of small smart wireless and robust sensors and networks, with sensors for monitoring of e.g. temperature, humidity, air velocity, strain and crack opening, acoustic emissions, vibration, inclination, chemical attack, ambient and UV light, with built-in deterioration and material models, data pre-processing, and alarm functions to inform responsible persons about changes of the object status.
Cold mountains are sensitive to climate change. Corresponding research and early warning in this environment of extreme lateral variability, however, are data-limited due to difficult access and harsh conditions. Here, the integration of robust and reliable distributed measurement systems and computer models is the most effective means to achieve significant progress. This integration of robust and reliable distributed measurement systems , however, is a dynamically evolving process of intense trans-disciplinary cooperation, engineering and research. This process of pursuing and operating real systems is the most promising way to materialize the true benefits of wireless sensor network technology in this application area. As a joint geo-science and engineering project, Permasense aims at materializing the benefit of new measurement systems for permafrost research. Current focal points are the advection of heat by running water in joint systems and corresponding potentials for deep linear thaw and rock destabilization as well as cryogenic rock movement.