Description: Reliable data are the base of all scientific analyses, interpretations and conclusions. Evaluating data in a smart way speeds up the process of interpretation and conclusion and highlights where, when and how additionally acquired data in the field will support knowledge gain. An extended SMART monitoring concept is introduced which includes SMART sensors, DataFlows, MetaData and Sampling approaches and tools. In the course of the Digital Earth project, the meaning of SMART monitoring has significantly evolved. It stands for a combination of hard- and software tools enhancing the traditional monitoring approach where a SMART monitoring DataFlow is processed and analyzed sequentially on the way from the sensor to a repository into an integrated analysis approach. The measured values itself, its metadata, and the status of the sensor, and additional auxiliary data can be made available in real time and analyzed to enhance the sensor output concerning accuracy and precision. Although several parts of the four tools are known, technically feasible and sometimes applied in Earth science studies, there is a large discrepancy between knowledge and our derived ambitions and what is feasible and commonly done in the reality and in the field.

Description: A thorough and reliable assessment of changes in sea surface water temperatures (SSWTs) is essential for understanding the effects of global warming on long-term trends in marine ecosystems and their communities. The first long-term temperature measurements were established almost a century ago, especially in coastal areas, and some of them are still in operation. However, while in earlier times these measurements were done by hand every day, current environmental long-term observation stations (ELTOS) are often fully automated and integrated in cabled underwater observatories (UWOs). With this new technology, year-round measurements became feasible even in remote or difficult to access areas, such as coastal areas of the Arctic Ocean in winter, where measurements were almost impossible just a decade ago. In this context, there is a question over what extent the sampling frequency and accuracy influence results in long-term monitoring approaches. In this paper, we address this with a combination of lab experiments on sensor accuracy and precision and a simulated sampling program with different sampling frequencies based on a continuous water temperature dataset from Svalbard, Arctic, from 2012 to 2017. Our laboratory experiments showed that temperature measurements with 12 different temperature sensor types at different price ranges all provided measurements accurate enough to resolve temperature changes over years on a level discussed in the literature when addressing climate change effects in coastal waters. However, the experiments also revealed that some sensors are more suitable for measuring absolute temperature changes over time, while others are more suitable for determining relative temperature changes. Our simulated sampling program in Svalbard coastal waters over 5 years revealed that the selection of a proper sampling frequency is most relevant for discriminating significant long-term temperature changes from random daily, seasonal, or interannual fluctuations. While hourly and daily sampling could deliver reliable, stable, and comparable results concerning temperature increases over time, weekly sampling was less able to reliably detect overall significant trends. With even lower sampling frequencies (monthly sampling), no significant temperature trend over time could be detected. Although the results were obtained for a specific site, they are transferable to other aquatic research questions and non-polar regions.

Description: Highlights • Nutrient and carbon fluxes are key processes in land-ocean interactions. • We sampled along the river-estuary-ocean system according to travel time of water. • The river was autotrophic with phytoplankton growth, high pH and oxygen concentration, and CO2 undersaturation. • Phytoplankton died off in the estuary causing low pH and oxygen concentration, CO2 supersaturation, and nutrient release. • The approach is suitable to investigate single events such as hydrological extremes. Nutrient and carbon dynamics within the river-estuary-coastal water systems are key processes in understanding the flux of matter from the terrestrial environment to the ocean. Here, we analysed those dynamics by following a sampling approach based on the travel time of water and an advanced calculation of nutrient fluxes in the tidal part. We started with a nearly Lagrangian sampling of the river (River Elbe, Germany; 580 km within 8 days). After a subsequent investigation of the estuary, we followed the plume of the river by raster sampling the German Bight (North Sea) using three ships simultaneously. In the river, we detected intensive longitudinal growth of phytoplankton connected with high oxygen saturation and pH values and an undersaturation of CO2, whereas concentrations of dissolved nutrients declined. In the estuary, the Elbe shifted from an autotrophic to a heterotrophic system: Phytoplankton died off upstream of the salinity gradient, causing minima in oxygen saturation and pH, supersaturation of CO2, and a release of nutrients. In the shelf region, phytoplankton and nutrient concentrations were low, oxygen was close to saturation, and pH was within a typical marine range. Over all sections, oxygen saturation was positively related to pH and negatively to pCO2. Corresponding to the significant particulated nutrient flux via phytoplankton, flux rates of dissolved nutrients from river into estuary were low and determined by depleted concentrations. In contrast, fluxes from the estuary to the coastal waters were higher and the pattern was determined by tidal current. Overall, the approach is appropriate to better understand land-ocean fluxes, particularly to illuminate the importance of these fluxes under different seasonal and hydrological conditions, including flood and drought events.