Periodic Reporting for period 2 - HiFreq (Smart high-frequency environmental sensor networks for quantifying nonlinear hydrological process dynamics across spatial scales)
Période du rapport: 2018-12-01 au 2022-02-28
Regulators and industries are challenged by the difficulty to analyse and predict the impact of nonlinear environmental processes on short-term and long-term responses of ecosystems to environmental change. Until very recently, the development of conventional monitoring, forecasting and prediction tools has been based on the assumption of stationary environmental systems. In the context of global change, these tools are increasingly pushed towards and even beyond their design limits. This project follows the rationale that only novel, high-frequency/high-resolution monitoring and predictive modelling will yield new process understanding of ecosystem functioning. Technological progress offers as many opportunities as it triggers challenges: what are needed now are new strategies to generate, manage and analyse BIG DATA at unprecedented spatial and temporal resolution. Innovation can only stand as a synonym for ‘significant positive changes’ if [a] we manage to clearly state the challenges (global change & non-stationarity) and problems (generating and managing high-frequency information) and [b] transform them into solutions. The timely outcomes of this project will hence be of great relevance for the scientific community, regulators, and the private sector.
The key aim of HiFreq project is to drive innovation in high-frequency environmental sensor network technologies and modelling, in particular, to quantify non-linear process dynamics in ecohydrological, biogeochemical and ecosystem monitoring. The wide-ranging activities of the action led not only to innovation in sensor and monitoring technologies but also support conclusions about the applicability of new sensor networks in operational contexts as well as the BIG DATA challenges going along with the development of large data quantities and their analysis. Solutions developed in this action particularly advance our ability to sense water pollution in-situ at higher frequency and accuracy than previously possible. Data science solutions include edge-processing algorithms for data reduction before transmission.
The key aim of HiFreq project is to drive innovation in high-frequency environmental sensor network technologies and modelling, in particular, to quantify non-linear process dynamics in ecohydrological, biogeochemical and ecosystem monitoring. The wide-ranging activities of the action led not only to innovation in sensor and monitoring technologies but also support conclusions about the applicability of new sensor networks in operational contexts as well as the BIG DATA challenges going along with the development of large data quantities and their analysis. Solutions developed in this action particularly advance our ability to sense water pollution in-situ at higher frequency and accuracy than previously possible. Data science solutions include edge-processing algorithms for data reduction before transmission.
The project has created scientific progress through individual secondments, facilitating the supra-disciplinary aims and objectives. From March 2020 until the end of the (extended) funding period, the undertaking of in person secondments became impossible due to COVID19 lockdowns and later restrictions to the movement between different countries that prevented the international as well as inter-sectoral exchange in the action, at least in person. The project leadership team efforts during this time were on ensuring the safety of project participants that were on active secondments and providing support to best conduct research while under local lockdown.
To mitigate these COVID induced limitations, a first project extension was applied for and granted, however, travel between participating countries and institutions hadn’t resumed in time. A second project extension was unfortunately not granted, preventing the project from implementing outstanding secondments. The main emphasis of the network has therefore been on trying to deliver as much as possible of the planned actions and deliverables without being able to actually conduct in-person secondments during this time.
WP 3 facilitated the development, testing and validation of in-situ sensor technology for high-frequency and high-spatial resolution monitoring. During the 2nd project period covered by this report, the research team endeavoured to further develop the new concepts initiated in the first project phase in the joint networking of multiple property sensors under dynamic flow conditions, and advanced capacities in monitoring the dynamic breakthrough of reactive fluorescence tracers under variable discharge and turbidity conditions. We used joint up approaches were used to deploy those innovations locally when project exchanges through secondments became impossible.
From first phase innovations, WP 4, focused on developing novel approaches for integrating high-frequency sensor technology for large-scale distributed sensor network monitoring. The main focus in this phase has been on creating automated calibration and validation scripts for the optimised analysis of fluorescence tracer breakthrough curves, the results of these have been published.
WP 5 aimed at providing innovative statistical approaches and modelling techniques for efficiently handling, processing and analysing complex sets of big data with variable frequency and resolution. We have been focussing on developing algorithms for the synthesis of the large interdisciplinary data sets generated in project and designed automated analysis methods for microbial metabolic activity tracers, and simulated nutrient dynamics in rivers using high-frequency nutrient data to reassess prior nutrient models.
Finally, WP 6 focussed on developing strategies for visualising and aiding decision making based on high-frequency environmental sensor network monitoring and adaptive modelling approaches. These developments were conducted to better guide real-time responses to multiple environmental stressors. Building on the first phase advances, we created predictive understanding of river corridor exchange: standardization of metadata, critical evaluation of the information content and support volumes for measurement techniques, multiscale model-data integration, and advancing theoretical models of river corridor exchange.
To mitigate these COVID induced limitations, a first project extension was applied for and granted, however, travel between participating countries and institutions hadn’t resumed in time. A second project extension was unfortunately not granted, preventing the project from implementing outstanding secondments. The main emphasis of the network has therefore been on trying to deliver as much as possible of the planned actions and deliverables without being able to actually conduct in-person secondments during this time.
WP 3 facilitated the development, testing and validation of in-situ sensor technology for high-frequency and high-spatial resolution monitoring. During the 2nd project period covered by this report, the research team endeavoured to further develop the new concepts initiated in the first project phase in the joint networking of multiple property sensors under dynamic flow conditions, and advanced capacities in monitoring the dynamic breakthrough of reactive fluorescence tracers under variable discharge and turbidity conditions. We used joint up approaches were used to deploy those innovations locally when project exchanges through secondments became impossible.
From first phase innovations, WP 4, focused on developing novel approaches for integrating high-frequency sensor technology for large-scale distributed sensor network monitoring. The main focus in this phase has been on creating automated calibration and validation scripts for the optimised analysis of fluorescence tracer breakthrough curves, the results of these have been published.
WP 5 aimed at providing innovative statistical approaches and modelling techniques for efficiently handling, processing and analysing complex sets of big data with variable frequency and resolution. We have been focussing on developing algorithms for the synthesis of the large interdisciplinary data sets generated in project and designed automated analysis methods for microbial metabolic activity tracers, and simulated nutrient dynamics in rivers using high-frequency nutrient data to reassess prior nutrient models.
Finally, WP 6 focussed on developing strategies for visualising and aiding decision making based on high-frequency environmental sensor network monitoring and adaptive modelling approaches. These developments were conducted to better guide real-time responses to multiple environmental stressors. Building on the first phase advances, we created predictive understanding of river corridor exchange: standardization of metadata, critical evaluation of the information content and support volumes for measurement techniques, multiscale model-data integration, and advancing theoretical models of river corridor exchange.
The project has substantially contributed to improving the technological solutions and mechanistic understanding of ecohydrological dynamics, biogeochemical fluxes and pollutant fate and transport in aquatic ecosystems. The advances made do not only improve our understanding of dynamic pollution source zone activation and connectivity during variable climatic and land use conditions but also provide direct benefits to the private sector and public sector throughout Europe. Innovations created in HiFreq will be directly relevant to future EU legislation, with the inter-sectoral consortium having created valuable synergies between science and practice. Relevant areas of activities, directly connected to HiFreq include the Water Framework Directive where innovative approaches to achieve a good chemical and ecological standard and strategies to evaluate success beyond the currently established indicators are required and where technological innovations made in HiFreq help to advance mechanistic process understanding, including the assessment of efficiency and functioning of mitigation and adaptation efforts, in particular in light of dynamic development of interacting ecosystem stressors under climate change. The resulting advances to our understanding of the highly dynamic functioning of complex aquatic ecosystems is crucial given expected increases in hydropower production and river regulation (cf. EU Sustainable Development Strategy, SDS). Through our innovative research and dissemination concept in the HiFreq consortium, knowledge, tools and models generated during the action will be continued to be applied also in the future to better assess the environmental effects of multi-stressor interactions on river ecosystems, which will be vital in moving towards clean energy without compromising long-term ecological services.