Observations and monitoring of earth systems across compartments are facing several challenges. One major challenge is the trade-off between intended spatial coverage (e.g. regional scale) and resolution required for the investigation of specific process as e.g. limited accessibility (e.g. subsurface in geosystems or the deep ocean) or a spatially continuous characterization of relevant system parameters (e.g. soil moisture and permeability) are often hampered. Indirect measurements and the use of proxies might overcome some of the problems detailed within the Show Cases but often suffer from site specific suitability. In WP1 we therefore aim to explore and develop suitable frameworks for selecting and further developing appropriate tools and put them in an adaptable scientific workflow (see WP2) by combining methods and tools for ground-truthing of proxies and for the investigation of Earth (Sub)-Systems.
The basic approach of the hierarchic concept consists of combing of methods of different spatial and temporal resolutions a) for an adaptive monitoring of selected areas b).
Monitoring ideas have been investigated on national, European and international levels for decades in various projects which, however, mainly focus(ed) on specific compartments (e.g. ESONET, EMSO, FIXO3, COSYNA, FRAM, IAGOS, or Oceans 2.0 from Ocean Networks Canada) and/or science discipline - or technology-specific efforts. Only few are designed to function across compartments such as e.g. ENVRI+, ICOS, TERENO. The concept of Digital Earth is to make monitoring designs and sensor layouts smarter: e.g. improving the observation efficiency of an environmental measurement-system by on-site/real-time data processing enabling easy interaction between measurements and data analysis and/or integrating opportunistic sensors like Commercial Microwave Link networks (CMLs), which can provide countrywide rainfall estimates in real-time (Chwala et al. 2016).
We propose an integrative hierarchic monitoring concept in which methods and technologies from different disciplines (chemistry, hydrogeology, hydrometeorology, geophysics, or biology) will be either combined or used complementary. This hierarchical approach in which methods range from remote sensing over regional measurements to local in-situ measurements will allow a consistent coverage on large spatial scales without compromising spatial and temporal resolution.
To address the challenges that will arise when combining measurements from terrestrial, oceanic and atmospheric monitoring stations or short-term field campaigns, four tasks have been defined in WP1. The base for the hierarchic monitoring concept is a profound understanding of available technologies and their applicability under different environmental and technological conditions and considering reliability and accuracy of the acquired data (QC/QA => Task 2.2). Thus mapping and monitoring systems will be analyzed within Task 1.1 and attention will be given to evaluate the inter-comparison of different sensors (e.g. between CML-, Radar- and gauge derived precipitation). Task 1.2 focuses on the Data-Flow from sensor to archives which is an essential prerequisite for efficient data analysis (fast evaluation of quality controlled data) and the effective combination of measurement technologies. The challenges when combining methods of different spatial and temporal resolution in the frame of our hierarchic monitoring concept will be addressed in Task 1.3.
The term SMART Monitoring has been chosen to highlight that traditional monitoring has to advance in such a way that the data flow from individual sensors in multiple observatories via data bases to the scientist is made possible (effortless) and allows automated (machine learning) and near real-time interactive data analyses/exploration. Analyses include e.g. the detection of malfunctioning sensors or the identifications of trends, outliers or events in time series. Such an advancement in monitoring will help to better adjust sensor settings and monitoring strategies in time and space in an iterative feedback of a ‘smart’ monitoring. Furthermore measured parameters and their values need to be specific, measurable, accepted, relevant, and trackable (SMART) for a sustainable use as data.
Task 1.1 Match monitoring/observation approaches with cross-compartmental needs
[KIT, AWI, FZJ, GEOMAR, GFZ, HZG, UFZ]
Task 1.2 Improve Data-Flow Framework from Sensor to Data-Exploration
[AWI, FZJ, GEOMAR, GFZ, HZG, UFZ, KIT]
[UFZ, AWI, FZJ, GEOMAR, GFZ, HMGU, HZG, KIT]