# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 5 Summary  
Scaling science is a paradigm used by [PERSON] and [PERSON] [153] to provide social impact for the public good. This approach underpins the GEO AquaWatch goal to develop and build the global capacity and utility of water quality data, products, and information to support water resource management and decision making. Scaling science is normally based on inclusive coordination, optimal scale, and dynamic evaluation, to transition from user ideas and needs, to knowledge and impacts. Inclusive coordination is where scientists actively engage and iterate with end-users, such as the iterative development model discussed previously. Optimal scale is acknowledging that there are trade-offs when the scale of study is too fine or too large. Dynamic evaluation is where scientists and stakeholders have a willingness to adapt to new technologies, such as CubeSats or drones, based on feedback. Moving to larger scale requires replication, where integrative  
Figure 8: ODYSSEA Platform workflow from sensors and modeling data through the Internet and integration steps leading to post-processing and interface display for a range of decision makers and stakeholders. components can be articulated and standardized [154]. The framework proposed in this paper includes those components. A growing list of integrated data to information systems specific to water quality management includes NOAA's tides and currents [155]; harmful algae bloom forecasting systems [156; 157]; Spain's Portus ([[https://portus.puertos.es](https://portus.puertos.es)]([https://portus.puertos.es](https://portus.puertos.es)), accessed on July 2021); EAWAG's Swiss lake information portal ([[https://simstrat.eawag.ch/](https://simstrat.eawag.ch/)]([https://simstrat.eawag.ch/](https://simstrat.eawag.ch/)) for 1-D and [[http://meteolakes.ch/](http://meteolakes.ch/)]([http://meteolakes.ch/](http://meteolakes.ch/)) for 3-D models, both links accessed on July 2021); the UK Lakes Portal ([[https://eip.ceh.ac.uk/apps/](https://eip.ceh.ac.uk/apps/)]([https://eip.ceh.ac.uk/apps/](https://eip.ceh.ac.uk/apps/)) lakes, accessed on July 2021) the HAB Satellite Analysis tool from San Francisco Estuary Institute ([[https://fhab.sfei.org/](https://fhab.sfei.org/)]([https://fhab.sfei.org/](https://fhab.sfei.org/)), accessed on July 2021); Datalakes online platform for Swiss lakes ([[https://live.datalakesapi.ch/](https://live.datalakesapi.ch/)]([https://live.datalakesapi.ch/](https://live.datalakesapi.ch/)), accessed on July 2021); CoBIOS: Coastal Biomass Observatory Services, the high biomass blooms in Europe's coastal waters models; and MoS2 project for southern North Sea [141]. All these systems aim at providing a synoptic view through user-friendly interfaces and information to help assess the state of water quality and, ultimately, mitigate the multiple reactions and hazards resulting from changes in conditions. In this paper, the framework for integrating data products, derived from multiple observations and modeling, through data assimilation techniques for regional and local water quality nowcasts and forecasts, is conceptualized. It is assumed that all models, satellite algorithms, and data products are already at a satisfactory level for stakeholder needs available as interpretation ready data. Prior requirements, such as calibration, validation, and data quality control and assurance, were outside the scope of consideration here. The sustainability of various observing systems remains a major concern. However, through improved communication between scientists and stakeholders, there is an improved likelihood that scientifically sound information can be transferred and understood by stakeholders for management decisions. Stakeholder obstacles include correct and easily accessible interpretation of data into information, particularly when programming skills may be required to derive additional information, and expert knowledge is required to understand technology limitations and capabilities.